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+[
+  {
+    "node_id": 0,
+    "label": 16,
+    "text": "Title: LMEye: An Interactive Perception Network for Large Language Models\nAbstract: Training a Large Visual Language Model (LVLM) from scratch, like GPT-4, is resource-intensive. Our paper presents a play-and-plug module for Large Language Models (LLMs), namely Interactive Perception Network (IPN), aiming to achieve a LVLM by incorporating the image understanding capability into LLMs. Previous methods incorporate visual information into LLMs with a simple visual mapping network, where the image feature is projected into the embedding space of LLMs via a linear layer. Such mapping network projects the image feature once yet does not consider the interaction between the image and the human input query. Hence, the obtained visual information with no connections with human intention may be inadequate for LLMs to make intention-following responses, which we term as static visual information. IPN addresses this issue by allowing the LLM to request the desired visual information aligned with various human instructions, which we term as the dynamic interaction between the LLM and visual information. Specifically, IPN consists of a simple visual mapping network to provide the basic perception of an image for LLMs. It also contains additional modules responsible for acquiring requests from LLMs, performing request-based visual information interaction, and transmitting the resulting interacted visual information to LLMs, respectively. In this way, LLMs act to understand the human query, deliver the corresponding request to the request-based visual information interaction module, and generate the response based on the interleaved multimodal information. We evaluate IPN through extensive experiments on multimodal question answering, reasoning, and so on, demonstrating that it significantly improves the zero-shot performance of LVLMs on various multimodal tasks compared to previous methods.",
+    "neighbors": [
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+      754,
+      887,
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+      1052,
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+      1148,
+      1659,
+      1668,
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+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1,
+    "label": 28,
+    "text": "Title: Density Devolution for Ordering Synthetic Channels\nAbstract: Constructing a polar code is all about selecting a subset of rows from a Kronecker power of $ \\left[ {\\begin{array}{c} {10} \\\\ {11} \\end{array}} \\right] $. It is known that, under successive cancellation decoder, some rows are Paretobetter than the other. For instance, whenever a user sees a substring 01 in the binary expansion of a row index and replaces it with 10, the user obtains a row index that is always more welcomed. We call this a \"rule\" and denote it by 10 \u227d 01. In present work, we first enumerate some rules over binary erasure channels such as 1001 \u227d 0110 and 10001 \u227d 01010 and 10101 \u227d 01110. We then summarize them using a \"rule of rules\": if 10a \u227d 01b is a rule, where a and b are arbitrary binary strings, then 100a \u227d 010b and 101a \u227d 011b are rules. This work\u2019s main contribution is using field theory, Galois theory, and numerical analysis to develop an algorithm that decides if a rule of rules is mathematically sound. We apply the algorithm to enumerate some rules of rules. Each rule of rule is capable of generating an infinite family of rules. For instance, 10c01 \u227d 01c10 for arbitrary binary string c can be generated. We found an application of 10c01 \u227d 01c10 that is related to integer partition and the dominance order therein.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2,
+    "label": 24,
+    "text": "Title: SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems\nAbstract: A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep generative CF models often work with user-selected features rather than focusing on the discriminative features of the black-box model. Consequently, such CF examples may not necessarily lie near the decision boundary, thereby contradicting the definition of CFs. To address this issue, we propose in this paper a novel approach that leverages saliency maps to generate more informative CF explanations. Source codes are available at: https://github.com/Amir-Samadi//Saliency_Aware_CF.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 3,
+    "label": 30,
+    "text": "Title: Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent\nAbstract: Large Language Models (LLMs) have demonstrated a remarkable ability to generalize zero-shot to various language-related tasks. This paper focuses on the study of exploring generative LLMs such as ChatGPT and GPT-4 for relevance ranking in Information Retrieval (IR). Surprisingly, our experiments reveal that properly instructed ChatGPT and GPT-4 can deliver competitive, even superior results than supervised methods on popular IR benchmarks. Notably, GPT-4 outperforms the fully fine-tuned monoT5-3B on MS MARCO by an average of 2.7 nDCG on TREC datasets, an average of 2.3 nDCG on eight BEIR datasets, and an average of 2.7 nDCG on ten low-resource languages Mr.TyDi. Subsequently, we delve into the potential for distilling the ranking capabilities of ChatGPT into a specialized model. Our small specialized model that trained on 10K ChatGPT generated data outperforms monoT5 trained on 400K annotated MS MARCO data on BEIR. The code to reproduce our results is available at www.github.com/sunnweiwei/RankGPT",
+    "neighbors": [
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+      1636,
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+      1915,
+      2094
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 4,
+    "label": 23,
+    "text": "Title: CCRep: Learning Code Change Representations via Pre-Trained Code Model and Query Back\nAbstract: Representing code changes as numeric feature vectors, i.e., code change representations, is usually an essential step to automate many software engineering tasks related to code changes, e.g., commit message generation and just-in-time defect prediction. Intuitively, the quality of code change representations is crucial for the effectiveness of automated approaches. Prior work on code changes usually designs and evaluates code change representation approaches for a specific task, and little work has investigated code change encoders that can be used and jointly trained on various tasks. To fill this gap, this work proposes a novel Code Change Representation learning approach named CCRep, which can learn to encode code changes as feature vectors for diverse downstream tasks. Specifically, CCRep regards a code change as the combination of its before-change and after-change code, leverages a pre-trained code model to obtain high-quality contextual embeddings of code, and uses a novel mechanism named query back to extract and encode the changed code fragments and make them explicitly interact with the whole code change. To evaluate CCRep and demonstrate its applicability to diverse code-change-related tasks, we apply it to three tasks: commit message generation, patch correctness assessment, and just-in-time defect prediction. Experimental results show that CCRep outperforms the state-of-the-art techniques on each task.",
+    "neighbors": [
+      546
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 5,
+    "label": 28,
+    "text": "Title: Indexed Multiple Access with Reconfigurable Intelligent Surfaces: The Reflection Tuning Potential\nAbstract: Indexed modulation (IM) is an evolving technique that has become popular due to its ability of parallel data communication over distinct combinations of transmission entities. In this article, we first provide a comprehensive survey of IM-enabled multiple access (MA) techniques, emphasizing the shortcomings of existing non-indexed MA schemes. Theoretical comparisons are presented to show how the notion of indexing eliminates the limitations of non-indexed solutions. We also discuss the benefits that the utilization of a reconfigurable intelligent surface (RIS) can offer when deployed as an indexing entity. In particular, we propose an RIS-indexed multiple access (RIMA) transmission scheme that utilizes dynamic phase tuning to embed multi-user information over a single carrier. The performance of the proposed RIMA is assessed in light of simulation results that confirm its performance gains. The article further includes a list of relevant open technical issues and research directions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 6,
+    "label": 24,
+    "text": "Title: Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks\nAbstract: This paper focuses on approximation and learning performance analysis for deep convolutional neural networks with zero-padding and max-pooling. We prove that, to approximate $r$-smooth function, the approximation rates of deep convolutional neural networks with depth $L$ are of order $ (L^2/\\log L)^{-2r/d} $, which is optimal up to a logarithmic factor. Furthermore, we deduce almost optimal learning rates for implementing empirical risk minimization over deep convolutional neural networks.",
+    "neighbors": [
+      648
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 7,
+    "label": 24,
+    "text": "Title: Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play\nAbstract: Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data. Therefore, approaches that improve the accuracy-group robustness trade-off frontier of a DNN model (i.e. improving worst-group accuracy without sacrificing average accuracy, or vice versa) is of crucial importance. Uncertainty-based active learning (AL) can potentially improve the frontier by preferentially sampling underrepresented subgroups to create a more balanced training dataset. However, the quality of uncertainty estimates from modern DNNs tend to degrade in the presence of spurious correlations and dataset bias, compromising the effectiveness of AL for sampling tail groups. In this work, we propose Introspective Self-play (ISP), a simple approach to improve the uncertainty estimation of a deep neural network under dataset bias, by adding an auxiliary introspection task requiring a model to predict the bias for each data point in addition to the label. We show that ISP provably improves the bias-awareness of the model representation and the resulting uncertainty estimates. On two real-world tabular and language tasks, ISP serves as a simple\"plug-in\"for AL model training, consistently improving both the tail-group sampling rate and the final accuracy-fairness trade-off frontier of popular AL methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 8,
+    "label": 24,
+    "text": "Title: PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs\nAbstract: While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equations (PDEs) is still lacking. This study introduces PINNacle, a benchmarking tool designed to fill this gap. PINNacle provides a diverse dataset, comprising over 20 distinct PDEs from various domains including heat conduction, fluid dynamics, biology, and electromagnetics. These PDEs encapsulate key challenges inherent to real-world problems, such as complex geometry, multi-scale phenomena, nonlinearity, and high dimensionality. PINNacle also offers a user-friendly toolbox, incorporating about 10 state-of-the-art PINN methods for systematic evaluation and comparison. We have conducted extensive experiments with these methods, offering insights into their strengths and weaknesses. In addition to providing a standardized means of assessing performance, PINNacle also offers an in-depth analysis to guide future research, particularly in areas such as domain decomposition methods and loss reweighting for handling multi-scale problems and complex geometry. While PINNacle does not guarantee success in all real-world scenarios, it represents a significant contribution to the field by offering a robust, diverse, and comprehensive benchmark suite that will undoubtedly foster further research and development in PINNs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 9,
+    "label": 24,
+    "text": "Title: Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data\nAbstract: Conformal prediction has emerged as a rigorous means of providing deep learning models with reliable uncertainty estimates and safety guarantees. Yet, its performance is known to degrade under distribution shift and long-tailed class distributions, which are often present in real world applications. Here, we characterize the performance of several post-hoc and training-based conformal prediction methods under these settings, providing the first empirical evaluation on large-scale datasets and models. We show that across numerous conformal methods and neural network families, performance greatly degrades under distribution shifts violating safety guarantees. Similarly, we show that in long-tailed settings the guarantees are frequently violated on many classes. Understanding the limitations of these methods is necessary for deployment in real world and safety-critical applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 10,
+    "label": 5,
+    "text": "Title: Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources\nAbstract: Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators. Here, we present our experiences deploying two AI-guided simulation workflows across such heterogeneous systems. A unique aspect of our approach is our use of cloud-hosted management services to manage challenging aspects of cross-resource authentication and authorization, function-as-a-service (FaaS) function invocation, and data transfer. We show that these methods can achieve performance parity with systems that rely on direct connection between resources. We achieve parity by integrating the FaaS system and data transfer capabilities with a system that passes data by reference among managers and workers, and a user-configurable steering algorithm to hide data transfer latencies. We anticipate that this ease of use can enable routine use of heterogeneous resources in computational science.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 11,
+    "label": 30,
+    "text": "Title: The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only\nAbstract: Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This curation process is believed to be necessary to produce performant models with broad zero-shot generalization abilities. However, as larger models requiring pretraining on trillions of tokens are considered, it is unclear how scalable is curation and whether we will run out of unique high-quality data soon. At variance with previous beliefs, we show that properly filtered and deduplicated web data alone can lead to powerful models; even significantly outperforming models from the state-of-the-art trained on The Pile. Despite extensive filtering, the high-quality data we extract from the web is still plentiful, and we are able to obtain five trillion tokens from CommonCrawl. We publicly release an extract of 600 billion tokens from our RefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.",
+    "neighbors": [
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+      2235,
+      2257
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 12,
+    "label": 25,
+    "text": "Title: RobustL2S: Speaker-Specific Lip-to-Speech Synthesis exploiting Self-Supervised Representations\nAbstract: Significant progress has been made in speaker dependent Lip-to-Speech synthesis, which aims to generate speech from silent videos of talking faces. Current state-of-the-art approaches primarily employ non-autoregressive sequence-to-sequence architectures to directly predict mel-spectrograms or audio waveforms from lip representations. We hypothesize that the direct mel-prediction hampers training/model efficiency due to the entanglement of speech content with ambient information and speaker characteristics. To this end, we propose RobustL2S, a modularized framework for Lip-to-Speech synthesis. First, a non-autoregressive sequence-to-sequence model maps self-supervised visual features to a representation of disentangled speech content. A vocoder then converts the speech features into raw waveforms. Extensive evaluations confirm the effectiveness of our setup, achieving state-of-the-art performance on the unconstrained Lip2Wav dataset and the constrained GRID and TCD-TIMIT datasets. Speech samples from RobustL2S can be found at https://neha-sherin.github.io/RobustL2S/",
+    "neighbors": [
+      1869
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 13,
+    "label": 24,
+    "text": "Title: Text analysis using deep neural networks in digital humanities and information science\nAbstract: Combining computational technologies and humanities is an ongoing effort aimed at making resources such as texts, images, audio, video, and other artifacts digitally available, searchable, and analyzable. In recent years, deep neural networks (DNN) dominate the field of automatic text analysis and natural language processing (NLP), in some cases presenting a super\u2010human performance. DNNs are the state\u2010of\u2010the\u2010art machine learning algorithms solving many NLP tasks that are relevant for Digital Humanities (DH) research, such as spell checking, language detection, entity extraction, author detection, question answering, and other tasks. These supervised algorithms learn patterns from a large number of \u201cright\u201d and \u201cwrong\u201d examples and apply them to new examples. However, using DNNs for analyzing the text resources in DH research presents two main challenges: (un)availability of training data and a need for domain adaptation. This paper explores these challenges by analyzing multiple use\u2010cases of DH studies in recent literature and their possible solutions and lays out a practical decision model for DH experts for when and how to choose the appropriate deep learning approaches for their research. Moreover, in this paper, we aim to raise awareness of the benefits of utilizing deep learning models in the DH community.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 14,
+    "label": 23,
+    "text": "Title: Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data\nAbstract: The complexity and dynamism of microservices pose significant challenges to system reliability, and thereby, automated troubleshooting is crucial. Effective root cause localization after anomaly detection is crucial for ensuring the reliability of microservice systems. However, two significant issues rest in existing approaches: (1) Microservices generate traces, system logs, and key performance indicators (KPIs), but existing approaches usually consider traces only, failing to understand the system fully as traces cannot depict all anomalies; (2) Troubleshooting microservices generally contains two main phases, i.e., anomaly detection and root cause localization. Existing studies regard these two phases as independent, ignoring their close correlation. Even worse, inaccurate detection results can deeply affect localization effectiveness. To overcome these limitations, we propose Eadro, the first end-to-end framework to integrate anomaly detection and root cause localization based on multi-source data for troubleshooting large-scale microservices. The key insights of Eadro are the anomaly manifestations on different data sources and the close connection between detection and localization. Thus, Eadro models intra-service behaviors and inter-service dependencies from traces, logs, and KPIs, all the while leveraging the shared knowledge of the two phases via multi-task learning. Experiments on two widely-used benchmark microservices demonstrate that Eadro outperforms state-of-the-art approaches by a large margin. The results also show the usefulness of integrating multi-source data. We also release our code and data to facilitate future research.",
+    "neighbors": [
+      919,
+      1131,
+      2173
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 15,
+    "label": 38,
+    "text": "Title: The disruption index is biased by citation inflation\nAbstract: A recent analysis of scientific publication and patent citation networks by Park et al. (Nature, 2023) suggests that publications and patents are becoming less disruptive over time. Here we show that the reported decrease in disruptiveness is an artifact of systematic shifts in the structure of citation networks unrelated to innovation system capacity. Instead, the decline is attributable to 'citation inflation', an unavoidable characteristic of real citation networks that manifests as a systematic time-dependent bias and renders cross-temporal analysis challenging. One driver of citation inflation is the ever-increasing lengths of reference lists over time, which in turn increases the density of links in citation networks, and causes the disruption index to converge to 0. A second driver is attributable to shifts in the construction of reference lists, which is increasingly impacted by self-citations that increase in the rate of triadic closure in citation networks, and thus confounds efforts to measure disruption, which is itself a measure of triadic closure. Combined, these two systematic shifts render the disruption index temporally biased, and unsuitable for cross-temporal analysis. The impact of this systematic bias further stymies efforts to correlate disruption to other measures that are also time-dependent, such as team size and citation counts. In order to demonstrate this fundamental measurement problem, we present three complementary lines of critique (deductive, empirical and computational modeling), and also make available an ensemble of synthetic citation networks that can be used to test alternative citation-based indices for systematic bias.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 16,
+    "label": 10,
+    "text": "Title: Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation\nAbstract: The use of Shap scores has become widespread in Explainable AI. However, their computation is in general intractable, in particular when done with a black-box classifier, such as neural network. Recent research has unveiled classes of open-box Boolean Circuit classifiers for which Shap can be computed efficiently. We show how to transform binary neural networks into those circuits for efficient Shap computation.We use logic-based knowledge compilation techniques. The performance gain is huge, as we show in the light of our experiments.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 17,
+    "label": 16,
+    "text": "Title: FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection\nAbstract: Lack of generalization to unseen domains/attacks is the Achilles heel of most face presentation attack detection (FacePAD) algorithms. Existing attempts to enhance the generalizability of FacePAD solutions assume that data from multiple source domains are available with a single entity to enable centralized training. In practice, data from different source domains may be collected by diverse entities, who are often unable to share their data due to legal and privacy constraints. While collaborative learning paradigms such as federated learning (FL) can overcome this problem, standard FL methods are ill-suited for domain generalization because they struggle to surmount the twin challenges of handling non-iid client data distributions during training and generalizing to unseen domains during inference. In this work, a novel framework called Federated Split learning with Intermediate representation Sampling (FedSIS) is introduced for privacy-preserving domain generalization. In FedSIS, a hybrid Vision Transformer (ViT) architecture is learned using a combination of FL and split learning to achieve robustness against statistical heterogeneity in the client data distributions without any sharing of raw data (thereby preserving privacy). To further improve generalization to unseen domains, a novel feature augmentation strategy called intermediate representation sampling is employed, and discriminative information from intermediate blocks of a ViT is distilled using a shared adapter network. The FedSIS approach has been evaluated on two well-known benchmarks for cross-domain FacePAD to demonstrate that it is possible to achieve state-of-the-art generalization performance without data sharing. Code: https://github.com/Naiftt/FedSIS",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 18,
+    "label": 31,
+    "text": "Title: Identifying document similarity using a fast estimation of the Levenshtein Distance based on compression and signatures\nAbstract: Identifying document similarity has many applications, e.g., source code analysis or plagiarism detection. However, identifying similarities is not trivial and can be time complex. For instance, the Levenshtein Distance is a common metric to define the similarity between two documents but has quadratic runtime which makes it impractical for large documents where large starts with a few hundred kilobytes. In this paper, we present a novel concept that allows estimating the Levenshtein Distance: the algorithm first compresses documents to signatures (similar to hash values) using a user-defined compression ratio. Signatures can then be compared against each other (some constrains apply) where the outcome is the estimated Levenshtein Distance. Our evaluation shows promising results in terms of runtime efficiency and accuracy. In addition, we introduce a significance score allowing examiners to set a threshold and identify related documents.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 19,
+    "label": 24,
+    "text": "Title: Generating Multidimensional Clusters With Support Lines\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 20,
+    "label": 27,
+    "text": "Title: TactoFind: A Tactile Only System for Object Retrieval\nAbstract: We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer. Successful solutions require localizing free objects, identifying specific object instances, and then grasping the identified objects, only using touch feedback. Unlike vision, where cameras can observe the entire scene, touch sensors are local and only observe parts of the scene that are in contact with the manipulator. Moreover, information gathering via touch sensors necessitates applying forces on the touched surface which may disturb the scene itself. Reasoning with touch, therefore, requires careful exploration and integration of information over time - a challenge we tackle. We present a system capable of using sparse tactile feedback from fingertip touch sensors on a dexterous hand to localize, identify and grasp novel objects without any visual feedback. Videos are available at https://sites.google.com/view/tactofind.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 21,
+    "label": 24,
+    "text": "Title: B2Opt: Learning to Optimize Black-box Optimization with Little Budget\nAbstract: The core challenge of high-dimensional and expensive black-box optimization (BBO) is how to obtain better performance faster with little function evaluation cost. The essence of the problem is how to design an efficient optimization strategy tailored to the target task. This paper designs a powerful optimization framework to automatically learn the optimization strategies from the target or cheap surrogate task without human intervention. However, current methods are weak for this due to poor representation of optimization strategy. To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies. Compared to the state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude performance improvement with less function evaluation cost. We validate our proposal on high-dimensional synthetic functions and two real-world applications. We also find that deep B2Opt performs better than shallow ones.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 22,
+    "label": 28,
+    "text": "Title: Joint data rate and EMF exposure analysis in user-centric cell-free massive MIMO networks\nAbstract: The objective of this study is to analyze the statistics of the data rate and of the incident power density (IPD) in user-centric cell-free networks (UCCFNs). To this purpose, our analysis proposes a number of performance metrics derived using stochastic geometry (SG). On the one hand, the \ufb01rst moments and the marginal distribution of the IPD are calculated. On the other hand, bounds on the joint distributions of rate and IPD are provided for two scenarios: when it is relevant to obtain IPD values above a given threshold (for energy harvesting purposes), and when these values should instead remain below the threshold (for public health reasons). In addition to deriving these metrics, this work incorporates features related to UCCFNs which are new in SG models: a power allocation based on collective channel statistics, as well as the presence of potential overlaps between adjacent clusters. Our numerical results illustrate the achievable trade-offs between the rate and IPD performance. For the considered system, these results also highlight the existence of an optimal node density maximizing the joint distributions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 23,
+    "label": 24,
+    "text": "Title: Learning to Learn from APIs: Black-Box Data-Free Meta-Learning\nAbstract: Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data. Existing DFML work can only meta-learn from (i) white-box and (ii) small-scale pre-trained models (iii) with the same architecture, neglecting the more practical setting where the users only have inference access to the APIs with arbitrary model architectures and model scale inside. To solve this issue, we propose a Bi-level Data-free Meta Knowledge Distillation (BiDf-MKD) framework to transfer more general meta knowledge from a collection of black-box APIs to one single meta model. Specifically, by just querying APIs, we inverse each API to recover its training data via a zero-order gradient estimator and then perform meta-learning via a novel bi-level meta knowledge distillation structure, in which we design a boundary query set recovery technique to recover a more informative query set near the decision boundary. In addition, to encourage better generalization within the setting of limited API budgets, we propose task memory replay to diversify the underlying task distribution by covering more interpolated tasks. Extensive experiments in various real-world scenarios show the superior performance of our BiDf-MKD framework.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 24,
+    "label": 28,
+    "text": "Title: Sum-rank metric codes\nAbstract: Sum-rank metric codes are a natural extension of both linear block codes and rank-metric codes. They have several applications in information theory, including multishot network coding and distributed storage systems. The aim of this chapter is to present the mathematical theory of sum-rank metric codes, paying special attention to the $\\mathbb{F}_q$-linear case in which different sizes of matrices are allowed. We provide a comprehensive overview of the main results in the area. In particular, we discuss invariants, optimal anticodes, and MSRD codes. In the last section, we concentrate on $\\mathbb{F}_{q^m}$-linear codes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 25,
+    "label": 10,
+    "text": "Title: ChatGPT for Robotics: Design Principles and Model Abilities\nAbstract: This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.",
+    "neighbors": [
+      600,
+      817,
+      1036,
+      1060,
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+      1353,
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+      1822,
+      1877,
+      2141,
+      2166
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 26,
+    "label": 22,
+    "text": "Title: Alice or Bob?: Process Polymorphism in Choreographies\nAbstract: We present PolyChor$\\lambda$, a language for higher-order functional \\emph{choreographic programming} -- an emerging paradigm by which programmers write the desired cooperative behaviour of a system of communicating processes and then compile it into distributed implementations for each process, a translation called \\emph{endpoint projection}. Unlike its predecessor, Chor$\\lambda$, PolyChor$\\lambda$ has both type and \\emph{process} polymorphism inspired by System F$_\\omega$. That is, PolyChor$\\lambda$ is the first (higher-order) functional choreographic language which gives programmers the ability to write generic choreographies and determine the participants at runtime. This novel combination of features also allows PolyChor$\\lambda$ processes to communicate \\emph{distributed values}, leading to a new and intuitive way to write delegation. While some of the functional features of PolyChor$\\lambda$ give it a weaker correspondence between the semantics of choreographies and their endpoint-projected concurrent systems than some other choreographic languages, we still get the hallmark end result of choreographic programming: projected programs are deadlock-free by design.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 27,
+    "label": 30,
+    "text": "Title: GPT detectors are biased against non-native English writers\nAbstract: nan",
+    "neighbors": [
+      42,
+      896,
+      1487,
+      1600,
+      1805,
+      2044
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 28,
+    "label": 8,
+    "text": "Title: BeamSense: Rethinking Wireless Sensing with MU-MIMO Wi-Fi Beamforming Feedback\nAbstract: In this paper, we propose BeamSense, a completely novel approach to implement standard-compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of channel state information (CSI) from Wi-Fi chips to classify activities, which is not supported by the Wi-Fi standard and hence requires the usage of specialized equipment. On the contrary, BeamSense leverages the standard-compliant beamforming feedback information (BFI) to characterize the propagation environment. Conversely from CSI, the BFI (i) can be easily recorded without any firmware modification, and (ii) captures the multiple channels between the access point and the stations, thus providing much better sensitivity. BeamSense includes a novel cross-domain few-shot learning (FSL) algorithm to handle unseen environments and subjects with few additional data points. We evaluate BeamSense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We show that our BFI-based approach achieves about 10% more accuracy when compared to CSI-based prior work, while our FSL strategy improves accuracy by up to 30% and 80% when compared with state-of-the-art cross-domain algorithms.",
+    "neighbors": [
+      730,
+      851
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 29,
+    "label": 13,
+    "text": "Title: Simplicial Hopfield networks\nAbstract: Hopfield networks are artificial neural networks which store memory patterns on the states of their neurons by choosing recurrent connection weights and update rules such that the energy landscape of the network forms attractors around the memories. How many stable, sufficiently-attracting memory patterns can we store in such a network using $N$ neurons? The answer depends on the choice of weights and update rule. Inspired by setwise connectivity in biology, we extend Hopfield networks by adding setwise connections and embedding these connections in a simplicial complex. Simplicial complexes are higher dimensional analogues of graphs which naturally represent collections of pairwise and setwise relationships. We show that our simplicial Hopfield networks increase memory storage capacity. Surprisingly, even when connections are limited to a small random subset of equivalent size to an all-pairwise network, our networks still outperform their pairwise counterparts. Such scenarios include non-trivial simplicial topology. We also test analogous modern continuous Hopfield networks, offering a potentially promising avenue for improving the attention mechanism in Transformer models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 30,
+    "label": 30,
+    "text": "Title: AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity using Contrastive Learning and Structured Knowledge\nAbstract: Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar. Conversely, aspect-based sentence embeddings provide similarities between texts based on certain predefined aspects. Thus, similarity predictions of texts are more targeted to specific requirements and more easily explainable. In this paper, we present AspectCSE, an approach for aspect-based contrastive learning of sentence embeddings. Results indicate that AspectCSE achieves an average improvement of 3.97% on information retrieval tasks across multiple aspects compared to the previous best results. We also propose using Wikidata knowledge graph properties to train models of multi-aspect sentence embeddings in which multiple specific aspects are simultaneously considered during similarity predictions. We demonstrate that multi-aspect embeddings outperform single-aspect embeddings on aspect-specific information retrieval tasks. Finally, we examine the aspect-based sentence embedding space and demonstrate that embeddings of semantically similar aspect labels are often close, even without explicit similarity training between different aspect labels.",
+    "neighbors": [
+      168
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 31,
+    "label": 30,
+    "text": "Title: Low-Resourced Machine Translation for Senegalese Wolof Language\nAbstract: Natural Language Processing (NLP) research has made great advancements in recent years with major breakthroughs that have established new benchmarks. However, these advances have mainly benefited a certain group of languages commonly referred to as resource-rich such as English and French. Majority of other languages with weaker resources are then left behind which is the case for most African languages including Wolof. In this work, we present a parallel Wolof/French corpus of 123,000 sentences on which we conducted experiments on machine translation models based on Recurrent Neural Networks (RNN) in different data configurations. We noted performance gains with the models trained on subworded data as well as those trained on the French-English language pair compared to those trained on the French-Wolof pair under the same experimental conditions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 32,
+    "label": 24,
+    "text": "Title: Outlier Robust Adversarial Training\nAbstract: Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with adversarial samples. While traditional robust learning methods and the recent adversarial training approaches are designed to handle each of the two challenges, to date, no work has been done to develop models that are robust with regard to the low-quality training data and the potential adversarial attack at inference time simultaneously. It is for this reason that we introduce Outlier Robust Adversarial Training (ORAT) in this work. ORAT is based on a bi-level optimization formulation of adversarial training with a robust rank-based loss function. Theoretically, we show that the learning objective of ORAT satisfies the $\\mathcal{H}$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss. Furthermore, we analyze its generalization ability and provide uniform convergence rates in high probability. ORAT can be optimized with a simple algorithm. Experimental evaluations on three benchmark datasets demonstrate the effectiveness and robustness of ORAT in handling outliers and adversarial attacks. Our code is available at https://github.com/discovershu/ORAT.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 33,
+    "label": 16,
+    "text": "Title: BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation\nAbstract: Scale-wise evaluation of object detectors is important for real-world applications. However, existing metrics are either coarse or not sufficiently reliable. In this paper, we propose novel scale-wise metrics that strike a balance between fineness and reliability, using a filter bank consisting of triangular and trapezoidal band-pass filters. We conduct experiments with two methods on two datasets and show that the proposed metrics can highlight the differences between the methods and between the datasets. Code is available at https://github.com/shinya7y/UniverseNet.",
+    "neighbors": [
+      700
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 34,
+    "label": 16,
+    "text": "Title: VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks\nAbstract: Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endowing them with immense potential across a range of applications. However, in the field of computer vision, despite the availability of numerous powerful vision foundation models (VFMs), they are still restricted to tasks in a pre-defined form, struggling to match the open-ended task capabilities of LLMs. In this work, we present an LLM-based framework for vision-centric tasks, termed VisionLLM. This framework provides a unified perspective for vision and language tasks by treating images as a foreign language and aligning vision-centric tasks with language tasks that can be flexibly defined and managed using language instructions. An LLM-based decoder can then make appropriate predictions based on these instructions for open-ended tasks. Extensive experiments show that the proposed VisionLLM can achieve different levels of task customization through language instructions, from fine-grained object-level to coarse-grained task-level customization, all with good results. It's noteworthy that, with a generalist LLM-based framework, our model can achieve over 60\\% mAP on COCO, on par with detection-specific models. We hope this model can set a new baseline for generalist vision and language models. The demo shall be released based on https://github.com/OpenGVLab/InternGPT. The code shall be released at https://github.com/OpenGVLab/VisionLLM.",
+    "neighbors": [
+      173,
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+      855,
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+      1315,
+      1459,
+      1540,
+      1913,
+      2030,
+      2036,
+      2064,
+      2155,
+      2203,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 35,
+    "label": 6,
+    "text": "Title: Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency\nAbstract: The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have shown that the Sense of Embodiment, and in particular the Sense of Agency, depends on the extent to which the avatar's movements mimic the user's movements. However, few works study such effect for tasks requiring a precise interaction with the environment, i.e., tasks that require accurate manipulation, precise foot stepping, or correct body poses. In these cases, users are likely to notice inconsistencies between their self-avatars and their actual pose. In this paper, we study the impact of the animation fidelity of the user avatar on a variety of tasks that focus on arm movement, leg movement and body posture. We compare three different animation techniques: two of them using Inverse Kinematics to reconstruct the pose from sparse input (6 trackers), and a third one using a professional motion capture system with 17 inertial sensors. We evaluate these animation techniques both quantitatively (completion time, unintentional collisions, pose accuracy) and qualitatively (Sense of Embodiment). Our results show that the animation quality affects the Sense of Embodiment. Inertial-based MoCap performs significantly better in mimicking body poses. Surprisingly, IK-based solutions using fewer sensors outperformed MoCap in tasks requiring accurate positioning, which we attribute to the higher latency and the positional drift that causes errors at the end-effectors, which are more noticeable in contact areas such as the feet.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 36,
+    "label": 30,
+    "text": "Title: Large Language Models for Information Retrieval: A Survey\nAbstract: As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, thereby reshaping the IR landscape, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs), typified by ChatGPT and GPT-4, has revolutionized natural language processing due to their remarkable language understanding, generation, generalization, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers. Additionally, we explore promising directions within this expanding field.",
+    "neighbors": [
+      3,
+      424,
+      529,
+      1052,
+      1194,
+      1203,
+      1481,
+      1560,
+      1678,
+      1834,
+      1863,
+      1915,
+      2013,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 37,
+    "label": 22,
+    "text": "Title: Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach\nAbstract: We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to many discrete inference problems, even with infinite support and continuous priors. To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine functions, (stochastic) branching, and conditioning on events. Our key tool is probability generating functions: they provide a compact closed-form representation of distributions that are definable by programs, thus enabling the exact computation of posterior probabilities, expectation, variance, and higher moments. Our inference method is provably correct, fully automated and uses automatic differentiation (specifically, Taylor polynomials), but does not require computer algebra. Our experiments show that its performance on a range of real-world examples is competitive with approximate Monte Carlo methods, while avoiding approximation errors.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 38,
+    "label": 24,
+    "text": "Title: FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods\nAbstract: This paper introduces the Fair Fairness Benchmark (\\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is critical for ethical and legal compliance. However, there exist challenges in comparing and developing of fairness methods due to inconsistencies in experimental settings, lack of accessible algorithmic implementations, and limited extensibility of current fairness packages and tools. To address these issues, we introduce an open-source, standardized benchmark for evaluating in-processing group fairness methods and provide a comprehensive analysis of state-of-the-art methods to ensure different notions of group fairness. This work offers the following key contributions: the provision of flexible, extensible, minimalistic, and research-oriented open-source code; the establishment of unified fairness method benchmarking pipelines; and extensive benchmarking, which yields key insights from $\\mathbf{45,079}$ experiments. We believe our work will significantly facilitate the growth and development of the fairness research community. The benchmark, including code and running logs, is available at https://github.com/ahxt/fair_fairness_benchmark",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 39,
+    "label": 24,
+    "text": "Title: Distributionally Robust Recourse Action\nAbstract: A recourse action aims to explain a particular algorithmic decision by showing one specific way in which the instance could be modified to receive an alternate outcome. Existing recourse generation methods often assume that the machine learning model does not change over time. However, this assumption does not always hold in practice because of data distribution shifts, and in this case, the recourse action may become invalid. To redress this shortcoming, we propose the Distributionally Robust Recourse Action (DiRRAc) framework, which generates a recourse action that has a high probability of being valid under a mixture of model shifts. We formulate the robustified recourse setup as a min-max optimization problem, where the max problem is specified by Gelbrich distance over an ambiguity set around the distribution of model parameters. Then we suggest a projected gradient descent algorithm to find a robust recourse according to the min-max objective. We show that our DiRRAc framework can be extended to hedge against the misspecification of the mixture weights. Numerical experiments with both synthetic and three real-world datasets demonstrate the benefits of our proposed framework over state-of-the-art recourse methods.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 40,
+    "label": 16,
+    "text": "Title: Hypotheses Tree Building for One-Shot Temporal Sentence Localization\nAbstract: Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on dense video frame annotations, which require a tremendous amount of human effort to collect. In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. Particularly, we propose an effective and novel tree-structure baseline for one-shot TSL, called Multiple Hypotheses Segment Tree (MHST), to capture the query-aware discriminative frame-wise information under the insufficient annotations. Each video frame is taken as the leaf-node, and the adjacent frames sharing the same visual-linguistic semantics will be merged into the upper non-leaf node for tree building. At last, each root node is an individual segment hypothesis containing the consecutive frames of its leaf-nodes. During the tree construction, we also introduce a pruning strategy to eliminate the interference of query-irrelevant nodes. With our designed self-supervised loss functions, our MHST is able to generate high-quality segment hypotheses for ranking and selection with the query. Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods.",
+    "neighbors": [
+      1732,
+      2287
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 41,
+    "label": 16,
+    "text": "Title: Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution\nAbstract: Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13$\\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \\url{https://github.com/jinyeying/nighttime_dehaze}.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 42,
+    "label": 30,
+    "text": "Title: Can AI-Generated Text be Reliably Detected?\nAbstract: In this paper, both empirically and theoretically, we show that several AI-text detectors are not reliable in practical scenarios. Empirically, we show that paraphrasing attacks, where a light paraphraser is applied on top of a large language model (LLM), can break a whole range of detectors, including ones using watermarking schemes as well as neural network-based detectors and zero-shot classifiers. Our experiments demonstrate that retrieval-based detectors, designed to evade paraphrasing attacks, are still vulnerable to recursive paraphrasing. We then provide a theoretical impossibility result indicating that as language models become more sophisticated and better at emulating human text, the performance of even the best-possible detector decreases. For a sufficiently advanced language model seeking to imitate human text, even the best-possible detector may only perform marginally better than a random classifier. Our result is general enough to capture specific scenarios such as particular writing styles, clever prompt design, or text paraphrasing. We also extend the impossibility result to include the case where pseudorandom number generators are used for AI-text generation instead of true randomness. We show that the same result holds with a negligible correction term for all polynomial-time computable detectors. Finally, we show that even LLMs protected by watermarking schemes can be vulnerable against spoofing attacks where adversarial humans can infer hidden LLM text signatures and add them to human-generated text to be detected as text generated by the LLMs, potentially causing reputational damage to their developers. We believe these results can open an honest conversation in the community regarding the ethical and reliable use of AI-generated text.",
+    "neighbors": [
+      27,
+      352,
+      580,
+      691,
+      896,
+      1436,
+      1487,
+      1574,
+      1600,
+      1805,
+      1863,
+      2044,
+      2249
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 43,
+    "label": 16,
+    "text": "Title: Diffusion Models for Image Restoration and Enhancement - A Comprehensive Survey\nAbstract: Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various forms of degradation. Recently, the diffusion model has achieved significant advancements in the visual generation of AIGC, thereby raising an intuitive question,\"whether diffusion model can boost image restoration\". To answer this, some pioneering studies attempt to integrate diffusion models into the image restoration task, resulting in superior performances than previous GAN-based methods. Despite that, a comprehensive and enlightening survey on diffusion model-based image restoration remains scarce. In this paper, we are the first to present a comprehensive review of recent diffusion model-based methods on image restoration, encompassing the learning paradigm, conditional strategy, framework design, modeling strategy, and evaluation. Concretely, we first introduce the background of the diffusion model briefly and then present two prevalent workflows that exploit diffusion models in image restoration. Subsequently, we classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR, intending to inspire future development. To evaluate existing methods thoroughly, we summarize the commonly-used dataset, implementation details, and evaluation metrics. Additionally, we present the objective comparison for open-sourced methods across three tasks, including image super-resolution, deblurring, and inpainting. Ultimately, informed by the limitations in existing works, we propose five potential and challenging directions for the future research of diffusion model-based IR, including sampling efficiency, model compression, distortion simulation and estimation, distortion invariant learning, and framework design.",
+    "neighbors": [
+      732,
+      800,
+      1173,
+      1539,
+      1902
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 44,
+    "label": 24,
+    "text": "Title: GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction\nAbstract: Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 45,
+    "label": 16,
+    "text": "Title: K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging\nAbstract: In this paper, we consider the problem of disease diagnosis. Unlike the conventional learning paradigm that treats labels independently, we propose a knowledge-enhanced framework, that enables training visual representation with the guidance of medical domain knowledge. In particular, we make the following contributions: First, to explicitly incorporate experts' knowledge, we propose to learn a neural representation for the medical knowledge graph via contrastive learning, implicitly establishing relations between different medical concepts. Second, while training the visual encoder, we keep the parameters of the knowledge encoder frozen and propose to learn a set of prompt vectors for efficient adaptation. Third, we adopt a Transformer-based disease-query module for cross-model fusion, which naturally enables explainable diagnosis results via cross attention. To validate the effectiveness of our proposed framework, we conduct thorough experiments on three x-ray imaging datasets across different anatomy structures, showing our model is able to exploit the implicit relations between diseases/findings, thus is beneficial to the commonly encountered problem in the medical domain, namely, long-tailed and zero-shot recognition, which conventional methods either struggle or completely fail to realize.",
+    "neighbors": [
+      607
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 46,
+    "label": 4,
+    "text": "Title: Privacy Dashboards for Citizens and GDPR Services for Small Data Holders: A Literature Review\nAbstract: Citizens have gained many rights with the GDPR, e.g. the right to get a copy of their personal data. In practice, however, this is fraught with problems for citizens and small data holders. We present a literature review on solutions promising relief in the form of privacy dashboards for citizens and GDPR services for small data holders. Covered topics are analyzed, categorized and compared. This is ought to be a step towards both enabling citizens to exercise their GDPR rights and supporting small data holders to comply with their GDPR duties.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 47,
+    "label": 16,
+    "text": "Title: OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception\nAbstract: Semantic occupancy perception is essential for autonomous driving, as automated vehicles require a fine-grained perception of the 3D urban structures. However, existing relevant benchmarks lack diversity in urban scenes, and they only evaluate front-view predictions. Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark. In the OpenOccupancy benchmark, we extend the large-scale nuScenes dataset with dense semantic occupancy annotations. Previous annotations rely on LiDAR points superimposition, where some occupancy labels are missed due to sparse LiDAR channels. To mitigate the problem, we introduce the Augmenting And Purifying (AAP) pipeline to ~2x densify the annotations, where ~4000 human hours are involved in the labeling process. Besides, camera-based, LiDAR-based and multi-modal baselines are established for the OpenOccupancy benchmark. Furthermore, considering the complexity of surrounding occupancy perception lies in the computational burden of high-resolution 3D predictions, we propose the Cascade Occupancy Network (CONet) to refine the coarse prediction, which relatively enhances the performance by ~30% than the baseline. We hope the OpenOccupancy benchmark will boost the development of surrounding occupancy perception algorithms.",
+    "neighbors": [
+      1571,
+      2198,
+      2308
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 48,
+    "label": 16,
+    "text": "Title: Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions\nAbstract: We propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images while optimizing the underlying scene, resulting in an optimized 3D scene that respects the edit instruction. We demonstrate that our proposed method is able to edit large-scale, real-world scenes, and is able to accomplish more realistic, targeted edits than prior work.",
+    "neighbors": [
+      286,
+      330,
+      624,
+      1205,
+      1355,
+      1773
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 49,
+    "label": 24,
+    "text": "Title: A novel framework for handling sparse data in traffic forecast\nAbstract: The ever increasing amount of GPS-equipped vehicles provides in real-time valuable traffic information for the roads traversed by the moving vehicles. In this way, a set of sparse and time evolving traffic reports is generated for each road. These time series are a valuable asset in order to forecast the future traffic condition. In this paper we present a deep learning framework that encodes the sparse recent traffic information and forecasts the future traffic condition. Our framework consists of a recurrent part and a decoder. The recurrent part employs an attention mechanism that encodes the traffic reports that are available at a particular time window. The decoder is responsible to forecast the future traffic condition.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 50,
+    "label": 16,
+    "text": "Title: Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval\nAbstract: Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal models to extract visual and textual features. However, these approaches lack the necessary underlying alignment capabilities required to match multimodal data effectively. Besides, these works use prior information to explore explicit part alignments, which may lead to the distortion of intra-modality information. To alleviate these issues, we present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework that learns relations between local visual-textual tokens and enhances global image-text matching without requiring additional prior supervision. Specifically, we first design an Implicit Relation Reasoning module in a masked language modeling paradigm. This achieves cross-modal interaction by integrating the visual cues into the textual tokens with a cross-modal multimodal interaction encoder. Secondly, to globally align the visual and textual embeddings, Similarity Distribution Matching is proposed to minimize the KL divergence between image-text similarity distributions and the normalized label matching distributions. The proposed method achieves new state-of-the-art results on all three public datasets, with a notable margin of about 3%-9% for Rank-1 accuracy compared to prior methods.",
+    "neighbors": [
+      2179
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 51,
+    "label": 10,
+    "text": "Title: Notation3 as an Existential Rule Language\nAbstract: Notation3 Logic (\\nthree) is an extension of RDF that allows the user to write rules introducing new blank nodes to RDF graphs. Many applications (e.g., ontology mapping) rely on this feature as blank nodes -- used directly or in auxiliary constructs -- are omnipresent on the Web. However, the number of fast \\nthree reasoners covering this very important feature of the logic is rather limited. On the other hand, there are engines like VLog or Nemo which do not directly support Semantic Web rule formats but which are developed and optimized for very similar constructs: existential rules. In this paper, we investigate the relation between \\nthree rules with blank nodes in their heads and existential rules. We identify a subset of \\nthree which can be mapped directly to existential rules and define such a mapping preserving the equivalence of \\nthree formulae. In order to also illustrate that in some cases \\nthree reasoning could benefit from our translation, we then employ this mapping in an implementation to compare the performance of the \\nthree reasoners EYE and cwm to VLog and Nemo on \\nthree rules and their mapped counterparts. Our tests show that the existential rule reasoners perform particularly well for use cases containing many facts while especially the EYE reasoner is very fast when dealing with a high number of dependent rules. We thus provide a tool enabling the Semantic Web community to directly use existing and future existential rule reasoners and benefit from the findings of this active community.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 52,
+    "label": 16,
+    "text": "Title: Fast Full-frame Video Stabilization with Iterative Optimization\nAbstract: Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 53,
+    "label": 10,
+    "text": "Title: Decentralized Adaptive Formation via Consensus-Oriented Multi-Agent Communication\nAbstract: Adaptive multi-agent formation control, which requires the formation to flexibly adjust along with the quantity variations of agents in a decentralized manner, belongs to one of the most challenging issues in multi-agent systems, especially under communication-limited constraints. In this paper, we propose a novel Consensus-based Decentralized Adaptive Formation (Cons-DecAF) framework. Specifically, we develop a novel multi-agent reinforcement learning method, Consensus-oriented Multi-Agent Communication (ConsMAC), to enable agents to perceive global information and establish the consensus from local states by effectively aggregating neighbor messages. Afterwards, we leverage policy distillation to accomplish the adaptive formation adjustment. Meanwhile, instead of pre-assigning specific positions of agents, we employ a displacement-based formation by Hausdorff distance to significantly improve the formation efficiency. The experimental results through extensive simulations validate that the proposed method has achieved outstanding performance in terms of both speed and stability.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 54,
+    "label": 16,
+    "text": "Title: RiDDLE: Reversible and Diversified De-identification with Latent Encryptor\nAbstract: This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor, to protect the identity information of people from being misused. Built upon a pre-learned StyleGAN2 generator, RiDDLE manages to encrypt and decrypt the facial identity within the latent space. The design of RiDDLE has three appealing properties. First, the encryption process is cipher-guided and hence allows diverse anonymization using different passwords. Second, the true identity can only be decrypted with the correct password, otherwise the system will produce another de-identified face to maintain the privacy. Third, both encryption and decryption share an efficient implementation, benefiting from a carefully tailored lightweight encryptor. Comparisons with existing alternatives confirm that our approach accomplishes the de-identification task with better quality, higher diversity, and stronger reversibility. We further demonstrate the effectiveness of RiDDLE in anonymizing videos. Code and models will be made publicly available.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 55,
+    "label": 24,
+    "text": "Title: Few-shot Node Classification with Extremely Weak Supervision\nAbstract: Few-shot node classification aims at classifying nodes with limited labeled nodes as references. Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i.e., meta-training classes) and then generalize to classes with limited labeled nodes (i.e., meta-test classes). Nevertheless, on real-world graphs, it is usually difficult to obtain abundant labeled nodes for many classes. In practice, each meta-training class can only consist of several labeled nodes, known as the extremely weak supervision problem. In few-shot node classification, with extremely limited labeled nodes for meta-training, the generalization gap between meta-training and meta-test will become larger and thus lead to suboptimal performance. To tackle this issue, we study a novel problem of few-shot node classification with extremely weak supervision and propose a principled framework X-FNC under the prevalent meta-learning framework. Specifically, our goal is to accumulate meta-knowledge across different meta-training tasks with extremely weak supervision and generalize such knowledge to meta-test tasks. To address the challenges resulting from extremely scarce labeled nodes, we propose two essential modules to obtain pseudo-labeled nodes as extra references and effectively learn from extremely limited supervision information. We further conduct extensive experiments on four node classification datasets with extremely weak supervision to validate the superiority of our framework compared to the state-of-the-art baselines.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 56,
+    "label": 24,
+    "text": "Title: A Coreset Learning Reality Check\nAbstract: Subsampling algorithms are a natural approach to reduce data size before fitting models on massive datasets. In recent years, several works have proposed methods for subsampling rows from a data matrix while maintaining relevant information for classification. While these works are supported by theory and limited experiments, to date there has not been a comprehensive evaluation of these methods. In our work, we directly compare multiple methods for logistic regression drawn from the coreset and optimal subsampling literature and discover inconsistencies in their effectiveness. In many cases, methods do not outperform simple uniform subsampling.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 57,
+    "label": 30,
+    "text": "Title: Toolformer: Language Models Can Teach Themselves to Use Tools\nAbstract: Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q\\&A system, two different search engines, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities.",
+    "neighbors": [
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+      1915,
+      2104,
+      2113,
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+      2184,
+      2216,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 58,
+    "label": 24,
+    "text": "Title: Learning from Hypervectors: A Survey on Hypervector Encoding\nAbstract: Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the data are encoded with long vectors, called hypervectors, typically with a length of 1K to 10K. The literature provides several encoding techniques to generate orthogonal or correlated hypervectors, depending on the intended application. The existing surveys in the literature often focus on the overall aspects of HDC systems, including system inputs, primary computations, and final outputs. However, this study takes a more specific approach. It zeroes in on the HDC system input and the generation of hypervectors, directly influencing the hypervector encoding process. This survey brings together various methods for hypervector generation from different studies and explores the limitations, challenges, and potential benefits they entail. Through a comprehensive exploration of this survey, readers will acquire a profound understanding of various encoding types in HDC and gain insights into the intricate process of hypervector generation for diverse applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 59,
+    "label": 35,
+    "text": "Title: Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing\nAbstract: For safety reasons, unprivileged users today have only limited ways to customize the kernel through the extended Berkeley Packet Filter (eBPF). This is unfortunate, especially since the eBPF framework itself has seen an increase in scope over the years. We propose SandBPF, a software-based kernel isolation technique that dynamically sandboxes eBPF programs to allow unprivileged users to safely extend the kernel, unleashing eBPF's full potential. Our early proof-of-concept shows that SandBPF can effectively prevent exploits missed by eBPF's native safety mechanism (i.e., static verification) while incurring 0%-10% overhead on web server benchmarks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 60,
+    "label": 28,
+    "text": "Title: Reconfigurable Intelligent Surfaces to Enable Energy-Efficient IoT Networks\nAbstract: In this article, we study the uplink (UL) channel of a cellular network of Internet of Things (IoT) devices assisted by a reconfigurable intelligent surface (RIS) with a limited number of reflecting angle configurations. Firstly, we derive an expression of the required transmit power for the machine-type devices (MTDs) to attain a target signal-to-noise ratio (SNR), considering a channel model that accounts for the RIS discretization into sub-wavelength reflecting elements. Such an expression demonstrates that the transmit power depends on the target SNR, the position of the MTD in the service area, and the RIS setup, which includes the number of reflecting elements and the available reflecting angle configurations. Secondly, we develop an expression for the expected battery lifetime (EBL) of the MTDs, which explicitly depends on the MTD transmit power. Numerical simulations on the energy efficiency (EE) evaluated via the EBL demonstrate the benefits of adopting RISs to enable energy-efficient IoT networks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 61,
+    "label": 36,
+    "text": "Title: Rational verification and checking for Nash and subgame-perfect equilibria in graph games\nAbstract: We study two natural problems about rational behaviors in multiplayer non-zero-sum sequential infinite duration games played on graphs: checking problems, that consist in deciding whether a strategy profile, defined by a Mealy machine, is rational; and rational verification, that consists in deciding whether all the rational answers to a given strategy satisfy some specification. We give the complexities of those problems for two major concepts of rationality: Nash equilibria and subgame-perfect equilibria, and for five major classes of payoff functions: parity, mean-payoff, quantitative reachability, energy, and discounted-sum.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 62,
+    "label": 4,
+    "text": "Title: When GPT Meets Program Analysis: Towards Intelligent Detection of Smart Contract Logic Vulnerabilities in GPTScan\nAbstract: Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or dataflow patterns, such as re-entrancy and integer overflow. However, a recent study on Web3 security bugs revealed that about 80% of these bugs cannot be audited by existing tools due to the lack of domain-specific property description and checking. Given recent advances in Generative Pretraining Transformer (GPT), it is worth exploring how GPT could aid in detecting logic vulnerabilities in smart contracts. In this paper, we propose GPTScan, the first tool combining GPT with static analysis for smart contract logic vulnerability detection. Instead of relying solely on GPT to identify vulnerabilities, which can lead to high false positives and is limited by GPT's pre-trained knowledge, we utilize GPT as a versatile code understanding tool. By breaking down each logic vulnerability type into scenarios and properties, GPTScan matches candidate vulnerabilities with GPT. To enhance accuracy, GPTScan further instructs GPT to intelligently recognize key variables and statements, which are then validated by static confirmation. Evaluation on diverse datasets with around 400 contract projects and 3K Solidity files shows that GPTScan achieves high precision (over 90%) for token contracts and acceptable precision (57.14%) for large projects like Web3Bugs. It effectively detects groundtruth logic vulnerabilities with a recall of over 80%, including 9 new vulnerabilities missed by human auditors. GPTScan is fast and cost-effective, taking an average of 14.39 seconds and 0.01 USD to scan per thousand lines of Solidity code. Moreover, static confirmation helps GPTScan reduce two-thirds of false positives.",
+    "neighbors": [
+      1527,
+      1622
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 63,
+    "label": 16,
+    "text": "Title: Text2Tex: Text-driven Texture Synthesis via Diffusion Models\nAbstract: We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts. Our method incorporates inpainting into a pre-trained depth-aware image diffusion model to progressively synthesize high resolution partial textures from multiple viewpoints. To avoid accumulating inconsistent and stretched artifacts across views, we dynamically segment the rendered view into a generation mask, which represents the generation status of each visible texel. This partitioned view representation guides the depth-aware inpainting model to generate and update partial textures for the corresponding regions. Furthermore, we propose an automatic view sequence generation scheme to determine the next best view for updating the partial texture. Extensive experiments demonstrate that our method significantly outperforms the existing text-driven approaches and GAN-based methods.",
+    "neighbors": [
+      1205,
+      1902,
+      1905,
+      2085,
+      2117
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 64,
+    "label": 24,
+    "text": "Title: MUDiff: Unified Diffusion for Complete Molecule Generation\nAbstract: Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing methods for molecule generation focus either on 2D graph structure or on 3D geometric structure, which is not sufficient to represent a complete molecule as 2D graph captures mainly topology while 3D geometry captures mainly spatial atom arrangements. Combining these representations is essential to better represent a molecule. In this paper, we present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates, by combining discrete and continuous diffusion processes. The use of diffusion processes allows for capturing the probabilistic nature of molecular processes and exploring the effect of different factors on molecular structures. Additionally, we propose a novel graph transformer architecture to denoise the diffusion process. The transformer adheres to 3D roto-translation equivariance constraints, allowing it to learn invariant atom and edge representations while preserving the equivariance of atom coordinates. This transformer can be used to learn molecular representations robust to geometric transformations. We evaluate the performance of our model through experiments and comparisons with existing methods, showing its ability to generate more stable and valid molecules. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.",
+    "neighbors": [
+      1922
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 65,
+    "label": 24,
+    "text": "Title: The Lie-Group Bayesian Learning Rule\nAbstract: The Bayesian Learning Rule provides a framework for generic algorithm design but can be difficult to use for three reasons. First, it requires a specific parameterization of exponential family. Second, it uses gradients which can be difficult to compute. Third, its update may not always stay on the manifold. We address these difficulties by proposing an extension based on Lie-groups where posteriors are parametrized through transformations of an arbitrary base distribution and updated via the group's exponential map. This simplifies all three difficulties for many cases, providing flexible parametrizations through group's action, simple gradient computation through reparameterization, and updates that always stay on the manifold. We use the new learning rule to derive a new algorithm for deep learning with desirable biologically-plausible attributes to learn sparse features. Our work opens a new frontier for the design of new algorithms by exploiting Lie-group structures.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 66,
+    "label": 36,
+    "text": "Title: Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games\nAbstract: The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation. It describes the behavior of a group of agents who share a common set of $F$ facilities and take actions as subsets with $k$ facilities. In this work, we study the online formulation of congestion games, where agents participate in the game repeatedly and observe feedback with randomness. We propose CongestEXP, a decentralized algorithm that applies the classic exponential weights method. By maintaining weights on the facility level, the regret bound of CongestEXP avoids the exponential dependence on the size of possible facility sets, i.e., $\\binom{F}{k} \\approx F^k$, and scales only linearly with $F$. Specifically, we show that CongestEXP attains a regret upper bound of $O(kF\\sqrt{T})$ for every individual player, where $T$ is the time horizon. On the other hand, exploiting the exponential growth of weights enables CongestEXP to achieve a fast convergence rate. If a strict Nash equilibrium exists, we show that CongestEXP can converge to the strict Nash policy almost exponentially fast in $O(F\\exp(-t^{1-\\alpha}))$, where $t$ is the number of iterations and $\\alpha \\in (1/2, 1)$.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 67,
+    "label": 34,
+    "text": "Title: Long Directed Detours: Reduction to 2-Disjoint Paths\nAbstract: In the Longest ( s, t ) -Detour problem, we look for an ( s, t ) -path that is at least k vertices longer than a shortest one. We study the parameterized complexity of Longest ( s, t ) -Detour when parameterized by k : this falls into the research paradigm of \u2018parameterization above guarantee\u2019. Whereas the problem is known to be \ufb01xed-parameter tractable (FPT) on undirected graphs, the status of Longest ( s, t ) -Detour on directed graphs remains highly unclear: it is not even known to be solvable in polynomial time for k = 1 . Recently, Fomin et al. made progress in this direction by showing that the problem is FPT on every class of directed graphs where the 3-Disjoint Paths problem is solvable in polynomial time. We improve upon their result by weakening this assumption: we show that only a polynomial-time algorithm for 2-Disjoint Paths is required. What is more, our approach yields an arguably simpler proof.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 68,
+    "label": 26,
+    "text": "Title: Impact of the Covid 19 outbreaks on the italian twitter vaccination debat: a network based analysis\nAbstract: Vaccine hesitancy, or the reluctance to be vaccinated, is a phenomenon that has recently become particularly significant, in conjunction with the vaccination campaign against COVID-19. During the lockdown period, necessary to control the spread of the virus, social networks have played an important role in the Italian debate on vaccination, generally representing the easiest and safest way to exchange opinions and maintain some form of sociability. Among social network platforms, Twitter has assumed a strategic role in driving the public opinion, creating compact groups of users sharing similar views towards the utility, uselessness or even dangerousness of vaccines. In this paper, we present a new, publicly available, dataset of Italian tweets, TwitterVax, collected in the period January 2019--May 2022. Considering monthly data, gathered into forty one retweet networks -- where nodes identify users and edges are present between users who have retweeted each other -- we performed community detection within the networks, analyzing their evolution and polarization with respect to NoVax and ProVax users through time. This allowed us to clearly discover debate trends as well as identify potential key moments and actors in opinion flows, characterizing the main features and tweeting behavior of the two communities.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 69,
+    "label": 30,
+    "text": "Title: Toxicity in ChatGPT: Analyzing Persona-assigned Language Models\nAbstract: Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service. Since users include people with critical information needs like students or patients engaging with chatbots, the safety of these systems is of prime importance. Therefore, a clear understanding of the capabilities and limitations of LLMs is necessary. To this end, we systematically evaluate toxicity in over half a million generations of ChatGPT, a popular dialogue-based LLM. We find that setting the system parameter of ChatGPT by assigning it a persona, say that of the boxer Muhammad Ali, significantly increases the toxicity of generations. Depending on the persona assigned to ChatGPT, its toxicity can increase up to 6x, with outputs engaging in incorrect stereotypes, harmful dialogue, and hurtful opinions. This may be potentially defamatory to the persona and harmful to an unsuspecting user. Furthermore, we find concerning patterns where specific entities (e.g., certain races) are targeted more than others (3x more) irrespective of the assigned persona, that reflect inherent discriminatory biases in the model. We hope that our findings inspire the broader AI community to rethink the efficacy of current safety guardrails and develop better techniques that lead to robust, safe, and trustworthy AI systems.",
+    "neighbors": [
+      1001,
+      1520,
+      1713,
+      1801,
+      1878,
+      1952
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 70,
+    "label": 27,
+    "text": "Title: Dynamic Object Removal for Effective Slam\nAbstract: This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization. The paper proposes a two-step process to address this challenge, which involves finding the dynamic objects in the scene using a Flow-based method and then using a deep Video inpainting algorithm to remove them. The study aims to test the validity of this approach by comparing it with baseline results using two state-of-the-art SLAM algorithms, ORB-SLAM2 and LSD, and understanding the impact of dynamic objects and the corresponding trade-offs. The proposed approach does not require any significant modifications to the baseline SLAM algorithms, and therefore, the computational effort required remains unchanged. The paper presents a detailed analysis of the results obtained and concludes that the proposed method is effective in removing dynamic objects from the scene, leading to improved SLAM performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 71,
+    "label": 16,
+    "text": "Title: SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving\nAbstract: Semantic scene completion (SSC) is crucial for holistic 3D scene understanding by jointly estimating semantics and geometry from sparse observations. However, progress in SSC, particularly in autonomous driving scenarios, is hindered by the scarcity of high-quality datasets. To overcome this challenge, we introduce SSCBench, a comprehensive benchmark that integrates scenes from widely-used automotive datasets (e.g., KITTI-360, nuScenes, and Waymo). SSCBench follows an established setup and format in the community, facilitating the easy exploration of the camera- and LiDAR-based SSC across various real-world scenarios. We present quantitative and qualitative evaluations of state-of-the-art algorithms on SSCBench and commit to continuously incorporating novel automotive datasets and SSC algorithms to drive further advancements in this field. Our resources are released on https://github.com/ai4ce/SSCBench.",
+    "neighbors": [
+      1260,
+      1571
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 72,
+    "label": 16,
+    "text": "Title: Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation\nAbstract: Semi-supervised semantic segmentation (SSS) has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based SSS methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework which aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new CCVC approach on the SSS benchmark datasets where our method achieves new state-of-the-art performance. Our code is available at https://github.com/xiaoyao3302/CCVC.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 73,
+    "label": 24,
+    "text": "Title: The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data\nAbstract: In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. This helps formulate hypotheses regarding molecular mechanisms that could potentially be targeted by future medicines. The CausalBench Challenge was an initiative to invite the machine learning community to advance the state of the art in constructing gene-gene interaction networks. These networks, derived from large-scale, real-world datasets of single cells under various perturbations, are crucial for understanding the causal mechanisms underlying disease biology. Using the framework provided by the CausalBench benchmark, participants were tasked with enhancing the capacity of the state of the art methods to leverage large-scale genetic perturbation data. This report provides an analysis and summary of the methods submitted during the challenge to give a partial image of the state of the art at the time of the challenge. The winning solutions significantly improved performance compared to previous baselines, establishing a new state of the art for this critical task in biology and medicine.",
+    "neighbors": [
+      160
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 74,
+    "label": 24,
+    "text": "Title: Byte Pair Encoding for Symbolic Music\nAbstract: The symbolic music modality is nowadays mostly represented as discrete and used with sequential models such as Transformers, for deep learning tasks. Recent research put efforts on the tokenization, i.e. the conversion of data into sequences of integers intelligible to such models. This can be achieved by many ways as music can be composed of simultaneous tracks, of simultaneous notes with several attributes. Until now, the proposed tokenizations are based on small vocabularies describing the note attributes and time events, resulting in fairly long token sequences. In this paper, we show how Byte Pair Encoding (BPE) can improve the results of deep learning models while improving its performances. We experiment on music generation and composer classification, and study the impact of BPE on how models learn the embeddings, and show that it can help to increase their isotropy, i.e., the uniformity of the variance of their positions in the space.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 75,
+    "label": 30,
+    "text": "Title: Whose Opinions Do Language Models Reflect?\nAbstract: Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large. In this work, we put forth a quantitative framework to investigate the opinions reflected by LMs -- by leveraging high-quality public opinion polls and their associated human responses. Using this framework, we create OpinionsQA, a new dataset for evaluating the alignment of LM opinions with those of 60 US demographic groups over topics ranging from abortion to automation. Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs (e.g., 65+ and widowed individuals). Our code and data are available at https://github.com/tatsu-lab/opinions_qa.",
+    "neighbors": [
+      352,
+      1259,
+      1520,
+      2140,
+      2258,
+      2305
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 76,
+    "label": 4,
+    "text": "Title: Exploring Smart Commercial Building Occupants\u2019 Perceptions and Notification Preferences of Internet of Things Data Collection in the United States\nAbstract: Data collection through the Internet of Things (IoT) devices, or smart devices, in commercial buildings enables possibilities for increased convenience and energy efficiency. However, such benefits face a large perceptual challenge when being implemented in practice, due to the different ways occupants working in the buildings understand and trust in the data collection. The semi-public, pervasive, and multi-modal nature of data collection in smart buildings points to the need to study occupants\u2019 understanding of data collection and notification preferences. We conduct an online study with 492 participants in the US who report working in smart commercial buildings regarding: 1) awareness and perception of data collection in smart commercial buildings, 2) privacy notification preferences, and 3) potential factors for privacy notification preferences. We find that around half of the participants are not fully aware of the data collection and use practices of IoT even though they notice the presence of IoT devices and sensors. We also discover many misunderstandings around different data practices. The majority of participants want to be notified of data practices in smart buildings, and they prefer push notifications to passive ones such as websites or physical signs. Surprisingly, mobile app notification, despite being a popular channel for smart homes, is the least preferred method for smart commercial buildings.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 77,
+    "label": 16,
+    "text": "Title: Learn More for Food Recognition via Progressive Self-Distillation\nAbstract: Food recognition has a wide range of applications, such as health-aware recommendation and self-service restaurants. Most previous methods of food recognition firstly locate informative regions in some weakly-supervised manners and then aggregate their features. However, location errors of informative regions limit the effectiveness of these methods to some extent. Instead of locating multiple regions, we propose a Progressive Self-Distillation (PSD) method, which progressively enhances the ability of network to mine more details for food recognition. The training of PSD simultaneously contains multiple self-distillations, in which a teacher network and a student network share the same embedding network. Since the student network receives a modified image from its teacher network by masking some informative regions, the teacher network outputs stronger semantic representations than the student network. Guided by such teacher network with stronger semantics, the student network is encouraged to mine more useful regions from the modified image by enhancing its own ability. The ability of the teacher network is also enhanced with the shared embedding network. By using progressive training, the teacher network incrementally improves its ability to mine more discriminative regions. In inference phase, only the teacher network is used without the help of the student network. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method and state-of-the-art performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 78,
+    "label": 24,
+    "text": "Title: A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems\nAbstract: This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 79,
+    "label": 10,
+    "text": "Title: Assessing Trustworthiness of Autonomous Systems\nAbstract: As Autonomous Systems (AS) become more ubiquitous in society, more responsible for our safety and our interaction with them more frequent, it is essential that they are trustworthy. Assessing the trustworthiness of AS is a mandatory challenge for the verification and development community. This will require appropriate standards and suitable metrics that may serve to objectively and comparatively judge trustworthiness of AS across the broad range of current and future applications. The meta-expression `trustworthiness' is examined in the context of AS capturing the relevant qualities that comprise this term in the literature. Recent developments in standards and frameworks that support assurance of autonomous systems are reviewed. A list of key challenges are identified for the community and we present an outline of a process that can be used as a trustworthiness assessment framework for AS.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 80,
+    "label": 27,
+    "text": "Title: Agent 3, change your route: possible conversation between a human manager and UAM Air Traffic Management (UATM)\nAbstract: This work in progress paper provides an example to show a detouring procedure through knowledge representation and reasoning. When a human manager requests a detouring, this should affect the related agents. Through non-monotonic reasoning process, we verify each step to be proceeded and provide all the successful connections of the reasoning. Following this progress and continuing this idea development, we expect that this simulated scenario can be a guideline to build the traffic management system in real. After a brief introduction including related works, we provide our problem formulation, primary work, discussion, and conclusions.",
+    "neighbors": [
+      2128
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 81,
+    "label": 30,
+    "text": "Title: Enabling Large Language Models to Generate Text with Citations\nAbstract: Large language models (LLMs) have emerged as a widely-used tool for information seeking, but their generated outputs are prone to hallucination. In this work, we aim to enable LLMs to generate text with citations, improving their factual correctness and verifiability. Existing work mainly relies on commercial search engines and human evaluation, making it challenging to reproduce and compare with different modeling approaches. We propose ALCE, the first benchmark for Automatic LLMs' Citation Evaluation. ALCE collects a diverse set of questions and retrieval corpora and requires building end-to-end systems to retrieve supporting evidence and generate answers with citations. We build automatic metrics along three dimensions -- fluency, correctness, and citation quality -- and demonstrate their strong correlation with human judgements. Our experiments with state-of-the-art LLMs and novel prompting strategies show that current systems have considerable room for improvements -- for example, on the ELI5 dataset, even the best model has 49% of its generations lacking complete citation support. Our extensive analyses further highlight promising future directions, including developing better retrievers, advancing long-context LLMs, and improving the ability to synthesize information from multiple sources.",
+    "neighbors": [
+      57,
+      1052,
+      1950,
+      1972,
+      2038
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 82,
+    "label": 24,
+    "text": "Title: Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources\nAbstract: Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce\"patchwork learning\"(PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows.",
+    "neighbors": [
+      1047,
+      1584,
+      1863
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 83,
+    "label": 16,
+    "text": "Title: An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation\nAbstract: Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii) 3D annotations on LiDAR point clouds are time-consuming and expensive. To fill this gap, we present an efficient semi-automated annotation tool that automatically annotates LiDAR sequences with tracking algorithms while offering a fully annotated infrastructure LiDAR dataset -- FLORIDA (Florida LiDAR-based Object Recognition and Intelligent Data Annotation) -- which will be made publicly available. Our advanced annotation tool seamlessly integrates multi-object tracking (MOT), single-object tracking (SOT), and suitable trajectory post-processing techniques. Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models. By repeating the process, we significantly increase the overall annotation speed by three to four times and obtain better qualitative annotations than a state-of-the-art annotation tool. The human annotation experiments verify the effectiveness of our annotation tool. In addition, we provide detailed statistics and object detection evaluation results for our dataset in serving as a benchmark for perception tasks at traffic intersections.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 84,
+    "label": 34,
+    "text": "Title: Algorithmically Effective Differentially Private Synthetic Data\nAbstract: We present a highly effective algorithmic approach for generating $\\varepsilon$-differentially private synthetic data in a bounded metric space with near-optimal utility guarantees under the 1-Wasserstein distance. In particular, for a dataset $X$ in the hypercube $[0,1]^d$, our algorithm generates synthetic dataset $Y$ such that the expected 1-Wasserstein distance between the empirical measure of $X$ and $Y$ is $O((\\varepsilon n)^{-1/d})$ for $d\\geq 2$, and is $O(\\log^2(\\varepsilon n)(\\varepsilon n)^{-1})$ for $d=1$. The accuracy guarantee is optimal up to a constant factor for $d\\geq 2$, and up to a logarithmic factor for $d=1$. Our algorithm has a fast running time of $O(\\varepsilon dn)$ for all $d\\geq 1$ and demonstrates improved accuracy compared to the method in (Boedihardjo et al., 2022) for $d\\geq 2$.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 85,
+    "label": 10,
+    "text": "Title: HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level\nAbstract: Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information. The internal structure of HKG can be represented as a hypergraph-based representation globally and a semantic sequence-based representation locally. However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs\u2019 representation. To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets. In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task. Our code is publicly available.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 86,
+    "label": 13,
+    "text": "Title: Toward spike-based stochastic neural computing\nAbstract: Inspired by the highly irregular spiking activity of cortical neurons, stochastic neural computing is an attractive theory for explaining the operating principles of the brain and the ability to represent uncertainty by intelligent agents. However, computing and learning with high-dimensional joint probability distributions of spiking neural activity across large populations of neurons present as a major challenge. To overcome this, we develop a novel moment embedding approach to enable gradient-based learning in spiking neural networks accounting for the propagation of correlated neural variability. We show under the supervised learning setting a spiking neural network trained this way is able to learn the task while simultaneously minimizing uncertainty, and further demonstrate its application to neuromorphic hardware. Built on the principle of spike-based stochastic neural computing, the proposed method opens up new opportunities for developing machine intelligence capable of computing uncertainty and for designing unconventional computing architectures.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 87,
+    "label": 28,
+    "text": "Title: The Freshness Game: Timely Communications in the Presence of an Adversary\nAbstract: We consider a communication system where a base station (BS) transmits update packets to $N$ users, one user at a time, over a wireless channel. We investigate the age of this status updating system with an adversary that jams the update packets in the downlink. We consider two system models: with diversity and without diversity. In the model without diversity, we show that if the BS schedules the users with a stationary randomized policy, then the optimal choice for the adversary is to block the user which has the lowest probability of getting scheduled by the BS, at the middle of the time horizon, consecutively for $\\alpha T$ time slots. In the model with diversity, we show that for large $T$, the uniform user scheduling algorithm together with the uniform sub-carrier choosing algorithm is $\\frac{2 N_{sub}}{N_{sub}-1}$ optimal. Next, we investigate the game theoretic equilibrium points of this status updating system. For the model without diversity, we show that a Nash equilibrium does not exist, however, a Stackelberg equilibrium exists when the scheduling algorithm of the BS acts as the leader and the adversary acts as the follower. For the model with diversity, we show that a Nash equilibrium exists and identify the Nash equilibrium. Finally, we extend the model without diversity to the case where the BS can serve multiple users and the adversary can jam multiple users, at a time.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 88,
+    "label": 16,
+    "text": "Title: On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization\nAbstract: Achieving high accuracy on data from domains unseen during training is a fundamental challenge in domain generalization (DG). While state-of-the-art DG classifiers have demonstrated impressive performance across various tasks, they have shown a bias towards domain-dependent information, such as image styles, rather than domain-invariant information, such as image content. This bias renders them unreliable for deployment in risk-sensitive scenarios such as autonomous driving where a misclassification could lead to catastrophic consequences. To enable risk-averse predictions from a DG classifier, we propose a novel inference procedure, Test-Time Neural Style Smoothing (TT-NSS), that uses a\"style-smoothed\"version of the DG classifier for prediction at test time. Specifically, the style-smoothed classifier classifies a test image as the most probable class predicted by the DG classifier on random re-stylizations of the test image. TT-NSS uses a neural style transfer module to stylize a test image on the fly, requires only black-box access to the DG classifier, and crucially, abstains when predictions of the DG classifier on the stylized test images lack consensus. Additionally, we propose a neural style smoothing (NSS) based training procedure that can be seamlessly integrated with existing DG methods. This procedure enhances prediction consistency, improving the performance of TT-NSS on non-abstained samples. Our empirical results demonstrate the effectiveness of TT-NSS and NSS at producing and improving risk-averse predictions on unseen domains from DG classifiers trained with SOTA training methods on various benchmark datasets and their variations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 89,
+    "label": 34,
+    "text": "Title: Deterministic Massively Parallel Symmetry Breaking for Sparse Graphs\nAbstract: We consider the problem of designing deterministic graph algorithms for the model of Massively Parallel Computation (MPC) that improve with the sparsity of the input graph, as measured by the standard notion of arboricity. For the problems of maximal independent set (MIS), maximal matching (MM), and vertex coloring, we improve the state of the art as follows. Let \u03bb denote the arboricity of the n-node input graph with maximum degree \u0394. MIS and MM We develop a low-space MPC algorithm that deterministically reduces the maximum degree to poly(\u03bb) in O(log log n) rounds, improving and simplifying the randomized O(log log n)-round poly(max(\u03bb, log n))-degree reduction of Ghaffari, Grunau, Jin [DISC'20]. Our approach when combined with the state-of-the-art O(log \u0394 + log log n)-round algorithm by Czumaj, Davies, Parter [SPAA'20, TALG'21] leads to an improved deterministic round complexity of O(log \u03bb + log log n). The above MIS and MM algorithm however works in the setting where the global memory allowed, i.e., the number of machines times the local memory per machine, is superlinear in the input size. We extend them to obtain the first low-space MIS and MM algorithms that work with linear global memory. Specifically, we show that both problems can be solved in deterministic time O(log \u03bb \u00b7 log log\u03bb n), and even in O(log log n) time for graphs with arboricity at most logO(1) log n. In this setting, only a O(log2 log n)-running time bound for trees was known due to Latypov and Uitto [ArXiv'21]. Vertex Coloring We present a O(1)-round deterministic algorithm for the problem of O(&955;)-coloring in the linear-memory regime of MPC, with relaxed global memory of n \u00b7 poly(&955;). This matches the round complexity of the state-of-the-art randomized algorithm by Ghaffari and Sayyadi [ICALP'19] and significantly improves upon the deterministic O(&955;\u03b5 )-round algorithm by Barenboim and Khazanov [CSR'18]. Our algorithm solves the problem after just one single graph partitioning step, in contrast to the involved local coloring simulations of the above state-of-the-art algorithms. Using O(n + m) global memory, we derive a O(log \u03bb)-round algorithm by combining the constant-round (\u0394 + 1)-list-coloring algorithm by Czumaj, Davies, Parter [PODC'20, SIAM J. Comput.'21] with that of Barenboim and Khazanov.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 90,
+    "label": 10,
+    "text": "Title: Concise QBF Encodings for Games on a Grid (extended version)\nAbstract: Encoding 2-player games in QBF correctly and efficiently is challenging and error-prone. To enable concise specifications and uniform encodings of games played on grid boards, like Tic-Tac-Toe, Connect-4, Domineering, Pursuer-Evader and Breakthrough, we introduce Board-game Domain Definition Language (BDDL), inspired by the success of PDDL in the planning domain. We provide an efficient translation from BDDL into QBF, encoding the existence of a winning strategy of bounded depth. Our lifted encoding treats board positions symbolically and allows concise definitions of conditions, effects and winning configurations, relative to symbolic board positions. The size of the encoding grows linearly in the input model and the considered depth. To show the feasibility of such a generic approach, we use QBF solvers to compute the critical depths of winning strategies for instances of several known games. For several games, our work provides the first QBF encoding. Unlike plan validation in SAT-based planning, validating QBF-based winning strategies is difficult. We show how to validate winning strategies using QBF certificates and interactive game play.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 91,
+    "label": 24,
+    "text": "Title: Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models\nAbstract: Most existing cross-modal generative methods based on diffusion models use guidance to provide control over the latent space to enable conditional generation across different modalities. Such methods focus on providing guidance through separately-trained models, each for one modality. As a result, these methods suffer from cross-modal information loss and are limited to unidirectional conditional generation. Inspired by how humans synchronously acquire multi-modal information and learn the correlation between modalities, we explore a multi-modal diffusion model training and sampling scheme that uses channel-wise image conditioning to learn cross-modality correlation during the training phase to better mimic the learning process in the brain. Our empirical results demonstrate that our approach can achieve data generation conditioned on all correlated modalities.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 92,
+    "label": 6,
+    "text": "Title: Virtual Reality Training of Social Skills in Adults with Autism Spectrum Disorder: An Examination of Acceptability, Usability, User Experience, Social Skills, and Executive Functions\nAbstract: Poor social skills in autism spectrum disorder (ASD) are associated with reduced independence in daily life. Current interventions for improving the social skills of individuals with ASD fail to represent the complexity of real-life social settings and situations. Virtual reality (VR) may facilitate social skills training in social environments and situations similar to those in real life; however, more research is needed to elucidate aspects such as the acceptability, usability, and user experience of VR systems in ASD. Twenty-five participants with ASD attended a neuropsychological evaluation and three sessions of VR social skills training, which incorporated five social scenarios with three difficulty levels. Participants reported high acceptability, system usability, and user experience. Significant correlations were observed between performance in social scenarios, self-reports, and executive functions. Working memory and planning ability were significant predictors of the functionality level in ASD and the VR system\u2019s perceived usability, respectively. Yet, performance in social scenarios was the best predictor of usability, acceptability, and functionality level. Planning ability substantially predicted performance in social scenarios, suggesting an implication in social skills. Immersive VR social skills training in individuals with ASD appears to be an appropriate service, but an errorless approach that is adaptive to the individual\u2019s needs should be preferred.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 93,
+    "label": 24,
+    "text": "Title: Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers\nAbstract: To address the problem of NLP classifiers learning spurious correlations between training features and target labels, a common approach is to make the model\u2019s predictions invariant to these features. However, this can be counter-productive when the features have a non-zero causal effect on the target label and thus are important for prediction. Therefore, using methods from the causal inference literature, we propose an algorithm to regularize the learnt effect of the features on the model\u2019s prediction to the estimated effect of feature on label. This results in an automated augmentation method that leverages the estimated effect of a feature to appropriately change the labels for new augmented inputs. On toxicity and IMDB review datasets, the proposed algorithm minimises spurious correlations and improves the minority group (i.e., samples breaking spurious correlations) accuracy, while also improving the total accuracy compared to standard training.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 94,
+    "label": 24,
+    "text": "Title: Numerical Association Rule Mining: A Systematic Literature Review\nAbstract: Numerical association rule mining is a widely used variant of the association rule mining technique, and it has been extensively used in discovering patterns and relationships in numerical data. Initially, researchers and scientists integrated numerical attributes in association rule mining using various discretization approaches; however, over time, a plethora of alternative methods have emerged in this field. Unfortunately, the increase of alternative methods has resulted into a significant knowledge gap in understanding diverse techniques employed in numerical association rule mining -- this paper attempts to bridge this knowledge gap by conducting a comprehensive systematic literature review. We provide an in-depth study of diverse methods, algorithms, metrics, and datasets derived from 1,140 scholarly articles published from the inception of numerical association rule mining in the year 1996 to 2022. In compliance with the inclusion, exclusion, and quality evaluation criteria, 68 papers were chosen to be extensively evaluated. To the best of our knowledge, this systematic literature review is the first of its kind to provide an exhaustive analysis of the current literature and previous surveys on numerical association rule mining. The paper discusses important research issues, the current status, and future possibilities of numerical association rule mining. On the basis of this systematic review, the article also presents a novel discretization measure that contributes by providing a partitioning of numerical data that meets well human perception of partitions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 95,
+    "label": 36,
+    "text": "Title: Synthesizing Permissive Winning Strategy Templates for Parity Games\nAbstract: We present a novel method to compute \\emph{permissive winning strategies} in two-player games over finite graphs with $ \\omega $-regular winning conditions. Given a game graph $G$ and a parity winning condition $\\Phi$, we compute a \\emph{winning strategy template} $\\Psi$ that collects an infinite number of winning strategies for objective $\\Phi$ in a concise data structure. We use this new representation of sets of winning strategies to tackle two problems arising from applications of two-player games in the context of cyber-physical system design -- (i) \\emph{incremental synthesis}, i.e., adapting strategies to newly arriving, \\emph{additional} $\\omega$-regular objectives $\\Phi'$, and (ii) \\emph{fault-tolerant control}, i.e., adapting strategies to the occasional or persistent unavailability of actuators. The main features of our strategy templates -- which we utilize for solving these challenges -- are their easy computability, adaptability, and compositionality. For \\emph{incremental synthesis}, we empirically show on a large set of benchmarks that our technique vastly outperforms existing approaches if the number of added specifications increases. While our method is not complete, our prototype implementation returns the full winning region in all 1400 benchmark instances, i.e., handling a large problem class efficiently in practice.",
+    "neighbors": [
+      2285
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 96,
+    "label": 4,
+    "text": "Title: Protecting the Decentralized Future: An Exploration of Common Blockchain Attacks and their Countermeasures\nAbstract: Blockchain technology transformed the digital sphere by providing a transparent, secure, and decentralized platform for data security across a range of industries, including cryptocurrencies and supply chain management. Blockchain's integrity and dependability have been jeopardized by the rising number of security threats, which have attracted cybercriminals as a target. By summarizing suggested fixes, this research aims to offer a thorough analysis of mitigating blockchain attacks. The objectives of the paper include identifying weak blockchain attacks, evaluating various solutions, and determining how effective and effective they are at preventing these attacks. The study also highlights how crucial it is to take into account the particular needs of every blockchain application. This study provides beneficial perspectives and insights for blockchain researchers and practitioners, making it essential reading for those interested in current and future trends in blockchain security research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 97,
+    "label": 24,
+    "text": "Title: Federated PAC-Bayesian Learning on Non-IID data\nAbstract: Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique prior knowledge for each client and variable aggregation weights. We also introduce an objective function and an innovative Gibbs-based algorithm for the optimization of the derived bound. The results are validated on real-world datasets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 98,
+    "label": 25,
+    "text": "Title: An Attention-based Approach to Hierarchical Multi-label Music Instrument Classification\nAbstract: Although music is typically multi-label, many works have studied hierarchical music tagging with simplified settings such as single-label data. Moreover, there lacks a framework to describe various joint training methods under the multi-label setting. In order to discuss the above topics, we introduce hierarchical multi-label music instrument classification task. The task provides a realistic setting where multi-instrument real music data is assumed. Various hierarchical methods that jointly train a DNN are summarized and explored in the context of the fusion of deep learning and conventional techniques. For the effective joint training in the multi-label setting, we propose two methods to model the connection between fine- and coarse-level tags, where one uses rule-based grouped max-pooling, the other one uses the attention mechanism obtained in a data-driven manner. Our evaluation reveals that the proposed methods have advantages over the method without joint training. In addition, the decision procedure within the proposed methods can be interpreted by visualizing attention maps or referring to fixed rules.",
+    "neighbors": [
+      1813
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 99,
+    "label": 16,
+    "text": "Title: Image Synthesis under Limited Data: A Survey and Taxonomy\nAbstract: Deep generative models, which target reproducing the given data distribution to produce novel samples, have made unprecedented advancements in recent years. Their technical breakthroughs have enabled unparalleled quality in the synthesis of visual content. However, one critical prerequisite for their tremendous success is the availability of a sufficient number of training samples, which requires massive computation resources. When trained on limited data, generative models tend to suffer from severe performance deterioration due to overfitting and memorization. Accordingly, researchers have devoted considerable attention to develop novel models that are capable of generating plausible and diverse images from limited training data recently. Despite numerous efforts to enhance training stability and synthesis quality in the limited data scenarios, there is a lack of a systematic survey that provides 1) a clear problem definition, critical challenges, and taxonomy of various tasks; 2) an in-depth analysis on the pros, cons, and remain limitations of existing literature; as well as 3) a thorough discussion on the potential applications and future directions in the field of image synthesis under limited data. In order to fill this gap and provide a informative introduction to researchers who are new to this topic, this survey offers a comprehensive review and a novel taxonomy on the development of image synthesis under limited data. In particular, it covers the problem definition, requirements, main solutions, popular benchmarks, and remain challenges in a comprehensive and all-around manner.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 100,
+    "label": 31,
+    "text": "Title: Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce\nAbstract: Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems. The assumption is to use a fixed Top-N to represent them. We propose that median impressions viewed from historical sessions per diner be used as a personalized value for N. We present preliminary exploratory results and list future steps to improve upon and evaluate the efficacy of these personalized metrics.",
+    "neighbors": [
+      1413
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 101,
+    "label": 16,
+    "text": "Title: CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation\nAbstract: Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by selftraining with pseudo-labeling pixels having high confidences for unlabeled images. However, using only highconfidence pixels for self-training may result in losing much of the information in the unlabeled datasets due to poor confidence calibration of modern deep learning networks. In this paper, we propose a class-adaptive semisupervision framework for semi-supervised semantic segmentation (CAFS) to cope with the loss of most information that occurs in existing high-confidence-based pseudolabeling methods. Unlike existing semi-supervised semantic segmentation frameworks, CAFS constructs a validation set on a labeled dataset, to leverage the calibration performance for each class. On this basis, we propose a calibration aware class-wise adaptive thresholding and classwise adaptive oversampling using the analysis results from the validation set. Our proposed CAFS achieves state-ofthe-art performance on the full data partition of the base PASCAL VOC 2012 dataset and on the 1/4 data partition of the Cityscapes dataset with significant margins of 83.0% and 80.4%, respectively. The code is available at https://github.com/cjf8899/CAFS.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 102,
+    "label": 8,
+    "text": "Title: Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels\nAbstract: With the continuous increment of maritime applications, the development of marine networks for data offloading becomes necessary. However, the limited maritime network resources are very difficult to satisfy real-time demands. Besides, how to effectively handle multiple compute-intensive tasks becomes another intractable issue. Hence, in this paper, we focus on the decision of maritime task offloading by the cooperation of unmanned aerial vehicles (UAVs) and vessels. Specifically, we first propose a cooperative offloading framework, including the demands from marine Internet of Things (MIoTs) devices and resource providers from UAVs and vessels. Due to the limited energy and computation ability of UAVs, it is necessary to help better apply the vessels to computation offloading. Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost. Then, we leverage Lyapunov optimization to convert the long-term constraints of the total execution time and energy cost into their short-term constraints, further yielding a set of per-time-slot optimization problems. Furthermore, we propose a Q-learning based approach to solve the short-term problem efficiently. Finally, simulation results are conducted to verify the correctness and effectiveness of the proposed algorithm.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 103,
+    "label": 16,
+    "text": "Title: A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics\nAbstract: nan",
+    "neighbors": [
+      2102
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 104,
+    "label": 16,
+    "text": "Title: Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information\nAbstract: In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by aligning the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 105,
+    "label": 24,
+    "text": "Title: FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?\nAbstract: Few-shot learning aims to train models that can be generalized to novel classes with only a few samples. Recently, a line of works are proposed to enhance few-shot learning with accessible semantic information from class names. However, these works focus on improving existing modules such as visual prototypes and feature extractors of the standard few-shot learning framework. This limits the full potential use of semantic information. In this paper, we propose a novel few-shot learning framework that uses pre-trained language models based on contrastive learning. To address the challenge of alignment between visual features and textual embeddings obtained from text-based pre-trained language model, we carefully design the textual branch of our framework and introduce a metric module to generalize the cosine similarity. For better transferability, we let the metric module adapt to different few-shot tasks and adopt MAML to train the model via bi-level optimization. Moreover, we conduct extensive experiments on multiple benchmarks to demonstrate the effectiveness of our method.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 106,
+    "label": 16,
+    "text": "Title: Review of Large Vision Models and Visual Prompt Engineering\nAbstract: Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilities. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.",
+    "neighbors": [
+      535,
+      584,
+      929,
+      1050,
+      1052,
+      1084,
+      1199,
+      1262,
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+      1863,
+      2057,
+      2113,
+      2203,
+      2296
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 107,
+    "label": 8,
+    "text": "Title: Improving the Efficiency of MIMO Simulations in ns-3\nAbstract: Channel modeling is a fundamental task for the design and evaluation of wireless technologies and networks, before actual prototyping, commercial product development and real deployments. The recent trends of current and future mobile networks, which include large antenna systems, massive deployments, and high-frequency bands, require complex channel models for the accurate simulation of massive MIMO (m-MIMO) in millimeter wave (mmWave) and Terahertz (THz) bands. To address the complexity/accuracy trade-off, a spatial channel model has been defined by 3GPP (TR 38.901), which has been shown to be the main bottleneck of current system-level simulations in ns-3. In this paper, we focus on improving the channel modeling efficiency for large-scale MIMO system-level simulations. Extensions are developed in two directions. First, we improve the efficiency of the current 3GPP TR 38.901 implementation code in ns-3, by allowing the use of the Eigen library for more efficient matrix algebra operations, among other optimizations and a more modular code structure. Second, we propose a new performance-oriented MIMO channel model for reduced complexity, as an alternative model suitable for mmWave/THz bands, and calibrate it against the 3GPP TR 38.901 model. Simulation results demonstrate the proper calibration of the newly introduced model for various scenarios and channel conditions, and exhibit an effective reduction of the simulation time (up to 16 times compared to the previous baseline) thanks to the various proposed improvements.",
+    "neighbors": [
+      839
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 108,
+    "label": 24,
+    "text": "Title: Symplectic Structure-Aware Hamiltonian (Graph) Embeddings\nAbstract: In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-inspired GNNs are proposed to address the dynamic nature of such embeddings by incorporating physical laws into node feature updates. In this work, we present SAH-GNN, a novel approach that generalizes Hamiltonian dynamics for more flexible node feature updates. Unlike existing Hamiltonian-inspired GNNs, SAH-GNN employs Riemannian optimization on the symplectic Stiefel manifold to adaptively learn the underlying symplectic structure during training, circumventing the limitations of existing Hamiltonian GNNs that rely on a pre-defined form of standard symplectic structure. This innovation allows SAH-GNN to automatically adapt to various graph datasets without extensive hyperparameter tuning. Moreover, it conserves energy during training such that the implicit Hamiltonian system is physically meaningful. To this end, we empirically validate SAH-GNN's superior performance and adaptability in node classification tasks across multiple types of graph datasets.",
+    "neighbors": [
+      236
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 109,
+    "label": 8,
+    "text": "Title: A Review of Gaps between Web 4.0 and Web 3.0 Intelligent Network Infrastructure\nAbstract: World Wide Web is speeding up its pace into an intelligent and decentralized ecosystem, as seen in the campaign of Web 3.0 and forthcoming Web 4.0. Marked by the Europe Commission's latest mention of Web 4.0, a race towards strategic Web 4.0 success has started. Web 4.0 is committed to bringing the next technological transition with an open, secure, trustworthy fairness and digital ecosystem for individuals and businesses in private and public sectors. Despite overlapping scopes and objectives of Web 3.0 and Web 4.0 from academic and industrial perspectives, there are distinct and definitive features and gaps for the next generation of WWW. In this review, a brief introduction to WWW development unravels the entangled but consistent requirement of a more vivid web experience, enhancing human-centric experience in both societal and technical aspects. Moreover, the review brings a decentralized intelligence prospect of view on native AI entities for Web 4.0, envisioning sustainable, autonomous and decentralized AI services for the entire Web 4.0 environment, powering a self-sustainable Decentralized Physical and Software Infrastructure for Computing Force Network, Semantic Network, Virtual/Mixed Reality, and Privacy-preserving content presumption. The review aims to reveal that Web 4.0 offers native intelligence with focused thinking on utilizing decentralized physical infrastructure, in addition to sole requirements on decentralization, bridging the gap between Web 4.0 and Web 3.0 advances with the latest future-shaping blockchain-enabled computing and network routing protocols.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 110,
+    "label": 6,
+    "text": "Title: StoryChat: Designing a Narrative-Based Viewer Participation Tool for Live Streaming Chatrooms\nAbstract: Live streaming platforms and existing viewer participation tools enable users to interact and engage with an online community, but the anonymity and scale of chat usually result in the spread of negative comments. However, only a few existing moderation tools investigate the influence of proactive moderation on viewers\u2019 engagement and prosocial behavior. To address this, we developed StoryChat, a narrative-based viewer participation tool that utilizes a dynamic graphical plot to reflect chatroom negativity. We crafted the narrative through a viewer-centered (N=65) iterative design process and evaluated the tool with 48 experienced viewers in a deployment study. We discovered that StoryChat encouraged viewers to contribute prosocial comments, increased viewer engagement, and fostered viewers\u2019 sense of community. Viewers reported a closer connection between streamers and other viewers because of the narrative design, suggesting that narrative-based viewer engagement tools have the potential to encourage community engagement and prosocial behaviors.",
+    "neighbors": [
+      452
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 111,
+    "label": 24,
+    "text": "Title: ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis\nAbstract: With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public ground-truth datasets. While there are a few available datasets for turn-based sports for action detection, these datasets severely lack structured source data and stroke-level records since these require high-cost labeling efforts from domain experts and are hard to detect using automatic techniques. Consequently, the development of artificial intelligence approaches is significantly hindered when existing models are applied to more challenging structured turn-based sequences. In this paper, we present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type with a choice of 18 distinct classes, the corresponding hitting locations, and the locations of both players at each stroke. In the experiments, we provide multiple benchmarks (i.e., stroke influence, stroke forecasting, and movement forecasting) with baselines to illustrate the practicability of using ShuttleSet for turn-based analytics, which is expected to stimulate both academic and sports communities. Over the past two years, a visualization platform has been deployed to illustrate the variability of analysis cases from ShuttleSet for coaches to delve into players' tactical preferences with human-interactive interfaces, which was also used by national badminton teams during multiple international high-ranking matches.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 112,
+    "label": 15,
+    "text": "Title: VarSim: A Fast Process Variation-aware Thermal Modeling Methodology Using Green's Functions\nAbstract: Despite temperature rise being a first-order design constraint, traditional thermal estimation techniques have severe limitations in modeling critical aspects affecting the temperature in modern-day chips. Existing thermal modeling techniques often ignore the effects of parameter variation, which can lead to significant errors. Such methods also ignore the dependence of conductivity on temperature and its variation. Leakage power is also incorporated inadequately by state-of-the-art techniques. Thermal modeling is a process that has to be repeated at least thousands of times in the design cycle, and hence speed is of utmost importance. To overcome these limitations, we propose VarSim, an ultrafast thermal simulator based on Green's functions. Green's functions have been shown to be faster than the traditional finite difference and finite element-based approaches but have rarely been employed in thermal modeling. Hence we propose a new Green's function-based method to capture the effects of leakage power as well as process variation analytically. We provide a closed-form solution for the Green's function considering the effects of variation on the process, temperature, and thermal conductivity. In addition, we propose a novel way of dealing with the anisotropicity introduced by process variation by splitting the Green's functions into shift-variant and shift-invariant components. Since our solutions are analytical expressions, we were able to obtain speedups that were several orders of magnitude over and above state-of-the-art proposals with a mean absolute error limited to 4% for a wide range of test cases. Furthermore, our method accurately captures the steady-state as well as the transient variation in temperature.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 113,
+    "label": 24,
+    "text": "Title: CenTime: Event-Conditional Modelling of Censoring in Survival Analysis\nAbstract: Survival analysis is a valuable tool for estimating the time until specific events, such as death or cancer recurrence, based on baseline observations. This is particularly useful in healthcare to prognostically predict clinically important events based on patient data. However, existing approaches often have limitations; some focus only on ranking patients by survivability, neglecting to estimate the actual event time, while others treat the problem as a classification task, ignoring the inherent time-ordered structure of the events. Furthermore, the effective utilization of censored samples - training data points where the exact event time is unknown - is essential for improving the predictive accuracy of the model. In this paper, we introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data. Furthermore, CenTime is easily integrated with deep learning models with no restrictions on batch size or the number of uncensored samples. We compare our approach with standard survival analysis methods, including the Cox proportional-hazard model and DeepHit. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance. Our implementation is publicly available at https://github.com/ahmedhshahin/CenTime.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 114,
+    "label": 4,
+    "text": "Title: ForensiBlock: A Provenance-Driven Blockchain Framework for Data Forensics and Auditability\nAbstract: Maintaining accurate provenance records is paramount in digital forensics, as they underpin evidence credibility and integrity, addressing essential aspects like accountability and reproducibility. Blockchains have several properties that can address these requirements. Previous systems utilized public blockchains, i.e., treated blockchain as a black box, and benefiting from the immutability property. However, the blockchain was accessible to everyone, giving rise to security concerns and moreover, efficient extraction of provenance faces challenges due to the enormous scale and complexity of digital data. This necessitates a tailored blockchain design for digital forensics. Our solution, Forensiblock has a novel design that automates investigation steps, ensures secure data access, traces data origins, preserves records, and expedites provenance extraction. Forensiblock incorporates Role-Based Access Control with Staged Authorization (RBAC-SA) and a distributed Merkle root for case tracking. These features support authorized resource access with an efficient retrieval of provenance records. Particularly, comparing two methods for extracting provenance records off chain storage retrieval with Merkle root verification and a brute-force search the offchain method is significantly better, especially as the blockchain size and number of cases increase. We also found that our distributed Merkle root creation slightly increases smart contract processing time but significantly improves history access. Overall, we show that Forensiblock offers secure, efficient, and reliable handling of digital forensic data",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 115,
+    "label": 16,
+    "text": "Title: Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification\nAbstract: Self-attention modules have demonstrated remarkable capabilities in capturing long-range relationships and improving the performance of point cloud tasks. However, point cloud objects are typically characterized by complex, disordered, and non-Euclidean spatial structures with multiple scales, and their behavior is often dynamic and unpredictable. The current self-attention modules mostly rely on dot product multiplication and dimension alignment among query-key-value features, which cannot adequately capture the multi-scale non-Euclidean structures of point cloud objects. To address these problems, this paper proposes a self-attention plug-in module with its variants, Multi-scale Geometry-aware Transformer (MGT). MGT processes point cloud data with multi-scale local and global geometric information in the following three aspects. At first, the MGT divides point cloud data into patches with multiple scales. Secondly, a local feature extractor based on sphere mapping is proposed to explore the geometry inner each patch and generate a fixed-length representation for each patch. Thirdly, the fixed-length representations are fed into a novel geodesic-based self-attention to capture the global non-Euclidean geometry between patches. Finally, all the modules are integrated into the framework of MGT with an end-to-end training scheme. Experimental results demonstrate that the MGT vastly increases the capability of capturing multi-scale geometry using the self-attention mechanism and achieves strong competitive performance on mainstream point cloud benchmarks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 116,
+    "label": 24,
+    "text": "Title: Fast Exact NPN Classification with Influence-aided Canonical Form\nAbstract: NPN classification has many applications in the synthesis and verification of digital circuits. The canonical-form-based method is the most common approach, designing a canonical form as representative for the NPN equivalence class first and then computing the transformation function according to the canonical form. Most works use variable symmetries and several signatures, mainly based on the cofactor, to simplify the canonical form construction and computation. This paper describes a novel canonical form and its computation algorithm by introducing Boolean influence to NPN classification, which is a basic concept in analysis of Boolean functions. We show that influence is input-negation-independent, input-permutation-dependent, and has other structural information than previous signatures for NPN classification. Therefore, it is a significant ingredient in speeding up NPN classification. Experimental results prove that influence plays an important role in reducing the transformation enumeration in computing the canonical form. Compared with the state-of-the-art algorithm implemented in ABC, our influence-aided canonical form for exact NPN classification gains up to 5.5x speedup.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 117,
+    "label": 16,
+    "text": "Title: Detecting and Grounding Multi-Modal Media Manipulation\nAbstract: Misinformation has become a pressing issue. Fake media, in both visual and textual forms, is widespread on the web. While various deepfake detection and text fake news detection methods have been proposed, they are only designed for single-modality forgery based on binary classification, let alone analyzing and reasoning subtle forgery traces across different modalities. In this paper, we high-light a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM4). DGM4 aims to not only detect the authenticity of multi-modal media, but also ground the manipulated content (i.e., image bounding boxes and text tokens), which requires deeper reasoning of multi-modal media manipulation. To support a large-scale investigation, we construct the first DGM4 dataset, where image-text pairs are manipulated by various approaches, with rich annotation of diverse manipulations. Moreover, we propose a novel HierArchical Multi-modal Manipulation rEasoning tRansformer (HAMMER) to fully capture the fine-grained interaction between different modalities. HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning. Dedicated manipulation detection and grounding heads are integrated from shallow to deep levels based on the interacted multi-modal information. Finally, we build an extensive bench-mark and set up rigorous evaluation metrics for this new research problem. Comprehensive experiments demonstrate the superiority of our model; several valuable observations are also revealed to facilitate future research in multi-modal media manipulation.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 118,
+    "label": 10,
+    "text": "Title: GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking\nAbstract: Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language processing tasks. In the real world, graph data is ubiquitous and an essential part of AGI and prevails in domains like social network analysis, bioinformatics and recommender systems. The training corpus of large language models often includes some algorithmic components, which allows them to achieve certain effects on some graph data-related problems. However, there is still little research on their performance on a broader range of graph-structured data. In this study, we conduct an extensive investigation to assess the proficiency of LLMs in comprehending graph data, employing a diverse range of structural and semantic-related tasks. Our analysis encompasses 10 distinct tasks that evaluate the LLMs' capabilities in graph understanding. Through our study, we not only uncover the current limitations of language models in comprehending graph structures and performing associated reasoning tasks but also emphasize the necessity for further advancements and novel approaches to enhance their graph processing capabilities. Our findings contribute valuable insights towards bridging the gap between language models and graph understanding, paving the way for more effective graph mining and knowledge extraction.",
+    "neighbors": [
+      57,
+      1238,
+      1544,
+      2109,
+      2113,
+      2136,
+      2281
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 119,
+    "label": 16,
+    "text": "Title: Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm\nAbstract: Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, we train YOLOv8 (the latest version of You Only Look Once) model on the GRAZPEDWRI-DX dataset, and use data augmentation to improve the model performance. The experimental results show that our model have reached the state-of-the-art (SOTA) real-time model performance. Specifically, compared to YOLOv8s models, the mean average precision (mAP 50) of our models improve from 0.604 and 0.625 to 0.612 and 0.631 at the input image size of 640 and 1024, respectively. To enable surgeons to use our model for fracture detection on pediatric wrist trauma X-ray images, we have designed the application\"Fracture Detection Using YOLOv8 App\"to assist surgeons in diagnosing fractures, reducing the probability of error analysis, and providing more useful information for surgery. Our implementation code is released at https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 120,
+    "label": 24,
+    "text": "Title: On student-teacher deviations in distillation: does it pay to disobey?\nAbstract: Knowledge distillation (KD) has been widely-used to improve the test accuracy of a ``student'' network by training the student to mimic soft probabilities of a trained\"teacher\"network. Yet, it has been shown in recent work that, despite being trained to fit the teacher's probabilities, the student not only significantly deviates from these probabilities, but also performs even better than the teacher. Our work aims to reconcile this seemingly paradoxical observation by characterizing the precise nature of the student-teacher deviations, and by arguing how they can co-occur with better generalization. First, through experiments on image and language data, we identify that these deviations correspond to the student systematically exaggerating the confidence levels of the teacher. Next, we theoretically and empirically establish in some simple settings that KD also exaggerates the implicit bias of gradient descent in converging faster along the top eigendirections of the data. Finally, we demonstrate that this exaggerated bias effect can simultaneously result in both (a) the exaggeration of confidence and (b) the improved generalization of the student, thus offering a resolution to the apparent paradox. Our analysis brings existing theory and practice closer by considering the role of gradient descent in KD and by demonstrating the exaggerated bias effect in both theoretical and empirical settings.",
+    "neighbors": [
+      203
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 121,
+    "label": 30,
+    "text": "Title: A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability\nAbstract: This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with different languages, settings, or scenarios, and the results demonstrate that ChatGPT has strong text-to-SQL abilities. Although there is still a gap from the current state-of-the-art (SOTA) model performance, considering that the experiment was conducted in a zero-shot scenario, ChatGPT's performance is still impressive. Notably, in the ADVETA (RPL) scenario, the zero-shot ChatGPT even outperforms the SOTA model that requires fine-tuning on the Spider dataset by 4.1\\%, demonstrating its potential for use in practical applications. To support further research in related fields, we have made the data generated by ChatGPT publicly available at https://github.com/THU-BPM/chatgpt-sql.",
+    "neighbors": [
+      924,
+      1636,
+      1797,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 122,
+    "label": 24,
+    "text": "Title: Combining Slow and Fast: Complementary Filtering for Dynamics Learning\nAbstract: Modeling an unknown dynamical system is crucial in order to predict the future behavior of the system. A standard approach is training recurrent models on measurement data. While these models typically provide exact short-term predictions, accumulating errors yield deteriorated long-term behavior. In contrast, models with reliable long-term predictions can often be obtained, either by training a robust but less detailed model, or by leveraging physics-based simulations. In both cases, inaccuracies in the models yield a lack of short-time details. Thus, different models with contrastive properties on different time horizons are available. This observation immediately raises the question: Can we obtain predictions that combine the best of both worlds? Inspired by sensor fusion tasks, we interpret the problem in the frequency domain and leverage classical methods from signal processing, in particular complementary filters. This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other. Essentially, the high-pass filter extracts high-frequencies, whereas the low-pass filter extracts low frequencies. Applying this concept to dynamics model learning enables the construction of models that yield accurate long- and short-term predictions. Here, we propose two methods, one being purely learning-based and the other one being a hybrid model that requires an additional physics-based simulator.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 123,
+    "label": 15,
+    "text": "Title: Sparq: A Custom RISC-V Vector Processor for Efficient Sub-Byte Quantized Inference\nAbstract: Convolutional Neural Networks (CNNs) are used in a wide range of applications, with full-precision CNNs achieving high accuracy at the expense of portability. Recent progress in quantization techniques has demonstrated that sub-byte Quantized Neural Networks (QNNs) achieve comparable or superior accuracy while significantly reducing the computational cost and memory footprint. However, sub-byte computation on commodity hardware is sub-optimal due to the lack of support for such precision. In this paper, we introduce Sparq, a Sub-byte vector Processor designed for the AcceleRation of QNN inference. This processor is based on a modified version of Ara, an open-source 64-bit RISC-V \u201cV\u201d compliant processor. Sparq is implemented in GLOBAL FOUNDRIES 22FDX FD-SOI technology and extends the Instruction Set Architecture (ISA) by adding a new multiply-shift-accumulate instruction to improve sub-byte computation effciency. The floating-point unit is also removed to minimize area and power usage. To demonstrate Sparq performance, we implement an ultra-low-precision (1-bit to 4-bit) vectorized conv2d operation taking advantage of the dedicated hardware. We show that Sparq can significantly accelerate sub-byte computations with respectively 3.2 times, and 1.7 times acceleration over an optimized 16-bit 2D convolution for 2-bit and 4-bit quantization.",
+    "neighbors": [
+      1301
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 124,
+    "label": 24,
+    "text": "Title: Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations\nAbstract: Knowledge tracing (KT) is a crucial technique to predict students\u2019 future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students\u2019 knowledge state variations at a \ufb01ne-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.",
+    "neighbors": [
+      263
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 125,
+    "label": 10,
+    "text": "Title: Engineering LaCAM$^\\ast$: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding\nAbstract: This paper addresses the challenges of real-time, large-scale, and near-optimal multi-agent pathfinding (MAPF) through enhancements to the recently proposed LaCAM* algorithm. LaCAM* is a scalable search-based algorithm that guarantees the eventual finding of optimal solutions for cumulative transition costs. While it has demonstrated remarkable planning success rates, surpassing various state-of-the-art MAPF methods, its initial solution quality is far from optimal, and its convergence speed to the optimum is slow. To overcome these limitations, this paper introduces several improvement techniques, partly drawing inspiration from other MAPF methods. We provide empirical evidence that the fusion of these techniques significantly improves the solution quality of LaCAM*, thus further pushing the boundaries of MAPF algorithms.",
+    "neighbors": [
+      1743
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 126,
+    "label": 24,
+    "text": "Title: AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback\nAbstract: Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their ability to follow user instructions well. Developing these LLMs involves a complex yet poorly understood workflow requiring training with human feedback. Replicating and understanding this instruction-following process faces three major challenges: the high cost of data collection, the lack of trustworthy evaluation, and the absence of reference method implementations. We address these challenges with AlpacaFarm, a simulator that enables research and development for learning from feedback at a low cost. First, we design LLM prompts to simulate human feedback that are 45x cheaper than crowdworkers and display high agreement with humans. Second, we propose an automatic evaluation and validate it against human instructions obtained on real-world interactions. Third, we contribute reference implementations for several methods (PPO, best-of-n, expert iteration, and more) that learn from pairwise feedback. Finally, as an end-to-end validation of AlpacaFarm, we train and evaluate eleven models on 10k pairs of real human feedback and show that rankings of models trained in AlpacaFarm match rankings of models trained on human data. As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003. We release all components of AlpacaFarm at https://github.com/tatsu-lab/alpaca_farm.",
+    "neighbors": [
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+      2257
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 127,
+    "label": 30,
+    "text": "Title: Language Models can Solve Computer Tasks\nAbstract: Agents capable of carrying out general tasks on a computer can improve efficiency and productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally, such agents should be able to solve new computer tasks presented to them through natural language commands. However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks. In this work, we show that a pre-trained large language model (LLM) agent can execute computer tasks guided by natural language using a simple prompting scheme where the agent Recursively Criticizes and Improves its output (RCI). The RCI approach significantly outperforms existing LLM methods for automating computer tasks and surpasses supervised learning (SL) and reinforcement learning (RL) approaches on the MiniWoB++ benchmark. We compare multiple LLMs and find that RCI with the InstructGPT-3+RLHF LLM is state-of-the-art on MiniWoB++, using only a handful of demonstrations per task rather than tens of thousands, and without a task-specific reward function. Furthermore, we demonstrate RCI prompting's effectiveness in enhancing LLMs' reasoning abilities on a suite of natural language reasoning tasks, outperforming chain of thought (CoT) prompting. We find that RCI combined with CoT performs better than either separately. Our code can be found here: https://github.com/posgnu/rci-agent.",
+    "neighbors": [
+      57,
+      240,
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+      704,
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+      1267,
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+      1490,
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+      1810,
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+      1906,
+      2016,
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+      2136,
+      2166
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 128,
+    "label": 16,
+    "text": "Title: EMP-SSL: Towards Self-Supervised Learning in One Training Epoch\nAbstract: Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are rather\"inefficient\"learners, typically taking hundreds of training epochs to fully converge. In this work, we show that the key towards efficient self-supervised learning is to increase the number of crops from each image instance. Leveraging one of the state-of-the-art SSL method, we introduce a simplistic form of self-supervised learning method called Extreme-Multi-Patch Self-Supervised-Learning (EMP-SSL) that does not rely on many heuristic techniques for SSL such as weight sharing between the branches, feature-wise normalization, output quantization, and stop gradient, etc, and reduces the training epochs by two orders of magnitude. We show that the proposed method is able to converge to 85.1% on CIFAR-10, 58.5% on CIFAR-100, 38.1% on Tiny ImageNet and 58.5% on ImageNet-100 in just one epoch. Furthermore, the proposed method achieves 91.5% on CIFAR-10, 70.1% on CIFAR-100, 51.5% on Tiny ImageNet and 78.9% on ImageNet-100 with linear probing in less than ten training epochs. In addition, we show that EMP-SSL shows significantly better transferability to out-of-domain datasets compared to baseline SSL methods. We will release the code in https://github.com/tsb0601/EMP-SSL.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 129,
+    "label": 9,
+    "text": "Title: Tight (Double) Exponential Bounds for NP-Complete Problems: Treewidth and Vertex Cover Parameterizations\nAbstract: Treewidth is as an important parameter that yields tractability for many problems. For example, graph problems expressible in Monadic Second Order (MSO) logic and QUANTIFIED SAT or, more generally, QUANTIFIED CSP, are fixed-parameter tractable parameterized by the treewidth of the input's (primal) graph plus the length of the MSO-formula [Courcelle, Information&Computation 1990] and the quantifier rank [Chen, ECAI 2004], respectively. The algorithms generated by these (meta-)results have running times whose dependence on treewidth is a tower of exponents. A conditional lower bound by Fichte et al. [LICS 2020] shows that, for QUANTIFIED SAT, the height of this tower is equal to the number of quantifier alternations. Lower bounds showing that at least double-exponential factors in the running time are necessary, exhibit the extraordinary computational hardness of such problems, and are rare: there are very few (for treewidth tw and vertex cover vc parameterizations) and they are for $\\Sigma_2^p$-, $\\Sigma_3^p$- or #NP-complete problems. We show, for the first time, that it is not necessary to go higher up in the polynomial hierarchy to obtain such lower bounds. Specifically, for the well-studied NP-complete metric graph problems METRIC DIMENSION, STRONG METRIC DIMENSION, and GEODETIC SET, we prove that they do not admit $2^{2^{o(tw)}} \\cdot n^{O(1)}$-time algorithms, even on bounded diameter graphs, unless the ETH fails. For STRONG METRIC DIMENSION, this lower bound holds even for vc. This is impossible for the other two as they admit $2^{O({vc}^2)} \\cdot n^{O(1)}$-time algorithms. We show that, unless the ETH fails, they do not admit $2^{o({vc}^2)}\\cdot n^{O(1)}$-time algorithms, thereby adding to the short list of problems admitting such lower bounds. The latter results also yield lower bounds on the vertex-kernel sizes. We complement all our lower bounds with matching upper bounds.",
+    "neighbors": [
+      2019
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 130,
+    "label": 27,
+    "text": "Title: Prediction of SLAM ATE Using an Ensemble Learning Regression Model and 1-D Global Pooling of Data Characterization\nAbstract: Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tolerance mechanisms to deal with performance degradation. In this work, we introduce a novel method for predicting SLAM localization error based on the characterization of raw sensor inputs. The proposed method relies on using a random forest regression model trained on 1-D global pooled features that are generated from characterized raw sensor data. The model is validated by using it to predict the performance of ORB-SLAM3 on three different datasets running on four different operating modes, resulting in an average prediction accuracy of up to 94.7\\%. The paper also studies the impact of 12 different 1-D global pooling functions on regression quality, and the superiority of 1-D global averaging is quantitatively proven. Finally, the paper studies the quality of prediction with limited training data, and proves that we are able to maintain proper prediction quality when only 20 \\% of the training examples are used for training, which highlights how the proposed model can optimize the evaluation footprint of SLAM systems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 131,
+    "label": 16,
+    "text": "Title: Grouped Knowledge Distillation for Deep Face Recognition\nAbstract: Compared with the feature-based distillation methods, logits distillation can liberalize the requirements of consistent feature dimension between teacher and student networks, while the performance is deemed inferior in face recognition. One major challenge is that the light-weight student network has difficulty fitting the target logits due to its low model capacity, which is attributed to the significant number of identities in face recognition. Therefore, we seek to probe the target logits to extract the primary knowledge related to face identity, and discard the others, to make the distillation more achievable for the student network. Specifically, there is a tail group with near-zero values in the prediction, containing minor knowledge for distillation. To provide a clear perspective of its impact, we first partition the logits into two groups, i.e., Primary Group and Secondary Group, according to the cumulative probability of the softened prediction. Then, we reorganize the Knowledge Distillation (KD) loss of grouped logits into three parts, i.e., Primary-KD, Secondary-KD, and Binary-KD. Primary-KD refers to distilling the primary knowledge from the teacher, Secondary-KD aims to refine minor knowledge but increases the difficulty of distillation, and Binary-KD ensures the consistency of knowledge distribution between teacher and student. We experimentally found that (1) Primary-KD and Binary-KD are indispensable for KD, and (2) Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. Extensive experimental results on popular face recognition benchmarks demonstrate the superiority of proposed GKD over state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 132,
+    "label": 24,
+    "text": "Title: Thompson Sampling for Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit\nAbstract: We study the real-valued combinatorial pure exploration of the multi-armed bandit (R-CPE-MAB) problem. In R-CPE-MAB, a player is given $d$ stochastic arms, and the reward of each arm $s\\in\\{1, \\ldots, d\\}$ follows an unknown distribution with mean $\\mu_s$. In each time step, a player pulls a single arm and observes its reward. The player's goal is to identify the optimal \\emph{action} $\\boldsymbol{\\pi}^{*} = \\argmax_{\\boldsymbol{\\pi} \\in \\mathcal{A}} \\boldsymbol{\\mu}^{\\top}\\boldsymbol{\\pi}$ from a finite-sized real-valued \\emph{action set} $\\mathcal{A}\\subset \\mathbb{R}^{d}$ with as few arm pulls as possible. Previous methods in the R-CPE-MAB assume that the size of the action set $\\mathcal{A}$ is polynomial in $d$. We introduce an algorithm named the Generalized Thompson Sampling Explore (GenTS-Explore) algorithm, which is the first algorithm that can work even when the size of the action set is exponentially large in $d$. We also introduce a novel problem-dependent sample complexity lower bound of the R-CPE-MAB problem, and show that the GenTS-Explore algorithm achieves the optimal sample complexity up to a problem-dependent constant factor.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 133,
+    "label": 24,
+    "text": "Title: A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization\nAbstract: The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks. Unfortunately, designing fast algorithms for the symmetric NMF is not as easy as for its nonsymmetric counterpart, since the latter admits the splitting property that allows state-of-the-art alternating-type algorithms. To overcome this issue, we first split the decision variable and transform the symmetric NMF to a penalized nonsymmetric one, paving the way for designing efficient alternating-type algorithms. We then show that solving the penalized nonsymmetric reformulation returns a solution to the original symmetric NMF. Moreover, we design a family of alternating-type algorithms and show that they all admit strong convergence guarantee: the generated sequence of iterates is convergent and converges at least sublinearly to a critical point of the original symmetric NMF. Finally, we conduct experiments on both synthetic data and real image clustering to support our theoretical results and demonstrate the performance of the alternating-type algorithms.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 134,
+    "label": 36,
+    "text": "Title: Sequential Principal-Agent Problems with Communication: Efficient Computation and Learning\nAbstract: We study a sequential decision making problem between a principal and an agent with incomplete information on both sides. In this model, the principal and the agent interact in a stochastic environment, and each is privy to observations about the state not available to the other. The principal has the power of commitment, both to elicit information from the agent and to provide signals about her own information. The principal and the agent communicate their signals to each other, and select their actions independently based on this communication. Each player receives a payoff based on the state and their joint actions, and the environment moves to a new state. The interaction continues over a finite time horizon, and both players act to optimize their own total payoffs over the horizon. Our model encompasses as special cases stochastic games of incomplete information and POMDPs, as well as sequential Bayesian persuasion and mechanism design problems. We study both computation of optimal policies and learning in our setting. While the general problems are computationally intractable, we study algorithmic solutions under a conditional independence assumption on the underlying state-observation distributions. We present an polynomial-time algorithm to compute the principal's optimal policy up to an additive approximation. Additionally, we show an efficient learning algorithm in the case where the transition probabilities are not known beforehand. The algorithm guarantees sublinear regret for both players.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 135,
+    "label": 23,
+    "text": "Title: Techniques for Improving the Energy Efficiency of Mobile Apps: A Taxonomy and Systematic Literature Review\nAbstract: Building energy efficient software is an increasingly important task for mobile developers. However, a cumulative body of knowledge of techniques that support this goal does not exist. We conduct a systematic literature review to gather information on existing techniques that allow developers to increase energy efficiency in mobile apps. Based on a synthesis of the 91 included primary studies, we propose a taxonomy of techniques for improving the energy efficiency in mobile apps. The taxonomy includes seven main categories of techniques and serves as a collection of available methods for developers and as a reference guide for software testers when performing energy efficiency testing by the means of benchmark tests.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 136,
+    "label": 16,
+    "text": "Title: CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning\nAbstract: Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM). Nevertheless, these Vision-Language Models (VLMs) are suffering from the drawback of hallucination -- due to insufficient understanding of vision and language modalities, VLMs may generate incorrect perception information when doing downstream applications, for example, captioning a non-existent entity. To address the hallucination phenomenon, on the one hand, we introduce a Contrastive Instruction Evaluation Method (CIEM), which is an automatic pipeline that leverages an annotated image-text dataset coupled with an LLM to generate factual/contrastive question-answer pairs for the evaluation of the hallucination of VLMs. On the other hand, based on CIEM, we further propose a new instruction tuning method called CIT (the abbreviation of Contrastive Instruction Tuning) to alleviate the hallucination of VLMs by automatically producing high-quality factual/contrastive question-answer pairs and corresponding justifications for model tuning. Through extensive experiments on CIEM and CIT, we pinpoint the hallucination issues commonly present in existing VLMs, the disability of the current instruction-tuning dataset to handle the hallucination phenomenon and the superiority of CIT-tuned VLMs over both CIEM and public datasets.",
+    "neighbors": [
+      392,
+      811,
+      887,
+      1052,
+      1148,
+      1485,
+      1863,
+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 137,
+    "label": 16,
+    "text": "Title: Depth-Aware Generative Adversarial Network for Talking Head Video Generation\nAbstract: Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving video. Existing works for this task heavily rely on 2D representations (e.g. appearance and motion) learned from the input images. However, dense 3D facial geometry (e.g. pixel-wise depth) is extremely important for this task as it is particularly beneficial for us to essentially generate accurate 3D face structures and distinguish noisy information from the possibly cluttered background. Nevertheless, dense 3D geometry annotations are prohibitively costly for videos and are typically not available for this video generation task. In this paper, we introduce a self-supervised face-depth learning method to automatically recover dense 3D facial geometry (i.e. depth) from the face videos without the requirement of any expensive 3D annotation data. Based on the learned dense depth maps, we further propose to leverage them to estimate sparse facial keypoints that capture the critical movement of the human head. In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i.e. appearance and depth) attention to guide the generation of motion fields for warping source image representations. All these contributions compose a novel depth-aware generative adversarial network (DaGAN) for talking head generation. Extensive experiments conducted demonstrate that our proposed method can generate highly realistic faces, and achieve significant results on the unseen human faces. 11https://github.com/harlanhong/CVPR2022-DaGAN",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 138,
+    "label": 16,
+    "text": "Title: Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey\nAbstract: The vulnerabilities of fingerprint authentication systems have raised security concerns when adapting them to highly secure access-control applications. Therefore, Fingerprint Presentation Attack Detection (FPAD) methods are essential for ensuring reliable fingerprint authentication. Owing to the lack of generation capacity of traditional handcrafted based approaches, deep learning-based FPAD has become mainstream and has achieved remarkable performance in the past decade. Existing reviews have focused more on hand-cratfed rather than deep learning-based methods, which are outdated. To stimulate future research, we will concentrate only on recent deep-learning-based FPAD methods. In this paper, we first briefly introduce the most common Presentation Attack Instruments (PAIs) and publicly available fingerprint Presentation Attack (PA) datasets. We then describe the existing deep-learning FPAD by categorizing them into contact, contactless, and smartphone-based approaches. Finally, we conclude the paper by discussing the open challenges at the current stage and emphasizing the potential future perspective.",
+    "neighbors": [
+      301
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 139,
+    "label": 35,
+    "text": "Title: SWAM: Revisiting Swap and OOMK for Improving Application Responsiveness on Mobile Devices\nAbstract: Existing memory reclamation policies on mobile devices may be no longer valid because they have negative effects on the response time of running applications. In this paper, we propose SWAM, a new integrated memory management technique that complements the shortcomings of both the swapping and killing mechanism in mobile devices and improves the application responsiveness. SWAM consists of (1) Adaptive Swap that performs swapping adaptively into memory or storage device while managing the swap space dynamically, (2) OOM Cleaner that reclaims shared object pages in the swap space to secure available memory and storage space, and (3) EOOM Killer that terminates processes in the worst case while prioritizing the lowest initialization cost applications as victim processes first. Experimental results demonstrate that SWAM significantly reduces the number of applications killed by OOMK (6.5x lower), and improves application launch time (36% faster) and response time (41% faster), compared to the conventional schemes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 140,
+    "label": 16,
+    "text": "Title: Change detection needs change information: improving deep 3D point cloud change detection\nAbstract: Change detection is an important task to rapidly identify modified areas, in particular when multi-temporal data are concerned. In landscapes with complex geometry such as urban environment, vertical information turn out to be a very useful knowledge not only to highlight changes but also to classify them into different categories. In this paper, we focus on change segmentation directly using raw 3D point clouds (PCs), to avoid any loss of information due to rasterization processes. While deep learning has recently proved its effectiveness for this particular task by encoding the information through Siamese networks, we investigate here the idea of also using change information in early steps of deep networks. To do this, we first propose to provide the Siamese KPConv State-of-The-Art (SoTA) network with hand-crafted features and especially a change-related one. This improves the mean of Intersection over Union (IoU) over classes of change by 4.70\\%. Considering that the major improvement was obtained thanks to the change-related feature, we propose three new architectures to address 3D PCs change segmentation: OneConvFusion, Triplet KPConv, and Encoder Fusion SiamKPConv. All the three networks take into account change information in early steps and outperform SoTA methods. In particular, the last network, entitled Encoder Fusion SiamKPConv, overtakes SoTA with more than 5% of mean of IoU over classes of change emphasizing the value of having the network focus on change information for change detection task.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 141,
+    "label": 24,
+    "text": "Title: AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW Basis\nAbstract: This paper proposes an efficient optimizer called AdaPlus which integrates Nesterov momentum and precise stepsize adjustment on AdamW basis. AdaPlus combines the advantages of AdamW, Nadam, and AdaBelief and, in particular, does not introduce any extra hyper-parameters. We perform extensive experimental evaluations on three machine learning tasks to validate the effectiveness of AdaPlus. The experiment results validate that AdaPlus (i) is the best adaptive method which performs most comparable with (even slightly better than) SGD with momentum on image classification tasks and (ii) outperforms other state-of-the-art optimizers on language modeling tasks and illustrates the highest stability when training GANs. The experiment code of AdaPlus is available at: https://github.com/guanleics/AdaPlus.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 142,
+    "label": 16,
+    "text": "Title: Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers\nAbstract: In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation Challenge and Expression (Expr) Classification Challenge. ABAW 2023 aims to tackle the challenge of affective behavior analysis in natural contexts, with the ultimate goal of creating intelligent machines and robots that possess the ability to comprehend human emotions, feelings, and behaviors. For the Expression Classification Challenge, we propose a streamlined approach that handles the challenges of classification effectively. However, our main contribution lies in our use of diverse models and tools to extract multimodal features such as audio and video cues from the Hume-Reaction dataset. By studying, analyzing, and combining these features, we significantly enhance the model\u2019s accuracy for sentiment prediction in a multimodal context. Furthermore, our method achieves outstanding results on the Emotional Reaction Intensity (ERI) Estimation Challenge, surpassing the baseline method by an impressive 84% increase, as measured by the Pearson Coefficient, on the validation dataset.",
+    "neighbors": [
+      1265,
+      1541,
+      2055
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 143,
+    "label": 30,
+    "text": "Title: C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models\nAbstract: New NLP benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present C-Eval, the first comprehensive Chinese evaluation suite designed to assess advanced knowledge and reasoning abilities of foundation models in a Chinese context. C-Eval comprises multiple-choice questions across four difficulty levels: middle school, high school, college, and professional. The questions span 52 diverse disciplines, ranging from humanities to science and engineering. C-Eval is accompanied by C-Eval Hard, a subset of very challenging subjects in C-Eval that requires advanced reasoning abilities to solve. We conduct a comprehensive evaluation of the most advanced LLMs on C-Eval, including both English- and Chinese-oriented models. Results indicate that only GPT-4 could achieve an average accuracy of over 60%, suggesting that there is still significant room for improvement for current LLMs. We anticipate C-Eval will help analyze important strengths and shortcomings of foundation models, and foster their development and growth for Chinese users.",
+    "neighbors": [
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+      2215
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 144,
+    "label": 16,
+    "text": "Title: DataComp: In search of the next generation of multimodal datasets\nAbstract: Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the ML ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training code and testing the resulting model on 38 downstream test sets. Our benchmark consists of multiple compute scales spanning four orders of magnitude, which enables the study of scaling trends and makes the benchmark accessible to researchers with varying resources. Our baseline experiments show that the DataComp workflow leads to better training sets. In particular, our best baseline, DataComp-1B, enables training a CLIP ViT-L/14 from scratch to 79.2% zero-shot accuracy on ImageNet, outperforming OpenAI's CLIP ViT-L/14 by 3.7 percentage points while using the same training procedure and compute. We release DataComp and all accompanying code at www.datacomp.ai.",
+    "neighbors": [
+      719,
+      2064
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 145,
+    "label": 24,
+    "text": "Title: One-Versus-Others Attention: Scalable Multimodal Integration\nAbstract: Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving. Despite the importance of multimodal learning, existing efforts focus on NLP applications, where the number of modalities is typically less than four (audio, video, text, images). However, data inputs in other domains, such as the medical field, may include X-rays, PET scans, MRIs, genetic screening, clinical notes, and more, creating a need for both efficient and accurate information fusion. Many state-of-the-art models rely on pairwise cross-modal attention, which does not scale well for applications with more than three modalities. For $n$ modalities, computing attention will result in $n \\choose 2$ operations, potentially requiring considerable amounts of computational resources. To address this, we propose a new domain-neutral attention mechanism, One-Versus-Others (OvO) attention, that scales linearly with the number of modalities and requires only $n$ attention operations, thus offering a significant reduction in computational complexity compared to existing cross-modal attention algorithms. Using three diverse real-world datasets as well as an additional simulation experiment, we show that our method improves performance compared to popular fusion techniques while decreasing computation costs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 146,
+    "label": 8,
+    "text": "Title: Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization\nAbstract: Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation.We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.",
+    "neighbors": [
+      490,
+      1601,
+      1684,
+      1863,
+      1908,
+      2059,
+      2245
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 147,
+    "label": 31,
+    "text": "Title: Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities\nAbstract: Community Question Answering (CQA) in different domains is growing at a large scale because of the availability of several platforms and huge shareable information among users. With the rapid growth of such online platforms, a massive amount of archived data makes it difficult for moderators to retrieve possible duplicates for a new question and identify and confirm existing question pairs as duplicates at the right time. This problem is even more critical in CQAs corresponding to large software systems like askubuntu where moderators need to be experts to comprehend something as a duplicate. Note that the prime challenge in such CQA platforms is that the moderators are themselves experts and are therefore usually extremely busy with their time being extraordinarily expensive. To facilitate the task of the moderators, in this work, we have tackled two significant issues for the askubuntu CQA platform: (1) retrieval of duplicate questions given a new question and (2) duplicate question confirmation time prediction. In the first task, we focus on retrieving duplicate questions from a question pool for a particular newly posted question. In the second task, we solve a regression problem to rank a pair of questions that could potentially take a long time to get confirmed as duplicates. For duplicate question retrieval, we propose a Siamese neural network based approach by exploiting both text and network-based features, which outperforms several state-of-the-art baseline techniques. Our method outperforms DupPredictor and DUPE by 5% and 7% respectively. For duplicate confirmation time prediction, we have used both the standard machine learning models and neural network along with the text and graph-based features. We obtain Spearman's rank correlation of 0.20 and 0.213 (statistically significant) for text and graph based features respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 148,
+    "label": 30,
+    "text": "Title: Semantic Parsing for Conversational Question Answering over Knowledge Graphs\nAbstract: In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 149,
+    "label": 30,
+    "text": "Title: Jointly Optimizing Translations and Speech Timing to Improve Isochrony in Automatic Dubbing\nAbstract: Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech. The new target language speech should satisfy isochrony; that is, the new speech should be time aligned with the original video, including mouth movements, pauses, hand gestures, etc. In this paper, we propose training a model that directly optimizes both the translation as well as the speech duration of the generated translations. We show that this system generates speech that better matches the timing of the original speech, compared to prior work, while simplifying the system architecture.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 150,
+    "label": 39,
+    "text": "Title: The Packing Chromatic Number of the Infinite Square Grid is 15\nAbstract: A packing $k$-coloring is a natural variation on the standard notion of graph $k$-coloring, where vertices are assigned numbers from $\\{1, \\ldots, k\\}$, and any two vertices assigned a common color $c \\in \\{1, \\ldots, k\\}$ need to be at a distance greater than $c$ (as opposed to $1$, in standard graph colorings). Despite a sequence of incremental work, determining the packing chromatic number of the infinite square grid has remained an open problem since its introduction in 2002. We culminate the search by proving this number to be 15. We achieve this result by improving the best-known method for this problem by roughly two orders of magnitude. The most important technique to boost performance is a novel, surprisingly effective propositional encoding for packing colorings. Additionally, we developed an alternative symmetry-breaking method. Since both new techniques are more complex than existing techniques for this problem, a verified approach is required to trust them. We include both techniques in a proof of unsatisfiability, reducing the trusted core to the correctness of the direct encoding.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 151,
+    "label": 16,
+    "text": "Title: Pluralistic Aging Diffusion Autoencoder\nAbstract: Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. Most existing methods often produce one deterministic estimation. This paper proposes a novel CLIP-driven Pluralistic Aging Diffusion Autoencoder (PADA) to enhance the diversity of aging patterns. First, we employ diffusion models to generate diverse low-level aging details via a sequential denoising reverse process. Second, we present Probabilistic Aging Embedding (PAE) to capture diverse high-level aging patterns, which represents age information as probabilistic distributions in the common CLIP latent space. A text-guided KL-divergence loss is designed to guide this learning. Our method can achieve pluralistic face aging conditioned on open-world aging texts and arbitrary unseen face images. Qualitative and quantitative experiments demonstrate that our method can generate more diverse and high-quality plausible aging results.",
+    "neighbors": [
+      2276
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 152,
+    "label": 5,
+    "text": "Title: BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction\nAbstract: Accurate real-time traffic flow prediction can be leveraged to relieve traffic congestion and associated negative impacts. The existing centralized deep learning methodologies have demonstrated high prediction accuracy, but suffer from privacy concerns due to the sensitive nature of transportation data. Moreover, the emerging literature on traffic prediction by distributed learning approaches, including federated learning, primarily focuses on offline learning. This paper proposes BFRT, a blockchained federated learning architecture for online traffic flow prediction using real-time data and edge computing. The proposed approach provides privacy for the underlying data, while enabling decentralized model training in real-time at the Internet of Vehicles edge. We federate GRU and LSTM models and conduct extensive experiments with dynamically collected arterial traffic data shards. We prototype the proposed permissioned blockchain network on Hyperledger Fabric and perform extensive tests using virtual machines to simulate the edge nodes. Experimental results outperform the centralized models, highlighting the feasibility of our approach for facili-tating privacy-preserving and decentralized real-time traffic flow prediction.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 153,
+    "label": 16,
+    "text": "Title: Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising\nAbstract: Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID.",
+    "neighbors": [
+      394
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 154,
+    "label": 16,
+    "text": "Title: Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality\nAbstract: Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the\"LSUN-Bedroom\"dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.",
+    "neighbors": [
+      1900,
+      2021,
+      2279
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 155,
+    "label": 2,
+    "text": "Title: Compositional Solution of Mean Payoff Games by String Diagrams\nAbstract: Following our recent development of a compositional model checking algorithm for Markov decision processes, we present a compositional framework for solving mean payoff games (MPGs). The framework is derived from category theory, specifically that of monoidal categories: MPGs (extended with open ends) get composed in so-called string diagrams and thus organized in a monoidal category; their solution is then expressed as a functor, whose preservation properties embody compositionality. As usual, the key question to compositionality is how to enrich the semantic domain; the categorical framework gives an informed guidance in solving the question by singling out the algebraic structure required in the extended semantic domain. We implemented our compositional solution in Haskell; depending on benchmarks, it can outperform an existing algorithm by an order of magnitude.",
+    "neighbors": [
+      707
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 156,
+    "label": 26,
+    "text": "Title: The blame game: Understanding blame assignment in social media\nAbstract: Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 157,
+    "label": 13,
+    "text": "Title: A Static Analysis of Informed Down-Samples\nAbstract: We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintains population-level test coverage when compared to random down-sampling. Additionally, we show that both forms of down-sampling cause greater test coverage loss than standard lexicase selection with no down-sampling. However, given more information about the population, we found that informed down-sampling can further reduce its test coverage loss. We also recommend wider adoption of the static population analyses we present in this work.",
+    "neighbors": [
+      1017,
+      1850
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 158,
+    "label": 16,
+    "text": "Title: PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation\nAbstract: The current 3D human pose estimators face challenges in adapting to new datasets due to the scarcity of 2D-3D pose pairs in target domain training sets. We present the \\textit{Multi-Hypothesis \\textbf{P}ose \\textbf{Syn}thesis \\textbf{D}omain \\textbf{A}daptation} (\\textbf{PoSynDA}) framework to overcome this issue without extensive target domain annotation. Utilizing a diffusion-centric structure, PoSynDA simulates the 3D pose distribution in the target domain, filling the data diversity gap. By incorporating a multi-hypothesis network, it creates diverse pose hypotheses and aligns them with the target domain. Target-specific source augmentation obtains the target domain distribution data from the source domain by decoupling the scale and position parameters. The teacher-student paradigm and low-rank adaptation further refine the process. PoSynDA demonstrates competitive performance on benchmarks, such as Human3.6M, MPI-INF-3DHP, and 3DPW, even comparable with the target-trained MixSTE model~\\cite{zhang2022mixste}. This work paves the way for the practical application of 3D human pose estimation. The code is available at https://github.com/hbing-l/PoSynDA.",
+    "neighbors": [
+      422,
+      1605,
+      2009
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 159,
+    "label": 24,
+    "text": "Title: FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networks\nAbstract: Federated learning (FL) is a distributed and privacy-preserving learning framework for predictive modeling with massive data generated at the edge by Internet of Things (IoT) devices. One major challenge preventing the wide adoption of FL in IoT is the pervasive power supply constraints of IoT devices due to the intensive energy consumption of battery-powered clients for local training and model updates. Low battery levels of clients eventually lead to their early dropouts from edge networks, loss of training data jeopardizing the performance of FL, and their availability to perform other designated tasks. In this paper, we propose FedLE, an energy-efficient client selection framework that enables lifespan extension of edge IoT networks. In FedLE, the clients first run for a minimum epoch to generate their local model update. The models are partially uploaded to the server for calculating similarities between each pair of clients. Clustering is performed against these client pairs to identify those with similar model distributions. In each round, low-powered clients have a lower probability of being selected, delaying the draining of their batteries. Empirical studies show that FedLE outperforms baselines on benchmark datasets and lasts more training rounds than FedAvg with battery power constraints.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 160,
+    "label": 24,
+    "text": "Title: Learning DAGs from Data with Few Root Causes\nAbstract: We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a linear SEM can be viewed as a linear transform that, in prior work, computes the data from a dense input vector of random valued root causes (as we will call them) associated with the nodes. Instead, we consider the case of (approximately) few root causes and also introduce noise in the measurement of the data. Intuitively, this means that the DAG data is produced by few data-generating events whose effect percolates through the DAG. We prove identifiability in this new setting and show that the true DAG is the global minimizer of the $L^0$-norm of the vector of root causes. For data with few root causes, with and without noise, we show superior performance compared to prior DAG learning methods.",
+    "neighbors": [
+      73,
+      1468
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 161,
+    "label": 24,
+    "text": "Title: xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions\nAbstract: Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 162,
+    "label": 24,
+    "text": "Title: Provably Efficient Model-Free Algorithms for Non-stationary CMDPs\nAbstract: We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost). In the non-stationary environment, reward, utility functions, and transition kernels can vary arbitrarily over time as long as the cumulative variations do not exceed certain variation budgets. We propose the first model-free, simulator-free RL algorithms with sublinear regret and zero constraint violation for non-stationary CMDPs in both tabular and linear function approximation settings with provable performance guarantees. Our results on regret bound and constraint violation for the tabular case match the corresponding best results for stationary CMDPs when the total budget is known. Additionally, we present a general framework for addressing the well-known challenges associated with analyzing non-stationary CMDPs, without requiring prior knowledge of the variation budget. We apply the approach for both tabular and linear approximation settings.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 163,
+    "label": 24,
+    "text": "Title: SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces\nAbstract: Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch global optimisation and quadrature with arbitrary acquisition functions and kernels over discrete and mixed spaces. The key to our approach is to reformulate batch selection for global optimisation as a quadrature problem, which relaxes acquisition function maximisation (non-convex) to kernel recombination (convex). Bridging global optimisation and quadrature can efficiently solve both tasks by balancing the merits of exploitative Bayesian optimisation and explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive baselines on 12 synthetic and diverse real-world tasks.",
+    "neighbors": [
+      850
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 164,
+    "label": 24,
+    "text": "Title: HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning\nAbstract: Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients. Secondly, HPL in each client captures the hierarchical information in local data with the supervision of shared class prototypes in the hyperbolic model space. Additionally, CA in the server mitigates the impact of the inconsistent deviations from clients to server. Extensive studies of four datasets prove that HyperFed is effective in enhancing the performance of FL under the non-IID setting.",
+    "neighbors": [
+      487
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 165,
+    "label": 23,
+    "text": "Title: SPSysML: A meta-model for quantitative evaluation of Simulation-Physical Systems\nAbstract: Robotic systems are complex cyber-physical systems (CPS) commonly equipped with multiple sensors and effectors. Recent simulation methods enable the Digital Twin (DT) concept realisation. However, DT employment in robotic system development, e.g. in-development testing, is unclear. During the system development, its parts evolve from simulated mockups to physical parts which run software deployed on the actual hardware. Therefore, a design tool and a flexible development procedure ensuring the integrity of the simulated and physical parts are required. We aim to maximise the integration between a CPS's simulated and physical parts in various setups. The better integration, the better simulation-based testing coverage of the physical part (hardware and software). We propose a Domain Specification Language (DSL) based on Systems Modeling Language (SysML) that we refer to as SPSysML (Simulation-Physical System Modeling Language). SPSysML defines the taxonomy of a Simulation-Physical System (SPSys), being a CPS consisting of at least a physical or simulated part. In particular, the simulated ones can be DTs. We propose a SPSys Development Procedure (SPSysDP) that enables the maximisation of the simulation-physical integrity of SPSys by evaluating the proposed factors. SPSysDP is used to develop a complex robotic system for the INCARE project. In subsequent iterations of SPSysDP, the simulation-physical integrity of the system is maximised. As a result, the system model consists of fewer components, and a greater fraction of the system components are shared between various system setups. We implement and test the system with popular frameworks, Robot Operating System (ROS) and Gazebo simulator. SPSysML with SPSysDP enables the design of SPSys (including DT and CPS), multi-setup system development featuring maximised integrity between simulation and physical parts in its setups.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 166,
+    "label": 24,
+    "text": "Title: WLD-Reg: A Data-dependent Within-layer Diversity Regularizer\nAbstract: Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage the diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. We present an extensive empirical study confirming that the proposed approach enhances the performance of several state-of-the-art neural network models in multiple tasks. The code is publically available at https://github.com/firasl/AAAI-23-WLD-Reg.",
+    "neighbors": [
+      2097
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 167,
+    "label": 24,
+    "text": "Title: SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs\nAbstract: Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally connected and can be represented with graphs, graph neural networks become increasingly popular in tackling the anomaly detection problem. Despite the promising results, research on anomaly detection has almost exclusively focused on static graphs while the mining of anomalous patterns from dynamic graphs is rarely studied but has significant application value. In addition, anomaly detection is typically tackled from semi-supervised perspectives due to the lack of sufficient labeled data. However, most proposed methods are limited to merely exploiting labeled data, leaving a large number of unlabeled samples unexplored. In this work, we present semi-supervised anomaly detection (SAD), an end-to-end framework for anomaly detection on dynamic graphs. By a combination of a time-equipped memory bank and a pseudo-label contrastive learning module, SAD is able to fully exploit the potential of large unlabeled samples and uncover underlying anomalies on evolving graph streams. Extensive experiments on four real-world datasets demonstrate that SAD efficiently discovers anomalies from dynamic graphs and outperforms existing advanced methods even when provided with only little labeled data.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 168,
+    "label": 30,
+    "text": "Title: Efficient Domain Adaptation of Sentence Embeddings using Adapters\nAbstract: Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model's weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1% of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6% of the parameters.",
+    "neighbors": [
+      30
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 169,
+    "label": 37,
+    "text": "Title: Using Learned Indexes to Improve Time Series Indexing Performance on Embedded Sensor Devices\nAbstract: Efficiently querying data on embedded sensor and IoT devices is challenging given the very limited memory and CPU resources. With the increasing volumes of collected data, it is critical to process, filter, and manipulate data on the edge devices where it is collected to improve efficiency and reduce network transmissions. Existing embedded index structures do not adapt to the data distribution and characteristics. This paper demonstrates how applying learned indexes that develop space efficient summaries of the data can dramatically improve the query performance and predictability. Learned indexes based on linear approximations can reduce the query I/O by 50 to 90% and improve query throughput by a factor of 2 to 5, while only requiring a few kilobytes of RAM. Experimental results on a variety of time series data sets demonstrate the advantages of learned indexes that considerably improve over the state-of-the-art index algorithms.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 170,
+    "label": 16,
+    "text": "Title: Vision + Language Applications: A Survey\nAbstract: Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://github.com/Yutong-Zhou-cv/Awesome-Text-to-Image.",
+    "neighbors": [
+      319,
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+      1758,
+      1768,
+      1863,
+      1902,
+      2161,
+      2190,
+      2242
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 171,
+    "label": 4,
+    "text": "Title: MFDPG: Multi-Factor Authenticated Password Management With Zero Stored Secrets\nAbstract: While password managers are a vital tool for internet security, they can also create a massive central point of failure, as evidenced by several major recent data breaches. For over 20 years, deterministic password generators (DPGs) have been proposed, and largely rejected, as a viable alternative to password management tools. In this paper, we survey 45 existing DPGs to asses the main security, privacy, and usability issues hindering their adoption. We then present a new multi-factor deterministic password generator (MFDPG) design that aims to address these shortcomings. The result not only achieves strong, practical password management with zero credential storage, but also effectively serves as a progressive client-side upgrade of weak password-only websites to strong multi-factor authentication.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 172,
+    "label": 36,
+    "text": "Title: The Computational Complexity of Single-Player Imperfect-Recall Games\nAbstract: We study single-player extensive-form games with imperfect recall, such as the Sleeping Beauty problem or the Absentminded Driver game. For such games, two natural equilibrium concepts have been proposed as alternative solution concepts to ex-ante optimality. One equilibrium concept uses generalized double halving (GDH) as a belief system and evidential decision theory (EDT), and another one uses generalized thirding (GT) as a belief system and causal decision theory (CDT). Our findings relate those three solution concepts of a game to solution concepts of a polynomial maximization problem: global optima, optimal points with respect to subsets of variables and Karush\u2013Kuhn\u2013Tucker (KKT) points. Based on these correspondences, we are able to settle various complexity-theoretic questions on the computation of such strategies. For ex-ante optimality and (EDT,GDH)-equilibria, we obtain NP-hardness and inapproximability, and for (CDT,GT)-equilibria we obtain CLS-completeness results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 173,
+    "label": 16,
+    "text": "Title: MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action\nAbstract: We propose MM-REACT, a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. In this paper, we define and explore a comprehensive list of advanced vision tasks that are intriguing to solve, but may exceed the capabilities of existing vision and vision-language models. To achieve such advanced visual intelligence, MM-REACT introduces a textual prompt design that can represent text descriptions, textualized spatial coordinates, and aligned file names for dense visual signals such as images and videos. MM-REACT's prompt design allows language models to accept, associate, and process multimodal information, thereby facilitating the synergetic combination of ChatGPT and various vision experts. Zero-shot experiments demonstrate MM-REACT's effectiveness in addressing the specified capabilities of interests and its wide application in different scenarios that require advanced visual understanding. Furthermore, we discuss and compare MM-REACT's system paradigm with an alternative approach that extends language models for multimodal scenarios through joint finetuning. Code, demo, video, and visualization are available at https://multimodal-react.github.io/",
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+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 174,
+    "label": 24,
+    "text": "Title: VDHLA: Variable Depth Hybrid Learning Automaton and Its Application to Defense Against the Selfish Mining Attack in Bitcoin\nAbstract: Learning Automaton (LA) is an adaptive self-organized model that improves its action-selection through interaction with an unknown environment. LA with finite action set can be classified into two main categories: fixed and variable structure. Furthermore, variable action-set learning automaton (VASLA) is one of the main subsets of variable structure learning automaton. In this paper, we propose VDHLA, a novel hybrid learning automaton model, which is a combination of fixed structure and variable action set learning automaton. In the proposed model, variable action set learning automaton can increase, decrease, or leave unchanged the depth of fixed structure learning automaton during the action switching phase. In addition, the depth of the proposed model can change in a symmetric (SVDHLA) or asymmetric (AVDHLA) manner. To the best of our knowledge, it is the first hybrid model that intelligently changes the depth of fixed structure learning automaton. Several computer simulations are conducted to study the performance of the proposed model with respect to the total number of rewards and action switching in stationary and non-stationary environments. The proposed model is compared with FSLA and VSLA. In order to determine the performance of the proposed model in a practical application, the selfish mining attack which threatens the incentive-compatibility of a proof-of-work based blockchain environment is considered. The proposed model is applied to defend against the selfish mining attack in Bitcoin and compared with the tie-breaking mechanism, which is a well-known defense. Simulation results in all environments have shown the superiority of the proposed model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 175,
+    "label": 24,
+    "text": "Title: Fast Temporal Wavelet Graph Neural Networks\nAbstract: Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate spatial structure, as well as the non-linear temporal dynamics of the network. To facilitate reliable and timely forecast for the human brain and traffic networks, we propose the Fast Temporal Wavelet Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for learning tasks on timeseries data with the underlying graph structure, thanks to the theories of multiresolution analysis and wavelet theory on discrete spaces. We employ Multiresolution Matrix Factorization (MMF) (Kondor et al., 2014) to factorize the highly dense graph structure and compute the corresponding sparse wavelet basis that allows us to construct fast wavelet convolution as the backbone of our novel architecture. Experimental results on real-world PEMS-BAY, METR-LA traffic datasets and AJILE12 ECoG dataset show that FTWGNN is competitive with the state-of-the-arts while maintaining a low computational footprint. Our PyTorch implementation is publicly available at https://github.com/HySonLab/TWGNN.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 176,
+    "label": 16,
+    "text": "Title: GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest\nAbstract: Instruction tuning large language model (LLM) on image-text pairs has achieved unprecedented vision-language multimodal abilities. However, their vision-language alignments are only built on image-level, the lack of region-level alignment limits their advancements to fine-grained multimodal understanding. In this paper, we propose instruction tuning on region-of-interest. The key design is to reformulate the bounding box as the format of spatial instruction. The interleaved sequences of visual features extracted by the spatial instruction and the language embedding are input to LLM, and trained on the transformed region-text data in instruction tuning format. Our region-level vision-language model, termed as GPT4RoI, brings brand new conversational and interactive experience beyond image-level understanding. (1) Controllability: Users can interact with our model by both language and spatial instructions to flexibly adjust the detail level of the question. (2) Capacities: Our model supports not only single-region spatial instruction but also multi-region. This unlocks more region-level multimodal capacities such as detailed region caption and complex region reasoning. (3) Composition: Any off-the-shelf object detector can be a spatial instruction provider so as to mine informative object attributes from our model, like color, shape, material, action, relation to other objects, etc. The code, data, and demo can be found at https://github.com/jshilong/GPT4RoI.",
+    "neighbors": [
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 177,
+    "label": 20,
+    "text": "Title: Scenic Routes in Rd\nAbstract: In this work, we introduce the problem of scenic routes among points in Rd. The key development is the nature of the problem in terms of both defining the concept of scenic points and scenic routes and then coming up with algorithms that meet different criteria for the generated scenic routes. The scenic routes problem provides a visual trajectory for a user to comprehend the layout of high-dimensional points. The nature of this trajectory and the visual layout of the points have applications in comprehending the results of machine learning supervised and unsupervised learning techniques. We study the problem in 2D and 3D (with two color points) before exploring the issues in Rd. The red/blue points in our examples could be to be in a class or not to be in a class. The applications could include landscape design to adhere to the scenic beauty of the artifacts on the ground. The generation of equally separated layouts for designing composite hardware where interference could be an issue.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 178,
+    "label": 34,
+    "text": "Title: Change a Bit to save Bytes: Compression for Floating Point Time-Series Data\nAbstract: The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 179,
+    "label": 29,
+    "text": "Title: Performance of the Gittins Policy in the G/G/1 and G/G/k, With and Without Setup Times\nAbstract: How should we schedule jobs to minimize mean queue length? In the preemptive M/G/1 queue, we know the optimal policy is the Gittins policy, which uses any available information about jobs' remaining service times to dynamically prioritize jobs. For models more complex than the M/G/1, optimal scheduling is generally intractable. This leads us to ask: beyond the M/G/1, does Gittins still perform well? Recent results indicate that Gittins performs well in the M/G/k, meaning that its additive suboptimality gap is bounded by an expression which is negligible in heavy traffic. But allowing multiple servers is just one way to extend the M/G/1, and most other extensions remain open. Does Gittins still perform well with non-Poisson arrival processes? Or if servers require setup times when transitioning from idle to busy? In this paper, we give the first analysis of the Gittins policy that can handle any combination of (a) multiple servers, (b) non-Poisson arrivals, and (c) setup times. Our results thus cover the G/G/1 and G/G/k, with and without setup times, bounding Gittins's suboptimality gap in each case. Each of (a), (b), and (c) adds a term to our bound, but all the terms are negligible in heavy traffic, thus implying Gittins's heavy-traffic optimality in all the systems we consider. Another consequence of our results is that Gittins is optimal in the M/G/1 with setup times at all loads.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 180,
+    "label": 28,
+    "text": "Title: Capacity Bounds for Vertically-Drifted First Arrival Position Channels under a Covariance Constraint\nAbstract: In this paper, we delve into the capacity problem of additive vertically-drifted first arrival position noise channel, which models a communication system where the position of molecules is harnessed to convey information. Drawing inspiration from the principles governing vector Gaussian interference channels, we examine this capacity problem within the context of a covariance constraint on input distributions. We offer analytical upper and lower bounds on this capacity for a three-dimensional spatial setting. This is achieved through a meticulous analysis of the characteristic function coupled with an investigation into the stability properties. The results of this study contribute to the ongoing effort to understand the fundamental limits of molecular communication systems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 181,
+    "label": 16,
+    "text": "Title: $\\texttt{NePhi}$: Neural Deformation Fields for Approximately Diffeomorphic Medical Image Registration\nAbstract: This work proposes $\\texttt{NePhi}$, a neural deformation model which results in approximately diffeomorphic transformations. In contrast to the predominant voxel-based approaches, $\\texttt{NePhi}$ represents deformations functionally which allows for memory-efficient training and inference. This is of particular importance for large volumetric registrations. Further, while medical image registration approaches representing transformation maps via multi-layer perceptrons have been proposed, $\\texttt{NePhi}$ facilitates both pairwise optimization-based registration $\\textit{as well as}$ learning-based registration via predicted or optimized global and local latent codes. Lastly, as deformation regularity is a highly desirable property for most medical image registration tasks, $\\texttt{NePhi}$ makes use of gradient inverse consistency regularization which empirically results in approximately diffeomorphic transformations. We show the performance of $\\texttt{NePhi}$ on two 2D synthetic datasets as well as on real 3D lung registration. Our results show that $\\texttt{NePhi}$ can achieve similar accuracies as voxel-based representations in a single-resolution registration setting while using less memory and allowing for faster instance-optimization.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 182,
+    "label": 16,
+    "text": "Title: Towards Realistic Unsupervised Fine-tuning with CLIP\nAbstract: The emergence of vision-language models (VLMs), such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some previous studies have explored the unsupervised fine-tuning of CLIP, they often rely on prior knowledge in the form of class names associated with ground truth labels. In this paper, we delve into a realistic unsupervised fine-tuning scenario by assuming that the unlabeled data might contain out-of-distribution samples from unknown classes. Furthermore, we emphasize the importance of simultaneously enhancing out-of-distribution detection capabilities alongside the recognition of instances associated with predefined class labels. To tackle this problem, we present a simple, efficient, and effective fine-tuning approach called Universal Entropy Optimization (UEO). UEO leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances. Apart from optimizing the textual prompts, UEO also incorporates optimization of channel-wise affine transformations within the visual branch of CLIP. Through extensive experiments conducted across 15 domains and 4 different types of prior knowledge, we demonstrate that UEO surpasses baseline methods in terms of both generalization and out-of-distribution detection.",
+    "neighbors": [
+      2232
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 183,
+    "label": 16,
+    "text": "Title: SwitchGPT: Adapting Large Language Models for Non-Text Outputs\nAbstract: Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text outputs. However, they falter when requests to generate non-text ones. Concurrently, modality conversion models, such as text-to-image, despite generating high-quality images, suffer from a lack of extensive textual pretraining. As a result, these models are only capable of accommodating specific image descriptions rather than comprehending more complex instructions. To bridge this gap, we propose a novel approach, \\methodname, from a modality conversion perspective that evolves a text-based LLM into a multi-modal one. We specifically employ a minimal dataset to instruct LLMs to recognize the intended output modality as directed by the instructions. Consequently, the adapted LLM can effectively summon various off-the-shelf modality conversion models from the model zoos to generate non-text responses. This circumvents the necessity for complicated pretraining that typically requires immense quantities of paired multi-modal data, while simultaneously inheriting the extensive knowledge of LLMs and the ability of high-quality generative models. To evaluate and compare the adapted multi-modal LLM with its traditional counterparts, we have constructed a multi-modal instruction benchmark that solicits diverse modality outputs. The experiment results reveal that, with minimal training, LLMs can be conveniently adapted to comprehend requests for non-text responses, thus achieving higher flexibility in multi-modal scenarios. Code and data will be made available at https://github.com/xinke-wang/SwitchGPT.",
+    "neighbors": [
+      0,
+      57,
+      363,
+      529,
+      817,
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+      1047,
+      1052,
+      2036,
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+      2235
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 184,
+    "label": 24,
+    "text": "Title: Online Laplace Model Selection Revisited\nAbstract: The Laplace approximation provides a closed-form model selection objective for neural networks (NN). Online variants, which optimise NN parameters jointly with hyperparameters, like weight decay strength, have seen renewed interest in the Bayesian deep learning community. However, these methods violate Laplace's method's critical assumption that the approximation is performed around a mode of the loss, calling into question their soundness. This work re-derives online Laplace methods, showing them to target a variational bound on a mode-corrected variant of the Laplace evidence which does not make stationarity assumptions. Online Laplace and its mode-corrected counterpart share stationary points where 1. the NN parameters are a maximum a posteriori, satisfying the Laplace method's assumption, and 2. the hyperparameters maximise the Laplace evidence, motivating online methods. We demonstrate that these optima are roughly attained in practise by online algorithms using full-batch gradient descent on UCI regression datasets. The optimised hyperparameters prevent overfitting and outperform validation-based early stopping.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 185,
+    "label": 16,
+    "text": "Title: A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment\nAbstract: Taking advantage of an event-based camera, the issues of motion blur, low dynamic range and low time sampling of standard cameras can all be addressed. However, there is a lack of event-based datasets dedicated to the benchmarking of segmentation algorithms, especially those that provide depth information which is critical for segmentation in occluded scenes. This paper proposes a new Event-based Segmentation Dataset (ESD), a high-quality 3D spatial and temporal dataset for object segmentation in an indoor cluttered environment. Our proposed dataset ESD comprises 145 sequences with 14,166 RGB frames that are manually annotated with instance masks. Overall 21.88 million and 20.80 million events from two event-based cameras in a stereo-graphic configuration are collected, respectively. To the best of our knowledge, this densely annotated and 3D spatial-temporal event-based segmentation benchmark of tabletop objects is the first of its kind. By releasing ESD, we expect to provide the community with a challenging segmentation benchmark with high quality.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 186,
+    "label": 27,
+    "text": "Title: Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned\nAbstract: This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-scale real-world deployments, with particular emphasis on handling intermittent and unreliable communication. Second, we collect and release challenging multi-robot benchmarking datasets obtained during live experiments conducted on the MIT campus, with accurate reference trajectories and maps for evaluation. The datasets include up to 8 robots traversing long distances (up to 8 km) and feature many challenging elements such as severe visual ambiguities (e.g., in underground tunnels and hallways), mixed indoor and outdoor trajectories with different lighting conditions, and dynamic entities (e.g., pedestrians and cars). Lastly, we evaluate the resilience of Kimera-Multi under different communication scenarios, and provide a quantitative comparison with a centralized baseline system. Based on the results from both live experiments and subsequent analysis, we discuss the strengths and weaknesses of Kimera-Multi, and suggest future directions for both algorithm and system design. We release the source code of Kimera-Multi and all datasets to facilitate further research towards the reliable real-world deployment of multi-robot SLAM systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 187,
+    "label": 17,
+    "text": "Title: Experiencing avatar direction in low cost theatrical mixed reality setup\nAbstract: We introduce1 the setup and programming framework of AvatarStaging theatrical mixed reality experiment. We focus on a configuration addressing movement issues between physical and 3D digital spaces from performers and directors' points of view. We propose 3 practical exercises.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 188,
+    "label": 16,
+    "text": "Title: A Control-Centric Benchmark for Video Prediction\nAbstract: Video is a promising source of knowledge for embodied agents to learn models of the world's dynamics. Large deep networks have become increasingly effective at modeling complex video data in a self-supervised manner, as evaluated by metrics based on human perceptual similarity or pixel-wise comparison. However, it remains unclear whether current metrics are accurate indicators of performance on downstream tasks. We find empirically that for planning robotic manipulation, existing metrics can be unreliable at predicting execution success. To address this, we propose a benchmark for action-conditioned video prediction in the form of a control benchmark that evaluates a given model for simulated robotic manipulation through sampling-based planning. Our benchmark, Video Prediction for Visual Planning ($VP^2$), includes simulated environments with 11 task categories and 310 task instance definitions, a full planning implementation, and training datasets containing scripted interaction trajectories for each task category. A central design goal of our benchmark is to expose a simple interface -- a single forward prediction call -- so it is straightforward to evaluate almost any action-conditioned video prediction model. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling by analyzing five highly-performant video prediction models, finding that while scale can improve perceptual quality when modeling visually diverse settings, other attributes such as uncertainty awareness can also aid planning performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 189,
+    "label": 24,
+    "text": "Title: Learning Unbiased News Article Representations: A Knowledge-Infused Approach\nAbstract: Quantification of the political leaning of online news articles can aid in understanding the dynamics of political ideology in social groups and measures to mitigating them. However, predicting the accurate political leaning of a news article with machine learning models is a challenging task. This is due to (i) the political ideology of a news article is defined by several factors, and (ii) the innate nature of existing learning models to be biased with the political bias of the news publisher during the model training. There is only a limited number of methods to study the political leaning of news articles which also do not consider the algorithmic political bias which lowers the generalization of machine learning models to predict the political leaning of news articles published by any new news publishers. In this work, we propose a knowledge-infused deep learning model that utilizes relatively reliable external data resources to learn unbiased representations of news articles using their global and local contexts. We evaluate the proposed model by setting the data in such a way that news domains or news publishers in the test set are completely unseen during the training phase. With this setup we show that the proposed model mitigates algorithmic political bias and outperforms baseline methods to predict the political leaning of news articles with up to 73% accuracy.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 190,
+    "label": 28,
+    "text": "Title: Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements\nAbstract: In this paper we consider the problem of recovering a low-rank Tucker approximation to a massive tensor based solely on structured random compressive measurements. Crucially, the proposed random measurement ensembles are both designed to be compactly represented (i.e., low-memory), and can also be efficiently computed in one-pass over the tensor. Thus, the proposed compressive sensing approach may be used to produce a low-rank factorization of a huge tensor that is too large to store in memory with a total memory footprint on the order of the much smaller desired low-rank factorization. In addition, the compressive sensing recovery algorithm itself (which takes the compressive measurements as input, and then outputs a low-rank factorization) also runs in a time which principally depends only on the size of the sought factorization, making its runtime sub-linear in the size of the large tensor one is approximating. Finally, unlike prior works related to (streaming) algorithms for low-rank tensor approximation from such compressive measurements, we present a unified analysis of both Kronecker and Khatri-Rao structured measurement ensembles culminating in error guarantees comparing the error of our recovery algorithm's approximation of the input tensor to the best possible low-rank Tucker approximation error achievable for the tensor by any possible algorithm. We further include an empirical study of the proposed approach that verifies our theoretical findings and explores various trade-offs of parameters of interest.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 191,
+    "label": 30,
+    "text": "Title: Topic-Selective Graph Network for Topic-Focused Summarization\nAbstract: Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 192,
+    "label": 16,
+    "text": "Title: Accurate Eye Tracking from Dense 3D Surface Reconstructions using Single-Shot Deflectometry\nAbstract: Eye-tracking plays a crucial role in the development of virtual reality devices, neuroscience research, and psychology. Despite its significance in numerous applications, achieving an accurate, robust, and fast eye-tracking solution remains a considerable challenge for current state-of-the-art methods. While existing reflection-based techniques (e.g.,\"glint tracking\") are considered the most accurate, their performance is limited by their reliance on sparse 3D surface data acquired solely from the cornea surface. In this paper, we rethink the way how specular reflections can be used for eye tracking: We propose a novel method for accurate and fast evaluation of the gaze direction that exploits teachings from single-shot phase-measuring-deflectometry (PMD). In contrast to state-of-the-art reflection-based methods, our method acquires dense 3D surface information of both cornea and sclera within only one single camera frame (single-shot). Improvements in acquired reflection surface points(\"glints\") of factors $>3300 \\times$ are easily achievable. We show the feasibility of our approach with experimentally evaluated gaze errors of only $\\leq 0.25^\\circ$ demonstrating a significant improvement over the current state-of-the-art.",
+    "neighbors": [
+      501
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 193,
+    "label": 10,
+    "text": "Title: Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning\nAbstract: Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find high quality solutions to ILPs faster than Branch and Bound. However, how to find the right heuristics to maximize the performance of LNS remains an open problem. In this paper, we propose a novel approach, CL-LNS, that delivers state-of-the-art anytime performance on several ILP benchmarks measured by metrics including the primal gap, the primal integral, survival rates and the best performing rate. Specifically, CL-LNS collects positive and negative solution samples from an expert heuristic that is slow to compute and learns a new one with a contrastive loss. We use graph attention networks and a richer set of features to further improve its performance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 194,
+    "label": 16,
+    "text": "Title: A Robust Likelihood Model for Novelty Detection\nAbstract: Current approaches to novelty or anomaly detection are based on deep neural networks. Despite their effectiveness, neural networks are also vulnerable to imperceptible deformations of the input data. This is a serious issue in critical applications, or when data alterations are generated by an adversarial attack. While this is a known problem that has been studied in recent years for the case of supervised learning, the case of novelty detection has received very limited attention. Indeed, in this latter setting the learning is typically unsupervised because outlier data is not available during training, and new approaches for this case need to be investigated. We propose a new prior that aims at learning a robust likelihood for the novelty test, as a defense against attacks. We also integrate the same prior with a state-of-the-art novelty detection approach. Because of the geometric properties of that approach, the resulting robust training is computationally very efficient. An initial evaluation of the method indicates that it is effective at improving performance with respect to the standard models in the absence and presence of attacks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 195,
+    "label": 30,
+    "text": "Title: Leveraging Large Text Corpora for End-to-End Speech Summarization\nAbstract: End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech recognition (ASR) and text summarization models, the E2E approach is more promising because it mitigates ASR errors, incorporates nonverbal information, and simplifies the overall system. However, since collecting a large amount of paired data (i.e., speech and summary) is difficult, the training data is usually insufficient to train a robust E2E SSum system. In this paper, we present two novel methods that leverage a large amount of external text summarization data for E2E SSum training. The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary. The second is a TTS-free method that directly inputs phoneme sequence instead of synthesized speech to the E2E SSum model. Experiments show that our proposed TTS- and phoneme-based methods improve several metrics on the How2 dataset. In particular, our best system outperforms a previous state-of-the-art one by a large margin (i.e., METEOR score improvements of more than 6 points). To the best of our knowledge, this is the first work to use external language resources for E2E SSum. Moreover, we report a detailed analysis of the How2 dataset to confirm the validity of our proposed E2E SSum system.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 196,
+    "label": 16,
+    "text": "Title: 3D-Aware Object Localization using Gaussian Implicit Occupancy Function\nAbstract: To automatically localize a target object in an image is crucial for many computer vision applications. To represent the 2D object, ellipse labels have recently been identified as a promising alternative to axis-aligned bounding boxes. This paper further considers 3D-aware ellipse labels, \\textit{i.e.}, ellipses which are projections of a 3D ellipsoidal approximation of the object, for 2D target localization. Indeed, projected ellipses carry more geometric information about the object geometry and pose (3D awareness) than traditional 3D-agnostic bounding box labels. Moreover, such a generic 3D ellipsoidal model allows for approximating known to coarsely known targets. We then propose to have a new look at ellipse regression and replace the discontinuous geometric ellipse parameters with the parameters of an implicit Gaussian distribution encoding object occupancy in the image. The models are trained to regress the values of this bivariate Gaussian distribution over the image pixels using a statistical loss function. We introduce a novel non-trainable differentiable layer, E-DSNT, to extract the distribution parameters. Also, we describe how to readily generate consistent 3D-aware Gaussian occupancy parameters using only coarse dimensions of the target and relative pose labels. We extend three existing spacecraft pose estimation datasets with 3D-aware Gaussian occupancy labels to validate our hypothesis. Labels and source code are publicly accessible here: https://cvi2.uni.lu/3d-aware-obj-loc/.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 197,
+    "label": 6,
+    "text": "Title: OldVisOnline: Curating a Dataset of Historical Visualizations\nAbstract: With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.",
+    "neighbors": [
+      2196
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 198,
+    "label": 6,
+    "text": "Title: Understanding Shared Control for Assistive Robotic Arms\nAbstract: Living a self-determined life independent of human caregivers or fully autonomous robots is a crucial factor for human dignity and the preservation of self-worth for people with motor impairments. Assistive robotic solutions - particularly robotic arms - are frequently deployed in domestic care, empowering people with motor impairments in performing ADLs independently. However, while assistive robotic arms can help them perform ADLs, currently available controls are highly complex and time-consuming due to the need to control multiple DoFs at once and necessary mode-switches. This work provides an overview of shared control approaches for assistive robotic arms, which aim to improve their ease of use for people with motor impairments. We identify three main takeaways for future research: Less is More, Pick-and-Place Matters, and Communicating Intent.",
+    "neighbors": [
+      1785
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 199,
+    "label": 24,
+    "text": "Title: Tight Memory-Regret Lower Bounds for Streaming Bandits\nAbstract: In this paper, we investigate the streaming bandits problem, wherein the learner aims to minimize regret by dealing with online arriving arms and sublinear arm memory. We establish the tight worst-case regret lower bound of $\\Omega \\left( (TB)^{\\alpha} K^{1-\\alpha}\\right), \\alpha = 2^{B} / (2^{B+1}-1)$ for any algorithm with a time horizon $T$, number of arms $K$, and number of passes $B$. The result reveals a separation between the stochastic bandits problem in the classical centralized setting and the streaming setting with bounded arm memory. Notably, in comparison to the well-known $\\Omega(\\sqrt{KT})$ lower bound, an additional double logarithmic factor is unavoidable for any streaming bandits algorithm with sublinear memory permitted. Furthermore, we establish the first instance-dependent lower bound of $\\Omega \\left(T^{1/(B+1)} \\sum_{\\Delta_x>0} \\frac{\\mu^*}{\\Delta_x}\\right)$ for streaming bandits. These lower bounds are derived through a unique reduction from the regret-minimization setting to the sample complexity analysis for a sequence of $\\epsilon$-optimal arms identification tasks, which maybe of independent interest. To complement the lower bound, we also provide a multi-pass algorithm that achieves a regret upper bound of $\\tilde{O} \\left( (TB)^{\\alpha} K^{1 - \\alpha}\\right)$ using constant arm memory.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 200,
+    "label": 2,
+    "text": "Title: Dynamic Logic of Communicating Hybrid Programs\nAbstract: This paper presents a dynamic logic $d\\mathcal{L}_\\text{CHP}$ for compositional deductive verification of communicating hybrid programs (CHPs). CHPs go beyond the traditional mixed discrete and continuous dynamics of hybrid systems by adding CSP-style operators for communication and parallelism. A compositional proof calculus is presented that modularly verifies CHPs including their parallel compositions from proofs of their subprograms by assumption-commitment reasoning in dynamic logic. Unlike Hoare-style assumption-commitments, $d\\mathcal{L}_\\text{CHP}$ supports intuitive symbolic execution via explicit recorder variables for communication primitives. Since $d\\mathcal{L}_\\text{CHP}$ is a conservative extension of differential dynamic logic $d\\mathcal{L}$, it can be used soundly along with the $d\\mathcal{L}$ proof calculus and $d\\mathcal{L}$'s complete axiomatization for differential equation invariants.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 201,
+    "label": 17,
+    "text": "Title: Deep and Fast Approximate Order Independent Transparency\nAbstract: We present a machine learning approach for efficiently computing order independent transparency (OIT). Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setup and is portable to all platforms running even with commodity GPUs. Our method requires a rendering pass to extract all features that are subsequently used to predict the overall OIT pixel color with a pre-trained neural network. We provide a comparative experimental evaluation and shader source code of all methods for reproduction of the experiments.",
+    "neighbors": [
+      317
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 202,
+    "label": 16,
+    "text": "Title: Thread Counting in Plain Weave for Old Paintings Using Semi-Supervised Regression Deep Learning Models\nAbstract: In this work, the authors develop regression approaches based on deep learning to perform thread density estimation for plain weave canvas analysis. Previous approaches were based on Fourier analysis, which is quite robust for some scenarios but fails in some others, in machine learning tools, that involve pre-labeling of the painting at hand, or the segmentation of thread crossing points, that provides good estimations in all scenarios with no need of pre-labeling. The segmentation approach is time-consuming as the estimation of the densities is performed after locating the crossing points. In this novel proposal, we avoid this step by computing the density of threads directly from the image with a regression deep learning model. We also incorporate some improvements in the initial preprocessing of the input image with an impact on the final error. Several models are proposed and analyzed to retain the best one. Furthermore, we further reduce the density estimation error by introducing a semi-supervised approach. The performance of our novel algorithm is analyzed with works by Ribera, Vel\\'azquez, and Poussin where we compare our results to the ones of previous approaches. Finally, the method is put into practice to support the change of authorship or a masterpiece at the Museo del Prado.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 203,
+    "label": 24,
+    "text": "Title: Supervision Complexity and its Role in Knowledge Distillation\nAbstract: Despite the popularity and efficacy of knowledge distillation, there is limited understanding of why it helps. In order to study the generalization behavior of a distilled student, we propose a new theoretical framework that leverages supervision complexity: a measure of alignment between teacher-provided supervision and the student's neural tangent kernel. The framework highlights a delicate interplay among the teacher's accuracy, the student's margin with respect to the teacher predictions, and the complexity of the teacher predictions. Specifically, it provides a rigorous justification for the utility of various techniques that are prevalent in the context of distillation, such as early stopping and temperature scaling. Our analysis further suggests the use of online distillation, where a student receives increasingly more complex supervision from teachers in different stages of their training. We demonstrate efficacy of online distillation and validate the theoretical findings on a range of image classification benchmarks and model architectures.",
+    "neighbors": [
+      120
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 204,
+    "label": 3,
+    "text": "Title: The LAVA Model: Learning Analytics Meets Visual Analytics\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 205,
+    "label": 20,
+    "text": "Title: Online and Dynamic Algorithms for Geometric Set Cover and Hitting Set\nAbstract: Set cover and hitting set are fundamental problems in combinatorial optimization which are well-studied in the offline, online, and dynamic settings. We study the geometric versions of these problems and present new online and dynamic algorithms for them. In the online version of set cover (resp. hitting set), $m$ sets (resp.~$n$ points) are give $n$ points (resp.~$m$ sets) arrive online, one-by-one. In the dynamic versions, points (resp. sets) can arrive as well as depart. Our goal is to maintain a set cover (resp. hitting set), minimizing the size of the computed solution. For online set cover for (axis-parallel) squares of arbitrary sizes, we present a tight $O(\\log n)$-competitive algorithm. In the same setting for hitting set, we provide a tight $O(\\log N)$-competitive algorithm, assuming that all points have integral coordinates in $[0,N)^{2}$. No online algorithm had been known for either of these settings, not even for unit squares (apart from the known online algorithms for arbitrary set systems). For both dynamic set cover and hitting set with $d$-dimensional hyperrectangles, we obtain $(\\log m)^{O(d)}$-approximation algorithms with $(\\log m)^{O(d)}$ worst-case update time. This partially answers an open question posed by Chan et al. [SODA'22]. Previously, no dynamic algorithms with polylogarithmic update time were known even in the setting of squares (for either of these problems). Our main technical contributions are an \\emph{extended quad-tree }approach and a \\emph{frequency reduction} technique that reduces geometric set cover instances to instances of general set cover with bounded frequency.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 206,
+    "label": 24,
+    "text": "Title: A CNN-LSTM Architecture for Marine Vessel Track Association Using Automatic Identification System (AIS) Data\nAbstract: In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel\u2019s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 207,
+    "label": 16,
+    "text": "Title: Vision-Based Human Pose Estimation via Deep Learning: A Survey\nAbstract: Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human\u2013computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 208,
+    "label": 6,
+    "text": "Title: Enough With \u201cHuman-AI Collaboration\u201d\nAbstract: Describing our interaction with Artificial Intelligence (AI) systems as \u2018collaboration\u2019 is well-intentioned, but flawed. Not only is it misleading, but it also takes away the credit of AI \u2018labour\u2019 from the humans behind it, and erases and obscures an often exploitative arrangement between AI producers and consumers. The AI \u2018collaboration\u2019 metaphor is merely the latest episode in a long history of labour appropriation and credit reassignment that disenfranchises labourers in the Global South. I propose that viewing AI as a tool or an instrument, rather than a collaborator, is more accurate, and ultimately fairer.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 209,
+    "label": 31,
+    "text": "Title: AutoMLP: Automated MLP for Sequential Recommendations\nAbstract: Sequential recommender systems aim to predict users\u2019 next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users\u2019 long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users\u2019 long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.",
+    "neighbors": [
+      1830
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 210,
+    "label": 13,
+    "text": "Title: Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis\nAbstract: Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both benchmarking and practical decision-making, neither has been conducted. In this context, first, this paper reviews existing regions of interest and quality indicators for preference-based evolutionary multi-objective optimization using the reference point. We point out that each quality indicator was designed for a different region of interest. Then, this paper investigates the properties of the quality indicators. We demonstrate that an achievement scalarizing function value is not always consistent with the distance from a solution to the reference point in the objective space. We observe that the regions of interest can be significantly different depending on the position of the reference point and the shape of the Pareto front. We identify undesirable properties of some quality indicators. We also show that the ranking of preference-based evolutionary multi-objective optimization algorithms depends on the choice of quality indicators.",
+    "neighbors": [
+      1164
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 211,
+    "label": 16,
+    "text": "Title: Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM\nAbstract: We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time. Co-SLAM represents the scene as a multi-resolution hash-grid to exploit its high convergence speed and ability to represent high-frequency local features. In addition, Co-SLAM incorporates one-blob encoding, to encourage surface coherence and completion in unobserved areas. This joint parametric-coordinate encoding enables real-time and robust performance by bringing the best of both worlds: fast convergence and surface hole filling. Moreover, our ray sampling strategy allows Co-SLAM to perform global bundle adjustment over all keyframes instead of requiring keyframe selection to maintain a small number of active keyframes as competing neural SLAM approaches do. Experimental results show that Co-SLAM runs at 10-17Hz and achieves state-of-the-art scene reconstruction results, and competitive tracking performance in various datasets and benchmarks (ScanNet, TUM, Replica, Synthetic RGBD). Project page: https://hengyiwang.github.io/projects/CoSLAM",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 212,
+    "label": 27,
+    "text": "Title: Communications-Aware Robotics: Challenges and Opportunities\nAbstract: The use of Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs) has seen significant growth in the research community, industry, and society. Many of these agents are equipped with communication systems that are essential for completing certain tasks successfully. This has led to the emergence of a new interdisciplinary field at the intersection of robotics and communications, which has been further driven by the integration of UAVs into 5G and 6G communication networks. However, one of the main challenges in this research area is how many researchers tend to oversimplify either the robotics or the communications aspects, hindering the full potential of this new interdisciplinary field. In this paper, we present some of the necessary modeling tools for addressing these problems from both a robotics and communications perspective, using the UAV communications relay as an example.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 213,
+    "label": 3,
+    "text": "Title: QI2 - an Interactive Tool for Data Quality Assurance\nAbstract: The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 214,
+    "label": 16,
+    "text": "Title: Vision Transformers for Mobile Applications: A Short Survey\nAbstract: Vision Transformers (ViTs) have demonstrated state-of-the-art performance on many Computer Vision Tasks. Unfortunately, deploying these large-scale ViTs is resource-consuming and impossible for many mobile devices. While most in the community are building for larger and larger ViTs, we ask a completely opposite question: How small can a ViT be within the tradeoffs of accuracy and inference latency that make it suitable for mobile deployment? We look into a few ViTs specifically designed for mobile applications and observe that they modify the transformer's architecture or are built around the combination of CNN and transformer. Recent work has also attempted to create sparse ViT networks and proposed alternatives to the attention module. In this paper, we study these architectures, identify the challenges and analyze what really makes a vision transformer suitable for mobile applications. We aim to serve as a baseline for future research direction and hopefully lay the foundation to choose the exemplary vision transformer architecture for your application running on mobile devices.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 215,
+    "label": 10,
+    "text": "Title: Human in the Loop Novelty Generation\nAbstract: Developing artificial intelligence approaches to overcome novel, unexpected circumstances is a difficult, unsolved problem. One challenge to advancing the state of the art in novelty accommodation is the availability of testing frameworks for evaluating performance against novel situations. Recent novelty generation approaches in domains such as Science Birds and Monopoly leverage human domain expertise during the search to discover new novelties. Such approaches introduce human guidance before novelty generation occurs and yield novelties that can be directly loaded into a simulated environment. We introduce a new approach to novelty generation that uses abstract models of environments (including simulation domains) that do not require domain-dependent human guidance to generate novelties. A key result is a larger, often infinite space of novelties capable of being generated, with the trade-off being a requirement to involve human guidance to select and filter novelties post generation. We describe our Human-in-the-Loop novelty generation process using our open-source novelty generation library to test baseline agents in two domains: Monopoly and VizDoom. Our results shows the Human-in-the-Loop method enables users to develop, implement, test, and revise novelties within 4 hours for both Monopoly and VizDoom domains.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 216,
+    "label": 30,
+    "text": "Title: AI-UPV at EXIST 2023 - Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime\nAbstract: With the increasing influence of social media platforms, it has become crucial to develop automated systems capable of detecting instances of sexism and other disrespectful and hateful behaviors to promote a more inclusive and respectful online environment. Nevertheless, these tasks are considerably challenging considering different hate categories and the author's intentions, especially under the learning with disagreements regime. This paper describes AI-UPV team's participation in the EXIST (sEXism Identification in Social neTworks) Lab at CLEF 2023. The proposed approach aims at addressing the task of sexism identification and characterization under the learning with disagreements paradigm by training directly from the data with disagreements, without using any aggregated label. Yet, performances considering both soft and hard evaluations are reported. The proposed system uses large language models (i.e., mBERT and XLM-RoBERTa) and ensemble strategies for sexism identification and classification in English and Spanish. In particular, our system is articulated in three different pipelines. The ensemble approach outperformed the individual large language models obtaining the best performances both adopting a soft and a hard label evaluation. This work describes the participation in all the three EXIST tasks, considering a soft evaluation, it obtained fourth place in Task 2 at EXIST and first place in Task 3, with the highest ICM-Soft of -2.32 and a normalized ICM-Soft of 0.79. The source code of our approaches is publicly available at https://github.com/AngelFelipeMP/Sexism-LLM-Learning-With-Disagreement.",
+    "neighbors": [
+      1052,
+      1219,
+      1683,
+      1965
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 217,
+    "label": 24,
+    "text": "Title: Explainable Data Poison Attacks on Human Emotion Evaluation Systems Based on EEG Signals\nAbstract: The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers\u2019 perspective. Human emotion evaluation using EEG signals has consistently attracted a lot of research attention. The identification of human emotional states based on EEG signals is effective to detect potential internal threats caused by insider individuals. Nevertheless, EEG signal-based human emotion evaluation systems have shown several vulnerabilities to data poison attacks. Besides, due to the instability and complexity of the EEG signals, it is challenging to explain and analyze how data poison attacks influence the decision process of EEG signal-based human emotion evaluation systems. In this paper, from the attackers\u2019 side, data poison attacks occurring in the training phases of six different Machine Learning models including Random Forest, Adaptive Boosting (AdaBoost), Extra Trees, XGBoost, Multilayer Perceptron (MLP), and K-Nearest Neighbors (KNN) intrude on the EEG signal-based human emotion evaluation systems using these Machine Learning models. This seeks to reduce the performance of the aforementioned Machine Learning models with regard to the classification task of 4 different human emotions using EEG signals. The findings of the experiments demonstrate that the suggested data poison assaults are model-independently successful, although various models exhibit varying levels of resilience to the attacks. In addition, the data poison attacks on the EEG signal-based human emotion evaluation systems are explained with several Explainable Artificial Intelligence (XAI) methods including Shapley Additive Explanation (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), and Generated Decision Trees. And the codes of this paper are publicly available on GitHub.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 218,
+    "label": 24,
+    "text": "Title: Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints\nAbstract: Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposals. To avoid this form of collapse, we propose a novel method for structured prediction of diverse trajectories. To this end, we complement an underlying pretrained generative model with a diversity component, based on a determinantal point process (DPP). We balance and structure this diversity with the inclusion of knowledge-based quality constraints, independent from the underlying generative model. We combine these two novel components with a gating operation, ensuring that the predictions are both diverse and within the drivable area. We demonstrate on the nuScenes driving dataset the relevance of our compound approach, which yields significant improvements in the diversity and the quality of the generated trajectories.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 219,
+    "label": 24,
+    "text": "Title: Representation Learning via Variational Bayesian Networks\nAbstract: We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the \"long-tail'', where the data is scarce. VBN provides better modeling for long-tail entities via two complementary mechanisms: First, VBN employs informative hierarchical priors that enable information propagation between entities sharing common ancestors. Additionally, VBN models explicit relations between entities that enforce complementary structure and consistency, guiding the learned representations towards a more meaningful arrangement in space. Second, VBN represents entities by densities (rather than vectors), hence modeling uncertainty that plays a complementary role in coping with data scarcity. Finally, we propose a scalable Variational Bayes optimization algorithm that enables fast approximate Bayesian inference. We evaluate the effectiveness of VBN on linguistic, recommendations, and medical inference tasks. Our findings show that VBN outperforms other existing methods across multiple datasets, and especially in the long-tail.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 220,
+    "label": 16,
+    "text": "Title: Vision Transformer for Action Units Detection\nAbstract: Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient Vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video Vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the Vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge, with a notable 14% difference in result. Furthermore, the achieved results are comparable to those of the top three teams in the previous ABAW 2022 challenge.",
+    "neighbors": [
+      1241,
+      1533,
+      1541
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 221,
+    "label": 16,
+    "text": "Title: Entropy Transformer Networks: A Learning Approach via Tangent Bundle Data Manifold\nAbstract: This paper focuses on an accurate and fast interpolation approach for image transformation employed in the design of CNN architectures. Standard Spatial Transformer Networks (STNs) use bilinear or linear interpolation as their interpolation, with unrealistic assumptions about the underlying data distributions, which leads to poor performance under scale variations. Moreover, STNs do not preserve the norm of gradients in propagation due to their dependency on sparse neighboring pixels. To address this problem, a novel Entropy STN (ESTN) is proposed that interpolates on the data manifold distributions. In particular, random samples are generated for each pixel in association with the tangent space of the data manifold, and construct a linear approximation of their intensity values with an entropy regularizer to compute the transformer parameters. A simple yet effective technique is also proposed to normalize the non-zero values of the convolution operation, to fine-tune the layers for gradients' norm-regularization during training. Experiments on challenging benchmarks show that the proposed ESTN can improve predictive accuracy over a range of computer vision tasks, including image reconstruction, and classification, while reducing the computational cost.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 222,
+    "label": 15,
+    "text": "Title: LEAPS: Topological-Layout-Adaptable Multi-die FPGA Placement for Super Long Line Minimization\nAbstract: Multi-die FPGAs are crucial components in modern computing systems, particularly for high-performance applications such as artificial intelligence and data centers. Super long lines (SLLs) provide interconnections between super logic regions (SLRs) for a multi-die FPGA on a silicon interposer. They have significantly higher delay compared to regular interconnects, which need to be minimized. With the increase in design complexity, the growth of SLLs gives rise to challenges in timing and power closure. Existing placement algorithms focus on optimizing the number of SLLs but often face limitations due to specific topologies of SLRs. Furthermore, they fall short of achieving continuous optimization of SLLs throughout the entire placement process. This highlights the necessity for more advanced and adaptable solutions. In this paper, we propose LEAPS, a comprehensive, systematic, and adaptable multi-die FPGA placement algorithm for SLL minimization. Our contributions are threefold: 1) proposing a high-performance global placement algorithm for multi-die FPGAs that optimizes the number of SLLs while addressing other essential design constraints such as wirelength, routability, and clock routing; 2) introducing a versatile method for more complex SLR topologies of multi-die FPGAs, surpassing the limitations of existing approaches; and 3) executing continuous optimization of SLLs across the whole placement stages, including global placement (GP), legalization (LG), and detailed placement (DP). Experimental results demonstrate the effectiveness of LEAPS in reducing SLLs and enhancing circuit performance. Compared with the most recent state-of-the-art (SOTA) method, LEAPS achieves an average reduction of 40.19% in SLLs and 9.99% in HPWL, while exhibiting a notable 34.34$\\times$ improvement in runtime.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 223,
+    "label": 4,
+    "text": "Title: Sparsity and Privacy in Secret Sharing: A Fundamental Trade-Off\nAbstract: This work investigates the design of sparse secret sharing schemes that encode a sparse private matrix into sparse shares. This investigation is motivated by distributed computing, where the multiplication of sparse and private matrices is moved from a computationally weak main node to untrusted worker machines. Classical secret-sharing schemes produce dense shares. However, sparsity can help speed up the computation. We show that, for matrices with i.i.d. entries, sparsity in the shares comes at a fundamental cost of weaker privacy. We derive a fundamental tradeoff between sparsity and privacy and construct optimal sparse secret sharing schemes that produce shares that leak the minimum amount of information for a desired sparsity of the shares. We apply our schemes to distributed sparse and private matrix multiplication schemes with no colluding workers while tolerating stragglers. For the setting of two non-communicating clusters of workers, we design a sparse one-time pad so that no private information is leaked to a cluster of untrusted and colluding workers, and the shares with bounded but non-zero leakage are assigned to a cluster of partially trusted workers. We conclude by discussing the necessity of using permutations for matrices with correlated entries.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 224,
+    "label": 30,
+    "text": "Title: Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization\nAbstract: Query-focused meeting summarization (QFMS) aims to generate summaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism. However, due to the dilution of key query-relevant information caused by long meeting transcripts, the original transformer-based model is insufficient to highlight the key parts related to the query. In this paper, we propose a query-aware framework with joint modeling token and utterance based on Query-Utterance Attention. It calculates the utterance-level relevance to the query with a dense retrieval module. Then both token-level query relevance and utterance-level query relevance are combined and incorporated into the generation process with attention mechanism explicitly. We show that the query relevance of different granularities contributes to generating a summary more related to the query. Experimental results on the QMSum dataset show that the proposed model achieves new state-of-the-art performance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 225,
+    "label": 30,
+    "text": "Title: Systematic Offensive Stereotyping (SOS) Bias in Language Models\nAbstract: Research has shown that language models (LMs) are socially biased. However, toxicity and offensive stereotyping bias in LMs are understudied. In this paper, we investigate the systematic offensive stereotype (SOS) bias in LMs. We propose a method to measure it. Then, we validate the SOS bias and investigate the effectiveness of debias methods from the literature on removing it. Finally, we investigate the impact of the SOS bias in LMs on their performance and their fairness on the task of hate speech detection. Our results suggest that all the inspected LMs are SOS biased. The results suggest that the SOS bias in LMs is reflective of the hate experienced online by the inspected marginalized groups. The results indicate that removing the SOS bias in LMs, using a popular debias method from the literature, leads to worse SOS bias scores. Finally, Our results show no strong evidence that the SOS bias in LMs is impactful on their performance on hate speech detection. On the other hand, there is evidence that the SOS bias in LMs is impactful on their fairness.",
+    "neighbors": [
+      767,
+      1705
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 226,
+    "label": 28,
+    "text": "Title: Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G\nAbstract: A revolutionary technology relying on Stacked Intelligent Metasurfaces (SIM) is capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave regime. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counterparts, such as reconfigurable intelligent surfaces (RIS) and metasurface lenses. We harness this new SIM for implementing holographic multiple-input multiple-output (HMIMO) communications without requiring excessive radio-frequency (RF) chains, which is a substantial benefit compared to existing implementations. First of all, we propose an HMIMO communication system based on a pair of SIM at the transmitter (TX) and receiver (RX), respectively. In sharp contrast to the conventional MIMO designs, SIM is capable of automatically accomplishing transmit precoding and receiver combining, as the EM waves propagate through them. As such, each spatial stream can be directly radiated and recovered from the corresponding transmit and receive port. Secondly, we formulate the problem of minimizing the error between the actual end-to-end channel matrix and the target diagonal one, representing a flawless interference-free system of parallel subchannels. This is achieved by jointly optimizing the phase shifts associated with all the metasurface layers of both the TX-SIM and RX-SIM. We then design a gradient descent algorithm to solve the resultant non-convex problem. Furthermore, we theoretically analyze the HMIMO channel capacity bound and provide some fundamental insights. Finally, extensive simulation results are provided for characterizing our SIM-aided HMIMO system, which quantifies its substantial performance benefits, e.g., 150% capacity improvement over both conventional MIMO and its RIS-aided counterparts.",
+    "neighbors": [
+      745,
+      865,
+      2269
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 227,
+    "label": 7,
+    "text": "Title: A New Algorithm to determine Adomian Polynomials for nonlinear polynomial functions\nAbstract: We present a new algorithm by which the Adomian polynomials can be determined for scalar-valued nonlinear polynomial functional in a Hilbert space. This algorithm calculates the Adomian polynomials without the complicated operations such as parametrization, expansion, regrouping, differentiation, etc. The algorithm involves only some matrix operations. Because of the simplicity in the mathematical operations, the new algorithm is faster and more efficient than the other algorithms previously reported in the literature. We also implement the algorithm in the MATHEMATICA code. The computing speed and efficiency of the new algorithm are compared with some other algorithms in the one-dimensional case.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 228,
+    "label": 30,
+    "text": "Title: Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning\nAbstract: The end-to-end speech translation (E2E-ST) model has gradually become a mainstream paradigm due to its low latency and less error propagation. However, it is non-trivial to train such a model well due to the task complexity and data scarcity. The speech-and-text modality differences result in the E2E-ST model performance usually inferior to the corresponding machine translation (MT) model. Based on the above observation, existing methods often use sharing mechanisms to carry out implicit knowledge transfer by imposing various constraints. However, the final model often performs worse on the MT task than the MT model trained alone, which means that the knowledge transfer ability of this method is also limited. To deal with these problems, we propose the FCCL (Fine- and Coarse- Granularity Contrastive Learning) approach for E2E-ST, which makes explicit knowledge transfer through cross-modal multi-grained contrastive learning. A key ingredient of our approach is applying contrastive learning at both sentence- and frame-level to give the comprehensive guide for extracting speech representations containing rich semantic information. In addition, we adopt a simple whitening method to alleviate the representation degeneration in the MT model, which adversely affects contrast learning. Experiments on the MuST-C benchmark show that our proposed approach significantly outperforms the state-of-the-art E2E-ST baselines on all eight language pairs. Further analysis indicates that FCCL can free up its capacity from learning grammatical structure information and force more layers to learn semantic information.",
+    "neighbors": [
+      582,
+      637
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 229,
+    "label": 24,
+    "text": "Title: An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations\nAbstract: This study used a multigrid-based convolutional neural network architecture known as MgNet in operator learning to solve numerical partial differential equations (PDEs). Given the property of smoothing iterations in multigrid methods where low-frequency errors decay slowly, we introduced a low-frequency correction structure for residuals to enhance the standard V-cycle MgNet. The enhanced MgNet model can capture the low-frequency features of solutions considerably better than the standard V-cycle MgNet. The numerical results obtained using some standard operator learning tasks are better than those obtained using many state-of-the-art methods, demonstrating the efficiency of our model.Moreover, numerically, our new model is more robust in case of low- and high-resolution data during training and testing, respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 230,
+    "label": 4,
+    "text": "Title: FACE-AUDITOR: Data Auditing in Facial Recognition Systems\nAbstract: Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images during the model deployment phase. However, the power of facial recognition systems enables entities with moderate resources to canvas the Internet and build well-performed facial recognition models without people's awareness and consent. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them, which inevitably destroys the semantic information, increases the difficulty of retroactivity, and is still prone to adaptive attacks. Therefore, an auditing method that does not interfere with the facial recognition model's utility and cannot be quickly bypassed is urgently needed. In this paper, we formulate the auditing process as a user-level membership inference problem and propose a complete toolkit FACE-AUDITOR that can carefully choose the probing set to query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model. We further propose to use the similarity scores between the original face images as reference information to improve the auditing performance. Extensive experiments on multiple real-world face image datasets show that FACE-AUDITOR can achieve auditing accuracy of up to $99\\%$. Finally, we show that FACE-AUDITOR is robust in the presence of several perturbation mechanisms to the training images or the target models. The source code of our experiments can be found at \\url{https://github.com/MinChen00/Face-Auditor}.",
+    "neighbors": [
+      1409
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 231,
+    "label": 28,
+    "text": "Title: Randomly punctured Reed-Solomon codes achieve list-decoding capacity over linear-sized fields\nAbstract: Reed--Solomon codes are a classic family of error-correcting codes consisting of evaluations of low-degree polynomials over a finite field on some sequence of distinct field elements. They are widely known for their optimal unique-decoding capabilities, but their list-decoding capabilities are not fully understood. Given the prevalence of Reed-Solomon codes, a fundamental question in coding theory is determining if Reed--Solomon codes can optimally achieve list-decoding capacity. A recent breakthrough by Brakensiek, Gopi, and Makam, established that Reed--Solomon codes are combinatorially list-decodable all the way to capacity. However, their results hold for randomly-punctured Reed--Solomon codes over an exponentially large field size $2^{O(n)}$, where $n$ is the block length of the code. A natural question is whether Reed--Solomon codes can still achieve capacity over smaller fields. Recently, Guo and Zhang showed that Reed--Solomon codes are list-decodable to capacity with field size $O(n^2)$. We show that Reed--Solomon codes are list-decodable to capacity with linear field size $O(n)$, which is optimal up to the constant factor. We also give evidence that the ratio between the alphabet size $q$ and code length $n$ cannot be bounded by an absolute constant. Our techniques also show that random linear codes are list-decodable up to (the alphabet-independent) capacity with optimal list-size $O(1/\\varepsilon)$ and near-optimal alphabet size $2^{O(1/\\varepsilon^2)}$, where $\\varepsilon$ is the gap to capacity. As far as we are aware, list-decoding up to capacity with optimal list-size $O(1/\\varepsilon)$ was previously not known to be achievable with any linear code over a constant alphabet size (even non-constructively). Our proofs are based on the ideas of Guo and Zhang, and we additionally exploit symmetries of reduced intersection matrices.",
+    "neighbors": [
+      2134
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 232,
+    "label": 31,
+    "text": "Title: Reconciling the accuracy-diversity trade-off in recommendations\nAbstract: In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories). As such, real-world recommender systems often explicitly incorporate diversity separately from accuracy. This approach, however, leaves a basic question unanswered: Why is there a trade-off in the first place? We show how the trade-off can be explained via a user's consumption constraints -- users typically only consume a few of the items they are recommended. In a stylized model we introduce, objectives that account for this constraint induce diverse recommendations, while objectives that do not account for this constraint induce homogeneous recommendations. This suggests that accuracy and diversity appear misaligned because standard accuracy metrics do not consider consumption constraints. Our model yields precise and interpretable characterizations of diversity in different settings, giving practical insights into the design of diverse recommendations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 233,
+    "label": 4,
+    "text": "Title: Application-aware Energy Attack Mitigation in the Battery-less Internet of Things\nAbstract: We study how to mitigate the effects of energy attacks in the batteryless Internet of Things (IoT). Battery-less IoT devices live and die with ambient energy, as they use energy harvesting to power their operation. They are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging their energy buffers. Experimental evidence exists that shows how controlling ambient energy allows an attacker to steer a device execution in unintended ways: energy provisioning effectively becomes an attack vector. We design, implement, and evaluate a mitigation system for energy attacks. By taking into account the specific application requirements and the output of an attack detection module, we tune task execution rates and optimize energy management. This ensures continued application execution in the event of an energy attack. When a device is under attack, our solution ensures the execution of 23.3% additional application cycles compared to the baselines we consider and increases task schedulability by at least 21%, while enabling a 34% higher peripheral availability.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 234,
+    "label": 4,
+    "text": "Title: Hybrid DLT as a data layer for real-time, data-intensive applications\nAbstract: We propose a new approach, termed Hybrid DLT, to address a broad range of industrial use cases where certain properties of both private and public DLTs are valuable, while other properties may be unnecessary or detrimental. The Hybrid DLT approach involves a system where private ledgers, with limited data block dissemination, are collaboratively created by nodes within a private network. The Notary, a publicly auditable authoritative component, maintains a single, official, coherent history for each private ledger without requiring access to data blocks. This is achieved by leveraging a public DLT solution to render the ledger histories tamper-proof, consequently providing tamper-evidence for ledger data disclosed to external actors. We present Traent Hybrid Blockchain, a commercial implementation of the Hybrid DLT approach: a real-time, data-intensive collaboration system for organizations seeking immutable data while also needing to comply with the European General Data Protection Regulation (GDPR).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 235,
+    "label": 24,
+    "text": "Title: Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction\nAbstract: Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that are computed considering a batch of scored triples and their corresponding labels. Traditional approaches consider the label of a triple to be either true or false. However, recent works suggest that all negative triples should not be valued equally. In line with this recent assumption, we posit that negative triples that are semantically valid w.r.t. domain and range constraints might be high-quality negative triples. As such, loss functions should treat them differently from semantically invalid negative ones. To this aim, we propose semantic-driven versions for the three main loss functions for link prediction. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results on three public benchmark KGs underpinned with different schemas, which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions do (1) lead to better MRR and Hits@10 values, (2) drive KGEMs towards better semantic awareness as measured by the Sem@K metric. This highlights that semantic information globally improves KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 236,
+    "label": 24,
+    "text": "Title: Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks\nAbstract: In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model the embedding update of a node feature as a Hamiltonian orbit over time. Since the Hamiltonian orbits generalize the exponential maps, this approach allows us to learn the underlying manifold of the graph in training, in contrast to most of the existing literature that assumes a fixed graph embedding manifold with a closed exponential map solution. Our proposed node embedding strategy can automatically learn, without extensive tuning, the underlying geometry of any given graph dataset even if it has diverse geometries. We test Hamiltonian functions of different forms and verify the performance of our approach on two graph node embedding downstream tasks: node classification and link prediction. Numerical experiments demonstrate that our approach adapts better to different types of graph datasets than popular state-of-the-art graph node embedding GNNs. The code is available at \\url{https://github.com/zknus/Hamiltonian-GNN}.",
+    "neighbors": [
+      108
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 237,
+    "label": 24,
+    "text": "Title: Explainability Techniques for Chemical Language Models\nAbstract: Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose an explainable AI technique that attributes the importance of individual atoms towards the predictions made by these models. Our method backpropagates the relevance information towards the chemical input string and visualizes the importance of individual atoms. We focus on self-attention Transformers operating on molecular string representations and leverage a pretrained encoder for finetuning. We showcase the method by predicting and visualizing solubility in water and organic solvents. We achieve competitive model performance while obtaining interpretable predictions, which we use to inspect the pretrained model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 238,
+    "label": 28,
+    "text": "Title: Interference Leakage Minimization in RIS-assisted MIMO Interference Channels\nAbstract: We address the problem of interference leakage (IL) minimization in the $K$-user multiple-input multiple-output (MIMO) interference channel (IC) assisted by a reconfigurable intelligent surface (RIS). We describe an iterative algorithm based on block coordinate descent to minimize the IL cost function. A reformulation of the problem provides a geometric interpretation and shows interesting connections with envelope precoding and phase-only zero-forcing beamforming problems. As a result of this analysis, we derive a set of necessary (but not sufficient) conditions for a phase-optimized RIS to be able to perfectly cancel the interference on the $K$-user MIMO IC.",
+    "neighbors": [
+      1143,
+      1410,
+      2253
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 239,
+    "label": 27,
+    "text": "Title: Multi-Shooting Differential Dynamic Programming for Hybrid Systems using Analytical Derivatives\nAbstract: Differential Dynamic Programming (DDP) is a popular technique used to generate motion for dynamic-legged robots in the recent past. However, in most cases, only the first-order partial derivatives of the underlying dynamics are used, resulting in the iLQR approach. Neglecting the second-order terms often slows down the convergence rate compared to full DDP. Multi-Shooting is another popular technique to improve robustness, especially if the dynamics are highly non-linear. In this work, we consider Multi-Shooting DDP for trajectory optimization of a bounding gait for a simplified quadruped model. As the main contribution, we develop Second-Order analytical partial derivatives of the rigid-body contact dynamics, extending our previous results for fixed/floating base models with multi-DoF joints. Finally, we show the benefits of a novel Quasi-Newton method for approximating second-order derivatives of the dynamics, leading to order-of-magnitude speedups in the convergence compared to the full DDP method.",
+    "neighbors": [
+      264
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 240,
+    "label": 10,
+    "text": "Title: Cognitive Architectures for Language Agents\nAbstract: Recent efforts have incorporated large language models (LLMs) with external resources (e.g., the Internet) or internal control flows (e.g., prompt chaining) for tasks requiring grounding or reasoning. However, these efforts have largely been piecemeal, lacking a systematic framework for constructing a fully-fledged language agent. To address this challenge, we draw on the rich history of agent design in symbolic artificial intelligence to develop a blueprint for a new wave of cognitive language agents. We first show that LLMs have many of the same properties as production systems, and recent efforts to improve their grounding or reasoning mirror the development of cognitive architectures built around production systems. We then propose Cognitive Architectures for Language Agents (CoALA), a conceptual framework to systematize diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework. Finally, we use the CoALA framework to highlight gaps and propose actionable directions toward more capable language agents in the future.",
+    "neighbors": [
+      57,
+      127,
+      247,
+      704,
+      817,
+      1047,
+      1203,
+      1267,
+      1490,
+      1840,
+      1863,
+      1877,
+      1878,
+      2029,
+      2100,
+      2136
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 241,
+    "label": 37,
+    "text": "Title: Fast Searching The Densest Subgraph And Decomposition With Local Optimality\nAbstract: Densest Subgraph Problem (DSP) is an important primitive problem with a wide range of applications, including fraud detection, community detection and DNA motif discovery. Edge-based density is one of the most common metrics in DSP. Although a maximum flow algorithm can exactly solve it in polynomial time, the increasing amount of data and the high complexity of algorithms motivate scientists to find approximation algorithms. Among these, its duality of linear programming derives several iterative algorithms including Greedy++, Frank-Wolfe and FISTA which redistribute edge weights to find the densest subgraph, however, these iterative algorithms vibrate around the optimal solution, which are not satisfactory for fast convergence. We propose our main algorithm Locally Optimal Weight Distribution (LOWD) to distribute the remaining edge weights in a locally optimal operation to converge to the optimal solution monotonically. Theoretically, we show that it will reach the optimal state of a specific linear programming which is called locally-dense decomposition. Besides, we show that it is not necessary to consider most of the edges in the original graph. Therefore, we develop a pruning algorithm using a modified Counting Sort to prune graphs by removing unnecessary edges and nodes, and then we can search the densest subgraph in a much smaller graph.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 242,
+    "label": 23,
+    "text": "Title: Understanding and Remediating Open-Source License Incompatibilities in the PyPI Ecosystem\nAbstract: The reuse and distribution of open-source software must be in compliance with its accompanying open-source license. In modern packaging ecosystems, maintaining such compliance is challenging because a package may have a complex multi-layered dependency graph with many packages, any of which may have an incompatible license. Although prior research finds that license incompatibilities are prevalent, empirical evidence is still scarce in some modern packaging ecosystems (e.g., PyPI). It also remains unclear how developers remediate the license incompatibilities in the dependency graphs of their packages (including direct and transitive dependencies), let alone any automated approaches. To bridge this gap, we conduct a large-scale empirical study of license incompatibilities and their remediation practices in the PyPI ecosystem. We find that 7.27% of the PyPI package releases have license incompatibilities and 61.3% of them are caused by transitive dependencies, causing challenges in their remediation; for remediation, developers can apply one of the five strategies: migration, removal, pinning versions, changing their own licenses, and negotiation. Inspired by our findings, we propose SILENCE, an SMT-solver-based approach to recommend license incompatibility remediations with minimal costs in package dependency graph. Our evaluation shows that the remediations proposed by SILENCE can match 19 historical real-world cases (except for migrations not covered by an existing knowledge base) and have been accepted by five popular PyPI packages whose developers were previously unaware of their license incompatibilities.",
+    "neighbors": [
+      1139
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 243,
+    "label": 24,
+    "text": "Title: Gradient Derivation for Learnable Parameters in Graph Attention Networks\nAbstract: This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powerful frameworks for processing graph-structured data and, hence, have been used in a range of applications. However, the achieved performance by these attempts has been found to be inconsistent across different datasets and the reasons for this remains an open research question. As the gradient flow provides valuable insights into the training dynamics of statistically learning models, this work obtains the gradients for the trainable model parameters of GATv2. The gradient derivations supplement the efforts of [2], where potential pitfalls of GATv2 are investigated.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 244,
+    "label": 7,
+    "text": "Title: PGD reduced-order modeling for structural dynamics applications\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 245,
+    "label": 24,
+    "text": "Title: Fundamental limits of overparametrized shallow neural networks for supervised learning\nAbstract: We carry out an information-theoretical analysis of a two-layer neural network trained from input-output pairs generated by a teacher network with matching architecture, in overparametrized regimes. Our results come in the form of bounds relating i) the mutual information between training data and network weights, or ii) the Bayes-optimal generalization error, to the same quantities but for a simpler (generalized) linear model for which explicit expressions are rigorously known. Our bounds, which are expressed in terms of the number of training samples, input dimension and number of hidden units, thus yield fundamental performance limits for any neural network (and actually any learning procedure) trained from limited data generated according to our two-layer teacher neural network model. The proof relies on rigorous tools from spin glasses and is guided by ``Gaussian equivalence principles'' lying at the core of numerous recent analyses of neural networks. With respect to the existing literature, which is either non-rigorous or restricted to the case of the learning of the readout weights only, our results are information-theoretic (i.e. are not specific to any learning algorithm) and, importantly, cover a setting where all the network parameters are trained.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 246,
+    "label": 27,
+    "text": "Title: High-speed electrical connector assembly by structured compliance in a finray-effect gripper\nAbstract: Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, requiring compensation of alignment errors while applying sufficient force in the insertion direction, ideally at high speeds and while grasping a range of components. Vision, tactile, or force sensors can compensate alignment errors, but have limited bandwidth, limiting the safe assembly speed. Passive compliance such as silicone-based fingers can reduce collision forces and grasp a range of components, but often cannot provide the accuracy or assembly forces required. To support high-speed mechanical search and self-aligning insertion, this paper proposes monolithic additively manufactured fingers which realize a moderate, structured compliance directly proximal to the gripped object. The geometry of finray-effect fingers are adapted to add form-closure features and realize a directionally-dependent stiffness at the fingertip, with a high stiffness to apply insertion forces and lower transverse stiffness to support alignment. Design parameters and mechanical properties of the fingers are investigated with FEM and empirical studies, analyzing the stiffness, maximum load, and viscoelastic effects. The fingers realize a remote center of compliance, which is shown to depend on the rib angle, and a directional stiffness ratio of $14-36$. The fingers are applied to a plug insertion task, realizing a tolerance window of $7.5$ mm and approach speeds of $1.3$ m/s.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 247,
+    "label": 30,
+    "text": "Title: ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate\nAbstract: Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have explored LLMs' potential as alternatives for human evaluation. While these single-agent-based approaches show promise, experimental results suggest that further advancements are needed to bridge the gap between their current effectiveness and human-level evaluation quality. Recognizing that best practices of human evaluation processes often involve multiple human annotators collaborating in the evaluation, we resort to a multi-agent debate framework, moving beyond single-agent prompting strategies. The multi-agent-based approach enables a group of LLMs to synergize with an array of intelligent counterparts, harnessing their distinct capabilities and expertise to enhance efficiency and effectiveness in handling intricate tasks. In this paper, we construct a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models on open-ended questions and traditional natural language generation (NLG) tasks. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments. Our code is available at https://github.com/chanchimin/ChatEval.",
+    "neighbors": [
+      240,
+      652,
+      811,
+      989,
+      1203,
+      1227,
+      1346,
+      1354,
+      1566,
+      1878,
+      1949,
+      2042,
+      2087
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 248,
+    "label": 10,
+    "text": "Title: Complexity and scalability of defeasible reasoning in many-valued weighted knowledge bases with typicality\nAbstract: Weighted knowledge bases for description logics with typicality under a\"concept-wise\"multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a $\\Pi^p_2$ upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfils the lack by providing a $P^{NP[log]}$-completeness result and new ASP encodings that deal with weighted knowledge bases with large search spaces.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 249,
+    "label": 22,
+    "text": "Title: Further Decimating the Inductive Programming Search Space with Instruction Digrams\nAbstract: Overlapping instruction subsets derived from human originated code have previously been shown to dramatically shrink the inductive programming search space, often by many orders of magnitude. Here we extend the instruction subset approach to consider direct instruction-instruction applications (or instruction digrams) as an additional search heuristic for inductive programming. In this study we analyse the frequency distribution of instruction digrams in a large sample of open source code. This indicates that the instruction digram distribution is highly skewed with over 93% of possible instruction digrams not represnted in the code sample. We demonstrate that instruction digrams can be used to constrain instruction selection during search, further reducing size of the the search space, in some cases by several orders of magnitude. This significantly increases the size of programs that can be generated using search based inductive programming techniques. We discuss the results and provide some suggestions for further work.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 250,
+    "label": 24,
+    "text": "Title: When Can Linear Learners be Robust to Indiscriminate Poisoning Attacks?\nAbstract: We study indiscriminate poisoning for linear learners where an adversary injects a few crafted examples into the training data with the goal of forcing the induced model to incur higher test error. Inspired by the observation that linear learners on some datasets are able to resist the best known attacks even without any defenses, we further investigate whether datasets can be inherently robust to indiscriminate poisoning attacks for linear learners. For theoretical Gaussian distributions, we rigorously characterize the behavior of an optimal poisoning attack, defined as the poisoning strategy that attains the maximum risk of the induced model at a given poisoning budget. Our results prove that linear learners can indeed be robust to indiscriminate poisoning if the class-wise data distributions are well-separated with low variance and the size of the constraint set containing all permissible poisoning points is also small. These findings largely explain the drastic variation in empirical attack performance of the state-of-the-art poisoning attacks on linear learners across benchmark datasets, making an important initial step towards understanding the underlying reasons some learning tasks are vulnerable to data poisoning attacks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 251,
+    "label": 37,
+    "text": "Title: DIALITE: Discover, Align and Integrate Open Data Tables\nAbstract: We demonstrate a novel table discovery pipeline called DIALITE that allows users to discover, integrate and analyze open data tables. DIALITE has three main stages. First, it allows users to discover tables from open data platforms using state-of-the-art table discovery techniques. Second, DIALITE integrates the discovered tables to produce an integrated table. Finally, it allows users to analyze the integration result by applying different downstreaming tasks over it. Our pipeline is flexible such that the user can easily add and compare additional discovery and integration algorithms.",
+    "neighbors": [
+      1157
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 252,
+    "label": 27,
+    "text": "Title: What You See Is (not) What You Get: A VR Framework for Correcting Robot Errors\nAbstract: Many solutions tailored for intuitive visualization or teleoperation of virtual, augmented and mixed (VAM) reality systems are not robust to robot failures, such as the inability to detect and recognize objects in the environment or planning unsafe trajectories. In this paper, we present a novel virtual reality (VR) framework where users can (i) recognize when the robot has failed to detect a real- world object, (ii) correct the error in VR, (iii) modify proposed object trajectories and, (iv) implement behaviors on a real-world robot. Finally, we propose a user study aimed at testing the efficacy of our framework. Project materials can be found in the OSF repository.",
+    "neighbors": [
+      1726
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 253,
+    "label": 24,
+    "text": "Title: Homological Convolutional Neural Networks\nAbstract: Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabular data still poses a challenge with classic machine learning approaches being often computationally cheaper and equally effective than increasingly complex deep learning architectures. The challenge arises from the fact that, in tabular data, the correlation among features is weaker than the one from spatial or semantic relationships in images or natural languages, and the dependency structures need to be modeled without any prior information. In this work, we propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations to gain spatial information from sparse tabular data. The resulting model leverages the power of convolutions and is centered on a limited number of concepts from network topology to guarantee (i) a data-centric, deterministic building pipeline; (ii) a high level of interpretability over the inference process; and (iii) an adequate room for scalability. We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models demonstrating that our approach reaches state-of-the-art performances on these challenging datasets. The code to reproduce all our experiments is provided at https://github.com/FinancialComputingUCL/HomologicalCNN.",
+    "neighbors": [
+      671,
+      1368
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 254,
+    "label": 23,
+    "text": "Title: AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection\nAbstract: The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to generate run-time log sequences without actually running the system. AutoLog starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AutoLog propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AutoLog acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. We hope AutoLog can facilitate the benchmarking and adoption of automated log analysis techniques.",
+    "neighbors": [
+      939
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 255,
+    "label": 5,
+    "text": "Title: Workflows Community Summit 2022: A Roadmap Revolution\nAbstract: Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enabling the publication of workflows and their associated products according to the FAIR principles. This document reports on discussions and findings from the 2022 international edition of the Workflows Community Summit that took place on November 29 and 30, 2022.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 256,
+    "label": 24,
+    "text": "Title: A study on a Q-Learning algorithm application to a manufacturing assembly problem\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 257,
+    "label": 24,
+    "text": "Title: IoT Federated Blockchain Learning at the Edge\nAbstract: IoT devices are sorely underutilized in the medical field, especially within machine learning for medicine, yet they offer unrivaled benefits. IoT devices are low-cost, energy-efficient, small and intelligent devices. In this paper, we propose a distributed federated learning framework for IoT devices, more specifically for IoMT (Internet of Medical Things), using blockchain to allow for a decentralized scheme improving privacy and efficiency over a centralized system; this allows us to move from the cloud-based architectures, that are prevalent, to the edge. The system is designed for three paradigms: 1) Training neural networks on IoT devices to allow for collaborative training of a shared model whilst decoupling the learning from the dataset to ensure privacy. Training is performed in an online manner simultaneously amongst all participants, allowing for the training of actual data that may not have been present in a dataset collected in the traditional way and dynamically adapt the system whilst it is being trained. 2) Training of an IoMT system in a fully private manner such as to mitigate the issue with confidentiality of medical data and to build robust, and potentially bespoke, models where not much, if any, data exists. 3) Distribution of the actual network training, something federated learning itself does not do, to allow hospitals, for example, to utilize their spare computing resources to train network models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 258,
+    "label": 27,
+    "text": "Title: Digital twin in virtual reality for human-vehicle interactions in the context of autonomous driving\nAbstract: This paper presents the results of tests of interactions between real humans and simulated vehicles in a virtual scenario. Human activity is inserted into the virtual world via a virtual reality interface for pedestrians. The autonomous vehicle is equipped with a virtual Human-Machine interface (HMI) and drives through the digital twin of a real crosswalk. The HMI was combined with gentle and aggressive braking maneuvers when the pedestrian intended to cross. The results of the interactions were obtained through questionnaires and measurable variables such as the distance to the vehicle when the pedestrian initiated the crossing action. The questionnaires show that pedestrians feel safer whenever HMI is activated and that varying the braking maneuver does not influence their perception of danger as much, while the measurable variables show that both HMI activation and the gentle braking maneuver cause the pedestrian to cross earlier.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 259,
+    "label": 6,
+    "text": "Title: ChameleonControl: Teleoperating Real Human Surrogates through Mixed Reality Gestural Guidance for Remote Hands-on Classrooms\nAbstract: We present ChameleonControl, a real-human teleoperation system for scalable remote instruction in hands-on classrooms. In contrast to existing video or AR/VR-based remote hands-on education, ChameleonControl uses a real human as a surrogate of a remote instructor. Building on existing human-based telepresence approaches, we contribute a novel method to teleoperate a human surrogate through synchronized mixed reality hand gestural navigation and verbal communication. By overlaying the remote instructor\u2019s virtual hands in the local user\u2019s MR view, the remote instructor can guide and control the local user as if they were physically present. This allows the local user/surrogate to synchronize their hand movements and gestures with the remote instructor, effectively teleoperating a real human. We deploy and evaluate our system in classrooms of physiotherapy training, as well as other application domains such as mechanical assembly, sign language and cooking lessons. The study results confirm that our approach can increase engagement and the sense of co-presence, showing potential for the future of remote hands-on classrooms.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 260,
+    "label": 16,
+    "text": "Title: Chasing Consistency in Text-to-3D Generation from a Single Image\nAbstract: Text-to-3D generation from a single-view image is a popular but challenging task in 3D vision. Although numerous methods have been proposed, existing works still suffer from the inconsistency issues, including 1) semantic inconsistency, 2) geometric inconsistency, and 3) saturation inconsistency, resulting in distorted, overfitted, and over-saturated generations. In light of the above issues, we present Consist3D, a three-stage framework Chasing for semantic-, geometric-, and saturation-Consistent Text-to-3D generation from a single image, in which the first two stages aim to learn parameterized consistency tokens, and the last stage is for optimization. Specifically, the semantic encoding stage learns a token independent of views and estimations, promoting semantic consistency and robustness. Meanwhile, the geometric encoding stage learns another token with comprehensive geometry and reconstruction constraints under novel-view estimations, reducing overfitting and encouraging geometric consistency. Finally, the optimization stage benefits from the semantic and geometric tokens, allowing a low classifier-free guidance scale and therefore preventing oversaturation. Experimental results demonstrate that Consist3D produces more consistent, faithful, and photo-realistic 3D assets compared to previous state-of-the-art methods. Furthermore, Consist3D also allows background and object editing through text prompts.",
+    "neighbors": [
+      436,
+      955,
+      1125,
+      1173,
+      1902,
+      2205
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 261,
+    "label": 27,
+    "text": "Title: Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots\nAbstract: Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to omnidirectional robots or lack a complete navigation and decision-making framework that operates in real time. This paper presents an autonomous nonholonomic multi-robot system and an end-to-end hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks, and the experiment results reveal their effectiveness and reliability in complex and dynamic environments.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 262,
+    "label": 24,
+    "text": "Title: AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices\nAbstract: In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 263,
+    "label": 24,
+    "text": "Title: simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing\nAbstract: Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recently, many works present lots of special methods for applying deep neural networks to KT from different perspectives like model architecture, adversarial augmentation and etc., which make the overall algorithm and system become more and more complex. Furthermore, due to the lack of standardized evaluation protocol \\citep{liu2022pykt}, there is no widely agreed KT baselines and published experimental comparisons become inconsistent and self-contradictory, i.e., the reported AUC scores of DKT on ASSISTments2009 range from 0.721 to 0.821 \\citep{minn2018deep,yeung2018addressing}. Therefore, in this paper, we provide a strong but simple baseline method to deal with the KT task named \\textsc{simpleKT}. Inspired by the Rasch model in psychometrics, we explicitly model question-specific variations to capture the individual differences among questions covering the same set of knowledge components that are a generalization of terms of concepts or skills needed for learners to accomplish steps in a task or a problem. Furthermore, instead of using sophisticated representations to capture student forgetting behaviors, we use the ordinary dot-product attention function to extract the time-aware information embedded in the student learning interactions. Extensive experiments show that such a simple baseline is able to always rank top 3 in terms of AUC scores and achieve 57 wins, 3 ties and 16 loss against 12 DLKT baseline methods on 7 public datasets of different domains. We believe this work serves as a strong baseline for future KT research. Code is available at \\url{https://github.com/pykt-team/pykt-toolkit}\\footnote{We merged our model to the \\textsc{pyKT} benchmark at \\url{https://pykt.org/}.}.",
+    "neighbors": [
+      124
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 264,
+    "label": 27,
+    "text": "Title: On Second-Order Derivatives of Rigid-Body Dynamics: Theory & Implementation\nAbstract: Model-based control for robots has increasingly been dependent on optimization-based methods like Differential Dynamic Programming and iterative LQR (iLQR). These methods can form the basis of Model-Predictive Control (MPC), which is commonly used for controlling legged robots. Computing the partial derivatives of the dynamics is often the most expensive part of these algorithms, regardless of whether analytical methods, Finite Difference, Automatic Differentiation (AD), or Chain-Rule accumulation is used. Since the second-order derivatives of dynamics result in tensor computations, they are often ignored, leading to the use of iLQR, instead of the full second-order DDP method. In this paper, we present analytical methods to compute the second-order derivatives of inverse and forward dynamics for open-chain rigid-body systems with multi-DoF joints and fixed/floating bases. An extensive comparison of accuracy and run-time performance with AD and other methods is provided, including the consideration of code-generation techniques in C/C++ to speed up the computations. For the 36 DoF ATLAS humanoid, the second-order Inverse, and the Forward dynamics derivatives take approx 200 mu s, and approx 2.1 ms respectively, resulting in a 3x speedup over the AD approach.",
+    "neighbors": [
+      239
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 265,
+    "label": 2,
+    "text": "Title: Decentralized Stream Runtime Verification for Timed Asynchronous Networks\nAbstract: Problem: We study the problem of monitoring distributed systems such as smart buildings, ambient living, wide area networks and other distributed systems that get monitored periodically in human scale times. In these systems computers communicate using message passing and share an almost synchronized clock. This is a realistic scenario for networks where the speed of the monitoring is sufficiently slow (like seconds or tens of seconds) to permit efficient clock synchronization, where clock deviations are small compared to the time precision and frequency required by the monitoring. Solution: More concretely, we propose a solution to monitor decentralized systems where monitors are expressed as stream runtime verification specifications. We solve the problem for \u201ctimed asynchronous networks\u201d, where computational nodes where the monitors run have a synchronized clock with a small bounded maximum drift. These nodes communicate using a network, where messages can take arbitrarily long but cannot be duplicated or lost. This setting is common in many cyber-physical systems like smart buildings and ambient living. This assumption generalizes the synchronous monitoring case. Previous approaches to decentralized monitoring were limited to synchronous networks, which are not easily implemented in practice because of network failures. Even when network failures are unusual, they can require several monitoring cycles to be repaired. Methodology: We describe formally the monitoring problem for timed-asynchronous networks, we describe a decentralized algorithm and provide proofs of its correctness. Afterwards, we formally analyze the complexity of our solutions and provide two analysis techniques to approximate the memory requirements. Finally, we implement the algorithm and perform an empirical evaluation with real data extracted from four different datasets. Contributions: We propose a solution to the timed asynchronous decentralized monitoring problem. We study the specifications and conditions on the network behavior that allow the monitoring to take place with bounded resources, independently of the trace length. Finally, we report the results of an empirical evaluation of an implementation and verify the theoretical results in terms of effectiveness and efficiency.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 266,
+    "label": 28,
+    "text": "Title: Secure Communication for Spatially Correlated RIS-Aided Multiuser Massive MIMO Systems: Analysis and Optimization\nAbstract: This letter investigates the secure communication in a reconfigurable intelligent surface (RIS)-aided multiuser massive multiple-input multiple-output (MIMO) system exploiting artificial noise (AN). We first derive a closed-form expression of the ergodic secrecy rate under spatially correlated MIMO channels. By using this derived result, we further optimize the power fraction of AN in closed form and the RIS phase shifts by developing a gradient-based algorithm, which requires only statistical channel state information (CSI). Our analysis shows that spatial correlation at the RIS provides an additional dimension for optimizing the RIS phase shifts. Numerical simulations validate the analytical results which show the insightful interplay among the system parameters and the degradation of secrecy performance due to high spatial correlation at the RIS.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 267,
+    "label": 24,
+    "text": "Title: Mutual Information Regularization for Vertical Federated Learning\nAbstract: Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works show that parties without labels (passive parties) in VFL can infer the sensitive label information owned by the party with labels (active party) or execute backdoor attacks to VFL. Meanwhile, active party can also infer sensitive feature information from passive party. All these pose new privacy and security challenges to VFL systems. We propose a new general defense method which limits the mutual information between private raw data, including both features and labels, and intermediate outputs to achieve a better trade-off between model utility and privacy. We term this defense Mutual Information Regularization Defense (MID). We theoretically and experimentally testify the effectiveness of our MID method in defending existing attacks in VFL, including label inference attacks, backdoor attacks and feature reconstruction attacks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 268,
+    "label": 30,
+    "text": "Title: WizardCoder: Empowering Code Large Language Models with Evol-Instruct\nAbstract: Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on extensive raw code data without instruction fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs with complex instruction fine-tuning, by adapting the Evol-Instruct method to the domain of code. Through comprehensive experiments on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000, we unveil the exceptional capabilities of our model. It surpasses all other open-source Code LLMs by a substantial margin. Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+. Our code, model weights, and data are public at https://github.com/nlpxucan/WizardLM",
+    "neighbors": [
+      855,
+      1052,
+      1112,
+      1114,
+      1171,
+      1249,
+      1515,
+      1606,
+      1735,
+      1840,
+      1863,
+      1879,
+      1907,
+      1950
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 269,
+    "label": 16,
+    "text": "Title: From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models\nAbstract: Diffusion models have revolted the field of text-to-image generation recently. The unique way of fusing text and image information contributes to their remarkable capability of generating highly text-related images. From another perspective, these generative models imply clues about the precise correlation between words and pixels. In this work, a simple but effective method is proposed to utilize the attention mechanism in the denoising network of text-to-image diffusion models. Without re-training nor inference-time optimization, the semantic grounding of phrases can be attained directly. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting and our method achieves superior performance to prior methods. In addition, the acquired word-pixel correlation is found to be generalizable for the learned text embedding of customized generation methods, requiring only a few modifications. To validate our discovery, we introduce a new practical task called\"personalized referring image segmentation\"with a new dataset. Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task. In summary, our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation.",
+    "neighbors": [
+      1262,
+      2009,
+      2186
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 270,
+    "label": 6,
+    "text": "Title: Deimos: A Grammar of Dynamic Embodied Immersive Visualisation Morphs and Transitions\nAbstract: We present Deimos, a grammar for specifying dynamic embodied immersive visualisation morphs and transitions. A morph is a collection of animated transitions that are dynamically applied to immersive visualisations at runtime and is conceptually modelled as a state machine. It is comprised of state, transition, and signal specifications. States in a morph are used to generate animation keyframes, with transitions connecting two states together. A transition is controlled by signals, which are composable data streams that can be used to enable embodied interaction techniques. Morphs allow immersive representations of data to transform and change shape through user interaction, facilitating the embodied cognition process. We demonstrate the expressivity of Deimos in an example gallery and evaluate its usability in an expert user study of six immersive analytics researchers. Participants found the grammar to be powerful and expressive, and showed interest in drawing upon Deimos\u2019 concepts and ideas in their own research.",
+    "neighbors": [
+      760,
+      1313
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 271,
+    "label": 24,
+    "text": "Title: NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks\nAbstract: Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions. Defense mechanisms are highly preferred in safety-critical applications. In this paper, firstly, we use the gradient class activation map (GradCAM) to analyze the behavior deviation of the VGG-16 network when its inputs are mixed with adversarial perturbation or Gaussian noise. In particular, our method can locate vulnerable layers that are sensitive to adversarial perturbation and Gaussian noise. We also show that the behavior deviation of vulnerable layers can be used to detect adversarial examples. Secondly, we propose a novel NoiseCAM algorithm that integrates information from globally and pixel-level weighted class activation maps. Our algorithm is susceptible to adversarial perturbations and will not respond to Gaussian random noise mixed in the inputs. Third, we compare detecting adversarial examples using both behavior deviation and NoiseCAM, and we show that NoiseCAM outperforms behavior deviation modeling in its overall performance. Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 272,
+    "label": 24,
+    "text": "Title: An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions\nAbstract: Obesity is a critical healthcare issue affecting the United States. The least risky treatments available for obesity are behavioral interventions meant to promote diet and exercise. Often these interventions contain a mobile component that allows interventionists to collect participants level data and provide participants with incentives and goals to promote long term behavioral change. Recently, there has been interest in using direct financial incentives to promote behavior change. However, adherence is challenging in these interventions, as each participant will react differently to different incentive structure and amounts, leading researchers to consider personalized interventions. The key challenge for personalization, is that the clinicians do not know a priori how best to administer incentives to participants, and given finite intervention budgets how to disburse costly resources efficiently. In this paper, we consider this challenge of designing personalized weight loss interventions that use direct financial incentives to motivate weight loss while remaining within a budget. We create a machine learning approach that is able to predict how individuals may react to different incentive schedules within the context of a behavioral intervention. We use this predictive model in an adaptive framework that over the course of the intervention computes what incentives to disburse to participants and remain within the study budget. We provide both theoretical guarantees for our modeling and optimization approaches as well as demonstrate their performance in a simulated weight loss study. Our results highlight the cost efficiency and effectiveness of our personalized intervention design for weight loss.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 273,
+    "label": 16,
+    "text": "Title: Training-Free Layout Control with Cross-Attention Guidance\nAbstract: Recent diffusion-based generators can produce high-quality images based only on textual prompts. However, they do not correctly interpret instructions that specify the spatial layout of the composition. We propose a simple approach that can achieve robust layout control without requiring training or fine-tuning the image generator. Our technique, which we call layout guidance, manipulates the cross-attention layers that the model uses to interface textual and visual information and steers the reconstruction in the desired direction given, e.g., a user-specified layout. In order to determine how to best guide attention, we study the role of different attention maps when generating images and experiment with two alternative strategies, forward and backward guidance. We evaluate our method quantitatively and qualitatively with several experiments, validating its effectiveness. We further demonstrate its versatility by extending layout guidance to the task of editing the layout and context of a given real image.",
+    "neighbors": [
+      706,
+      908,
+      955,
+      1079,
+      1902,
+      2161,
+      2277,
+      2306
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 274,
+    "label": 34,
+    "text": "Title: Learning-Augmented Online TSP on Rings, Trees, Flowers and (almost) Everywhere Else\nAbstract: We study the Online Traveling Salesperson Problem (OLTSP) with predictions. In OLTSP, a sequence of initially unknown requests arrive over time at points (locations) of a metric space. The goal is, starting from a particular point of the metric space (the origin), to serve all these requests while minimizing the total time spent. The server moves with unit speed or is\"waiting\"(zero speed) at some location. We consider two variants: in the open variant, the goal is achieved when the last request is served. In the closed one, the server additionally has to return to the origin. We adopt a prediction model, introduced for OLTSP on the line, in which the predictions correspond to the locations of the requests and extend it to more general metric spaces. We first propose an oracle-based algorithmic framework, inspired by previous work. This framework allows us to design online algorithms for general metric spaces that provide competitive ratio guarantees which, given perfect predictions, beat the best possible classical guarantee (consistency). Moreover, they degrade gracefully along with the increase in error (smoothness), but always within a constant factor of the best known competitive ratio in the classical case (robustness). Having reduced the problem to designing suitable efficient oracles, we describe how to achieve this for general metric spaces as well as specific metric spaces (rings, trees and flowers), the resulting algorithms being tractable in the latter case. The consistency guarantees of our algorithms are tight in almost all cases, and their smoothness guarantees only suffer a linear dependency on the error, which we show is necessary. Finally, we provide robustness guarantees improving previous results.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 275,
+    "label": 24,
+    "text": "Title: An active inference model of car following: Advantages and applications\nAbstract: Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable than rule-based models but are limited by the need for large training datasets and their lack of interpretability, i.e., an understandable link between input data and output behaviors. We propose a novel car following modeling approach using active inference, which has comparable behavioral flexibility to data-driven models while maintaining interpretability. We assessed the proposed model, the Active Inference Driving Agent (AIDA), through a benchmark analysis against the rule-based Intelligent Driver Model, and two neural network Behavior Cloning models. The models were trained and tested on a real-world driving dataset using a consistent process. The testing results showed that the AIDA predicted driving controls significantly better than the rule-based Intelligent Driver Model and had similar accuracy to the data-driven neural network models in three out of four evaluations. Subsequent interpretability analyses illustrated that the AIDA's learned distributions were consistent with driver behavior theory and that visualizations of the distributions could be used to directly comprehend the model's decision making process and correct model errors attributable to limited training data. The results indicate that the AIDA is a promising alternative to black-box data-driven models and suggest a need for further research focused on modeling driving style and model training with more diverse datasets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 276,
+    "label": 24,
+    "text": "Title: Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits\nAbstract: The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.",
+    "neighbors": [
+      1982
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 277,
+    "label": 16,
+    "text": "Title: ASPIRE: Language-Guided Augmentation for Robust Image Classification\nAbstract: Neural image classifiers can often learn to make predictions by overly relying on non-predictive features that are spuriously correlated with the class labels in the training data. This leads to poor performance in real-world atypical scenarios where such features are absent. Supplementing the training dataset with images without such spurious features can aid robust learning against spurious correlations via better generalization. This paper presents ASPIRE (Language-guided data Augmentation for SPurIous correlation REmoval), a simple yet effective solution for expanding the training dataset with synthetic images without spurious features. ASPIRE, guided by language, generates these images without requiring any form of additional supervision or existing examples. Precisely, we employ LLMs to first extract foreground and background features from textual descriptions of an image, followed by advanced language-guided image editing to discover the features that are spuriously correlated with the class label. Finally, we personalize a text-to-image generation model to generate diverse in-domain images without spurious features. We demonstrate the effectiveness of ASPIRE on 4 datasets, including the very challenging Hard ImageNet dataset, and 9 baselines and show that ASPIRE improves the classification accuracy of prior methods by 1% - 38%. Code soon at: https://github.com/Sreyan88/ASPIRE.",
+    "neighbors": [
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 278,
+    "label": 24,
+    "text": "Title: Doubly Robust Counterfactual Classification\nAbstract: We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a general counterfactual classifier, where we can incorporate flexible constraints by casting the classification problem as a nonlinear mathematical program involving counterfactuals. We go on to analyze the rates of convergence of the estimator and provide a closed-form expression for its asymptotic distribution. Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast $\\sqrt{n}$ rates with tractable inference even when using nonparametric machine learning approaches. We study the empirical performance of our methods by simulation and apply them for recidivism risk prediction.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 279,
+    "label": 15,
+    "text": "Title: CUDA-PIM: End-to-End Integration of Digital Processing-in-Memory from High-Level C++ to Microarchitectural Design\nAbstract: Digital processing-in-memory (PIM) architectures mitigate the memory wall problem by facilitating parallel bitwise operations directly within memory. Recent works have demonstrated their algorithmic potential for accelerating data-intensive applications; however, there remains a significant gap in the programming model and microarchitectural design. This is further exacerbated by the emerging model of partitions, which significantly complicates control and periphery. Therefore, inspired by NVIDIA CUDA, this paper provides an end-to-end architectural integration of digital memristive PIM from an abstract high-level C++ programming interface for vector operations to the low-level microarchitecture. We begin by proposing an efficient microarchitecture and instruction set architecture (ISA) that bridge the gap between the low-level control periphery and an abstraction of PIM parallelism into warps and threads. We subsequently propose a PIM compilation library that converts high-level C++ to ISA instructions, and a PIM driver that translates ISA instructions into PIM micro-operations. This drastically simplifies the development of PIM applications and enables PIM integration within larger existing C++ CPU/GPU programs for heterogeneous computing with significant ease. Lastly, we present an efficient GPU-accelerated simulator for the proposed PIM microarchitecture. Although slower than a theoretical PIM chip, this simulator provides an accessible platform for developers to start executing and debugging PIM algorithms. To validate our approach, we implement state-of-the-art matrix operations and FFT PIM-based algorithms as case studies. These examples demonstrate drastically simplified development without compromising performance, showing the potential and significance of CUDA-PIM.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 280,
+    "label": 31,
+    "text": "Title: Information Retrieval: Recent Advances and Beyond\nAbstract: This paper provides an extensive and thorough overview of the models and techniques utilized in the first and second stages of the typical information retrieval processing chain. Our discussion encompasses the current state-of-the-art models, covering a wide range of methods and approaches in the field of information retrieval. We delve into the historical development of these models, analyze the key advancements and breakthroughs, and address the challenges and limitations faced by researchers and practitioners in the domain. By offering a comprehensive understanding of the field, this survey is a valuable resource for researchers, practitioners, and newcomers to the information retrieval domain, fostering knowledge growth, innovation, and the development of novel ideas and techniques.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 281,
+    "label": 16,
+    "text": "Title: Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection\nAbstract: Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descriptions with powerful pre-trained 2D neural networks. To this end, this study proposes Complementary Pseudo Multimodal Feature (CPMF) that incorporates local geometrical information in 3D modality using handcrafted PCD descriptors and global semantic information in the generated pseudo 2D modality using pre-trained 2D neural networks. For global semantics extraction, CPMF projects the origin PCD into a pseudo 2D modality containing multi-view images. These images are delivered to pre-trained 2D neural networks for informative 2D modality feature extraction. The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection. Extensive experiments demonstrate the complementary capacity between 2D and 3D modality features and the effectiveness of CPMF, with 95.15% image-level AU-ROC and 92.93% pixel-level PRO on the MVTec3D benchmark. Code is available on https://github.com/caoyunkang/CPMF.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 282,
+    "label": 16,
+    "text": "Title: Causalainer: Causal Explainer for Automatic Video Summarization\nAbstract: The goal of video summarization is to automatically shorten videos such that it conveys the overall story without losing relevant information. In many application scenarios, improper video summarization can have a large impact. For example in forensics, the quality of the generated video summary will affect an investigator\u2019s judgment while in journalism it might yield undesired bias. Because of this, modeling explainability is a key concern. One of the best ways to address the explainability challenge is to uncover the causal relations that steer the process and lead to the result. Current machine learning-based video summarization algorithms learn optimal parameters but do not uncover causal relationships. Hence, they suffer from a relative lack of explainability. In this work, a Causal Explainer, dubbed Causalainer, is proposed to address this issue. Multiple meaningful random variables and their joint distributions are introduced to characterize the behaviors of key components in the problem of video summarization. In addition, helper distributions are introduced to enhance the effectiveness of model training. In visual-textual input scenarios, the extra input can decrease the model performance. A causal semantics extractor is designed to tackle this issue by effectively distilling the mutual information from the visual and textual inputs. Experimental results on commonly used benchmarks demonstrate that the proposed method achieves state-of-the-art performance while being more explainable.",
+    "neighbors": [
+      1951,
+      2266
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 283,
+    "label": 13,
+    "text": "Title: Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design\nAbstract: Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate solution for energy-efficient edge computing. Yet, with the growing complexity of SNN algorithms, it isn't easy to properly benchmark and optimize their computational cost without hardware in the loop. Although digital neuromorphic processors have been widely adopted to benchmark SNN algorithms, their black-box nature is problematic for algorithm-hardware co-optimization. In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architecture. For convenient benchmarking and optimization, we provide the energy cost of the essential neuromorphic components in SENeCA, including neuron models and learning rules. Moreover, we exploit the SENeCA's hierarchical memory and exhibit an advantage over existing neuromorphic processors. We show the energy efficiency of SNN algorithms for video processing and online learning, and demonstrate the potential of our work for optimizing algorithm designs. Overall, we present a practical approach to enable algorithm designers to accurately benchmark SNN algorithms and pave the way towards effective algorithm-hardware co-design.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 284,
+    "label": 24,
+    "text": "Title: A Tale of Two Approximations: Tightening Over-Approximation for DNN Robustness Verification via Under-Approximation\nAbstract: The robustness of deep neural networks (DNNs) is crucial to the hosting system\u2019s reliability and security. Formal verification has been demonstrated to be effective in providing provable robustness guarantees. To improve its scalability, over-approximating the non-linear activation functions in DNNs by linear constraints has been widely adopted, which transforms the verification problem into an efficiently solvable linear programming problem. Many efforts have been dedicated to defining the so-called tightest approximations to reduce overestimation imposed by over-approximation. In this paper, we study existing approaches and identify a dominant factor in defining tight approximation, namely the approximation domain of the activation function. We find out that tight approximations defined on approximation domains may not be as tight as the ones on their actual domains, yet existing approaches all rely only on approximation domains. Based on this observation, we propose a novel dual-approximation approach to tighten overapproximations, leveraging an activation function\u2019s underestimated domain to define tight approximation bounds. We implement our approach with two complementary algorithms based respectively on Monte Carlo simulation and gradient descent into a tool called DualApp. We assess it on a comprehensive benchmark of DNNs with different architectures. Our experimental results show that DualApp significantly outperforms the state-of-the-art approaches with 100% \u2212 1000% improvement on the verified robustness ratio and 10.64% on average (up to 66.53%) on the certified lower bound.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 285,
+    "label": 27,
+    "text": "Title: Distributed Optimization Methods for Multi-Robot Systems: Part I - A Tutorial\nAbstract: Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems. This tutorial constitutes the first part of a two-part series on distributed optimization applied to multi-robot problems, which seeks to advance the application of distributed optimization in robotics. In this tutorial, we demonstrate that many canonical multi-robot problems can be cast within the distributed optimization framework, such as multi-robot simultaneous localization and planning (SLAM), multi-robot target tracking, and multi-robot task assignment problems. We identify three broad categories of distributed optimization algorithms: distributed first-order methods, distributed sequential convex programming, and the alternating direction method of multipliers (ADMM). We describe the basic structure of each category and provide representative algorithms within each category. We then work through a simulation case study of multiple drones collaboratively tracking a ground vehicle. We compare solutions to this problem using a number of different distributed optimization algorithms. In addition, we implement a distributed optimization algorithm in hardware on a network of Rasberry Pis communicating with XBee modules to illustrate robustness to the challenges of real-world communication networks.",
+    "neighbors": [
+      539,
+      2164
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 286,
+    "label": 16,
+    "text": "Title: Watch Your Steps: Local Image and Scene Editing by Text Instructions\nAbstract: Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We leverage InstructPix2Pix (IP2P) and identify the discrepancy between IP2P predictions with and without the instruction. This discrepancy is referred to as the relevance map. The relevance map conveys the importance of changing each pixel to achieve the edits, and is used to to guide the modifications. This guidance ensures that the irrelevant pixels remain unchanged. Relevance maps are further used to enhance the quality of text-guided editing of 3D scenes in the form of neural radiance fields. A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made. We perform iterative updates on the training views guided by rendered relevance maps from the relevance field. Our method achieves state-of-the-art performance on both image and NeRF editing tasks. Project page: https://ashmrz.github.io/WatchYourSteps/",
+    "neighbors": [
+      48,
+      1125,
+      1355
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 287,
+    "label": 24,
+    "text": "Title: Adversarial Attacks on Adversarial Bandits\nAbstract: We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinear (T-o(T)) number of rounds, while incurring only sublinear (o(T)) cumulative attack cost. This result implies critical security concern in real-world bandit-based systems, e.g., in online recommendation, an attacker might be able to hijack the recommender system and promote a desired product. Our proposed attack algorithms require knowledge of only the regret rate, thus are agnostic to the concrete bandit algorithm employed by the victim player. We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur. The lower bound matches the upper bound achieved by our attack, which shows that our attack is asymptotically optimal.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 288,
+    "label": 16,
+    "text": "Title: Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification\nAbstract: Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors. Despite this, little work has been done to incorporate appearance cues beyond simple heuristic models that lack robustness to feature degradation. In this paper, we propose a novel way to leverage objects' appearances to adaptively integrate appearance matching into existing high-performance motion-based methods. Building upon the pure motion-based method OC-SORT, we achieve 1st place on MOT20 and 2nd place on MOT17 with 63.9 and 64.9 HOTA, respectively. We also achieve 61.3 HOTA on the challenging DanceTrack benchmark as a new state-of-the-art even compared to more heavily-designed methods. The code and models are available at \\url{https://github.com/GerardMaggiolino/Deep-OC-SORT}.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 289,
+    "label": 37,
+    "text": "Title: Lero: A Learning-to-Rank Query Optimizer\nAbstract: \n A recent line of works apply machine learning techniques to assist or rebuild cost-based query optimizers in DBMS. While exhibiting superiority in some benchmarks, their deficiencies, e.g., unstable performance, high training cost, and slow model updating, stem from the inherent hardness of predicting the cost or latency of execution plans using machine learning models. In this paper, we introduce a\n learning-to-rank\n query optimizer, called Lero, which builds on top of a native query optimizer and continuously learns to improve the optimization performance. The key observation is that the relative order or\n rank\n of plans, rather than the exact cost or latency, is sufficient for query optimization. Lero employs a\n pairwise\n approach to train a classifier to compare any two plans and tell which one is better. Such a binary classification task is much easier than the regression task to predict the cost or latency, in terms of model efficiency and accuracy. Rather than building a learned optimizer from scratch, Lero is designed to leverage decades of wisdom of databases and improve the native query optimizer. With its non-intrusive design, Lero can be implemented on top of any existing DBMS with minimal integration efforts. We implement Lero and demonstrate its outstanding performance using PostgreSQL. In our experiments, Lero achieves near optimal performance on several benchmarks. It reduces the plan execution time of the native optimizer in PostgreSQL by up to 70% and other learned query optimizers by up to 37%. Meanwhile, Lero continuously learns and automatically adapts to query workloads and changes in data.\n",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 290,
+    "label": 4,
+    "text": "Title: A First Study of MEV on an Up-and-Coming Blockchain: Algorand\nAbstract: Maximal Extractable Value (MEV) significantly influences network incentives, consensus safety, and economic dynamics, and has been extensively studied within the Ethereum blockchain domain. However, MEV is not specific to Ethereum, and extends to other blockchain platforms with differing properties, such as Algorand. Algorand, a smart-contract-based blockchain employing a Byzantine-Fault Tolerant consensus mechanism and Pure-Proof-of-Stake, is characterized by a First-Come-First-Serve transaction ordering mechanism and minimal fixed transaction fees. This paper provides the first exploration of the MEV landscape on Algorand, focusing on arbitrage MEV patterns, key actors, their strategic preferences, transaction positioning strategies, and the influence of Algorand's network infrastructure on MEV searching. We observed 1,142,970 arbitrage cases, with a single searcher executing 653,001. Different searchers demonstrated diverse strategies, reflected in the varied distribution of profitable block positions. Nonetheless, the even spread of arbitrage positions across a block indicates an emphasis on immediate backrunning executions. Furthermore, we identified 265,637 instances of Batch Transaction Issuances, where an address occupied over 80% of a block with a singular transaction type.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 291,
+    "label": 24,
+    "text": "Title: PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees\nAbstract: Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high communication and computation costs or (2) overfit to local data, which can be limited in scope, and are vulnerable to evolved test samples with natural shifts. In this paper, we propose PerAda, a parameter-efficient pFL framework that reduces communication and computational costs and exhibits superior generalization performance, especially under test-time distribution shifts. PerAda reduces the costs by leveraging the power of pretrained models and only updates and communicates a small number of additional parameters from adapters. PerAda has good generalization since it regularizes each client's personalized adapter with a global adapter, while the global adapter uses knowledge distillation to aggregate generalized information from all clients. Theoretically, we provide generalization bounds to explain why PerAda improves generalization, and we prove its convergence to stationary points under non-convex settings. Empirically, PerAda demonstrates competitive personalized performance (+4.85% on CheXpert) and enables better out-of-distribution generalization (+5.23% on CIFAR-10-C) on different datasets across natural and medical domains compared with baselines, while only updating 12.6% of parameters per model based on the adapter.",
+    "neighbors": [
+      487
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 292,
+    "label": 4,
+    "text": "Title: ODDFuzz: Discovering Java Deserialization Vulnerabilities via Structure-Aware Directed Greybox Fuzzing\nAbstract: Java deserialization vulnerability is a severe threat in practice. Researchers have proposed static analysis solutions to locate candidate vulnerabilities and fuzzing solutions to generate proof-of-concept (PoC) serialized objects to trigger them. However, existing solutions have limited effectiveness and efficiency.In this paper, we propose a novel hybrid solution ODDFuzz to efficiently discover Java deserialization vulnerabilities. First, ODDFuzz performs lightweight static taint analysis to identify candidate gadget chains that may cause deserialization vulnerabilities. In this step, ODDFuzz tries to locate all candidates and avoid false negatives. Then, ODDFuzz performs directed greybox fuzzing (DGF) to explore those candidates and generate PoC testcases to mitigate false positives. Specifically, ODDFuzz applies a structure-aware seed generation method to guarantee the validity of the testcases, and adopts a novel hybrid feedback and a step-forward strategy to guide the directed fuzzing.We implemented a prototype of ODDFuzz and evaluated it on the popular Java deserialization repository ysoserial. Results show that, ODDFuzz could discover 16 out of 34 known gadget chains, while two state-of-the-art baselines only identify three of them. In addition, we evaluated ODDFuzz on real-world applications including Oracle WebLogic Server, Apache Dubbo, Sonatype Nexus, and protostuff, and found six previously unreported exploitable gadget chains with five CVEs assigned.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 293,
+    "label": 26,
+    "text": "Title: Non-Markovian paths and cycles in NFT trades\nAbstract: Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 294,
+    "label": 10,
+    "text": "Title: Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals\nAbstract: Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e.g., with GDPR), and (iii) reliance on the contrastive nature of human explanation. Although counterfactual explanations are normally used to explain individual predictive-instances, we explore a novel use case in which groups of similar instances are explained in a collective fashion using ``group counterfactuals'' (e.g., to highlight a repeating pattern of illness in a group of patients). These group counterfactuals meet a human preference for coherent, broad explanations covering multiple events/instances. A novel, group-counterfactual algorithm is proposed to generate high-coverage explanations that are faithful to the to-be-explained model. This explanation strategy is also evaluated in a large, controlled user study (N=207), using objective (i.e., accuracy) and subjective (i.e., confidence, explanation satisfaction, and trust) psychological measures. The results show that group counterfactuals elicit modest but definite improvements in people's understanding of an AI system. The implications of these findings for counterfactual methods and for XAI are discussed.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 295,
+    "label": 4,
+    "text": "Title: Fast and Private Inference of Deep Neural Networks by Co-designing Activation Functions\nAbstract: Machine Learning as a Service (MLaaS) is an increasingly popular design where a company with abundant computing resources trains a deep neural network and offers query access for tasks like image classification. The challenge with this design is that MLaaS requires the client to reveal their potentially sensitive queries to the company hosting the model. Multi-party computation (MPC) protects the client's data by allowing encrypted inferences. However, current approaches suffer prohibitively large inference times. The inference time bottleneck in MPC is the evaluation of non-linear layers such as ReLU activation functions. Motivated by the success of previous work co-designing machine learning and MPC aspects, we develop an activation function co-design. We replace all ReLUs with a polynomial approximation and evaluate them with single-round MPC protocols, which give state-of-the-art inference times in wide-area networks. Furthermore, to address the accuracy issues previously encountered with polynomial activations, we propose a novel training algorithm that gives accuracy competitive with plaintext models. Our evaluation shows between $4$ and $90\\times$ speedups in inference time on large models with up to $23$ million parameters while maintaining competitive inference accuracy.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 296,
+    "label": 24,
+    "text": "Title: Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning\nAbstract: Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 297,
+    "label": 16,
+    "text": "Title: DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos\nAbstract: Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. The code is available at https://github.com/aboah1994/DeepSegment.git.",
+    "neighbors": [
+      2202
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 298,
+    "label": 16,
+    "text": "Title: Population-Based Evolutionary Gaming for Unsupervised Person Re-identification\nAbstract: nan",
+    "neighbors": [
+      718
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 299,
+    "label": 26,
+    "text": "Title: GeoCovaxTweets: COVID-19 Vaccines and Vaccination-specific Global Geotagged Twitter Conversations\nAbstract: Social media platforms provide actionable information during crises and pandemic outbreaks. The COVID-19 pandemic has imposed a chronic public health crisis worldwide, with experts considering vaccines as the ultimate prevention to achieve herd immunity against the virus. A proportion of people may turn to social media platforms to oppose vaccines and vaccination, hindering government efforts to eradicate the virus. This paper presents the COVID-19 vaccines and vaccination-specific global geotagged tweets dataset, GeoCovaxTweets, that contains more than 1.8 million tweets, with location information and longer temporal coverage, originating from 233 countries and territories between January 2020 and November 2022. The paper discusses the dataset's curation method and how it can be re-created locally, and later explores the dataset through multiple tweets distributions and briefly discusses its potential use cases. We anticipate that the dataset will assist the researchers in the crisis computing domain to explore the conversational dynamics of COVID-19 vaccines and vaccination Twitter discourse through numerous spatial and temporal dimensions concerning trends, shifts in opinions, misinformation, and anti-vaccination campaigns.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 300,
+    "label": 24,
+    "text": "Title: Active Learning in Symbolic Regression with Physical Constraints\nAbstract: Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather in an active learning setting with physical constraints. SR with active learning proposes which experiments to do next. Active learning is done with query by committee, where the Pareto frontier of equations is the committee. The physical constraints improve proposed equations in very low data settings. These approaches reduce the data required for SR and achieves state of the art results in data required to rediscover known equations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 301,
+    "label": 16,
+    "text": "Title: Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?\nAbstract: The rapid evolution of high-end smartphones with advanced high-resolution cameras has resulted in contactless capture of fingerprint biometrics that are more reliable and suitable for verification. Similar to other biometric systems, contactless fingerprint-verification systems are vulnerable to presentation attacks. In this paper, we present a comparative study on the generalizability of seven different pre-trained Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) to reliably detect presentation attacks. Extensive experiments were carried out on publicly available smartphone-based presentation attack datasets using four different Presentation Attack Instruments (PAI). The detection performance of the eighth deep feature technique was evaluated using the leave-one-out protocol to benchmark the generalization performance for unseen PAI. The obtained results indicated the best generalization performance with the ResNet50 CNN.",
+    "neighbors": [
+      138
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 302,
+    "label": 24,
+    "text": "Title: Importance of methodological choices in data manipulation for validating epileptic seizure detection models\nAbstract: Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, difficulty comparing results, and low reproducibility. Therefore, this article identifies a wide range of methodological decisions that must be made and reported when training and evaluating the performance of epilepsy detection systems. We characterize the influence of individual choices using a typical ensemble random-forest model and the publicly available CHB-MIT database, providing a broader picture of each decision and giving good-practice recommendations, based on our experience, where possible.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 303,
+    "label": 28,
+    "text": "Title: Moments of Autocorrelation Demerit Factors of Binary Sequences\nAbstract: Sequences with low aperiodic autocorrelation are used in communications and remote sensing for synchronization and ranging. The autocorrelation demerit factor of a sequence is the sum of the squared magnitudes of its autocorrelation values at every nonzero shift when we normalize the sequence to have unit Euclidean length. The merit factor, introduced by Golay, is the reciprocal of the demerit factor. We consider the uniform probability measure on the $2^\\ell$ binary sequences of length $\\ell$ and investigate the distribution of the demerit factors of these sequences. Previous researchers have calculated the mean and variance of this distribution. We develop new combinatorial techniques to calculate the $p$th central moment of the demerit factor for binary sequences of length $\\ell$. These techniques prove that for $p\\geq 2$ and $\\ell \\geq 4$, all the central moments are strictly positive. For any given $p$, one may use the technique to obtain an exact formula for the $p$th central moment of the demerit factor as a function of the length $\\ell$. The previously obtained formula for variance is confirmed by our technique with a short calculation, and we demonstrate that our techniques go beyond this by also deriving an exact formula for the skewness.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 304,
+    "label": 24,
+    "text": "Title: On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats\nAbstract: Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for training such models, where the computations are usually performed in single-precision floating-point number format. The convergence of single-precision SGD is normally aligned with the theoretical results of real numbers since they exhibit negligible error. However, the numerical error increases when the computations are performed in low-precision number formats. This provides compelling reasons to study the SGD convergence adapted for low-precision computations. We present both deterministic and stochastic analysis of the SGD algorithm, obtaining bounds that show the effect of number format. Such bounds can provide guidelines as to how SGD convergence is affected when constraints render the possibility of performing high-precision computations remote.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 305,
+    "label": 23,
+    "text": "Title: Best performance and reliability for your time: budget-aware search-based optimization of software model refactoring\nAbstract: Context: Software model optimization is a process that automatically generates design alternatives, typically to enhance quantifiable non-functional properties of software systems, such as performance and reliability. Multi-objective evolutionary algorithms have shown to be effective in this context for assisting the designer in identifying trade-offs between the desired non-functional properties. Objective: In this work, we investigate the effects of imposing a time budget to limit the search for design alternatives, which inevitably affects the quality of the resulting alternatives. Method: The effects of time budgets are analyzed by investigating both the quality of the generated design alternatives and their structural features when varying the budget and the genetic algorithm (NSGA-II, PESA2, SPEA2). This is achieved by employing multi-objective quality indicators and a tree-based representation of the search space. Results: The study reveals that the time budget significantly affects the quality of Pareto fronts, especially for performance and reliability. NSGA-II is the fastest algorithm, while PESA2 generates the highest-quality solutions. The imposition of a time budget results in structurally distinct models compared to those obtained without a budget, indicating that the search process is influenced by both the budget and algorithm selection. Conclusions: In software model optimization, imposing a time budget can be effective in saving optimization time, but designers should carefully consider the trade-off between time and solution quality in the Pareto front, along with the structural characteristics of the generated models. By making informed choices about the specific genetic algorithm, designers can achieve different trade-offs.",
+    "neighbors": [
+      403
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 306,
+    "label": 16,
+    "text": "Title: Lightweight, Pre-trained Transformers for Remote Sensing Timeseries\nAbstract: Machine learning algorithms for parsing remote sensing data have a wide range of societally relevant applications, but labels used to train these algorithms can be difficult or impossible to acquire. This challenge has spurred research into self-supervised learning for remote sensing data aiming to unlock the use of machine learning in geographies or application domains where labelled datasets are small. Current self-supervised learning approaches for remote sensing data draw significant inspiration from techniques applied to natural images. However, remote sensing data has important differences from natural images -- for example, the temporal dimension is critical for many tasks and data is collected from many complementary sensors. We show that designing models and self-supervised training techniques specifically for remote sensing data results in both smaller and more performant models. We introduce the Pretrained Remote Sensing Transformer (Presto), a transformer-based model pre-trained on remote sensing pixel-timeseries data. Presto excels at a wide variety of globally distributed remote sensing tasks and outperforms much larger models. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.",
+    "neighbors": [
+      1561
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 307,
+    "label": 22,
+    "text": "Title: Suspension Analysis and Selective Continuation-Passing Style for Higher-Order Probabilistic Programming Languages\nAbstract: Probabilistic programming languages (PPLs) make encoding and automatically solving statistical inference problems relatively easy by separating models from the inference algorithm. A popular choice for solving inference problems is to use Monte Carlo inference algorithms. For higher-order functional PPLs, these inference algorithms rely on execution suspension to perform inference, most often enabled through a full continuation-passing style (CPS) transformation. However, standard CPS transformations for PPL compilers introduce significant overhead, a problem the community has generally overlooked. State-of-the-art solutions either perform complete CPS transformations with performance penalties due to unnecessary closure allocations or use efficient, but complex, low-level solutions that are often not available in high-level languages. In contrast to prior work, we develop a new approach that is both efficient and easy to implement using higher-order languages. Specifically, we design a novel static suspension analysis technique that determines the parts of a program that require suspension, given a particular inference algorithm. The analysis result allows selectively CPS transforming the program only where necessary. We formally prove the correctness of the suspension analysis and implement both the suspension analysis and selective CPS transformation in the Miking CorePPL compiler. We evaluate the implementation for a large number of Monte Carlo inference algorithms on real-world models from phylogenetics, epidemiology, and topic modeling. The evaluation results demonstrate significant improvements across all models and inference algorithms.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 308,
+    "label": 30,
+    "text": "Title: The Touch\u00e923-ValueEval Dataset for Identifying Human Values behind Arguments\nAbstract: We present the Touch\\'e23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we collected 9324 arguments from 6 diverse sources, covering religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. The Touch\\'e23-ValueEval dataset extends the Webis-ArgValues-22. In comparison to the previous dataset, the effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT model increases. Therefore, though the classification difficulty increased as per the label distribution, the larger dataset allows for training better models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 309,
+    "label": 24,
+    "text": "Title: Conformal PID Control for Time Series Prediction\nAbstract: We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. Our theory both simplifies and strengthens existing analyses in online conformal prediction. Experiments on 4-week-ahead forecasting of statewide COVID-19 death counts in the U.S. show an improvement in coverage over the ensemble forecaster used in official CDC communications. We also run experiments on predicting electricity demand, market returns, and temperature using autoregressive, Theta, Prophet, and Transformer models. We provide an extendable codebase for testing our methods and for the integration of new algorithms, data sets, and forecasting rules.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 310,
+    "label": 16,
+    "text": "Title: Joint-Relation Transformer for Multi-Person Motion Prediction\nAbstract: Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based methods have shown promising results on this task, but they miss the explicit relation representation between joints, such as skeleton structure and pairwise distance, which is crucial for accurate interaction modeling. In this paper, we propose the Joint-Relation Transformer, which utilizes relation information to enhance interaction modeling and improve future motion prediction. Our relation information contains the relative distance and the intra-/inter-person physical constraints. To fuse relation and joint information, we design a novel joint-relation fusion layer with relation-aware attention to update both features. Additionally, we supervise the relation information by forecasting future distance. Experiments show that our method achieves a 13.4% improvement of 900ms VIM on 3DPW-SoMoF/RC and 17.8%/12.0% improvement of 3s MPJPE on CMU-Mpcap/MuPoTS-3D dataset.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 311,
+    "label": 27,
+    "text": "Title: Lavender Autonomous Navigation with Semantic Segmentation at the Edge\nAbstract: Achieving success in agricultural activities heavily relies on precise navigation in row crop fields. Recently, segmentation-based navigation has emerged as a reliable technique when GPS-based localization is unavailable or higher accuracy is needed due to vegetation or unfavorable weather conditions. It also comes in handy when plants are growing rapidly and require an online adaptation of the navigation algorithm. This work applies a segmentation-based visual agnostic navigation algorithm to lavender fields, considering both simulation and real-world scenarios. The effectiveness of this approach is validated through a wide set of experimental tests, which show the capability of the proposed solution to generalize over different scenarios and provide highly-reliable results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 312,
+    "label": 30,
+    "text": "Title: Making the Implicit Explicit: Implicit Content as a First Class Citizen in NLP\nAbstract: Language is multifaceted. A given utterance can be re-expressed in equivalent forms, and its implicit and explicit content support various logical and pragmatic inferences. When processing an utterance, we consider these different aspects, as mediated by our interpretive goals -- understanding that\"it's dark in here\"may be a veiled direction to turn on a light. Nonetheless, NLP methods typically operate over the surface form alone, eliding this nuance. In this work, we represent language with language, and direct an LLM to decompose utterances into logical and plausible inferences. The reduced complexity of the decompositions makes them easier to embed, opening up novel applications. Variations on our technique lead to state-of-the-art improvements on sentence embedding benchmarks, a substantive application in computational political science, and to a novel construct-discovery process, which we validate with human annotations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 313,
+    "label": 16,
+    "text": "Title: AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence\nAbstract: In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a well-organized subjective experiment is conducted to assess the human visual preferences for each image from three perspectives including quality, authenticity and correspondence. Finally, based on this large-scale database, we conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.",
+    "neighbors": [
+      1902,
+      1969,
+      2007
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 314,
+    "label": 16,
+    "text": "Title: SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection\nAbstract: The rapid proliferation of non-cooperative spacecraft and space debris in orbit has precipitated a surging demand for on-orbit servicing and space debris removal at a scale that only autonomous missions can address, but the prerequisite autonomous navigation and flightpath planning to safely capture an unknown, non-cooperative, tumbling space object is an open problem. Planning safe, effective trajectories requires real-time, automated spacecraft feature recognition algorithms to pinpoint the locations of collision hazards (e.g., solar panels or antennas) and safe docking features (e.g., satellite bodies or thrusters). Prior work in this area reveals that computer vision models' performance is highly dependent on the training dataset and its coverage of scenarios visually similar to the real scenarios that occur in deployment. Hence, the algorithm may have degraded performance under certain lighting conditions even when the rendezvous maneuver conditions of the chaser to the target spacecraft are the same. This work delves into how humans perform these tasks through a survey of how people experienced with spacecraft shapes and components recognize features of the three spacecraft: Landsat, Envisat, Anik, and the orbiter Mir. The survey reveals that the most common patterns in the human detection process were to consider the shape and texture of the features-antenna, solar panels, thrusters, and satellite bodies. This work introduces a novel algorithm called Space YOLO, which uses context-based decision processes, specifically shape and texture information, to perform object detection. Unlike traditional object detectors, the method demands far fewer labor hours for synthetic data preparation. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments under different lighting and chaser maneuver conditions at the ORION facility at Florida Tech.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 315,
+    "label": 16,
+    "text": "Title: Are Diffusion Models Vulnerable to Membership Inference Attacks?\nAbstract: Diffusion-based generative models have shown great potential for image synthesis, but there is a lack of research on the security and privacy risks they may pose. In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern. Our results indicate that existing MIAs designed for GANs or VAE are largely ineffective on diffusion models, either due to inapplicable scenarios (e.g., requiring the discriminator of GANs) or inappropriate assumptions (e.g., closer distances between synthetic samples and member samples). To address this gap, we propose Step-wise Error Comparing Membership Inference (SecMI), a query-based MIA that infers memberships by assessing the matching of forward process posterior estimation at each timestep. SecMI follows the common overfitting assumption in MIA where member samples normally have smaller estimation errors, compared with hold-out samples. We consider both the standard diffusion models, e.g., DDPM, and the text-to-image diffusion models, e.g., Latent Diffusion Models and Stable Diffusion. Experimental results demonstrate that our methods precisely infer the membership with high confidence on both of the two scenarios across multiple different datasets. Code is available at https://github.com/jinhaoduan/SecMI.",
+    "neighbors": [
+      579,
+      1481,
+      1713,
+      2279
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 316,
+    "label": 24,
+    "text": "Title: QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective Time Series Forecasting\nAbstract: In the telecom domain, precise forecasting of time series patterns, such as cell key performance indicators (KPIs), plays a pivotal role in enhancing service quality and operational efficiency. State-of-the-art forecasting approaches prioritize forecasting accuracy at the expense of computational performance, rendering them less suitable for data-intensive applications encompassing systems with a multitude of time series variables. To address this issue, we introduce QBSD, a live forecasting approach tailored to optimize the trade-off between accuracy and computational complexity. We have evaluated the performance of QBSD against state-of-the-art forecasting approaches on publicly available datasets. We have also extended this investigation to our curated network KPI dataset, now publicly accessible, to showcase the effect of dynamic operating ranges that varies with time. The results demonstrate that the proposed method excels in runtime efficiency compared to the leading algorithms available while maintaining competitive forecast accuracy.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 317,
+    "label": 17,
+    "text": "Title: Neural Shadow Mapping\nAbstract: We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing visual results on par with ray traced hard and soft shadows. We show that combining memory bandwidth-aware architecture specialization and careful temporal-window training leads to a fast, compact and easy-to-train neural shadowing method. Our technique is memory bandwidth conscious, eliminates the need for post-process temporal anti-aliasing or denoising, and supports scenes with dynamic view, emitters and geometry while remaining robust to unseen objects.",
+    "neighbors": [
+      201
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 318,
+    "label": 24,
+    "text": "Title: Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting\nAbstract: Accurate traffic forecasting is vital to intelligent transportation systems, which are widely adopted to solve urban traffic issues. Existing traffic forecasting studies focus on modeling spatial-temporal dynamics in traffic data, among which the graph convolution network (GCN) is at the center for exploiting the spatial dependency embedded in the road network graphs. However, these GCN-based methods operate intrinsically on the node level (e.g., road and intersection) only whereas overlooking the spatial hierarchy of the whole city. Nodes such as intersections and road segments can form clusters (e.g., regions), which could also have interactions with each other and share similarities at a higher level. In this work, we propose an Adaptive Hierarchical SpatioTemporal Network (AHSTN) to promote traffic forecasting by exploiting the spatial hierarchy and modeling multi-scale spatial correlations. Apart from the node-level spatiotemporal blocks, AHSTN introduces the adaptive spatiotemporal downsampling module to infer the spatial hierarchy for spatiotemporal modeling at the cluster level. Then, an adaptive spatiotemporal upsampling module is proposed to upsample the cluster-level representations to the node-level and obtain the multi-scale representations for generating predictions. Experiments on two real-world datasets show that AHSTN achieves better performance over several strong baselines.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 319,
+    "label": 16,
+    "text": "Title: Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models\nAbstract: ChatGPT is attracting a cross-field interest as it provides a language interface with remarkable conversational competency and reasoning capabilities across many domains. However, since ChatGPT is trained with languages, it is currently not capable of processing or generating images from the visual world. At the same time, Visual Foundation Models, such as Visual Transformers or Stable Diffusion, although showing great visual understanding and generation capabilities, they are only experts on specific tasks with one-round fixed inputs and outputs. To this end, We build a system called \\textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps. 3) providing feedback and asking for corrected results. We design a series of prompts to inject the visual model information into ChatGPT, considering models of multiple inputs/outputs and models that require visual feedback. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models. Our system is publicly available at \\url{https://github.com/microsoft/visual-chatgpt}.",
+    "neighbors": [
+      0,
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+      887,
+      902,
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+      1050,
+      1060,
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+      1148,
+      1262,
+      1315,
+      1327,
+      1348,
+      1353,
+      1467,
+      1574,
+      1626,
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+      1788,
+      1810,
+      1899,
+      1902,
+      1906,
+      1913,
+      1990,
+      2018,
+      2030,
+      2036,
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+      2095,
+      2113,
+      2155,
+      2166,
+      2216,
+      2274,
+      2286
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 320,
+    "label": 24,
+    "text": "Title: Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability\nAbstract: Recent research has observed that in machine learning optimization, gradient descent (GD) often operates at the edge of stability (EoS) [Cohen, et al., 2021], where the stepsizes are set to be large, resulting in non-monotonic losses induced by the GD iterates. This paper studies the convergence and implicit bias of constant-stepsize GD for logistic regression on linearly separable data in the EoS regime. Despite the presence of local oscillations, we prove that the logistic loss can be minimized by GD with any constant stepsize over a long time scale. Furthermore, we prove that with any constant stepsize, the GD iterates tend to infinity when projected to a max-margin direction (the hard-margin SVM direction) and converge to a fixed vector that minimizes a strongly convex potential when projected to the orthogonal complement of the max-margin direction. In contrast, we also show that in the EoS regime, GD iterates may diverge catastrophically under the exponential loss, highlighting the superiority of the logistic loss. These theoretical findings are in line with numerical simulations and complement existing theories on the convergence and implicit bias of GD, which are only applicable when the stepsizes are sufficiently small.",
+    "neighbors": [
+      1746
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 321,
+    "label": 28,
+    "text": "Title: Wireless Channel Charting: Theory, Practice, and Applications\nAbstract: Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart. Channel charting summarizes the entire CSI dataset in a self-supervised manner, which opens up a range of applications that are tied to user location. In this article, we introduce the theoretical underpinnings of channel charting and present an overview of recent algorithmic developments and experimental results obtained in the field. We furthermore discuss concrete application examples of channel charting to network- and user-related applications, and we provide a perspective on future developments and challenges as well as the role of channel charting in next-generation wireless networks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 322,
+    "label": 16,
+    "text": "Title: CLR: Channel-wise Lightweight Reprogramming for Continual Learning\nAbstract: Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned tasks after learning new tasks, i.e., to avoid catastrophic forgetting. We propose a Channel-wise Lightweight Reprogramming (CLR) approach that helps convolutional neural networks (CNNs) overcome catastrophic forgetting during continual learning. We show that a CNN model trained on an old task (or self-supervised proxy task) could be ``reprogrammed\"to solve a new task by using our proposed lightweight (very cheap) reprogramming parameter. With the help of CLR, we have a better stability-plasticity trade-off to solve continual learning problems: To maintain stability and retain previous task ability, we use a common task-agnostic immutable part as the shared ``anchor\"parameter set. We then add task-specific lightweight reprogramming parameters to reinterpret the outputs of the immutable parts, to enable plasticity and integrate new knowledge. To learn sequential tasks, we only train the lightweight reprogramming parameters to learn each new task. Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting. To minimize the parameter requirement of reprogramming to learn new tasks, we make reprogramming lightweight by only adjusting essential kernels and learning channel-wise linear mappings from anchor parameters to task-specific domain knowledge. We show that, for general CNNs, the CLR parameter increase is less than 0.6\\% for any new task. Our method outperforms 13 state-of-the-art continual learning baselines on a new challenging sequence of 53 image classification datasets. Code and data are available at https://github.com/gyhandy/Channel-wise-Lightweight-Reprogramming",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 323,
+    "label": 24,
+    "text": "Title: Improving physics-informed DeepONets with hard constraints\nAbstract: Current physics-informed (standard or operator) neural networks still rely on accurately learning the initial conditions of the system they are solving. In contrast, standard numerical methods evolve such initial conditions without needing to learn these. In this study, we propose to improve current physics-informed deep learning strategies such that initial conditions do not need to be learned and are represented exactly in the predicted solution. Moreover, this method guarantees that when a DeepONet is applied multiple times to time step a solution, the resulting function is continuous.",
+    "neighbors": [
+      1435
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 324,
+    "label": 27,
+    "text": "Title: ImMesh: An Immediate LiDAR Localization and Meshing Framework\nAbstract: In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 325,
+    "label": 27,
+    "text": "Title: Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design\nAbstract: Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool. The code for running the experimentation and installing the toolchain can be found here https://github.com/vardhah/ConstraintBOUUVHullDesign.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 326,
+    "label": 16,
+    "text": "Title: MedViT: A Robust Vision Transformer for Generalized Medical Image Classification\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 327,
+    "label": 10,
+    "text": "Title: Tractable Diversity: Scalable Multiperspective Ontology Management via Standpoint EL\nAbstract: The tractability of the lightweight description logic EL has allowed for the construction of large and widely used ontologies that support semantic interoperability. However, comprehensive domains with a broad user base are often at odds with strong axiomatisations otherwise useful for inferencing, since these are usually context dependent and subject to diverging perspectives.\n\n\n\nIn this paper we introduce Standpoint EL, a multi-modal extension of EL that allows for the integrated representation of domain knowledge relative to diverse, possibly conflicting standpoints (or contexts), which can be hierarchically organised and put in relation to each other. We establish that Standpoint EL still exhibits EL's favourable PTime standard reasoning, whereas introducing additional features like empty standpoints, rigid roles, and nominals makes standard reasoning tasks intractable.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 328,
+    "label": 3,
+    "text": "Title: A Comparative Study of Reference Reliability in Multiple Language Editions of Wikipedia\nAbstract: Information presented in Wikipedia articles must be attributable to reliable published sources in the form of references. This study examines over 5 million Wikipedia articles to assess the reliability of references in multiple language editions. We quantify the cross-lingual patterns of the perennial sources list, a collection of reliability labels for web domains identified and collaboratively agreed upon by Wikipedia editors. We discover that some sources (or web domains) deemed untrustworthy in one language (i.e., English) continue to appear in articles in other languages. This trend is especially evident with sources tailored for smaller communities. Furthermore, non-authoritative sources found in the English version of a page tend to persist in other language versions of that page. We finally present a case study on the Chinese, Russian, and Swedish Wikipedias to demonstrate a discrepancy in reference reliability across cultures. Our finding highlights future challenges in coordinating global knowledge on source reliability.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 329,
+    "label": 29,
+    "text": "Title: An Analysis of the Completion Time of the BB84 Protocol\nAbstract: The BB84 QKD protocol is based on the idea that the sender and the receiver can reconcile a certain fraction of the teleported qubits to detect eavesdropping or noise and decode the rest to use as a private key. Under the present hardware infrastructure, decoherence of quantum states poses a significant challenge to performing perfect or efficient teleportation, meaning that a teleportation-based protocol must be run multiple times to observe success. Thus, performance analyses of such protocols usually consider the completion time, i.e., the time until success, rather than the duration of a single attempt. Moreover, due to decoherence, the success of an attempt is in general dependent on the duration of individual phases of that attempt, as quantum states must wait in memory while the success or failure of a generation phase is communicated to the relevant parties. In this work, we do a performance analysis of the completion time of the BB84 protocol in a setting where the sender and the receiver are connected via a single quantum repeater and the only quantum channel between them does not see any adversarial attack. Assuming certain distributional forms for the generation and communication phases of teleportation, we provide a method to compute the MGF of the completion time and subsequently derive an estimate of the CDF and a bound on the tail probability. This result helps us gauge the (tail) behaviour of the completion time in terms of the parameters characterising the elementary phases of teleportation, without having to run the protocol multiple times. We also provide an efficient simulation scheme to generate the completion time, which relies on expressing the completion time in terms of aggregated teleportation times. We numerically compare our approach with a full-scale simulation and observe good agreement between them.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 330,
+    "label": 16,
+    "text": "Title: Control4D: Dynamic Portrait Editing by Learning 4D GAN from 2D Diffusion-based Editor\nAbstract: Recent years have witnessed considerable achievements in editing images with text instructions. When applying these editors to dynamic scene editing, the new-style scene tends to be temporally inconsistent due to the frame-by-frame nature of these 2D editors. To tackle this issue, we propose Control4D, a novel approach for high-fidelity and temporally consistent 4D portrait editing. Control4D is built upon an efficient 4D representation with a 2D diffusion-based editor. Instead of using direct supervisions from the editor, our method learns a 4D GAN from it and avoids the inconsistent supervision signals. Specifically, we employ a discriminator to learn the generation distribution based on the edited images and then update the generator with the discrimination signals. For more stable training, multi-level information is extracted from the edited images and used to facilitate the learning of the generator. Experimental results show that Control4D surpasses previous approaches and achieves more photo-realistic and consistent 4D editing performances. The link to our project website is https://control4darxiv.github.io.",
+    "neighbors": [
+      48,
+      1020,
+      1125,
+      1251,
+      1279,
+      1449,
+      1902,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 331,
+    "label": 16,
+    "text": "Title: AdvART: Adversarial Art for Camouflaged Object Detection Attacks\nAbstract: A majority of existing physical attacks in the real world result in conspicuous and eye-catching patterns for generated patches, which made them identifiable/detectable by humans. To overcome this limitation, recent work has proposed several approaches that aim at generating naturalistic patches using generative adversarial networks (GANs), which may not catch human's attention. However, these approaches are computationally intensive and do not always converge to natural looking patterns. In this paper, we propose a novel lightweight framework that systematically generates naturalistic adversarial patches without using GANs. To illustrate the proposed approach, we generate adversarial art (AdvART), which are patches generated to look like artistic paintings while maintaining high attack efficiency. In fact, we redefine the optimization problem by introducing a new similarity objective. Specifically, we leverage similarity metrics to construct a similarity loss that is added to the optimized objective function. This component guides the patch to follow a predefined artistic patterns while maximizing the victim model's loss function. Our patch achieves high success rates with $12.53\\%$ mean average precision (mAP) on YOLOv4tiny for INRIA dataset.",
+    "neighbors": [
+      1287,
+      1737
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 332,
+    "label": 24,
+    "text": "Title: Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data\nAbstract: We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 333,
+    "label": 30,
+    "text": "Title: Using Large Language Models to Automate Category and Trend Analysis of Scientific Articles: An Application in Ophthalmology\nAbstract: Purpose: In this paper, we present an automated method for article classification, leveraging the power of Large Language Models (LLM). The primary focus is on the field of ophthalmology, but the model is extendable to other fields. Methods: We have developed a model based on Natural Language Processing (NLP) techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we have employed zero-shot learning (ZSL) LLM models and compared against Bidirectional and Auto-Regressive Transformers (BART) and its variants, and Bidirectional Encoder Representations from Transformers (BERT), and its variant such as distilBERT, SciBERT, PubmedBERT, BioBERT. Results: The classification results demonstrate the effectiveness of LLMs in categorizing large number of ophthalmology papers without human intervention. Results: To evalute the LLMs, we compiled a dataset (RenD) of 1000 ocular disease-related articles, which were expertly annotated by a panel of six specialists into 15 distinct categories. The model achieved mean accuracy of 0.86 and mean F1 of 0.85 based on the RenD dataset. Conclusion: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval in other domains too. We performed trend analysis that enables the researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 334,
+    "label": 24,
+    "text": "Title: Approximation Algorithms for Fair Range Clustering\nAbstract: This paper studies the fair range clustering problem in which the data points are from different demographic groups and the goal is to pick $k$ centers with the minimum clustering cost such that each group is at least minimally represented in the centers set and no group dominates the centers set. More precisely, given a set of $n$ points in a metric space $(P,d)$ where each point belongs to one of the $\\ell$ different demographics (i.e., $P = P_1 \\uplus P_2 \\uplus \\cdots \\uplus P_\\ell$) and a set of $\\ell$ intervals $[\\alpha_1, \\beta_1], \\cdots, [\\alpha_\\ell, \\beta_\\ell]$ on desired number of centers from each group, the goal is to pick a set of $k$ centers $C$ with minimum $\\ell_p$-clustering cost (i.e., $(\\sum_{v\\in P} d(v,C)^p)^{1/p}$) such that for each group $i\\in \\ell$, $|C\\cap P_i| \\in [\\alpha_i, \\beta_i]$. In particular, the fair range $\\ell_p$-clustering captures fair range $k$-center, $k$-median and $k$-means as its special cases. In this work, we provide efficient constant factor approximation algorithms for fair range $\\ell_p$-clustering for all values of $p\\in [1,\\infty)$.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 335,
+    "label": 10,
+    "text": "Title: Biomedical Knowledge Graph Embeddings with Negative Statements\nAbstract: A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption. Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction. However, no attention has been given to the exploration of negative statements by knowledge graph embedding approaches despite the potential of negative statements to produce more accurate representations of entities in a knowledge graph. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. In particular, we present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation in ontology-rich knowledge graphs. This is of particular importance for applications in the biomedical domain, where the inadequacy of embedding approaches regarding negative statements at the ontology level has been identified as a crucial limitation. We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in two different predictive tasks based on KG embeddings: protein-protein interaction prediction and gene-disease association prediction. We conduct an extensive analysis over established benchmarks and demonstrate that our method is able to improve the performance of knowledge graph embeddings on all tasks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 336,
+    "label": 30,
+    "text": "Title: Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning\nAbstract: Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive and time-consuming manual labeling of large datasets. Automatic label extraction from radiology reports can reduce the time required to obtain labeled datasets, but this task is challenging due to semantically similar words and missing annotated data. In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler. We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model and fine-tuned on a small dataset of manually labeled reports. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks. Our findings highlight the benefits of employing deep learning-based models even in scenarios with sparse data and the use of the rule-based labeler as a tool for weak supervision.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 337,
+    "label": 5,
+    "text": "Title: Towards General and Efficient Online Tuning for Spark\nAbstract: The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search. In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 338,
+    "label": 36,
+    "text": "Title: Complexity of Conformant Election Manipulation\nAbstract: It is important to study how strategic agents can affect the outcome of an election. There has been a long line of research in the computational study of elections on the complexity of manipulative actions such as manipulation and bribery. These problems model scenarios such as voters casting strategic votes and agents campaigning for voters to change their votes to make a desired candidate win. A common assumption is that the preferences of the voters follow the structure of a domain restriction such as single peakedness, and so manipulators only consider votes that also satisfy this restriction. We introduce the model where the preferences of the voters define their own restriction and strategic actions must ``conform'' by using only these votes. In this model, the election after manipulation will retain common domain restrictions. We explore the computational complexity of conformant manipulative actions and we discuss how conformant manipulative actions relate to other manipulative actions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 339,
+    "label": 16,
+    "text": "Title: NVTC: Nonlinear Vector Transform Coding\nAbstract: In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (RD) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://github.com/USTC-IMCL/NVTC.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 340,
+    "label": 16,
+    "text": "Title: Orientation-Independent Chinese Text Recognition in Scene Images\nAbstract: Scene text recognition (STR) has attracted much attention due to its broad applications. The previous works pay more attention to dealing with the recognition of Latin text images with complex backgrounds by introducing language models or other auxiliary networks. Different from Latin texts, many vertical Chinese texts exist in natural scenes, which brings difficulties to current state-of-the-art STR methods. In this paper, we take the first attempt to extract orientation-independent visual features by disentangling content and orientation information of text images, thus recognizing both horizontal and vertical texts robustly in natural scenes. Specifically, we introduce a Character Image Reconstruction Network (CIRN) to recover corresponding printed character images with disentangled content and orientation information. We conduct experiments on a scene dataset for benchmarking Chinese text recognition, and the results demonstrate that the proposed method can indeed improve performance through disentangling content and orientation information. To further validate the effectiveness of our method, we additionally collect a Vertical Chinese Text Recognition (VCTR) dataset. The experimental results show that the proposed method achieves 45.63\\% improvement on VCTR when introducing CIRN to the baseline model.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 341,
+    "label": 16,
+    "text": "Title: Faster Segment Anything: Towards Lightweight SAM for Mobile Applications\nAbstract: Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like image editing with fine-grained control). Many of such applications need to be run on resource-constraint edge devices, like mobile phones. In this work, we aim to make SAM mobile-friendly by replacing the heavyweight image encoder with a lightweight one. A naive way to train such a new SAM as in the original SAM paper leads to unsatisfactory performance, especially when limited training sources are available. We find that this is mainly caused by the coupled optimization of the image encoder and mask decoder, motivated by which we propose decoupled distillation. Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM. The training can be completed on a single GPU within less than one day, and the resulting lightweight SAM is termed MobileSAM which is more than 60 times smaller yet performs on par with the original SAM. For inference speed, With a single GPU, MobileSAM runs around 10ms per image: 8ms on the image encoder and 4ms on the mask decoder. With superior performance, our MobileSAM is around 5 times faster than the concurrent FastSAM and 7 times smaller, making it more suitable for mobile applications. Moreover, we show that MobileSAM can run relatively smoothly on CPU. The code for our project is provided at \\href{https://github.com/ChaoningZhang/MobileSAM}{\\textcolor{red}{MobileSAM}}), with a demo showing that MobileSAM can run relatively smoothly on CPU.",
+    "neighbors": [
+      719,
+      1006,
+      1084,
+      1207,
+      1663,
+      1690,
+      1932
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 342,
+    "label": 30,
+    "text": "Title: Larger language models do in-context learning differently\nAbstract: We study how in-context learning (ICL) in language models is affected by semantic priors versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with semantically-unrelated labels-across various model families (GPT-3, InstructGPT, Codex, PaLM, and Flan-PaLM). First, experiments on ICL with flipped labels show that overriding semantic priors is an emergent ability of model scale. While small language models ignore flipped labels presented in-context and thus rely primarily on semantic priors from pretraining, large models can override semantic priors when presented with in-context exemplars that contradict priors, despite the stronger semantic priors that larger models may hold. We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e.g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task. The ability to do SUL-ICL also emerges primarily with scale, and large-enough language models can even perform linear classification in a SUL-ICL setting. Finally, we evaluate instruction-tuned models and find that instruction tuning strengthens both the use of semantic priors and the capacity to learn input-label mappings, but more of the former.",
+    "neighbors": [
+      1026,
+      1146,
+      1306,
+      1617,
+      1867,
+      2226
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 343,
+    "label": 25,
+    "text": "Title: Audio-visual video-to-speech synthesis with synthesized input audio\nAbstract: Video-to-speech synthesis involves reconstructing the speech signal of a speaker from a silent video. The implicit assumption of this task is that the sound signal is either missing or contains a high amount of noise/corruption such that it is not useful for processing. Previous works in the literature either use video inputs only or employ both video and audio inputs during training, and discard the input audio pathway during inference. In this work we investigate the effect of using video and audio inputs for video-to-speech synthesis during both training and inference. In particular, we use pre-trained video-to-speech models to synthesize the missing speech signals and then train an audio-visual-to-speech synthesis model, using both the silent video and the synthesized speech as inputs, to predict the final reconstructed speech. Our experiments demonstrate that this approach is successful with both raw waveforms and mel spectrograms as target outputs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 344,
+    "label": 24,
+    "text": "Title: AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks\nAbstract: The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language Models (LLMs) have experienced a rapid series of breakthroughs very recently. We envision that the two fields can radically push the boundaries of each other through tight integration. To showcase this vision, we explore the potential of a symbiotic relationship between AutoML and LLMs, shedding light on how they can benefit each other. In particular, we investigate both the opportunities to enhance AutoML approaches with LLMs from different perspectives and the challenges of leveraging AutoML to further improve LLMs. To this end, we survey existing work, and we critically assess risks. We strongly believe that the integration of the two fields has the potential to disrupt both fields, NLP and AutoML. By highlighting conceivable synergies, but also risks, we aim to foster further exploration at the intersection of AutoML and LLMs.",
+    "neighbors": [
+      1052,
+      1307,
+      1972,
+      2109,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 345,
+    "label": 4,
+    "text": "Title: HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication\nAbstract: New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 346,
+    "label": 30,
+    "text": "Title: Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek\nAbstract: \u2014Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited. In this work we propose M2DS2, a simple and sample-ef\ufb01cient \ufb01netuning strategy for large pretrained speech models, based on mixed source and target domain self-supervision. We \ufb01nd that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a 120 hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a testbed for multi-domain evaluation of Greek ASR systems. In our experiments we \ufb01nd that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environ- ment, M2DS2 yields signi\ufb01cant improvements for cross-domain adaptation, even when a only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we \ufb01nd that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.",
+    "neighbors": [
+      2078
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 347,
+    "label": 24,
+    "text": "Title: Domain Generalization without Excess Empirical Risk\nAbstract: Given data from diverse sets of distinct distributions, domain generalization aims to learn models that generalize to unseen distributions. A common approach is designing a data-driven surrogate penalty to capture generalization and minimize the empirical risk jointly with the penalty. We argue that a significant failure mode of this recipe is an excess risk due to an erroneous penalty or hardness in joint optimization. We present an approach that eliminates this problem. Instead of jointly minimizing empirical risk with the penalty, we minimize the penalty under the constraint of optimality of the empirical risk. This change guarantees that the domain generalization penalty cannot impair optimization of the empirical risk, i.e., in-distribution performance. To solve the proposed optimization problem, we demonstrate an exciting connection to rate-distortion theory and utilize its tools to design an efficient method. Our approach can be applied to any penalty-based domain generalization method, and we demonstrate its effectiveness by applying it to three examplar methods from the literature, showing significant improvements.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 348,
+    "label": 24,
+    "text": "Title: Data-Driven Identification of Quadratic Symplectic Representations of Nonlinear Hamiltonian Systems\nAbstract: We present a framework for learning Hamiltonian systems using data. This work is based on the lifting hypothesis, which posits that nonlinear Hamiltonian systems can be written as nonlinear systems with cubic Hamiltonians. By leveraging this, we obtain quadratic dynamics that are Hamiltonian in a transformed coordinate system. To that end, for given generalized position and momentum data, we propose a methodology to learn quadratic dynamical systems, enforcing the Hamiltonian structure in combination with a symplectic auto-encoder. The enforced Hamiltonian structure exhibits long-term stability of the system, while the cubic Hamiltonian function provides relatively low model complexity. For low-dimensional data, we determine a higher-order transformed coordinate system, whereas, for high-dimensional data, we find a lower-order coordinate system with the desired properties. We demonstrate the proposed methodology by means of both low-dimensional and high-dimensional nonlinear Hamiltonian systems.",
+    "neighbors": [
+      1385
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 349,
+    "label": 24,
+    "text": "Title: A Framework for Incentivized Collaborative Learning\nAbstract: Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that cannot be accomplished by a single entity alone. This is likely due to factors such as security constraints, privacy concerns, and limitations in computation resources. As a result, collaborative learning (CL) research has been gaining momentum. However, a significant challenge in practical applications of CL is how to effectively incentivize multiple entities to collaborate before any collaboration occurs. In this study, we propose ICL, a general framework for incentivized collaborative learning, and provide insights into the critical issue of when and why incentives can improve collaboration performance. Furthermore, we show the broad applicability of ICL to specific cases in federated learning, assisted learning, and multi-armed bandit with both theory and experimental results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 350,
+    "label": 27,
+    "text": "Title: On the Simulation of Perception Errors in Autonomous Vehicles\nAbstract: Even though virtual testing of Autonomous Vehicles (AVs) has been well recognized as essential for safety assessment, AV simulators are still undergoing active development. One particularly challenging question is to effectively include the Sensing and Perception (S&P) subsystem into the simulation loop. In this article, we define Perception Error Models (PEM), a virtual simulation component that can enable the analysis of the impact of perception errors on AV safety, without the need to model the sensors themselves. We propose a generalized data-driven procedure towards parametric modeling and evaluate it using Apollo, an open-source driving software, and nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an open-source vehicle simulator. Furthermore, we demonstrate the usefulness of PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups. Our virtual tests highlight limitations in the current evaluation metrics, and the proposed approach can help study the impact of perception errors on AV safety.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 351,
+    "label": 10,
+    "text": "Title: The Update Equivalence Framework for Decision-Time Planning\nAbstract: The process of revising (or constructing) a policy immediately prior to execution -- known as decision-time planning -- is key to achieving superhuman performance in perfect-information settings like chess and Go. A recent line of work has extended decision-time planning to more general imperfect-information settings, leading to superhuman performance in poker. However, these methods requires considering subgames whose sizes grow quickly in the amount of non-public information, making them unhelpful when the amount of non-public information is large. Motivated by this issue, we introduce an alternative framework for decision-time planning that is not based on subgames but rather on the notion of update equivalence. In this framework, decision-time planning algorithms simulate updates of synchronous learning algorithms. This framework enables us to introduce a new family of principled decision-time planning algorithms that do not rely on public information, opening the door to sound and effective decision-time planning in settings with large amounts of non-public information. In experiments, members of this family produce comparable or superior results compared to state-of-the-art approaches in Hanabi and improve performance in 3x3 Abrupt Dark Hex and Phantom Tic-Tac-Toe.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 352,
+    "label": 30,
+    "text": "Title: Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks\nAbstract: Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results. However, crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs, as crowd workers have financial incentives to use LLMs to increase their productivity and income. To investigate this concern, we conducted a case study on the prevalence of LLM usage by crowd workers. We reran an abstract summarization task from the literature on Amazon Mechanical Turk and, through a combination of keystroke detection and synthetic text classification, estimate that 33-46% of crowd workers used LLMs when completing the task. Although generalization to other, less LLM-friendly tasks is unclear, our results call for platforms, researchers, and crowd workers to find new ways to ensure that human data remain human, perhaps using the methodology proposed here as a stepping stone. Code/data: https://github.com/epfl-dlab/GPTurk",
+    "neighbors": [
+      42,
+      75,
+      401,
+      1384,
+      1487,
+      1949,
+      1992,
+      2094,
+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 353,
+    "label": 16,
+    "text": "Title: FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection\nAbstract: Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. In this paper, we find that the above trade-off can be better mitigated by leveraging the distinct frequency biases between normal and abnormal reconstruction errors. To this end, we propose Frequency-aware Image Restoration (FAIR), a novel self-supervised image restoration task that restores images from their high-frequency components. It enables precise reconstruction of normal patterns while mitigating unfavorable generalization to anomalies. Using only a simple vanilla UNet, FAIR achieves state-of-the-art performance with higher efficiency on various defect detection datasets. Code: https://github.com/liutongkun/FAIR.",
+    "neighbors": [
+      2098
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 354,
+    "label": 30,
+    "text": "Title: InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling\nAbstract: Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (InfoCTM). Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method. This works as a regularization to properly align topics and prevent degenerate topic representations of words, which mitigates the repetitive topic issue. To address the low-coverage dictionary issue, we further propose a cross-lingual vocabulary linking method that finds more linked cross-lingual words for topic alignment beyond the translations of a given dictionary. Extensive experiments on English, Chinese, and Japanese datasets demonstrate that our method outperforms state-of-the-art baselines, producing more coherent, diverse, and well-aligned topics and showing better transferability for cross-lingual classification tasks.",
+    "neighbors": [
+      953,
+      1184
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 355,
+    "label": 27,
+    "text": "Title: Exploring Levels of Control for a Navigation Assistant for Blind Travelers\nAbstract: Only a small percentage of blind and low-vision people use traditional mobility aids such as a cane or a guide dog. Various assistive technologies have been proposed to address the limitations of traditional mobility aids. These devices often give either the user or the device majority of the control. In this work, we explore how varying levels of control affect the users' sense of agency, trust in the device, confidence, and successful navigation. We present Glide, a novel mobility aid with two modes for control: Glide-directed and User-directed. We employ Glide in a study (N=9) in which blind or low-vision participants used both modes to navigate through an indoor environment. Overall, participants found that Glide was easy to use and learn. Most participants trusted Glide despite its current limitations, and their confidence and performance increased as they continued to use Glide. Users' control mode preferences varied in different situations; no single mode \"won\" in all situations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 356,
+    "label": 16,
+    "text": "Title: Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder\nAbstract: Masked autoencoder (MAE) is a promising self-supervised pre-training technique that can improve the representation learning of a neural network without human intervention. However, applying MAE directly to volumetric medical images poses two challenges: (i) a lack of global information that is crucial for understanding the clinical context of the holistic data, (ii) no guarantee of stabilizing the representations learned from randomly masked inputs. To address these limitations, we propose the \\textbf{G}lobal-\\textbf{L}ocal \\textbf{M}asked \\textbf{A}uto\\textbf{E}ncoder (GL-MAE), a simple yet effective self-supervised pre-training strategy. In addition to reconstructing masked local views, as in previous methods, GL-MAE incorporates global context learning by reconstructing masked global views. Furthermore, a complete global view is integrated as an anchor to guide the reconstruction and stabilize the learning process through global-to-global consistency learning and global-to-local consistency learning. Finetuning results on multiple datasets demonstrate the superiority of our method over other state-of-the-art self-supervised algorithms, highlighting its effectiveness on versatile volumetric medical image segmentation tasks, even when annotations are scarce. Our codes and models will be released upon acceptance.",
+    "neighbors": [
+      1488,
+      1525
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 357,
+    "label": 16,
+    "text": "Title: SyncDreamer: Generating Multiview-consistent Images from a Single-view Image\nAbstract: In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views from a single-view image of an object. However, maintaining consistency in geometry and colors for the generated images remains a challenge. To address this issue, we propose a synchronized multiview diffusion model that models the joint probability distribution of multiview images, enabling the generation of multiview-consistent images in a single reverse process. SyncDreamer synchronizes the intermediate states of all the generated images at every step of the reverse process through a 3D-aware feature attention mechanism that correlates the corresponding features across different views. Experiments show that SyncDreamer generates images with high consistency across different views, thus making it well-suited for various 3D generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.",
+    "neighbors": [
+      1125,
+      1418,
+      2049,
+      2117,
+      2205
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 358,
+    "label": 24,
+    "text": "Title: Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings\nAbstract: Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personalization is necessary at deployment time to fine-tune the model, a person-specific dataset needs to be collected online by interacting with the users. Optimizing the collection of labels in such phase is instrumental to impose a tolerable burden on the users while maximizing personal improvement. In this paper, we consider a fine-grain stress detection problem based on wearable sensors targeting everyday settings, and propose a novel context-aware active learning strategy capable of jointly maximizing the meaningfulness of the signal samples we request the user to label and the response rate. We develop a multilayered sensor-edge-cloud platform to periodically capture physiological signals and process them in real-time, as well as to collect labels and retrain the detection model. We collect a large dataset and show that the context-aware active learning technique we propose achieves a desirable detection performance using 88% and 32% fewer queries from users compared to a randomized strategy and a traditional active learning strategy, respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 359,
+    "label": 16,
+    "text": "Title: Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems\nAbstract: Deep learning models are susceptible to adversarial samples in white and black-box environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved, who can identify whether a given sample is benign or malicious. However, in white-box environments, interpretable deep learning systems (IDLSes) have been shown to be vulnerable to malicious manipulations. In black-box settings, as access to the components of IDLSes is limited, it becomes more challenging for the adversary to fool the system. In this work, we propose a Query-efficient Score-based black-box attack against IDLSes, QuScore, which requires no knowledge of the target model and its coupled interpretation model. QuScore is based on transfer-based and score-based methods by employing an effective microbial genetic algorithm. Our method is designed to reduce the number of queries necessary to carry out successful attacks, resulting in a more efficient process. By continuously refining the adversarial samples created based on feedback scores from the IDLS, our approach effectively navigates the search space to identify perturbations that can fool the system. We evaluate the attack's effectiveness on four CNN models (Inception, ResNet, VGG, DenseNet) and two interpretation models (CAM, Grad), using both ImageNet and CIFAR datasets. Our results show that the proposed approach is query-efficient with a high attack success rate that can reach between 95% and 100% and transferability with an average success rate of 69% in the ImageNet and CIFAR datasets. Our attack method generates adversarial examples with attribution maps that resemble benign samples. We have also demonstrated that our attack is resilient against various preprocessing defense techniques and can easily be transferred to different DNN models.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 360,
+    "label": 24,
+    "text": "Title: Practical Differentially Private Hyperparameter Tuning with Subsampling\nAbstract: Tuning the hyperparameters of differentially private (DP) machine learning (ML) algorithms often requires use of sensitive data and this may leak private information via hyperparameter values. Recently, Papernot and Steinke (2022) proposed a certain class of DP hyperparameter tuning algorithms, where the number of random search samples is randomized itself. Commonly, these algorithms still considerably increase the DP privacy parameter $\\varepsilon$ over non-tuned DP ML model training and can be computationally heavy as evaluating each hyperparameter candidate requires a new training run. We focus on lowering both the DP bounds and the computational cost of these methods by using only a random subset of the sensitive data for the hyperparameter tuning and by extrapolating the optimal values to a larger dataset. We provide a R\\'enyi differential privacy analysis for the proposed method and experimentally show that it consistently leads to better privacy-utility trade-off than the baseline method by Papernot and Steinke.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 361,
+    "label": 10,
+    "text": "Title: Discussion Paper: The Threat of Real Time Deepfakes\nAbstract: Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 362,
+    "label": 27,
+    "text": "Title: Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning\nAbstract: Preference Based Reinforcement Learning has shown much promise for utilizing human binary feedback on queried trajectory pairs to recover the underlying reward model of the Human in the Loop (HiL). While works have attempted to better utilize the queries made to the human, in this work we make two observations about the unlabeled trajectories collected by the agent and propose two corresponding loss functions that ensure participation of unlabeled trajectories in the reward learning process, and structure the embedding space of the reward model such that it reflects the structure of state space with respect to action distances. We validate the proposed method on one locomotion domain and one robotic manipulation task and compare with the state-of-the-art baseline PEBBLE. We further present an ablation of the proposed loss components across both the domains and find that not only each of the loss components perform better than the baseline, but the synergic combination of the two has much better reward recovery and human feedback sample efficiency.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 363,
+    "label": 30,
+    "text": "Title: API-Bank: A Benchmark for Tool-Augmented LLMs\nAbstract: Recent research has shown that Large Language Models (LLMs) can utilize external tools to improve their contextual processing abilities, moving away from the pure language modeling paradigm and paving the way for Artificial General Intelligence. Despite this, there has been a lack of systematic evaluation to demonstrate the efficacy of LLMs using tools to respond to human instructions. This paper presents API-Bank, the first benchmark tailored for Tool-Augmented LLMs. API-Bank includes 53 commonly used API tools, a complete Tool-Augmented LLM workflow, and 264 annotated dialogues that encompass a total of 568 API calls. These resources have been designed to thoroughly evaluate LLMs' ability to plan step-by-step API calls, retrieve relevant APIs, and correctly execute API calls to meet human needs. The experimental results show that GPT-3.5 emerges the ability to use the tools relative to GPT3, while GPT-4 has stronger planning performance. Nevertheless, there remains considerable scope for further improvement when compared to human performance. Additionally, detailed error analysis and case studies demonstrate the feasibility of Tool-Augmented LLMs for daily use, as well as the primary challenges that future research needs to address.",
+    "neighbors": [
+      57,
+      183,
+      704,
+      817,
+      1001,
+      1044,
+      1353,
+      1430,
+      1878,
+      2166,
+      2265
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 364,
+    "label": 16,
+    "text": "Title: NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation\nAbstract: In this paper, we propose NUWA-XL, a novel Diffusion over Diffusion architecture for eXtremely Long video generation. Most current work generates long videos segment by segment sequentially, which normally leads to the gap between training on short videos and inferring long videos, and the sequential generation is inefficient. Instead, our approach adopts a \u201ccoarse-to-fine\u201d process, in which the video can be generated in parallel at the same granularity. A global diffusion model is applied to generate the keyframes across the entire time range, and then local diffusion models recursively fill in the content between nearby frames. This simple yet effective strategy allows us to directly train on long videos (3376 frames) to reduce the training-inference gap and makes it possible to generate all segments in parallel. To evaluate our model, we build FlintstonesHD dataset, a new benchmark for long video generation. Experiments show that our model not only generates high-quality long videos with both global and local coherence, but also decreases the average inference time from 7.55min to 26s (by 94.26%) at the same hardware setting when generating 1024 frames. The homepage link is [NUWA-XL](https://msra-nuwa.azurewebsites.net)",
+    "neighbors": [
+      1707,
+      2085
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 365,
+    "label": 5,
+    "text": "Title: Supercharging Distributed Computing Environments For High Performance Data Engineering\nAbstract: The data engineering and data science community has embraced the idea of using Python&R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to process terabytes of data. They can easily exceed the capabilities of a single machine, but also demand significant developer time&effort. Therefore it is essential to design scalable dataframe solutions. There have been multiple attempts to tackle this problem, the most notable being the dataframe systems developed using distributed computing environments such as Dask and Ray. Even though Dask/Ray distributed computing features look very promising, we perceive that the Dask Dataframes/Ray Datasets still have room for optimization. In this paper, we present CylonFlow, an alternative distributed dataframe execution methodology that enables state-of-the-art performance and scalability on the same Dask/Ray infrastructure (thereby supercharging them!). To achieve this, we integrate a high performance dataframe system Cylon, which was originally based on an entirely different execution paradigm, into Dask and Ray. Our experiments show that on a pipeline of dataframe operators, CylonFlow achieves 30x more distributed performance than Dask Dataframes. Interestingly, it also enables superior sequential performance due to the native C++ execution of Cylon. We believe the success of Cylon&CylonFlow extends beyond the data engineering domain, and can be used to consolidate high performance computing and distributed computing ecosystems.",
+    "neighbors": [
+      922
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 366,
+    "label": 4,
+    "text": "Title: ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search\nAbstract: We present ProvG-Searcher, a novel approach for detecting known APT behaviors within system security logs. Our approach leverages provenance graphs, a comprehensive graph representation of event logs, to capture and depict data provenance relations by mapping system entities as nodes and their interactions as edges. We formulate the task of searching provenance graphs as a subgraph matching problem and employ a graph representation learning method. The central component of our search methodology involves embedding of subgraphs in a vector space where subgraph relationships can be directly evaluated. We achieve this through the use of order embeddings that simplify subgraph matching to straightforward comparisons between a query and precomputed subgraph representations. To address challenges posed by the size and complexity of provenance graphs, we propose a graph partitioning scheme and a behavior-preserving graph reduction method. Overall, our technique offers significant computational efficiency, allowing most of the search computation to be performed offline while incorporating a lightweight comparison step during query execution. Experimental results on standard datasets demonstrate that ProvG-Searcher achieves superior performance, with an accuracy exceeding 99% in detecting query behaviors and a false positive rate of approximately 0.02%, outperforming other approaches.",
+    "neighbors": [
+      1442
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 367,
+    "label": 16,
+    "text": "Title: Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 368,
+    "label": 24,
+    "text": "Title: Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs\nAbstract: In reward-free reinforcement learning (RL), an agent explores the environment first without any reward information, in order to achieve certain learning goals afterwards for any given reward. In this paper we focus on reward-free RL under low-rank MDP models, in which both the representation and linear weight vectors are unknown. Although various algorithms have been proposed for reward-free low-rank MDPs, the corresponding sample complexity is still far from being satisfactory. In this work, we first provide the first known sample complexity lower bound that holds for any algorithm under low-rank MDPs. This lower bound implies it is strictly harder to find a near-optimal policy under low-rank MDPs than under linear MDPs. We then propose a novel model-based algorithm, coined RAFFLE, and show it can both find an $\\epsilon$-optimal policy and achieve an $\\epsilon$-accurate system identification via reward-free exploration, with a sample complexity significantly improving the previous results. Such a sample complexity matches our lower bound in the dependence on $\\epsilon$, as well as on $K$ in the large $d$ regime, where $d$ and $K$ respectively denote the representation dimension and action space cardinality. Finally, we provide a planning algorithm (without further interaction with true environment) for RAFFLE to learn a near-accurate representation, which is the first known representation learning guarantee under the same setting.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 369,
+    "label": 3,
+    "text": "Title: Designing the Metaverse: A Scoping Review to Map Current Research Effort on Ethical Implications\nAbstract: The metaverse and digital, virtual environments have been part of recent history as places in which people can socialize, work and spend time playing games. However, the infancy of the development of these digital, virtual environments brings some challenges that are still not fully depicted. With this article, we seek to identify and map the currently available knowledge and scientific effort to discover what principles, guidelines, laws, policies, and practices are currently in place to allow for the design of digital, virtual environments, and the metaverse. Through a scoping review, we aimed to systematically survey the existing literature and discern gaps in knowledge within the domain of metaverse research from sociological, anthropological, cultural, and experiential perspectives. The objective of this review was twofold: (1) to examine the focus of the literature studying the metaverse from various angles and (2) to formulate a research agenda for the design and development of ethical digital, virtual environments. With this paper, we identified several works and articles detailing experiments and research on the design of digital, virtual environments and metaverses. We found an increased number of publications in the year 2022. This finding, together with the fact that only a few articles were focused on the domain of ethics, culture and society shows that there is still a vast amount of work to be done to create awareness, principles and policies that could help to design safe, secure and inclusive digital, virtual environments and metaverses.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 370,
+    "label": 16,
+    "text": "Title: StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training\nAbstract: In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario.",
+    "neighbors": [
+      2025
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 371,
+    "label": 16,
+    "text": "Title: Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction\nAbstract: Exploring spatial-temporal dependencies from observed motions is one of the core challenges of human motion prediction. Previous methods mainly focus on dedicated network structures to model the spatial and temporal dependencies. This paper considers a new direction by introducing a model learning framework with auxiliary tasks. In our auxiliary tasks, partial body joints' coordinates are corrupted by either masking or adding noise and the goal is to recover corrupted coordinates depending on the rest coordinates. To work with auxiliary tasks, we propose a novel auxiliary-adapted transformer, which can handle incomplete, corrupted motion data and achieve coordinate recovery via capturing spatial-temporal dependencies. Through auxiliary tasks, the auxiliary-adapted transformer is promoted to capture more comprehensive spatial-temporal dependencies among body joints' coordinates, leading to better feature learning. Extensive experimental results have shown that our method outperforms state-of-the-art methods by remarkable margins of 7.2%, 3.7%, and 9.4% in terms of 3D mean per joint position error (MPJPE) on the Human3.6M, CMU Mocap, and 3DPW datasets, respectively. We also demonstrate that our method is more robust under data missing cases and noisy data cases. Code is available at https://github.com/MediaBrain-SJTU/AuxFormer.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 372,
+    "label": 8,
+    "text": "Title: QUIC Library Hunter: Identifying Server Libraries Across the Internet\nAbstract: The new QUIC protocol can be implemented in user space, and various implementations already exist. While they follow the same specification and general interoperability is given, differences in performance, functionality, but also security (e.g., due to bugs) can be expected. Therefore, knowledge about the implementation of an endpoint on the Internet can help researchers, operators and users to better analyze connections, evaluations and findings. We provide an approach to identify used libraries of QUIC servers based on CONNECTION_CLOSE frames and transport parameter orders. We apply our methodology to Internet-wide scans and identify at least one deployment for 18 QUIC libraries. In total, we can identify the library of 8.8 M IPv4 and 2.5 M IPv6 addresses.",
+    "neighbors": [
+      578
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 373,
+    "label": 16,
+    "text": "Title: Compensation Learning in Semantic Segmentation\nAbstract: Label noise and ambiguities between similar classes are challenging problems in developing new models and annotating new data for semantic segmentation. In this paper, we propose Compensation Learning in Semantic Segmentation, a framework to identify and compensate ambiguities as well as label noise. More specifically, we add a ground truth depending and globally learned bias to the classification logits and introduce a novel uncertainty branch for neural networks to induce the compensation bias only to relevant regions. Our method is employed into state-of-the-art segmentation frameworks and several experiments demonstrate that our proposed compensation learns inter-class relations that allow global identification of challenging ambiguities as well as the exact localization of subsequent label noise. Additionally, it enlarges robustness against label noise during training and allows target-oriented manipulation during inference. We evaluate the proposed method on Cityscapes, KITTI-STEP, ADE20k, and COCO-stuff10k.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 374,
+    "label": 24,
+    "text": "Title: Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control\nAbstract: There are many physical processes that have inherent discontinuities in their mathematical formulations. This paper is motivated by the specific case of collisions between two rigid or deformable bodies and the intrinsic nature of that discontinuity. The impulse response to a collision is discontinuous with the lack of any response when no collision occurs, which causes difficulties for numerical approaches that require differentiability which are typical in machine learning, inverse problems, and control. We theoretically and numerically demonstrate that the derivative of the collision time with respect to the parameters becomes infinite as one approaches the barrier separating colliding from not colliding, and use lifting to complexify the solution space so that solutions on the other side of the barrier are directly attainable as precise values. Subsequently, we mollify the barrier posed by the unbounded derivatives, so that one can tunnel back and forth in a smooth and reliable fashion facilitating the use of standard numerical approaches. Moreover, we illustrate that standard approaches fail in numerous ways mostly due to a lack of understanding of the mathematical nature of the problem (e.g. typical backpropagation utilizes many rules of differentiation, but ignores L'Hopital's rule).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 375,
+    "label": 16,
+    "text": "Title: Urban Regional Function Guided Traffic Flow Prediction\nAbstract: The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.",
+    "neighbors": [
+      591,
+      1620
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 376,
+    "label": 24,
+    "text": "Title: Variance-reduced Clipping for Non-convex Optimization\nAbstract: Gradient clipping is a standard training technique used in deep learning applications such as large-scale language modeling to mitigate exploding gradients. Recent experimental studies have demonstrated a fairly special behavior in the smoothness of the training objective along its trajectory when trained with gradient clipping. That is, the smoothness grows with the gradient norm. This is in clear contrast to the well-established assumption in folklore non-convex optimization, a.k.a. $L$--smoothness, where the smoothness is assumed to be bounded by a constant $L$ globally. The recently introduced $(L_0,L_1)$--smoothness is a more relaxed notion that captures such behavior in non-convex optimization. In particular, it has been shown that under this relaxed smoothness assumption, SGD with clipping requires $O(\\epsilon^{-4})$ stochastic gradient computations to find an $\\epsilon$--stationary solution. In this paper, we employ a variance reduction technique, namely SPIDER, and demonstrate that for a carefully designed learning rate, this complexity is improved to $O(\\epsilon^{-3})$ which is order-optimal. Our designed learning rate comprises the clipping technique to mitigate the growing smoothness. Moreover, when the objective function is the average of $n$ components, we improve the existing $O(n\\epsilon^{-2})$ bound on the stochastic gradient complexity to $O(\\sqrt{n} \\epsilon^{-2} + n)$, which is order-optimal as well. In addition to being theoretically optimal, SPIDER with our designed parameters demonstrates comparable empirical performance against variance-reduced methods such as SVRG and SARAH in several vision tasks.",
+    "neighbors": [
+      1347
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 377,
+    "label": 30,
+    "text": "Title: Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4\nAbstract: Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as\"advanced\"at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-of-distribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on out-of-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval.",
+    "neighbors": [
+      682,
+      857,
+      975,
+      1001,
+      1259,
+      1713,
+      1915,
+      1952,
+      2215
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 378,
+    "label": 16,
+    "text": "Title: TrickVOS: A Bag of Tricks for Video Object Segmentation\nAbstract: Space-time memory (STM) network methods have been dominant in semi-supervised video object segmentation (SVOS) due to their remarkable performance. In this work, we identify three key aspects where we can improve such methods; i) supervisory signal, ii) pretraining and iii) spatial awareness. We then propose TrickVOS; a generic, method-agnostic bag of tricks addressing each aspect with i) a structure-aware hybrid loss, ii) a simple decoder pretraining regime and iii) a cheap tracker that imposes spatial constraints in model predictions. Finally, we propose a lightweight network and show that when trained with TrickVOS, it achieves competitive results to state-of-the-art methods on DAVIS and YouTube benchmarks, while being one of the first STM-based SVOS methods that can run in real-time on a mobile device.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 379,
+    "label": 10,
+    "text": "Title: DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection\nAbstract: The growing popularity of social media platforms has simplified the creation and distribution of news articles but also creates a conduit for spreading fake news. In consequence, the need arises for effective context-aware fake news detection mechanisms, where the contextual information can be built either from the textual content of posts or from available social data (e.g., information about the users, reactions to posts, or the social network). In this paper, we propose DANES, a Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection. DANES comprises a Text Branch for a textual content-based context and a Social Branch for the social context. These two branches are used to create a novel Network Embedding. Preliminary ablation results on 3 real-world datasets, i.e., BuzzFace, Twitter15, and Twitter16, are promising, with an accuracy that outperforms state-of-the-art solutions when employing both social and textual content features.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 380,
+    "label": 16,
+    "text": "Title: BiBench: Benchmarking and Analyzing Network Binarization\nAbstract: Network binarization emerges as one of the most promising compression approaches offering extraordinary computation and memory savings by minimizing the bit-width. However, recent research has shown that applying existing binarization algorithms to diverse tasks, architectures, and hardware in realistic scenarios is still not straightforward. Common challenges of binarization, such as accuracy degradation and efficiency limitation, suggest that its attributes are not fully understood. To close this gap, we present BiBench, a rigorously designed benchmark with in-depth analysis for network binarization. We first carefully scrutinize the requirements of binarization in the actual production and define evaluation tracks and metrics for a comprehensive and fair investigation. Then, we evaluate and analyze a series of milestone binarization algorithms that function at the operator level and with extensive influence. Our benchmark reveals that 1) the binarized operator has a crucial impact on the performance and deployability of binarized networks; 2) the accuracy of binarization varies significantly across different learning tasks and neural architectures; 3) binarization has demonstrated promising efficiency potential on edge devices despite the limited hardware support. The results and analysis also lead to a promising paradigm for accurate and efficient binarization. We believe that BiBench will contribute to the broader adoption of binarization and serve as a foundation for future research. The code for our BiBench is released https://github.com/htqin/BiBench .",
+    "neighbors": [
+      1777
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 381,
+    "label": 28,
+    "text": "Title: Optimized Design of Joint Mirror Array and Liquid Crystal-Based RIS-Aided VLC Systems\nAbstract: Most studies of reflecting intelligent surfaces (RISs)-assisted visible light communication (VLC) systems have focused on the integration of RISs in the channel to combat the line-of-sight (LoS) blockage and to enhance the corresponding achievable data rate. Some recent efforts have investigated the integration of liquid crystal (LC)-RIS in the VLC receiver to also improve the corresponding achievable data rate. To jointly benefit from the previously mentioned appealing capabilities of the RIS technology in both the channel and the receiver, in this work, we propose a novel indoor VLC system that is jointly assisted by a mirror array-based RIS in the channel and an LC-based RIS aided-VLC receiver. To illustrate the performance of the proposed system, a rate maximization problem is formulated, solved, and evaluated. This maximization problem jointly optimizes the roll and yaw angles of the mirror array-based RIS as well as the refractive index of the LC-based RIS VLC receiver. Moreover, this maximization problem considers practical assumptions, such as the presence of non-users blockers in the LoS path between the transmitter-receiver pair and the user's random device orientation (i.e., the user's self-blockage). Due to the non-convexity of the formulated optimization problem, a low-complexity algorithm is utilized to get the global optimal solution. A multi-user scenario of the proposed scheme is also presented. Furthermore, the energy efficiency of the proposed system is also investigated. Simulation results are provided, confirming that the proposed system yields a noteworthy improvement in data rate and energy efficiency performances compared to several baseline schemes.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 382,
+    "label": 22,
+    "text": "Title: SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR\nAbstract: High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware designs produced by HLS tools still suffer from a significant performance gap compared to manual implementations. This is because the input HLS programs must still be written using hardware design principles. Existing techniques either leave the program source unchanged or perform a fixed sequence of source transformation passes, potentially missing opportunities to find the optimal design. We propose a super-optimization approach for HLS that automatically rewrites an arbitrary software program into efficient HLS code that can be used to generate an optimized hardware design. We developed a toolflow named SEER, based on the e-graph data structure, to efficiently explore equivalent implementations of a program at scale. SEER provides an extensible framework, orchestrating existing software compiler passes and hardware synthesis optimizers. Our work is the first attempt to exploit e-graph rewriting for large software compiler frameworks, such as MLIR. Across a set of open-source benchmarks, we show that SEER achieves up to 38x the performance within 1.4x the area of the original program. Via an Intel-provided case study, SEER demonstrates the potential to outperform manually optimized designs produced by hardware experts.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 383,
+    "label": 10,
+    "text": "Title: Markov Conditions and Factorization in Logical Credal Networks\nAbstract: We examine the recently proposed language of Logical Credal Networks, in particular investigating the consequences of various Markov conditions. We introduce the notion of structure for a Logical Credal Network and show that a structure without directed cycles leads to a well-known factorization result. For networks with directed cycles, we analyze the differences between Markov conditions, factorization results, and specification requirements.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 384,
+    "label": 6,
+    "text": "Title: Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent\nAbstract: The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no \"true\" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global problem for which there is currently no good solution. As an explainable AI (XAI) system, the agent provides explanations for its decisions on the misinformed nature of social media content. In this late-breaking study, we explored the roles of trust (attitude) and reliance (behaviour) as key elements of XAI user experience (UX) and whether these influenced the illusion of consensus. Findings show no effect of trust, but an effect of reliance on consensus-based explanations. This work may guide the design of anti-misinformation systems that use XAI, especially the user-centred design of explanations.",
+    "neighbors": [
+      695
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 385,
+    "label": 28,
+    "text": "Title: Unsourced Random Access with the MIMO Receiver: Projection Decoding Analysis\nAbstract: We consider unsourced random access with MIMO receiver - a crucial communication scenario for future 5G/6G wireless networks. We perform a projection-based decoder analysis and derive energy efficiency achievability bounds when channel state information is unknown at transmitters and the receiver (no-CSI scenario). The comparison to the maximum-likelihood (ML) achievability bounds by Gao et al. (2023) is performed. We show that there is a region where the new bound outperforms the ML bound. The latter fact should not surprise the reader as both decoding criteria are suboptimal when considering per-user probability of error (PUPE). Moreover, transition to projection decoding allows for significant dimensionality reduction, which greatly reduces the computation time.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 386,
+    "label": 24,
+    "text": "Title: Probing the limit of hydrologic predictability with the Transformer network\nAbstract: For a number of years since its introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) have proven remarkably difficult to surpass in terms of daily hydrograph metrics on known, comparable benchmarks. Outside of hydrology, Transformers have now become the model of choice for sequential prediction tasks, making it a curious architecture to investigate. Here, we first show that a vanilla Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS dataset, and lagged especially for the high-flow metrics due to short-term processes. However, a recurrence-free variant of Transformer can obtain mixed comparisons with LSTM, producing the same Kling-Gupta efficiency coefficient (KGE), along with other metrics. The lack of advantages for the Transformer is linked to the Markovian nature of the hydrologic prediction problem. Similar to LSTM, the Transformer can also merge multiple forcing dataset to improve model performance. While the Transformer results are not higher than current state-of-the-art, we still learned some valuable lessons: (1) the vanilla Transformer architecture is not suitable for hydrologic modeling; (2) the proposed recurrence-free modification can improve Transformer performance so future work can continue to test more of such modifications; and (3) the prediction limits on the dataset should be close to the current state-of-the-art model. As a non-recurrent model, the Transformer may bear scale advantages for learning from bigger datasets and storing knowledge. This work serves as a reference point for future modifications of the model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 387,
+    "label": 16,
+    "text": "Title: On the challenges to learn from Natural Data Streams\nAbstract: \u2014In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e . data characterized by a streaming nature, unbalanced distribution, data drift over a long time frame and strong correlation of samples in short time ranges. Moreover, a clear separation between the tradi- tional training and deployment phases is usually lacking. This data organization and fruition represents an interesting and challenging scenario for both traditional Machine and Deep Learning algorithms and incremental learning agents, i.e . agents that have the ability to incrementally improve their knowledge through the past experience. In this paper, we investigate the classi\ufb01cation performance of a variety of algorithms that belong to various research \ufb01eld, i.e . Continual, Streaming and Online Learning, that receives as training input Natural Data Streams. The experimental validation is carried out on three different datasets, expressly organized to replicate this challenging setting.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 388,
+    "label": 24,
+    "text": "Title: Revisiting Weighted Strategy for Non-stationary Parametric Bandits\nAbstract: Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and restart strategies. As many non-stationary environments exhibit gradual drifting patterns, the weighted strategy is commonly adopted in real-world applications. However, previous theoretical studies show that its analysis is more involved and the algorithms are either computationally less efficient or statistically suboptimal. This paper revisits the weighted strategy for non-stationary parametric bandits. In linear bandits (LB), we discover that this undesirable feature is due to an inadequate regret analysis, which results in an overly complex algorithm design. We propose a refined analysis framework, which simplifies the derivation and importantly produces a simpler weight-based algorithm that is as efficient as window/restart-based algorithms while retaining the same regret as previous studies. Furthermore, our new framework can be used to improve regret bounds of other parametric bandits, including Generalized Linear Bandits (GLB) and Self-Concordant Bandits (SCB). For example, we develop a simple weighted GLB algorithm with an $\\widetilde{O}(k_\\mu^{\\frac{5}{4}} c_\\mu^{-\\frac{3}{4}} d^{\\frac{3}{4}} P_T^{\\frac{1}{4}}T^{\\frac{3}{4}})$ regret, improving the $\\widetilde{O}(k_\\mu^{2} c_\\mu^{-1}d^{\\frac{9}{10}} P_T^{\\frac{1}{5}}T^{\\frac{4}{5}})$ bound in prior work, where $k_\\mu$ and $c_\\mu$ characterize the reward model's nonlinearity, $P_T$ measures the non-stationarity, $d$ and $T$ denote the dimension and time horizon.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 389,
+    "label": 24,
+    "text": "Title: Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces\nAbstract: Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 390,
+    "label": 4,
+    "text": "Title: Up-to-date Threat Modelling for Soft Privacy on Smart Cars\nAbstract: Physical persons playing the role of car drivers consume data that is sourced from the Internet and, at the same time, themselves act as sources of relevant data. It follows that citizens' privacy is potentially at risk while they drive, hence the need to model privacy threats in this application domain. This paper addresses the privacy threats by updating a recent threat-modelling methodology and by tailoring it specifically to the soft privacy target property, which ensures citizens' full control on their personal data. The methodology now features the sources of documentation as an explicit variable that is to be considered. It is demonstrated by including a new version of the de-facto standard LINDDUN methodology as well as an additional source by ENISA which is found to be relevant to soft privacy. The main findings are a set of 23 domain-independent threats, 43 domain-specific assets and 525 domain-dependent threats for the target property in the automotive domain. While these exceed their previous versions, their main value is to offer self-evident support to at least two arguments. One is that LINDDUN has evolved much the way our original methodology already advocated because a few of our previously suggested extensions are no longer outstanding. The other one is that ENISA's treatment of privacy aboard smart cars should be extended considerably because our 525 threats fall in the same scope.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 391,
+    "label": 24,
+    "text": "Title: DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning\nAbstract: Differential private optimization for nonconvex smooth objective is considered. In the previous work, the best known utility bound is $\\widetilde O(\\sqrt{d}/(n\\varepsilon_\\mathrm{DP}))$ in terms of the squared full gradient norm, which is achieved by Differential Private Gradient Descent (DP-GD) as an instance, where $n$ is the sample size, $d$ is the problem dimensionality and $\\varepsilon_\\mathrm{DP}$ is the differential privacy parameter. To improve the best known utility bound, we propose a new differential private optimization framework called \\emph{DIFF2 (DIFFerential private optimization via gradient DIFFerences)} that constructs a differential private global gradient estimator with possibly quite small variance based on communicated \\emph{gradient differences} rather than gradients themselves. It is shown that DIFF2 with a gradient descent subroutine achieves the utility of $\\widetilde O(d^{2/3}/(n\\varepsilon_\\mathrm{DP})^{4/3})$, which can be significantly better than the previous one in terms of the dependence on the sample size $n$. To the best of our knowledge, this is the first fundamental result to improve the standard utility $\\widetilde O(\\sqrt{d}/(n\\varepsilon_\\mathrm{DP}))$ for nonconvex objectives. Additionally, a more computational and communication efficient subroutine is combined with DIFF2 and its theoretical analysis is also given. Numerical experiments are conducted to validate the superiority of DIFF2 framework.",
+    "neighbors": [
+      1347
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 392,
+    "label": 16,
+    "text": "Title: MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models\nAbstract: Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully reflect the performance of MLLM, lacking a comprehensive evaluation. In this paper, we fill in this blank, presenting the first MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks. In order to avoid data leakage that may arise from direct use of public datasets for evaluation, the annotations of instruction-answer pairs are all manually designed. The concise instruction design allows us to fairly compare MLLMs, instead of struggling in prompt engineering. Besides, with such an instruction, we can also easily carry out quantitative statistics. A total of 12 advanced MLLMs are comprehensively evaluated on our MME, which not only suggests that existing MLLMs still have a large room for improvement, but also reveals the potential directions for the subsequent model optimization.",
+    "neighbors": [
+      34,
+      136,
+      319,
+      522,
+      836,
+      887,
+      1001,
+      1047,
+      1052,
+      1148,
+      1344,
+      1485,
+      1537,
+      1765,
+      1863,
+      2036,
+      2064,
+      2113,
+      2155,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 393,
+    "label": 3,
+    "text": "Title: Monitoring Gender Gaps via LinkedIn Advertising Estimates: the case study of Italy\nAbstract: Women remain underrepresented in the labour market. Although significant advancements are being made to increase female participation in the workforce, the gender gap is still far from being bridged. We contribute to the growing literature on gender inequalities in the labour market, evaluating the potential of the LinkedIn estimates to monitor the evolution of the gender gaps sustainably, complementing the official data sources. In particular, assessing the labour market patterns at a subnational level in Italy. Our findings show that the LinkedIn estimates accurately capture the gender disparities in Italy regarding sociodemographic attributes such as gender, age, geographic location, seniority, and industry category. At the same time, we assess data biases such as the digitalisation gap, which impacts the representativity of the workforce in an imbalanced manner, confirming that women are under-represented in Southern Italy. Additionally to confirming the gender disparities to the official census, LinkedIn estimates are a valuable tool to provide dynamic insights; we showed an immigration flow of highly skilled women, predominantly from the South. Digital surveillance of gender inequalities with detailed and timely data is particularly significant to enable policymakers to tailor impactful campaigns.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 394,
+    "label": 16,
+    "text": "Title: RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution\nAbstract: Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network. In particular, we emphasize the role of the base-frame and utilize it as a key prompt to guide the knowledge acquisition from other frames in every recurrence. Moreover, we introduce an implicit weighting loss to improve the model's flexibility in facing input frames with variable numbers. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves better results than state-of-the-art ones. Codes and pre-trained models are available at https://github.com/ZcsrenlongZ/RBSR.",
+    "neighbors": [
+      153,
+      1019
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 395,
+    "label": 5,
+    "text": "Title: Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning\nAbstract: Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on x86-64 and ARM platforms and an emerging RISC-V one. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.",
+    "neighbors": [
+      991
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 396,
+    "label": 16,
+    "text": "Title: ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer\nAbstract: Deep learning (DL) has advanced the field of dense prediction, while gradually dissolving the inherent barriers between different tasks. However, most existing works focus on designing architectures and constructing visual cues only for the specific task, which ignores the potential uniformity introduced by the DL paradigm. In this paper, we attempt to construct a novel \\underline{ComP}lementary \\underline{tr}ansformer, \\textbf{ComPtr}, for diverse bi-source dense prediction tasks. Specifically, unlike existing methods that over-specialize in a single task or a subset of tasks, ComPtr starts from the more general concept of bi-source dense prediction. Based on the basic dependence on information complementarity, we propose consistency enhancement and difference awareness components with which ComPtr can evacuate and collect important visual semantic cues from different image sources for diverse tasks, respectively. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer. This task-generic design provides a smooth foundation for constructing the unified model that can simultaneously deal with various bi-source information. In extensive experiments across several representative vision tasks, i.e. remote sensing change detection, RGB-T crowd counting, RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed method consistently obtains favorable performance. The code will be available at \\url{https://github.com/lartpang/ComPtr}.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 397,
+    "label": 10,
+    "text": "Title: Ontologies in Digital Twins: A Systematic Literature Review\nAbstract: Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 398,
+    "label": 24,
+    "text": "Title: Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey\nAbstract: In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacypreserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We rst review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a uni ed and comprehensive secure graph machine learning system.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 399,
+    "label": 4,
+    "text": "Title: Resource-aware Cyber Deception in Cloud-Native Environments\nAbstract: Cyber deception can be a valuable addition to traditional cyber defense mechanisms, especially for modern cloud-native environments with a fading security perimeter. However, pre-built decoys used in classical computer networks are not effective in detecting and mitigating malicious actors due to their inability to blend with the variety of applications in such environments. On the other hand, decoys cloning the deployed microservices of an application can offer a high-fidelity deception mechanism to intercept ongoing attacks within production environments. However, to fully benefit from this approach, it is essential to use a limited amount of decoy resources and devise a suitable cloning strategy to minimize the impact on legitimate services performance. Following this observation, we formulate a non-linear integer optimization problem that maximizes the number of attack paths intercepted by the allocated decoys within a fixed resource budget. Attack paths represent the attacker's movements within the infrastructure as a sequence of violated microservices. We also design a heuristic decoy placement algorithm to approximate the optimal solution and overcome the computational complexity of the proposed formulation. We evaluate the performance of the optimal and heuristic solutions against other schemes that use local vulnerability metrics to select which microservices to clone as decoys. Our results show that the proposed allocation strategy achieves a higher number of intercepted attack paths compared to these schemes while requiring approximately the same number of decoys.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 400,
+    "label": 24,
+    "text": "Title: Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning\nAbstract: In large-scale machine learning, recent works have studied the effects of compressing gradients in stochastic optimization in order to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in large-scale, multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? In this paper, we investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our main technical contribution is to show that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. We then extend our results significantly to nonlinear stochastic approximation algorithms and multi-agent settings. In particular, we prove that for multi-agent TD learning, one can achieve linear convergence speedups in the number of agents while communicating just $\\tilde{O}(1)$ bits per agent at each time step. Our work is the first to provide finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our analysis hinges on studying the drift of a novel Lyapunov function that captures the dynamics of a memory variable introduced by error feedback.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 401,
+    "label": 30,
+    "text": "Title: Uncertainty in Natural Language Generation: From Theory to Applications\nAbstract: Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like summarisation or translation, but also serve as a natural language interface to a variety of applications. As such, it is crucial that NLG systems are trustworthy and reliable, for example by indicating when they are likely to be wrong; and supporting multiple views, backgrounds and writing styles -- reflecting diverse human sub-populations. In this paper, we argue that a principled treatment of uncertainty can assist in creating systems and evaluation protocols better aligned with these goals. We first present the fundamental theory, frameworks and vocabulary required to represent uncertainty. We then characterise the main sources of uncertainty in NLG from a linguistic perspective, and propose a two-dimensional taxonomy that is more informative and faithful than the popular aleatoric/epistemic dichotomy. Finally, we move from theory to applications and highlight exciting research directions that exploit uncertainty to power decoding, controllable generation, self-assessment, selective answering, active learning and more.",
+    "neighbors": [
+      57,
+      352,
+      1044,
+      1052,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 402,
+    "label": 10,
+    "text": "Title: Integrated Conflict Management for UAM with Strategic Demand Capacity Balancing and Learning-based Tactical Deconfliction\nAbstract: Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations within and around the urban environment. Before the commercialization and adoption of this emerging transportation mode, however, aviation safety must be guaranteed, i.e., all the aircraft have to be safely separated by strategic and tactical deconfliction. Reinforcement learning has demonstrated effectiveness in the tactical deconfliction of en route commercial air traffic in simulation. However, its performance is found to be dependent on the traffic density. In this project, we propose a novel framework that combines demand capacity balancing (DCB) for strategic conflict management and reinforcement learning for tactical separation. By using DCB to precondition traffic to proper density levels, we show that reinforcement learning can achieve much better performance for tactical safety separation. Our results also indicate that this DCB preconditioning can allow target levels of safety to be met that are otherwise impossible. In addition, combining strategic DCB with reinforcement learning for tactical separation can meet these safety levels while achieving greater operational efficiency than alternative solutions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 403,
+    "label": 23,
+    "text": "Title: Many-Objective Optimization of Non-Functional Attributes based on Refactoring of Software Models\nAbstract: Software quality estimation is a challenging and time-consuming activity, and models are crucial to face the complexity of such activity on modern software applications. In this context, software refactoring is a crucial activity within development life-cycles where requirements and functionalities rapidly evolve. One main challenge is that the improvement of distinctive quality attributes may require contrasting refactoring actions on software, as for trade-off between performance and reliability (or other non-functional attributes). In such cases, multi-objective optimization can provide the designer with a wider view on these trade-offs and, consequently, can lead to identify suitable refactoring actions that take into account independent or even competing objectives. In this paper, we present an approach that exploits NSGA-II as the genetic algorithm to search optimal Pareto frontiers for software refactoring while considering many objectives. We consider performance and reliability variations of a model alternative with respect to an initial model, the amount of performance antipatterns detected on the model alternative, and the architectural distance, which quantifies the effort to obtain a model alternative from the initial one. We applied our approach on two case studies: a Train Ticket Booking Service, and CoCoME. We observed that our approach is able to improve performance (by up to 42\\%) while preserving or even improving the reliability (by up to 32\\%) of generated model alternatives. We also observed that there exists an order of preference of refactoring actions among model alternatives. We can state that performance antipatterns confirmed their ability to improve performance of a subject model in the context of many-objective optimization. In addition, the metric that we adopted for the architectural distance seems to be suitable for estimating the refactoring effort.",
+    "neighbors": [
+      305,
+      1328,
+      1875
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 404,
+    "label": 36,
+    "text": "Title: Stability of Multi-Agent Learning: Convergence in Network Games with Many Players\nAbstract: The behaviour of multi-agent learning in many player games has been shown to display complex dynamics outside of restrictive examples such as network zero-sum games. In addition, it has been shown that convergent behaviour is less likely to occur as the number of players increase. To make progress in resolving this problem, we study Q-Learning dynamics and determine a sufficient condition for the dynamics to converge to a unique equilibrium in any network game. We find that this condition depends on the nature of pairwise interactions and on the network structure, but is explicitly independent of the total number of agents in the game. We evaluate this result on a number of representative network games and show that, under suitable network conditions, stable learning dynamics can be achieved with an arbitrary number of agents.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 405,
+    "label": 2,
+    "text": "Title: A Complete Inference System for Skip-free Guarded Kleene Algebra with Tests\nAbstract: Guarded Kleene Algebra with Tests (GKAT) is a fragment of Kleene Algebra with Tests (KAT) that was recently introduced to reason efficiently about imperative programs. In contrast to KAT, GKAT does not have an algebraic axiomatization, but relies on an analogue of Salomaa's axiomatization of Kleene Algebra. In this paper, we present an algebraic axiomatization and prove two completeness results for a large fragment of GKAT consisting of skip-free programs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 406,
+    "label": 26,
+    "text": "Title: Random Walk on Multiple Networks\nAbstract: Random Walk is a basic algorithm to explore the structure of networks, which can be used in many tasks, such as local community detection and network embedding. Existing random walk methods are based on single networks that contain limited information. In contrast, real data often contain entities with different types or/and from different sources, which are comprehensive and can be better modeled by multiple networks. To take the advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq1-3221668.gif\"/></alternatives></inline-formula>. <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq2-3221668.gif\"/></alternatives></inline-formula> is flexible and supports both multiplex networks and general multiple networks, which may form many-to-many node mappings between networks. <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq3-3221668.gif\"/></alternatives></inline-formula> sends a random walker on each network to obtain the local proximity (i.e., node visiting probabilities) w.r.t. the starting nodes. Walkers with similar visiting probabilities reinforce each other. We theoretically analyze the convergence properties of <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq4-3221668.gif\"/></alternatives></inline-formula>. Two approximation methods with theoretical performance guarantees are proposed for efficient computation. We apply <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq5-3221668.gif\"/></alternatives></inline-formula> in link prediction, network embedding, and local community detection. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of <inline-formula><tex-math notation=\"LaTeX\">$\\mathsf {RWM}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"sans-serif\">RWM</mml:mi></mml:math><inline-graphic xlink:href=\"luo-ieq6-3221668.gif\"/></alternatives></inline-formula>.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 407,
+    "label": 2,
+    "text": "Title: Bottom-Up Stratified Probabilistic Logic Programming with Fusemate\nAbstract: This paper introduces the Fusemate probabilistic logic programming system. Fusemate's inference engine comprises a grounding component and a variable elimination method for probabilistic inference. Fusemate differs from most other systems by grounding the program in a bottom-up way instead of the common top-down way. While bottom-up grounding is attractive for a number of reasons, e.g., for dynamically creating distributions of varying support sizes, it makes it harder to control the amount of ground clauses generated. We address this problem by interleaving grounding with a query-guided relevance test which prunes rules whose bodies are inconsistent with the query. % This is done We present our method in detail and demonstrate it with examples that involve\"time\", such as (hidden) Markov models. Our experiments demonstrate competitive or better performance compared to a state-of-the art probabilistic logic programming system, in particular for high branching problems.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 408,
+    "label": 6,
+    "text": "Title: DeepLens: Interactive Out-of-distribution Data Detection in NLP Models\nAbstract: Machine Learning (ML) has been widely used in Natural Language Processing (NLP) applications. A fundamental assumption in ML is that training data and real-world data should follow a similar distribution. However, a deployed ML model may suffer from out-of-distribution (OOD) issues due to distribution shifts in the real-world data. Though many algorithms have been proposed to detect OOD data from text corpora, there is still a lack of interactive tool support for ML developers. In this work, we propose DeepLens, an interactive system that helps users detect and explore OOD issues in massive text corpora. Users can efficiently explore different OOD types in DeepLens with the help of a text clustering method. Users can also dig into a specific text by inspecting salient words highlighted through neuron activation analysis. In a within-subjects user study with 24 participants, participants using DeepLens were able to find nearly twice more types of OOD issues accurately with 22% more confidence compared with a variant of DeepLens that has no interaction or visualization support.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 409,
+    "label": 6,
+    "text": "Title: Design and Assessment of a Bimanual Haptic Epidural Needle Insertion Simulator.\nAbstract: The case experience of anesthesiologists is one of the leading causes of accidental dural punctures and failed epidurals - the most common complications of epidural analgesia used for pain relief during delivery. We designed a bimanual haptic simulator to train anesthesiologists and optimize epidural analgesia skill acquisition. We present an assessment study conducted with 22 anesthesiologists of different competency levels from several Israeli hospitals. Our simulator emulates the forces applied to the epidural (Touhy) needle, held by one hand, and those applied to the Loss of Resistance (LOR) syringe, held by the other one. The resistance is calculated based on a model of the epidural region layers parameterized by the weight of the patient. We measured the movements of both haptic devices and quantified the results' rate (success, failed epidurals, and dural punctures), insertion strategies, and the participants' answers to questionnaires about their perception of the simulation realism. We demonstrated good construct validity by showing that the simulator can distinguish between real-life novices and experts. Face and content validity were examined by studying users' impressions regarding the simulator's realism and fulfillment of purpose. We found differences in strategies between different level anesthesiologists, and suggest trainee-based instruction in advanced training stages.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 410,
+    "label": 10,
+    "text": "Title: Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs\nAbstract: Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 411,
+    "label": 26,
+    "text": "Title: Social Media Analytics in Disaster Response: A Comprehensive Review\nAbstract: Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 412,
+    "label": 30,
+    "text": "Title: Syntactically Robust Training on Partially-Observed Data for Open Information Extraction\nAbstract: Open Information Extraction models have shown promising results with sufficient supervision. However, these models face a fundamental challenge that the syntactic distribution of training data is partially observable in comparison to the real world. In this paper, we propose a syntactically robust training framework that enables models to be trained on a syntactic-abundant distribution based on diverse paraphrase generation. To tackle the intrinsic problem of knowledge deformation of paraphrasing, two algorithms based on semantic similarity matching and syntactic tree walking are used to restore the expressionally transformed knowledge. The training framework can be generally applied to other syntactic partial observable domains. Based on the proposed framework, we build a new evaluation set called CaRB-AutoPara, a syntactically diverse dataset consistent with the real-world setting for validating the robustness of the models. Experiments including a thorough analysis show that the performance of the model degrades with the increase of the difference in syntactic distribution, while our framework gives a robust boundary. The source code is publicly available at https://github.com/qijimrc/RobustOIE.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 413,
+    "label": 24,
+    "text": "Title: Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization\nAbstract: Using noisy crowdsourced labels from multiple annotators, a deep learning-based end-to-end (E2E) system aims to learn the label correction mechanism and the neural classifier simultaneously. To this end, many E2E systems concatenate the neural classifier with multiple annotator-specific ``label confusion'' layers and co-train the two parts in a parameter-coupled manner. The formulated coupled cross-entropy minimization (CCEM)-type criteria are intuitive and work well in practice. Nonetheless, theoretical understanding of the CCEM criterion has been limited. The contribution of this work is twofold: First, performance guarantees of the CCEM criterion are presented. Our analysis reveals for the first time that the CCEM can indeed correctly identify the annotators' confusion characteristics and the desired ``ground-truth'' neural classifier under realistic conditions, e.g., when only incomplete annotator labeling and finite samples are available. Second, based on the insights learned from our analysis, two regularized variants of the CCEM are proposed. The regularization terms provably enhance the identifiability of the target model parameters in various more challenging cases. A series of synthetic and real data experiments are presented to showcase the effectiveness of our approach.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 414,
+    "label": 25,
+    "text": "Title: AudioLDM: Text-to-Audio Generation with Latent Diffusion Models\nAbstract: Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the continuous audio representations from contrastive language-audio pretraining (CLAP) latents. The pretrained CLAP models enable us to train LDMs with audio embedding while providing text embedding as a condition during sampling. By learning the latent representations of audio signals and their compositions without modeling the cross-modal relationship, AudioLDM is advantageous in both generation quality and computational efficiency. Trained on AudioCaps with a single GPU, AudioLDM achieves state-of-the-art TTA performance measured by both objective and subjective metrics (e.g., frechet distance). Moreover, AudioLDM is the first TTA system that enables various text-guided audio manipulations (e.g., style transfer) in a zero-shot fashion. Our implementation and demos are available at https://audioldm.github.io.",
+    "neighbors": [
+      736,
+      1156,
+      1307,
+      1958,
+      2103
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 415,
+    "label": 10,
+    "text": "Title: Towards a Better Understanding of Learning with Multiagent Teams\nAbstract: While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily more effective than smaller ones. In this paper, we study why and under which conditions certain team structures promote effective learning for a population of individual learning agents. We show that, depending on the environment, some team structures help agents learn to specialize into specific roles, resulting in more favorable global results. However, large teams create credit assignment challenges that reduce coordination, leading to large teams performing poorly compared to smaller ones. We support our conclusions with both theoretical analysis and empirical results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 416,
+    "label": 10,
+    "text": "Title: LogicRec: Recommendation with Users' Logical Requirements\nAbstract: Users may demand recommendations with highly personalized requirements involving logical operations, e.g., the intersection of two requirements, where such requirements naturally form structured logical queries on knowledge graphs (KGs). To date, existing recommender systems lack the capability to tackle users' complex logical requirements. In this work, we formulate the problem of recommendation with users' logical requirements (LogicRec) and construct benchmark datasets for LogicRec. Furthermore, we propose an initial solution for LogicRec based on logical requirement retrieval and user preference retrieval, where we face two challenges. First, KGs are incomplete in nature. Therefore, there are always missing true facts, which entails that the answers to logical requirements can not be completely found in KGs. In this case, item selection based on the answers to logical queries is not applicable. We thus resort to logical query embedding (LQE) to jointly infer missing facts and retrieve items based on logical requirements. Second, answer sets are under-exploited. Existing LQE methods can only deal with query-answer pairs, where queries in our case are the intersected user preferences and logical requirements. However, the logical requirements and user preferences have different answer sets, offering us richer knowledge about the requirements and preferences by providing requirement-item and preference-item pairs. Thus, we design a multi-task knowledge-sharing mechanism to exploit these answer sets collectively. Extensive experimental results demonstrate the significance of the LogicRec task and the effectiveness of our proposed method.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 417,
+    "label": 30,
+    "text": "Title: Probing Taxonomic and Thematic Embeddings for Taxonomic Information\nAbstract: Modelling taxonomic and thematic relatedness is important for building AI with comprehen-sive natural language understanding. The goal of this paper is to learn more about how taxonomic information is structurally encoded in embeddings. To do this, we design a new hypernym-hyponym probing task and perform a comparative probing study of taxonomic and thematic SGNS and GloVe embeddings. Our experiments indicate that both types of embeddings encode some taxonomic information, but the amount, as well as the geometric properties of the encodings, are independently related to both the encoder architecture, as well as the embedding training data. Speci\ufb01cally, we \ufb01nd that only taxonomic embeddings carry taxonomic information in their norm, which is determined by the underlying distribution in the data.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 418,
+    "label": 24,
+    "text": "Title: STORM-GAN: Spatio-Temporal Meta-GAN for Cross-City Estimation of Human Mobility Responses to COVID-19\nAbstract: Human mobility estimation is crucial during the COVID-19 pandemic due to its significant guidance for policymakers to make non-pharmaceutical interventions. While deep learning approaches outperform conventional estimation techniques on tasks with abundant training data, the continuously evolving pandemic poses a significant challenge to solving this problem due to data non-stationarity, limited observations, and complex social contexts. Prior works on mobility estimation either focus on a single city or lack the ability to model the spatio-temporal dependencies across cities and time periods. To address these issues, we make the first attempt to tackle the cross-city human mobility estimation problem through a deep meta-generative framework. We propose a Spatio-Temporal Meta-Generative Adversarial Network (STORM-GAN) model that estimates dynamic human mobility responses under a set of social and policy conditions related to COVID-19. Facilitated by a novel spatio-temporal task-based graph (STTG) embedding, STORM-GAN is capable of learning shared knowledge from a spatio-temporal distribution of estimation tasks and quickly adapting to new cities and time periods with limited training samples. The STTG embedding component is designed to capture the similarities among cities to mitigate cross-task heterogeneity. Experimental results on real-world data show that the proposed approach can greatly improve estimation performance and outperform baselines.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 419,
+    "label": 16,
+    "text": "Title: Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation\nAbstract: Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations.",
+    "neighbors": [
+      1314
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 420,
+    "label": 10,
+    "text": "Title: Alien Coding\nAbstract: We introduce a self-learning algorithm for synthesizing programs for OEIS sequences. The algorithm starts from scratch initially generating programs at random. Then it runs many iterations of a self-learning loop that interleaves (i) training neural machine translation to learn the correspondence between sequences and the programs discovered so far, and (ii) proposing many new programs for each OEIS sequence by the trained neural machine translator. The algorithm discovers on its own programs for more than 78000 OEIS sequences, sometimes developing unusual programming methods. We analyze its behavior and the invented programs in several experiments.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 421,
+    "label": 16,
+    "text": "Title: Combating Online Misinformation Videos: Characterization, Detection, and Future Directions\nAbstract: With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem. Though previous studies have made much progress in detecting misinformation in text and image formats, video-based misinformation brings new and unique challenges to automatic detection systems: 1) high information heterogeneity brought by various modalities, 2) blurred distinction between misleading video manipulation and nonmalicious artistic video editing, and 3) new patterns of misinformation propagation due to the dominant role of recommendation systems on online video platforms. To facilitate research on this challenging task, we conduct this survey to present advances in misinformation video detection. We first analyze and characterize the misinformation video from three levels including signals, semantics, and intents. Based on the characterization, we systematically review existing works for detection from features of various modalities to techniques for clue integration. We also introduce existing resources including representative datasets and useful tools. Besides summarizing existing studies, we discuss related areas and outline open issues and future directions to encourage and guide more research on misinformation video detection. The corresponding repository is at https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection.",
+    "neighbors": [
+      2136
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 422,
+    "label": 16,
+    "text": "Title: Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation\nAbstract: In this paper, a novel Diffusion-based 3D Pose estimation (D3DP) method with Joint-wise reProjection-based Multi-hypothesis Aggregation (JPMA) is proposed for probabilistic 3D human pose estimation. On the one hand, D3DP generates multiple possible 3D pose hypotheses for a single 2D observation. It gradually diffuses the ground truth 3D poses to a random distribution, and learns a denoiser conditioned on 2D keypoints to recover the uncontaminated 3D poses. The proposed D3DP is compatible with existing 3D pose estimators and supports users to balance efficiency and accuracy during inference through two customizable parameters. On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use. It reprojects 3D pose hypotheses to the 2D camera plane, selects the best hypothesis joint-by-joint based on the reprojection errors, and combines the selected joints into the final pose. The proposed JPMA conducts aggregation at the joint level and makes use of the 2D prior information, both of which have been overlooked by previous approaches. Extensive experiments on Human3.6M and MPI-INF-3DHP datasets show that our method outperforms the state-of-the-art deterministic and probabilistic approaches by 1.5% and 8.9%, respectively. Code is available at https://github.com/paTRICK-swk/D3DP.",
+    "neighbors": [
+      158
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 423,
+    "label": 24,
+    "text": "Title: Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning\nAbstract: Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (ADMGs), which describe ancestral relations between variables using directed and bidirected edges. However, existing methods using ADMGs are based on either linear functional assumptions or a discrete search that is complicated to use and lacks computational tractability for large datasets. In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data. We first show that the presence of latent confounding is identifiable under the assumptions of bow-free ADMGs with non-linear additive noise models. With this insight, we propose a novel neural causal model based on autoregressive flows for ADMG learning. This not only enables us to determine complex causal structural relationships behind the data in the presence of latent confounding, but also estimate their functional relationships (hence treatment effects) simultaneously. We further validate our approach via experiments on both synthetic and real-world datasets, and demonstrate the competitive performance against relevant baselines.",
+    "neighbors": [
+      1189
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 424,
+    "label": 31,
+    "text": "Title: Recommender Systems in the Era of Large Language Models (LLMs)\nAbstract: With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component of our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating textual side information, DNN-based methods still face limitations, such as difficulties in understanding users' interests and capturing textual side information, inabilities in generalizing to various recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities. As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, to provide researchers in relevant fields with an in-depth understanding. Therefore, in this paper, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting. More specifically, we first introduce representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we review recent techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss future directions in this emerging field.",
+    "neighbors": [
+      36,
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+      1182,
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+      1667,
+      1762,
+      1863,
+      2013,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 425,
+    "label": 24,
+    "text": "Title: Federated Learning for Water Consumption Forecasting in Smart Cities\nAbstract: \u2014Water consumption remains a major concern among the world\u2019s future challenges. For applications like load moni-toring and demand response, deep learning models are trained using enormous volumes of consumption data in smart cities. On the one hand, the information used is private. For instance, the precise information gathered by a smart meter that is a part of the system\u2019s IoT architecture at a consumer\u2019s residence may give details about the appliances and, consequently, the consumer\u2019s behavior at home. On the other hand, enormous data volumes with suf\ufb01cient variation are needed for the deep learning models to be trained properly. This paper introduces a novel model for water consumption prediction in smart cities while preserving privacy regarding monthly consumption. The pro- posed approach leverages federated learning (FL) as a machine learning paradigm designed to train a machine learning model in a distributed manner while avoiding sharing the users data with a central training facility. In addition, this approach is promising to reduce the overhead utilization through decreasing the frequency of data transmission between the users and the central entity. Extensive simulation illustrate that the proposed approach shows an enhancement in predicting water consumption for different households.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 426,
+    "label": 5,
+    "text": "Title: Managing data replication and distribution in the fog with FReD\nAbstract: The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re\u2010implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low\u2010latency, high\u2010bandwidth, and privacy\u2010sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 427,
+    "label": 16,
+    "text": "Title: DINOv2: Learning Robust Visual Features without Supervision\nAbstract: The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim at accelerating and stabilizing the training at scale. In terms of data, we propose an automatic pipeline to build a dedicated, diverse, and curated image dataset instead of uncurated data, as typically done in the self-supervised literature. In terms of models, we train a ViT model (Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of smaller models that surpass the best available all-purpose features, OpenCLIP (Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.",
+    "neighbors": [
+      1052,
+      1211,
+      1926,
+      2031,
+      2093,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 428,
+    "label": 16,
+    "text": "Title: DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization\nAbstract: Stable Diffusion (SD) customization approaches enable users to personalize SD model outputs, greatly enhancing the flexibility and diversity of AI art. However, they also allow individuals to plagiarize specific styles or subjects from copyrighted images, which raises significant concerns about potential copyright infringement. To address this issue, we propose an invisible data-free universal adversarial watermark (DUAW), aiming to protect a myriad of copyrighted images from different customization approaches across various versions of SD models. First, DUAW is designed to disrupt the variational autoencoder during SD customization. Second, DUAW operates in a data-free context, where it is trained on synthetic images produced by a Large Language Model (LLM) and a pretrained SD model. This approach circumvents the necessity of directly handling copyrighted images, thereby preserving their confidentiality. Once crafted, DUAW can be imperceptibly integrated into massive copyrighted images, serving as a protective measure by inducing significant distortions in the images generated by customized SD models. Experimental results demonstrate that DUAW can effectively distort the outputs of fine-tuned SD models, rendering them discernible to both human observers and a simple classifier.",
+    "neighbors": [
+      887,
+      1237,
+      1730,
+      1731,
+      2154
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 429,
+    "label": 4,
+    "text": "Title: Game Theory in Distributed Systems Security: Foundations, Challenges, and Future Directions\nAbstract: Many of our critical infrastructure systems and personal computing systems have a distributed computing systems structure. The incentives to attack them have been growing rapidly as has their attack surface due to increasing levels of connectedness. Therefore, we feel it is time to bring in rigorous reasoning to secure such systems. The distributed system security and the game theory technical communities can come together to effectively address this challenge. In this article, we lay out the foundations from each that we can build upon to achieve our goals. Next, we describe a set of research challenges for the community, organized into three categories -- analytical, systems, and integration challenges, each with\"short term\"time horizon (2-3 years) and\"long term\"(5-10 years) items. This article was conceived of through a community discussion at the 2022 NSF SaTC PI meeting.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 430,
+    "label": 30,
+    "text": "Title: Pretraining Language Models with Human Preferences\nAbstract: Language models (LMs) are pretrained to imitate internet text, including content that would violate human preferences if generated by an LM: falsehoods, offensive comments, personally identifiable information, low-quality or buggy code, and more. Here, we explore alternative objectives for pretraining LMs in a way that also guides them to generate text aligned with human preferences. We benchmark five objectives for pretraining with human feedback across three tasks and study how they affect the trade-off between alignment and capabilities of pretrained LMs. We find a Pareto-optimal and simple approach among those we explored: conditional training, or learning distribution over tokens conditional on their human preference scores given by a reward model. Conditional training reduces the rate of undesirable content by up to an order of magnitude, both when generating without a prompt and with an adversarially-chosen prompt. Moreover, conditional training maintains the downstream task performance of standard LM pretraining, both before and after task-specific finetuning. Pretraining with human feedback results in much better preference satisfaction than standard LM pretraining followed by finetuning with feedback, i.e., learning and then unlearning undesirable behavior. Our results suggest that we should move beyond imitation learning when pretraining LMs and incorporate human preferences from the start of training.",
+    "neighbors": [
+      126,
+      899,
+      1114,
+      1237,
+      1490,
+      1647,
+      1735,
+      2016,
+      2235,
+      2257,
+      2258,
+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 431,
+    "label": 16,
+    "text": "Title: Incomplete Multimodal Learning for Remote Sensing Data Fusion\nAbstract: The mechanism of connecting multimodal signals through self-attention operation is a key factor in the success of multimodal Transformer networks in remote sensing data fusion tasks. However, traditional approaches assume access to all modalities during both training and inference, which can lead to severe degradation when dealing with modal-incomplete inputs in downstream applications. To address this limitation, our proposed approach introduces a novel model for incomplete multimodal learning in the context of remote sensing data fusion. This approach can be used in both supervised and self-supervised pretraining paradigms and leverages the additional learned fusion tokens in combination with Bi-LSTM attention and masked self-attention mechanisms to collect multimodal signals. The proposed approach employs reconstruction and contrastive loss to facilitate fusion in pre-training while allowing for random modality combinations as inputs in network training. Our approach delivers state-of-the-art performance on two multimodal datasets for tasks such as building instance / semantic segmentation and land-cover mapping tasks when dealing with incomplete inputs during inference.",
+    "neighbors": [
+      2228
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 432,
+    "label": 23,
+    "text": "Title: Predicting Defective Visual Code Changes in a Multi-Language AAA Video Game Project\nAbstract: Video game development increasingly relies on using visual programming languages as the primary way to build video game features. The aim of using visual programming is to move game logic into the hands of game designers, who may not be as well versed in textual coding. In this paper, we empirically observe that there are more defect-inducing commits containing visual code than textual code in a AAA video game project codebase. This indicates that the existing textual code Just-in-Time (JIT) defect prediction models under evaluation by Electronic Arts (EA) may be ineffective as they do not account for changes in visual code. Thus, we focus our research on constructing visual code defect prediction models that encompass visual code metrics and evaluate the models against defect prediction models that use language agnostic features, and textual code metrics. We test our models using features extracted from the historical codebase of a AAA video game project, as well as the historical codebases of 70 open source projects that use textual and visual code. We find that defect prediction models have better performance overall in terms of the area under the ROC curve (AUC), and Mathews Correlation Coefficient (MCC) when incorporating visual code features for projects that contain more commits with visual code than textual code.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 433,
+    "label": 34,
+    "text": "Title: The Complexity of Distributed Approximation of Packing and Covering Integer Linear Programs\nAbstract: In this paper, we present a low-diameter decomposition algorithm in the LOCAL model of distributed computing that succeeds with probability 1 \u2212 1/poly(n). Specifically, we show how to compute an (\u03f5, O((log n) / \u03f5)) low-diameter decomposition in O((log3(1/\u03f5) log n) / \u03f5) rounds. Further developing our techniques, we show new distributed algorithms for approximating general packing and covering integer linear programs in the LOCAL model. For packing problems, our algorithm finds an (1 \u2212 \u03f5)-approximate solution in O((log3(1/\u03f5) log n) / \u03f5) rounds with probability 1 \u2212 1/poly (n). For covering problems, our algorithm finds an (1 + \u03f5)-approximate solution in O(((log log n+log(1/\u03f5))3 log n) / \u03f5) rounds with probability 1 \u2212 1/poly(n). These results improve upon the previous O((log3 n) / \u03f5)-round algorithm by Ghaffari, Kuhn, and Maus [STOC 2017] which is based on network decompositions. Our algorithms are near-optimal for many fundamental combinatorial graph optimization problems in the LOCAL model, such as minimum vertex cover and minimum dominating set, as their (1 \u00b1 \u03f5)-approximate solutions require \u03a9 ((log n) / \u03f5) rounds to compute.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 434,
+    "label": 4,
+    "text": "Title: A Survey on Cross-Architectural IoT Malware Threat Hunting\nAbstract: In recent years, the increase in non-Windows malware threats had turned the focus of the cybersecurity community. Research works on hunting Windows PE-based malwares are maturing, whereas the developments on Linux malware threat hunting are relatively scarce. With the advent of the Internet of Things (IoT) era, smart devices that are getting integrated into human life have become a hackers\u2019 highway for their malicious activities. The IoT devices employ various Unix-based architectures that follow ELF (Executable and Linkable Format) as their standard binary file specification. This study aims at providing a comprehensive survey on the latest developments in cross-architectural IoT malware detection and classification approaches. Aided by a modern taxonomy, we discuss the feature representations, feature extraction techniques, and machine learning models employed in the surveyed works. We further provide more insights on the practical challenges involved in cross-architectural IoT malware threat hunting and discuss various avenues to instill potential future research.",
+    "neighbors": [
+      1127
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 435,
+    "label": 24,
+    "text": "Title: A new Gradient TD Algorithm with only One Step-size: Convergence Rate Analysis using L-\u03bb Smoothness\nAbstract: Gradient Temporal Difference (GTD) algorithms (Sutton et al., 2008, 2009) are the first $O(d)$ ($d$ is the number features) algorithms that have convergence guarantees for off-policy learning with linear function approximation. Liu et al. (2015) and Dalal et. al. (2018) proved the convergence rates of GTD, GTD2 and TDC are $O(t^{-\\alpha/2})$ for some $\\alpha \\in (0,1)$. This bound is tight (Dalal et al., 2020), and slower than $O(1/\\sqrt{t})$. GTD algorithms also have two step-size parameters, which are difficult to tune. In literature, there is a\"single-time-scale\"formulation of GTD. However, this formulation still has two step-size parameters. This paper presents a truly single-time-scale GTD algorithm for minimizing the Norm of Expected td Update (NEU) objective, and it has only one step-size parameter. We prove that the new algorithm, called Impression GTD, converges at least as fast as $O(1/t)$. Furthermore, based on a generalization of the expected smoothness (Gower et al. 2019), called $L$-$\\lambda$ smoothness, we are able to prove that the new GTD converges even faster, in fact, with a linear rate. Our rate actually also improves Gower et al.'s result with a tighter bound under a weaker assumption. Besides Impression GTD, we also prove the rates of three other GTD algorithms, one by Yao and Liu (2008), another called A-transpose-TD (Sutton et al., 2008), and a counterpart of A-transpose-TD. The convergence rates of all the four GTD algorithms are proved in a single generic GTD framework to which $L$-$\\lambda$ smoothness applies. Empirical results on Random walks, Boyan chain, and Baird counterexample show that Impression GTD converges much faster than existing GTD algorithms for both on-policy and off-policy learning problems, with well-performing step-sizes in a big range.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 436,
+    "label": 16,
+    "text": "Title: ReVersion: Diffusion-Based Relation Inversion from Images\nAbstract: Diffusion models gain increasing popularity for their generative capabilities. Recently, there have been surging needs to generate customized images by inverting diffusion models from exemplar images. However, existing inversion methods mainly focus on capturing object appearances. How to invert object relations, another important pillar in the visual world, remains unexplored. In this work, we propose ReVersion for the Relation Inversion task, which aims to learn a specific relation (represented as\"relation prompt\") from exemplar images. Specifically, we learn a relation prompt from a frozen pre-trained text-to-image diffusion model. The learned relation prompt can then be applied to generate relation-specific images with new objects, backgrounds, and styles. Our key insight is the\"preposition prior\"- real-world relation prompts can be sparsely activated upon a set of basis prepositional words. Specifically, we propose a novel relation-steering contrastive learning scheme to impose two critical properties of the relation prompt: 1) The relation prompt should capture the interaction between objects, enforced by the preposition prior. 2) The relation prompt should be disentangled away from object appearances. We further devise relation-focal importance sampling to emphasize high-level interactions over low-level appearances (e.g., texture, color). To comprehensively evaluate this new task, we contribute ReVersion Benchmark, which provides various exemplar images with diverse relations. Extensive experiments validate the superiority of our approach over existing methods across a wide range of visual relations.",
+    "neighbors": [
+      260,
+      1582
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 437,
+    "label": 27,
+    "text": "Title: Towards a Safe Real-Time Motion Planning Framework for Autonomous Driving Systems: An MPPI Approach\nAbstract: Planning safe trajectories in Autonomous Driving Systems (ADS) is a complex problem to solve in real-time. The main challenge to solve this problem arises from the various conditions and constraints imposed by road geometry, semantics and traffic rules, as well as the presence of dynamic agents. Recently, Model Predictive Path Integral (MPPI) has shown to be an effective framework for optimal motion planning and control in robot navigation in unstructured and highly uncertain environments. In this paper, we formulate the motion planning problem in ADS as a nonlinear stochastic dynamic optimization problem that can be solved using an MPPI strategy. The main technical contribution of this work is a method to handle obstacles within the MPPI formulation safely. In this method, obstacles are approximated by circles that can be easily integrated into the MPPI cost formulation while considering safety margins. The proposed MPPI framework has been efficiently implemented in our autonomous vehicle and experimentally validated using three different primitive scenarios. Experimental results show that generated trajectories are safe, feasible and perfectly achieve the planning objective. The video results as well as the open-source implementation are available at: https://gitlab.uni.lu/360lab-public/mppi",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 438,
+    "label": 16,
+    "text": "Title: VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection\nAbstract: The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful visual representations with rich semantics. An open and worthwhile problem is efficiently adapting such a strong model to the video domain and designing a robust video anomaly detector. In this work, we propose VadCLIP, a new paradigm for weakly supervised video anomaly detection (WSVAD) by leveraging the frozen CLIP model directly without any pre-training and fine-tuning process. Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes full use of fine-grained associations between vision and language on the strength of CLIP and involves dual branch. One branch simply utilizes visual features for coarse-grained binary classification, while the other fully leverages the fine-grained language-image alignment. With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task. We conduct extensive experiments on two commonly-used benchmarks, demonstrating that VadCLIP achieves the best performance on both coarse-grained and fine-grained WSVAD, surpassing the state-of-the-art methods by a large margin. Specifically, VadCLIP achieves 84.51% AP and 88.02% AUC on XD-Violence and UCF-Crime, respectively. Code and features will be released to facilitate future VAD research.",
+    "neighbors": [
+      2115
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 439,
+    "label": 24,
+    "text": "Title: Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints\nAbstract: This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal-dual contextual Bayesian optimization algorithm is proposed that achieves sublinear cumulative regret with respect to the dynamic optimal solution under certain regularity conditions. Furthermore, the algorithm achieves zero time-average constraint violation, ensuring that the average value of the constraint function satisfies the desired constraint. The method is applied to both sampled instances from Gaussian processes and a continuous stirred tank reactor parameter tuning problem; simulation results show that the method simultaneously provides close-to-optimal performance and maintains constraint feasibility on average. This contrasts current state-of-the-art methods, which either suffer from large cumulative regret or severe constraint violations for the case studies presented.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 440,
+    "label": 24,
+    "text": "Title: Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes\nAbstract: Predicting the evolution of systems that exhibit spatio-temporal dynamics in response to external stimuli is a key enabling technology fostering scientific innovation. Traditional equations-based approaches leverage first principles to yield predictions through the numerical approximation of high-dimensional systems of differential equations, thus calling for large-scale parallel computing platforms and requiring large computational costs. Data-driven approaches, instead, enable the description of systems evolution in low-dimensional latent spaces, by leveraging dimensionality reduction and deep learning algorithms. We propose a novel architecture, named Latent Dynamics Network (LDNet), which is able to discover low-dimensional intrinsic dynamics of possibly non-Markovian dynamical systems, thus predicting the time evolution of space-dependent fields in response to external inputs. Unlike popular approaches, in which the latent representation of the solution manifold is learned by means of auto-encoders that map a high-dimensional discretization of the system state into itself, LDNets automatically discover a low-dimensional manifold while learning the latent dynamics, without ever operating in the high-dimensional space. Furthermore, LDNets are meshless algorithms that do not reconstruct the output on a predetermined grid of points, but rather at any point of the domain, thus enabling weight-sharing across query-points. These features make LDNets lightweight and easy-to-train, with excellent accuracy and generalization properties, even in time-extrapolation regimes. We validate our method on several test cases and we show that, for a challenging highly-nonlinear problem, LDNets outperform state-of-the-art methods in terms of accuracy (normalized error 5 times smaller), by employing a dramatically smaller number of trainable parameters (more than 10 times fewer).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 441,
+    "label": 25,
+    "text": "Title: The HCCL system for VoxCeleb Speaker Recognition Challenge 2022\nAbstract: This report describes our submission to track1 and track3 for VoxCeleb Speaker Recognition Challenge 2022(VoxSRC2022). Our best system achieves minDCF 0.1397 and EER 2.414 in track1, minDCF 0.388 and EER 7.030 in track3.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 442,
+    "label": 4,
+    "text": "Title: Smart Learning to Find Dumb Contracts\nAbstract: We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising oracle can only judge source. DLVA's training algorithm is general: we extend a source code analysis to bytecode without any manual feature engineering, predefined patterns, or expert rules. DLVA's training algorithm is also robust: it overcame a 1.25% error rate mislabeled contracts, and--the student surpassing the teacher--found vulnerable contracts that Slither mislabeled. DLVA is much faster than other smart contract vulnerability detectors: DLVA checks contracts for 29 vulnerabilities in 0.2 seconds, a 10-1,000x speedup. DLVA has three key components. First, Smart Contract to Vector (SC2V) uses neural networks to map smart contract bytecode to a high-dimensional floating-point vector. We benchmark SC2V against 4 state-of-the-art graph neural networks and show that it improves model differentiation by 2.2%. Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55.7% of the contracts in our test set, it has a Slither-predictive accuracy of 97.4% with a false positive rate of only 0.1%. Third, Core Classifier (CC) uses neural networks to infer vulnerable contracts regardless of vector distance. We benchmark DLVA's CC with 10 ML techniques and show that the CC improves accuracy by 11.3%. Overall, DLVA predicts Slither's labels with an overall accuracy of 92.7% and associated false positive rate of 7.2%. Lastly, we benchmark DLVA against nine well-known smart contract analysis tools. Despite using much less analysis time, DLVA completed every query, leading the pack with an average accuracy of 99.7%, pleasingly balancing high true positive rates with low false positive rates.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 443,
+    "label": 24,
+    "text": "Title: Human-Timescale Adaptation in an Open-Ended Task Space\nAbstract: Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL). In this work, we demonstrate that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans. In a vast space of held-out environment dynamics, our adaptive agent (AdA) displays on-the-fly hypothesis-driven exploration, efficient exploitation of acquired knowledge, and can successfully be prompted with first-person demonstrations. Adaptation emerges from three ingredients: (1) meta-reinforcement learning across a vast, smooth and diverse task distribution, (2) a policy parameterised as a large-scale attention-based memory architecture, and (3) an effective automated curriculum that prioritises tasks at the frontier of an agent's capabilities. We demonstrate characteristic scaling laws with respect to network size, memory length, and richness of the training task distribution. We believe our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 444,
+    "label": 26,
+    "text": "Title: No Love Among Haters: Negative Interactions Reduce Hate Community Engagement\nAbstract: While online hate groups pose significant risks to the health of online platforms and safety of marginalized groups, little is known about what causes users to become active in hate groups and the effect of social interactions on furthering their engagement. We address this gap by first developing tools to find hate communities within Reddit, and then augment 11 subreddits extracted with 14 known hateful subreddits (25 in total). Using causal inference methods, we evaluate the effect of replies on engagement in hateful subreddits by comparing users who receive replies to their first comment (the treatment) to equivalent control users who do not. We find users who receive replies are less likely to become engaged in hateful subreddits than users who do not, while the opposite effect is observed for a matched sample of similar-sized non-hateful subreddits. Using the Google Perspective API and VADER, we discover that hateful community first-repliers are more toxic, negative, and attack the posters more often than non-hateful first-repliers. In addition, we uncover a negative correlation between engagement and attacks or toxicity of first-repliers. We simulate the cumulative engagement of hateful and non-hateful subreddits under the contra-positive scenario of friendly first-replies, finding that attacks dramatically reduce engagement in hateful subreddits. These results counter-intuitively imply that, although under-moderated communities allow hate to fester, the resulting environment is such that direct social interaction does not encourage further participation, thus endogenously constraining the harmful role that these communities could play as recruitment venues for antisocial beliefs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 445,
+    "label": 10,
+    "text": "Title: Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning\nAbstract: Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities. We empirically show that our analogy-based approach outperforms LSTM, Random Forest, SVM, and MLP, with a drastically lower number of parameters. We also evaluate its generalization potential in a transfer learning setting. These results advocate for the further integration of analogy-based inference in tasks related to the KG lifecycle.",
+    "neighbors": [
+      2250
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 446,
+    "label": 24,
+    "text": "Title: Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model\nAbstract: A trained neural network model contains information on the training data. Given such a model, malicious parties can leverage the\"knowledge\"in this model and design ways to print out any usable information (known as model inversion attack). Therefore, it is valuable to explore the ways to conduct a such attack and demonstrate its severity. In this work, we proposed ways to generate a data point of the target class without prior knowledge of the exact target distribution by using a pre-trained diffusion model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 447,
+    "label": 30,
+    "text": "Title: Improving Contextualized Topic Models with Negative Sampling\nAbstract: Topic modeling has emerged as a dominant method for exploring large document collections. Recent approaches to topic modeling use large contextualized language models and variational autoencoders. In this paper, we propose a negative sampling mechanism for a contextualized topic model to improve the quality of the generated topics. In particular, during model training, we perturb the generated document-topic vector and use a triplet loss to encourage the document reconstructed from the correct document-topic vector to be similar to the input document and dissimilar to the document reconstructed from the perturbed vector. Experiments for different topic counts on three publicly available benchmark datasets show that in most cases, our approach leads to an increase in topic coherence over that of the baselines. Our model also achieves very high topic diversity.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 448,
+    "label": 16,
+    "text": "Title: BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images\nAbstract: Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high recall rates of retrieval, but their performance may degrade in the case of view variation or scene changes. In this work, we explore the potential of a different representation in place recognition, i.e. bird's eye view (BEV) images. We observe that the structural contents of BEV images are less influenced by rotations and translations of point clouds. We validate that, without any delicate design, a simple VGGNet trained on BEV images achieves comparable performance with the state-of-the-art place recognition methods in scenes of slight viewpoint changes. For more robust place recognition, we design a rotation-invariant network called BEVPlace. We use group convolution to extract rotation-equivariant local features from the images and NetVLAD for global feature aggregation. In addition, we observe that the distance between BEV features is correlated with the geometry distance of point clouds. Based on the observation, we develop a method to estimate the position of the query cloud, extending the usage of place recognition. The experiments conducted on large-scale public datasets show that our method 1) achieves state-of-the-art performance in terms of recall rates, 2) is robust to view changes, 3) shows strong generalization ability, and 4) can estimate the positions of query point clouds. Source codes are publicly available at https://github.com/zjuluolun/BEVPlace.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 449,
+    "label": 16,
+    "text": "Title: Robust Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers\nAbstract: Deep neural networks (DNNs) can be manipulated to exhibit specific behaviors when exposed to specific trigger patterns, without affecting their performance on normal samples. This type of attack is known as a backdoor attack. Recent research has focused on designing invisible triggers for backdoor attacks to ensure visual stealthiness. These triggers have demonstrated strong attack performance even under backdoor defense, which aims to eliminate or suppress the backdoor effect in the model. However, through experimental observations, we have noticed that these carefully designed invisible triggers are often susceptible to visual distortion during inference, such as Gaussian blurring or environmental variations in real-world scenarios. This phenomenon significantly undermines the effectiveness of attacks in practical applications. Unfortunately, this issue has not received sufficient attention and has not been thoroughly investigated. To address this limitation, we propose a novel approach called the Visible, Semantic, Sample-Specific, and Compatible trigger (VSSC-trigger), which leverages a recent powerful image method known as the stable diffusion model. In this approach, a text trigger is utilized as a prompt and combined with a benign image. The resulting combination is then processed by a pre-trained stable diffusion model, generating a corresponding semantic object. This object is seamlessly integrated with the original image, resulting in a new realistic image, referred to as the poisoned image. Extensive experimental results and analysis validate the effectiveness and robustness of our proposed attack method, even in the presence of visual distortion. We believe that the new trigger proposed in this work, along with the proposed idea to address the aforementioned issues, will have significant prospective implications for further advancements in this direction.",
+    "neighbors": [
+      1902
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 450,
+    "label": 24,
+    "text": "Title: Fast Submodular Function Maximization\nAbstract: Submodular functions have many real-world applications, such as document summarization, sensor placement, and image segmentation. For all these applications, the key building block is how to compute the maximum value of a submodular function efficiently. We consider both the online and offline versions of the problem: in each iteration, the data set changes incrementally or is not changed, and a user can issue a query to maximize the function on a given subset of the data. The user can be malicious, issuing queries based on previous query results to break the competitive ratio for the online algorithm. Today, the best-known algorithm for online submodular function maximization has a running time of $O(n k d^2)$ where $n$ is the total number of elements, $d$ is the feature dimension and $k$ is the number of elements to be selected. We propose a new method based on a novel search tree data structure. Our algorithm only takes $\\widetilde{O}(nk + kd^2 + nd)$ time.",
+    "neighbors": [
+      748,
+      1559
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 451,
+    "label": 31,
+    "text": "Title: Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning\nAbstract: Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation. In each turn of CRS, there naturally have two decision-making processes with different roles that influence each other: 1) director, which is to select the follow-up option (i.e., ask or recommend) that is more effective for reducing the action space and acquiring user preferences; and 2) actor, which is to accordingly choose primitive actions (i.e., asked attribute or recommended item) to estimate the effectiveness of the director\u2019s option. However, existing methods heavily rely on a unified decision-making module or heuristic rules, while neglecting to distinguish the roles of different decision procedures, as well as the mutual influences between them. To address this, we propose a novel Director-Actor Hierarchical Conversational Recommender (DAHCR), where the director selects the most effective option, followed by the actor accordingly choosing primitive actions that satisfy user preferences. Specifically, we develop a dynamic hypergraph to model user preferences and introduce an intrinsic motivation to train from weak supervision over the director. Finally, to alleviate the bad effect of model bias on the mutual influence between the director and actor, we model the director\u2019s option by sampling from a categorical distribution. Extensive experiments demonstrate that DAHCR outperforms state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 452,
+    "label": 6,
+    "text": "Title: Let's Play Together through Channels: Understanding the Practices and Experience of Danmaku Participation Game Players in China\nAbstract: Live streaming is becoming increasingly popular in recent years, as most channels prioritize the delivery of engaging content to their viewers. Among various live streaming channels, Danmaku participation game (DPG) has emerged in China as a mixture of live streaming and online gaming, offering an immersive gaming experience to players. Although prior research has explored audience participation games (APGs) in North America and Europe, it primarily focuses on discussing prototypes and lacks observation of players in natural settings. Little is known about how players perceive DPGs and their player experience. To fill the research gap, we observed a series of DPG channels and conducted an interview-based study to gain insights into the practices and experiences of DPG players. Our work reveals that DPGs can effectively synergize live streaming and online games, amplifying both player engagement and a profound sense of accomplishment to players.",
+    "neighbors": [
+      110
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 453,
+    "label": 16,
+    "text": "Title: DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal\nAbstract: Medical ultrasound (US) is widely used to evaluate and stage vascular diseases, in particular for the preliminary screening program, due to the advantage of being radiation-free. However, automatic segmentation of small tubular structures (e.g., the ulnar artery) from cross-sectional US images is still challenging. To address this challenge, this paper proposes the DopUS-Net and a vessel re-identification module that leverage the Doppler effect to enhance the final segmentation result. Firstly, the DopUS-Net combines the Doppler images with B-mode images to increase the segmentation accuracy and robustness of small blood vessels. It incorporates two encoders to exploit the maximum potential of the Doppler signal and recurrent neural network modules to preserve sequential information. Input to the first encoder is a two-channel duplex image representing the combination of the grey-scale Doppler and B-mode images to ensure anatomical spatial correctness. The second encoder operates on the pure Doppler images to provide a region proposal. Secondly, benefiting from the Doppler signal, this work first introduces an online artery re-identification module to qualitatively evaluate the real-time segmentation results and automatically optimize the probe pose for enhanced Doppler images. This quality-aware module enables the closed-loop control of robotic screening to further improve the confidence and robustness of image segmentation. The experimental results demonstrate that the proposed approach with the re-identification process can significantly improve the accuracy and robustness of the segmentation results (dice score: from 0:54 to 0:86; intersection over union: from 0:47 to 0:78).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 454,
+    "label": 27,
+    "text": "Title: Time Optimal Ergodic Search\nAbstract: Robots with the ability to balance time against the thoroughness of search have the potential to provide time-critical assistance in applications such as search and rescue. Current advances in ergodic coverage-based search methods have enabled robots to completely explore and search an area in a fixed amount of time. However, optimizing time against the quality of autonomous ergodic search has yet to be demonstrated. In this paper, we investigate solutions to the time-optimal ergodic search problem for fast and adaptive robotic search and exploration. We pose the problem as a minimum time problem with an ergodic inequality constraint whose upper bound regulates and balances the granularity of search against time. Solutions to the problem are presented analytically using Pontryagin's conditions of optimality and demonstrated numerically through a direct transcription optimization approach. We show the efficacy of the approach in generating time-optimal ergodic search trajectories in simulation and with drone experiments in a cluttered environment. Obstacle avoidance is shown to be readily integrated into our formulation, and we perform ablation studies that investigate parameter dependence on optimized time and trajectory sensitivity for search.",
+    "neighbors": [
+      1494
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 455,
+    "label": 27,
+    "text": "Title: Bipedal Walking on Constrained Footholds with MPC Footstep Control\nAbstract: Bipedal robots promise the ability to traverse rough terrain quickly and efficiently, and indeed, humanoid robots can now use strong ankles and careful foot placement to traverse discontinuous terrain. However, more agile underactuated bipeds have small feet and weak ankles, and must constantly adjust their planned footstep position to maintain balance. We introduce a new model-predictive footstep controller which jointly optimizes over the robot's discrete choice of stepping surface, impending footstep position sequence, ankle torque in the sagittal plane, and center of mass trajectory, to track a velocity command. The controller is formulated as a single Mixed Integer Quadratic Program (MIQP) which is solved at 50-200 Hz, depending on terrain complexity. We implement a state of the art real-time elevation mapping and convex terrain decomposition framework to inform the controller of its surroundings in the form on convex polygons representing steppable terrain. We investigate the capabilities and challenges of our approach through hardware experiments on the underactuated biped Cassie.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 456,
+    "label": 16,
+    "text": "Title: Do humans and machines have the same eyes? Human-machine perceptual differences on image classification\nAbstract: Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels. Most efforts in recent vision research focus on measuring the model task performance using standardized benchmarks. Limited work has been done to understand the perceptual difference between humans and machines. To fill this gap, our study first quantifies and analyzes the statistical distributions of mistakes from the two sources. We then explore human vs. machine expertise after ranking tasks by difficulty levels. Even when humans and machines have similar overall accuracies, the distribution of answers may vary. Leveraging the perceptual difference between humans and machines, we empirically demonstrate a post-hoc human-machine collaboration that outperforms humans or machines alone.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 457,
+    "label": 28,
+    "text": "Title: A Simplistic Model of Neural Scaling Laws: Multiperiodic Santa Fe Processes\nAbstract: It was observed that large language models exhibit a power-law decay of cross entropy with respect to the number of parameters and training tokens. When extrapolated literally, this decay implies that the entropy rate of natural language is zero. To understand this phenomenon -- or an artifact -- better, we construct a simple stationary stochastic process and its memory-based predictor that exhibit a power-law decay of cross entropy with the vanishing entropy rate. Our example is based on previously discussed Santa Fe processes, which decompose a random text into a process of narration and time-independent knowledge. Previous discussions assumed that narration is a memoryless source with Zipf's distribution. In this paper, we propose a model of narration that has the vanishing entropy rate and applies a randomly chosen deterministic sequence called a multiperiodic sequence. Under a suitable parameterization, multiperiodic sequences exhibit asymptotic relative frequencies given by Zipf's law. Remaining agnostic about the value of the entropy rate of natural language, we discuss relevance of similar constructions for language modeling.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 458,
+    "label": 16,
+    "text": "Title: No Free Lunch in Self Supervised Representation Learning\nAbstract: Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features. However few extensive explorations of the impact of transformation design have been conducted in the literature. In particular, the dependence of downstream performances to transformation design has been established, but not studied in depth. In this work, we explore this relationship, its impact on a domain other than natural images, and show that designing the transformations can be viewed as a form of supervision. First, we demonstrate that not only do transformations have an effect on downstream performance and relevance of clustering, but also that each category in a supervised dataset can be impacted in a different way. Following this, we explore the impact of transformation design on microscopy images, a domain where the difference between classes is more subtle and fuzzy than in natural images. In this case, we observe a greater impact on downstream tasks performances. Finally, we demonstrate that transformation design can be leveraged as a form of supervision, as careful selection of these by a domain expert can lead to a drastic increase in performance on a given downstream task.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 459,
+    "label": 16,
+    "text": "Title: Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos\nAbstract: Remote photoplethysmography (rPPG) based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, \\etc, in a non-contact manner. Existing mainstream CNN-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native Transformer-based framework called Dual-path TokenLearner (Dual-TL) is proposed in this paper, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed Dual-TL uses a Spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a Temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The Dual-TL achieves state-of-the-art performances in both intra- and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement. The source code is available at \\href{https://github.com/VUT-HFUT/Dual-TL}{https://github.com/VUT-HFUT/Dual-TL}",
+    "neighbors": [
+      2055
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 460,
+    "label": 28,
+    "text": "Title: On the difficulty to beat the first linear programming bound for binary codes\nAbstract: The first linear programming bound of McEliece, Rodemich, Rumsey, and Welch is the best known asymptotic upper bound for binary codes, for a certain subrange of distances. Starting from the work of Friedman and Tillich, there are, by now, some arguably easier and more direct arguments for this bound. We show that this more recent line of argument runs into certain difficulties if one tries to go beyond this bound (say, towards the second linear programming bound of McEliece, Rodemich, Rumsey, and Welch).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 461,
+    "label": 24,
+    "text": "Title: Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions\nAbstract: Promotions play a crucial role in e-commerce platforms, and various cost structures are employed to drive user engagement. This paper focuses on promotions with response-dependent costs, where expenses are incurred only when a purchase is made. Such promotions include discounts and coupons. While existing uplift model approaches aim to address this challenge, these approaches often necessitate training multiple models, like meta-learners, or encounter complications when estimating profit due to zero-inflated values stemming from non-converted individuals with zero cost and profit. To address these challenges, we introduce Incremental Profit per Conversion (IPC), a novel uplift measure of promotional campaigns' efficiency in unit economics. Through a proposed response transformation, we demonstrate that IPC requires only converted data, its propensity, and a single model to be estimated. As a result, IPC resolves the issues mentioned above while mitigating the noise typically associated with the class imbalance in conversion datasets and biases arising from the many-to-one mapping between search and purchase data. Lastly, we validate the efficacy of our approach by presenting results obtained from a synthetic simulation of a discount coupon campaign.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 462,
+    "label": 4,
+    "text": "Title: Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection\nAbstract: Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmarks circuits are generated with multiple shortcomings, i.e., i) they are heavily biased by the designers' mindset when they are created, and ii) they are created through a one-dimensional lens, mainly the signal activity of nets. To address these shortcomings, we introduce the first automated reinforcement learning (RL) HT insertion and detection framework. In the insertion phase, an RL agent explores the circuits and finds different locations that are best for keeping inserted HTs hidden. On the defense side, we introduce a multi-criteria RL-based detector that generates test vectors to discover the existence of HTs. Using the proposed framework, one can explore the HT insertion and detection design spaces to break the human mindset limitations as well as the benchmark issues, ultimately leading toward the next-generation of innovative detectors. Our HT toolset is open-source to accelerate research in this field and reduce the initial setup time for newcomers. We demonstrate the efficacy of our framework on ISCAS-85 benchmarks and provide the attack and detection success rates and define a methodology for comparing our techniques.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 463,
+    "label": 16,
+    "text": "Title: Deep Metric Multi-View Hashing for Multimedia Retrieval\nAbstract: Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar samples, leading to limited retrieval precision. Current methods utilize weighted sum or concatenation to fuse the multi-view features. We argue that these fusion methods cannot capture the interaction among different views. Furthermore, these methods ignored the information provided by the dissimilar samples. We propose a novel deep metric multi-view hashing (DMMVH) method to address the mentioned problems. Extensive empirical evidence is presented to show that gate-based fusion is better than typical methods. We introduce deep metric learning to the multi-view hashing problems, which can utilize metric information of dissimilar samples. On the MIR-Flickr25K, MS COCO, and NUS-WIDE, our method outperforms the current state-of-the-art methods by a large margin (up to 15.28 mean Average Precision (mAP) improvement).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 464,
+    "label": 6,
+    "text": "Title: Challenges and Opportunities in Data Visualization Education: A Call to Action\nAbstract: This paper is a call to action for research and discussion on data visualization education. As visualization evolves and spreads through our professional and personal lives, we need to understand how to support and empower a broad and diverse community of learners in visualization. Data Visualization is a diverse and dynamic discipline that combines knowledge from different fields, is tailored to suit diverse audiences and contexts, and frequently incorporates tacit knowledge. This complex nature leads to a series of interrelated challenges for data visualization education. Driven by a lack of consolidated knowledge, overview, and orientation for visualization education, the 21 authors of this paper-educators and researchers in data visualization-identify and describe 19 challenges informed by our collective practical experience. We organize these challenges around seven themes People, Goals&Assessment, Environment, Motivation, Methods, Materials, and Change. Across these themes, we formulate 43 research questions to address these challenges. As part of our call to action, we then conclude with 5 cross-cutting opportunities and respective action items: embrace DIVERSITY+INCLUSION, build COMMUNITIES, conduct RESEARCH, act AGILE, and relish RESPONSIBILITY. We aim to inspire researchers, educators and learners to drive visualization education forward and discuss why, how, who and where we educate, as we learn to use visualization to address challenges across many scales and many domains in a rapidly changing world: viseducationchallenges.github.io.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 465,
+    "label": 31,
+    "text": "Title: Evaluating Online Bandit Exploration In Large-Scale Recommender System\nAbstract: Bandit learning has been an increasingly popular design choice for recommender system. Despite the strong interest in bandit learning from the community, there remains multiple bottlenecks that prevent many bandit learning approaches from productionalization. One major bottleneck is how to test the effectiveness of bandit algorithm with fairness and without data leakage. Different from supervised learning algorithms, bandit learning algorithms emphasize greatly on the data collection process through their explorative nature. Such explorative behavior may induce unfair evaluation in a classic A/B test setting. In this work, we apply upper confidence bound (UCB) to our large scale short video recommender system and present a test framework for the production bandit learning life-cycle with a new set of metrics. Extensive experiment results show that our experiment design is able to fairly evaluate the performance of bandit learning in the recommender system.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 466,
+    "label": 16,
+    "text": "Title: Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling\nAbstract: Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine-grained spatial scene perception and the insufficiently modeling of video temporality. Towards this end, this work explores a novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal dynamics of videos for VidSRL. Built upon the HostSG, we present a nichetargeting VidSRL framework. A scene-event mapping mechanism is first designed to bridge the gap between the underlying scene structure and the high-level event semantic structure, resulting in an overall hierarchical scene-event (termed ICE) graph structure. We further perform iterative structure refinement to optimize the ICE graph, such that the overall structure representation can best coincide with end task demand. Finally, three subtask predictions of VidSRL are jointly decoded, where the end-to-end paradigm effectively avoids error propagation. On the benchmark dataset, our framework boosts significantly over the current best-performing model. Further analyses are shown for a better understanding of the advances of our methods.",
+    "neighbors": [
+      754,
+      1792
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 467,
+    "label": 28,
+    "text": "Title: Cascaded Code Distributed Computing With Low Complexity and Improved Flexibility\nAbstract: Coded distributed computing, proposed by Li et al., offers significant potential for reducing the communication load in MapReduce computing systems. In the setting of the \\emph{cascaded} coded distributed computing that consisting of $K$ nodes, $N$ input files, and $Q$ output functions, the objective is to compute each output function through $s\\geq 1$ nodes with a computation load $r\\geq 1$, enabling the application of coding techniques during the Shuffle phase to achieve minimum communication load. However, for most existing coded distributed computing schemes, a major limitation lies in their demand for splitting the original data into an exponentially growing number of input files in terms of $N/\\binom{K}{r} \\in\\mathbb{N}$ and requiring an exponentially large number of output functions $Q/\\binom{K}{s} \\in\\mathbb{N}$, which imposes stringent requirements for implementation and results in significant coding complexity when $K$ is large. In this paper, we focus on the cascaded case of $K/s\\in\\mathbb{N} $, deliberately designing the strategy of input files store and output functions assignment based on a grouping method, such that a low-complexity two-round Shuffle phase is available. The main advantages of our proposed scheme contains: 1) the communication load is quilt close to or surprisingly better than the optimal state-of-the-art scheme proposed by Li et al.; 2) our scheme requires significantly less number of input files and output functions; 3) all the operations are implemented over the minimum binary field $\\mathbb{F}_2$.",
+    "neighbors": [
+      1674,
+      1998
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 468,
+    "label": 34,
+    "text": "Title: Locally Consistent Decomposition of Strings with Applications to Edit Distance Sketching\nAbstract: In this paper we provide a new locally consistent decomposition of strings. Each string x is decomposed into blocks that can be described by grammars of size O(k) (using some amount of randomness). If we take two strings x and y of edit distance at most k then their block decomposition uses the same number of grammars and the i-th grammar of x is the same as the i-th grammar of y except for at most k indexes i. The edit distance of x and y equals to the sum of edit distances of pairs of blocks where x and y differ. Our decomposition can be used to design a sketch of size O(k2) for edit distance, and also a rolling sketch for edit distance of size O(k2). The rolling sketch allows to update the sketched string by appending a symbol or removing a symbol from the beginning of the string.",
+    "neighbors": [
+      1318
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 469,
+    "label": 22,
+    "text": "Title: Exact Probabilistic Inference Using Generating Functions\nAbstract: Probabilistic programs are typically normal-looking programs describing posterior probability distributions. They intrinsically code up randomized algorithms and have long been at the heart of modern machine learning and approximate computing. We explore the theory of generating functions [19] and investigate its usage in the exact quantitative reasoning of probabilistic programs. Important topics include the exact representation of program semantics [13], proving exact program equivalence [5], and -- as our main focus in this extended abstract -- exact probabilistic inference. In probabilistic programming, inference aims to derive a program's posterior distribution. In contrast to approximate inference, inferring exact distributions comes with several benefits [8], e.g., no loss of precision, natural support for symbolic parameters, and efficiency on models with certain structures. Exact probabilistic inference, however, is a notoriously hard task [6,12,17,18]. The challenges mainly arise from three program constructs: (1) unbounded while-loops and/or recursion, (2) infinite-support distributions, and (3) conditioning (via posterior observations). We present our ongoing research in addressing these challenges (with a focus on conditioning) leveraging generating functions and show their potential in facilitating exact probabilistic inference for discrete probabilistic programs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 470,
+    "label": 16,
+    "text": "Title: Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking\nAbstract: This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects. Thus, we name it \u201cPast- and-Future reasoning for Tracking\u201d (PF-Track). Specifically, our method adopts the \u201ctracking by attention\u201d framework and represents tracked instances coherently over time with object queries. To explicitly use historical cues, our \u201cPast Reasoning\u201d module learns to refine the tracks and enhance the object features by cross-attending to queries from previous frames and other objects. The \u201cFuture Reasoning\u201d module digests historical information and predicts robust future trajectories. In the case of long-term occlusions, our method maintains the object positions and enables re-association by integrating motion predictions. On the nuScenes dataset, our method improves AMOTA by a large margin and remarkably reduces ID-Switches by 90% compared to prior approaches, which is an order of magnitude less. The code and models are made available at https://github.com/TRI-ML/PF-Track.",
+    "neighbors": [
+      1283
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 471,
+    "label": 30,
+    "text": "Title: Automated speech- and text-based classification of neuropsychiatric conditions in a multidiagnostic setting\nAbstract: Speech patterns have been identified as potential diagnostic markers for neuropsychiatric conditions. However, most studies only compare a single clinical group to healthy controls, whereas clinical practice often requires differentiating between multiple potential diagnoses (multiclass settings). To address this, we assembled a dataset of repeated recordings from 420 participants (67 with major depressive disorder, 106 with schizophrenia and 46 with autism, as well as matched controls), and tested the performance of a range of conventional machine learning models and advanced Transformer models on both binary and multiclass classification, based on voice and text features. While binary models performed comparably to previous research (F1 scores between 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major depressive disorder, MDD; and 0.71-0.83 for schizophrenia); when differentiating between multiple diagnostic groups performance decreased markedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for schizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models yielded increased performance, suggesting that they capture complementary diagnostic information. Our results indicate that models trained on binary classification may learn to rely on markers of generic differences between clinical and non-clinical populations, or markers of clinical features that overlap across conditions, rather than identifying markers specific to individual conditions. We provide recommendations for future research in the field, suggesting increased focus on developing larger transdiagnostic datasets that include more fine-grained clinical features, and that can support the development of models that better capture the complexity of neuropsychiatric conditions and naturalistic diagnostic assessment.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 472,
+    "label": 24,
+    "text": "Title: Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation\nAbstract: Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing randomized techniques are first devised under the order-d (d>= 3) T-SVD framework. On this basis, we then further investigate the robust high-order tensor completion (RHTC) problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. To the best of our knowledge, this is the first study to incorporate the randomized low-rank approximation into the RHTC problem. Empirical studies on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.",
+    "neighbors": [
+      625
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 473,
+    "label": 24,
+    "text": "Title: Canonical and Noncanonical Hamiltonian Operator Inference\nAbstract: A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.",
+    "neighbors": [
+      1385
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 474,
+    "label": 16,
+    "text": "Title: Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization\nAbstract: Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 475,
+    "label": 36,
+    "text": "Title: The Good, the Bad and the Submodular: Fairly Allocating Mixed Manna Under Order-Neutral Submodular Preferences\nAbstract: We study the problem of fairly allocating indivisible goods (positively valued items) and chores (negatively valued items) among agents with decreasing marginal utilities over items. Our focus is on instances where all the agents have simple preferences; specifically, we assume the marginal value of an item can be either $-1$, $0$ or some positive integer $c$. Under this assumption, we present an efficient algorithm to compute leximin allocations for a broad class of valuation functions we call order-neutral submodular valuations. Order-neutral submodular valuations strictly contain the well-studied class of additive valuations but are a strict subset of the class of submodular valuations. We show that these leximin allocations are Lorenz dominating and approximately proportional. We also show that, under further restriction to additive valuations, these leximin allocations are approximately envy-free and guarantee each agent their maxmin share. We complement this algorithmic result with a lower bound showing that the problem of computing leximin allocations is NP-hard when $c$ is a rational number.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 476,
+    "label": 24,
+    "text": "Title: Projection-free Online Exp-concave Optimization\nAbstract: We consider the setting of online convex optimization (OCO) with \\textit{exp-concave} losses. The best regret bound known for this setting is $O(n\\log{}T)$, where $n$ is the dimension and $T$ is the number of prediction rounds (treating all other quantities as constants and assuming $T$ is sufficiently large), and is attainable via the well-known Online Newton Step algorithm (ONS). However, ONS requires on each iteration to compute a projection (according to some matrix-induced norm) onto the feasible convex set, which is often computationally prohibitive in high-dimensional settings and when the feasible set admits a non-trivial structure. In this work we consider projection-free online algorithms for exp-concave and smooth losses, where by projection-free we refer to algorithms that rely only on the availability of a linear optimization oracle (LOO) for the feasible set, which in many applications of interest admits much more efficient implementations than a projection oracle. We present an LOO-based ONS-style algorithm, which using overall $O(T)$ calls to a LOO, guarantees in worst case regret bounded by $\\widetilde{O}(n^{2/3}T^{2/3})$ (ignoring all quantities except for $n,T$). However, our algorithm is most interesting in an important and plausible low-dimensional data scenario: if the gradients (approximately) span a subspace of dimension at most $\\rho$, $\\rho<<n$, the regret bound improves to $\\widetilde{O}(\\rho^{2/3}T^{2/3})$, and by applying standard deterministic sketching techniques, both the space and average additional per-iteration runtime requirements are only $O(\\rho{}n)$ (instead of $O(n^2)$). This improves upon recently proposed LOO-based algorithms for OCO which, while having the same state-of-the-art dependence on the horizon $T$, suffer from regret/oracle complexity that scales with $\\sqrt{n}$ or worse.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 477,
+    "label": 24,
+    "text": "Title: Robust Budget Pacing with a Single Sample\nAbstract: Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an advertiser's value and also competing advertisers' values over time, a commonly used approach is to learn a target expenditure plan that specifies a target spend as a function of time, and then run a controller that tracks this plan. This raises the question: how many historical samples are required to learn a good expenditure plan? We study this question by considering an advertiser repeatedly participating in $T$ second-price auctions, where the tuple of her value and the highest competing bid is drawn from an unknown time-varying distribution. The advertiser seeks to maximize her total utility subject to her budget constraint. Prior work has shown the sufficiency of $T\\log T$ samples per distribution to achieve the optimal $O(\\sqrt{T})$-regret. We dramatically improve this state-of-the-art and show that just one sample per distribution is enough to achieve the near-optimal $\\tilde O(\\sqrt{T})$-regret, while still being robust to noise in the sampling distributions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 478,
+    "label": 28,
+    "text": "Title: Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming\nAbstract: This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points are jointly serving the communication users and sensing the targets. For this setup, we first develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communication-prioritized sensing beamforming and sensing-prioritized communication beamforming. Then, we consider the joint sensing and communication (JSC) beamforming design and derive the optimal structure of these JSC beamforming vectors based on a max-min fairness formulation. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) that of the communication-prioritized sensing beamforming solutions with almost the same sensing SNR of the sensing-prioritized communication beamforming approaches, yielding a promising strategy for cell-free ISAC MIMO systems.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 479,
+    "label": 15,
+    "text": "Title: DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training\nAbstract: On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance and energy boost of dedicated accelerators. We present DARKSIDE, a System-on-Chip with a heterogeneous cluster of eight RISC-V cores enhanced with 2-b to 32-b mixed-precision integer arithmetic. To boost the performance and efficiency on key compute-intensive DNN kernels, the cluster is enriched with three digital accelerators: 1) a specialized engine for low-data-reuse depthwise convolution kernels (up to 30 MAC/cycle); 2) a minimal overhead datamover to marshal 1\u201332-b data on-the-fly; and 3) a 16-b floating-point tensor product engine (TPE) for tiled matrix-multiplication acceleration. DARKSIDE is implemented in 65-nm CMOS technology. The cluster achieves a peak integer performance of 65 GOPS and a peak efficiency of 835 GOPS/W when working on 2-b integer DNN kernels. When targeting floating-point tensor operations, the TPE provides up to 18.2 GFLOPS of performance or 300 GFLOPS/W of efficiency\u2014enough to enable on-chip floating-point training at competitive speed coupled with ultralow power quantized inference.",
+    "neighbors": [
+      1301
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 480,
+    "label": 25,
+    "text": "Title: A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion\nAbstract: Speech Emotion Recognition (SER) is a challenging task. In this paper, we introduce a modality conversion concept aimed at enhancing emotion recognition performance on the MELD dataset. We assess our approach through two experiments: first, a method named Modality-Conversion that employs automatic speech recognition (ASR) systems, followed by a text classifier; second, we assume perfect ASR output and investigate the impact of modality conversion on SER, this method is called Modality-Conversion++. Our findings indicate that the first method yields substantial results, while the second method outperforms state-of-the-art (SOTA) speech-based approaches in terms of SER weighted-F1 (WF1) score on the MELD dataset. This research highlights the potential of modality conversion for tasks that can be conducted in alternative modalities.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 481,
+    "label": 16,
+    "text": "Title: Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-Resolution\nAbstract: Three-dimensional (3-D) synthetic aperture radar (SAR) is widely used in many security and industrial applications requiring high-resolution imaging of concealed or occluded objects. The ability to resolve intricate 3-D targets is essential to the performance of such applications and depends directly on system bandwidth. However, because high-bandwidth systems face several prohibitive hurdles, an alternative solution is to operate multiple radars at distinct frequency bands and fuse the multiband signals. Current multiband signal fusion methods assume a simple target model and a small number of point reflectors, which is invalid for realistic security screening and industrial imaging scenarios wherein the target model effectively consists of a large number of reflectors. To the best of our knowledge, this study presents the first use of deep learning for multiband signal fusion. The proposed network, called kR-Net, employs a hybrid, dual-domain complex-valued convolutional neural network (CV-CNN) to fuse multiband signals and impute the missing samples in the frequency gaps between subbands. By exploiting the relationships in both the wavenumber domain and wavenumber spectral domain, the proposed framework overcomes the drawbacks of existing multiband imaging techniques for realistic scenarios at a fraction of the computation time of existing multiband fusion algorithms. Our method achieves high-resolution imaging of intricate targets previously impossible using conventional techniques and enables finer resolution capacity for concealed weapon detection and occluded object classification using multiband signaling without requiring more advanced hardware. Furthermore, a fully integrated multiband imaging system is developed using commercially available millimeter-wave (mmWave) radars for efficient multiband imaging.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 482,
+    "label": 23,
+    "text": "Title: Developers' Visuo-spatial Mental Model and Program Comprehension\nAbstract: Previous works from research and industry have proposed a spatial representation of code in a canvas, arguing that a navigational code space confers developers the freedom to organise elements according to their understanding. By allowing developers to translate logical relatedness into spatial proximity, this code representation could aid in code navigation and comprehension. However, the association between developers' code comprehension and their visuo-spatial mental model of the code is not yet well understood. This mental model is affected on the one hand by the spatial code representation and on the other by the visuo-spatial working memory of developers. We address this knowledge gap by conducting an online experiment with 20 developers following a between-subject design. The control group used a conventional tab-based code visualization, while the experimental group used a code canvas to complete three code comprehension tasks. Furthermore, we measure the participants' visuo-spatial working memory using a Corsi Block test at the end of the tasks. Our results suggest that, overall, neither the spatial representation of code nor the visuo-spatial working memory of developers has a significant impact on comprehension performance. However, we identified significant differences in the time dedicated to different comprehension activities such as navigation, annotation, and UI interactions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 483,
+    "label": 16,
+    "text": "Title: SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition\nAbstract: Contrastive learning has achieved great success in skeleton-based action recognition. However, most existing approaches encode the skeleton sequences as entangled spatiotemporal representations and confine the contrasts to the same level of representation. Instead, this paper introduces a novel contrastive learning framework, namely Spatiotemporal Clues Disentanglement Network (SCD-Net). Specifically, we integrate the decoupling module with a feature extractor to derive explicit clues from spatial and temporal domains respectively. As for the training of SCD-Net, with a constructed global anchor, we encourage the interaction between the anchor and extracted clues. Further, we propose a new masking strategy with structural constraints to strengthen the contextual associations, leveraging the latest development from masked image modelling into the proposed SCD-Net. We conduct extensive evaluations on the NTU-RGB+D (60&120) and PKU-MMD (I&II) datasets, covering various downstream tasks such as action recognition, action retrieval, transfer learning, and semi-supervised learning. The experimental results demonstrate the effectiveness of our method, which outperforms the existing state-of-the-art (SOTA) approaches significantly.",
+    "neighbors": [
+      1693
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 484,
+    "label": 16,
+    "text": "Title: Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations\nAbstract: Vehicle light detection, association, and localization are required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning. Currently, many vehicle light detectors use single-stage detectors which predict bounding boxes to identify a vehicle light, in a manner decoupled from vehicle instances. In this paper, we present a method for detecting a vehicle light given an upstream vehicle detection and approximation of a visible light's center. Our method predicts four approximate corners associated with each vehicle light. We experiment with CNN architectures, data augmentation, and contextual preprocessing methods designed to reduce surrounding-vehicle confusion. We achieve an average distance error from the ground truth corner of 4.77 pixels, about 16.33% of the size of the vehicle light on average. We train and evaluate our model on the LISA Lights Dataset, allowing us to thoroughly evaluate our vehicle light corner detection model on a large variety of vehicle light shapes and lighting conditions. We propose that this model can be integrated into a pipeline with vehicle detection and vehicle light center detection to make a fully-formed vehicle light detection network, valuable to identifying trajectory-informative signals in driving scenes.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 485,
+    "label": 30,
+    "text": "Title: ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer\nAbstract: ChatGPT Analysis",
+    "neighbors": [
+      173,
+      613,
+      748,
+      971,
+      975,
+      1001,
+      1596,
+      1863,
+      1952,
+      2016,
+      2038,
+      2062
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 486,
+    "label": 24,
+    "text": "Title: Continuous-time convolutions model of event sequences\nAbstract: Massive samples of event sequences data occur in various domains, including e-commerce, healthcare, and finance. There are two main challenges regarding inference of such data: computational and methodological. The amount of available data and the length of event sequences per client are typically large, thus it requires long-term modelling. Moreover, this data is often sparse and non-uniform, making classic approaches for time series processing inapplicable. Existing solutions include recurrent and transformer architectures in such cases. To allow continuous time, the authors introduce specific parametric intensity functions defined at each moment on top of existing models. Due to the parametric nature, these intensities represent only a limited class of event sequences. We propose the COTIC method based on a continuous convolution neural network suitable for non-uniform occurrence of events in time. In COTIC, dilations and multi-layer architecture efficiently handle dependencies between events. Furthermore, the model provides general intensity dynamics in continuous time - including self-excitement encountered in practice. The COTIC model outperforms existing approaches on majority of the considered datasets, producing embeddings for an event sequence that can be used to solve downstream tasks - e.g. predicting next event type and return time. The code of the proposed method can be found in the GitHub repository (https://github.com/VladislavZh/COTIC).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 487,
+    "label": 24,
+    "text": "Title: Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data\nAbstract: Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the modeling of decentralized data with non-independent and identical distributions (non-IID). In non-IID settings, there are intra-client inconsistency that comes from the imbalanced data modeling, and inter-client inconsistency among heterogeneous client distributions, which not only hinders sufficient representation of the minority data, but also brings discrepant model deviations. However, previous work overlooks to tackle the above two coupling inconsistencies together. In this work, we propose FedRANE, which consists of two main modules, i.e., local relational augmentation (LRA) and global Nash equilibrium (GNE), to resolve intra- and inter-client inconsistency simultaneously. Specifically, in each client, LRA mines the similarity relations among different data samples and enhances the minority sample representations with their neighbors using attentive message passing. In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums. We conduct extensive experiments on four benchmark datasets to show the superiority of FedRANE in enhancing the performance of FL with non-IID data.",
+    "neighbors": [
+      164,
+      291
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 488,
+    "label": 8,
+    "text": "Title: Towards an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets\nAbstract: This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ is conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The combination of different communication technologies within a common measurement methodology provides insights that can be exploited by Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection. Moreover, the datasets are publicly available, labelled and prefiltered for fast on-boarding and applicability.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 489,
+    "label": 3,
+    "text": "Title: Disincentivizing Polarization in Social Networks\nAbstract: On social networks, algorithmic personalization drives users into filter bubbles where they rarely see content that deviates from their interests. We present a model for content curation and personalization that avoids filter bubbles, along with algorithmic guarantees and nearly matching lower bounds. In our model, the platform interacts with $n$ users over $T$ timesteps, choosing content for each user from $k$ categories. The platform receives stochastic rewards as in a multi-arm bandit. To avoid filter bubbles, we draw on the intuition that if some users are shown some category of content, then all users should see at least a small amount of that content. We first analyze a naive formalization of this intuition and show it has unintended consequences: it leads to ``tyranny of the majority'' with the burden of diversification borne disproportionately by those with minority interests. This leads us to our model which distributes this burden more equitably. We require that the probability any user is shown a particular type of content is at least $\\gamma$ times the average probability all users are shown that type of content. Full personalization corresponds to $\\gamma = 0$ and complete homogenization corresponds to $\\gamma = 1$; hence, $\\gamma$ encodes a hard cap on the level of personalization. We also analyze additional formulations where the platform can exceed its cap but pays a penalty proportional to its constraint violation. We provide algorithmic guarantees for optimizing recommendations subject to these constraints. These include nearly matching upper and lower bounds for the entire range of $\\gamma \\in [0,1]$ showing that the reward of a multi-agent variant of UCB is nearly optimal. Using real-world preference data, we empirically verify that under our model, users share the burden of diversification with only minor utility loss under our constraints.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 490,
+    "label": 16,
+    "text": "Title: Conditional Image-to-Video Generation with Latent Flow Diffusion Models\nAbstract: Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video starting from an image (e.g., a person's face) and a condition (e.g., an action class label like smile). The key challenge of the cI2V task lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given image and condition. In this paper, we propose an approach for cI2V using novel latent flow diffusion models (LFDM) that synthesize an optical flow sequence in the latent space based on the given condition to warp the given image. Compared to previous direct-synthesis-based works, our proposed LFDM can better synthesize spatial details and temporal motion by fully utilizing the spatial content of the given image and warping it in the latent space according to the generated temporally-coherent flow. The training of LFDM consists of two separate stages: (1) an unsupervised learning stage to train a latent flow auto-encoder for spatial content generation, including a flow predictor to estimate latent flow between pairs of video frames, and (2) a conditional learning stage to train a 3D-UNet-based diffusion model (DM) for temporal latent flow generation. Unlike previous DMs operating in pixel space or latent feature space that couples spatial and temporal information, the DM in our LFDM only needs to learn a low-dimensional latent flow space for motion generation, thus being more computationally efficient. We conduct comprehensive experiments on multiple datasets, where LFDM consistently outperforms prior arts. Furthermore, we show that LFDM can be easily adapted to new domains by simply finetuning the image decoder. Our code is available at https://github.com/nihaomiao/CVPR23_LFDM.",
+    "neighbors": [
+      146,
+      860,
+      1978,
+      2085
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 491,
+    "label": 23,
+    "text": "Title: When Conversations Turn Into Work: A Taxonomy of Converted Discussions and Issues in GitHub\nAbstract: Popular and large contemporary open-source projects now embrace a diverse set of documentation for communication channels. Examples include contribution guidelines (i.e., commit message guidelines, coding rules, submission guidelines), code of conduct (i.e., rules and behavior expectations), governance policies, and Q&A forum. In 2020, GitHub released Discussion to distinguish between communication and collaboration. However, it remains unclear how developers maintain these channels, how trivial it is, and whether deciding on conversion takes time. We conducted an empirical study on 259 NPM and 148 PyPI repositories, devising two taxonomies of reasons for converting discussions into issues and vice-versa. The most frequent conversion from a discussion to an issue is when developers request a contributor to clarify their idea into an issue (Reporting a Clarification Request -35.1% and 34.7%, respectively), while agreeing that having non actionable topic (QA, ideas, feature requests -55.0% and 42.0%, respectively}) is the most frequent reason of converting an issue into a discussion. Furthermore, we show that not all reasons for conversion are trivial (e.g., not a bug), and raising a conversion intent potentially takes time (i.e., a median of 15.2 and 35.1 hours, respectively, taken from issues to discussions). Our work contributes to complementing the GitHub guidelines and helping developers effectively utilize the Issue and Discussion communication channels to maintain their collaboration.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 492,
+    "label": 24,
+    "text": "Title: ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure\nAbstract: Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions. Traditionally, post-processing methods have been used to calibrate the model after training. In recent years, various trainable calibration measures have been proposed to incorporate them directly into the training process. However, these methods all incorporate internal hyperparameters, and the performance of these calibration objectives relies on tuning these hyperparameters, incurring more computational costs as the size of neural networks and datasets become larger. As such, we present Expected Squared Difference (ESD), a tuning-free (i.e., hyperparameter-free) trainable calibration objective loss, where we view the calibration error from the perspective of the squared difference between the two expectations. With extensive experiments on several architectures (CNNs, Transformers) and datasets, we demonstrate that (1) incorporating ESD into the training improves model calibration in various batch size settings without the need for internal hyperparameter tuning, (2) ESD yields the best-calibrated results compared with previous approaches, and (3) ESD drastically improves the computational costs required for calibration during training due to the absence of internal hyperparameter. The code is publicly accessible at https://github.com/hee-suk-yoon/ESD.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 493,
+    "label": 15,
+    "text": "Title: Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing\nAbstract: Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and nano-robotics need to run many diverse tasks within a power envelope of a few tens of mW over a wide range of operating conditions: compute-intensive but strongly quantized Deep Neural Network (DNN) inference, as well as signal processing and control requiring high-precision floating-point. We present Marsellus, an all-digital heterogeneous SoC for AI-IoT end-nodes fabricated in GlobalFoundries 22nm FDX that combines 1) a general-purpose cluster of 16 RISC-V Digital Signal Processing (DSP) cores attuned for the execution of a diverse range of workloads exploiting 4-bit and 2-bit arithmetic extensions (XpulpNN), combined with fused MAC&LOAD operations and floating-point support; 2) a 2-8bit Reconfigurable Binary Engine (RBE) to accelerate 3x3 and 1x1 (pointwise) convolutions in DNNs; 3) a set of On-Chip Monitoring (OCM) blocks connected to an Adaptive Body Biasing (ABB) generator and a hardware control loop, enabling on-the-fly adaptation of transistor threshold voltages. Marsellus achieves up to 180 Gop/s or 3.32 Top/s/W on 2-bit precision arithmetic in software, and up to 637 Gop/s or 12.4 Top/s/W on hardware-accelerated DNN layers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 494,
+    "label": 24,
+    "text": "Title: Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training\nAbstract: Given the massive cost of language model pre-training, a non-trivial improvement of the optimization algorithm would lead to a material reduction on the time and cost of training. Adam and its variants have been state-of-the-art for years, and more sophisticated second-order (Hessian-based) optimizers often incur too much per-step overhead. In this paper, we propose Sophia, Second-order Clipped Stochastic Optimization, a simple scalable second-order optimizer that uses a light-weight estimate of the diagonal Hessian as the pre-conditioner. The update is the moving average of the gradients divided by the moving average of the estimated Hessian, followed by element-wise clipping. The clipping controls the worst-case update size and tames the negative impact of non-convexity and rapid change of Hessian along the trajectory. Sophia only estimates the diagonal Hessian every handful of iterations, which has negligible average per-step time and memory overhead. On language modeling with GPT-2 models of sizes ranging from 125M to 770M, Sophia achieves a 2x speed-up compared with Adam in the number of steps, total compute, and wall-clock time. Theoretically, we show that Sophia adapts to the curvature in different components of the parameters, which can be highly heterogeneous for language modeling tasks. Our run-time bound does not depend on the condition number of the loss.",
+    "neighbors": [
+      748,
+      1052,
+      1906,
+      2093
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 495,
+    "label": 30,
+    "text": "Title: Conversation Derailment Forecasting with Graph Convolutional Networks\nAbstract: Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. Current state-of-the-art approaches to address this problem rely on sequence models that treat dialogues as text streams. We propose a novel model based on a graph convolutional neural network that considers dialogue user dynamics and the influence of public perception on conversation utterances. Through empirical evaluation, we show that our model effectively captures conversation dynamics and outperforms the state-of-the-art models on the CGA and CMV benchmark datasets by 1.5\\% and 1.7\\%, respectively.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 496,
+    "label": 24,
+    "text": "Title: Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling\nAbstract: There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g., prior activity level, location, etc.). Online RL is a promising data-driven approach for this problem as it learns based on each user's historical responses and uses that knowledge to personalize these decisions. However, to decide whether the RL algorithm should be included in an ``optimized'' intervention for real-world deployment, we must assess the data evidence indicating that the RL algorithm is actually personalizing the treatments to its users. Due to the stochasticity in the RL algorithm, one may get a false impression that it is learning in certain states and using this learning to provide specific treatments. We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity. We illustrate our methodology with a case study by analyzing the data from a physical activity clinical trial called HeartSteps, which included the use of an online RL algorithm. We demonstrate how our approach enhances data-driven truth-in-advertising of algorithm personalization both across all users as well as within specific users in the study.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 497,
+    "label": 28,
+    "text": "Title: MDS Array Codes With Small Sub-packetization Levels and Small Repair Degrees\nAbstract: High-rate minimum storage regenerating (MSR) codes are known to require a large sub-packetization level, which can make meta-data management difficult and hinder implementation in practical systems. A few maximum distance separable (MDS) array code constructions have been proposed to attain a much smaller sub-packetization level by sacrificing a bit of repair bandwidth. However, to the best of our knowledge, only one construction by Guruswami et al. can support the repair of a failed node without contacting all the surviving nodes. This construction is certainly of theoretical interest but not yet practical due to its requirement for very large code parameters. In this paper, we propose a generic transformation that can convert any $(\\overline{n}, \\overline{k})$ MSR code with a repair degree of $\\overline{d}<\\overline{n}-1$ into another $(n=s\\overline{n},k)$ MDS array code that supports $d<n-1$ with a small sub-packetization level and $(1+\\epsilon)$-optimal repair bandwidth (i.e., $1+\\epsilon$ times the optimal value) under a specific condition. We obtain three MDS array codes with small sub-packetization levels and $(1+\\epsilon)$-optimal repair bandwidth by applying this transformation to three known MSR codes. All the new MDS array codes have a small repair degree of $d<n-1$ and work for both small and large code parameters.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 498,
+    "label": 28,
+    "text": "Title: Space-Time Shift Keying Aided OTFS Modulation for Orthogonal Multiple Access\nAbstract: Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) {and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 499,
+    "label": 16,
+    "text": "Title: WiSARD: A Labeled Visual and Thermal Image Dataset for Wilderness Search and Rescue\nAbstract: Sensor-equipped unoccupied aerial vehicles (UAVs) have the potential to help reduce search times and alleviate safety risks for first responders carrying out Wilderness Search and Rescue (WiSAR) operations, the process of finding and rescuing person(s) lost in wilderness areas. Unfortunately, visual sensors alone do not address the need for robustness across all the possible terrains, weather, and lighting conditions that WiSAR operations can be conducted in. The use of multi-modal sensors, specifically visual-thermal cameras, is critical in enabling WiSAR UAVs to perform in diverse operating conditions. However, due to the unique challenges posed by the wilderness context, existing dataset benchmarks are inadequate for developing vision-based algorithms for autonomous WiSAR UAVs. To this end, we present WiSARD, a dataset with roughly 56,000 labeled visual and thermal images collected from UAV flights in various terrains, seasons, weather, and lighting conditions. To the best of our knowledge, WiSARD is the first large-scale dataset collected with multi-modal sensors for autonomous WiSAR operations. We envision that our dataset will provide researchers with a diverse and challenging benchmark that can test the robustness of their algorithms when applied to real-world (life-saving) applications. Link to dataset: https://sites.google.com/uw.edu/wisard/",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 500,
+    "label": 9,
+    "text": "Title: On the Complexity of Robust Eventual Inequality Testing for C-Finite Functions\nAbstract: We study the computational complexity of a robust version of the problem of testing two univariate C-finite functions for eventual inequality at large times. Specifically, working in the bit-model of real computation, we consider the eventual inequality testing problem for real functions that are specified by homogeneous linear Cauchy problems with arbitrary real coefficients and initial values. In order to assign to this problem a well-defined computational complexity, we develop a natural notion of polynomial-time decidability of subsets of computable metric spaces which extends our recently introduced notion of maximal partial decidability. We show that eventual inequality of C-finite functions is polynomial-time decidable in this sense.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 501,
+    "label": 16,
+    "text": "Title: Optimization-Based Eye Tracking using Deflectometric Information\nAbstract: Eye tracking is an important tool with a wide range of applications in Virtual, Augmented, and Mixed Reality (VR/AR/MR) technologies. State-of-the-art eye tracking methods are either reflection-based and track reflections of sparse point light sources, or image-based and exploit 2D features of the acquired eye image. In this work, we attempt to significantly improve reflection-based methods by utilizing pixel-dense deflectometric surface measurements in combination with optimization-based inverse rendering algorithms. Utilizing the known geometry of our deflectometric setup, we develop a differentiable rendering pipeline based on PyTorch3D that simulates a virtual eye under screen illumination. Eventually, we exploit the image-screen-correspondence information from the captured measurements to find the eye's rotation, translation, and shape parameters with our renderer via gradient descent. In general, our method does not require a specific pattern and can work with ordinary video frames of the main VR/AR/MR screen itself. We demonstrate real-world experiments with evaluated mean relative gaze errors below 0.45 degrees at a precision better than 0.11 degrees. Moreover, we show an improvement of 6X over a representative reflection-based state-of-the-art method in simulation.",
+    "neighbors": [
+      192
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 502,
+    "label": 24,
+    "text": "Title: Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection\nAbstract: Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks. However, ACL needs tremendous running time to generate the adversarial variants of all training data, which limits its scalability to large datasets. To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method. RCS does not require label information and searches for an informative subset that minimizes a representational divergence, which is the distance of the representation between natural data and their virtual adversarial variants. The vanilla solution of RCS via traversing all possible subsets is computationally prohibitive. Therefore, we theoretically transform RCS into a surrogate problem of submodular maximization, of which the greedy search is an efficient solution with an optimality guarantee for the original problem. Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the large-scale ImageNet-1K dataset to obtain an effective robust representation via RCS.",
+    "neighbors": [
+      1065,
+      2256
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 503,
+    "label": 16,
+    "text": "Title: Correspondence-free online human motion retargeting\nAbstract: We present a novel data-driven framework for unsupervised human motion retargeting which animates a target body shape with a source motion. This allows to retarget motions between different characters by animating a target subject with a motion of a source subject. Our method is correspondence-free,~\\ie neither spatial correspondences between the source and target shapes nor temporal correspondences between different frames of the source motion are required. Our proposed method directly animates a target shape with arbitrary sequences of humans in motion, possibly captured using 4D acquisition platforms or consumer devices. Our framework takes into account long-term temporal context of $1$ second during retargeting while accounting for surface details. To achieve this, we take inspiration from two lines of existing work: skeletal motion retargeting, which leverages long-term temporal context at the cost of surface detail, and surface-based retargeting, which preserves surface details without considering long-term temporal context. We unify the advantages of these works by combining a learnt skinning field with a skeletal retargeting approach. During inference, our method runs online,~\\ie the input can be processed in a serial way, and retargeting is performed in a single forward pass per frame. Experiments show that including long-term temporal context during training improves the method's accuracy both in terms of the retargeted skeletal motion and the detail preservation. Furthermore, our method generalizes well on unobserved motions and body shapes. We demonstrate that the proposed framework achieves state-of-the-art results on two test datasets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 504,
+    "label": 8,
+    "text": "Title: Play to Earn in the Metaverse with Mobile Edge Computing over Wireless Networks: A Deep Reinforcement Learning Approach\nAbstract: The Metaverse play-to-earn games have been gaining popularity as they enable players to earn in-game tokens which can be translated to real-world profits. With the advancements in augmented reality (AR) technologies, users can play AR games in the Metaverse. However, these high-resolution games are compute-intensive, and in-game graphical scenes need to be offloaded from mobile devices to an edge server for computation. In this work, we consider an optimization problem where the Metaverse Service Provider (MSP)'s objective is to reduce downlink transmission latency of in-game graphics, the latency of uplink data transmission, and the worst-case (greatest) battery charge expenditure of user equipments (UEs), while maximizing the worst-case (lowest) UE resolution-influenced in-game earning potential through optimizing the downlink UE-Metaverse Base Station (UE-MBS) assignment and the uplink transmission power selection. The downlink and uplink transmissions are then executed asynchronously. We propose a multi-agent, loss-sharing (MALS) reinforcement learning model to tackle the asynchronous and asymmetric problem. We then compare the MALS model with other baseline models and show its superiority over other methods. Finally, we conduct multi-variable optimization weighting analyses and show the viability of using our proposed MALS algorithm to tackle joint optimization problems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 505,
+    "label": 24,
+    "text": "Title: Large Language Models as Tool Makers\nAbstract: Recent research shows the potential of enhancing the problem-solving ability of large language models (LLMs) through the use of external tools. However, prior work along this line depends on the availability of existing tools. In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs As Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving. Our approach consists of two key phases: 1) tool making: an LLM acts as the tool maker that crafts tools for given tasks, where a tool is implemented as a Python utility function. 2) tool using: an LLM acts as the tool user, which applies the tool built by the tool maker for problem-solving. The tool user can be either the same or a different LLM from the tool maker. Tool-making enables an LLM to continually generate tools that can be applied to different requests so that future requests can call the corresponding APIs when beneficial for solving the tasks. Furthermore, the division of labor among LLMs for tool-making and tool-using phases introduces the opportunity to achieve cost effectiveness without degrading the quality of generated tools and problem solutions. For example, recognizing that tool-making demands more sophisticated capabilities than tool-using, we can apply a powerful yet resource-intensive model as the tool maker, and a lightweight while cost-effective model as the tool user. We validate the effectiveness of our approach across a variety of complex reasoning tasks, including Big-Bench tasks. With GPT-4 as the tool maker and GPT-3.5 as the tool user, LATM can achieve performance that is on par with using GPT-4 for both tool making and tool using, while the inference cost is significantly reduced.",
+    "neighbors": [
+      57,
+      173,
+      618,
+      1002,
+      1026,
+      1044,
+      1267,
+      1306,
+      1327,
+      1353,
+      1490,
+      1556,
+      1878,
+      1983,
+      2166,
+      2171
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 506,
+    "label": 24,
+    "text": "Title: Generalized Implicit Follow-The-Regularized-Leader\nAbstract: We propose a new class of online learning algorithms, generalized implicit Follow-The-Regularized-Leader (FTRL), that expands the scope of FTRL framework. Generalized implicit FTRL can recover known algorithms, as FTRL with linearized losses and implicit FTRL, and it allows the design of new update rules, as extensions of aProx and Mirror-Prox to FTRL. Our theory is constructive in the sense that it provides a simple unifying framework to design updates that directly improve the worst-case upper bound on the regret. The key idea is substituting the linearization of the losses with a Fenchel-Young inequality. We show the flexibility of the framework by proving that some known algorithms, like the Mirror-Prox updates, are instantiations of the generalized implicit FTRL. Finally, the new framework allows us to recover the temporal variation bound of implicit OMD, with the same computational complexity.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 507,
+    "label": 26,
+    "text": "Title: Social Media COVID-19 Contact Tracing Using Mobile Social Payments and Facebook Data\nAbstract: Many in the US were reluctant to report their COVID-19 cases at the height of the pandemic (e.g., for fear of missing work or other obligations due to quarantine mandates). Other methods such as using public social media data can therefore help augment current approaches to surveilling pandemics. This study evaluated the effectiveness of using social media data as a data source for tracking public health pandemics. There have been several attempts at using social media data from platforms like Twitter for analyzing the COVID-19 pandemic. While these provide a multitude of useful insights, new platforms like Venmo, a popular U.S. mobile social payment app often used during in-person activities, remain understudied. We developed unique computational methods (combining Venmo- and Facebook- derived data) to classify post content, including the location where the content was likely posted. This approach enabled geotemporal COVID-19-related infoveillance. By examining 135M publicly available Venmo transactions from 22.1M unique users, we found significant spikes in the use of COVID-19 related keywords in March 2020. Using Facebook-based geotags for 9K users along with transaction geo-parsing (i.e., parsing text to detect place names), we identified 38K location-based clusters. Within these groups, we found a strong correlation (0.81) between the use of COVID-19 keywords in a region and the number of reported COVID-19 cases as well as an aggregate decrease in transactions during lockdowns and an increase when lockdowns are lifted. Surprisingly, we saw a weak negative correlation between the number of transactions and reported cases over time (-0.49). Our results indicate that using non-Twitter social media trace data can aid pandemic- and other health-related infoveillance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 508,
+    "label": 30,
+    "text": "Title: Cross-lingual Argument Mining in the Medical Domain\nAbstract: Nowadays the medical domain is receiving more and more attention in applications in-volving Arti\ufb01cial Intelligence. Clinicians have to deal with an enormous amount of unstructured textual data to make a conclusion about patients\u2019 health in their everyday life. Argument mining helps to provide a structure to such data by detecting argumentative components in the text and classifying the relations between them. However, as it is the case for many tasks in Natural Language Processing in general and in medical text processing in particular, the large majority of the work on computational argumentation has been done only for English. This is also the case with the only dataset available for argumentation in the medical domain, namely, the annotated medical data of abstracts of Randomized Controlled Trials (RCT) from the MEDLINE database. In order to mitigate the lack of annotated data for other languages, we empirically investigate several strategies to perform argument mining and classi\ufb01cation in medical texts for a language for which no annotated data is available. This project shows that automatically translating and project annotations from English to a target language (Spanish) is an e\ufb00ective way to generate annotated data without manual intervention. Furthermore, our experiments demonstrate that the translation and projection approach outperforms zero-shot cross-lingual approaches using a large masked multilingual language model. Finally, we show how the automatically generated data in Spanish can also be used to improve results in the original English evaluation setting.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 509,
+    "label": 24,
+    "text": "Title: Robust Representation Learning for Unreliable Partial Label Learning\nAbstract: Partial Label Learning (PLL) is a type of weakly supervised learning where each training instance is assigned a set of candidate labels, but only one label is the ground-truth. However, this idealistic assumption may not always hold due to potential annotation inaccuracies, meaning the ground-truth may not be present in the candidate label set. This is known as Unreliable Partial Label Learning (UPLL) that introduces an additional complexity due to the inherent unreliability and ambiguity of partial labels, often resulting in a sub-optimal performance with existing methods. To address this challenge, we propose the Unreliability-Robust Representation Learning framework (URRL) that leverages unreliability-robust contrastive learning to help the model fortify against unreliable partial labels effectively. Concurrently, we propose a dual strategy that combines KNN-based candidate label set correction and consistency-regularization-based label disambiguation to refine label quality and enhance the ability of representation learning within the URRL framework. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art PLL methods on various datasets with diverse degrees of unreliability and ambiguity. Furthermore, we provide a theoretical analysis of our approach from the perspective of the expectation maximization (EM) algorithm. Upon acceptance, we pledge to make the code publicly accessible.",
+    "neighbors": [
+      565
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 510,
+    "label": 30,
+    "text": "Title: Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models\nAbstract: The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counselling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging Large Language Models (LLMs) for question-answering in psychological consultation settings to ease the demand for mental health professions. Our framework combines pre-trained LLMs with real-world professional Q\\&A from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, with human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 511,
+    "label": 30,
+    "text": "Title: Representing interlingual meaning in lexical databases\nAbstract: nan",
+    "neighbors": [
+      1554
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 512,
+    "label": 27,
+    "text": "Title: UAV-based Receding Horizon Control for 3D Inspection Planning\nAbstract: Nowadays, unmanned aerial vehicles or UAVs are being used for a wide range of tasks, including infrastructure inspection, automated monitoring and coverage. This paper investigates the problem of 3D inspection planning with an autonomous UAV agent which is subject to dynamical and sensing constraints. We propose a receding horizon 3D inspection planning control approach for generating optimal trajectories which enable an autonomous UAV agent to inspect a finite number of feature-points scattered on the surface of a cuboid-like structure of interest. The inspection planning problem is formulated as a constrained open-loop optimal control problem and is solved using mixed integer programming (MIP) optimization. Quantitative and qualitative evaluation demonstrates the effectiveness of the proposed approach.",
+    "neighbors": [
+      569,
+      2293
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 513,
+    "label": 16,
+    "text": "Title: An Overview about Emerging Technologies of Autonomous Driving\nAbstract: Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems.",
+    "neighbors": [
+      663,
+      736,
+      1348,
+      1491,
+      1537,
+      1663,
+      1901
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 514,
+    "label": 24,
+    "text": "Title: Unbiased and Efficient Self-Supervised Incremental Contrastive Learning\nAbstract: Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning. However, the incremental learning issue of CL has rarely been studied, which brings the limitation in applying it to real-world applications. Contrastive learning identifies the samples with the negative ones from the noise distribution that changes in the incremental scenarios. Therefore, only fitting the change of data without noise distribution causes bias, and directly retraining results in low efficiency. To bridge this research gap, we propose a self-supervised Incremental Contrastive Learning (ICL) framework consisting of (i) a novel Incremental InfoNCE (NCE-II) loss function by estimating the change of noise distribution for old data to guarantee no bias with respect to the retraining, (ii) a meta-optimization with deep reinforced Learning Rate Learning (LRL) mechanism which can adaptively learn the learning rate according to the status of the training processes and achieve fast convergence which is critical for incremental learning. Theoretically, the proposed ICL is equivalent to retraining, which is based on solid mathematical derivation. In practice, extensive experiments in different domains demonstrate that, without retraining a new model, ICL achieves up to 16.7x training speedup and 16.8x faster convergence with competitive results.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 515,
+    "label": 24,
+    "text": "Title: PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy\nAbstract: Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation owing to its strong potential in capturing underlying data statistics while preserving data privacy. However, in cases of practical data heterogeneity among FL clients, existing FL frameworks still exhibit deficiency in capturing the overall feature properties of local client data that exhibit disparate distributions. In response, generative adversarial networks (GANs) have recently been exploited in FL to address data heterogeneity since GANs can be integrated for data regeneration without exposing original raw data. Despite some successes, existing GAN-related FL frameworks often incur heavy communication cost and also elicit other privacy concerns, which limit their applications in real scenarios. To this end, this work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions across clients and to strengthen privacy preservation at reduced communication cost, especially over wireless networks. Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework. Through experimental results based on several well-known benchmark datasets, our proposed PS-FedGAN shows great promise to tackle FL under non-IID client data distributions, while securing data privacy and lowering communication overhead.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 516,
+    "label": 24,
+    "text": "Title: Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrieval\nAbstract: A recent advancement in the domain of biomedical Entity Linking is the development of powerful two-stage algorithms \u2013 an initial candidate retrieval stage that generates a shortlist of entities for each mention, followed by a candidate ranking stage. However, the effectiveness of both stages are inextricably dependent on computationally expensive components. Specifically, in candidate retrieval via dense representation retrieval it is important to have hard negative samples, which require repeated forward passes and nearest neighbour searches across the entire entity label set throughout training. In this work, we show that pairing a proxy-based metric learning loss with an adversarial regularizer provides an efficient alternative to hard negative sampling in the candidate retrieval stage. In particular, we show competitive performance on the recall@1 metric, thereby providing the option to leave out the expensive candidate ranking step. Finally, we demonstrate how the model can be used in a zero-shot setting to discover out of knowledge base biomedical entities.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 517,
+    "label": 16,
+    "text": "Title: Sampling to Distill: Knowledge Transfer from Open-World Data\nAbstract: Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the teacher network without original training data. Despite encouraging results, existing DFKD methods rely heavily on generation modules with high computational costs. Meanwhile, they ignore the fact that the generated and original data exist domain shifts due to the lack of supervision information. Moreover, knowledge is transferred through each example, ignoring the implicit relationship among multiple examples. To this end, we propose a novel Open-world Data Sampling Distillation (ODSD) method without a redundant generation process. First, we try to sample open-world data close to the original data's distribution by an adaptive sampling module. Then, we introduce a low-noise representation to alleviate the domain shifts and build a structured relationship of multiple data examples to exploit data knowledge. Extensive experiments on CIFAR-10, CIFAR-100, NYUv2, and ImageNet show that our ODSD method achieves state-of-the-art performance. Especially, we improve 1.50\\%-9.59\\% accuracy on the ImageNet dataset compared with the existing results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 518,
+    "label": 24,
+    "text": "Title: Hybrid Algorithm Selection and Hyperparameter Tuning on Distributed Machine Learning Resources: A Hierarchical Agent-based Approach\nAbstract: Algorithm selection and hyperparameter tuning are critical steps in both academic and applied machine learning. On the other hand, these steps are becoming ever increasingly delicate due to the extensive rise in the number, diversity, and distributedness of machine learning resources. Multi-agent systems, when applied to the design of machine learning platforms, bring about several distinctive characteristics such as scalability, flexibility, and robustness, just to name a few. This paper proposes a fully automatic and collaborative agent-based mechanism for selecting distributedly organized machine learning algorithms and simultaneously tuning their hyperparameters. Our method builds upon an existing agent-based hierarchical machine-learning platform and augments its query structure to support the aforementioned functionalities without being limited to specific learning, selection, and tuning mechanisms. We have conducted theoretical assessments, formal verification, and analytical study to demonstrate the correctness, resource utilization, and computational efficiency of our technique. According to the results, our solution is totally correct and exhibits linear time and space complexity in relation to the size of available resources. To provide concrete examples of how the proposed methodologies can effectively adapt and perform across a range of algorithmic options and datasets, we have also conducted a series of experiments using a system comprised of 24 algorithms and 9 datasets.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 519,
+    "label": 30,
+    "text": "Title: ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark\nAbstract: ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions. In this report, we aim to evaluate ChatGPT on the Grammatical Error Correction(GEC) task, and compare it with commercial GEC product (e.g., Grammarly) and state-of-the-art models (e.g., GECToR). By testing on the CoNLL2014 benchmark dataset, we find that ChatGPT performs not as well as those baselines in terms of the automatic evaluation metrics (e.g., $F_{0.5}$ score), particularly on long sentences. We inspect the outputs and find that ChatGPT goes beyond one-by-one corrections. Specifically, it prefers to change the surface expression of certain phrases or sentence structure while maintaining grammatical correctness. Human evaluation quantitatively confirms this and suggests that ChatGPT produces less under-correction or mis-correction issues but more over-corrections. These results demonstrate that ChatGPT is severely under-estimated by the automatic evaluation metrics and could be a promising tool for GEC.",
+    "neighbors": [
+      971,
+      1133,
+      1636,
+      1713,
+      1801,
+      1952,
+      2254,
+      2289
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 520,
+    "label": 23,
+    "text": "Title: The Double-Edged Sword of Diversity: How Diversity, Conflict, and Psychological Safety Impact Agile Software Teams\nAbstract: Team diversity can be seen as a double-edged sword. It brings additional cognitive resources to teams at the risk of increased conflict. Few studies have investigated how different types of diversity impact Agile software teams. This study views diversity through the lens of the categorization-elaboration model (CEM). We investigated how diversity in gender, age, role, and cultural background impacts team effectiveness and conflict, and how these associations are moderated by psychological safety. Our sample consisted of 1,118 participants from 161 teams and was analyzed with Covariance-Based Structural Equation Modeling (CB-SEM). We found a positive effect of age diversity on team effectiveness and gender diversity on relational conflict. Psychological safety contributed directly to effective teamwork and less conflict but did not moderate the diversity-effectiveness link. While our results are consistent with the CEM theory for age and gender diversity, other types of diversity did not yield similar results. We discuss several reasons for this, including curvilinear effects, moderators such as task interdependence, or the presence of a diversity mindset. With this paper, we argue that a dichotomous nature of diversity is oversimplified. Indeed, it is a complex relationship where context plays a pivotal role. A deeper understanding of diversity through the lens of theories such as the CEM may lead to more effective teamwork.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 521,
+    "label": 24,
+    "text": "Title: Best-Effort Adaptation\nAbstract: We study a problem of best-effort adaptation motivated by several applications and considerations, which consists of determining an accurate predictor for a target domain, for which a moderate amount of labeled samples are available, while leveraging information from another domain for which substantially more labeled samples are at one's disposal. We present a new and general discrepancy-based theoretical analysis of sample reweighting methods, including bounds holding uniformly over the weights. We show how these bounds can guide the design of learning algorithms that we discuss in detail. We further show that our learning guarantees and algorithms provide improved solutions for standard domain adaptation problems, for which few labeled data or none are available from the target domain. We finally report the results of a series of experiments demonstrating the effectiveness of our best-effort adaptation and domain adaptation algorithms, as well as comparisons with several baselines. We also discuss how our analysis can benefit the design of principled solutions for fine-tuning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 522,
+    "label": 16,
+    "text": "Title: MIMIC-IT: Multi-Modal In-Context Instruction Tuning\nAbstract: High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language tasks involving intricate visual scenes, a large quantity of diverse and creative instruction-response pairs should be imperative to tune vision-language models (VLMs). Nevertheless, the current availability of vision-language instruction-response pairs in terms of quantity, diversity, and creativity remains limited, posing challenges to the generalization of interactive VLMs. Here we present MultI-Modal In-Context Instruction Tuning (MIMIC-IT), a dataset comprising 2.8 million multimodal instruction-response pairs, with 2.2 million unique instructions derived from images and videos. Each pair is accompanied by multi-modal in-context information, forming conversational contexts aimed at empowering VLMs in perception, reasoning, and planning. The instruction-response collection process, dubbed as Syphus, is scaled using an automatic annotation pipeline that combines human expertise with GPT's capabilities. Using the MIMIC-IT dataset, we train a large VLM named Otter. Based on extensive evaluations conducted on vision-language benchmarks, it has been observed that Otter demonstrates remarkable proficiency in multi-modal perception, reasoning, and in-context learning. Human evaluation reveals it effectively aligns with the user's intentions. We release the MIMIC-IT dataset, instruction-response collection pipeline, benchmarks, and the Otter model.",
+    "neighbors": [
+      173,
+      319,
+      392,
+      602,
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+      836,
+      887,
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+      1039,
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+      1052,
+      1315,
+      1467,
+      1668,
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+      1863,
+      1899,
+      2030,
+      2036,
+      2155
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 523,
+    "label": 24,
+    "text": "Title: GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets\nAbstract: Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice. Existing methods partly address this issue through feature imputation. However, these techniques (i) assume uniformity of feature set across nodes, (ii) are transductive by nature, and (iii) fail to work when features are added or removed over time. In this work, we address these limitations through a novel GNN framework called GRAFENNE. GRAFENNE performs a novel allotropic transformation on the original graph, wherein the nodes and features are decoupled through a bipartite encoding. Through a carefully chosen message passing framework on the allotropic transformation, we make the model parameter size independent of the number of features and thereby inductive to both unseen nodes and features. We prove that GRAFENNE is at least as expressive as any of the existing message-passing GNNs in terms of Weisfeiler-Leman tests, and therefore, the additional inductivity to unseen features does not come at the cost of expressivity. In addition, as demonstrated over four real-world graphs, GRAFENNE empowers the underlying GNN with high empirical efficacy and the ability to learn in continual fashion over streaming feature sets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 524,
+    "label": 24,
+    "text": "Title: Hard Regularization to Prevent Collapse in Online Deep Clustering without Data Augmentation\nAbstract: Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. Successful existing models have employed various techniques to avoid this problem, most of which require data augmentation or which aim to make the average soft assignment across the dataset the same for each cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments. Using a Bayesian framework, we derive an intuitive optimization objective that can be straightforwardly included in the training of the encoder network. Tested on four image datasets, we show that it consistently avoids collapse more robustly than other methods and that it leads to more accurate clustering. We also conduct further experiments and analyses justifying our choice to regularize the hard cluster assignments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 525,
+    "label": 24,
+    "text": "Title: A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data\nAbstract: The interactive exploration of large and evolving datasets is challenging as relationships between underlying variables may not be fully understood. There may be hidden trends and patterns in the data that are worthy of further exploration and analysis.We present a system that methodically explores multiple combinations of variables using a searchlight subsect scan algorithm to identify outliers. An iterative k-means clustering algorithm is applied to features derived through a split-apply-combine paradigm used in the database literature. Outliers are identified as singleton or small clusters. This algorithm is swept across the dataset in a searchlight manner. The dimensions that contain outliers are combined with other dimensions using an efficient subset scan technique to gain further insight into the outliers. We illustrate this system by analyzing open health care data released by New York State. We apply our iterative k-means searchlight followed by subset scanning. Several anomalous trends in the data are identified, including cost overruns at specific hospitals, and increases in diagnoses such as suicides. These constitute novel findings in the literature, and are of potential use to regulatory agencies, policy makers and concerned citizens.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 526,
+    "label": 16,
+    "text": "Title: Event-Based Frame Interpolation with Ad-hoc Deblurring\nAbstract: The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene. Even though previous works recognize the utility of asynchronous event information for this task, they ignore the fact that motion may or may not result in blur in the input video to be interpolated, depending on the length of the exposure time of the frames and the speed of the motion, and assume either that the input video is sharp, restricting themselves to frame interpolation, or that it is blurry, including an explicit, separate deblurring stage before interpolation in their pipeline. We instead propose a general method for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that naturally incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. In addition, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments on the standard CoPro benchmark and on our dataset show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring and the joint task of interpolation and deblurring. Our code and dataset are available at https://github.com/AHupuJR/REFID.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 527,
+    "label": 4,
+    "text": "Title: Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales\nAbstract: The numbers of persons who can be enrolled by their iris patterns with no identity collisions is studied in relation to the biometric entropy extracted, and the decision operating threshold. The population size at which identity collision becomes likelier than not, given those variables, defines iris\"capacity.\"The general solution to this combinatorial problem is derived, in analogy with the well-known\"birthday problem.\"Its application to unique biometric identification on national population scales is shown, referencing empirical data from US NIST (National Institute of Standards and Technology) trials involving 1.2 trillion (1.2 x 10^(12) ) iris comparisons. The entropy of a given person's two iris patterns suffices for global identity uniqueness.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 528,
+    "label": 23,
+    "text": "Title: Hazards in Deep Learning Testing: Prevalence, Impact and Recommendations\nAbstract: Much research on Machine Learning testing relies on empirical studies that evaluate and show their potential. However, in this context empirical results are sensitive to a number of parameters that can adversely impact the results of the experiments and potentially lead to wrong conclusions (Type I errors, i.e., incorrectly rejecting the Null Hypothesis). To this end, we survey the related literature and identify 10 commonly adopted empirical evaluation hazards that may significantly impact experimental results. We then perform a sensitivity analysis on 30 influential studies that were published in top-tier SE venues, against our hazard set and demonstrate their criticality. Our findings indicate that all 10 hazards we identify have the potential to invalidate experimental findings, such as those made by the related literature, and should be handled properly. Going a step further, we propose a point set of 10 good empirical practices that has the potential to mitigate the impact of the hazards. We believe our work forms the first step towards raising awareness of the common pitfalls and good practices within the software engineering community and hopefully contribute towards setting particular expectations for empirical research in the field of deep learning testing.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 529,
+    "label": 24,
+    "text": "Title: QLoRA: Efficient Finetuning of Quantized LLMs\nAbstract: We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) double quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) paged optimziers to manage memory spikes. We use QLoRA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training.",
+    "neighbors": [
+      36,
+      183,
+      602,
+      619,
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+      1002,
+      1039,
+      1052,
+      1114,
+      1237,
+      1327,
+      1617,
+      1733,
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+      1906,
+      1907,
+      1950,
+      1972,
+      2042,
+      2251,
+      2257
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 530,
+    "label": 24,
+    "text": "Title: Faster Training of Neural ODEs Using Gau\u00df-Legendre Quadrature\nAbstract: Neural ODEs demonstrate strong performance in generative and time-series modelling. However, training them via the adjoint method is slow compared to discrete models due to the requirement of numerically solving ODEs. To speed neural ODEs up, a common approach is to regularise the solutions. However, this approach may affect the expressivity of the model; when the trajectory itself matters, this is particularly important. In this paper, we propose an alternative way to speed up the training of neural ODEs. The key idea is to speed up the adjoint method by using Gau{\\ss}-Legendre quadrature to solve integrals faster than ODE-based methods while remaining memory efficient. We also extend the idea to training SDEs using the Wong-Zakai theorem, by training a corresponding ODE and transferring the parameters. Our approach leads to faster training of neural ODEs, especially for large models. It also presents a new way to train SDE-based models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 531,
+    "label": 15,
+    "text": "Title: Neuromorphic Neuromodulation: Towards the next generation of on-device AI-revolution in electroceuticals\nAbstract: Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of artificial intelligence (AI) holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as the solution-in-the-making to resource-constraint implantable neuromodulation systems. This perspective introduces the concept of Neuromorphic Neuromodulation, a new breed of closed-loop responsive feedback system. It highlights its potential to revolutionize implantable brain-machine microsystems for patient-specific treatment",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 532,
+    "label": 4,
+    "text": "Title: Communication Lower Bounds for Cryptographic Broadcast Protocols\nAbstract: Broadcast protocols enable a set of $n$ parties to agree on the input of a designated sender, even facing attacks by malicious parties. In the honest-majority setting, randomization and cryptography were harnessed to achieve low-communication broadcast with sub-quadratic total communication and balanced sub-linear cost per party. However, comparatively little is known in the dishonest-majority setting. Here, the most communication-efficient constructions are based on Dolev and Strong (SICOMP '83), and sub-quadratic broadcast has not been achieved. On the other hand, the only nontrivial $\\omega(n)$ communication lower bounds are restricted to deterministic protocols, or against strong adaptive adversaries that can perform\"after the fact\"removal of messages. We provide new communication lower bounds in this space, which hold against arbitrary cryptography and setup assumptions, as well as a simple protocol showing near tightness of our first bound. 1) We demonstrate a tradeoff between resiliency and communication for protocols secure against $n-o(n)$ static corruptions. For example, $\\Omega(n\\cdot {\\sf polylog}(n))$ messages are needed when the number of honest parties is $n/{\\sf polylog}(n)$; $\\Omega(n\\sqrt{n})$ messages are needed for $O(\\sqrt{n})$ honest parties; and $\\Omega(n^2)$ messages are needed for $O(1)$ honest parties. Complementarily, we demonstrate broadcast with $O(n\\cdot{\\sf polylog}(n))$ total communication facing any constant fraction of static corruptions. 2) Our second bound considers $n/2 + k$ corruptions and a weakly adaptive adversary that cannot remove messages\"after the fact.\"We show that any broadcast protocol within this setting can be attacked to force an arbitrary party to send messages to $k$ other parties. This rules out, for example, broadcast facing 51% corruptions in which all non-sender parties have sublinear communication locality.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 533,
+    "label": 16,
+    "text": "Title: Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing\nAbstract: Contrastive pretraining on parallel image-text data has attained great success in vision-language processing (VLP), as exemplified by CLIP and related methods. However, prior explorations tend to focus on general domains in the web. Biomedical images and text are rather different, but publicly available datasets are small and skew toward chest X-ray, thus severely limiting progress. In this paper, we conducted by far the largest study on biomedical VLP, using 15 million figure-caption pairs extracted from biomedical research articles in PubMed Central. Our dataset (PMC-15M) is two orders of magnitude larger than existing biomedical image-text datasets such as MIMIC-CXR, and spans a diverse range of biomedical images. The standard CLIP method is suboptimal for the biomedical domain. We propose BiomedCLIP with domain-specific adaptations tailored to biomedical VLP. We conducted extensive experiments and ablation studies on standard biomedical imaging tasks from retrieval to classification to visual question-answering (VQA). BiomedCLIP established new state of the art in a wide range of standard datasets, substantially outperformed prior VLP approaches. Surprisingly, BiomedCLIP even outperformed radiology-specific state-of-the-art models such as BioViL on radiology-specific tasks such as RSNA pneumonia detection, thus highlighting the utility in large-scale pretraining across all biomedical image types. We will release our models at https://aka.ms/biomedclip to facilitate future research in biomedical VLP.",
+    "neighbors": [
+      1666,
+      1670,
+      2219
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 534,
+    "label": 25,
+    "text": "Title: Phase Repair for Time-Domain Convolutional Neural Networks in Music Super-Resolution\nAbstract: Audio Super-Resolution (SR) is an important topic in the field of audio processing. Many models are designed in time domain due to the advantage of waveform processing, such as being able to avoid the phase problem. However, in prior works it is shown that Time-Domain Convolutional Neural Network (TD-CNN) approaches tend to produce annoying artifacts in their output. In order to confirm the source of the artifact, we conduct an AB listening test and found phase to be the cause. We further propose Time-Domain Phase Repair (TD-PR) to improve TD-CNNs' performance by repairing the phase of the TD-CNNs' output. In this paper, we focus on the music SR task, which is challenging due to the wide frequency response and dynamic range of music. Our proposed method can handle various narrow-bandwidth from 2.5kHz to 4kHz with a target bandwidth of 8kHz. We conduct both objective and subjective evaluation to assess the proposed method. The objective evaluation result indicates the proposed method achieves the SR task effectively. Moreover, the proposed TD-PR obtains the much higher mean opinion scores than all TD-CNN baselines, which indicates that the proposed TD-PR significantly improves perceptual quality. Samples are available on the demo page.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 535,
+    "label": 16,
+    "text": "Title: Segment Anything Model for Medical Image Analysis: an Experimental Study\nAbstract: nan",
+    "neighbors": [
+      106,
+      613,
+      1006,
+      1863,
+      1932,
+      2272
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 536,
+    "label": 34,
+    "text": "Title: Computing matching statistics on Wheeler DFAs\nAbstract: Matching statistics were introduced to solve the approximate string matching problem, which is a recurrent subroutine in bioinformatics applications. In 2010, Ohlebusch et al. [SPIRE 2010] proposed a time and space efficient algorithm for computing matching statistics which relies on some components of a compressed suffix tree-notably, the longest common prefix (LCP) array. In this paper, we show how their algorithm can be generalized from strings to Wheeler deterministic finite automata. Most importantly, we introduce a notion of LCP array for Wheeler automata, thus establishing a first clear step towards extending (compressed) suffix tree functionalities to labeled graphs.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 537,
+    "label": 16,
+    "text": "Title: CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention\nAbstract: While features of different scales are perceptually important to visual inputs, existing vision transformers do not yet take advantage of them explicitly. To this end, we first propose a cross-scale vision transformer, CrossFormer. It introduces a cross-scale embedding layer (CEL) and a long-short distance attention (LSDA). On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features. On the other hand, LSDA splits the self-attention module into a short-distance one and a long-distance counterpart, which not only reduces the computational burden but also keeps both small-scale and large-scale features in the tokens. Moreover, through experiments on CrossFormer, we observe another two issues that affect vision transformers' performance, i.e. the enlarging self-attention maps and amplitude explosion. Thus, we further propose a progressive group size (PGS) paradigm and an amplitude cooling layer (ACL) to alleviate the two issues, respectively. The CrossFormer incorporating with PGS and ACL is called CrossFormer++. Extensive experiments show that CrossFormer++ outperforms the other vision transformers on image classification, object detection, instance segmentation, and semantic segmentation tasks. The code will be available at: https://github.com/cheerss/CrossFormer.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 538,
+    "label": 16,
+    "text": "Title: SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments\nAbstract: Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details. Existing methods in the field of clothing animation are limited to either static behavior or specific network models for individual garments, which hinders their applicability in real-world scenarios where diverse animated garments are required. Our proposed method overcomes these limitations by providing a unified framework that predicts dynamic behavior for different garments with arbitrary topology and looseness, resulting in versatile and realistic deformations. First, we observe that the problem of bias towards low frequency always hampers supervised learning and leads to overly smooth deformations. To address this issue, we introduce a frequency-control strategy from a spectral perspective that enhances the generation of high-frequency details of the deformation. In addition, to make the network highly generalizable and able to learn various clothing deformations effectively, we propose a spectral descriptor to achieve a generalized description of the global shape information. Building on the above strategies, we develop a dynamic clothing deformation estimator that integrates frequency-controllable attention mechanisms with long short-term memory. The estimator takes as input expressive features from garments and human bodies, allowing it to automatically output continuous deformations for diverse clothing types, independent of mesh topology or vertex count. Finally, we present a neural collision handling method to further enhance the realism of garments. Our experimental results demonstrate the effectiveness of our approach on a variety of free-swinging garments and its superiority over state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 539,
+    "label": 27,
+    "text": "Title: Distributed Optimization Methods for Multi-Robot Systems: Part II - A Survey\nAbstract: Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. This survey constitutes the second part of a two-part series on distributed optimization applied to multi-robot problems. In this paper, we survey three main classes of distributed optimization algorithms -- distributed first-order methods, distributed sequential convex programming methods, and alternating direction method of multipliers (ADMM) methods -- focusing on fully-distributed methods that do not require coordination or computation by a central computer. We describe the fundamental structure of each category and note important variations around this structure, designed to address its associated drawbacks. Further, we provide practical implications of noteworthy assumptions made by distributed optimization algorithms, noting the classes of robotics problems suitable for these algorithms. Moreover, we identify important open research challenges in distributed optimization, specifically for robotics problem.",
+    "neighbors": [
+      285,
+      2164
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 540,
+    "label": 24,
+    "text": "Title: adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems\nAbstract: Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy-sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user\u2019s private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability, and their preferences and behavior evolve. The effect of one privacy leak mitigation can differ for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depending on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off. On average, adaPARL improves the utility by while reducing the privacy leak by on average.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 541,
+    "label": 16,
+    "text": "Title: BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation\nAbstract: We present a large-scale video subtitle translation dataset, BigVideo, to facilitate the study of multi-modality machine translation. Compared with the widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger, consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also introduce two deliberately designed test sets to verify the necessity of visual information: Ambiguous with the presence of ambiguous words, and Unambiguous in which the text context is self-contained for translation. To better model the common semantics shared across texts and videos, we introduce a contrastive learning method in the cross-modal encoder. Extensive experiments on the BigVideo show that: a) Visual information consistently improves the NMT model in terms of BLEU, BLEURT, and COMET on both Ambiguous and Unambiguous test sets. b) Visual information helps disambiguation, compared to the strong text baseline on terminology-targeted scores and human evaluation. Dataset and our implementations are available at https://github.com/DeepLearnXMU/BigVideo-VMT.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 542,
+    "label": 33,
+    "text": "Title: A generic characterization of generalized unary temporal logic and two-variable first-order logic\nAbstract: We investigate an operator on classes of languages. For each class $C$, it outputs a new class $FO^2(I_C)$ associated with a variant of two-variable first-order logic equipped with a signature$I_C$ built from $C$. For $C = \\{\\emptyset, A^*\\}$, we get the variant $FO^2(<)$ equipped with the linear order. For $C = \\{\\emptyset, \\{\\varepsilon\\},A^+, A^*\\}$, we get the variant $FO^2(<,+1)$, which also includes the successor. If $C$ consists of all Boolean combinations of languages $A^*aA^*$ where $a$ is a letter, we get the variant $FO^2(<,Bet)$, which also includes ``between relations''. We prove a generic algebraic characterization of the classes $FO^2(I_C)$. It smoothly and elegantly generalizes the known ones for all aforementioned cases. Moreover, it implies that if $C$ has decidable separation (plus mild properties), then $FO^2(I_C)$ has a decidable membership problem. We actually work with an equivalent definition of \\fodc in terms of unary temporal logic. For each class $C$, we consider a variant $TL(C)$ of unary temporal logic whose future/past modalities depend on $C$ and such that $TL(C) = FO^2(I_C)$. Finally, we also characterize $FL(C)$ and $PL(C)$, the pure-future and pure-past restrictions of $TL(C)$. These characterizations as well imply that if \\Cs is a class with decidable separation, then $FL(C)$ and $PL(C)$ have decidable membership.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 543,
+    "label": 10,
+    "text": "Title: Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach\nAbstract: The Bin Packing Problem is a classic problem with wide industrial applicability. In fact, the efficient packing of items into bins is one of the toughest challenges in many logistic corporations and is a critical issue for reducing storage costs or improving vehicle space allocation. In this work, we resort to our previously published quantum-classical framework known as Q4RealBPP, and elaborate on the solving of real-world oriented instances of the Bin Packing Problem. With this purpose, this paper gravitates on the following characteristics: i) the existence of heterogeneous bins, ii) the extension of the framework to solve not only three-dimensional, but also one- and two-dimensional instances of the problem, iii) requirements for item-bin associations, and iv) delivery priorities. All these features have been tested in this paper, as well as the ability of Q4RealBPP to solve real-world oriented instances.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 544,
+    "label": 24,
+    "text": "Title: On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network\nAbstract: This paper explores the expressive power of deep neural networks through the framework of function compositions. We demonstrate that the repeated compositions of a single fixed-size ReLU network exhibit surprising expressive power, despite the limited expressive capabilities of the individual network itself. Specifically, we prove by construction that $\\mathcal{L}_2\\circ \\boldsymbol{g}^{\\circ r}\\circ \\boldsymbol{\\mathcal{L}}_1$ can approximate $1$-Lipschitz continuous functions on $[0,1]^d$ with an error $\\mathcal{O}(r^{-1/d})$, where $\\boldsymbol{g}$ is realized by a fixed-size ReLU network, $\\boldsymbol{\\mathcal{L}}_1$ and $\\mathcal{L}_2$ are two affine linear maps matching the dimensions, and $\\boldsymbol{g}^{\\circ r}$ denotes the $r$-times composition of $\\boldsymbol{g}$. Furthermore, we extend such a result to generic continuous functions on $[0,1]^d$ with the approximation error characterized by the modulus of continuity. Our results reveal that a continuous-depth network generated via a dynamical system has immense approximation power even if its dynamics function is time-independent and realized by a fixed-size ReLU network.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 545,
+    "label": 6,
+    "text": "Title: Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis\nAbstract: Construction technology researchers and forward-thinking companies are experimenting with collaborative robots (aka cobots), powered by artificial intelligence (AI), to explore various automation scenarios as part of the digital transformation of the industry. Intelligent cobots are expected to be the dominant type of robots in the future of work in construction. However, the black-box nature of AI-powered cobots and unknown technical and psychological aspects of introducing them to job sites are precursors to trust challenges. By analyzing the results of semi-structured interviews with construction practitioners using grounded theory, this paper investigates the characteristics of trustworthy AI-powered cobots in construction. The study found that while the key trust factors identified in a systematic literature review -- conducted previously by the authors -- resonated with the field experts and end users, other factors such as financial considerations and the uncertainty associated with change were also significant barriers against trusting AI-powered cobots in construction.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 546,
+    "label": 23,
+    "text": "Title: Learning to Represent Patches\nAbstract: Patch representation is crucial in automating various software engineering tasks, like determining patch accuracy or summarizing code changes. While recent research has employed deep learning for patch representation, focusing on token sequences or Abstract Syntax Trees (ASTs), they often miss the change's semantic intent and the context of modified lines. To bridge this gap, we introduce a novel method, Patcherizer. It delves into the intentions of context and structure, merging the surrounding code context with two innovative representations. These capture the intention in code changes and the intention in AST structural modifications pre and post-patch. This holistic representation aptly captures a patch's underlying intentions. Patcherizer employs graph convolutional neural networks for structural intention graph representation and transformers for intention sequence representation. We evaluated Patcherizer's embeddings' versatility in three areas: (1) Patch description generation, (2) Patch accuracy prediction, and (3) Patch intention identification. Our experiments demonstrate the representation's efficacy across all tasks, outperforming state-of-the-art methods. For example, in patch description generation, Patcherizer excels, showing an average boost of 19.39% in BLEU, 8.71% in ROUGE-L, and 34.03% in METEOR scores.",
+    "neighbors": [
+      4
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 547,
+    "label": 23,
+    "text": "Title: Systematically Detecting Packet Validation Vulnerabilities in Embedded Network Stacks\nAbstract: Embedded Network Stacks (ENS) enable low-resource devices to communicate with the outside world, facilitating the development of the Internet of Things and Cyber-Physical Systems. Some defects in ENS are thus high-severity cybersecurity vulnerabilities: they are remotely triggerable and can impact the physical world. While prior research has shed light on the characteristics of defects in many classes of software systems, no study has described the properties of ENS defects nor identified a systematic technique to expose them. The most common automated approach to detecting ENS defects is feedback-driven randomized dynamic analysis (\"fuzzing\"), a costly and unpredictable technique. This paper provides the first systematic characterization of cybersecurity vulnerabilities in ENS. We analyzed 61 vulnerabilities across 6 open-source ENS. Most of these ENS defects are concentrated in the transport and network layers of the network stack, require reaching different states in the network protocol, and can be triggered by only 1-2 modifications to a single packet. We therefore propose a novel systematic testing framework that focuses on the transport and network layers, uses seeds that cover a network protocol's states, and systematically modifies packet fields. We evaluated this framework on 4 ENS and replicated 12 of the 14 reported IP/TCP/UDP vulnerabilities. On recent versions of these ENSs, it discovered 7 novel defects (6 assigned CVES) during a bounded systematic test that covered all protocol states and made up to 3 modifications per packet. We found defects in 3 of the 4 ENS we tested that had not been found by prior fuzzing research. Our results suggest that fuzzing should be deferred until after systematic testing is employed.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 548,
+    "label": 4,
+    "text": "Title: Blockchain-enabled Parametric Solar Energy Insurance via Remote Sensing\nAbstract: Despite its popularity, the nature of solar energy is highly uncertain and weather dependent, affecting the business viability and investment of solar energy generation, especially for household users. To stabilize the income from solar energy generation, there have been limited traditional options, such as using energy storage to pool excessive solar energy in off-peak periods or financial derivatives from future markets to hedge energy prices. In this paper, we explore a novel idea of parametric solar energy insurance, by which solar panel owners can insure their solar energy generation based on a verifiable geographically specific index (surface solar irradiation). Parametric solar energy insurance offers opportunities of financial subsidies for insufficient solar energy generation and amortizes the fluctuations of renewable energy generation geographically. Furthermore, we propose to leverage blockchain and remote sensing (satellite imagery) to provide a publicly verifiable platform for solar energy insurance, which not only automates the underwriting and claims of a solar energy insurance policy, but also improves its accountability and transparency. We utilize the state-of-the-art succinct zero-knowledge proofs (zk-SNARK) to realize privacy-preserving blockchain-based solar energy insurance on real-world permissionless blockchain platform Ethereum.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 549,
+    "label": 30,
+    "text": "Title: ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations\nAbstract: This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. Given ChatGPT's promising performance across various tasks, we conduct extensive evaluations on the whole test sets of 13 datasets, including temporal and causal relations, PDTB2.0-based and dialogue-based discourse relations, and downstream applications on discourse understanding. To achieve reliable results, we adopt three tailored prompt templates for each task, including the zero-shot prompt template, zero-shot prompt engineering (PE) template, and in-context learning (ICL) prompt template, to establish the initial baseline scores for all popular sentence-pair relation classification tasks for the first time. We find that ChatGPT exhibits strong performance in detecting and reasoning about causal relations, while it may not be proficient in identifying the temporal order between two events. It can recognize most discourse relations with existing explicit discourse connectives, but the implicit discourse relation still remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue discourse parsing task that requires structural understanding in a dialogue before being aware of the discourse relation.",
+    "neighbors": [
+      685,
+      1133,
+      1384,
+      1863,
+      1952,
+      1992,
+      2094,
+      2158,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 550,
+    "label": 24,
+    "text": "Title: Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 551,
+    "label": 30,
+    "text": "Title: Evaluate What You Can't Evaluate: Unassessable Generated Responses Quality\nAbstract: LLMs (large language models) such as ChatGPT have shown remarkable language understanding and generation capabilities. Although reference-free evaluators based on LLMs show better human alignment than traditional reference-based evaluators, there are many challenges in using reference-free evaluators based on LLMs. Reference-free evaluators are more suitable for open-ended examples with different semantics responses. But not all examples are open-ended. For closed-ended examples with unique correct semantic response, reference-free evaluators will still consider it high quality when giving a response that is inconsistent with the facts and the semantic of reference. In order to comprehensively evaluate the reliability of evaluators based on LLMs, we construct two adversarial meta-evaluation dialogue generation datasets KdConv-ADV and DSTC7-ADV based on KdConv and DSTC7-AVSD, respectively. Compared to previous meta-evaluation benchmarks, KdConv-ADV and DSTC7-ADV are much more challenging since they requires evaluators to be able to reasonably evaluate closed-ended examples with the help of external knowledge or even its own knowledge. Empirical results show that the ability of LLMs to identify unreasonable responses is insufficient. There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.",
+    "neighbors": [
+      975,
+      1052,
+      1384,
+      1727,
+      1863,
+      1949,
+      2042,
+      2087,
+      2254
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 552,
+    "label": 10,
+    "text": "Title: A Divide-Align-Conquer Strategy for Program Synthesis\nAbstract: A major bottleneck in search-based program synthesis is the exponentially growing search space which makes learning large programs intractable. Humans mitigate this problem by leveraging the compositional nature of the real world: In structured domains, a logical speci\ufb01cation can often be decomposed into smaller, complemen-tary solution programs. We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems. For each example, we search for a decomposition into smaller units which maximizes the reconstruction accuracy in the output under a latent task program. A structural alignment of the constituent parts in the input and output leads to pairwise correspondences used to guide the program synthesis search. In order to align the input/output structures, we make use of the Structure-Mapping Theory (SMT), a formal model of human analogical reasoning which originated in the cognitive sciences. We show that decomposition-driven program synthesis with structural alignment outperforms Inductive Logic Programming (ILP) baselines on string transformation tasks even with minimal knowledge priors. Unlike existing methods, the predictive accuracy of our agent monotonically increases for additional examples and achieves an average time complexity of O ( m ) in the number m of partial programs for highly structured domains such as strings. We extend this method to the complex setting of visual reasoning in the Abstraction and Reasoning Corpus (ARC) for which ILP methods were previously infeasible.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 553,
+    "label": 30,
+    "text": "Title: Modeling Speaker-Listener Interaction for Backchannel Prediction\nAbstract: We present our latest findings on backchannel modeling novelly motivated by the canonical use of the minimal responses Yeah and Uh-huh in English and their correspondent tokens in German, and the effect of encoding the speaker-listener interaction. Backchanneling theories emphasize the active and continuous role of the listener in the course of the conversation, their effects on the speaker's subsequent talk, and the consequent dynamic speaker-listener interaction. Therefore, we propose a neural-based acoustic backchannel classifier on minimal responses by processing acoustic features from the speaker speech, capturing and imitating listeners' backchanneling behavior, and encoding speaker-listener interaction. Our experimental results on the Switchboard and GECO datasets reveal that in almost all tested scenarios the speaker or listener behavior embeddings help the model make more accurate backchannel predictions. More importantly, a proper interaction encoding strategy, i.e., combining the speaker and listener embeddings, leads to the best performance on both datasets in terms of F1-score.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 554,
+    "label": 28,
+    "text": "Title: Applications of Absorptive Reconfigurable Intelligent Surfaces in Interference Mitigation and Physical Layer Security\nAbstract: This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; (3) The physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 555,
+    "label": 30,
+    "text": "Title: TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model\nAbstract: Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. RNNs are fast but monotonic (cannot correct earlier output, which can be necessary in incremental processing). Transformers, on the other hand, consume whole sequences, and hence are by nature non-incremental. A restart-incremental interface that repeatedly passes longer input prefixes can be used to obtain partial outputs, while providing the ability to revise. However, this method becomes costly as the sentence grows longer. In this work, we propose the Two-pass model for AdaPtIve Revision (TAPIR) and introduce a method to obtain an incremental supervision signal for learning an adaptive revision policy. Experimental results on sequence labelling show that our model has better incremental performance and faster inference speed compared to restart-incremental Transformers, while showing little degradation on full sequences.",
+    "neighbors": [
+      1577
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 556,
+    "label": 6,
+    "text": "Title: The Role of Communication and Reference Songs in the Mixing Process: Insights from Professional Mix Engineers\nAbstract: Effective music mixing requires technical and creative finesse, but clear communication with the client is crucial. The mixing engineer must grasp the client's expectations, and preferences, and collaborate to achieve the desired sound. The tacit agreement for the desired sound of the mix is often established using guides like reference songs and demo mixes exchanged between the artist and the engineer and sometimes verbalised using semantic terms. This paper presents the findings of a two-phased exploratory study aimed at understanding how professional mixing engineers interact with clients and use their feedback to guide the mixing process. For phase one, semi-structured interviews were conducted with five mixing engineers with the aim of gathering insights about their communication strategies, creative processes, and decision-making criteria. Based on the inferences from these interviews, an online questionnaire was designed and administered to a larger group of 22 mixing engineers during the second phase. The results of this study shed light on the importance of collaboration, empathy, and intention in the mixing process, and can inform the development of smart multi-track mixing systems that better support these practices. By highlighting the significance of these findings, this paper contributes to the growing body of research on the collaborative nature of music production and provides actionable recommendations for the design and implementation of innovative mixing tools.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 557,
+    "label": 24,
+    "text": "Title: An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs\nAbstract: In recent years, Transformer-based language models have become the standard approach for natural language processing tasks. However, stringent throughput and latency requirements in industrial applications are limiting their adoption. To mitigate the gap, model compression techniques such as structured pruning are being used to improve inference efficiency. However, most existing neural network inference runtimes lack adequate support for structured sparsity. In this paper, we propose an efficient sparse deep learning inference software stack for Transformer-based language models where the weights are pruned with constant block size. Our sparse software accelerator leverages Intel Deep Learning Boost to maximize the performance of sparse matrix - dense matrix multiplication (commonly abbreviated as SpMM) on CPUs. Our SpMM kernel outperforms the existing sparse libraries (oneMKL, TVM, and LIBXSMM) by an order of magnitude on a wide range of GEMM shapes under 5 representative sparsity ratios (70%, 75%, 80%, 85%, 90%). Moreover, our SpMM kernel shows up to 5x speedup over dense GEMM kernel of oneDNN, a well-optimized dense library widely used in industry. We apply our sparse accelerator on widely-used Transformer-based language models including Bert-Mini, DistilBERT, Bert-Base, and BERT-Large. Our sparse inference software shows up to 1.5x speedup over Neural Magic's Deepsparse under same configurations on Xeon on Amazon Web Services under proxy production latency constraints. We also compare our solution with two framework-based inference solutions, ONNX Runtime and PyTorch, and demonstrate up to 37x speedup over ONNX Runtime and 345x over PyTorch on Xeon under the latency constraints. All the source code is publicly available on Github: https://github.com/intel/intel-extension-for-transformers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 558,
+    "label": 24,
+    "text": "Title: Local Learning with Neuron Groups\nAbstract: Traditional deep network training methods optimize a monolithic objective function jointly for all the components. This can lead to various inefficiencies in terms of potential parallelization. Local learning is an approach to model-parallelism that removes the standard end-to-end learning setup and utilizes local objective functions to permit parallel learning amongst model components in a deep network. Recent works have demonstrated that variants of local learning can lead to efficient training of modern deep networks. However, in terms of how much computation can be distributed, these approaches are typically limited by the number of layers in a network. In this work we propose to study how local learning can be applied at the level of splitting layers or modules into sub-components, adding a notion of width-wise modularity to the existing depth-wise modularity associated with local learning. We investigate local-learning penalties that permit such models to be trained efficiently. Our experiments on the CIFAR-10, CIFAR-100, and Imagenet32 datasets demonstrate that introducing width-level modularity can lead to computational advantages over existing methods based on local learning and opens new opportunities for improved model-parallel distributed training. Code is available at: https://github.com/adeetyapatel12/GN-DGL.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 559,
+    "label": 30,
+    "text": "Title: Mitigating Temporal Misalignment by Discarding Outdated Facts\nAbstract: While large language models are able to retain vast amounts of world knowledge seen during pretraining, such knowledge is prone to going out of date and is nontrivial to update. Furthermore, these models are often used under temporal misalignment, tasked with answering questions about the present, despite having only been trained on data collected in the past. To mitigate the effects of temporal misalignment, we propose fact duration prediction: the task of predicting how long a given fact will remain true. In our experiments, we demonstrate how identifying facts that are prone to rapid change can help models avoid from reciting outdated information and identify which predictions require seeking out up-to-date knowledge sources. We also show how modeling fact duration improves calibration for knowledge-intensive tasks, such as open-retrieval question answering, under temporal misalignment by discarding volatile facts. Our data and code will be released publicly at https://github.com/mikejqzhang/mitigating_misalignment.",
+    "neighbors": [
+      1055,
+      1863
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 560,
+    "label": 24,
+    "text": "Title: Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization\nAbstract: Low Rank Decomposition (LRD) is a model compression technique applied to the weight tensors of deep learning models in order to reduce the number of trainable parameters and computational complexity. However, due to high number of new layers added to the architecture after applying LRD, it may not lead to a high training/inference acceleration if the decomposition ranks are not small enough. The issue is that using small ranks increases the risk of significant accuracy drop after decomposition. In this paper, we propose two techniques for accelerating low rank decomposed models without requiring to use small ranks for decomposition. These methods include rank optimization and sequential freezing of decomposed layers. We perform experiments on both convolutional and transformer-based models. Experiments show that these techniques can improve the model throughput up to 60% during training and 37% during inference when combined together while preserving the accuracy close to that of the original models",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 561,
+    "label": 24,
+    "text": "Title: Traffic4cast at NeurIPS 2022 - Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors\nAbstract: The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 562,
+    "label": 24,
+    "text": "Title: Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes\nAbstract: We prove that the set of functions representable by ReLU neural networks with integer weights strictly increases with the network depth while allowing arbitrary width. More precisely, we show that $\\lceil\\log_2(n)\\rceil$ hidden layers are indeed necessary to compute the maximum of $n$ numbers, matching known upper bounds. Our results are based on the known duality between neural networks and Newton polytopes via tropical geometry. The integrality assumption implies that these Newton polytopes are lattice polytopes. Then, our depth lower bounds follow from a parity argument on the normalized volume of faces of such polytopes.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 563,
+    "label": 6,
+    "text": "Title: \"An Adapt-or-Die Type of Situation\": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals\nAbstract: Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 49 sub-themes on professionals' perception, adoption and use of TTIG systems in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG to benefit people and games as cultural artefacts.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 564,
+    "label": 28,
+    "text": "Title: Quantum Multiple Access Wiretap Channel: On the One-Shot Achievable Secrecy Rate Regions\nAbstract: In this paper, we want to investigate classical-quantum multiple access wiretap channels (CQ-MA-WTC) under one-shot setting. In this regard, we analyze the CQ-MA-WTC using simultaneous position-based decoder for reliable decoding and using a newly introduced technique in order to decode securely. Also, for the sake of comparison, we analyze the CQ-MA-WTC using Sen's one-shot joint typicality lemma for reliable decoding. The simultaneous position-based decoder tends to a multiple hypothesis testing problem. Also, using convex splitting to analyze the privacy criteria in a simultaneous scenario becomes problematic. To overcome both problems, we first introduce a new channel that can be considered as a dual to the CQ-MA-WTC. This channel is called a point-to-point quantum wiretap channel with multiple messages (PP-QWTC). In the following, as a strategy to solve the problem, we also investigate and analyze quantum broadcast channels (QBCs) under the one-shot setting.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 565,
+    "label": 24,
+    "text": "Title: Partial-Label Regression\nAbstract: Partial-label learning is a popular weakly supervised learning setting that allows each training example to be annotated with a set of candidate labels. Previous studies on partial-label learning only focused on the classification setting where candidate labels are all discrete, which cannot handle continuous labels with real values. In this paper, we provide the first attempt to investigate partial-label regression, where each training example is annotated with a set of real-valued candidate labels. To solve this problem, we first propose a simple baseline method that takes the average loss incurred by candidate labels as the predictive loss. The drawback of this method lies in that the loss incurred by the true label may be overwhelmed by other false labels. To overcome this drawback, we propose an identification method that takes the least loss incurred by candidate labels as the predictive loss. We further improve it by proposing a progressive identification method to differentiate candidate labels using progressively updated weights for incurred losses. We prove that the latter two methods are model-consistent and provide convergence analysis showing the optimal parametric convergence rate. Our proposed methods are theoretically grounded and can be compatible with any models, optimizers, and losses. Experiments validate the effectiveness of our proposed methods.",
+    "neighbors": [
+      509
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 566,
+    "label": 30,
+    "text": "Title: LIMA: Less Is More for Alignment\nAbstract: Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences. We measure the relative importance of these two stages by training LIMA, a 65B parameter LLaMa language model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling. LIMA demonstrates remarkably strong performance, learning to follow specific response formats from only a handful of examples in the training data, including complex queries that range from planning trip itineraries to speculating about alternate history. Moreover, the model tends to generalize well to unseen tasks that did not appear in the training data. In a controlled human study, responses from LIMA are either equivalent or strictly preferred to GPT-4 in 43% of cases; this statistic is as high as 58% when compared to Bard and 65% versus DaVinci003, which was trained with human feedback. Taken together, these results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output.",
+    "neighbors": [
+      840,
+      855,
+      1002,
+      1052,
+      1346,
+      1430,
+      1516,
+      1892,
+      1907,
+      1950,
+      1972,
+      2038,
+      2122,
+      2208,
+      2305
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 567,
+    "label": 24,
+    "text": "Title: A Category-theoretical Meta-analysis of Definitions of Disentanglement\nAbstract: Disentangling the factors of variation in data is a fundamental concept in machine learning and has been studied in various ways by different researchers, leading to a multitude of definitions. Despite the numerous empirical studies, more theoretical research is needed to fully understand the defining properties of disentanglement and how different definitions relate to each other. This paper presents a meta-analysis of existing definitions of disentanglement, using category theory as a unifying and rigorous framework. We propose that the concepts of the cartesian and monoidal products should serve as the core of disentanglement. With these core concepts, we show the similarities and crucial differences in dealing with (i) functions, (ii) equivariant maps, (iii) relations, and (iv) stochastic maps. Overall, our meta-analysis deepens our understanding of disentanglement and its various formulations and can help researchers navigate different definitions and choose the most appropriate one for their specific context.",
+    "neighbors": [
+      1534
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 568,
+    "label": 23,
+    "text": "Title: Divide and Conquer the EmpiRE: A Community-Maintainable Knowledge Graph of Empirical Research in Requirements Engineering\nAbstract: [Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its\"current\"state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020 - 2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000 - 2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 569,
+    "label": 27,
+    "text": "Title: Unscented Optimal Control for 3D Coverage Planning with an Autonomous UAV Agent\nAbstract: We propose a novel probabilistically robust controller for the guidance of an unmanned aerial vehicle (UAV) in coverage planning missions, which can simultaneously optimize both the UAV\u2019s motion, and camera control inputs for the 3D coverage of a given object of interest. Specifically, the coverage planning problem is formulated in this work as an optimal control problem with logical constraints to enable the UAV agent to jointly: a) select a series of discrete camera field-of-view states which satisfy a set of coverage constraints, and b) optimize its motion control inputs according to a specified mission objective. We show how this hybrid optimal control problem can be solved with standard optimization tools by converting the logical expressions in the constraints into equality/inequality constraints involving only continuous variables. Finally, probabilistic robustness is achieved by integrating the unscented transformation to the proposed controller, thus enabling the design of robust open-loop coverage plans which take into account the future posterior distribution of the UAV\u2019s state inside the planning horizon.",
+    "neighbors": [
+      512,
+      2293
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 570,
+    "label": 30,
+    "text": "Title: Specializing Smaller Language Models towards Multi-Step Reasoning\nAbstract: The surprising ability of Large Language Models (LLMs) to perform well on complex reasoning with only few-shot chain-of-thought prompts is believed to emerge only in very large-scale models (100+ billion parameters). We show that such abilities can, in fact, be distilled down from GPT-3.5 ($\\ge$ 175B) to T5 variants ($\\le$ 11B). We propose model specialization, to specialize the model's ability towards a target task. The hypothesis is that large models (commonly viewed as larger than 100B) have strong modeling power, but are spread on a large spectrum of tasks. Small models (commonly viewed as smaller than 10B) have limited model capacity, but if we concentrate their capacity on a specific target task, the model can achieve a decent improved performance. We use multi-step math reasoning as our testbed because it is a very typical emergent ability. We show two important aspects of model abilities: (1). there exists a very complex balance/ tradeoff between language models' multi-dimensional abilities; (2). by paying the price of decreased generic ability, we can clearly lift up the scaling curve of models smaller than 10B towards a specialized multi-step math reasoning ability. We further give comprehensive discussions about important design choices for better generalization, including the tuning data format, the start model checkpoint, and a new model selection method. We hope our practice and discoveries can serve as an important attempt towards specialized smaller models in the new research paradigm set by LLMs.",
+    "neighbors": [
+      3,
+      619,
+      1011,
+      1617,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 571,
+    "label": 2,
+    "text": "Title: Investigations into Proof Structures\nAbstract: We introduce and elaborate a novel formalism for the manipulation and analysis of proofs as objects in a global manner. In this first approach the formalism is restricted to first-order problems characterized by condensed detachment. It is applied in an exemplary manner to a coherent and comprehensive formal reconstruction and analysis of historical proofs of a widely-studied problem due to {\\L}ukasiewicz. The underlying approach opens the door towards new systematic ways of generating lemmas in the course of proof search to the effects of reducing the search effort and finding shorter proofs. Among the numerous reported experiments along this line, a proof of {\\L}ukasiewicz's problem was automatically discovered that is much shorter than any proof found before by man or machine.",
+    "neighbors": [
+      1550
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 572,
+    "label": 10,
+    "text": "Title: Gradient strikes back: How filtering out high frequencies improves explanations\nAbstract: Recent years have witnessed an explosion in the development of novel prediction-based attribution methods, which have slowly been supplanting older gradient-based methods to explain the decisions of deep neural networks. However, it is still not clear why prediction-based methods outperform gradient-based ones. Here, we start with an empirical observation: these two approaches yield attribution maps with very different power spectra, with gradient-based methods revealing more high-frequency content than prediction-based methods. This observation raises multiple questions: What is the source of this high-frequency information, and does it truly reflect decisions made by the system? Lastly, why would the absence of high-frequency information in prediction-based methods yield better explainability scores along multiple metrics? We analyze the gradient of three representative visual classification models and observe that it contains noisy information emanating from high-frequencies. Furthermore, our analysis reveals that the operations used in Convolutional Neural Networks (CNNs) for downsampling appear to be a significant source of this high-frequency content -- suggesting aliasing as a possible underlying basis. We then apply an optimal low-pass filter for attribution maps and demonstrate that it improves gradient-based attribution methods. We show that (i) removing high-frequency noise yields significant improvements in the explainability scores obtained with gradient-based methods across multiple models -- leading to (ii) a novel ranking of state-of-the-art methods with gradient-based methods at the top. We believe that our results will spur renewed interest in simpler and computationally more efficient gradient-based methods for explainability.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 573,
+    "label": 27,
+    "text": "Title: Guidance & Control Networks for Time-Optimal Quadcopter Flight\nAbstract: Reaching fast and autonomous flight requires computationally efficient and robust algorithms. To this end, we train Guidance&Control Networks to approximate optimal control policies ranging from energy-optimal to time-optimal flight. We show that the policies become more difficult to learn the closer we get to the time-optimal 'bang-bang' control profile. We also assess the importance of knowing the maximum angular rotor velocity of the quadcopter and show that over- or underestimating this limit leads to less robust flight. We propose an algorithm to identify the current maximum angular rotor velocity onboard and a network that adapts its policy based on the identified limit. Finally, we extend previous work on Guidance&Control Networks by learning to take consecutive waypoints into account. We fly a 4x3m track in similar lap times as the differential-flatness-based minimum snap benchmark controller while benefiting from the flexibility that Guidance&Control Networks offer.",
+    "neighbors": [
+      1569
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 574,
+    "label": 10,
+    "text": "Title: The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models\nAbstract: Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. Nevertheless, their relative inability to defend against adversarial attacks has spurred skepticism about their natural language understanding. In this paper, we ask whether training with unanswerable questions in SQuAD 2.0 can help improve the robustness of MRC models against adversarial attacks. To explore that question, we fine-tune three state-of-the-art language models on either SQuAD 1.1 or SQuAD 2.0 and then evaluate their robustness under adversarial attacks. Our experiments reveal that current models fine-tuned on SQuAD 2.0 do not initially appear to be any more robust than ones fine-tuned on SQuAD 1.1, yet they reveal a measure of hidden robustness that can be leveraged to realize actual performance gains. Furthermore, we find that robustness of models fine-tuned on SQuAD 2.0 extends on additional out-of-domain datasets. Finally, we introduce a new adversarial attack to reveal of SQuAD 2.0 that current MRC models are learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 575,
+    "label": 24,
+    "text": "Title: Bounce: a Reliable Bayesian Optimization Algorithm for Combinatorial and Mixed Spaces\nAbstract: Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions with mixed and combinatorial input spaces. While Bayesian optimization has recently made significant progress in solving such problems, an in-depth analysis reveals that the current state-of-the-art methods are not reliable. Their performances degrade substantially when the unknown optima of the function do not have a certain structure. To fill the need for a reliable algorithm for combinatorial and mixed spaces, this paper proposes Bounce that relies on a novel map of various variable types into nested embeddings of increasing dimensionality. Comprehensive experiments show that Bounce reliably achieves and often even improves upon state-of-the-art performance on a variety of high-dimensional problems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 576,
+    "label": 16,
+    "text": "Title: D2TV: Dual Knowledge Distillation and Target-oriented Vision Modeling for Many-to-Many Multimodal Summarization\nAbstract: Many-to-many multimodal summarization (M$^3$S) task aims to generate summaries in any language with document inputs in any language and the corresponding image sequence, which essentially comprises multimodal monolingual summarization (MMS) and multimodal cross-lingual summarization (MXLS) tasks. Although much work has been devoted to either MMS or MXLS and has obtained increasing attention in recent years, little research pays attention to the M$^3$S task. Besides, existing studies mainly focus on 1) utilizing MMS to enhance MXLS via knowledge distillation without considering the performance of MMS or 2) improving MMS models by filtering summary-unrelated visual features with implicit learning or explicitly complex training objectives. In this paper, we first introduce a general and practical task, i.e., M$^3$S. Further, we propose a dual knowledge distillation and target-oriented vision modeling framework for the M$^3$S task. Specifically, the dual knowledge distillation method guarantees that the knowledge of MMS and MXLS can be transferred to each other and thus mutually prompt both of them. To offer target-oriented visual features, a simple yet effective target-oriented contrastive objective is designed and responsible for discarding needless visual information. Extensive experiments on the many-to-many setting show the effectiveness of the proposed approach. Additionally, we will contribute a many-to-many multimodal summarization (M$^3$Sum) dataset.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 577,
+    "label": 24,
+    "text": "Title: Timing Process Interventions with Causal Inference and Reinforcement Learning\nAbstract: The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process interventions are the cornerstones of effective optimization. Prescriptive process monitoring (PresPM) is the sub-field of process mining that concentrates on process optimization. The emerging PresPM literature identifies state-of-the-art methods, causal inference (CI) and reinforcement learning (RL), without presenting a quantitative comparison. Most experiments are carried out using historical data, causing problems with the accuracy of the methods' evaluations and preempting online RL. Our contribution consists of experiments on timed process interventions with synthetic data that renders genuine online RL and the comparison to CI possible, and allows for an accurate evaluation of the results. Our experiments reveal that RL's policies outperform those from CI and are more robust at the same time. Indeed, the RL policies approach perfect policies. Unlike CI, the unaltered online RL approach can be applied to other, more generic PresPM problems such as next best activity recommendations. Nonetheless, CI has its merits in settings where online learning is not an option.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 578,
+    "label": 8,
+    "text": "Title: Target Acquired? Evaluating Target Generation Algorithms for IPv6\nAbstract: Internet measurements are a crucial foundation of IPv6-related research. Due to the infeasibility of full address space scans for IPv6 however, those measurements rely on collections of reliably responsive, unbiased addresses, as provided e.g., by the IPv6 Hitlist service. Although used for various use cases, the hitlist provides an unfiltered list of responsive addresses, the hosts behind which can come from a range of different networks and devices, such as web servers, customer-premises equipment (CPE) devices, and Internet infrastructure. In this paper, we demonstrate the importance of tailoring hitlists in accordance with the research goal in question. By using PeeringDB we classify hitlist addresses into six different network categories, uncovering that 42% of hitlist addresses are in ISP networks. Moreover, we show the different behavior of those addresses depending on their respective category, e.g., ISP addresses exhibiting a relatively low lifetime. Furthermore, we analyze different Target Generation Algorithms (TGAs), which are used to increase the coverage of IPv6 measurements by generating new responsive targets for scans. We evaluate their performance under various conditions and find generated addresses to show vastly differing responsiveness levels for different TGAs.",
+    "neighbors": [
+      372
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 579,
+    "label": 16,
+    "text": "Title: A Reproducible Extraction of Training Images from Diffusion Models\nAbstract: Recently, Carlini et al. demonstrated the widely used model Stable Diffusion can regurgitate real training samples, which is troublesome from a copyright perspective. In this work, we provide an efficient extraction attack on par with the recent attack, with several order of magnitudes less network evaluations. In the process, we expose a new phenomena, which we dub template verbatims, wherein a diffusion model will regurgitate a training sample largely in tact. Template verbatims are harder to detect as they require retrieval and masking to correctly label. Furthermore, they are still generated by newer systems, even those which de-duplicate their training set, and we give insight into why they still appear during generation. We extract training images from several state of the art systems, including Stable Diffusion 2.0, Deep Image Floyd, and finally Midjourney v4. We release code to verify our extraction attack, perform the attack, as well as all extracted prompts at \\url{https://github.com/ryanwebster90/onestep-extraction}.",
+    "neighbors": [
+      315,
+      736,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 580,
+    "label": 30,
+    "text": "Title: Towards Codable Text Watermarking for Large Language Models\nAbstract: As large language models (LLMs) generate texts with increasing fluency and realism, there is a growing need to identify the source of texts to prevent the abuse of LLMs. Text watermarking techniques have proven reliable in distinguishing whether a text is generated by LLMs by injecting hidden patterns into the generated texts. However, we argue that existing watermarking methods for LLMs are encoding-inefficient (only contain one bit of information - whether it is generated from an LLM or not) and cannot flexibly meet the diverse information encoding needs (such as encoding model version, generation time, user id, etc.) in different LLMs application scenarios. In this work, we conduct the first systematic study on the topic of Codable Text Watermarking for LLMs (CTWL) that allows text watermarks to carry more customizable information. First of all, we study the taxonomy of LLM watermarking technology and give a mathematical formulation for CTWL. Additionally, we provide a comprehensive evaluation system for CTWL: (1) watermarking success rate, (2) robustness against various corruptions, (3) coding rate of payload information, (4) encoding and decoding efficiency, (5) impacts on the quality of the generated text. To meet the requirements of these non-Pareto-improving metrics, we devise a CTWL method named Balance-Marking, based on the motivation of ensuring that available and unavailable vocabularies for encoding information have approximately equivalent probabilities. Compared to the random vocabulary partitioning extended from the existing work, a probability-balanced vocabulary partition can significantly improve the quality of the generated text. Extensive experimental results have shown that our method outperforms a direct baseline under comprehensive evaluation.",
+    "neighbors": [
+      42,
+      1052,
+      1487,
+      1600,
+      2044
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 581,
+    "label": 30,
+    "text": "Title: Hi Sheldon! Creating Deep Personalized Characters from TV Shows\nAbstract: Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big Bang Theory, as a DEEP copy from appearance to personality. Towards this fantastic multimodal chatting scenario, we propose a novel task, named Deep Personalized Character Creation (DPCC): creating multimodal chat personalized characters from multimodal data such as TV shows. Specifically, given a single- or multi-modality input (text, audio, video), the goal of DPCC is to generate a multi-modality (text, audio, video) response, which should be well-matched the personality of a specific character such as Sheldon, and of high quality as well. To support this novel task, we further collect a character centric multimodal dialogue dataset, named Deep Personalized Character Dataset (DPCD), from TV shows. DPCD contains character-specific multimodal dialogue data of ~10k utterances and ~6 hours of audio/video per character, which is around 10 times larger compared to existing related datasets.On DPCD, we present a baseline method for the DPCC task and create 5 Deep personalized digital Characters (DeepCharacters) from Big Bang TV Shows. We conduct both subjective and objective experiments to evaluate the multimodal response from DeepCharacters in terms of characterization and quality. The results demonstrates that, on our collected DPCD dataset, the proposed baseline can create personalized digital characters for generating multimodal response.Our collected DPCD dataset, the code of data collection and our baseline will be published soon.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 582,
+    "label": 30,
+    "text": "Title: Decouple Non-parametric Knowledge Distillation For End-to-end Speech Translation\nAbstract: Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training. However, transcriptions are not always available, and how to improve the ST model performance without transcription, i.e., data efficiency, has rarely been studied in the literature. In this paper, we propose Decoupled Non-parametric Knowledge Distillation (DNKD) from data perspective to improve the data efficiency. Our method follows the knowledge distillation paradigm. However, instead of obtaining the teacher distribution from a sophisticated MT model, we construct it from a non-parametric datastore via k-Nearest-Neighbor (kNN) retrieval, which removes the dependence on transcription and MT model. Then we decouple the classic knowledge distillation loss into target and non-target distillation to enhance the effect of the knowledge among non-target logits, which is the prominent\"dark knowledge\". Experiments on MuST-C corpus show that, the proposed method can achieve consistent improvement over the strong baseline without requiring any transcription.",
+    "neighbors": [
+      228,
+      637
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 583,
+    "label": 16,
+    "text": "Title: Visual-Language Prompt Tuning with Knowledge-Guided Context Optimization\nAbstract: Prompt tuning is an effective way to adapt the pretrained visual-language model (VLM) to the downstream task using task-related textual tokens. Representative CoOp-based work combines the learnable textual tokens with the class tokens to obtain specific textual knowledge. However, the specific textual knowledge is worse generalization to the unseen classes because it forgets the essential general textual knowledge having a strong generalization ability. To tackle this issue, we introduce a novel Knowledge-guided Context Optimization (KgCoOp) to enhance the generalization ability of the learnable prompt for unseen classes. The key insight of KgCoOp is that the forgetting about essential knowledge can be alleviated by reducing the discrepancy between the learnable prompt and the hand-crafted prompt. Especially, KgCoOp minimizes the discrepancy between the textual embeddings generated by learned prompts and the hand-crafted prompts. Finally, adding the KgCoOp upon the contrastive loss can make a discriminative prompt for both seen and unseen tasks. Extensive evaluation of several benchmarks demonstrates that the proposed Knowledge-guided Context Optimization is an efficient method for prompt tuning, i.e., achieves better performance with less training time. code.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 584,
+    "label": 16,
+    "text": "Title: Segment and Track Anything\nAbstract: This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to select multiple objects in videos for tracking, corresponding to their specific requirements. These interaction methods comprise click, stroke, and text, each possessing unique benefits and capable of being employed in combination. As a result, SAM-Track can be used across an array of fields, ranging from drone technology, autonomous driving, medical imaging, augmented reality, to biological analysis. SAM-Track amalgamates Segment Anything Model (SAM), an interactive key-frame segmentation model, with our proposed AOT-based tracking model (DeAOT), which secured 1st place in four tracks of the VOT 2022 challenge, to facilitate object tracking in video. In addition, SAM-Track incorporates Grounding-DINO, which enables the framework to support text-based interaction. We have demonstrated the remarkable capabilities of SAM-Track on DAVIS-2016 Val (92.0%), DAVIS-2017 Test (79.2%)and its practicability in diverse applications. The project page is available at: https://github.com/z-x-yang/Segment-and-Track-Anything.",
+    "neighbors": [
+      106,
+      719,
+      1006,
+      1068,
+      1207,
+      1932,
+      2233
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 585,
+    "label": 24,
+    "text": "Title: MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts\nAbstract: Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COmparing COntinuous Optimizers (COCO) platform, we propose in this work a further generalization that allows multiple affine combinations of the original instances and arbitrarily chosen locations of the global optima. We demonstrate that the MA-BBOB generator can help fill the instance space, while overall patterns in algorithm performance are preserved. By combining the landscape features of the problems with the performance data, we pose the question of whether these features are as useful for algorithm selection as previous studies suggested. MA-BBOB is built on the publicly available IOHprofiler platform, which facilitates standardized experimentation routines, provides access to the interactive IOHanalyzer module for performance analysis and visualization, and enables comparisons with the rich and growing data collection available for the (MA-)BBOB functions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 586,
+    "label": 24,
+    "text": "Title: Wasserstein Dictionaries of Persistence Diagrams\nAbstract: This paper presents a computational framework for the concise encoding of an ensemble of persistence diagrams, in the form of weighted Wasserstein barycenters [100], [102] of a dictionary of atom diagrams. We introduce a multi-scale gradient descent approach for the efficient resolution of the corresponding minimization problem, which interleaves the optimization of the barycenter weights with the optimization of the atom diagrams. Our approach leverages the analytic expressions for the gradient of both sub-problems to ensure fast iterations and it additionally exploits shared-memory parallelism. Extensive experiments on public ensembles demonstrate the efficiency of our approach, with Wasserstein dictionary computations in the orders of minutes for the largest examples. We show the utility of our contributions in two applications. First, we apply Wassserstein dictionaries to data reduction and reliably compress persistence diagrams by concisely representing them with their weights in the dictionary. Second, we present a dimensionality reduction framework based on a Wasserstein dictionary defined with a small number of atoms (typically three) and encode the dictionary as a low dimensional simplex embedded in a visual space (typically in 2D). In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used to reproduce our results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 587,
+    "label": 28,
+    "text": "Title: Curve-lifted codes for local recovery using lines\nAbstract: In this paper, we introduce curve-lifted codes over fields of arbitrary characteristic, inspired by Hermitian-lifted codes over $\\mathbb{F}_{2^r}$. These codes are designed for locality and availability, and their particular parameters depend on the choice of curve and its properties. Due to the construction, the numbers of rational points of intersection between curves and lines play a key role. To demonstrate that and generate new families of locally recoverable codes (LRCs) with high availabilty, we focus on norm-trace-lifted codes. In some cases, they are easier to define than their Hermitian counterparts and consequently have a better asymptotic bound on the code rate.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 588,
+    "label": 27,
+    "text": "Title: BonnBot-I: A Precise Weed Management and Crop Monitoring Platform\nAbstract: Cultivation and weeding are two of the primary tasks performed by farmers today. A recent challenge for weeding is the desire to reduce herbicide and pesticide treatments while maintaining crop quality and quantity. In this paper we introduce BonnBot-I a precise weed management platform which can also performs field monitoring. Driven by crop monitoring approaches which can accurately locate and classify plants (weed and crop) we further improve their performance by fusing the platform available GNSS and wheel odometry. This improves tracking accuracy of our crop monitoring approach from a normalized average error of 8.3% to 3.5%, evaluated on a new publicly available corn dataset. We also present a novel arrangement of weeding tools mounted on linear actuators evaluated in simulated environments. We replicate weed distributions from a real field, using the results from our monitoring approach, and show the validity of our work-space division techniques which require significantly less movement (a 50% reduction) to achieve similar results. Overall, BonnBot-I is a significant step forward in precise weed management with a novel method of selectively spraying and controlling weeds in an arable field.",
+    "neighbors": [
+      884
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 589,
+    "label": 24,
+    "text": "Title: Free energy of Bayesian Convolutional Neural Network with Skip Connection\nAbstract: Since the success of Residual Network(ResNet), many of architectures of Convolutional Neural Networks(CNNs) have adopted skip connection. While the generalization performance of CNN with skip connection has been explained within the framework of Ensemble Learning, the dependency on the number of parameters have not been revealed. In this paper, we show that Bayesian free energy of Convolutional Neural Network both with and without skip connection in Bayesian learning. The upper bound of free energy of Bayesian CNN with skip connection does not depend on the oveparametrization and, the generalization error of Bayesian CNN has similar property.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 590,
+    "label": 4,
+    "text": "Title: A Four-Pronged Defense Against Byzantine Attacks in Federated Learning\nAbstract: \\textit{Federated learning} (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a \\underline{\\textbf{f}}our-\\underline{\\textbf{p}}ronged \\underline{\\textbf{d}}efense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is \\textit{not independent and identically distributed} (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with $30\\%$ improvement on model accuracy).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 591,
+    "label": 16,
+    "text": "Title: SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training\nAbstract: Skeleton sequence representation learning has shown great advantages for action recognition due to its promising ability to model human joints and topology. However, the current methods usually require sufficient labeled data for training computationally expensive models, which is labor-intensive and time-consuming. Moreover, these methods ignore how to utilize the fine-grained dependencies among different skeleton joints to pre-train an efficient skeleton sequence learning model that can generalize well across different datasets. In this paper, we propose an efficient skeleton sequence learning framework, named Skeleton Sequence Learning (SSL). To comprehensively capture the human pose and obtain discriminative skeleton sequence representation, we build an asymmetric graph-based encoder-decoder pre-training architecture named SkeletonMAE, which embeds skeleton joint sequence into Graph Convolutional Network (GCN) and reconstructs the masked skeleton joints and edges based on the prior human topology knowledge. Then, the pre-trained SkeletonMAE encoder is integrated with the Spatial-Temporal Representation Learning (STRL) module to build the SSL framework. Extensive experimental results show that our SSL generalizes well across different datasets and outperforms the state-of-the-art self-supervised skeleton-based action recognition methods on FineGym, Diving48, NTU 60 and NTU 120 datasets. Additionally, we obtain comparable performance to some fully supervised methods. The code is avaliable at https://github.com/HongYan1123/SkeletonMAE.",
+    "neighbors": [
+      375
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 592,
+    "label": 31,
+    "text": "Title: Optimizing Feature Set for Click-Through Rate Prediction\nAbstract: Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both features and their interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the feature field, which is too coarse to determine subtle features. They also do not filter useless feature interactions, leading to higher computation costs and degraded model performance. The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. To unify the selection of features and their interaction, we decompose the selection of each feature interaction into the selection of two correlated features. Such a decomposition makes the model end-to-end trainable given various feature interaction operations. By adopting feature-level search space, we set a learnable gate to determine whether each feature should be within the feature set. Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates. Hence, OptFS generates the feature set containing features that improve the final prediction results. Experimentally, we evaluate OptFS on three public datasets, demonstrating OptFS can optimize feature sets which enhance the model performance and further reduce both the storage and computational cost.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 593,
+    "label": 27,
+    "text": "Title: Autonomous Drone Racing: A Survey\nAbstract: Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms which can operate drones in complex and uncertain environments. Additionally, flying fast enables drones to cover more ground which in turn increases productivity and further strengthens their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing, where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 kph and 4 g respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of autonomous drone racing across model-based and learning-based approaches. We provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.",
+    "neighbors": [
+      686,
+      1924
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 594,
+    "label": 6,
+    "text": "Title: Explainable Human-Robot Training and Cooperation with Augmented Reality\nAbstract: The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 595,
+    "label": 16,
+    "text": "Title: Progressive Open Space Expansion for Open-Set Model Attribution\nAbstract: Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting limits the application in real-world scenarios for handling contents generated by arbitrary models. In this study, we focus on a challenging task, namely Open-Set Model Attribution (OSMA), to simultaneously attribute images to known models and identify those from unknown ones. Compared to existing openset recognition (OSR) tasks focusing on semantic novelty, OSMA is more challenging as the distinction between images from known and unknown models may only lie in visually imperceptible traces. To this end, we propose a Progressive Open Space Expansion (POSE) solution, which simulates open-set samples that maintain the same semantics as closed-set samples but embedded with different imperceptible traces. Guided by a diversity constraint, the open space is simulated progressively by a set of lightweight augmentation models. We consider three real-world scenarios and construct an OSMA benchmark dataset, including unknown models trained with different random seeds, architectures, and datasets from known ones. Extensive experiments on the dataset demonstrate POSE is superior to both existing model attribution methods and off-the-shelf OSR methods. Github: https://github.com/ICTMCG/POSE",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 596,
+    "label": 22,
+    "text": "Title: Certified Compilation of Choreographies with hacc\nAbstract: Programming communicating processes is challenging, because it requires writing separate programs that perform compatible send and receive actions at the right time during execution. Leaving this task to the programmer can easily lead to bugs. Choreographic programming addresses this challenge by equipping developers with high-level abstractions for codifying the desired communication structures from a global viewpoint. Given a choreography, implementations of the involved processes can be automatically generated by endpoint projection (EPP). While choreographic programming prevents manual mistakes in the implementation of communications, the correctness of a choreographic programming framework crucially hinges on the correctness of its complex compiler, which has motivated formalisation of theories of choreographic programming in theorem provers. In this paper, we build upon one of these formalisations to construct a toolchain that produces executable code from a choreography.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 597,
+    "label": 4,
+    "text": "Title: Anomaly Detection Techniques in Smart Grid Systems: A Review\nAbstract: Smart grid data can be evaluated for anomaly detection in numerous fields, including cyber-security, fault detection, electricity theft, etc. The strange anomalous behaviors may have been caused by various reasons, including peculiar load usage patterns of the consumers, grid infrastructure issues, blackouts, outside cyberattacks, or energy fraud. Recently, anomaly detection of the smart grid has attracted a large amount of interest from researchers, and it is widely applied in a number of high-impact fields. Implementing effective anomaly detection for a variety of aberrant behaviors is one of the smart grid's biggest challenges. In the context of smart grids, we provide a scoping overview of research from recent advances in anomaly detection in this work. We categorize our study from numerous aspects for deep understanding and inspection of the research challenges so far. Finally, after analyzing the gap in the reviewed paper, the direction for future research on anomaly detection in smart-grid systems has been provided briefly.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 598,
+    "label": 13,
+    "text": "Title: Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey\nAbstract: Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are focusing more and more interest on biologically grounded mechanisms, which are appealing due to the impressive capabilities exhibited by biological brains. This survey explores a range of these biologically inspired models of synaptic plasticity, their application in DL scenarios, and the connections with models of plasticity in Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL) represents an exciting research direction, aiming at advancing not only our current technologies but also our understanding of intelligence.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 599,
+    "label": 24,
+    "text": "Title: Risk-Averse MDPs under Reward Ambiguity\nAbstract: We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 600,
+    "label": 27,
+    "text": "Title: Language-Guided Traffic Simulation via Scene-Level Diffusion\nAbstract: Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are difficult for practitioners to use. To remedy this, we present CTG++, a scene-level conditional diffusion model that can be guided by language instructions. Developing this requires tackling two challenges: the need for a realistic and controllable traffic model backbone, and an effective method to interface with a traffic model using language. To address these challenges, we first propose a scene-level diffusion model equipped with a spatio-temporal transformer backbone, which generates realistic and controllable traffic. We then harness a large language model (LLM) to convert a user's query into a loss function, guiding the diffusion model towards query-compliant generation. Through comprehensive evaluation, we demonstrate the effectiveness of our proposed method in generating realistic, query-compliant traffic simulations.",
+    "neighbors": [
+      25,
+      736,
+      1044,
+      1047
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 601,
+    "label": 24,
+    "text": "Title: Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity\nAbstract: We present a partially personalized formulation of Federated Learning (FL) that strikes a balance between the flexibility of personalization and cooperativeness of global training. In our framework, we split the variables into global parameters, which are shared across all clients, and individual local parameters, which are kept private. We prove that under the right split of parameters, it is possible to find global parameters that allow each client to fit their data perfectly, and refer to the obtained problem as overpersonalized. For instance, the shared global parameters can be used to learn good data representations, whereas the personalized layers are fine-tuned for a specific client. Moreover, we present a simple algorithm for the partially personalized formulation that offers significant benefits to all clients. In particular, it breaks the curse of data heterogeneity in several settings, such as training with local steps, asynchronous training, and Byzantine-robust training.",
+    "neighbors": [
+      2034
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 602,
+    "label": 16,
+    "text": "Title: Large Multimodal Models: Notes on CVPR 2023 Tutorial\nAbstract: This tutorial note summarizes the presentation on ``Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4'', a part of CVPR 2023 tutorial on ``Recent Advances in Vision Foundation Models''. The tutorial consists of three parts. We first introduce the background on recent GPT-like large models for vision-and-language modeling to motivate the research in instruction-tuned large multimodal models (LMMs). As a pre-requisite, we describe the basics of instruction-tuning in large language models, which is further extended to the multimodal space. Lastly, we illustrate how to build the minimum prototype of multimodal GPT-4 like models with the open-source resource, and review the recently emerged topics.",
+    "neighbors": [
+      11,
+      34,
+      522,
+      529,
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+      792,
+      887,
+      907,
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+      1039,
+      1047,
+      1052,
+      1148,
+      1307,
+      1344,
+      1485,
+      1537,
+      1574,
+      1613,
+      1765,
+      1863,
+      2030,
+      2036,
+      2095,
+      2155
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 603,
+    "label": 28,
+    "text": "Title: Joint Beamforming and Compression Design for Per-Antenna Power Constrained Cooperative Cellular Networks\nAbstract: In the cooperative cellular network, relay-like base stations are connected to the central processor (CP) via rate-limited fronthaul links and the joint processing is performed at the CP, which thus can effectively mitigate the multiuser interference. In this paper, we consider the joint beamforming and compression problem with per-antenna power constraints in the cooperative cellular network. We first establish the equivalence between the considered problem and its semidefinite relaxation (SDR). Then we further derive the partial Lagrangian dual of the SDR problem and show that the objective function of the obtained dual problem is differentiable. Based on the differentiability, we propose two efficient projected gradient ascent algorithms for solving the dual problem, which are projected exact gradient ascent (PEGA) and projected inexact gradient ascent (PIGA). While PEGA is guaranteed to find the global solution of the dual problem (and hence the global solution of the original problem), PIGA is more computationally efficient due to the lower complexity in inexactly computing the gradient. Global optimality and high efficiency of the proposed algorithms are demonstrated via numerical experiments.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 604,
+    "label": 5,
+    "text": "Title: Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning\nAbstract: Federated Learning (FL) is a machine learning approach that enables the creation of shared models for powerful applications while allowing data to remain on devices. This approach provides benefits such as improved data privacy, security, and reduced latency. However, in some systems, direct communication between clients and servers may not be possible, such as remote areas without proper communication infrastructure. To overcome this challenge, a new framework called FedEx (Federated Learning via Model Express Delivery) is proposed. This framework employs mobile transporters, such as UAVs, to establish indirect communication channels between the server and clients. These transporters act as intermediaries and allow for model information exchange. The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection. To address this, two algorithms, FedEx-Sync and FedEx-Async, are proposed for synchronized and asynchronized learning at the transporter level. Additionally, a bi-level optimization algorithm is proposed to solve the joint client assignment and route planning problem. Experimental validation using two public datasets in a simulated network demonstrates consistent results with the theory, proving the efficacy of FedEx.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 605,
+    "label": 28,
+    "text": "Title: A Construction of Asymptotically Optimal Cascaded CDC Schemes via Combinatorial Designs\nAbstract: A coded distributed computing (CDC) system aims to reduce the communication load in the MapReduce framework. Such a system has $K$ nodes, $N$ input files, and $Q$ Reduce functions. Each input file is mapped by $r$ nodes and each Reduce function is computed by $s$ nodes. The objective is to achieve the maximum multicast gain. There are known CDC schemes that achieve optimal communication load. In some prominent known schemes, however, $N$ and $Q$ grow too fast in terms of $K$, greatly reducing their gains in practical scenarios. To mitigate the situation, some asymptotically optimal cascaded CDC schemes with $r=s$ have been proposed by using symmetric designs. In this paper, we put forward new asymptotically optimal cascaded CDC schemes with $r=s$ by using $1$-designs. Compared with earlier schemes from symmetric designs, ours have much smaller computation loads while keeping the other relevant parameters the same. We also obtain new asymptotically optimal cascaded CDC schemes with more flexible parameters compared with previously best-performing schemes.",
+    "neighbors": [
+      1674,
+      1998
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 606,
+    "label": 28,
+    "text": "Title: Beamfocusing Optimization for Near-Field Wideband Multi-User Communications\nAbstract: A near-field wideband communication system is studied, wherein a base station (BS) employs an extremely large-scale antenna array (ELAA) to serve multiple users situated within its near-field region. To facilitate the near-field beamfocusing and mitigate the wideband beam split, true-time delayer (TTD)-based hybrid beamforming architectures are employed at the BS. Apart from the fully-connected TTD-based architecture, a new sub-connected TTD-based architecture is proposed for enhancing energy efficiency. Three wideband beamfocusing optimization approaches are proposed to maximize spectral efficiency for both architectures. 1) Fully-digital approximation (FDA) approach: In this approach, the TTD-based hybrid beamformers are optimized to approximate the optimal fully-digital beamformers using block coordinate descent. 2) Penalty-based FDA approach: In this approach, the penalty method is leveraged in the FDA approach to guarantee the convergence to a stationary point of the spectral maximization problem. 3) Heuristic two-stage (HTS) approach: In this approach, the closed-form TTD-based analog beamformers are first designed based on the outcomes of near-field beam training and the piecewise-near-field approximation. Subsequently, the low-dimensional digital beamformer is optimized using knowledge of the low-dimensional equivalent channels, resulting in reduced computational complexity and channel estimation complexity. Our numerical results unveil that 1) the proposed approaches effectively eliminate the near-field beam split effect, and 2) compared to the fully-connected architecture, the proposed sub-connected architecture exhibits higher energy efficiency and imposes fewer hardware limitations on TTDs and system bandwidth.",
+    "neighbors": [
+      841,
+      1108,
+      2028
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 607,
+    "label": 16,
+    "text": "Title: Towards Generalist Foundation Model for Radiology\nAbstract: In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM.We consider the construction of foundational models from the perspectives of dataset construction, model design, and thorough evaluation. Our contribution can be concluded as follows: (i), we construct a large-scale Medical Multi-modal Dataset, MedMD, which consists of 16M 2D and 3D medical scans with high-quality text descriptions or reports across various data formats, modalities, and tasks, covering over 5000 distinct diseases. To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans; (ii ), we propose an architecture that enables visually conditioned generative pre-training, i.e., allowing for integration of text input with 2D or 3D medical scans, and generate responses for diverse radiologic tasks. The model was initially pre-trained on MedMD and subsequently fine-tuned on the domain-specific dataset, which is a radiologic cleaned version of MedMD, containing 3M radiologic visual-language pairs, termed as RadMD; (iii), we propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis, aiming to comprehensively assess the capability of foundation models in handling practical clinical problems. We conduct both automatic and human evaluation on RadBench, in both cases, RadFM significantly outperforms existing multi-modal foundation models. The codes, data, and model checkpoint will all be made publicly available to promote further research and development in the field.",
+    "neighbors": [
+      45,
+      696,
+      887,
+      1052,
+      1735,
+      1863
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 608,
+    "label": 8,
+    "text": "Title: Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points\nAbstract: The Internet of Things is in constant growth, with millions of devices used every day in our homes and workplaces to ease our lives. Such a strict coexistence between humans and smart devices makes the latter digital witnesses of our everyday lives through their sensor systems. This opens up to a new area of digital investigation named IoT Forensics, where digital traces produced by smart devices (network traffic, in primis) are leveraged as evidences for forensic purposes. It is therefore important to create tools able to capture, store and possibly analyse easily such digital traces to ease the job of forensic investigators. This work presents one of such tools, named Feature-Sniffer, which is thought explicitly for Wi-Fi enabled smart devices used in Smart Building/Smart Home scenarios. Feature-Sniffer is an add-on for OpenWrt-based access points and allows to easily perform online traffic feature extraction, avoiding to store large PCAP files. We present Feature-Sniffer with an accurate description of the implementation details, and we show its possible uses with practical examples for device identification and activity classification from encrypted traffic produced by IoT cameras. We release Feature-Sniffer publicly for reproducible research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 609,
+    "label": 24,
+    "text": "Title: Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning\nAbstract: Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 610,
+    "label": 30,
+    "text": "Title: A Data Fusion Framework for Multi-Domain Morality Learning\nAbstract: Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 611,
+    "label": 16,
+    "text": "Title: Vehicle Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps\nAbstract: Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the sensor data, the non-deterministic nature of future, and complex behavior of agents. In this paper, we tackle this problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic labels to the occupied cells and incorporating map information. We propose a novel framework that combines deep-learning-based spatio-temporal and probabilistic approaches to predict vehicle behaviors.Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset and show that our model shows superior ability to predict both static and dynamic vehicles compared to OGM predictions. Furthermore, we perform an ablation study and assess the role of semantic labels and map in the architecture.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 612,
+    "label": 25,
+    "text": "Title: Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model\nAbstract: Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment~(FA) and provide timestamps, while end-to-end ASR systems especially AED based ones are short of such ability. This paper proposes to perform timestamp prediction~(TP) while recognizing by utilizing continuous integrate-and-fire~(CIF) mechanism in non-autoregressive ASR model - Paraformer. Foucing on the fire place bias issue of CIF, we conduct post-processing strategies including fire-delay and silence insertion. Besides, we propose to use scaled-CIF to smooth the weights of CIF output, which is proved beneficial for both ASR and TP task. Accumulated averaging shift~(AAS) and diarization error rate~(DER) are adopted to measure the quality of timestamps and we compare these metrics of proposed system and conventional hybrid force-alignment system. The experiment results over manually-marked timestamps testset show that the proposed optimization methods significantly improve the accuracy of CIF timestamps, reducing 66.7\\% and 82.1\\% of AAS and DER respectively. Comparing to Kaldi force-alignment trained with the same data, optimized CIF timestamps achieved 12.3\\% relative AAS reduction.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 613,
+    "label": 10,
+    "text": "Title: A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT\nAbstract: Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. BERT learns bidirectional encoder representations from Transformers, which are trained on large datasets as contextual language models. Similarly, the generative pretrained transformer (GPT) method employs Transformers as the feature extractor and is trained using an autoregressive paradigm on large datasets. Recently, ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few shot prompting. The remarkable achievements of PFM have brought significant breakthroughs to various fields of AI. Numerous studies have proposed different methods, raising the demand for an updated survey. This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. The review covers the basic components and existing pretraining methods used in natural language processing, computer vision, and graph learning. Additionally, it explores advanced PFMs used for different data modalities and unified PFMs that consider data quality and quantity. The review also discusses research related to the fundamentals of PFMs, such as model efficiency and compression, security, and privacy. Finally, the study provides key implications, future research directions, challenges, and open problems in the field of PFMs. Overall, this survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence.",
+    "neighbors": [
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+      2085,
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+      2230,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 614,
+    "label": 13,
+    "text": "Title: Multi-Objective Archiving\nAbstract: Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multi-objective optimisation (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators; there is lack of systematic study of archiving methods from a general theoretical perspective. In this paper, we attempt to conduct a systematic overview of multi-objective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto compliant indicators (e.g., epsilon-indicator), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets.",
+    "neighbors": [
+      1164,
+      1700
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 615,
+    "label": 33,
+    "text": "Title: Parameterized Verification under TSO with Data Types\nAbstract: We consider parameterized verification of systems executing according to the total store ordering (TSO) semantics. The processes manipulate abstract data types over potentially infinite domains. We present a framework that translates the reachability problem for such systems to the reachability problem for register machines enriched with the given abstract data type. We use the translation to obtain tight complexity bounds for TSO-based parameterized verification over several abstract data types, such as push-down automata, ordered multi push-down automata, one-counter nets, one-counter automata, and Petri nets. We apply the framework to get complexity bounds for higher order stack and counter variants as well.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 616,
+    "label": 16,
+    "text": "Title: Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels\nAbstract: In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models. DPT operates in three stages: training a classifier on partially labeled data to predict pseudo-labels; training a conditional generative model using these pseudo-labels to generate pseudo images; and retraining the classifier with a mix of real and pseudo images. Empirically, DPT consistently achieves SOTA performance of semi-supervised generation and classification across various settings. In particular, with one or two labels per class, DPT achieves a Fr\\'echet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet 256x256, surpassing strong diffusion models with full labels, such as IDDPM, CDM, ADM, and LDM. Besides, DPT outperforms competitive semi-supervised baselines substantially on ImageNet classification tasks, achieving top-1 accuracies of 59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per class, respectively. Notably, our results demonstrate that diffusion can generate realistic images with only a few labels (e.g.,<0.1%) and generative augmentation remains viable for semi-supervised classification.",
+    "neighbors": [
+      736,
+      1049,
+      1835
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 617,
+    "label": 28,
+    "text": "Title: Near-Field Communications: What Will Be Different?\nAbstract: The design dilemma of\"What will be different between near-field communications (NFC) and far-field communications (FFC)?\"is addressed from four perspectives. 1) From the channel modelling perspective, the differences between near-field and far-field channel models are discussed. A novel Green's function-based channel model is proposed for continuous-aperture antennas, which is contrasted to conventional channel models tailored for spatially-discrete antennas. 2) From the performance analysis perspective, analytical results for characterizing the degrees of freedom and the power scaling laws in the near-field region are provided for both spatially-discrete and continuous-aperture antennas. 3) From the beamforming perspective, far-field beamforming is analogous to a\"flashlight\"that enables beamsteering, while near-field beamforming can be likened to a\"spotlight\"that facilitates beamfocusing. As a further advance, a couple of new beamforming structures are proposed for exploiting the new characteristics of NFC. 4) From the application perspective, new designs are discussed in the context of promising next-generation technologies in NFC, where our preliminary numerical results demonstrate that distance-aware target sensing and enhanced physical layer security can be realized in NFC. Finally, several future research directions of NFC are discussed.",
+    "neighbors": [
+      841,
+      1108,
+      1289
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 618,
+    "label": 30,
+    "text": "Title: Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models\nAbstract: Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent limitations as they are incapable of accessing up-to-date information (stored on the Web or in task-specific knowledge bases), using external tools, and performing precise mathematical and logical reasoning. In this paper, we present Chameleon, an AI system that mitigates these limitations by augmenting LLMs with plug-and-play modules for compositional reasoning. Chameleon synthesizes programs by composing various tools (e.g., LLMs, off-the-shelf vision models, web search engines, Python functions, and heuristic-based modules) for accomplishing complex reasoning tasks. At the heart of Chameleon is an LLM-based planner that assembles a sequence of tools to execute to generate the final response. We showcase the effectiveness of Chameleon on two multi-modal knowledge-intensive reasoning tasks: ScienceQA and TabMWP. Chameleon, powered by GPT-4, achieves an 86.54% overall accuracy on ScienceQA, improving the best published few-shot result by 11.37%. On TabMWP, GPT-4-powered Chameleon improves the accuracy by 17.0%, lifting the state of the art to 98.78%. Our analysis also shows that the GPT-4-powered planner exhibits more consistent and rational tool selection via inferring potential constraints from instructions, compared to a ChatGPT-powered planner.",
+    "neighbors": [
+      0,
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+      1050,
+      1052,
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+      1990,
+      2036,
+      2166,
+      2216,
+      2286
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 619,
+    "label": 30,
+    "text": "Title: A Survey on Model Compression for Large Language Models\nAbstract: Large Language Models (LLMs) have revolutionized natural language processing tasks with remarkable success. However, their formidable size and computational demands present significant challenges for practical deployment, especially in resource-constrained environments. As these challenges become increasingly pertinent, the field of model compression has emerged as a pivotal research area to alleviate these limitations. This paper presents a comprehensive survey that navigates the landscape of model compression techniques tailored specifically for LLMs. Addressing the imperative need for efficient deployment, we delve into various methodologies, encompassing quantization, pruning, knowledge distillation, and more. Within each of these techniques, we highlight recent advancements and innovative approaches that contribute to the evolving landscape of LLM research. Furthermore, we explore benchmarking strategies and evaluation metrics that are essential for assessing the effectiveness of compressed LLMs. By providing insights into the latest developments and practical implications, this survey serves as an invaluable resource for both researchers and practitioners. As LLMs continue to evolve, this survey aims to facilitate enhanced efficiency and real-world applicability, establishing a foundation for future advancements in the field.",
+    "neighbors": [
+      529,
+      570,
+      1001,
+      1011,
+      1021,
+      1544,
+      2047,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 620,
+    "label": 27,
+    "text": "Title: GP-guided MPPI for Efficient Navigation in Complex Unknown Cluttered Environments\nAbstract: Robotic navigation in unknown, cluttered environments with limited sensing capabilities poses significant challenges in robotics. Local trajectory optimization methods, such as Model Predictive Path Intergal (MPPI), are a promising solution to this challenge. However, global guidance is required to ensure effective navigation, especially when encountering challenging environmental conditions or navigating beyond the planning horizon. This study presents the GP-MPPI, an online learning-based control strategy that integrates MPPI with a local perception model based on Sparse Gaussian Process (SGP). The key idea is to leverage the learning capability of SGP to construct a variance (uncertainty) surface, which enables the robot to learn about the navigable space surrounding it, identify a set of suggested subgoals, and ultimately recommend the optimal subgoal that minimizes a predefined cost function to the local MPPI planner. Afterward, MPPI computes the optimal control sequence that satisfies the robot and collision avoidance constraints. Such an approach eliminates the necessity of a global map of the environment or an offline training process. We validate the efficiency and robustness of our proposed control strategy through both simulated and real-world experiments of 2D autonomous navigation tasks in complex unknown environments, demonstrating its superiority in guiding the robot safely towards its desired goal while avoiding obstacles and escaping entrapment in local minima. The GPU implementation of GP-MPPI, including the supplementary video, is available at https://github.com/IhabMohamed/GP-MPPI.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 621,
+    "label": 24,
+    "text": "Title: Parallel Coordinates for Discovery of Interpretable Machine Learning Models\nAbstract: This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to understand for end users. It is suggested to use mixed and pure hyperblocks in the proposed data classifier algorithm Hyper. It is shown that Hyper models generalize decision trees. The algorithm is presented in several settings and options to discover interactively or automatically overlapping or non-overlapping hyperblocks. Additionally, the use of hyperblocks in conjunction with language descriptions of visual patterns is demonstrated. The benchmark data from the UCI ML repository were used to evaluate the Hyper algorithm. It enabled the discovery of mixed and pure HBs evaluated using 10-fold cross validation. Connections among hyperblocks, dimension reduction and visualization have been established. The capability of end users to find and observe hyperblocks, as well as the ability of side-by-side visualizations to make patterns evident, are among major advantages ofhyperblock technology and the Hyper algorithm. A new method to visualize incomplete n-D data with missing values is proposed, while the traditional parallel coordinates do not support it. The ability of HBs to better prevent both overgeneralization and overfitting of data over decision trees is demonstrated as another benefit of the hyperblocks. The features of VisCanvas 2.0 software tool that implements Hyper technology are presented.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 622,
+    "label": 34,
+    "text": "Title: Approximation schemes for capacity vehicle routing problems: A survey\nAbstract: Finding the shortest travelling tour of vehicles with capacity k from the depot to the customers is called the Capacity vehicle routing problem (CVRP). CVRP plays an essential position in logistics systems, and it is the most intensively studied problem in combinatorial optimization. In complexity, CVRP with k $\\ge$ 3 is an NP-hard problem, and it is APX-hard as well. We already knew that it could not be approximated in metric space. Moreover, it is the first problem resisting Arora's famous approximation framework. So, whether there is, a polynomial-time (1+$\\epsilon$)-approximation for the Euclidean CVRP for any $\\epsilon>0$ is still an open problem. This paper will summarize the research progress from history to up-to-date developments. The survey will be updated periodically.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 623,
+    "label": 24,
+    "text": "Title: Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding\nAbstract: Solving linear inverse problems plays a crucial role in numerous applications. Algorithm unfolding based, model-aware data-driven approaches have gained significant attention for effectively addressing these problems. Learned iterative soft-thresholding algorithm (LISTA) and alternating direction method of multipliers compressive sensing network (ADMM-CSNet) are two widely used such approaches, based on ISTA and ADMM algorithms, respectively. In this work, we study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs, for finite-layer unfolded networks such as LISTA and ADMM-CSNet with smooth soft-thresholding in an over-parameterized (OP) regime. We achieve this by leveraging a modified version of the Polyak-Lojasiewicz, denoted PL$^*$, condition. Satisfying the PL$^*$ condition within a specific region of the loss landscape ensures the existence of a global minimum and exponential convergence from initialization using gradient descent based methods. Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL$^*$ condition to hold. We achieve this by deriving the Hessian spectral norm of these networks. Additionally, we show that the threshold on the number of training samples increases with the increase in the network width. Furthermore, we compare the threshold on training samples of unfolded networks with that of a standard fully-connected feed-forward network (FFNN) with smooth soft-thresholding non-linearity. We prove that unfolded networks have a higher threshold value than FFNN. Consequently, one can expect a better expected error for unfolded networks than FFNN.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 624,
+    "label": 16,
+    "text": "Title: Survey on Controlable Image Synthesis with Deep Learning\nAbstract: Image synthesis has attracted emerging research interests in academic and industry communities. Deep learning technologies especially the generative models greatly inspired controllable image synthesis approaches and applications, which aim to generate particular visual contents with latent prompts. In order to further investigate low-level controllable image synthesis problem which is crucial for fine image rendering and editing tasks, we present a survey of some recent works on 3D controllable image synthesis using deep learning. We first introduce the datasets and evaluation indicators for 3D controllable image synthesis. Then, we review the state-of-the-art research for geometrically controllable image synthesis in two aspects: 1) Viewpoint/pose-controllable image synthesis; 2) Structure/shape-controllable image synthesis. Furthermore, the photometrically controllable image synthesis approaches are also reviewed for 3D re-lighting researches. While the emphasis is on 3D controllable image synthesis algorithms, the related applications, products and resources are also briefly summarized for practitioners.",
+    "neighbors": [
+      48,
+      1125,
+      1481,
+      1902
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 625,
+    "label": 24,
+    "text": "Title: Guaranteed Tensor Recovery Fused Low-rankness and Smoothness\nAbstract: Tensor recovery is a fundamental problem in tensor research field. It generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Recent researches have made significant progress by adopting two insightful tensor priors, i.e., global low-rankness (L) and local smoothness (S), which are always encoded as a sum of two separate regularizers into recovery models. However, unlike the primary theoretical developments on low-rank tensor recovery, these joint \u201cL+S\u201d models have no theoretical exact-recovery guarantees yet, making the methods lack reliability in real practice. To this crucial issue, in this work, we build a unique regularizer termed as tensor correlated total variation (t-CTV), which essentially encodes both L and S priors of a tensor simultaneously. Especially, by equipping t-CTV into the recovery models, we can rigorously prove the exact recovery guarantees for two typical tensor recovery tasks, i.e., tensor completion and tensor robust principal component analysis. To the best of our knowledge, this should be the first exact-recovery results among all related \u201cL+S\u201d methods for tensor recovery. We further propose ADMM algorithms with fine convergence to solve the proposed models. Significant recovery accuracy improvements are observed in extensive experiments. Typically, our method achieves a workable performance when the missing rate is extremely large, e.g., 99.5%, for the color image inpainting task, while all its peers totally fail in such a challenging case. Code is released at https://github.com/wanghailin97.",
+    "neighbors": [
+      472
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 626,
+    "label": 24,
+    "text": "Title: Contrastive Learning under Heterophily\nAbstract: Graph Neural Networks are powerful tools for learning node representations when task-specific node labels are available. However, obtaining labels for graphs is expensive in many applications. This is particularly the case for large graphs. To address this, there has been a body of work to learn node representations in a self-supervised manner without labels. Contrastive learning (CL), has been particularly popular to learn representations in a self-supervised manner. In general, CL methods work by maximizing the similarity between representations of augmented views of the same example, and minimizing the similarity between augmented views of different examples. However, existing graph CL methods cannot learn high-quality representations under heterophily, where connected nodes tend to belong to different classes. This is because under heterophily, augmentations of the same example may not be similar to each other. In this work, we address the above problem by proposing the first graph CL method, HLCL, for learning node representations, under heterophily. HLCL uses a high-pass and a low-pass graph filter to generate different views of the same node. Then, it contrasts the two filtered views to learn the final node representations. Effectively, the high-pass filter captures the dissimilarity between nodes in a neighborhood and the low-pass filter captures the similarity between neighboring nodes.Contrasting the two filtered views allows HLCL to learn rich node representations for graphs, under heterophily and homophily.Empirically, HLCL outperforms state-of-the-art graph CL methods on benchmark heterophily datasets and large-scale real-world datasets by up to 10%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 627,
+    "label": 31,
+    "text": "Title: Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction\nAbstract: Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long-term and short-term user behavior data. But there are still some unresolved issues. More specially, (1) rule and truncation based methods to extract information from long-term behavior are easy to cause information loss, and (2) single feedback behavior regardless of scenario to extract information from short-term behavior lead to information confusion and noise. To fill this gap, we propose a Graph based Long-term and Short-term interest Model, termed GLSM. It consists of a multi-interest graph structure for capturing long-term user behavior, a multi-scenario heterogeneous sequence model for modeling short-term information, then an adaptive fusion mechanism to fused information from long-term and short-term behaviors. Comprehensive experiments on real-world datasets, GLSM achieved SOTA score on offline metrics. At the same time, the GLSM algorithm has been deployed in our industrial application, bringing 4.9% CTR and 4.3% GMV lift, which is significant to the business",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 628,
+    "label": 24,
+    "text": "Title: Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving\nAbstract: Physics-informed neural networks (PINNs) have emerged as promising surrogate modes for solving partial differential equations (PDEs). Their effectiveness lies in the ability to capture solution-related features through neural networks. However, original PINNs often suffer from bottlenecks, such as low accuracy and non-convergence, limiting their applicability in complex physical contexts. To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs. We also employ the gradient cosine similarity algorithm to integrate auxiliary problem loss with the primary problem loss in ATL-PINNs, which aims to enhance the effectiveness of the auxiliary-task learning modes. To the best of our knowledge, this is the first study to introduce auxiliary-task learning modes in the context of physics-informed learning. We conduct experiments on three PDE problems across different fields and scenarios. Our findings demonstrate that the proposed auxiliary-task learning modes can significantly improve solution accuracy, achieving a maximum performance boost of 96.62% (averaging 28.23%) compared to the original single-task PINNs. The code and dataset are open source at https://github.com/junjun-yan/ATL-PINN.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 629,
+    "label": 24,
+    "text": "Title: In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance\nAbstract: In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese nationa115-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 630,
+    "label": 30,
+    "text": "Title: EDM3: Event Detection as Multi-task Text Generation\nAbstract: Event detection refers to identifying event occurrences in a text and comprises of two subtasks; event identification and classification. We present EDM3, a novel approach for Event Detection that formulates three generative tasks: identification, classification, and combined detection. We show that EDM3 helps to learn transferable knowledge that can be leveraged to perform Event Detection and its subtasks concurrently, mitigating the error propagation inherent in pipelined approaches. Unlike previous dataset- or domain-specific approaches, EDM3 utilizes the existing knowledge of language models, allowing it to be trained over any classification schema. We evaluate EDM3 on multiple event detection datasets: RAMS, WikiEvents, MAVEN, and MLEE, showing that EDM3 outperforms 1) single-task performance by 8.4% on average and 2) multi-task performance without instructional prompts by 2.4% on average. We obtain SOTA results on RAMS (71.3% vs. 65.1% F-1) and competitive performance on other datasets. We analyze our approach to demonstrate its efficacy in low-resource and multi-sentence settings. We also show the effectiveness of this approach on non-standard event configurations such as multi-word and multi-class event triggers. Overall, our results show that EDM3 is a promising approach for Event Detection that has the potential for real-world applications.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 631,
+    "label": 30,
+    "text": "Title: Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations\nAbstract: Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa. These models deliver state-of-the-art results in several Natural Language Processing tasks, namely Emotion Recognition in Conversations (ERC), but suffer from the lack of interpretability and explainability. In this paper, we propose to combine the two approaches to perform ERC, as a means to obtain simpler and more interpretable Large Language Models-based classifiers. We propose to feed the utterances and their previous conversational turns to a pre-trained RoBERTa, obtaining contextual embedding utterance representations, that are then supplied to an adapted Fuzzy Fingerprint classification module. We validate our approach on the widely used DailyDialog ERC benchmark dataset, in which we obtain state-of-the-art level results using a much lighter model.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 632,
+    "label": 24,
+    "text": "Title: Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity\nAbstract: Normalizing Flows (NF) are Generative models which are particularly robust and allow for exact sampling of the learned distribution. They however require the design of an invertible mapping, whose Jacobian determinant has to be computable. Recently introduced, Neural Hamiltonian Flows (NHF) are based on Hamiltonian dynamics-based Flows, which are continuous, volume-preserving and invertible and thus make for natural candidates for robust NF architectures. In particular, their similarity to classical Mechanics could lead to easier interpretability of the learned mapping. However, despite being Physics-inspired architectures, the originally introduced NHF architecture still poses a challenge to interpretability. For this reason, in this work, we introduce a fixed kinetic energy version of the NHF model. Inspired by physics, our approach improves interpretability and requires less parameters than previously proposed architectures. We then study the robustness of the NHF architectures to the choice of hyperparameters. We analyze the impact of the number of leapfrog steps, the integration time and the number of neurons per hidden layer, as well as the choice of prior distribution, on sampling a multimodal 2D mixture. The NHF architecture is robust to these choices, especially the fixed-kinetic energy model. Finally, we adapt NHF to the context of Bayesian inference and illustrate our method on sampling the posterior distribution of two cosmological parameters knowing type Ia supernovae observations.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 633,
+    "label": 8,
+    "text": "Title: SFC: Near-Source Congestion Signaling and Flow Control\nAbstract: State-of-the-art congestion control algorithms for data centers alone do not cope well with transient congestion and high traffic bursts. To help with these, we revisit the concept of direct \\emph{backward} feedback from switches and propose Back-to-Sender (BTS) signaling to many concurrent incast senders. Combining it with our novel approach to in-network caching, we achieve near-source sub-RTT congestion signaling. Source Flow Control (SFC) combines these two simple signaling mechanisms to instantly pause traffic sources, hence avoiding the head-of-line blocking problem of conventional hop-by-hop flow control. Our prototype system and scale simulations demonstrate that near-source signaling can significantly reduce the message completion time of various workloads in the presence of incast, complementing existing congestion control algorithms. Our results show that SFC can reduce the $99^{th}$-percentile flow completion times by $1.2-6\\times$ and the peak switch buffer usage by $2-3\\times$ compared to the recent incast solutions.",
+    "neighbors": [
+      963
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 634,
+    "label": 16,
+    "text": "Title: RMM: Reinforced Memory Management for Class-Incremental Learning\nAbstract: Class-Incremental Learning (CIL) [40] trains classi\ufb01ers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the past, and future data are strictly non-accessible during the incremental phases. We solve this by training the policy function of RMM on pseudo CIL tasks, e.g., the tasks built on the data of the 0 -th phase, and then applying it to target tasks. RMM propagates two levels of actions: Level-1 determines how to split the memory between old and new classes, and Level-2 allocates memory for each speci\ufb01c class. In essence, it is an optimizable and general method for memory management that can be used in any replaying-based CIL method. For evaluation, we plug RMM into two top-performing baselines (LUCIR+AANets and POD+AANets [30]) and conduct experiments on three benchmarks (CIFAR-100, ImageNet-Subset, and ImageNet-Full). Our results show clear improvements, e.g., boosting POD+AANets by 3 . 6% , 4 . 4% , and 1 . 9% in the 25 -Phase settings of the above benchmarks, respectively. The code is available at https://class-il.mpi-inf.mpg.de/rmm",
+    "neighbors": [
+      897,
+      1115,
+      2291
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 635,
+    "label": 28,
+    "text": "Title: Age of Information Under Frame Slotted ALOHA-Based Status Updating Protocol\nAbstract: We propose a frame slotted ALOHA (FSA)-based protocol for a random access network where sources transmit status updates to their intended destinations. We evaluate the effect of such a protocol on the network\u2019s timeliness performance using the Age of Information (AoI) metric. Specifically, we leverage tools from stochastic geometry to model the spatial positions of the source-destination pairs and capture the entanglement amongst the nodes\u2019 spatial-temporal attributes through the interference they caused to each other. We derive analytical expressions for the average and variance of AoI over a typical transmission link in Poisson bipolar and cellular networks, respectively. Our analysis shows that in densely deployed networks, the FSA-based status updating protocol can significantly decrease the average AoI and in addition, stabilizes the age performance by substantially reducing the variance of AoI. Furthermore, under the same updating frequency, converting a slotted ALOHA protocol into an FSA-based one always leads to a reduction in the average AoI. Moreover, implementing FSA in conjunction with power control can further benefit the AoI performance, although the particular values of framesize and power control factor must be adequately tuned to achieve the optimal gain.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 636,
+    "label": 24,
+    "text": "Title: Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection\nAbstract: Modern machine learning models deployed in the wild can encounter both covariate and semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and OOD detection respectively. While both problems have received significant research attention lately, they have been pursued independently. This may not be surprising, since the two tasks have seemingly conflicting goals. This paper provides a new unified approach that is capable of simultaneously generalizing to covariate shifts while robustly detecting semantic shifts. We propose a margin-based learning framework that exploits freely available unlabeled data in the wild that captures the environmental test-time OOD distributions under both covariate and semantic shifts. We show both empirically and theoretically that the proposed margin constraint is the key to achieving both OOD generalization and detection. Extensive experiments show the superiority of our framework, outperforming competitive baselines that specialize in either OOD generalization or OOD detection. Code is publicly available at https://github.com/deeplearning-wisc/scone.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 637,
+    "label": 30,
+    "text": "Title: DropDim: A Regularization Method for Transformer Networks\nAbstract: We introduce DropDim, a structured dropout method designed for regularizing the self-attention mechanism, which is a key component of the transformer. In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions. In this way, the semantic information can be completely discarded. Thus, the excessive co-adapting between different embedding dimensions can be broken, and the self-attention is forced to encode meaningful features with a certain number of embedding dimensions erased. Experiments on a wide range of tasks executed on the MUST-C English-Germany dataset show that DropDim can effectively improve model performance, reduce over-fitting, and show complementary effects with other regularization methods. When combined with label smoothing, the WER can be reduced from 19.1% to 15.1% on the ASR task, and the BLEU value can be increased from 26.90 to 28.38 on the MT task. On the ST task, the model can reach a BLEU score of 22.99, an increase by 1.86 BLEU points compared to the strong baseline.",
+    "neighbors": [
+      228,
+      582
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 638,
+    "label": 27,
+    "text": "Title: Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control\nAbstract: Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models. Unfortunately, applying such models to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require. On the other hand, language-conditioned robotic policies that learn from interaction data can provide the necessary grounding that allows the agent to be correctly situated in the real world, but such policies are limited by the lack of high-level semantic understanding due to the limited breadth of the interaction data available for training them. Thus, if we want to make use of the semantic knowledge in a language model while still situating it in an embodied setting, we must construct an action sequence that is both likely according to the language model and also realizable according to grounded models of the environment. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate this guided decoding strategy is able to solve complex, long-horizon embodiment tasks in a robotic setting by leveraging the knowledge of both models. The project's website can be found at grounded-decoding.github.io.",
+    "neighbors": [
+      1451
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 639,
+    "label": 24,
+    "text": "Title: Ae2I: A Double Autoencoder for Imputation of Missing Values\nAbstract: The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column. This paper introduces a double autoencoder for imputation ($Ae^2I$) that simultaneously and collaboratively uses both row-row relationship and column-column relationship to impute the missing values. Empirical tests on Movielens 1M dataset demonstrated that $Ae^2I$ outperforms the current state-of-the-art models for recommender systems by a significant margin.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 640,
+    "label": 27,
+    "text": "Title: Towards Multi-robot Exploration: A Decentralized Strategy for UAV Forest Exploration\nAbstract: Efficient exploration strategies are vital in tasks such as search-and-rescue missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become particularly popular in such applications, promising to cover large areas at high speeds. Moreover, with the increasing maturity of onboard UAV perception, research focus has been shifting toward higher-level reasoning for single- and multi-robot missions. However, autonomous navigation and exploration of previously unknown large spaces still constitutes an open challenge, especially when the environment is cluttered and exhibits large and frequent occlusions due to high obstacle density, as is the case of forests. Moreover, the problem of long-distance wireless communication in such scenes can become a limiting factor, especially when automating the navigation of a UAV swarm. In this spirit, this work proposes an exploration strategy that enables UAVs, both individually and in small swarms, to quickly explore complex scenes in a decentralized fashion. By providing the decision-making capabilities to each UAV to switch between different execution modes, the proposed strategy strikes a great balance between cautious exploration of yet completely unknown regions and more aggressive exploration of smaller areas of unknown space. This results in full coverage of forest areas of variable density, consistently faster than the state of the art. Demonstrating successful deployment with a single UAV as well as a swarm of up to three UAVs, this work sets out the basic principles for multi-root exploration of cluttered scenes, with up to 65% speed up in the single UAV case and 40% increase in explored area for the same mission time in multi-UAV setups.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 641,
+    "label": 30,
+    "text": "Title: CamChoice: A Corpus of Multiple Choice Questions and Candidate Response Distributions\nAbstract: Multiple choice exams are widely used to assess candidates across a diverse range of domains and tasks. To moderate question quality, newly proposed questions often pass through pre-test evaluation stages before being deployed into real-world exams. Currently, this evaluation process is manually intensive, which can lead to time lags in the question development cycle. Streamlining this process via automation can significantly enhance efficiency, however, there's a current lack of datasets with adequate pre-test analysis information. In this paper we introduce CamChoice; a multiple-choice comprehension dataset of questions at different target levels, with corresponding candidate selection distributions. We introduce the task of candidate distribution matching, propose several evaluation metrics for the task, and demonstrate that automatic systems trained on RACE++ can be leveraged as baselines for our task. We further demonstrate that these automatic systems can be used for practical pre-test evaluation tasks such as detecting underperforming distractors, where our detection systems can automatically identify poor distractors that few candidates select. We release the data publicly for future research.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 642,
+    "label": 16,
+    "text": "Title: Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports\nAbstract: Deep learning-based object detectors have driven notable progress in multi-object tracking algorithms. Yet, current tracking methods mainly focus on simple, regular motion patterns in pedestrians or vehicles. This leaves a gap in tracking algorithms for targets with nonlinear, irregular motion, like athletes. Additionally, relying on the Kalman filter in recent tracking algorithms falls short when object motion defies its linear assumption. To overcome these issues, we propose a novel online and robust multi-object tracking approach named deep ExpansionIoU (Deep-EIoU), which focuses on multi-object tracking for sports scenarios. Unlike conventional methods, we abandon the use of the Kalman filter and leverage the iterative scale-up ExpansionIoU and deep features for robust tracking in sports scenarios. This approach achieves superior tracking performance without adopting a more robust detector, all while keeping the tracking process in an online fashion. Our proposed method demonstrates remarkable effectiveness in tracking irregular motion objects, achieving a score of 77.2% HOTA on the SportsMOT dataset and 85.4% HOTA on the SoccerNet-Tracking dataset. It outperforms all previous state-of-the-art trackers on various large-scale multi-object tracking benchmarks, covering various kinds of sports scenarios.",
+    "neighbors": [
+      838
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 643,
+    "label": 36,
+    "text": "Title: Single-Peaked Jump Schelling Games\nAbstract: Schelling games model the wide-spread phenomenon of residential segregation in metropolitan areas from a game-theoretic point of view. In these games agents of different types each strategically select a node on a given graph that models the residential area to maximize their individual utility. The latter solely depends on the types of the agents on neighboring nodes and it has been a standard assumption to consider utility functions that are monotone in the number of same-type neighbors. This simplifying assumption has recently been challenged since sociological poll results suggest that real-world agents actually favor diverse neighborhoods. We contribute to the recent endeavor of investigating residential segregation models with realistic agent behavior by studying Jump Schelling Games with agents having a single-peaked utility function. In such games, there are empty nodes in the graph and agents can strategically jump to such nodes to improve their utility. We investigate the existence of equilibria and show that they exist under specific conditions. Contrasting this, we prove that even on simple topologies like paths or rings such stable states are not guaranteed to exist. Regarding the game dynamics, we show that improving response cycles exist independently of the position of the peak in the utility function. Moreover, we show high almost tight bounds on the Price of Anarchy and the Price of Stability with respect to the recently proposed degree of integration, which counts the number of agents with a diverse neighborhood and which serves as a proxy for measuring the segregation strength. Last but not least, we show that computing a beneficial state with high integration is NP-complete and, as a novel conceptual contribution, we also show that it is NP-hard to decide if an equilibrium state can be found via improving response dynamics starting from a given initial state.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 644,
+    "label": 30,
+    "text": "Title: Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks\nAbstract: The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills. Are these skills general and transferable, or specialized to specific tasks seen during pretraining? To disentangle these effects, we propose an evaluation framework based on\"counterfactual\"task variants that deviate from the default assumptions underlying standard tasks. Across a suite of 11 tasks, we observe nontrivial performance on the counterfactual variants, but nevertheless find that performance substantially and consistently degrades compared to the default conditions. This suggests that while current LMs may possess abstract task-solving skills to a degree, they often also rely on narrow, non-transferable procedures for task-solving. These results motivate a more careful interpretation of language model performance that teases apart these aspects of behavior.",
+    "neighbors": [
+      3,
+      748,
+      1001,
+      1002,
+      1044,
+      1142,
+      1349,
+      1596,
+      1735,
+      1958,
+      2016,
+      2136,
+      2244
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 645,
+    "label": 1,
+    "text": "Title: Semantic Communications System with Model Division Multiple Access and Controllable Coding Rate for Point Cloud\nAbstract: Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to communication, which hinders the efficient transmission of point clouds in the era of artificial intelligence (AI). This paper proposes a point cloud based semantic communication system (PCSC), which uses AI-based encoding techniques to extract the semantic information of the point cloud and joint source-channel coding (JSCC) technology to overcome the distortion caused by noise channels and solve the\"cliff effect\"in traditional communication. In addition, the system realizes the controllable coding rate without fine-tuning the network. The method analyzes the coded semantic vector's importance and discards semantically-unimportant information, thereby improving the transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal model division multiple access (MDMA) technology are combined to design a point cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant experimental results show that the proposed method outperforms the traditional method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2 metrics. In terms of transmission, the proposed method can effectively solve the\"cliff effect\"in the traditional methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 646,
+    "label": 9,
+    "text": "Title: Matrix Multiplication and Number On the Forehead Communication\nAbstract: Three-player Number On the Forehead communication may be thought of as a three-player Number In the Hand promise model, in which each player is given the inputs that are supposedly on the other two players' heads, and promised that they are consistent with the inputs of of the other players. The set of all allowed inputs under this promise may be thought of as an order-3 tensor. We surprisingly observe that this tensor is exactly the matrix multiplication tensor, which is widely studied in the design of fast matrix multiplication algorithms. Using this connection, we prove a number of results about both Number On the Forehead communication and matrix multiplication, each by using known results or techniques about the other. For example, we show how the Laser method, a key technique used to design the best matrix multiplication algorithms, can also be used to design communication protocols for a variety of problems. We also show how known lower bounds for Number On the Forehead communication can be used to bound properties of the matrix multiplication tensor such as its zeroing out subrank. Finally, we substantially generalize known methods based on slice-rank for studying communication, and show how they directly relate to the matrix multiplication exponent $\\omega$.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 647,
+    "label": 24,
+    "text": "Title: On the Fusion Strategies for Federated Decision Making\nAbstract: We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their private data with the central processor or each other. We analyze the non-Bayesian social learning strategy in which agents incorporate their individual observations into their opinions (i.e., soft-decisions) with Bayes rule, and the central processor aggregates these opinions by arithmetic or geometric averaging. Building on our previous work, we establish that both pooling strategies result in asymptotic normality characterization of the system, which, for instance, can be utilized to derive approximate expressions for the error probability. We verify the theoretical findings with simulations and compare both strategies.",
+    "neighbors": [
+      1955
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 648,
+    "label": 24,
+    "text": "Title: Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning\nAbstract: This paper studies the performance of deep convolutional neural networks (DCNNs) with zero-padding in feature extraction and learning. After verifying the roles of zero-padding in enabling translation-equivalence, and pooling in its translation-invariance driven nature, we show that with similar number of free parameters, any deep fully connected networks (DFCNs) can be represented by DCNNs with zero-padding. This demonstrates that DCNNs with zero-padding is essentially better than DFCNs in feature extraction. Consequently, we derive universal consistency of DCNNs with zero-padding and show its translation-invariance in the learning process. All our theoretical results are verified by numerical experiments including both toy simulations and real-data running.",
+    "neighbors": [
+      6
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 649,
+    "label": 2,
+    "text": "Title: Eigenvariables, bracketing and the decidability of positive minimal predicate logic\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 650,
+    "label": 24,
+    "text": "Title: NPRL: Nightly Profile Representation Learning for Early Sepsis Onset Prediction in ICU Trauma Patients\nAbstract: Sepsis is a syndrome that develops in response to the presence of infection. It is characterized by severe organ dysfunction and is one of the leading causes of mortality in Intensive Care Units (ICUs) worldwide. These complications can be reduced through early application of antibiotics, hence the ability to anticipate the onset of sepsis early is crucial to the survival and well-being of patients. Current machine learning algorithms deployed inside medical infrastructures have demonstrated poor performance and are insufficient for anticipating sepsis onset early. In recent years, deep learning methodologies have been proposed to predict sepsis, but some fail to capture the time of onset (e.g., classifying patients' entire visits as developing sepsis or not) and others are unrealistic to be deployed into medical facilities (e.g., creating training instances using a fixed time to onset where the time of onset needs to be known apriori). Therefore, in this paper, we first propose a novel but realistic prediction framework that predicts each morning whether sepsis onset will occur within the next 24 hours with the help of most recent data collected at night, when patient-provider ratios are higher due to cross-coverage resulting in limited observation to each patient. However, as we increase the prediction rate into daily, the number of negative instances will increase while that of positive ones remain the same. Thereafter, we have a severe class imbalance problem, making a machine learning model hard to capture rare sepsis cases. To address this problem, we propose to do nightly profile representation learning (NPRL) for each patient. We prove that NPRL can theoretically alleviate the rare event problem. Our empirical study using data from a level-1 trauma center further demonstrates the effectiveness of our proposal.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 651,
+    "label": 34,
+    "text": "Title: Determinantal Sieving\nAbstract: We introduce determinantal sieving, a new, remarkably powerful tool in the toolbox of algebraic FPT algorithms. Given a polynomial $P(X)$ on a set of variables $X=\\{x_1,\\ldots,x_n\\}$ and a linear matroid $M=(X,\\mathcal{I})$ of rank $k$, both over a field $\\mathbb{F}$ of characteristic 2, in $2^k$ evaluations we can sieve for those terms in the monomial expansion of $P$ which are multilinear and whose support is a basis for $M$. Alternatively, using $2^k$ evaluations of $P$ we can sieve for those monomials whose odd support spans $M$. Applying this framework, we improve on a range of algebraic FPT algorithms, such as: 1. Solving $q$-Matroid Intersection in time $O^*(2^{(q-2)k})$ and $q$-Matroid Parity in time $O^*(2^{qk})$, improving on $O^*(4^{qk})$ (Brand and Pratt, ICALP 2021) 2. $T$-Cycle, Colourful $(s,t)$-Path, Colourful $(S,T)$-Linkage in undirected graphs, and the more general Rank $k$ $(S,T)$-Linkage problem, all in $O^*(2^k)$ time, improving on $O^*(2^{k+|S|})$ respectively $O^*(2^{|S|+O(k^2 \\log(k+|\\mathbb{F}|))})$ (Fomin et al., SODA 2023) 3. Many instances of the Diverse X paradigm, finding a collection of $r$ solutions to a problem with a minimum mutual distance of $d$ in time $O^*(2^{r(r-1)d/2})$, improving solutions for $k$-Distinct Branchings from time $2^{O(k \\log k)}$ to $O^*(2^k)$ (Bang-Jensen et al., ESA 2021), and for Diverse Perfect Matchings from $O^*(2^{2^{O(rd)}})$ to $O^*(2^{r^2d/2})$ (Fomin et al., STACS 2021) All matroids are assumed to be represented over a field of characteristic 2. Over general fields, we achieve similar results at the cost of using exponential space by working over the exterior algebra. For a class of arithmetic circuits we call strongly monotone, this is even achieved without any loss of running time. However, the odd support sieving result appears to be specific to working over characteristic 2.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 652,
+    "label": 30,
+    "text": "Title: Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?\nAbstract: Large Language Models (LLMs) have demonstrated impressive performance on Natural Language Processing (NLP) tasks, such as Question Answering, Summarization, and Classification. The use of LLMs as evaluators, that can rank or score the output of other models (usually LLMs) has become increasingly popular, due to the limitations of current evaluation techniques including the lack of appropriate benchmarks, metrics, cost, and access to human annotators. While LLMs are capable of handling approximately 100 languages, the majority of languages beyond the top 20 lack systematic evaluation across various tasks, metrics, and benchmarks. This creates an urgent need to scale up multilingual evaluation to ensure a precise understanding of LLM performance across diverse languages. LLM-based evaluators seem like the perfect solution to this problem, as they do not require human annotators, human-created references, or benchmarks and can theoretically be used to evaluate any language covered by the LLM. In this paper, we investigate whether LLM-based evaluators can help scale up multilingual evaluation. Specifically, we calibrate LLM-based evaluation against 20k human judgments of five metrics across three text-generation tasks in eight languages. Our findings indicate that LLM-based evaluators may exhibit bias towards higher scores and should be used with caution and should always be calibrated with a dataset of native speaker judgments, particularly in low-resource and non-Latin script languages.",
+    "neighbors": [
+      247,
+      1001,
+      1227,
+      1267,
+      1322,
+      1346,
+      1735,
+      1949,
+      2042,
+      2087,
+      2094,
+      2235
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 653,
+    "label": 30,
+    "text": "Title: Unimodal Intermediate Training for Multimodal Meme Sentiment Classification\nAbstract: Internet Memes remain a challenging form of user-generated content for automated sentiment classification. The availability of labelled memes is a barrier to developing sentiment classifiers of multimodal memes. To address the shortage of labelled memes, we propose to supplement the training of a multimodal meme classifier with unimodal (image-only and text-only) data. In this work, we present a novel variant of supervised intermediate training that uses relatively abundant sentiment-labelled unimodal data. Our results show a statistically significant performance improvement from the incorporation of unimodal text data. Furthermore, we show that the training set of labelled memes can be reduced by 40% without reducing the performance of the downstream model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 654,
+    "label": 27,
+    "text": "Title: A Multi-Heuristic Search-based Motion Planning for Automated Parking\nAbstract: In unstructured environments like parking lots or construction sites, due to the large search-space and kinodynamic constraints of the vehicle, it is challenging to achieve real-time planning. Several state-of-the-art planners utilize heuristic search-based algorithms. However, they heavily rely on the quality of the single heuristic function, used to guide the search. Therefore, they are not capable to achieve reasonable computational performance, resulting in unnecessary delays in the response of the vehicle. In this work, we are adopting a Multi-Heuristic Search approach, that enables the use of multiple heuristic functions and their individual advantages to capture different complexities of a given search space. Based on our knowledge, this approach was not used previously for this problem. For this purpose, multiple admissible and non-admissible heuristic functions are defined, the original Multi-Heuristic A* Search was extended for bidirectional use and dealing with hybrid continuous-discrete search space, and a mechanism for adapting scale of motion primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A* algorithm is benchmarked against a very popular heuristic search-based algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in both terms, computation efficiency and motion plan (path) quality.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 655,
+    "label": 24,
+    "text": "Title: Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models\nAbstract: In recent years, large-scale models have demonstrated state-of-the-art performance across various domains. However, training such models requires various techniques to address the problem of limited computing power and memory on devices such as GPUs. Some commonly used techniques include pipeline parallelism, tensor parallelism, and activation checkpointing. While existing works have focused on finding efficient distributed execution plans (Zheng et al. 2022) and activation checkpoint scheduling (Herrmann et al. 2019, Beaumont et al. 2021}, there has been no method proposed to optimize these two plans jointly. Moreover, ahead-of-time compilation relies heavily on accurate memory and computing overhead estimation, which is often time-consuming and misleading. Existing training systems and machine learning pipelines either physically execute each operand or estimate memory usage with a scaled input tensor. To address these challenges, we introduce a system that can jointly optimize distributed execution and gradient checkpointing plans. Additionally, we provide an easy-to-use symbolic profiler that generates memory and computing statistics for any PyTorch model with a minimal time cost. Our approach allows users to parallelize their model training on the given hardware with minimum code change based. The source code is publicly available at Colossal-AI GitHub or https://github.com/hpcaitech/ColossalAI",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 656,
+    "label": 16,
+    "text": "Title: Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification\nAbstract: In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at \\url{https://github.com/xmed-lab/Fed-MAS}.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 657,
+    "label": 30,
+    "text": "Title: New Linear-time Algorithm for SubTree Kernel Computation based on Root-Weighted Tree Automata\nAbstract: Tree kernels have been proposed to be used in many areas as the automatic learning of natural language applications. In this paper, we propose a new linear time algorithm based on the concept of weighted tree automata for SubTree kernel computation. First, we introduce a new class of weighted tree automata, called Root-Weighted Tree Automata, and their associated formal tree series. Then we define, from this class, the SubTree automata that represent compact computational models for finite tree languages. This allows us to design a theoretically guaranteed linear-time algorithm for computing the SubTree Kernel based on weighted tree automata intersection. The key idea behind the proposed algorithm is to replace DAG reduction and nodes sorting steps used in previous approaches by states equivalence classes computation allowed in the weighted tree automata approach. Our approach has three major advantages: it is output-sensitive, it is free sensitive from the tree types (ordered trees versus unordered trees), and it is well adapted to any incremental tree kernel based learning methods. Finally, we conduct a variety of comparative experiments on a wide range of synthetic tree languages datasets adapted for a deep algorithm analysis. The obtained results show that the proposed algorithm outperforms state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 658,
+    "label": 16,
+    "text": "Title: In-Rack Test Tube Pose Estimation Using RGB-D Data\nAbstract: Accurate robotic manipulation of test tubes in biology and medical industries is becoming increasingly important to address workforce shortages and improve worker safety. The detection and localization of test tubes are essential for the robots to successfully manipulate test tubes. In this paper, we present a framework to detect and estimate poses for the in-rack test tubes using color and depth data. The methodology involves the utilization of a YOLO object detector to effectively classify and localize both the test tubes and the tube racks within the provided image data. Subsequently, the pose of the tube rack is estimated through point cloud registration techniques. During the process of estimating the poses of the test tubes, we capitalize on constraints derived from the arrangement of rack slots. By employing an optimization-based algorithm, we effectively evaluate and refine the pose of the test tubes. This strategic approach ensures the robustness of pose estimation, even when confronted with noisy and incomplete point cloud data.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 659,
+    "label": 16,
+    "text": "Title: WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation\nAbstract: This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical importance for understanding a classification network and launching WSSS. We observe that different attention heads of ViT focus on different image areas. Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects. Besides, we propose a ViT-based gradient clipping decoder for online retraining with the CAM results to complete the WSSS task. We name this plain Transformer-based Weakly-supervised learning framework WeakTr. It achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 78.4% mIoU on the val set of PASCAL VOC 2012 and 50.3% mIoU on the val set of COCO 2014. Code is available at https://github.com/hustvl/WeakTr.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 660,
+    "label": 27,
+    "text": "Title: Data Models Applied to Soft Robot Modeling and Control: A Review\nAbstract: Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots are leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial gripper. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data models are utilized in pairs or separately. This review classifies these applied data models into five kinds, which are the Jacobian model, analytical model, statistical model, neural network, and reinforcement learning, and compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. A discussion about the development of the existing modeling and control approaches is presented, and we forecast that the combination of offline-trained and online-learning controllers will be the widespread implementation in the future.",
+    "neighbors": [
+      1333
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 661,
+    "label": 24,
+    "text": "Title: Open problems in causal structure learning: A case study of COVID-19 in the UK\nAbstract: Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models and data sets publicly available online.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 662,
+    "label": 24,
+    "text": "Title: Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits\nAbstract: In the stochastic multi-armed bandit problem, a randomized probability matching policy called Thompson sampling (TS) has shown excellent performance in various reward models. In addition to the empirical performance, TS has been shown to achieve asymptotic problem-dependent lower bounds in several models. However, its optimality has been mainly addressed under light-tailed or one-parameter models that belong to exponential families. In this paper, we consider the optimality of TS for the Pareto model that has a heavy tail and is parameterized by two unknown parameters. Specifically, we discuss the optimality of TS with probability matching priors that include the Jeffreys prior and the reference priors. We first prove that TS with certain probability matching priors can achieve the optimal regret bound. Then, we show the suboptimality of TS with other priors, including the Jeffreys and the reference priors. Nevertheless, we find that TS with the Jeffreys and reference priors can achieve the asymptotic lower bound if one uses a truncation procedure. These results suggest carefully choosing noninformative priors to avoid suboptimality and show the effectiveness of truncation procedures in TS-based policies.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 663,
+    "label": 16,
+    "text": "Title: Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges\nAbstract: Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This work reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlook challenges between current academic research and real-world applications. The project page is https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Driving",
+    "neighbors": [
+      513
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 664,
+    "label": 16,
+    "text": "Title: SCITUNE: Aligning Large Language Models with Scientific Multimodal Instructions\nAbstract: Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving the LLMs to align existing foundation models with scientific disciplines, concepts and goals. In this work, we present SciTune as a tuning framework to improve the ability of LLMs to follow scientific multimodal instructions. To test our methodology, we use a human-generated scientific instruction tuning dataset and train a large multimodal model LLaMA-SciTune that connects a vision encoder and LLM for science-focused visual and language understanding. In comparison to the models that are finetuned with machine generated data only, LLaMA-SciTune surpasses human performance on average and in many sub-categories on the ScienceQA benchmark.",
+    "neighbors": [
+      1002,
+      1039,
+      1044,
+      1052,
+      1613,
+      1668,
+      2036,
+      2155,
+      2286
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 665,
+    "label": 24,
+    "text": "Title: A Unified Algebraic Perspective on Lipschitz Neural Networks\nAbstract: Important research efforts have focused on the design and training of neural networks with a controlled Lipschitz constant. The goal is to increase and sometimes guarantee the robustness against adversarial attacks. Recent promising techniques draw inspirations from different backgrounds to design 1-Lipschitz neural networks, just to name a few: convex potential layers derive from the discretization of continuous dynamical systems, Almost-Orthogonal-Layer proposes a tailored method for matrix rescaling. However, it is today important to consider the recent and promising contributions in the field under a common theoretical lens to better design new and improved layers. This paper introduces a novel algebraic perspective unifying various types of 1-Lipschitz neural networks, including the ones previously mentioned, along with methods based on orthogonality and spectral methods. Interestingly, we show that many existing techniques can be derived and generalized via finding analytical solutions of a common semidefinite programming (SDP) condition. We also prove that AOL biases the scaled weight to the ones which are close to the set of orthogonal matrices in a certain mathematical manner. Moreover, our algebraic condition, combined with the Gershgorin circle theorem, readily leads to new and diverse parameterizations for 1-Lipschitz network layers. Our approach, called SDP-based Lipschitz Layers (SLL), allows us to design non-trivial yet efficient generalization of convex potential layers. Finally, the comprehensive set of experiments on image classification shows that SLLs outperform previous approaches on certified robust accuracy. Code is available at https://github.com/araujoalexandre/Lipschitz-SLL-Networks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 666,
+    "label": 36,
+    "text": "Title: Bayes correlated equilibria and no-regret dynamics\nAbstract: This paper explores equilibrium concepts for Bayesian games, which are fundamental models of games with incomplete information. We aim at three desirable properties of equilibria. First, equilibria can be naturally realized by introducing a mediator into games. Second, an equilibrium can be computed efficiently in a distributed fashion. Third, any equilibrium in that class approximately maximizes social welfare, as measured by the price of anarchy, for a broad class of games. These three properties allow players to compute an equilibrium and realize it via a mediator, thereby settling into a stable state with approximately optimal social welfare. Our main result is the existence of an equilibrium concept that satisfies these three properties. Toward this goal, we characterize various (non-equivalent) extensions of correlated equilibria, collectively known as Bayes correlated equilibria. In particular, we focus on communication equilibria (also known as coordination mechanisms), which can be realized by a mediator who gathers each player's private information and then sends correlated recommendations to the players. We show that if each player minimizes a variant of regret called untruthful swap regret in repeated play of Bayesian games, the empirical distribution of these dynamics converges to a communication equilibrium. We present an efficient algorithm for minimizing untruthful swap regret with a sublinear upper bound, which we prove to be tight up to a multiplicative constant. As a result, by simulating the dynamics with our algorithm, we can efficiently compute an approximate communication equilibrium. Furthermore, we extend existing lower bounds on the price of anarchy based on the smoothness arguments from Bayes Nash equilibria to equilibria obtained by the proposed dynamics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 667,
+    "label": 24,
+    "text": "Title: Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning\nAbstract: Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment between LLMs' knowledge and the environment can be wrong and limit functional competence due to lack of grounding. In this paper, we study an approach (named GLAM) to achieve this alignment through functional grounding: we consider an agent using an LLM as a policy that is progressively updated as the agent interacts with the environment, leveraging online Reinforcement Learning to improve its performance to solve goals. Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks? 2) How can it boost different forms of generalization? 3) What is the impact of online learning? We study these questions by functionally grounding several variants (size, architecture) of FLAN-T5.",
+    "neighbors": [
+      127,
+      704,
+      1349,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 668,
+    "label": 27,
+    "text": "Title: Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning\nAbstract: Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also actively looking for strategies to overtake/stay ahead of the opponent. In this work we propose a head-to-head racing environment for reinforcement learning which accurately models vehicle dynamics. Some previous works have tried learning a policy directly in the complex vehicle dynamics environment but have failed to learn an optimal policy. In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy. We also propose a control barrier function-based safe reinforcement learning algorithm to enforce the safety of the agent in a more effective way while not compromising on optimality.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 669,
+    "label": 25,
+    "text": "Title: Visually-Guided Sound Source Separation with Audio-Visual Predictive Coding\nAbstract: The framework of visually guided sound source separation generally consists of three parts: visual feature extraction, multimodal feature fusion, and sound signal processing. An ongoing trend in this field has been to tailor involved visual feature extractor for informative visual guidance and separately devise module for feature fusion, while utilizing U-Net by default for sound analysis. However, such a divide-and-conquer paradigm is parameter-inefficient and, meanwhile, may obtain suboptimal performance as jointly optimizing and harmonizing various model components is challengeable. By contrast, this article presents a novel approach, dubbed audio-visual predictive coding (AVPC), to tackle this task in a parameter-efficient and more effective manner. The network of AVPC features a simple ResNet-based video analysis network for deriving semantic visual features, and a predictive coding (PC)-based sound separation network that can extract audio features, fuse multimodal information, and predict sound separation masks in the same architecture. By iteratively minimizing the prediction error between features, AVPC integrates audio and visual information recursively, leading to progressively improved performance. In addition, we develop a valid self-supervised learning strategy for AVPC via copredicting two audio-visual representations of the same sound source. Extensive evaluations demonstrate that AVPC outperforms several baselines in separating musical instrument sounds, while reducing the model size significantly. Code is available at: https://github.com/zjsong/Audio-Visual-Predictive-Coding.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 670,
+    "label": 24,
+    "text": "Title: A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU\nAbstract: Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer Learning. We examine the structure, applications, benefits, and limitations of each model. Furthermore, we perform an analysis using three publicly available datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 671,
+    "label": 24,
+    "text": "Title: Topological Feature Selection\nAbstract: In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We model dependency structures among features using a family of chordal graphs (the Triangulated Maximally Filtered Graph), and we maximise the likelihood of features' relevance by studying their relative position inside the network. Such an approach presents three aspects that are particularly satisfactory compared to its alternatives: (i) it is highly tunable and easily adaptable to the nature of input data; (ii) it is fully explainable, maintaining, at the same time, a remarkable level of simplicity; (iii) it is computationally cheaper compared to its alternatives. We test our algorithm on 16 benchmark datasets from different applicative domains showing that it outperforms or matches the current state-of-the-art under heterogeneous evaluation conditions.",
+    "neighbors": [
+      253,
+      1368
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 672,
+    "label": 24,
+    "text": "Title: survex: an R package for explaining machine learning survival models\nAbstract: Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 673,
+    "label": 24,
+    "text": "Title: Variational Latent Branching Model for Off-Policy Evaluation\nAbstract: Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov decision processes (MDPs), which are used to rollout simulated trajectories and estimate the performance of policies. Model-based OPE methods face two key challenges. First, as offline trajectories are usually fixed, they tend to cover limited state and action space. Second, the performance of model-based methods can be sensitive to the initialization of their parameters. In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled. Specifically, VLBM leverages and extends the variational inference framework with the recurrent state alignment (RSA), which is designed to capture as much information underlying the limited training data, by smoothing out the information flow between the variational (encoding) and generative (decoding) part of VLBM. Moreover, we also introduce the branching architecture to improve the model's robustness against randomly initialized model weights. The effectiveness of the VLBM is evaluated on the deep OPE (DOPE) benchmark, from which the training trajectories are designed to result in varied coverage of the state-action space. We show that the VLBM outperforms existing state-of-the-art OPE methods in general.",
+    "neighbors": [
+      1664
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 674,
+    "label": 30,
+    "text": "Title: The Ways of Words: The Impact of Word Choice on Information Engagement and Decision Making\nAbstract: Little research has explored how information engagement (IE), the degree to which individuals interact with and use information in a manner that manifests cognitively, behaviorally, and affectively. This study explored the impact of phrasing, specifically word choice, on IE and decision making. Synthesizing two theoretical models, User Engagement Theory UET and Information Behavior Theory IBT, a theoretical framework illustrating the impact of and relationships among the three IE dimensions of perception, participation, and perseverance was developed and hypotheses generated. The framework was empirically validated in a large-scale user study measuring how word choice impacts the dimensions of IE. The findings provide evidence that IE differs from other forms of engagement in that it is driven and fostered by the expression of the information itself, regardless of the information system used to view, interact with, and use the information. The findings suggest that phrasing can have a significant effect on the interpretation of and interaction with digital information, indicating the importance of expression of information, in particular word choice, on decision making and IE. The research contributes to the literature by identifying methods for assessment and improvement of IE and decision making with digital text.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 675,
+    "label": 24,
+    "text": "Title: Practical Homomorphic Aggregation for Byzantine ML\nAbstract: Due to the large-scale availability of data, machine learning (ML) algorithms are being deployed in distributed topologies, where different nodes collaborate to train ML models over their individual data by exchanging model-related information (e.g., gradients) with a central server. However, distributed learning schemes are notably vulnerable to two threats. First, Byzantine nodes can single-handedly corrupt the learning by sending incorrect information to the server, e.g., erroneous gradients. The standard approach to mitigate such behavior is to use a non-linear robust aggregation method at the server. Second, the server can violate the privacy of the nodes. Recent attacks have shown that exchanging (unencrypted) gradients enables a curious server to recover the totality of the nodes' data. The use of homomorphic encryption (HE), a gold standard security primitive, has extensively been studied as a privacy-preserving solution to distributed learning in non-Byzantine scenarios. However, due to HE's large computational demand especially for high-dimensional ML models, there has not yet been any attempt to design purely homomorphic operators for non-linear robust aggregators. In this work, we present SABLE, the first completely homomorphic and Byzantine robust distributed learning algorithm. SABLE essentially relies on a novel plaintext encoding method that enables us to implement the robust aggregator over batching-friendly BGV. Moreover, this encoding scheme also accelerates state-of-the-art homomorphic sorting with larger security margins and smaller ciphertext size. We perform extensive experiments on image classification tasks and show that our algorithm achieves practical execution times while matching the ML performance of its non-private counterpart.",
+    "neighbors": [
+      1056,
+      1448
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 676,
+    "label": 15,
+    "text": "Title: Rule-based High-level Hardware-RTL Synthesis of Algorithms, Virtualizing Machines, and Communication Protocols with FPGAs based on Concurrent Communicating Sequential Processes and the ConPro Synthesis Framework\nAbstract: Virtualization is the abstraction of details. Algorithms and programming languages provide abstraction, too. Virtualization of hardware and embedded systems is becoming more and more important in heterogeneous environments and networks, e.g., distributed and material-integrated sensor networks. Communication and data processing with a broad range of hardware and low-level protocols can be unified and accessed uniquely by introducing virtualization layers implemented directly in hardware on chip. Hardware design is today still component-driven (like a circuit board), rather than transforming algorithms as an abstraction layer directly into hardware designs. Programs and protocols are algorithms, so do not handle them as devices like in traditional high-level synthesis design flows! Complex reactive systems with dominant and complex control paths play an increasing role in SoC-design. The major contribution to concurrency appears at the control path level. This article gives an in-depth introduction to SoC-design methodology using the Highest-Level Synthesis ConPro compiler framework and a process-oriented programming language that provides a programming model based on concurrently executing and communicating sequential processes (CCSP) with an extensive set of interprocess-communication primitives. Circuits are modelled and programmed on an algorithmic level, more convenient and natural than component-driven designs. Extended case studies of a smart communication protocol router and an advanced stack-based processor providing a programmatical virtualization layer are shown and evaluated. Both are used together as a smart node architecture deployed in high density sensor-actuator-networks, e.g., for material-integrated intelligent systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 677,
+    "label": 24,
+    "text": "Title: Replicability in Reinforcement Learning\nAbstract: We initiate the mathematical study of replicability as an algorithmic property in the context of reinforcement learning (RL). We focus on the fundamental setting of discounted tabular MDPs with access to a generative model. Inspired by Impagliazzo et al. [2022], we say that an RL algorithm is replicable if, with high probability, it outputs the exact same policy after two executions on i.i.d. samples drawn from the generator when its internal randomness is the same. We first provide an efficient $\\rho$-replicable algorithm for $(\\varepsilon, \\delta)$-optimal policy estimation with sample and time complexity $\\widetilde O\\left(\\frac{N^3\\cdot\\log(1/\\delta)}{(1-\\gamma)^5\\cdot\\varepsilon^2\\cdot\\rho^2}\\right)$, where $N$ is the number of state-action pairs. Next, for the subclass of deterministic algorithms, we provide a lower bound of order $\\Omega\\left(\\frac{N^3}{(1-\\gamma)^3\\cdot\\varepsilon^2\\cdot\\rho^2}\\right)$. Then, we study a relaxed version of replicability proposed by Kalavasis et al. [2023] called TV indistinguishability. We design a computationally efficient TV indistinguishable algorithm for policy estimation whose sample complexity is $\\widetilde O\\left(\\frac{N^2\\cdot\\log(1/\\delta)}{(1-\\gamma)^5\\cdot\\varepsilon^2\\cdot\\rho^2}\\right)$. At the cost of $\\exp(N)$ running time, we transform these TV indistinguishable algorithms to $\\rho$-replicable ones without increasing their sample complexity. Finally, we introduce the notion of approximate-replicability where we only require that two outputted policies are close under an appropriate statistical divergence (e.g., Renyi) and show an improved sample complexity of $\\widetilde O\\left(\\frac{N\\cdot\\log(1/\\delta)}{(1-\\gamma)^5\\cdot\\varepsilon^2\\cdot\\rho^2}\\right)$.",
+    "neighbors": [
+      1213,
+      1471
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 678,
+    "label": 11,
+    "text": "Title: iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning\nAbstract: Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations. We model two distinct incentives for agents' strategies: Behavioral Incentive for high-level decision-making based on their driving behavior or personality and Instant Incentive for motion planning for collision avoidance based on the current traffic state. Our approach enables agents to infer their opponents' behavior incentives and integrate this inferred information into their decision-making and motion-planning processes. We perform experiments on two simulation environments, Non-Cooperative Navigation and Heterogeneous Highway. In Heterogeneous Highway, results show that, compared with centralized training decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our method yields a 4.3% and 38.4% higher episodic reward in mild and chaotic traffic, with 48.1% higher success rate and 80.6% longer survival time in chaotic traffic. We also compare with a decentralized training decentralized execution (DTDE) baseline IPPO and demonstrate a higher episodic reward of 12.7% and 6.3% in mild traffic and chaotic traffic, 25.3% higher success rate, and 13.7% longer survival time.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 679,
+    "label": 16,
+    "text": "Title: A Recipe for Watermarking Diffusion Models\nAbstract: Recently, diffusion models (DMs) have demonstrated their advantageous potential for generative tasks. Widespread interest exists in incorporating DMs into downstream applications, such as producing or editing photorealistic images. However, practical deployment and unprecedented power of DMs raise legal issues, including copyright protection and monitoring of generated content. In this regard, watermarking has been a proven solution for copyright protection and content monitoring, but it is underexplored in the DMs literature. Specifically, DMs generate samples from longer tracks and may have newly designed multimodal structures, necessitating the modification of conventional watermarking pipelines. To this end, we conduct comprehensive analyses and derive a recipe for efficiently watermarking state-of-the-art DMs (e.g., Stable Diffusion), via training from scratch or finetuning. Our recipe is straightforward but involves empirically ablated implementation details, providing a solid foundation for future research on watermarking DMs. Our Code: https://github.com/yunqing-me/WatermarkDM.",
+    "neighbors": [
+      1487,
+      1902,
+      2221
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 680,
+    "label": 16,
+    "text": "Title: AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation\nAbstract: The study of ancient writings has great value for archaeology and philology. Essential forms of material are photographic characters, but manual photographic character recognition is extremely time-consuming and expertise-dependent. Automatic classification is therefore greatly desired. However, the current performance is limited due to the lack of annotated data. Data generation is an inexpensive but useful solution to data scarcity. Nevertheless, the diverse glyph shapes and complex background textures of photographic ancient characters make the generation task difficult, leading to unsatisfactory results of existing methods. To this end, we propose an unsupervised generative adversarial network called AGTGAN in this paper. By explicitly modeling global and local glyph shape styles, followed by a stroke-aware texture transfer and an associate adversarial learning mechanism, our method can generate characters with diverse glyphs and realistic textures. We evaluate our method on photographic ancient character datasets, e.g., OBC306 and CSDD. Our method outperforms other state-of-the-art methods in terms of various metrics and performs much better in terms of the diversity and authenticity of generated samples. With our generated images, experiments on the largest photographic oracle bone character dataset show that our method can achieve a significant increase in classification accuracy, up to 16.34%. The source code is available at https://github.com/Hellomystery/AGTGAN.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 681,
+    "label": 4,
+    "text": "Title: Security in Online Freelance Software Development: A case for Distributed Security Responsibility\nAbstract: Secure software is a cornerstone to safe and resilient digital ecosystems. It offers strong foundation to protect users' sensitive data and guard against cyber-threats. The rapidly increasing landscape of digital economy has encouraged developers from different socio-technical and socio-economic backgrounds to join online freelance marketplaces. While, secure software practices facilitate software developers in developing secure software, there is paucity of research on how freelance developers adhere to security practices and how they can be facilitated to improve their security behavior in under-resourced environments. Moreover, freelance developers are often held responsible for producing insecure code. In this position paper, we review existing literature and argue for the case of distributed security responsibilities in online freelance environment. We propose a research agenda aimed at offering an organized and systematic effort by researchers to address security needs and challenges of online freelance marketplaces. These include: characterising software security and defining separation of responsibilities, building trust in online freelance development communities, leveraging the potential of online freelancing platforms in the promotion of secure software development and building adaptive security interventions for online freelance software development. The research has the potential to bring forth existing security solutions to wider developer community and deliver substantial benefits to the broader security ecosystem.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 682,
+    "label": 30,
+    "text": "Title: PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts\nAbstract: The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts. In response to this vital need, we introduce PromptBench, a robustness benchmark designed to measure LLMs' resilience to adversarial prompts. This study uses a plethora of adversarial textual attacks targeting prompts across multiple levels: character, word, sentence, and semantic. These prompts are then employed in diverse tasks, such as sentiment analysis, natural language inference, reading comprehension, machine translation, and math problem-solving. Our study generates 4,032 adversarial prompts, meticulously evaluated over 8 tasks and 13 datasets, with 567,084 test samples in total. Our findings demonstrate that contemporary LLMs are vulnerable to adversarial prompts. Furthermore, we present comprehensive analysis to understand the mystery behind prompt robustness and its transferability. We then offer insightful robustness analysis and pragmatic recommendations for prompt composition, beneficial to both researchers and everyday users. We make our code, prompts, and methodologies to generate adversarial prompts publicly accessible, thereby enabling and encouraging collaborative exploration in this pivotal field: https://github.com/microsoft/promptbench.",
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+      2244,
+      2257
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 683,
+    "label": 24,
+    "text": "Title: Exact identification of nonlinear dynamical systems by Trimmed Lasso\nAbstract: Identification of nonlinear dynamical systems has been popularized by sparse identification of the nonlinear dynamics (SINDy) via the sequentially thresholded least squares (STLS) algorithm. Many extensions SINDy have emerged in the literature to deal with experimental data which are finite in length and noisy. Recently, the computationally intensive method of ensembling bootstrapped SINDy models (E-SINDy) was proposed for model identification, handling finite, highly noisy data. While the extensions of SINDy are numerous, their sparsity-promoting estimators occasionally provide sparse approximations of the dynamics as opposed to exact recovery. Furthermore, these estimators suffer under multicollinearity, e.g. the irrepresentable condition for the Lasso. In this paper, we demonstrate that the Trimmed Lasso for robust identification of models (TRIM) can provide exact recovery under more severe noise, finite data, and multicollinearity as opposed to E-SINDy. Additionally, the computational cost of TRIM is asymptotically equal to STLS since the sparsity parameter of the TRIM can be solved efficiently by convex solvers. We compare these methodologies on challenging nonlinear systems, specifically the Lorenz 63 system, the Bouc Wen oscillator from the nonlinear dynamics benchmark of No\\\"el and Schoukens, 2016, and a time delay system describing tool cutting dynamics. This study emphasizes the comparisons between STLS, reweighted $\\ell_1$ minimization, and Trimmed Lasso in identification with respect to problems faced by practitioners: the problem of finite and noisy data, the performance of the sparse regression of when the library grows in dimension (multicollinearity), and automatic methods for choice of regularization parameters.",
+    "neighbors": [
+      2065
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 684,
+    "label": 24,
+    "text": "Title: Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary Systems\nAbstract: Learning precise surrogate models of complex computer simulations and physical machines often require long-lasting or expensive experiments. Furthermore, the modeled physical dependencies exhibit nonlinear and nonstationary behavior. Machine learning methods that are used to produce the surrogate model should therefore address these problems by providing a scheme to keep the number of queries small, e.g. by using active learning and be able to capture the nonlinear and nonstationary properties of the system. One way of modeling the nonstationarity is to induce input-partitioning, a principle that has proven to be advantageous in active learning for Gaussian processes. However, these methods either assume a known partitioning, need to introduce complex sampling schemes or rely on very simple geometries. In this work, we present a simple, yet powerful kernel family that incorporates a partitioning that: i) is learnable via gradient-based methods, ii) uses a geometry that is more flexible than previous ones, while still being applicable in the low data regime. Thus, it provides a good prior for active learning procedures. We empirically demonstrate excellent performance on various active learning tasks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 685,
+    "label": 30,
+    "text": "Title: AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models\nAbstract: Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchmark specifically designed to assess foundation model in the context of human-centric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark. Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam. This demonstrates the extraordinary performance of contemporary foundation models. In contrast, we also find that GPT-4 is less proficient in tasks that require complex reasoning or specific domain knowledge. Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal these models' strengths and limitations, providing valuable insights into future directions for enhancing their general capabilities. By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a more meaningful and robust evaluation of foundation models' performance in real-world scenarios. The data, code, and all model outputs are released in https://github.com/ruixiangcui/AGIEval.",
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+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 686,
+    "label": 27,
+    "text": "Title: Aggressive Trajectory Generation for A Swarm of Autonomous Racing Drones\nAbstract: Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a 35m * 35m space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a 4m * 2m flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.",
+    "neighbors": [
+      593,
+      1838
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 687,
+    "label": 24,
+    "text": "Title: A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations\nAbstract: Universality is a key hypothesis in mechanistic interpretability -- that different models learn similar features and circuits when trained on similar tasks. In this work, we study the universality hypothesis by examining how small neural networks learn to implement group composition. We present a novel algorithm by which neural networks may implement composition for any finite group via mathematical representation theory. We then show that networks consistently learn this algorithm by reverse engineering model logits and weights, and confirm our understanding using ablations. By studying networks of differing architectures trained on various groups, we find mixed evidence for universality: using our algorithm, we can completely characterize the family of circuits and features that networks learn on this task, but for a given network the precise circuits learned -- as well as the order they develop -- are arbitrary.",
+    "neighbors": [
+      2060
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 688,
+    "label": 16,
+    "text": "Title: PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps\nAbstract: We propose PressureTransferNet, a novel method for Human Activity Recognition (HAR) using ground pressure information. Our approach generates body-specific dynamic ground pressure profiles for specific activities by leveraging existing pressure data from different individuals. PressureTransferNet is an encoder-decoder model taking a source pressure map and a target human attribute vector as inputs, producing a new pressure map reflecting the target attribute. To train the model, we use a sensor simulation to create a diverse dataset with various human attributes and pressure profiles. Evaluation on a real-world dataset shows its effectiveness in accurately transferring human attributes to ground pressure profiles across different scenarios. We visually confirm the fidelity of the synthesized pressure shapes using a physics-based deep learning model and achieve a binary R-square value of 0.79 on areas with ground contact. Validation through classification with F1 score (0.911$\\pm$0.015) on physical pressure mat data demonstrates the correctness of the synthesized pressure maps, making our method valuable for data augmentation, denoising, sensor simulation, and anomaly detection. Applications span sports science, rehabilitation, and bio-mechanics, contributing to the development of HAR systems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 689,
+    "label": 16,
+    "text": "Title: Distance Matters For Improving Performance Estimation Under Covariate Shift\nAbstract: Performance estimation under covariate shift is a crucial component of safe AI model deployment, especially for sensitive use-cases. Recently, several solutions were proposed to tackle this problem, most leveraging model predictions or softmax confidence to derive accuracy estimates. However, under dataset shifts, confidence scores may become ill-calibrated if samples are too far from the training distribution. In this work, we show that taking into account distances of test samples to their expected training distribution can significantly improve performance estimation under covariate shift. Precisely, we introduce a\"distance-check\"to flag samples that lie too far from the expected distribution, to avoid relying on their untrustworthy model outputs in the accuracy estimation step. We demonstrate the effectiveness of this method on 13 image classification tasks, across a wide-range of natural and synthetic distribution shifts and hundreds of models, with a median relative MAE improvement of 27% over the best baseline across all tasks, and SOTA performance on 10 out of 13 tasks. Our code is publicly available at https://github.com/melanibe/distance_matters_performance_estimation.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 690,
+    "label": 16,
+    "text": "Title: Virtual Occlusions Through Implicit Depth\nAbstract: For augmented reality (AR), it is important that virtual assets appear to \u2018sit among\u2019 real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consistent over time as the viewer's camera moves. Unfortunately, small mistakes in the estimated scene depth can ruin the downstream occlusion mask, and thereby the AR illusion. Especially in real-time settings, depths inferred near boundaries or across time can be inconsistent. In this paper, we challenge the need for depth-regression as an intermediate step. We instead propose an implicit model for depth and use that to predict the occlusion mask directly. The inputs to our network are one or more color images, plus the known depths of any virtual geometry. We show how our occlusion predictions are more accurate and more temporally stable than predictions derived from traditional depth-estimation models. We obtain state-of-the-art occlusion results on the challenging ScanNetv2 dataset and superior qualitative results on real scenes.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 691,
+    "label": 30,
+    "text": "Title: The Science of Detecting LLM-Generated Texts\nAbstract: The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disruptions in the education system. Although many detection approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This survey aims to provide an overview of existing LLM-generated text detection techniques and enhance the control and regulation of language generation models. Furthermore, we emphasize crucial considerations for future research, including the development of comprehensive evaluation metrics and the threat posed by open-source LLMs, to drive progress in the area of LLM-generated text detection.",
+    "neighbors": [
+      42,
+      896,
+      924,
+      1307,
+      1487,
+      1604,
+      1940,
+      2044,
+      2244
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 692,
+    "label": 24,
+    "text": "Title: Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing\nAbstract: nan",
+    "neighbors": [
+      1833
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 693,
+    "label": 23,
+    "text": "Title: LGBTQIA+ (In) Visibility in Computer Science and Software Engineering Education\nAbstract: Modern society is diverse, multicultural, and multifaceted. Because of these characteristics, we are currently observing an increase in the debates about equity, diversity, and inclusion in different areas, especially because several groups of individuals are underrepresented in many environments. In computer science and software engineering, it seems counterintuitive that these areas, which are responsible for creating technological solutions and systems for billions of users around the world, do not reflect the diversity of the society to which it serves. In trying to solve this diversity crisis in the software industry, researchers started to investigate strategies that can be applied to increase diversity and improve inclusion in academia and the software industry. However, the lack of diversity in computer science and related courses, including software engineering, is still a problem, in particular when some specific groups are considered. LGBTQIA+ students, for instance, face several challenges to fit into technology courses, even though most students in universities right now belong to Generation Z, which is described as open-minded to aspects of gender and sexuality. In this study, we aimed to discuss the state-of-art of publications about the inclusion of LGBTQIA+ students in computer science education. Using a mapping study, we identified eight studies published in the past six years that focused on this public. We present strategies developed to adapt curricula and lectures to be more inclusive to LGBTQIA+ students and discuss challenges and opportunities for future research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 694,
+    "label": 24,
+    "text": "Title: Refining a k-nearest neighbor graph for a computationally efficient spectral clustering\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 695,
+    "label": 6,
+    "text": "Title: Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent\nAbstract: Misinformation is a global problem in modern social media platforms with few solutions known to be effective. Social media platforms have offered tools to raise awareness of information, but these are closed systems that have not been empirically evaluated. Others have developed novel tools and strategies, but most have been studied out of context using static stimuli, researcher prompts, or low fidelity prototypes. We offer a new anti-misinformation agent grounded in theories of metacognition that was evaluated within Twitter. We report on a pilot study (n=17) and multi-part experimental study (n=57, n=49) where participants experienced three versions of the agent, each deploying a different strategy. We found that no single strategy was superior over the control. We also confirmed the necessity of transparency and clarity about the agent\u2019s underlying logic, as well as concerns about repeated exposure to misinformation and lack of user engagement.",
+    "neighbors": [
+      384
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 696,
+    "label": 16,
+    "text": "Title: PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering\nAbstract: In this paper, we focus on the problem of Medical Visual Question Answering (MedVQA), which is crucial in efficiently interpreting medical images with vital clinic-relevant information. Firstly, we reframe the problem of MedVQA as a generation task that naturally follows the human-machine interaction, we propose a generative-based model for medical visual understanding by aligning visual information from a pre-trained vision encoder with a large language model. Secondly, we establish a scalable pipeline to construct a large-scale medical visual question-answering dataset, named PMC-VQA, which contains 227k VQA pairs of 149k images that cover various modalities or diseases. Thirdly, we pre-train our proposed model on PMC-VQA and then fine-tune it on multiple public benchmarks, e.g., VQA-RAD and SLAKE, outperforming existing work by a large margin. Additionally, we propose a test set that has undergone manual verification, which is significantly more challenging, even the best models struggle to solve.",
+    "neighbors": [
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+      607,
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+      1052,
+      1142,
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+      1950,
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+      2216
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 697,
+    "label": 28,
+    "text": "Title: Codes over the non-unital non-commutative ring E using simplicial complexes\nAbstract: There are exactly two non-commutative rings of size $4$, namely, $E = \\langle a, b ~\\vert ~ 2a = 2b = 0, a^2 = a, b^2 = b, ab= a, ba = b\\rangle$ and its opposite ring $F$. These rings are non-unital. A subset $D$ of $E^m$ is defined with the help of simplicial complexes, and utilized to construct linear left-$E$-codes $C^L_D=\\{(v\\cdot d)_{d\\in D} : v\\in E^m\\}$ and right-$E$-codes $C^R_D=\\{(d\\cdot v)_{d\\in D} : v\\in E^m\\}$. We study their corresponding binary codes obtained via a Gray map. The weight distributions of all these codes are computed. We achieve a couple of infinite families of optimal codes with respect to the Griesmer bound. Ashikhmin-Barg's condition for minimality of a linear code is satisfied by most of the binary codes we constructed here. All the binary codes in this article are few-weight codes, and self-orthogonal codes under certain mild conditions. This is the first attempt to study the structure of linear codes over non-unital non-commutative rings using simplicial complexes.",
+    "neighbors": [
+      845
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 698,
+    "label": 6,
+    "text": "Title: Practicing Information Sensibility: How Gen Z Engages with Online Information\nAbstract: Assessing the trustworthiness of information online is complicated. Literacy-based paradigms are both widely used to help and widely critiqued. We conducted a study with 35 Gen Zers from across the U.S. to understand how they assess information online. We found that they tended to encounter\u2014rather than search for\u2014information, and that those encounters were shaped more by social motivations than by truth-seeking queries. For them, information processing is fundamentally a social practice. Gen Zers interpreted online information together, as aspirational members of social groups. Our participants sought information sensibility: a socially-informed awareness of the value of information encountered online. We outline key challenges they faced and practices they used to make sense of information. Our findings suggest that like their information sensibility practices, solutions and strategies to address misinformation should be embedded in social contexts online.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 699,
+    "label": 24,
+    "text": "Title: To be Robust and to be Fair: Aligning Fairness with Robustness\nAbstract: Adversarial training has been shown to be reliable in improving robustness against adversarial samples. However, the problem of adversarial training in terms of fairness has not yet been properly studied, and the relationship between fairness and accuracy attack still remains unclear. Can we simultaneously improve robustness w.r.t. both fairness and accuracy? To tackle this topic, in this paper, we study the problem of adversarial training and adversarial attack w.r.t. both metrics. We propose a unified structure for fairness attack which brings together common notions in group fairness, and we theoretically prove the equivalence of fairness attack against different notions. Moreover, we show the alignment of fairness and accuracy attack, and theoretically demonstrate that robustness w.r.t. one metric benefits from robustness w.r.t. the other metric. Our study suggests a novel way to unify adversarial training and attack w.r.t. fairness and accuracy, and experimental results show that our proposed method achieves better performance in terms of robustness w.r.t. both metrics.",
+    "neighbors": [
+      717
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 700,
+    "label": 16,
+    "text": "Title: MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results\nAbstract: Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset 1 is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset 2, the baseline code 3, and the website for evaluation on the public testset 4 are publicly available.",
+    "neighbors": [
+      33
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 701,
+    "label": 10,
+    "text": "Title: Enactivism & Objectively Optimal Super-Intelligence\nAbstract: Software's effect upon the world hinges upon the hardware that interprets it. This tends not to be an issue, because we standardise hardware. AI is typically conceived of as a software ``mind'' running on such interchangeable hardware. This formalises mind-body dualism, in that a software ``mind'' can be run on any number of standardised bodies. While this works well for simple applications, we argue that this approach is less than ideal for the purposes of formalising artificial general intelligence (AGI) or artificial super-intelligence (ASI). The general reinforcement learning agent AIXI is pareto optimal. However, this claim regarding AIXI's performance is highly subjective, because that performance depends upon the choice of interpreter. We examine this problem and formulate an approach based upon enactive cognition and pancomputationalism to address the issue. Weakness is a measure of plausibility, a ``proxy for intelligence'' unrelated to compression or simplicity. If hypotheses are evaluated in terms of weakness rather than length, then we are able to make objective claims regarding performance (how effectively one adapts, or ``generalises'' from limited information). Subsequently, we propose a definition of AGI which is objectively optimal given a ``vocabulary'' (body etc) in which cognition is enacted, and of ASI as that which finds the optimal vocabulary for a purpose and then constructs an AGI.",
+    "neighbors": [
+      1704,
+      1864
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 702,
+    "label": 30,
+    "text": "Title: Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation\nAbstract: While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the plan in order to improve or control the generated output.A short video demonstrating our system is available at https://goo.gle/text-blueprint-demo",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 703,
+    "label": 8,
+    "text": "Title: Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions\nAbstract: Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 704,
+    "label": 30,
+    "text": "Title: Augmented Language Models: a Survey\nAbstract: This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs can leverage these augmentations separately or in combination via heuristics, or learn to do so from demonstrations. While adhering to a standard missing tokens prediction objective, such augmented LMs can use various, possibly non-parametric external modules to expand their context processing ability, thus departing from the pure language modeling paradigm. We therefore refer to them as Augmented Language Models (ALMs). The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks and even outperforming most regular LMs on several benchmarks. In this work, after reviewing current advance in ALMs, we conclude that this new research direction has the potential to address common limitations of traditional LMs such as interpretability, consistency, and scalability issues.",
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+      1307,
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+      2166,
+      2171,
+      2184,
+      2235,
+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 705,
+    "label": 22,
+    "text": "Title: Decalf: A Directed, Effectful Cost-Aware Logical Framework\nAbstract: We present ${\\bf decalf}$, a ${\\bf d}$irected, ${\\bf e}$ffectful ${\\bf c}$ost-${\\bf a}$ware ${\\bf l}$ogical ${\\bf f}$ramework for studying quantitative aspects of functional programs with effects. Like ${\\bf calf}$, the language is based on a formal phase distinction between the extension and the intension of a program, its pure behavior as distinct from its cost measured by an effectful step-counting primitive. The type theory ensures that the behavior is unaffected by the cost accounting. Unlike ${\\bf calf}$, the present language takes account of effects, such as probabilistic choice and mutable state; this extension requires a reformulation of ${\\bf calf}$'s approach to cost accounting: rather than rely on a\"separable\"notion of cost, here a cost bound is simply another program. To make this formal, we equip every type with an intrinsic preorder, relaxing the precise cost accounting intrinsic to a program to a looser but nevertheless informative estimate. For example, the cost bound of a probabilistic program is itself a probabilistic program that specifies the distribution of costs. This approach serves as a streamlined alternative to the standard method of isolating a recurrence that bounds the cost in a manner that readily extends to higher-order, effectful programs. The development proceeds by first introducing the ${\\bf decalf}$ type system, which is based on an intrinsic ordering among terms that restricts in the extensional phase to extensional equality, but in the intensional phase reflects an approximation of the cost of a program of interest. This formulation is then applied to a number of illustrative examples, including pure and effectful sorting algorithms, simple probabilistic programs, and higher-order functions. Finally, we justify ${\\bf decalf}$ via a model in the topos of augmented simplicial sets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 706,
+    "label": 16,
+    "text": "Title: Dense Text-to-Image Generation with Attention Modulation\nAbstract: Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image region. To address this, we propose DenseDiffusion, a training-free method that adapts a pre-trained text-to-image model to handle such dense captions while offering control over the scene layout. We first analyze the relationship between generated images' layouts and the pre-trained model's intermediate attention maps. Next, we develop an attention modulation method that guides objects to appear in specific regions according to layout guidance. Without requiring additional fine-tuning or datasets, we improve image generation performance given dense captions regarding both automatic and human evaluation scores. In addition, we achieve similar-quality visual results with models specifically trained with layout conditions.",
+    "neighbors": [
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+      1418,
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+      1768,
+      1902,
+      2161,
+      2242,
+      2306
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 707,
+    "label": 2,
+    "text": "Title: Compositional Probabilistic Model Checking with String Diagrams of MDPs\nAbstract: nan",
+    "neighbors": [
+      155
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 708,
+    "label": 16,
+    "text": "Title: Contrastive Knowledge Amalgamation for Unsupervised Image Classification\nAbstract: Knowledge amalgamation (KA) aims to learn a compact student model to handle the joint objective from multiple teacher models that are are specialized for their own tasks respectively. Current methods focus on coarsely aligning teachers and students in the common representation space, making it difficult for the student to learn the proper decision boundaries from a set of heterogeneous teachers. Besides, the KL divergence in previous works only minimizes the probability distribution difference between teachers and the student, ignoring the intrinsic characteristics of teachers. Therefore, we propose a novel Contrastive Knowledge Amalgamation (CKA) framework, which introduces contrastive losses and an alignment loss to achieve intra-class cohesion and inter-class separation.Contrastive losses intra- and inter- models are designed to widen the distance between representations of different classes. The alignment loss is introduced to minimize the sample-level distribution differences of teacher-student models in the common representation space.Furthermore, the student learns heterogeneous unsupervised classification tasks through soft targets efficiently and flexibly in the task-level amalgamation. Extensive experiments on benchmarks demonstrate the generalization capability of CKA in the amalgamation of specific task as well as multiple tasks. Comprehensive ablation studies provide a further insight into our CKA.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 709,
+    "label": 10,
+    "text": "Title: Reinforcement Learning for Syntax-Guided Synthesis\nAbstract: Program synthesis is the task of automatically generating code based on a specification. In Syntax-Guided Synthesis(SyGuS) this specification is a combination of a syntactic template and a logical formula, and any generated code is proven to satisfy both. Techniques like SyGuS are critical to guaranteeing correct synthesis results. Despite the proliferation of machine learning in other types of program synthesis, state-of-the-art techniques in SyGuS are still driven by automated reasoning tools and simple enumeration. We hypothesize this is for two reasons: first the complexity of the search problem, and second the relatively small data sets available. In this work, we tackle these challenges by framing general SyGuS problems as a tree-search, and present a reinforcement learning guided synthesis algorithm for SyGuS based on Monte-Carlo Tree Search (MCTS). Our algorithm incorporates learned policy and value functions combined with the upper confidence bound for trees to balance exploration and exploitation. We incorporate this search procedure in a reinforcement learning setup in order to iteratively improve our policy and value estimators which are based on boosted tree models. To address the scarcity of training data, we present a method for automatically generating training data for SyGuS based on \\emph{anti-unification} of existing first-order satisfiability problems, which we use to train our MCTS policy. We implement and evaluate this setup and demonstrate that learned policy and value improve the synthesis performance over a baseline enumerator by over $26$ percentage points in the training and testing sets. With these results our tool outperforms state-of-the-art-tools such as CVC5 on the training set and performs comparably on the testing set. We make our data set publicly available, enabling further application of machine learning methods to the SyGuS problem.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 710,
+    "label": 16,
+    "text": "Title: How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks\nAbstract: As the use of collaborative robots (cobots) in industrial manufacturing continues to grow, human action recognition for effective human-robot collaboration becomes increasingly important. This ability is crucial for cobots to act autonomously and assist in assembly tasks. Recently, skeleton-based approaches are often used as they tend to generalize better to different people and environments. However, when processing skeletons alone, information about the objects a human interacts with is lost. Therefore, we present a novel approach of integrating object information into skeleton-based action recognition. We enhance two state-of-the-art methods by treating object centers as further skeleton joints. Our experiments on the assembly dataset IKEA ASM show that our approach improves the performance of these state-of-the-art methods to a large extent when combining skeleton joints with objects predicted by a state-of-the-art instance segmentation model. Our research sheds light on the benefits of combining skeleton joints with object information for human action recognition in assembly tasks. We analyze the effect of the object detector on the combination for action classification and discuss the important factors that must be taken into account.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 711,
+    "label": 6,
+    "text": "Title: A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey\nAbstract: The growing interest in the Metaverse has generated momentum for members of academia and industry to innovate toward realizing the Metaverse world. The Metaverse is a unique, continuous, and shared virtual world where humans embody a digital form within an online platform. Through a digital avatar, Metaverse users should have a perceptual presence within the environment and can interact and control the virtual world around them. Thus, a human-centric design is a crucial element of the Metaverse. The human users are not only the central entity but also the source of multi-sensory data that can be used to enrich the Metaverse ecosystem. In this survey, we study the potential applications of Brain-Computer Interface (BCI) technologies that can enhance the experience of Metaverse users. By directly communicating with the human brain, the most complex organ in the human body, BCI technologies hold the potential for the most intuitive human-machine system operating at the speed of thought. BCI technologies can enable various innovative applications for the Metaverse through this neural pathway, such as user cognitive state monitoring, digital avatar control, virtual interactions, and imagined speech communications. This survey first outlines the fundamental background of the Metaverse and BCI technologies. We then discuss the current challenges of the Metaverse that can potentially be addressed by BCI, such as motion sickness when users experience virtual environments or the negative emotional states of users in immersive virtual applications. After that, we propose and discuss a new research direction called Human Digital Twin, in which digital twins can create an intelligent and interactable avatar from the user's brain signals. We also present the challenges and potential solutions in synchronizing and communicating between virtual and physical entities in the Metaverse.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 712,
+    "label": 24,
+    "text": "Title: Factor Graph Neural Networks\nAbstract: In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs. By aiming to provide expressive methods for representation learning instead of computing marginals or most likely configurations, GNNs provide flexibility in the choice of information flowing rules while maintaining good performance. Despite their success and inspirations, they lack efficient ways to represent and learn higher-order relations among variables/nodes. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs. We then neuralize the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules; this facilitates both efficient inference and powerful end-to-end learning. We further show that with a suitable choice of message aggregation operators, our FGNN is also able to represent Max-Product belief propagation, providing a single family of architecture that can represent both Max and Sum-Product loopy belief propagation. Our extensive experimental evaluation on synthetic as well as real datasets demonstrates the potential of the proposed model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 713,
+    "label": 24,
+    "text": "Title: On the Steganographic Capacity of Selected Learning Models\nAbstract: Machine learning and deep learning models are potential vectors for various attack scenarios. For example, previous research has shown that malware can be hidden in deep learning models. Hiding information in a learning model can be viewed as a form of steganography. In this research, we consider the general question of the steganographic capacity of learning models. Specifically, for a wide range of models, we determine the number of low-order bits of the trained parameters that can be overwritten, without adversely affecting model performance. For each model considered, we graph the accuracy as a function of the number of low-order bits that have been overwritten, and for selected models, we also analyze the steganographic capacity of individual layers. The models that we test include the classic machine learning techniques of Linear Regression (LR) and Support Vector Machine (SVM); the popular general deep learning models of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN); the highly-successful Recurrent Neural Network (RNN) architecture of Long Short-Term Memory (LSTM); the pre-trained transfer learning-based models VGG16, DenseNet121, InceptionV3, and Xception; and, finally, an Auxiliary Classifier Generative Adversarial Network (ACGAN). In all cases, we find that a majority of the bits of each trained parameter can be overwritten before the accuracy degrades. Of the models tested, the steganographic capacity ranges from 7.04 KB for our LR experiments, to 44.74 MB for InceptionV3. We discuss the implications of our results and consider possible avenues for further research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 714,
+    "label": 24,
+    "text": "Title: A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance\nAbstract: We present accumulator-aware quantization (A2Q), a novel weight quantization method designed to train quantized neural networks (QNNs) to avoid overflow when using low-precision accumulators during inference. A2Q introduces a unique formulation inspired by weight normalization that constrains the L1-norm of model weights according to accumulator bit width bounds that we derive. Thus, in training QNNs for low-precision accumulation, A2Q also inherently promotes unstructured weight sparsity to guarantee overflow avoidance. We apply our method to deep learning-based computer vision tasks to show that A2Q can train QNNs for low-precision accumulators while maintaining model accuracy competitive with a floating-point baseline. In our evaluations, we consider the impact of A2Q on both general-purpose platforms and programmable hardware. However, we primarily target model deployment on FPGAs because they can be programmed to fully exploit custom accumulator bit widths. Our experimentation shows accumulator bit width significantly impacts the resource efficiency of FPGA-based accelerators. On average across our benchmarks, A2Q offers up to a 2.3x reduction in resource utilization over 32-bit accumulator counterparts with 99.2% of the floating-point model accuracy.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 715,
+    "label": 27,
+    "text": "Title: Beyond Flat GelSight Sensors: Simulation of Optical Tactile Sensors of Complex Morphologies for Sim2Real Learning\nAbstract: Recently, several morphologies, each with its advantages, have been proposed for the \\textit{GelSight} high-resolution tactile sensors. However, existing simulation methods are limited to flat-surface sensors, which prevents their usage with the newer sensors of non-flat morphologies in Sim2Real experiments. In this paper, we extend a previously proposed GelSight simulation method developed for flat-surface sensors and propose a novel method for curved sensors. In particular, we address the simulation of light rays travelling through a curved tactile membrane in the form of geodesic paths. The method is validated by simulating the finger-shaped GelTip sensor and comparing the generated synthetic tactile images against the corresponding real images. Our extensive experiments show that combining the illumination generated from the geodesic paths, with a background image from the real sensor, produces the best results when compared to the lighting generated by direct linear paths in the same conditions. As the method is parameterised by the sensor mesh, it can be applied in principle to simulate a tactile sensor of any morphology. The proposed method not only unlocks simulating existing optical tactile sensors of complex morphologies but also enables experimenting with sensors of novel morphologies, before the fabrication of the real sensor. Project website: https://danfergo.github.io/geltip-sim",
+    "neighbors": [
+      1140,
+      1961
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 716,
+    "label": 24,
+    "text": "Title: Feature Enforcing PINN (FE-PINN): A Framework for Learning the Underlying-Physics to Resolve Unbalancing in the Objective Function Terms\nAbstract: In this study, we propose a new data-free framework, Feature Enforcing Physics Informed Neural Network (FE-PINN), to overcome the challenge of an imbalanced loss function in vanilla PINNs. The imbalance is caused by the presence of two terms in the loss function: the partial differential loss and the boundary condition mean squared error. A standard solution is to use loss weighting, but it requires hyperparameter tuning. To address this challenge, we introduce a process called smart initialization to force the neural network to learn only the boundary conditions before the final training in a designed process. In this method, clustered domain points are used to train a neural network with designed weights, resulting in the creation of a neural network called Foundation network. This results in a network with unique weights that understand boundary conditions. Then, additional layers are used to improve the accuracy. This solves the problem of an imbalanced loss function without further need for hyperparameter tuning. For 2D flow over a cylinder as a benchmark, smart initialization in FE-PINN is 574 times faster than hyperparameter tuning in vanilla PINN. Even with the optimal loss weight value, FE-PINN outperforms vanilla PINN by speeding up the average training time by 1.98. Also, the ability of the proposed approach is shown for an inverse problem. To find the inlet velocity for a 2D flow over a cylinder, FE-PINN is twice faster than vanilla PINN with the knowledge of optimal weight loss value for vanilla PINN. Our results show that FE-PINN not only eliminates the time-consuming process of loss weighting but also improves convergence speed compared to vanilla PINN, even when the optimal weight value is used in its loss function. In conclusion, this framework can be used as a fast and accurate tool for solving a wide range of Partial Differential Equations across various fields.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 717,
+    "label": 24,
+    "text": "Title: Causality-Aided Trade-off Analysis for Machine Learning Fairness\nAbstract: There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To ractically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widelyused fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies.",
+    "neighbors": [
+      699
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 718,
+    "label": 16,
+    "text": "Title: Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification\nAbstract: In visible-infrared video person re-identification (re-ID), extracting features not affected by complex scenes (such as modality, camera views, pedestrian pose, background, etc.) changes, and mining and utilizing motion information are the keys to solving cross-modal pedestrian identity matching. To this end, the paper proposes a new visible-infrared video person re-ID method from a novel perspective, i.e., adversarial self-attack defense and spatial-temporal relation mining. In this work, the changes of views, posture, background and modal discrepancy are considered as the main factors that cause the perturbations of person identity features. Such interference information contained in the training samples is used as an adversarial perturbation. It performs adversarial attacks on the re-ID model during the training to make the model more robust to these unfavorable factors. The attack from the adversarial perturbation is introduced by activating the interference information contained in the input samples without generating adversarial samples, and it can be thus called adversarial self-attack. This design allows adversarial attack and defense to be integrated into one framework. This paper further proposes a spatial-temporal information-guided feature representation network to use the information in video sequences. The network cannot only extract the information contained in the video-frame sequences but also use the relation of the local information in space to guide the network to extract more robust features. The proposed method exhibits compelling performance on large-scale cross-modality video datasets. The source code of the proposed method will be released at https://github.com/lhf12278/xxx.",
+    "neighbors": [
+      298,
+      744
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 719,
+    "label": 16,
+    "text": "Title: Foundational Models Defining a New Era in Vision: A Survey and Outlook\nAbstract: Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locations, ambiguities, and variations in the real-world environment can be better described in human language, naturally governed by grammatical rules and other modalities such as audio and depth. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. These models are referred to as foundational models. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions. In this survey, we provide a comprehensive review of such emerging foundational models, including typical architecture designs to combine different modalities (vision, text, audio, etc), training objectives (contrastive, generative), pre-training datasets, fine-tuning mechanisms, and the common prompting patterns; textual, visual, and heterogeneous. We discuss the open challenges and research directions for foundational models in computer vision, including difficulties in their evaluations and benchmarking, gaps in their real-world understanding, limitations of their contextual understanding, biases, vulnerability to adversarial attacks, and interpretability issues. We review recent developments in this field, covering a wide range of applications of foundation models systematically and comprehensively. A comprehensive list of foundational models studied in this work is available at \\url{https://github.com/awaisrauf/Awesome-CV-Foundational-Models}.",
+    "neighbors": [
+      34,
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+      902,
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+      1044,
+      1047,
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+      1485,
+      1520,
+      1526,
+      1537,
+      1601,
+      1626,
+      1668,
+      1735,
+      1765,
+      1822,
+      1828,
+      1863,
+      1893,
+      1940,
+      2009,
+      2029,
+      2036,
+      2047,
+      2095,
+      2113,
+      2155,
+      2203,
+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 720,
+    "label": 16,
+    "text": "Title: Gap and Overlap Detection in Automated Fiber Placement\nAbstract: The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts produced through Automated Fiber Placement (AFP). These imperfections are the most commonly observed issues that can significantly impact the overall quality of the composite parts. Manual inspection is both time-consuming and labor-intensive, making it an inefficient approach. To overcome this challenge, the implementation of an automated defect detection system serves as the optimal solution. In this paper, we introduce a novel method that uses an Optical Coherence Tomography (OCT) sensor and computer vision techniques to detect and locate gaps and overlaps in composite parts. Our approach involves generating a depth map image of the composite surface that highlights the elevation of composite tapes (or tows) on the surface. By detecting the boundaries of each tow, our algorithm can compare consecutive tows and identify gaps or overlaps that may exist between them. Any gaps or overlaps exceeding a predefined tolerance threshold are considered manufacturing defects. To evaluate the performance of our approach, we compare the detected defects with the ground truth annotated by experts. The results demonstrate a high level of accuracy and efficiency in gap and overlap segmentation.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 721,
+    "label": 36,
+    "text": "Title: On the Parameterized Complexity of Controlling Approval-Based Multiwinner Voting: Destructive Model & Sequential Rules\nAbstract: Over the past few years, the (parameterized) complexity landscape of constructive control for many prevalent approval-based multiwinner voting (ABMV) rules has been explored. We expand these results in two directions. First, we study constructive control for sequential Thiele's rules. Second, we study destructive counterparts of these problems. Our exploration leads to a comprehensive understanding of the complexity of these problems. Along the way, we also study several interesting axiomatic properties of ABMV rules, and obtain generic results for rules fulfilling these properties. In particular, we show that for many rules satisfying these properties, election control problems are generally hard to solve from a parameterized complexity point of view, even when restricted to certain special cases.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 722,
+    "label": 37,
+    "text": "Title: Belnap-Dunn logic and query answering in inconsistent databases with null values\nAbstract: This paper concerns an expansion of first-order Belnap-Dunn logic, named $\\mathrm{BD}^{\\supset,\\mathsf{F}}$, and an application of this logic in the area of relational database theory. The notion of a relational database, the notion of a query applicable to a relational database, and several notions of an answer to a query with respect to a relational database are considered from the perspective of this logic, taking into account that a database may be an inconsistent database or a database with null values. The chosen perspective enables among other things the definition of a notion of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. For each of the notions of an answer considered, being an answer to a query with respect to a database of the kind considered is decidable.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 723,
+    "label": 23,
+    "text": "Title: JBMC: A Bounded Model Checking Tool for Java Bytecode\nAbstract: JBMC is an open-source SAT- and SMT-based bounded model checking tool for verifying Java bytecode. JBMC relies on an operational model of the Java libraries, which conservatively approximates their semantics, to verify assertion violations, array out-of-bounds, unintended arithmetic overflows, and other kinds of functional and runtime errors in Java bytecode. JBMC can be used to either falsify properties or prove program correctness if an upper bound on the depth of the state-space is known. Practical applications of JBMC include but are not limited to bug finding, property checking, test input generation, detection of security vulnerabilities, and program synthesis. Here we provide a detailed description of JBMC's architecture and its functionalities, including an in-depth discussion of its background theories and underlying technologies, including a state-of-the-art string solver to ensure safety and security of Java bytecode.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 724,
+    "label": 27,
+    "text": "Title: AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers\nAbstract: For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. The recent and remarkable advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, many existing approaches either translate the natural language directly into robot trajectories, or factor the inference process by decomposing language into task sub-goals, then relying on a motion planner to execute each sub-goal. When complex environmental and temporal constraints are involved, inference over planning tasks must be performed jointly with motion plans using traditional task-and-motion planning (TAMP) algorithms, making such factorization untenable. Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediary task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan. To improve translation, we automatically detect and correct both syntactic and semantic errors via autoregressive re-prompting, resulting in significant improvements in task completion. We show that our approach outperforms several methods using LLMs as planners in complex task domains.",
+    "neighbors": [
+      1877
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 725,
+    "label": 24,
+    "text": "Title: Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective\nAbstract: Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine-learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 726,
+    "label": 16,
+    "text": "Title: Masked Image Modeling with Local Multi-Scale Reconstruction\nAbstract: Masked Image Modeling (MIM) achieves outstanding success in self-supervised representation learning. Unfortunately, MIM models typically have huge computational burden and slow learning process, which is an inevitable obstacle for their industrial applications. Although the lower layers play the key role in MIM, existing MIM models conduct reconstruction task only at the top layer of encoder. The lower layers are not explicitly guided and the interaction among their patches is only used for calculating new activations. Considering the reconstruction task requires non-trivial inter-patch interactions to reason target signals, we apply it to multiple local layers including lower and upper layers. Further, since the multiple layers expect to learn the information of different scales, we design local multi-scale reconstruction, where the lower and upper layers reconstruct fine-scale and coarse-scale supervision signals respectively. This design not only accelerates the representation learning process by explicitly guiding multiple layers, but also facilitates multi-scale semantical understanding to the input. Extensive experiments show that with significantly less pre-training burden, our model achieves comparable or better performance on classification, detection and segmentation tasks than existing MIM models. Code is available with both MindSpore and PyTorch.",
+    "neighbors": [
+      1595,
+      1637,
+      2179
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 727,
+    "label": 24,
+    "text": "Title: Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity\nAbstract: Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link prediction tasks. However, the existing studies mostly only consider local connectivity while ignoring long-range connectivity and the roles of nodes. In this paper, we propose Unified Graph Transformer Networks (UGT) that effectively integrate local and global structural information into fixed-length vector representations. First, UGT learns local structure by identifying the local substructures and aggregating features of the $k$-hop neighborhoods of each node. Second, we construct virtual edges, bridging distant nodes with structural similarity to capture the long-range dependencies. Third, UGT learns unified representations through self-attention, encoding structural distance and $p$-step transition probability between node pairs. Furthermore, we propose a self-supervised learning task that effectively learns transition probability to fuse local and global structural features, which could then be transferred to other downstream tasks. Experimental results on real-world benchmark datasets over various downstream tasks showed that UGT significantly outperformed baselines that consist of state-of-the-art models. In addition, UGT reaches the expressive power of the third-order Weisfeiler-Lehman isomorphism test (3d-WL) in distinguishing non-isomorphic graph pairs. The source code is available at https://github.com/NSLab-CUK/Unified-Graph-Transformer.",
+    "neighbors": [
+      1986
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 728,
+    "label": 26,
+    "text": "Title: A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social Networks\nAbstract: Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, controversy and other important phenomena over controversial social issues, which have been the subject of many recent works. In this paper, we study the problem of minimizing risk of conflict in social networks by modifying the initial opinions of a small number of nodes. We show that the objective function of the combinatorial optimization problem is monotone and supermodular. We then propose a naive greedy algorithm with a (1-1/e) approximation ratio that solves the problem in cubic time. To overcome the computation challenge for large networks, we further integrate several effective approximation strategies to provide a nearly linear time algorithm with a (1-1/e-\u03b5) approximation ratio for any error parameter \u03b5>0. Extensive experiments on various real-world datasets demonstrate both the efficiency and effectiveness of our algorithms. In particular, the fast one scales to large networks with more than two million nodes, and achieves up to 20x speed-up over the state-of-the-art algorithm.",
+    "neighbors": [
+      876
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 729,
+    "label": 16,
+    "text": "Title: OverlapNetVLAD: A Coarse-to-Fine Framework for LiDAR-based Place Recognition\nAbstract: Place recognition is a challenging yet crucial task in robotics. Existing 3D LiDAR place recognition methods suffer from limited feature representation capability and long search times. To address these challenges, we propose a novel coarse-to-fine framework for 3D LiDAR place recognition that combines Birds' Eye View (BEV) feature extraction, coarse-grained matching, and fine-grained verification. In the coarse stage, our framework leverages the rich contextual information contained in BEV features to produce global descriptors. Then the top-\\textit{K} most similar candidates are identified via descriptor matching, which is fast but coarse-grained. In the fine stage, our overlap estimation network reuses the corresponding BEV features to predict the overlap region, enabling meticulous and precise matching. Experimental results on the KITTI odometry benchmark demonstrate that our framework achieves leading performance compared to state-of-the-art methods. Our code is available at: \\url{https://github.com/fcchit/OverlapNetVLAD}.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 730,
+    "label": 8,
+    "text": "Title: SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals\nAbstract: Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1 TB dataset and code repository1 [1].1https://github.com/kfoysalhaque/SiMWiSense",
+    "neighbors": [
+      28,
+      851
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 731,
+    "label": 24,
+    "text": "Title: Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error\nAbstract: Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Outof-distribution (OOD) data is known to increase the probability of error and research into metrics that identify what dissimilarities in data affect model performance is on-going. Recently, combinatorial coverage metrics have been explored in the literature as an alternative to distribution-based metrics. Results show that coverage metrics can correlate with classification error. However, other results show that the utility of coverage metrics is highly dataset-dependent. In this paper, we show that this dataset-dependence can be alleviated with metric learning, a machine learning technique for learning latent spaces where data from different classes is further apart. In a study of 6 open-source datasets, we find that metric learning increased the difference between set-difference coverage metrics (SDCCMs) calculated on correctly and incorrectly classified data, thereby demonstrating that metric learning improves the ability of SDCCMs to anticipate classification error. Paired t-tests validate the statistical significance of our findings. Overall, we conclude that metric learning improves the ability of coverage metrics to anticipate classifier error and identify when OOD data is likely to degrade model performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 732,
+    "label": 16,
+    "text": "Title: MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Network\nAbstract: Recent advances in deep learning have been pushing image denoising techniques to a new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most common methods. However, most of the existing BSN algorithms use a dot-based central mask, which is recognized as inefficient for images with large-scale spatially correlated noise. In this paper, we give the definition of large-noise and propose a multi-mask strategy using multiple convolutional kernels masked in different shapes to further break the noise spatial correlation. Furthermore, we propose a novel self-supervised image denoising method that combines the multi-mask strategy with BSN (MM-BSN). We show that different masks can cause significant performance differences, and the proposed MM-BSN can efficiently fuse the features extracted by multi-masked layers, while recovering the texture structures destroyed by multi-masking and information transmission. Our MM-BSN can be used to address the problem of large-noise denoising, which cannot be efficiently handled by other BSN methods. Extensive experiments on public real-world datasets demonstrate that the proposed MM-BSN achieves state-of-the-art performance among self-supervised and even unpaired image denoising methods for sRGB images denoising, without any labelling effort or prior knowledge. Code can be found in https://github.com/dannie125/MM-BSN.",
+    "neighbors": [
+      43
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 733,
+    "label": 16,
+    "text": "Title: QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge\nAbstract: This technical report describes our QuAVF@NTU-NVIDIA submission to the Ego4D Talking to Me (TTM) Challenge 2023. Based on the observation from the TTM task and the provided dataset, we propose to use two separate models to process the input videos and audio. By doing so, we can utilize all the labeled training data, including those without bounding box labels. Furthermore, we leverage the face quality score from a facial landmark prediction model for filtering noisy face input data. The face quality score is also employed in our proposed quality-aware fusion for integrating the results from two branches. With the simple architecture design, our model achieves 67.4% mean average precision (mAP) on the test set, which ranks first on the leaderboard and outperforms the baseline method by a large margin. Code is available at: https://github.com/hsi-che-lin/Ego4D-QuAVF-TTM-CVPR23",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 734,
+    "label": 24,
+    "text": "Title: Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms\nAbstract: We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we analyze the distribution of different classifier performances over a sample of standard benchmark data sets. Our results promisingly demonstrate that our approach differs substantially from existing benchmarking approaches and, therefore, adds a new perspective to the vivid debate on the comparison of classifiers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 735,
+    "label": 30,
+    "text": "Title: Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study\nAbstract: Large Language Models (LLMs) like ChatGPT have proven a great shallow understanding of many traditional NLP tasks, such as translation, summarization, etc. However, its performance on high-level understanding, such as dialogue discourse analysis task that requires a higher level of understanding and reasoning, remains less explored. This study investigates ChatGPT's capabilities in three dialogue discourse tasks: topic segmentation, discourse relation recognition, and discourse parsing, of varying difficulty levels. To adapt ChatGPT to these tasks, we propose discriminative and generative paradigms and introduce the Chain of Thought (COT) approach to improve ChatGPT's performance in more difficult tasks. The results show that our generative paradigm allows ChatGPT to achieve comparative performance in the topic segmentation task comparable to state-of-the-art methods but reveals room for improvement in the more complex tasks of discourse relation recognition and discourse parsing. Notably, the COT can significantly enhance ChatGPT's performance with the help of understanding complex structures in more challenging tasks. Through a series of case studies, our in-depth analysis suggests that ChatGPT can be a good annotator in topic segmentation but has difficulties understanding complex rhetorical structures. We hope these findings provide a foundation for future research to refine dialogue discourse analysis approaches in the era of LLMs.",
+    "neighbors": [
+      971,
+      1142,
+      1694,
+      1797,
+      2252
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 736,
+    "label": 24,
+    "text": "Title: On the Design Fundamentals of Diffusion Models: A Survey\nAbstract: Diffusion models are generative models, which gradually add and remove noise to learn the underlying distribution of training data for data generation. The components of diffusion models have gained significant attention with many design choices proposed. Existing reviews have primarily focused on higher-level solutions, thereby covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review on component-wise design choices in diffusion models. Specifically, we organize this review according to their three key components, namely the forward process, the reverse process, and the sampling procedure. This allows us to provide a fine-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the applicability of design choices, and the implementation of diffusion models.",
+    "neighbors": [
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+      2242,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 737,
+    "label": 16,
+    "text": "Title: Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding\nAbstract: Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.",
+    "neighbors": [
+      2287
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 738,
+    "label": 34,
+    "text": "Title: On the Complexity of Algorithms with Predictions for Dynamic Graph Problems\nAbstract: {\\em Algorithms with predictions} incorporate machine learning predictions into algorithm design. A plethora of recent works incorporated predictions to improve on worst-case optimal bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike in online algorithms, the main goal in dynamic data structures is to maintain the solution {\\em efficiently} with every update. Motivated by work in online algorithms, we investigate three natural models of predictions: (1) $\\varepsilon$-accurate predictions where each predicted request matches the true request with probability at least $\\varepsilon$, (2) list-accurate predictions where a true request comes from a list of possible requests, and (3) bounded delay predictions where the true requests are some (unknown) permutations of the predicted requests. For $\\varepsilon$-accurate predictions, we show that lower bounds from the non-prediction setting of a problem carry over, up to a $1-\\varepsilon$ factor. Then we give general reductions among the prediction models for a problem, showing that lower bounds for bounded delay imply lower bounds for list-accurate predictions, which imply lower bounds for $\\varepsilon$-accurate predictions. Further, we identify two broad problem classes based on lower bounds due to the Online Matrix Vector (OMv) conjecture. Specifically, we show that dynamic problems that are {\\em locally correctable} have strong conditional lower bounds for list-accurate predictions that are equivalent to the non-prediction setting, unless list-accurate predictions are perfect. Moreover, dynamic problems that are {\\em locally reducible} have a smooth transition in the running time. We categorize problems accordingly and give upper bounds that show that our lower bounds are almost tight, including problems in dynamic graphs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 739,
+    "label": 27,
+    "text": "Title: Chat with the Environment: Interactive Multimodal Perception using Large Language Models\nAbstract: Programming robot behavior in a complex world faces challenges on multiple levels, from dextrous low-level skills to high-level planning and reasoning. Recent pre-trained Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. However, it remains challenging to ground LLMs in multimodal sensory input and continuous action output, while enabling a robot to interact with its environment and acquire novel information as its policies unfold. We develop a robot interaction scenario with a partially observable state, which necessitates a robot to decide on a range of epistemic actions in order to sample sensory information among multiple modalities, before being able to execute the task correctly. An interactive perception framework is therefore proposed with an LLM as its backbone, whose ability is exploited to instruct epistemic actions and to reason over the resulting multimodal sensations (vision, sound, haptics, proprioception), as well as to plan an entire task execution based on the interactively acquired information. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment, while multimodal modules with the context of the environmental state help ground the LLMs and extend their processing ability. The project website can be found at \\href{https://matcha-model.github.io}{\\textcolor{blue}{https://matcha-model.github.io/}}.",
+    "neighbors": [
+      704,
+      719,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 740,
+    "label": 7,
+    "text": "Title: Isogeometric analysis using G-spline surfaces with arbitrary unstructured quadrilateral layout\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 741,
+    "label": 23,
+    "text": "Title: Visualising Game Engine Subsystem Coupling\nAbstract: Game engines support video game development by providing functionalities such as graphics rendering or input/output device management. However, their architectures are often overlooked, which hinders their integration and extension. In this paper, we use an approach for architecture recovery to create architectural models for 10 open-source game engines. We use these models to answer the following questions: Which subsystems more often couple with one another? Do game engines share subsystem coupling patterns? We observe that the Low-Level Renderer, Platform Independence Layer and Resource Manager are frequently coupled to the game engine Core. By identifying the most frequent coupling patterns, we describe an emergent game engine architecture and discuss how it can be used by practitioners to improve system understanding and maintainability.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 742,
+    "label": 30,
+    "text": "Title: Grounding and Distinguishing Conceptual Vocabulary Through Similarity Learning in Embodied Simulations\nAbstract: We present a novel method for using agent experiences gathered through an embodied simulation to ground contextualized word vectors to object representations. We use similarity learning to make comparisons between different object types based on their properties when interacted with, and to extract common features pertaining to the objects' behavior. We then use an affine transformation to calculate a projection matrix that transforms contextualized word vectors from different transformer-based language models into this learned space, and evaluate whether new test instances of transformed token vectors identify the correct concept in the object embedding space. Our results expose properties of the embedding spaces of four different transformer models and show that grounding object token vectors is usually more helpful to grounding verb and attribute token vectors than the reverse, which reflects earlier conclusions in the analogical reasoning and psycholinguistic literature.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 743,
+    "label": 4,
+    "text": "Title: Cryptographic ransomware encryption detection: Survey\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 744,
+    "label": 16,
+    "text": "Title: Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification\nAbstract: Due to the modality gap between visible and infrared images with high visual ambiguity, learning diverse modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect that erasing body-shape-related semantic concepts in the learned features can force the ReID model to extract more and other modality-shared features for identification. To this end, we propose shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. Jointly learning shape-related feature in one subspace and shape-erased features in the orthogonal complement achieves a conditional mutual information maximization between shape-erased feature and identity discarding body shape information, thus enhancing the diversity of the learned representation explicitly. Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.",
+    "neighbors": [
+      718
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 745,
+    "label": 28,
+    "text": "Title: Pilot Power Allocation for Channel Estimation in a Multi-RIS Aided Communication System\nAbstract: Reconfigurable intelligent surface (RIS) is a promising technology that enables the customization of electromagnetic propagation environments in next-generation wireless networks. In this paper, we investigate the optimal pilot power allocation during the channel estimation stage to improve the ergodic channel gain of RIS-assisted systems under practical imperfect channel state information (CSI). Specifically, we commence by deriving an explicit closed-form expression of the ergodic channel gain of a multi-RIS-aided communication system that takes into account channel estimation errors. Then, we formulate the pilot power allocation problem to maximize the ergodic channel gain under imperfect CSI, subject to the average pilot power constraint. Then, the method of Lagrange multipliers is invoked to obtain the optimal pilot power allocation solution, which indicates that allocating more power to the pilots for estimating the weak reflection channels is capable of effectively improving the ergodic channel gain under imperfect CSI. Finally, extensive simulation results corroborate our theoretical analysis.",
+    "neighbors": [
+      226,
+      865
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 746,
+    "label": 16,
+    "text": "Title: A Survey on Deep Multi-modal Learning for Body Language Recognition and Generation\nAbstract: Body language (BL) refers to the non-verbal communication expressed through physical movements, gestures, facial expressions, and postures. It is a form of communication that conveys information, emotions, attitudes, and intentions without the use of spoken or written words. It plays a crucial role in interpersonal interactions and can complement or even override verbal communication. Deep multi-modal learning techniques have shown promise in understanding and analyzing these diverse aspects of BL. The survey emphasizes their applications to BL generation and recognition. Several common BLs are considered i.e., Sign Language (SL), Cued Speech (CS), Co-speech (CoS), and Talking Head (TH), and we have conducted an analysis and established the connections among these four BL for the first time. Their generation and recognition often involve multi-modal approaches. Benchmark datasets for BL research are well collected and organized, along with the evaluation of SOTA methods on these datasets. The survey highlights challenges such as limited labeled data, multi-modal learning, and the need for domain adaptation to generalize models to unseen speakers or languages. Future research directions are presented, including exploring self-supervised learning techniques, integrating contextual information from other modalities, and exploiting large-scale pre-trained multi-modal models. In summary, this survey paper provides a comprehensive understanding of deep multi-modal learning for various BL generations and recognitions for the first time. By analyzing advancements, challenges, and future directions, it serves as a valuable resource for researchers and practitioners in advancing this field. n addition, we maintain a continuously updated paper list for deep multi-modal learning for BL recognition and generation: https://github.com/wentaoL86/awesome-body-language.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 747,
+    "label": 27,
+    "text": "Title: A Comparison of Pneumatic Actuators for Soft Growing Vine Robots\nAbstract: Soft pneumatic actuators are used to steer soft growing\"vine\"robots while being flexible enough to undergo the tip eversion required for growth. In this study, we compared the performance of three types of pneumatic actuators in terms of their ability to perform eversion, quasi-static bending, dynamic motion, and force output: the pouch motor, the cylindrical pneumatic artificial muscle (cPAM), and the fabric pneumatic artificial muscle (fPAM). The pouch motor is advantageous for prototyping due to its simple manufacturing process. The cPAM exhibits superior bending behavior and produces the highest forces, while the fPAM actuates fastest and everts at the lowest pressure. We evaluated a range of dimensions for each actuator type. Larger actuators can produce more significant deformations and forces, but smaller actuators inflate faster and can evert at a lower pressure. Because vine robots are lightweight, the effect of gravity on the functionality of different actuators is minimal. We developed a new analytical model that predicts the pressure-to-bending behavior of vine robot actuators. Using the actuator results, we designed and demonstrated a 4.8 m long vine robot equipped with highly maneuverable 60x60 mm cPAMs in a three-dimensional obstacle course. The vine robot was able to move around sharp turns, travel through a passage smaller than its diameter, and lift itself against gravity.",
+    "neighbors": [
+      1827,
+      1914
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 748,
+    "label": 34,
+    "text": "Title: A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time\nAbstract: Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \\sum_{j_0 = 1}^n \\sum_{i_0 = 1}^d ( \\langle \\langle \\exp( \\mathsf{A}_{j_0} x ) , {\\bf 1}_n \\rangle^{-1} \\exp( \\mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \\rangle - b_{j_0,i_0} )^2$. Here $\\mathsf{A} \\in \\mathbb{R}^{n^2 \\times d^2}$ is Kronecker product between $A_1 \\in \\mathbb{R}^{n \\times d}$ and $A_2 \\in \\mathbb{R}^{n \\times d}$. $A_3$ is a matrix in $\\mathbb{R}^{n \\times d}$, $\\mathsf{A}_{j_0} \\in \\mathbb{R}^{n \\times d^2}$ is the $j_0$-th block of $\\mathsf{A}$. The $X, Y \\in \\mathbb{R}^{d \\times d}$ are variables we want to learn. $B \\in \\mathbb{R}^{n \\times d}$ and $b_{j_0,i_0} \\in \\mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \\in \\mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \\in \\mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \\in \\mathbb{R}^{n \\times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \\in \\mathbb{R}^{n \\times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\\epsilon$ that runs in $\\widetilde{O}( ({\\cal T}_{\\mathrm{mat}}(n,n,d) + {\\cal T}_{\\mathrm{mat}}(n,d,d) + d^{2\\omega}) \\log(1/\\epsilon) )$ time. Here ${\\cal T}_{\\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \\times b$ matrix another $b \\times c$ matrix, and $\\omega\\approx 2.37$ denotes the exponent of matrix multiplication.",
+    "neighbors": [
+      450,
+      485,
+      494,
+      644,
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+      1035,
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+      1559,
+      1636,
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+      2114,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 749,
+    "label": 23,
+    "text": "Title: Is Scenario Generation Ready for SOTIF? A Systematic Literature Review\nAbstract: Scenario-based testing is considered state-of-the-art to verify and validate Advanced Driver Assistance Systems or Automated Driving Systems. Due to the official launch of the SOTIF-standard (ISO 21448), scenario-based testing becomes more and more relevant for releasing those Highly Automated Driving Systems. However, an essential missing detail prevent the practical application of the SOTIF-standard: How to practically generate scenarios for scenario-based testing? In this paper, we perform a Systematic Literature Review to identify techniques that generate scenarios complying with requirements of the SOTIF-standard. We classify existing scenario generation techniques and evaluate the characteristics of generated scenarios wrt. SOTIF requirements. We investigate which details of the real-world are covered by generated scenarios, whether scenarios are specific for a system under test or generic, and whether scenarios are designed to minimize the set of unknown and hazardous scenarios. We conclude that scenarios generated with existing techniques do not comply with requirements implied by the SOTIF-standard; hence, we propose directions for future research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 750,
+    "label": 16,
+    "text": "Title: Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers\nAbstract: Disaggregated performance metrics across demographic groups are a hallmark of fairness assessments in computer vision. These metrics successfully incentivized performance improvements on person-centric tasks such as face analysis and are used to understand risks of modern models. However, there is a lack of discussion on the vulnerabilities of these measurements for more complex computer vision tasks. In this paper, we consider multi-label image classification and, specifically, object categorization tasks. First, we highlight design choices and trade-offs for measurement that involve more nuance than discussed in prior computer vision literature. These challenges are related to the necessary scale of data, definition of groups for images, choice of metric, and dataset imbalances. Next, through two case studies using modern vision models, we demonstrate that naive implementations of these assessments are brittle. We identify several design choices that look merely like implementation details but significantly impact the conclusions of assessments, both in terms of magnitude and direction (on which group the classifiers work best) of disparities. Based on ablation studies, we propose some recommendations to increase the reliability of these assessments. Finally, through a qualitative analysis we find that concepts with large disparities tend to have varying definitions and representations between groups, with inconsistencies across datasets and annotators. While this result suggests avenues for mitigation through more consistent data collection, it also highlights that ambiguous label definitions remain a challenge when performing model assessments. Vision models are expanding and becoming more ubiquitous; it is even more important that our disparity assessments accurately reflect the true performance of models.",
+    "neighbors": [
+      1688
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 751,
+    "label": 4,
+    "text": "Title: Leakage-Abuse Attacks Against Forward and Backward Private Searchable Symmetric Encryption\nAbstract: Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly proposed DSSE schemes. Those schemes are generally constructed in a way to break the linkability across search and update queries to a given keyword. However, it remains underexplored whether forward and backward private DSSE is resilient against practical leakage-abuse attacks (LAAs), where an attacker attempts to recover query keywords from the leakage passively collected during queries. In this paper, we aim to be the first to answer this question firmly through two non-trivial efforts. First, we revisit the spectrum of forward and backward private DSSE schemes over the past few years, and unveil some inherent constructional limitations in most schemes. Those limitations allow attackers to exploit query equality and establish a guaranteed linkage among different (refreshed) query tokens surjective to a candidate keyword. Second, we refine volumetric leakage profiles of updates and queries by associating each with a specific operation. By further exploiting update volume and query response volume, we demonstrate that all forward and backward private DSSE schemes can leak the same volumetric information (e.g., insertion volume, deletion volume) as those without such security guarantees. To testify our findings, we realize two generic LAAs, i.e., frequency matching attack and volumetric inference attack, and we evaluate them over various experimental settings in the dynamic context. Finally, we call for new efficient schemes to protect query equality and volumetric information across search and update queries.",
+    "neighbors": [
+      1095,
+      2301
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 752,
+    "label": 24,
+    "text": "Title: Risk-sensitive Actor-free Policy via Convex Optimization\nAbstract: Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences. In this paper, we propose an optimal actor-free policy that optimizes a risk-sensitive criterion based on the conditional value at risk. The risk-sensitive objective function is modeled using an input-convex neural network ensuring convexity with respect to the actions and enabling the identification of globally optimal actions through simple gradient-following methods. Experimental results demonstrate the efficacy of our approach in maintaining effective risk control.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 753,
+    "label": 37,
+    "text": "Title: From Shapley Value to Model Counting and Back\nAbstract: In this paper we investigate the problem of quantifying the contribution of each variable to the satisfying assignments of a Boolean function based on the Shapley value. Our main result is a polynomial-time equivalence between computing Shapley values and model counting for any class of Boolean functions that are closed under substitutions of variables with disjunctions of fresh variables. This result settles an open problem raised in prior work, which sought to connect the Shapley value computation to probabilistic query evaluation. We show two applications of our result. First, the Shapley values can be computed in polynomial time over deterministic and decomposable circuits, since they are closed under OR-substitutions. Second, there is a polynomial-time equivalence between computing the Shapley value for the tuples contributing to the answer of a Boolean conjunctive query and counting the models in the lineage of the query. This equivalence allows us to immediately recover the dichotomy for Shapley value computation in case of self-join-free Boolean conjunctive queries; in particular, the hardness for non-hierarchical queries can now be shown using a simple reduction from the #P-hard problem of model counting for lineage in positive bipartite disjunctive normal form.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 754,
+    "label": 16,
+    "text": "Title: Transfer Visual Prompt Generator across LLMs\nAbstract: While developing a new vision-language LLM (VL-LLM) by pre-training on tremendous image-text pairs from scratch can be exceedingly resource-consuming, connecting an existing LLM with a comparatively lightweight visual prompt generator (VPG) becomes a feasible paradigm. However, further tuning the VPG part of the VL-LLM still suffers from indispensable computational costs, i.e., requiring thousands of GPU hours and millions of training data. One alternative solution is to transfer an existing VPG from any existing VL-LLMs for the target VL-LLM. In this work, we for the first time investigate the VPG transferability across LLMs, and explore a solution to reduce the cost of VPG transfer. We first study the VPG transfer across different LLM sizes (e.g., small-to-large), and across different LLM types, through which we diagnose the key factors to maximize the transfer efficiency. Based on our observation, we design a two-stage transfer framework named VPGTrans, which is simple yet highly effective. Through extensive experiments, we demonstrate that VPGTrans helps significantly speed up the transfer learning process without compromising performance. Remarkably, it helps achieve the VPG transfer from BLIP-2 OPT$_\\text{2.7B}$ to BLIP-2 OPT$_\\text{6.7B}$ with over 10 times speed-up and 10.7% training data compared with connecting a VPG to OPT$_\\text{6.7B}$ from scratch. Further, a series of intriguing findings and potential rationales behind them are provided and discussed. Finally, we showcase the practical value of our VPGTrans approach, by customizing two novel VL-LLMs, including VL-LLaMA and VL-Vicuna, with recently released LLaMA and Vicuna LLMs.",
+    "neighbors": [
+      0,
+      466,
+      719,
+      887,
+      1044,
+      1047,
+      1052,
+      1071,
+      1344,
+      2155
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 755,
+    "label": 9,
+    "text": "Title: Testing Junta Truncation\nAbstract: We consider the basic statistical problem of detecting truncation of the uniform distribution on the Boolean hypercube by juntas. More concretely, we give upper and lower bounds on the problem of distinguishing between i.i.d. sample access to either (a) the uniform distribution over $\\{0,1\\}^n$, or (b) the uniform distribution over $\\{0,1\\}^n$ conditioned on the satisfying assignments of a $k$-junta $f: \\{0,1\\}^n\\to\\{0,1\\}$. We show that (up to constant factors) $\\min\\{2^k + \\log{n\\choose k}, {2^{k/2}\\log^{1/2}{n\\choose k}}\\}$ samples suffice for this task and also show that a $\\log{n\\choose k}$ dependence on sample complexity is unavoidable. Our results suggest that testing junta truncation requires learning the set of relevant variables of the junta.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 756,
+    "label": 28,
+    "text": "Title: Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning\nAbstract: A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Information (EMI) are used as learning and objective functions. Coincidentally, EMI is essentially the same as Semantic Mutual Information (SeMI) proposed by the author 30 years ago. This paper first reviews the evolutionary histories of semantic information measures and learning functions. Then, it briefly introduces the author\u2019s semantic information G theory with the rate-fidelity function R(G) (G denotes SeMI, and R(G) extends R(D)) and its applications to multi-label learning, the maximum Mutual Information (MI) classification, and mixture models. Then it discusses how we should understand the relationship between SeMI and Shannon\u2019s MI, two generalized entropies (fuzzy entropy and coverage entropy), Autoencoders, Gibbs distributions, and partition functions from the perspective of the R(G) function or the G theory. An important conclusion is that mixture models and Restricted Boltzmann Machines converge because SeMI is maximized, and Shannon\u2019s MI is minimized, making information efficiency G/R close to 1. A potential opportunity is to simplify deep learning by using Gaussian channel mixture models for pre-training deep neural networks\u2019 latent layers without considering gradients. It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep learning but is far from enough. Combining semantic information theory and deep learning will accelerate their development.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 757,
+    "label": 10,
+    "text": "Title: On Identifiability of Conditional Causal Effects\nAbstract: We address the problem of identifiability of an arbitrary conditional causal effect given both the causal graph and a set of any observational and/or interventional distributions of the form $Q[S]:=P(S|do(V\\setminus S))$, where $V$ denotes the set of all observed variables and $S\\subseteq V$. We call this problem conditional generalized identifiability (c-gID in short) and prove the completeness of Pearl's $do$-calculus for the c-gID problem by providing sound and complete algorithm for the c-gID problem. This work revisited the c-gID problem in Lee et al. [2020], Correa et al. [2021] by adding explicitly the positivity assumption which is crucial for identifiability. It extends the results of [Lee et al., 2019, Kivva et al., 2022] on general identifiability (gID) which studied the problem for unconditional causal effects and Shpitser and Pearl [2006b] on identifiability of conditional causal effects given merely the observational distribution $P(\\mathbf{V})$ as our algorithm generalizes the algorithms proposed in [Kivva et al., 2022] and [Shpitser and Pearl, 2006b].",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 758,
+    "label": 16,
+    "text": "Title: DiffusionRet: Generative Text-Video Retrieval with Diffusion Model\nAbstract: Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query). This is accomplished through a diffusion-based text-video retrieval framework (DiffusionRet), which models the retrieval task as a process of gradually generating joint distribution from noise. During training, DiffusionRet is optimized from both the generation and discrimination perspectives, with the generator being optimized by generation loss and the feature extractor trained with contrastive loss. In this way, DiffusionRet cleverly leverages the strengths of both generative and discriminative methods. Extensive experiments on five commonly used text-video retrieval benchmarks, including MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo, with superior performances, justify the efficacy of our method. More encouragingly, without any modification, DiffusionRet even performs well in out-domain retrieval settings. We believe this work brings fundamental insights into the related fields. Code is available at https://github.com/jpthu17/DiffusionRet.",
+    "neighbors": [
+      1151,
+      1787
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 759,
+    "label": 16,
+    "text": "Title: Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition\nAbstract: Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weak-nesses of face recognition systems and evaluate their ro-bustness before deployed. However, most existing physical attacks are either detectable readily or ineffective against commercial recognition systems. The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems. It requires that this technique can simultaneously deceive black-box recognition models and evade defensive mechanisms. To fulfill this, we design adversarial textured 3D meshes (AT3D) with an elaborate topology on a human face, which can be 3D-printed and pasted on the attacker's face to evade the defenses. However, the mesh-based op-timization regime calculates gradients in high-dimensional mesh space, and can be trapped into local optima with un-satisfactory transferability. To deviate from the mesh-based space, we propose to perturb the low-dimensional coefficient space based on 3D Morphable Model, which signifi-cantly improves black-box transferability meanwhile enjoying faster search efficiency and better visual quality. Exten-sive experiments in digital and physical scenarios show that our method effectively explores the security vulnerabilities of multiple popular commercial services, including three recognition A PIs, four anti-spoofing A PIs, two prevailing mobile phones and two automated access control systems.",
+    "neighbors": [
+      1287
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 760,
+    "label": 6,
+    "text": "Title: Metrics-Based Evaluation and Comparison of Visualization Notations\nAbstract: A visualization notation is a recurring pattern of symbols used to author specifications of visualizations, from data transformation to visual mapping. Programmatic notations use symbols defined by grammars or domain-specific languages (e.g., ggplot2, dplyr, Vega-Lite) or libraries (e.g., Matplotlib, Pandas). Designers and prospective users of grammars and libraries often evaluate visualization notations by inspecting galleries of examples. While such collections demonstrate usage and expressiveness, their construction and evaluation are usually ad hoc, making comparisons of different notations difficult. More rarely, experts analyze notations via usability heuristics, such as the Cognitive Dimensions of Notations framework. These analyses, akin to structured close readings of text, can reveal design deficiencies, but place a burden on the expert to simultaneously consider many facets of often complex systems. To alleviate these issues, we introduce a metrics-based approach to usability evaluation and comparison of notations in which metrics are computed for a gallery of examples across a suite of notations. While applicable to any visualization domain, we explore the utility of our approach via a case study considering statistical graphics that explores 40 visualizations across 9 widely used notations. We facilitate the computation of appropriate metrics and analysis via a new tool called NotaScope. We gathered feedback via interviews with authors or maintainers of prominent charting libraries (n=6). We find that this approach is a promising way to formalize, externalize, and extend evaluations and comparisons of visualization notations.",
+    "neighbors": [
+      270
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 761,
+    "label": 30,
+    "text": "Title: AlpaGasus: Training A Better Alpaca with Fewer Data\nAbstract: Large language models~(LLMs) obtain instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce AlpaGasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. AlpaGasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and its 13B variant matches $>90\\%$ performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes \\footnote{We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4$\\times$NVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.}. Overall, AlpaGasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Our project page is available at: \\url{https://lichang-chen.github.io/AlpaGasus/}.",
+    "neighbors": [
+      126,
+      811,
+      1039,
+      1052,
+      1112,
+      1346,
+      1556,
+      1617,
+      1735,
+      1972,
+      2038,
+      2208
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 762,
+    "label": 25,
+    "text": "Title: Expressive paragraph text-to-speech synthesis with multi-step variational autoencoder\nAbstract: Neural networks have been able to generate high-quality single-sentence speech with substantial expressiveness. However, it remains a challenge concerning paragraph-level speech synthesis due to the need for coherent acoustic features while delivering fluctuating speech styles. Meanwhile, training these models directly on over-length speech leads to a deterioration in the quality of synthesis speech. To address these problems, we propose a high-quality and expressive paragraph speech synthesis system with a multi-step variational autoencoder. Specifically, we employ multi-step latent variables to capture speech information at different grammatical levels before utilizing these features in parallel to generate speech waveform. We also propose a three-step training method to improve the decoupling ability. Our model was trained on a single-speaker French audiobook corpus released at Blizzard Challenge 2023. Experimental results underscore the significant superiority of our system over baseline models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 763,
+    "label": 16,
+    "text": "Title: A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation\nAbstract: Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 764,
+    "label": 27,
+    "text": "Title: Social Robot Navigation through Constrained Optimization: a Comparative Study of Uncertainty-based Objectives and Constraints\nAbstract: This work is dedicated to the study of how uncertainty estimation of the human motion prediction can be embedded into constrained optimization techniques, such as Model Predictive Control (MPC) for the social robot navigation. We propose several cost objectives and constraint functions obtained from the uncertainty of predicting pedestrian positions and related to the probability of the collision that can be applied to the MPC, and all the different variants are compared in challenging scenes with multiple agents. The main question this paper tries to answer is: what are the most important uncertainty-based criteria for social MPC? For that, we evaluate the proposed approaches with several social navigation metrics in an extensive set of scenarios of different complexity in reproducible synthetic environments. The main outcome of our study is a foundation for a practical guide on when and how to use uncertainty-aware approaches for social robot navigation in practice and what are the most effective criteria.",
+    "neighbors": [
+      1635
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 765,
+    "label": 28,
+    "text": "Title: User Scheduling and Passive Beamforming for FDMA/OFDMA in Intelligent Reflection Surface\nAbstract: Most prior works on intelligent reflecting surface (IRS) merely consider point-to-point communications, including a single user, for ease of analysis. Nevertheless, a practical wireless system needs to accommodate multiple users simultaneously. Due to the lack of frequency-selective reflection, namely the set of phase shifts cannot be different across frequency subchannels, the integration of IRS imposes a fundamental challenge to frequency-multiplexing approaches such as frequency-division multiple access (FDMA) and the widely adopted technique called orthogonal FDMA (OFDMA). It motivates us to study (O)FDMA-based multi-user IRS communications to clarify which user scheduling and passive beamforming are favorable under this non-frequency-selective reflection environment. Theoretical analysis and numerical evaluation reveal that (O)FDMA does not need user scheduling when there are a few users. If the number of users becomes large, neither user scheduling nor IRS reflection optimization is necessary. These findings help substantially simplify the design of (O)FDMA-based IRS communications.",
+    "neighbors": [
+      1844
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 766,
+    "label": 4,
+    "text": "Title: Know Your Cybercriminal: Evaluating Attacker Preferences by Measuring Profile Sales on an Active, Leading Criminal Market for User Impersonation at Scale\nAbstract: In this paper we exploit market features proper of a leading Russian cybercrime market for user impersonation at scale to evaluate attacker preferences when purchasing stolen user profiles, and the overall economic activity of the market. We run our data collection over a period of $161$ days and collect data on a sample of $1'193$ sold user profiles out of $11'357$ advertised products in that period and their characteristics. We estimate a market trade volume of up to approximately $700$ profiles per day, corresponding to estimated daily sales of up to $4'000$ USD and an overall market revenue within the observation period between $540k$ and $715k$ USD. We find profile provision to be rather stable over time and mainly focused on European profiles, whereas actual profile acquisition varies significantly depending on other profile characteristics. Attackers' interests focus disproportionally on profiles of certain types, including those originating in North America and featuring $crypto$ resources. We model and evaluate the relative importance of different profile characteristics in the final decision of an attacker to purchase a profile, and discuss implications for defenses and risk evaluation.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 767,
+    "label": 30,
+    "text": "Title: Thesis Distillation: Investigating The Impact of Bias in NLP Models on Hate Speech Detection\nAbstract: This paper is a summary of the work in my PhD thesis. In which, I investigate the impact of bias in NLP models on the task of hate speech detection from three perspectives: explainability, offensive stereotyping bias, and fairness. I discuss the main takeaways from my thesis and how they can benefit the broader NLP community. Finally, I discuss important future research directions. The findings of my thesis suggest that bias in NLP models impacts the task of hate speech detection from all three perspectives. And that unless we start incorporating social sciences in studying bias in NLP models, we will not effectively overcome the current limitations of measuring and mitigating bias in NLP models.",
+    "neighbors": [
+      225,
+      1705,
+      2225
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 768,
+    "label": 36,
+    "text": "Title: Asynchronous Proportional Response Dynamics in Markets with Adversarial Scheduling\nAbstract: We study Proportional Response Dynamics (PRD) in linear Fisher markets where participants act asynchronously. We model this scenario as a sequential process in which in every step, an adversary selects a subset of the players that will update their bids, subject to liveness constraints. We show that if every bidder individually uses the PRD update rule whenever they are included in the group of bidders selected by the adversary, then (in the generic case) the entire dynamic converges to a competitive equilibrium of the market. Our proof technique uncovers further properties of linear Fisher markets, such as the uniqueness of the equilibrium for generic parameters and the convergence of associated best-response dynamics and no-swap regret dynamics under certain conditions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 769,
+    "label": 16,
+    "text": "Title: Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality\nAbstract: Lighting understanding plays an important role in virtual object composition, including mobile augmented reality (AR) applications. Prior work often targets recovering lighting from the physical environment to support photorealistic AR rendering. Because the common workflow is to use a back-facing camera to capture the physical world for overlaying virtual objects, we refer to this usage pattern as back-facing AR. However, existing methods often fall short in supporting emerging front-facing mobile AR applications, e.g., virtual try-on where a user leverages a front-facing camera to explore the effect of various products (e.g., glasses or hats) of different styles. This lack of support can be attributed to the unique challenges of obtaining 360\u00b0 HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques. In this paper, we propose to leverage dual-camera streaming to generate a high-quality environment map by combining multi-view lighting reconstruction and parametric directional lighting estimation. Our preliminary results show improved rendering quality using a dual-camera setup for front-facing AR compared to a commercial solution.",
+    "neighbors": [
+      1214
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 770,
+    "label": 34,
+    "text": "Title: Sliding Window String Indexing in Streams\nAbstract: Given a string $S$ over an alphabet $\\Sigma$, the 'string indexing problem' is to preprocess $S$ to subsequently support efficient pattern matching queries, i.e., given a pattern string $P$ report all the occurrences of $P$ in $S$. In this paper we study the 'streaming sliding window string indexing problem'. Here the string $S$ arrives as a stream, one character at a time, and the goal is to maintain an index of the last $w$ characters, called the 'window', for a specified parameter $w$. At any point in time a pattern matching query for a pattern $P$ may arrive, also streamed one character at a time, and all occurrences of $P$ within the current window must be returned. The streaming sliding window string indexing problem naturally captures scenarios where we want to index the most recent data (i.e. the window) of a stream while supporting efficient pattern matching. Our main result is a simple $O(w)$ space data structure that uses $O(\\log w)$ time with high probability to process each character from both the input string $S$ and the pattern string $P$. Reporting each occurrence from $P$ uses additional constant time per reported occurrence. Compared to previous work in similar scenarios this result is the first to achieve an efficient worst-case time per character from the input stream. We also consider a delayed variant of the problem, where a query may be answered at any point within the next $\\delta$ characters that arrive from either stream. We present an $O(w + \\delta)$ space data structure for this problem that improves the above time bounds to $O(\\log(w/\\delta))$. In particular, for a delay of $\\delta = \\epsilon w$ we obtain an $O(w)$ space data structure with constant time processing per character. The key idea to achieve our result is a novel and simple hierarchical structure of suffix trees of independent interest, inspired by the classic log-structured merge trees.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 771,
+    "label": 16,
+    "text": "Title: Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime\nAbstract: This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets. We explore several candidate methods to improve low-data performance, including: (i) adapting generic pre-trained models to novel image and text domains (i.e. medical imaging and reports) via unimodal self-supervision; (ii) using local (e.g. GLoRIA)&global (e.g. InfoNCE) contrastive loss functions as well as a combination of the two; (iii) extra supervision during VLM training, via: (a) image- and text-only self-supervision, and (b) creating additional positive image-text pairs for training through augmentation and nearest-neighbour search. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports. Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with the data. A similar pattern is found in the downstream task classification of CXR-related conditions with our method outperforming CLIP and also BioVIL, a strong CXR VLM benchmark, in the zero-shot and linear probing settings. We conclude with a set of recommendations for researchers aiming to train vision-language models on other medical imaging modalities when training data is scarce. To facilitate further research, we will make our code and models publicly available.",
+    "neighbors": [
+      2219
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 772,
+    "label": 10,
+    "text": "Title: Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review\nAbstract: Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot motions, data-efficiency in training of reinforcement learning, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 773,
+    "label": 5,
+    "text": "Title: IRQ Coloring and the Subtle Art of Mitigating Interrupt-generated Interference\nAbstract: Integrating workloads with differing criticality levels presents a formidable challenge in achieving the stringent spatial and temporal isolation requirements imposed by safety-critical standards such as ISO26262. The shift towards high-performance multicore platforms has been posing increasing issues to the so-called mixed-criticality systems (MCS) due to the reciprocal interference created by consolidated subsystems vying for access to shared (microarchitectural) resources (e.g., caches, bus interconnect, memory controller). The research community has acknowledged all these challenges. Thus, several techniques, such as cache partitioning and memory throttling, have been proposed to mitigate such interference; however, these techniques have some drawbacks and limitations that impact performance, memory footprint, and availability. In this work, we look from a different perspective. Departing from the observation that safety-critical workloads are typically event- and thus interrupt-driven, we mask\"colored\"interrupts based on the \\ac{QoS} assessment, providing fine-grain control to mitigate interference on critical workloads without entirely suspending non-critical workloads. We propose the so-called IRQ coloring technique. We implement and evaluate the IRQ Coloring on a reference high-performance multicore platform, i.e., Xilinx ZCU102. Results demonstrate negligible performance overhead, i.e.,<1% for a 100 microseconds period, and reasonable throughput guarantees for medium-critical workloads. We argue that the IRQ coloring technique presents predictability and intermediate guarantees advantages compared to state-of-art mechanisms",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 774,
+    "label": 36,
+    "text": "Title: Nash equilibrium seeking over digraphs with row-stochastic matrices and network-independent step-sizes\nAbstract: In this paper, we address the challenge of Nash equilibrium (NE) seeking in non-cooperative convex games with partial-decision information. We propose a distributed algorithm, where each agent refines its strategy through projected-gradient steps and an averaging procedure. Each agent uses estimates of competitors' actions obtained solely from local neighbor interactions, in a directed communication network. Unlike previous approaches that rely on (strong) monotonicity assumptions, this work establishes the convergence towards a NE under a diagonal dominance property of the pseudo-gradient mapping, that can be checked locally by the agents. Further, this condition is physically interpretable and of relevance for many applications, as it suggests that an agent's objective function is primarily influenced by its individual strategic decisions, rather than by the actions of its competitors. In virtue of a novel block-infinity norm convergence argument, we provide explicit bounds for constant step-size that are independent of the communication structure, and can be computed in a totally decentralized way. Numerical simulations on an optical network's power control problem validate the algorithm's effectiveness.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 775,
+    "label": 27,
+    "text": "Title: TrafficMCTS: A Closed-Loop Traffic Flow Generation Framework with Group-Based Monte Carlo Tree Search\nAbstract: Digital twins for intelligent transportation systems are currently attracting great interests, in which generating realistic, diverse, and human-like traffic flow in simulations is a formidable challenge. Current approaches often hinge on predefined driver models, objective optimization, or reliance on pre-recorded driving datasets, imposing limitations on their scalability, versatility, and adaptability. In this paper, we introduce TrafficMCTS, an innovative framework that harnesses the synergy of groupbased Monte Carlo tree search (MCTS) and Social Value Orientation (SVO) to engender a multifaceted traffic flow replete with varying driving styles and cooperative tendencies. Anchored by a closed-loop architecture, our framework enables vehicles to dynamically adapt to their environment in real time, and ensure feasible collision-free trajectories. Through comprehensive comparisons with state-of-the-art methods, we illuminate the advantages of our approach in terms of computational efficiency, planning success rate, intent completion time, and diversity metrics. Besides, we simulate highway and roundabout scenarios to illustrate the effectiveness of the proposed framework and highlight its ability to induce diverse social behaviors within the traffic flow. Finally, we validate the scalability of TrafficMCTS by showcasing its prowess in simultaneously mass vehicles within a sprawling road network, cultivating a landscape of traffic flow that mirrors the intricacies of human behavior.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 776,
+    "label": 16,
+    "text": "Title: I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation\nAbstract: Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss, and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5 \u2192Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 777,
+    "label": 34,
+    "text": "Title: Finding Maximal Exact Matches in Graphs\nAbstract: We study the problem of finding maximal exact matches (MEMs) between a query string $Q$ and a labeled graph $G$. MEMs are an important class of seeds, often used in seed-chain-extend type of practical alignment methods because of their strong connections to classical metrics. A principled way to speed up chaining is to limit the number of MEMs by considering only MEMs of length at least $\\kappa$ ($\\kappa$-MEMs). However, on arbitrary input graphs, the problem of finding MEMs cannot be solved in truly sub-quadratic time under SETH (Equi et al., ICALP 2019) even on acyclic graphs. In this paper we show an $O(n\\cdot L \\cdot d^{L-1} + m + M_{\\kappa,L})$-time algorithm finding all $\\kappa$-MEMs between $Q$ and $G$ spanning exactly $L$ nodes in $G$, where $n$ is the total length of node labels, $d$ is the maximum degree of a node in $G$, $m = |Q|$, and $M_{\\kappa,L}$ is the number of output MEMs. We use this algorithm to develop a $\\kappa$-MEM finding solution on indexable Elastic Founder Graphs (Equi et al., Algorithmica 2022) running in time $O(nH^2 + m + M_\\kappa)$, where $H$ is the maximum number of nodes in a block, and $M_\\kappa$ is the total number of $\\kappa$-MEMs. Our results generalize to the analysis of multiple query strings (MEMs between $G$ and any of the strings). Additionally, we provide some preliminary experimental results showing that the number of graph MEMs is an order of magnitude smaller than the number of string MEMs of the corresponding concatenated collection.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 778,
+    "label": 16,
+    "text": "Title: Diffusion Model with Clustering-based Conditioning for Food Image Generation\nAbstract: Image-based dietary assessment serves as an efficient and accurate solution for recording and analyzing nutrition intake using eating occasion images as input. Deep learning-based techniques are commonly used to perform image analysis such as food classification, segmentation, and portion size estimation, which rely on large amounts of food images with annotations for training. However, such data dependency poses significant barriers to real-world applications, because acquiring a substantial, diverse, and balanced set of food images can be challenging. One potential solution is to use synthetic food images for data augmentation. Although existing work has explored the use of generative adversarial networks (GAN) based structures for generation, the quality of synthetic food images still remains subpar. In addition, while diffusion-based generative models have shown promising results for general image generation tasks, the generation of food images can be challenging due to the substantial intra-class variance. In this paper, we investigate the generation of synthetic food images based on the conditional diffusion model and propose an effective clustering-based training framework, named ClusDiff, for generating high-quality and representative food images. The proposed method is evaluated on the Food-101 dataset and shows improved performance when compared with existing image generation works. We also demonstrate that the synthetic food images generated by ClusDiff can help address the severe class imbalance issue in long-tailed food classification using the VFN-LT dataset.",
+    "neighbors": [
+      1917
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 779,
+    "label": 16,
+    "text": "Title: An Outlook into the Future of Egocentric Vision\nAbstract: What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.",
+    "neighbors": [
+      1537
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 780,
+    "label": 1,
+    "text": "Title: MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding\nAbstract: The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for\"end-to-end optimized neural video coding.\"The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 781,
+    "label": 26,
+    "text": "Title: The Role of the IRA in Twitter during the 2016 US Presidential Election: Unveiling Amplification and Influence of Suspended Accounts\nAbstract: The impact of the social media campaign conducted by the Internet Research Agency (IRA) during the 2016 U.S. presidential election continues to be a topic of ongoing debate. While it is widely acknowledged that the objective of this campaign was to support Donald Trump, the true extent of its influence on Twitter users remains uncertain. Previous research has primarily focused on analyzing the interactions between IRA users and the broader Twitter community to assess the campaign's impact. In this study, we propose an alternative perspective that suggests the existing approach may underestimate the true extent of the IRA campaign. Our analysis uncovers the presence of a notable group of suspended Twitter users, whose size surpasses the IRA user group size by a factor of 60. These suspended users exhibit close interactions with IRA accounts, suggesting potential collaboration or coordination. Notably, our findings reveal the significant role played by these previously unnoticed accounts in amplifying the impact of the IRA campaign, surpassing even the reach of the IRA accounts themselves by a factor of 10. In contrast to previous findings, our study reveals that the combined efforts of the Internet Research Agency (IRA) and the identified group of suspended Twitter accounts had a significant influence on individuals categorized as undecided or weak supporters, probably with the intention of swaying their opinions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 782,
+    "label": 24,
+    "text": "Title: LibAUC: A Deep Learning Library for X-Risk Optimization\nAbstract: This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks. X-risks refer to a family of compositional functions in which the loss function of each data point is defined in a way that contrasts the data point with a large number of others. They have broad applications in AI for solving classical and emerging problems, including but not limited to classification for imbalanced data (CID), learning to rank (LTR), and contrastive learning of representations (CLR). The motivation of developing LibAUC is to address the convergence issues of existing libraries for solving these problems. In particular, existing libraries may not converge or require very large mini-batch sizes in order to attain good performance for these problems, due to the usage of the standard mini-batch technique in the empirical risk minimization (ERM) framework. Our library is for deep X-risk optimization (DXO) that has achieved great success in solving a variety of tasks for CID, LTR and CLR. The contributions of this paper include: (1) It introduces a new mini-batch based pipeline for implementing DXO algorithms, which differs from existing DL pipeline in the design of controlled data samplers and dynamic mini-batch losses; (2) It provides extensive benchmarking experiments for ablation studies and comparison with existing libraries. The LibAUC library features scalable performance for millions of items to be contrasted, faster and better convergence than existing libraries for optimizing X-risks, seamless PyTorch deployment and versatile APIs for various loss optimization. Our library is available to the open source community at https://github.com/Optimization-AI/LibAUC, to facilitate further academic research and industrial applications.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 783,
+    "label": 16,
+    "text": "Title: A View From Somewhere: Human-Centric Face Representations\nAbstract: Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS) -- a dataset of 638,180 human judgments of face similarity. We demonstrate the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human-decision making process, and the importance distinct annotators place on each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.",
+    "neighbors": [
+      1592
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 784,
+    "label": 16,
+    "text": "Title: A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head\nAbstract: Objective:Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction. Method: To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity. Results: Evaluating the model performance in large-scale Road Damage Dataset (RDD-2018), at a detection rate of 62.4 FPS, DenseSPH-YOLOv5 obtains a mean average precision (mAP) value of 85.25 %, F1-score of 81.18 %, and precision (P) value of 89.51 % outperforming current state-of-the-art models. Significance: The present research provides an effective and efficient damage localization model addressing the shortcoming of existing DL-based damage detection models by providing highly accurate localized bounding box prediction. Current work constitutes a step towards an accurate and robust automated damage detection system in real-time in-field applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 785,
+    "label": 28,
+    "text": "Title: RIS-aided Mixed RF-FSO Wireless Networks: Secrecy Performance Analysis with Simultaneous Eavesdropping\nAbstract: The appearance of sixth-generation networks has resulted in the proposal of several solutions to tackle signal loss. One of these solutions is the utilization of reconfigurable intelligent surfaces (RIS), which can reflect or refract signals as required. This integration offers significant potential to improve the coverage area from the sender to the receiver. In this paper, we present a comprehensive framework for analyzing the secrecy performance of a RIS-aided mixed radio frequency (RF)-free space optics (FSO) system, for the first time. Our study assumes that a secure message is transmitted from a RF transmitter to a FSO receiver through an intermediate relay. The RF link experiences Rician fading while the FSO link experiences M\\'alaga distributed turbulence with pointing errors. We examine three scenarios: 1) RF-link eavesdropping, 2) FSO-link eavesdropping, and 3) a simultaneous eavesdropping attack on both RF and FSO links. We evaluate the secrecy performance using analytical expressions to compute secrecy metrics such as the average secrecy capacity, secrecy outage probability, strictly positive secrecy capacity, effective secrecy throughput, and intercept probability. Our results are confirmed via Monte-Carlo simulations and demonstrate that fading parameters, atmospheric turbulence conditions, pointing errors, and detection techniques play a crucial role in enhancing secrecy performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 786,
+    "label": 24,
+    "text": "Title: Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions\nAbstract: Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and interpretable queries about the data that are most informative for the task. While this allows for built-in interpretability in predictive models, applying V-IP to any task requires data samples with dense concept-labeling by domain experts, limiting the application of V-IP to small-scale tasks where manual data annotation is feasible. In this work, we extend the V-IP framework with Foundational Models (FMs) to address this limitation. More specifically, we use a two-step process, by first leveraging Large Language Models (LLMs) to generate a sufficiently large candidate set of task-relevant interpretable concepts, then using Large Multimodal Models to annotate each data sample by semantic similarity with each concept in the generated concept set. While other interpretable-by-design frameworks such as Concept Bottleneck Models (CBMs) require an additional step of removing repetitive and non-discriminative concepts to have good interpretability and test performance, we mathematically and empirically justify that, with a sufficiently informative and task-relevant query (concept) set, the proposed FM+V-IP method does not require any type of concept filtering. In addition, we show that FM+V-IP with LLM generated concepts can achieve better test performance than V-IP with human annotated concepts, demonstrating the effectiveness of LLMs at generating efficient query sets. Finally, when compared to other interpretable-by-design frameworks such as CBMs, FM+V-IP can achieve competitive test performance using fewer number of concepts/queries in both cases with filtered or unfiltered concept sets.",
+    "neighbors": [
+      1044
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 787,
+    "label": 27,
+    "text": "Title: Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\nAbstract: This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available.",
+    "neighbors": [
+      800,
+      844,
+      964,
+      1186,
+      1684,
+      1908,
+      2018,
+      2245
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 788,
+    "label": 20,
+    "text": "Title: Revisiting Graph Persistence for Updates and Efficiency\nAbstract: It is well known that ordinary persistence on graphs can be computed more efficiently than the general persistence. Recently, it has been shown that zigzag persistence on graphs also exhibits similar behavior. Motivated by these results, we revisit graph persistence and propose efficient algorithms especially for local updates on filtrations, similar to what is done in ordinary persistence for computing the vineyard. We show that, for a filtration of length $m$, (i) switches (transpositions) in ordinary graph persistence can be done in $O(\\log m)$ time; (ii) zigzag persistence on graphs can be computed in $O(m\\log m)$ time, which improves a recent $O(m\\log^4n)$ time algorithm assuming $n$, the size of the union of all graphs in the filtration, satisfies $n\\in\\Omega({m^\\varepsilon})$ for any fixed $0<\\varepsilon<1$; (iii) open-closed, closed-open, and closed-closed bars in dimension $0$ for graph zigzag persistence can be updated in $O(\\log m)$ time, whereas the open-open bars in dimension $0$ and closed-closed bars in dimension $1$ can be done in $O(\\sqrt{m}\\,\\log m)$ time.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 789,
+    "label": 4,
+    "text": "Title: Augmented Symbolic Execution for Information Flow in Hardware Designs\nAbstract: We present SEIF, a methodology that combines static analysis with symbolic execution to verify and explicate information flow paths in a hardware design. SEIF begins with a statically built model of the information flow through a design and uses guided symbolic execution to recognize and eliminate non-flows with high precision or to find corresponding paths through the design state for true flows. We evaluate SEIF on two open-source CPUs, an AES core, and the AKER access control module. SEIF can exhaustively explore 10-12 clock cycles deep in 4-6 seconds on average, and can automatically account for 86-90% of the paths in the statically built model. Additionally, SEIF can be used to find multiple violating paths for security properties, providing a new angle for security verification.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 790,
+    "label": 6,
+    "text": "Title: DynamicRead: Exploring Robust Gaze Interaction Methods for Reading on Handheld Mobile Devices under Dynamic Conditions\nAbstract: Enabling gaze interaction in real-time on handheld mobile devices has attracted significant attention in recent years. An increasing number of research projects have focused on sophisticated appearance-based deep learning models to enhance the precision of gaze estimation on smartphones. This inspires important research questions, including how the gaze can be used in a real-time application, and what type of gaze interaction methods are preferable under dynamic conditions in terms of both user acceptance and delivering reliable performance. To address these questions, we design four types of gaze scrolling techniques: three explicit technique based on Gaze Gesture, Dwell time, and Pursuit; and one implicit technique based on reading speed to support touch-free, page-scrolling on a reading application. We conduct a 20-participant user study under both sitting and walking settings and our results reveal that Gaze Gesture and Dwell time-based interfaces are more robust while walking and Gaze Gesture has achieved consistently good scores on usability while not causing high cognitive workload.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 791,
+    "label": 23,
+    "text": "Title: Detecting Code Quality Issues in Pre-written Templates of Programming Tasks in Online Courses\nAbstract: In this work, we developed an algorithm for detecting code quality issues in the templates of online programming tasks, validated it, and conducted an empirical study on the dataset of student solutions. The algorithm consists of analyzing recurring unfixed issues in solutions of different students, matching them with the code of the template, and then filtering the results. Our manual validation on a subset of tasks demonstrated a precision of 80.8% and a recall of 73.3%. We used the algorithm on 415 Java tasks from the JetBrains Academy platform and discovered that as much as 14.7% of tasks have at least one issue in their template, thus making it harder for students to learn good code quality practices. We describe our results in detail, provide several motivating examples and specific cases, and share the feedback of the developers of the platform, who fixed 51 issues based on the output of our approach.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 792,
+    "label": 16,
+    "text": "Title: M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning\nAbstract: Instruction tuning has significantly advanced large language models (LLMs) such as ChatGPT, enabling them to align with human instructions across diverse tasks. However, progress in open vision-language models (VLMs) has been limited due to the scarcity of high-quality instruction datasets. To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions. Our M$^3$IT dataset comprises 40 carefully curated datasets, including 2.4 million instances and 400 manually written task instructions, reformatted into a vision-to-text structure. Key tasks are translated into 80 languages with an advanced translation system, ensuring broader accessibility. M$^3$IT surpasses previous datasets regarding task coverage, instruction number and instance scale. Moreover, we develop Ying-VLM, a VLM model trained on our M$^3$IT dataset, showcasing its potential to answer complex questions requiring world knowledge, generalize to unseen video tasks, and comprehend unseen instructions in Chinese. We have open-sourced the dataset to encourage further research.",
+    "neighbors": [
+      602,
+      855,
+      887,
+      1039,
+      1052,
+      1129,
+      1346,
+      1617,
+      1755,
+      1899,
+      2036,
+      2094,
+      2155,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 793,
+    "label": 3,
+    "text": "Title: Towards Understanding of Deepfake Videos in the Wild\nAbstract: Deepfakes have become a growing concern in recent years, prompting researchers to develop benchmark datasets and detection algorithms to tackle the issue. However, existing datasets suffer from significant drawbacks that hamper their effectiveness. Notably, these datasets fail to encompass the latest deepfake videos produced by state-of-the-art methods that are being shared across various platforms. This limitation impedes the ability to keep pace with the rapid evolution of generative AI techniques employed in real-world deepfake production. Our contributions in this IRB-approved study are to bridge this knowledge gap from current real-world deepfakes by providing in-depth analysis. We first present the largest and most diverse and recent deepfake dataset (RWDF-23) collected from the wild to date, consisting of 2,000 deepfake videos collected from 4 platforms targeting 4 different languages span created from 21 countries: Reddit, YouTube, TikTok, and Bilibili. By expanding the dataset's scope beyond the previous research, we capture a broader range of real-world deepfake content, reflecting the ever-evolving landscape of online platforms. Also, we conduct a comprehensive analysis encompassing various aspects of deepfakes, including creators, manipulation strategies, purposes, and real-world content production methods. This allows us to gain valuable insights into the nuances and characteristics of deepfakes in different contexts. Lastly, in addition to the video content, we also collect viewer comments and interactions, enabling us to explore the engagements of internet users with deepfake content. By considering this rich contextual information, we aim to provide a holistic understanding of the {evolving} deepfake phenomenon and its impact on online platforms.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 794,
+    "label": 16,
+    "text": "Title: RHINO: Rotated DETR with Dynamic Denoising via Hungarian Matching for Oriented Object Detection\nAbstract: With the publication of DINO, a variant of the Detection Transformer (DETR), Detection Transformers are breaking the record in the object detection benchmark with the merits of their end-to-end design and scalability. However, the extension of DETR to oriented object detection has not been thoroughly studied although more benefits from its end-to-end architecture are expected such as removing NMS and anchor-related costs. In this paper, we propose a first strong DINO-based baseline for oriented object detection. We found that straightforward employment of DETRs for oriented object detection does not guarantee non-duplicate prediction, and propose a simple cost to mitigate this. Furthermore, we introduce a $\\textit{dynamic denoising}$ strategy that uses Hungarian matching to filter redundant noised queries and $\\textit{query alignment}$ to preserve matching consistency between Transformer decoder layers. Our proposed model outperforms previous rotated DETRs and other counterparts, achieving state-of-the-art performance in DOTA-v1.0/v1.5/v2.0, and DIOR-R benchmarks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 795,
+    "label": 37,
+    "text": "Title: Towards a Rosetta Stone for (meta)data: Learning from natural language to improve semantic and cognitive interoperability\nAbstract: In order to effectively manage the overwhelming influx of data, it is crucial to ensure that data is findable, accessible, interoperable, and reusable (FAIR). While ontologies and knowledge graphs have been employed to enhance FAIRness, challenges remain regarding semantic and cognitive interoperability. We explore how English facilitates reliable communication of terms and statements, and transfer our findings to a framework of ontologies and knowledge graphs, while treating terms and statements as minimal information units. We categorize statement types based on their predicates, recognizing the limitations of modeling non-binary predicates with multiple triples, which negatively impacts interoperability. Terms are associated with different frames of reference, and different operations require different schemata. Term mappings and schema crosswalks are therefore vital for semantic interoperability. We propose a machine-actionable Rosetta Stone Framework for (meta)data, which uses reference terms and schemata as an interlingua to minimize mappings and crosswalks. Modeling statements rather than a human-independent reality ensures cognitive familiarity and thus better interoperability of data structures. We extend this Rosetta modeling paradigm to reference schemata, resulting in simple schemata with a consistent structure across statement types, empowering domain experts to create their own schemata using the Rosetta Editor, without requiring knowledge of semantics. The Editor also allows specifying textual and graphical display templates for each schema, delivering human-readable data representations alongside machine-actionable data structures. The Rosetta Query Builder derives queries based on completed input forms and the information from corresponding reference schemata. This work sets the conceptual ground for the Rosetta Stone Framework that we plan to develop in the future.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 796,
+    "label": 2,
+    "text": "Title: Truth Values Algebras and Proof Normalization\nAbstract: nan",
+    "neighbors": [
+      2282
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 797,
+    "label": 28,
+    "text": "Title: Bayesian Extensive-Rank Matrix Factorization with Rotational Invariant Priors\nAbstract: We consider a statistical model for matrix factorization in a regime where the rank of the two hidden matrix factors grows linearly with their dimension and their product is corrupted by additive noise. Despite various approaches, statistical and algorithmic limits of such problems have remained elusive. We study a Bayesian setting with the assumptions that (a) one of the matrix factors is symmetric, (b) both factors as well as the additive noise have rotational invariant priors, (c) the priors are known to the statistician. We derive analytical formulas for Rotation Invariant Estimators to reconstruct the two matrix factors, and conjecture that these are optimal in the large-dimension limit, in the sense that they minimize the average mean-square-error. We provide numerical checks which confirm the optimality conjecture when confronted to Oracle Estimators which are optimal by definition, but involve the ground-truth. Our derivation relies on a combination of tools, namely random matrix theory transforms, spherical integral formulas, and the replica method from statistical mechanics.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 798,
+    "label": 30,
+    "text": "Title: Glancing Future for Simultaneous Machine Translation\nAbstract: Simultaneous machine translation (SiMT) outputs translation while reading the source sentence. Unlike conventional sequence-to-sequence (seq2seq) training, existing SiMT methods adopt the prefix-to-prefix (prefix2prefix) training, where the model predicts target tokens based on partial source tokens. However, the prefix2prefix training diminishes the ability of the model to capture global information and introduces forced predictions due to the absence of essential source information. Consequently, it is crucial to bridge the gap between the prefix2prefix training and seq2seq training to enhance the translation capability of the SiMT model. In this paper, we propose a novel method that glances future in curriculum learning to achieve the transition from the seq2seq training to prefix2prefix training. Specifically, we gradually reduce the available source information from the whole sentence to the prefix corresponding to that latency. Our method is applicable to a wide range of SiMT methods and experiments demonstrate that our method outperforms strong baselines.",
+    "neighbors": [
+      1849
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 799,
+    "label": 30,
+    "text": "Title: Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks\nAbstract: The widespread dissemination of toxic online posts is increasingly damaging to society. However, research on detecting toxic language in Chinese has lagged significantly due to limited datasets. Existing datasets suffer from a lack of fine-grained annotations, such as the toxic type and expressions with indirect toxicity. These fine-grained annotations are crucial factors for accurately detecting the toxicity of posts involved with lexical knowledge, which has been a challenge for researchers. To tackle this problem, we facilitate the fine-grained detection of Chinese toxic language by building a new dataset with benchmark results. First, we devised Monitor Toxic Frame, a hierarchical taxonomy to analyze the toxic type and expressions. Then, we built a fine-grained dataset ToxiCN, including both direct and indirect toxic samples. ToxiCN is based on an insulting vocabulary containing implicit profanity. We further propose a benchmark model, Toxic Knowledge Enhancement (TKE), by incorporating lexical features to detect toxic language. We demonstrate the usability of ToxiCN and the effectiveness of TKE based on a systematic quantitative and qualitative analysis.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 800,
+    "label": 16,
+    "text": "Title: On the Importance of Noise Scheduling for Diffusion Models\nAbstract: We empirically study the effect of noise scheduling strategies for denoising diffusion generative models. There are three findings: (1) the noise scheduling is crucial for the performance, and the optimal one depends on the task (e.g., image sizes), (2) when increasing the image size, the optimal noise scheduling shifts towards a noisier one (due to increased redundancy in pixels), and (3) simply scaling the input data by a factor of $b$ while keeping the noise schedule function fixed (equivalent to shifting the logSNR by $\\log b$) is a good strategy across image sizes. This simple recipe, when combined with recently proposed Recurrent Interface Network (RIN), yields state-of-the-art pixel-based diffusion models for high-resolution images on ImageNet, enabling single-stage, end-to-end generation of diverse and high-fidelity images at 1024$\\times$1024 resolution (without upsampling/cascades).",
+    "neighbors": [
+      43,
+      736,
+      787,
+      996
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 801,
+    "label": 24,
+    "text": "Title: On Computable Online Learning\nAbstract: We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for\"optimality\"in terms of the mistake bound. Our main contribution is to give a necessary and sufficient condition for optimal c-online learning and show that the Littlestone dimension no longer characterizes the optimal mistake bound of c-online learning. Furthermore, we introduce anytime optimal (a-optimal) online learning, a more natural conceptualization of\"optimality\"and a generalization of Littlestone's Standard Optimal Algorithm. We show the existence of a computational separation between a-optimal and optimal online learning, proving that a-optimal online learning is computationally more difficult. Finally, we consider online learning with no requirements for optimality, and show, under a weaker notion of computability, that the finiteness of the Littlestone dimension no longer characterizes whether a class is c-online learnable with finite mistake bound. A potential avenue for strengthening this result is suggested by exploring the relationship between c-online and CPAC learning, where we show that c-online learning is as difficult as improper CPAC learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 802,
+    "label": 16,
+    "text": "Title: 3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks\nAbstract: Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ~13% and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 803,
+    "label": 24,
+    "text": "Title: Efficient Testable Learning of Halfspaces with Adversarial Label Noise\nAbstract: We give the first polynomial-time algorithm for the testable learning of halfspaces in the presence of adversarial label noise under the Gaussian distribution. In the recently introduced testable learning model, one is required to produce a tester-learner such that if the data passes the tester, then one can trust the output of the robust learner on the data. Our tester-learner runs in time $\\poly(d/\\eps)$ and outputs a halfspace with misclassification error $O(\\opt)+\\eps$, where $\\opt$ is the 0-1 error of the best fitting halfspace. At a technical level, our algorithm employs an iterative soft localization technique enhanced with appropriate testers to ensure that the data distribution is sufficiently similar to a Gaussian.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 804,
+    "label": 3,
+    "text": "Title: Automation and AI Technology in Surface Mining With a Brief Introduction to Open-Pit Operations in the Pilbara\nAbstract: This survey article provides a synopsis on some of the engineering problems, technological innovations, robotic development and automation efforts encountered in the mining industry -- particularly in the Pilbara iron-ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of AI and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development, mine-, rail- and port operations. From mineral exploration to ore shipment, there are roughly nine steps in between. These include: geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crush and screen, stockpile and load-out, rail network distribution, and ore-car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry-university R&D partnership.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 805,
+    "label": 16,
+    "text": "Title: RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation\nAbstract: Retinal vessel segmentation is generally grounded in image-based datasets collected with bench-top devices. The static images naturally lose the dynamic characteristics of retina fluctuation, resulting in diminished dataset richness, and the usage of bench-top devices further restricts dataset scalability due to its limited accessibility. Considering these limitations, we introduce the first video-based retinal dataset by employing handheld devices for data acquisition. The dataset comprises 635 smartphone-based fundus videos collected from four different clinics, involving 415 patients from 50 to 75 years old. It delivers comprehensive and precise annotations of retinal structures in both spatial and temporal dimensions, aiming to advance the landscape of vasculature segmentation. Specifically, the dataset provides three levels of spatial annotations: binary vessel masks for overall retinal structure delineation, general vein-artery masks for distinguishing the vein and artery, and fine-grained vein-artery masks for further characterizing the granularities of each artery and vein. In addition, the dataset offers temporal annotations that capture the vessel pulsation characteristics, assisting in detecting ocular diseases that require fine-grained recognition of hemodynamic fluctuation. In application, our dataset exhibits a significant domain shift with respect to data captured by bench-top devices, thus posing great challenges to existing methods. In the experiments, we provide evaluation metrics and benchmark results on our dataset, reflecting both the potential and challenges it offers for vessel segmentation tasks. We hope this challenging dataset would significantly contribute to the development of eye disease diagnosis and early prevention.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 806,
+    "label": 27,
+    "text": "Title: Study on Soft Robotic Pinniped Locomotion\nAbstract: Legged locomotion is a highly promising but under\u2013researched subfield within the field of soft robotics. The compliant limbs of soft-limbed robots offer numerous benefits, including the ability to regulate impacts, tolerate falls, and navigate through tight spaces. These robots have the potential to be used for various applications, such as search and rescue, inspection, surveillance, and more. The state-of-the-art still faces many challenges, including limited degrees of freedom, a lack of diversity in gait trajectories, insufficient limb dexterity, and limited payload capabilities. To address these challenges, we develop a modular soft-limbed robot that can mimic the locomotion of pinnipeds. By using a modular design approach, we aim to create a robot that has improved degrees of freedom, gait trajectory diversity, limb dexterity, and payload capabilities. We derive a complete floating-base kinematic model of the proposed robot and use it to generate and experimentally validate a variety of locomotion gaits. Results show that the proposed robot is capable of replicating these gaits effectively. We compare the locomotion trajectories under different gait parameters against our modeling results to demonstrate the validity of our proposed gait models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 807,
+    "label": 34,
+    "text": "Title: Shortcut Partitions in Minor-Free Graphs: Steiner Point Removal, Distance Oracles, Tree Covers, and More\nAbstract: The notion of shortcut partition, introduced recently by Chang, Conroy, Le, Milenkovi\\'c, Solomon, and Than [CCLMST23], is a new type of graph partition into low-diameter clusters. Roughly speaking, the shortcut partition guarantees that for every two vertices $u$ and $v$ in the graph, there exists a path between $u$ and $v$ that intersects only a few clusters. They proved that any planar graph admits a shortcut partition and gave several applications, including a construction of tree cover for arbitrary planar graphs with stretch $1+\\varepsilon$ and $O(1)$ many trees for any fixed $\\varepsilon \\in (0,1)$. However, the construction heavily exploits planarity in multiple steps, and is thus inherently limited to planar graphs. In this work, we breach the\"planarity barrier\"to construct a shortcut partition for $K_r$-minor-free graphs for any $r$. To this end, we take a completely different approach -- our key contribution is a novel deterministic variant of the cop decomposition in minor-free graphs [And86, AGG14]. Our shortcut partition for $K_r$-minor-free graphs yields several direct applications. Most notably, we construct the first optimal distance oracle for $K_r$-minor-free graphs, with $1+\\varepsilon$ stretch, linear space, and constant query time for any fixed $\\varepsilon \\in (0,1)$. The previous best distance oracle [AG06] uses $O(n\\log n)$ space and $O(\\log n)$ query time, and its construction relies on Robertson-Seymour structural theorem and other sophisticated tools. We also obtain the first tree cover of $O(1)$ size for minor-free graphs with stretch $1+\\varepsilon$, while the previous best $(1+\\varepsilon)$-tree cover has size $O(\\log^2 n)$ [BFN19].",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 808,
+    "label": 2,
+    "text": "Title: Combining Global and Local Merges in Logic-based Entity Resolution\nAbstract: In the recently proposed LACE framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tuples) are deemed equal and can be merged. By contrast, a local form of merge is often more natural when identifying pairs of data values, e.g. some occurrences of 'J. Smith' may be equated with 'Joe Smith', while others should merge with 'Jane Smith'. This motivates us to extend LACE with local merges of values and explore the computational properties of the resulting formalism.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 809,
+    "label": 10,
+    "text": "Title: Semantic Technologies in Sensor-Based Personal Health Monitoring Systems: A Systematic Mapping Study\nAbstract: In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. Semantic technologies have emerged as an effective way to not only deal with the issue of interoperability associated with heterogeneous health sensor data, but also to represent expert health knowledge to support complex reasoning required for decision-making. This study evaluates the state of the art in the use of semantic technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 40 systems representing the state of the art in the field are analysed. Through this analysis, six key challenges that such systems must overcome for optimal and effective health monitoring are identified: interoperability, context awareness, situation detection, situation prediction, decision support, and uncertainty handling. The study critically evaluates the extent to which these systems incorporate semantic technologies to deal with these challenges and identifies the prominent architectures, system development and evaluation methodologies that are used. The study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research directions.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 810,
+    "label": 7,
+    "text": "Title: Circuit simulation using explicit methods\nAbstract: Use of explicit methods for simulating electrical circuits, especially for power electronics applications, is described. Application of the forward Euler method to a half-wave rectifier is discussed, and the limitations of a fixed-step method are pointed out. Implementation of the Runge-Kutta-Fehlberg (RKF) method, which allows variable time steps, for the half-wave rectifier circuit is discussed, and its advantages pointed out. Formulation of circuit equations for the purpose of simulation using the RKF method is described for some more examples. Stability and accuracy issues related to power electronic circuits are brought out, and mechanisms to address them are presented. Future plans related to this work are described.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 811,
+    "label": 30,
+    "text": "Title: Judging LLM-as-a-judge with MT-Bench and Chatbot Arena\nAbstract: Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, including position, verbosity, and self-enhancement biases, as well as limited reasoning ability, and propose solutions to mitigate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80\\% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA and Vicuna. We will publicly release MT-bench questions, 3K expert votes, and 30K conversations with human preferences from Chatbot Arena.",
+    "neighbors": [
+      126,
+      136,
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+      247,
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+      1001,
+      1002,
+      1039,
+      1044,
+      1052,
+      1112,
+      1114,
+      1227,
+      1237,
+      1346,
+      1430,
+      1566,
+      1613,
+      1617,
+      1655,
+      1727,
+      1735,
+      1755,
+      1972,
+      2038,
+      2094,
+      2122
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 812,
+    "label": 21,
+    "text": "Title: An Efficient Early-breaking Estimation and Tree-splitting Missing RFID Tag Identification Protocol\nAbstract: Recent statistics have demonstrated that missing items have become the main cause of loss for retailers in inventory management. To quickly identify missing tags, traditional protocols adopt Aloha-based strategies which take a long time, especially when the number of tags is large. Among them, few works considered the effect of unexpected unknown tags on the missing tag identification process. With the presence of unknown tags, some missing tags may be falsely identified as present. Thus, the system's reliability is hardly guaranteed. In this work, we propose an efficient early-breaking estimation and tree-splitting-based missing tag identification (ETMTI) protocol for large-scale RFID systems. In ETMTI, a new early-breaking estimation and deactivation method is developed to effectively estimate the number of unknown tags and deactivate them within a short time. Next, a new tree-splitting-based missing tag identification method is proposed to quickly identify missing tags with a B-ary splitting tree. Besides, a bit-tracking response strategy is designed to further reduce the time cost. The optimal parameters, time cost, and false negative rate of ETMTI are analyzed theoretically. Simulation results are presented to demonstrate that the proposed ETMTI protocol takes a smaller time and has a lower false negative rate than the best-performing benchmarks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 813,
+    "label": 4,
+    "text": "Title: Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning\nAbstract: Split Learning (SL) is a new collaborative learning technique that allows participants, e.g. a client and a server, to train machine learning models without the client sharing raw data. In this setting, the client initially applies its part of the machine learning model on the raw data to generate activation maps and then sends them to the server to continue the training process. Previous works in the field demonstrated that reconstructing activation maps could result in privacy leakage of client data. In addition to that, existing mitigation techniques that overcome the privacy leakage of SL prove to be significantly worse in terms of accuracy. In this paper, we improve upon previous works by constructing a protocol based on U-shaped SL that can operate on homomorphically encrypted data. More precisely, in our approach, the client applies Homomorphic Encryption (HE) on the activation maps before sending them to the server, thus protecting user privacy. This is an important improvement that reduces privacy leakage in comparison to other SL-based works. Finally, our results show that, with the optimum set of parameters, training with HE data in the U-shaped SL setting only reduces accuracy by 2.65% compared to training on plaintext. In addition, raw training data privacy is preserved.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 814,
+    "label": 25,
+    "text": "Title: Improving Deep Attractor Network by BGRU and GMM for Speech Separation\nAbstract: Deep Attractor Network (DANet) is the state-of-the-art technique in speech separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but the complexity of the DANet model is very high. In this paper, a simplified and powerful DANet model is proposed using Bidirectional Gated neural network (BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the k-means was applied in DANet as a clustering algorithm to reduce the complexity and increase the learning speed and accuracy. The metrics used in this paper are Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ) score. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate the proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ scores respectively, which were better than the original DANet model. Other improvements were 20.7% and 17.9% in the number of parameters and time training respectively. The model was applied on mixed Arabic speech signals and the results were better than that in English.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 815,
+    "label": 27,
+    "text": "Title: Design, Modeling, and Redundancy Resolution of Soft Robot for Effective Harvesting\nAbstract: Blackberry harvesting is a labor-intensive and costly process, consuming up to 50\\% of the total annual crop hours. This paper presents a solution for robotic harvesting through the design, manufacturing, integration, and control of a pneumatically actuated, kinematically redundant soft arm with a tendon-driven soft robotic gripper. The hardware design is optimized for durability and modularity for practical use. The harvesting process is divided into four stages: initial placement, fine positioning, grasp, and move back to home position. For initial placement, we propose a real-time, continuous gain-scheduled redundancy resolution algorithm for simultaneous position and orientation control with joint-limit avoidance. The algorithm relies solely on visual feedback from an eye-to-hand camera and achieved a position and orientation tracking error of $0.64\\pm{0.27}$ mm and $1.08\\pm{1.5}^{\\circ}$, respectively, in benchtop settings. Following accurate initial placement of the robotic arm, fine positioning is achieved using a combination of eye-in-hand and eye-to-hand visual feedback, reaching an accuracy of $0.75\\pm{0.36}$ mm. The system's hardware, feedback framework, and control methods are thoroughly validated through benchtop and field tests, confirming feasibility for practical applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 816,
+    "label": 16,
+    "text": "Title: Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images\nAbstract: Aiming at answering questions based on the content of remotely sensed images, visual question answering for remote sensing data (RSVQA) has attracted much attention nowadays. However, previous works in RSVQA have focused little on the robustness of RSVQA. As we aim to enhance the reliability of RSVQA models, how to learn robust representations against new words and different question templates with the same meaning is the key challenge. With the proposed augmented dataset, we are able to obtain more questions in addition to the original ones with the same meaning. To make better use of this information, in this study, we propose a contrastive learning strategy for training robust RSVQA models against diverse question templates and words. Experimental results demonstrate that the proposed augmented dataset is effective in improving the robustness of the RSVQA model. In addition, the contrastive learning strategy performs well on the low resolution (LR) dataset.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 817,
+    "label": 10,
+    "text": "Title: ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs\nAbstract: Despite the advancements of open-source large language models (LLMs) and their variants, e.g., LLaMA and Vicuna, they remain significantly limited in performing higher-level tasks, such as following human instructions to use external tools (APIs). This is because current instruction tuning largely focuses on basic language tasks instead of the tool-use domain. This is in contrast to state-of-the-art (SOTA) LLMs, e.g., ChatGPT, which have demonstrated excellent tool-use capabilities but are unfortunately closed source. To facilitate tool-use capabilities within open-source LLMs, we introduce ToolLLM, a general tool-use framework of data construction, model training and evaluation. We first present ToolBench, an instruction-tuning dataset for tool use, which is created automatically using ChatGPT. Specifically, we collect 16,464 real-world RESTful APIs spanning 49 categories from RapidAPI Hub, then prompt ChatGPT to generate diverse human instructions involving these APIs, covering both single-tool and multi-tool scenarios. Finally, we use ChatGPT to search for a valid solution path (chain of API calls) for each instruction. To make the searching process more efficient, we develop a novel depth-first search-based decision tree (DFSDT), enabling LLMs to evaluate multiple reasoning traces and expand the search space. We show that DFSDT significantly enhances the planning and reasoning capabilities of LLMs. For efficient tool-use assessment, we develop an automatic evaluator: ToolEval. We fine-tune LLaMA on ToolBench and obtain ToolLLaMA. Our ToolEval reveals that ToolLLaMA demonstrates a remarkable ability to execute complex instructions and generalize to unseen APIs, and exhibits comparable performance to ChatGPT. To make the pipeline more practical, we devise a neural API retriever to recommend appropriate APIs for each instruction, negating the need for manual API selection.",
+    "neighbors": [
+      11,
+      25,
+      57,
+      183,
+      240,
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+      363,
+      989,
+      1001,
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+      1044,
+      1052,
+      1112,
+      1128,
+      1606,
+      1659,
+      1878,
+      2136,
+      2166,
+      2208
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 818,
+    "label": 30,
+    "text": "Title: Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback\nAbstract: Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available.",
+    "neighbors": [
+      57,
+      618,
+      857,
+      902,
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+      1039,
+      1267,
+      1348,
+      1520,
+      1678,
+      1915,
+      2016,
+      2038,
+      2113
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 819,
+    "label": 30,
+    "text": "Title: Empathetic Response Generation via Emotion Cause Transition Graph\nAbstract: Empathetic dialogue is a human-like behavior that requires the perception of both affective factors (e.g., emotion status) and cognitive factors (e.g., cause of the emotion). Besides concerning emotion status in early work, the latest approaches study emotion causes in empathetic dialogue. These approaches focus on understanding and duplicating emotion causes in the context to show empathy for the speaker. However, instead of only repeating the contextual causes, the real empathic response often demonstrate a logical and emotion-centered transition from the causes in the context to those in the responses. In this work, we propose an emotion cause transition graph to explicitly model the natural transition of emotion causes between two adjacent turns in empathetic dialogue. With this graph, the concept words of the emotion causes in the next turn can be predicted and used by a specifically designed concept-aware decoder to generate the empathic response. Automatic and human experimental results on the benchmark dataset demonstrate that our method produces more empathetic, coherent, informative, and specific responses than existing models.",
+    "neighbors": [
+      1212,
+      1378
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 820,
+    "label": 24,
+    "text": "Title: Graph-level Anomaly Detection via Hierarchical Memory Networks\nAbstract: Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules -- node and graph memory modules -- via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs. Extensive empirical results on 16 real-world graph datasets from various domains show that i) HimNet significantly outperforms the state-of-art methods and ii) it is robust to anomaly contamination. Codes are available at: https://github.com/Niuchx/HimNet.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 821,
+    "label": 6,
+    "text": "Title: Remote Monitoring and Teleoperation of Autonomous Vehicles - Is Virtual Reality an Option?\nAbstract: While the promise of autonomous vehicles has led to significant scientific and industrial progress, fully automated, SAE level 5 conform cars will likely not see mass adoption anytime soon. Instead, in many applications, human supervision, such as remote monitoring and teleoperation, will be required for the foreseeable future. While Virtual Reality (VR) has been proposed as one potential interface for teleoperation, its benefits and drawbacks over physical monitoring and teleoperation solutions have not been thoroughly investigated. To this end, we contribute three user studies, comparing and quantifying the performance of and subjective feedback for a VR-based system with an existing monitoring and teleoperation system, which is in industrial use today. Through these three user studies, we contribute to a better understanding of future virtual monitoring and teleoperation solutions for autonomous vehicles. The results of our first user study (n=16) indicate that a VR interface replicating the physical interface does not outperform the physical interface. It also quantifies the negative effects that combined monitoring and teleoperating tasks have on users irrespective of the interface being used. The results of the second user study (n=24) indicate that the perceptual and ergonomic issues caused by VR outweigh its benefits, like better concentration through isolation. The third follow-up user study (n=24) specifically targeted the perceptual and ergonomic issues of VR; the subjective feedback of this study indicates that newer-generation VR headsets have the potential to catch up with the current physical displays.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 822,
+    "label": 8,
+    "text": "Title: Remote Procedure Call as a Managed System Service\nAbstract: Remote Procedure Call (RPC) is a widely used abstraction for cloud computing. The programmer specifies type information for each remote procedure, and a compiler generates stub code linked into each application to marshal and unmarshal arguments into message buffers. Increasingly, however, application and service operations teams need a high degree of visibility and control over the flow of RPCs between services, leading many installations to use sidecars or service mesh proxies for manageability and policy flexibility. These sidecars typically involve inspection and modification of RPC data that the stub compiler had just carefully assembled, adding needless overhead. Further, upgrading diverse application RPC stubs to use advanced hardware capabilities such as RDMA or DPDK is a long and involved process, and often incompatible with sidecar policy control. In this paper, we propose, implement, and evaluate a novel approach, where RPC marshalling and policy enforcement are done as a system service rather than as a library linked into each application. Applications specify type information to the RPC system as before, while the RPC service executes policy engines and arbitrates resource use, and then marshals data customized to the underlying network hardware capabilities. Our system, mRPC, also supports live upgrades so that both policy and marshalling code can be updated transparently to application code. Compared with using a sidecar, mRPC speeds up a standard microservice benchmark, DeathStarBench, by up to 2.5$\\times$ while having a higher level of policy flexibility and availability.",
+    "neighbors": [
+      963
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 823,
+    "label": 24,
+    "text": "Title: Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space\nAbstract: In this paper, we revisit the regret of undiscounted reinforcement learning in MDPs with a birth and death structure. Specifically, we consider a controlled queue with impatient jobs and the main objective is to optimize a trade-off between energy consumption and user-perceived performance. Within this setting, the \\emph{diameter} $D$ of the MDP is $\\Omega(S^S)$, where $S$ is the number of states. Therefore, the existing lower and upper bounds on the regret at time$T$, of order $O(\\sqrt{DSAT})$ for MDPs with $S$ states and $A$ actions, may suggest that reinforcement learning is inefficient here. In our main result however, we exploit the structure of our MDPs to show that the regret of a slightly-tweaked version of the classical learning algorithm {\\sc Ucrl2} is in fact upper bounded by $\\tilde{\\mathcal{O}}(\\sqrt{E_2AT})$ where $E_2$ is related to the weighted second moment of the stationary measure of a reference policy. Importantly, $E_2$ is bounded independently of $S$. Thus, our bound is asymptotically independent of the number of states and of the diameter. This result is based on a careful study of the number of visits performed by the learning algorithm to the states of the MDP, which is highly non-uniform.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 824,
+    "label": 30,
+    "text": "Title: Why Can\u2019t Discourse Parsing Generalize? A Thorough Investigation of the Impact of Data Diversity\nAbstract: Recent advances in discourse parsing performance create the impression that, as in other NLP tasks, performance for high-resource languages such as English is finally becoming reliable. In this paper we demonstrate that this is not the case, and thoroughly investigate the impact of data diversity on RST parsing stability. We show that state-of-the-art architectures trained on the standard English newswire benchmark do not generalize well, even within the news domain. Using the two largest RST corpora of English with text from multiple genres, we quantify the impact of genre diversity in training data for achieving generalization to text types unseen during training. Our results show that a heterogeneous training regime is critical for stable and generalizable models, across parser architectures. We also provide error analyses of model outputs and out-of-domain performance. To our knowledge, this study is the first to fully evaluate cross-corpus RST parsing generalizability on complete trees, examine between-genre degradation within an RST corpus, and investigate the impact of genre diversity in training data composition.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 825,
+    "label": 24,
+    "text": "Title: Target-based Surrogates for Stochastic Optimization\nAbstract: We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning and adversarial training. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \\emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the $SSO$ algorithm, which can be viewed as projected stochastic gradient descent in the target space. This connection enables us to prove theoretical guarantees for $SSO$ when minimizing convex functions. Our framework allows the use of standard stochastic optimization algorithms to construct surrogates which can be minimized by any deterministic optimization method. To evaluate our framework, we consider a suite of supervised learning and imitation learning problems. Our experiments indicate the benefits of target optimization and the effectiveness of $SSO$.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 826,
+    "label": 3,
+    "text": "Title: Virtual Reality Photo-based Tours for Teaching Filipino Vocabulary in an Online Class in Japan: Transitioning into the New Normal\nAbstract: When educational institutions worldwide scrambled for ways to continue their classes during lockdowns caused by the COVID-19 pandemic, the use of information and communication technology (ICT) for remote teaching has become widely considered to be a potential solution. As universities raced to implement emergency remote teaching (ERT) strategies in Japan, some have explored innovative interventions other than webinar platforms and learning management systems to bridge the gap caused by restricted mobility among teachers and learners. One such innovation is virtual reality (VR). VR has been changing the landscape of higher education because of its ability to \"teleport\" learners to various places by simulating real-world environments in the virtual world. Some teachers, including the authors of this paper, explored integrating VR into their activities to address issues caused by geographical limitations brought about by the heightened restrictions in 2020.\u00a0 Results were largely encouraging. However, rules started relaxing in the succeeding years as more people got vaccinated. Thus, some fully online classes in Japan shifted to blended learning as they moved toward fully returning to in-person classes prompting educators to modify how they implemented their VR-based interventions. This paper describes how a class of university students in Japan who were taking a Filipino language course experienced a VR-based intervention in blended mode, which was originally prototyped during the peak of the ERT era. Moreover, adjustments and comparisons regarding methodological idiosyncrasies and findings between the fully online iteration and the recently implemented blended one are reported in detail.\u00a0 \nKeywords: virtual reality, immersive open pedagogies, immersive learning",
+    "neighbors": [
+      1631
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 827,
+    "label": 30,
+    "text": "Title: Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations\nAbstract: We propose EmoDistill, a novel speech emotion recognition (SER) framework that leverages cross-modal knowledge distillation during training to learn strong linguistic and prosodic representations of emotion from speech. During inference, our method only uses a stream of speech signals to perform unimodal SER thus reducing computation overhead and avoiding run-time transcription and prosodic feature extraction errors. During training, our method distills information at both embedding and logit levels from a pair of pre-trained Prosodic and Linguistic teachers that are fine-tuned for SER. Experiments on the IEMOCAP benchmark demonstrate that our method outperforms other unimodal and multimodal techniques by a considerable margin, and achieves state-of-the-art performance of 77.49% unweighted accuracy and 78.91% weighted accuracy. Detailed ablation studies demonstrate the impact of each component of our method.",
+    "neighbors": [
+      1447
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 828,
+    "label": 3,
+    "text": "Title: Privacy Perceptions and Behaviors of Google Personal Account Holders in Saudi Arabia\nAbstract: While privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies. To bridge this gap, we interviewed 30 Google personal account holders in Saudi Arabia about their privacy perceptions (awareness, attitudes, preferences, and concerns) regarding the activity data that Google saves about them, as well as any steps they take to control Google's collection or use of this data. Our study focuses on Google's Activity Controls, which enable users to control whether, and how, Google saves their Web&App Activity, Location History, and YouTube History. Our results show that although most participants have some level of awareness about Google's data practices and the Activity Controls, many have only vague awareness, and the majority have not used the available controls. When participants viewed their saved activity data, many were surprised by what had been saved. While many participants find Google's use of their data to improve the services provided to them acceptable, the majority find the use of their data for ad purposes unacceptable. We observe that our Saudi participants exhibit similar trends and patterns in privacy awareness, attitudes, preferences, concerns, and behaviors to what has been found in studies in the US. However, our study is not a replication of any of the US studies, and further research is needed to directly compare US and Saudi participants. Our results emphasize the need for: (1) improved techniques to inform users about privacy settings during account sign-up, to remind users about their settings, and to raise awareness about privacy settings; (2) improved privacy setting interfaces to reduce the costs that deter many users from changing the settings; and (3) further research to explore privacy concerns in non-Western cultures.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 829,
+    "label": 23,
+    "text": "Title: QChecker: Detecting Bugs in Quantum Programs via Static Analysis\nAbstract: Static analysis is the process of analyzing software code without executing the software. It can help find bugs and potential problems in software that may only appear at runtime. Although many static analysis tools have been developed for classical software, due to the nature of quantum programs, these existing tools are unsuitable for analyzing quantum programs. This paper presents QChecker, a static analysis tool that supports finding bugs in quantum programs in Qiskit. QChecker consists of two main modules: a module for extracting program information based on abstract syntax tree (AST), and a module for detecting bugs based on patterns. We evaluate the performance of QChecker using the Bugs4Q benchmark. The evaluation results show that QChecker can effectively detect various bugs in quantum programs.",
+    "neighbors": [
+      1278
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 830,
+    "label": 4,
+    "text": "Title: Sparsity in neural networks can increase their privacy\nAbstract: This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 831,
+    "label": 16,
+    "text": "Title: Dynamic Large Language Models on Blockchains\nAbstract: Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic large language model on blockchains, which have high computation performance and are distributed across a network of computers. A blockchain is a secure, decentralized, and transparent system that allows for the creation of a tamper-proof ledger for transactions without the need for intermediaries. The dynamic large language models can continuously learn from the user input after the training process. Our method provides a new way to develop the large language models and also sheds a light on the next generation artificial intelligence systems.",
+    "neighbors": [
+      1538,
+      1668,
+      1790,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 832,
+    "label": 27,
+    "text": "Title: A Novel Lockable Spring-loaded Prismatic Spine to Support Agile Quadrupedal Locomotion\nAbstract: This paper introduces a way to systematically investigate the effect of compliant prismatic spines in quadrupedal robot locomotion. We develop a novel spring-loaded lockable spine module, together with a new Spinal Compliance-Integrated Quadruped (SCIQ) platform for both empirical and numerical research. Individual spine tests reveal beneficial spinal characteristics like a degressive spring, and validate the efficacy of a proposed compact locking/unlocking mechanism for the spine. Benchmark vertical jumping and landing tests with our robot show comparable jumping performance between the rigid and compliant spines. An observed advantage of the compliant spine module is that it can alleviate more challenging landing conditions by absorbing impact energy and dissipating the remainder via feet slipping through much in cat-like stretching fashion.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 833,
+    "label": 34,
+    "text": "Title: Strong spatial mixing for colorings on trees and its algorithmic applications\nAbstract: Strong spatial mixing (SSM) is an important quantitative notion of correlation decay for Gibbs distributions arising in statistical physics, probability theory, and theoretical computer science. A longstanding conjecture is that the uniform distribution on proper $q$-colorings on a $\\Delta$-regular tree exhibits SSM whenever $q \\ge \\Delta+1$. Moreover, it is widely believed that as long as SSM holds on bounded-degree trees with $q$ colors, one would obtain an efficient sampler for $q$-colorings on all bounded-degree graphs via simple Markov chain algorithms. It is surprising that such a basic question is still open, even on trees, but then again it also highlights how much we still have to learn about random colorings. In this paper, we show the following: (1) For any $\\Delta \\ge 3$, SSM holds for random $q$-colorings on trees of maximum degree $\\Delta$ whenever $q \\ge \\Delta + 3$. Thus we almost fully resolve the aforementioned conjecture. Our result substantially improves upon the previously best bound which requires $q \\ge 1.59\\Delta+\\gamma^*$ for an absolute constant $\\gamma^*>0$. (2) For any $\\Delta\\ge 3$ and girth $g = \\Omega_\\Delta(1)$, we establish optimal mixing of the Glauber dynamics for $q$-colorings on graphs of maximum degree $\\Delta$ and girth $g$ whenever $q \\ge \\Delta+3$. Our approach is based on a new general reduction from spectral independence on large-girth graphs to SSM on trees that is of independent interest. Using the same techniques, we also prove near-optimal bounds on weak spatial mixing (WSM), a closely-related notion to SSM, for the antiferromagnetic Potts model on trees.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 834,
+    "label": 10,
+    "text": "Title: Large Language Models and Knowledge Graphs: Opportunities and Challenges\nAbstract: Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.",
+    "neighbors": [
+      887,
+      1052,
+      1520,
+      1560,
+      1663,
+      1797,
+      1799,
+      2062
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 835,
+    "label": 16,
+    "text": "Title: Asymmetric Face Recognition with Cross Model Compatible Ensembles\nAbstract: The asymmetrical retrieval setting is a well suited solution for resource constrained face recognition. In this setting a large model is used for indexing the gallery while a lightweight model is used for querying. The key principle in such systems is ensuring that both models share the same embedding space. Most methods in this domain are based on knowledge distillation. While useful, they suffer from several drawbacks: they are upper-bounded by the performance of the single best model found and cannot be extended to use an ensemble of models in a straightforward manner. In this paper we present an approach that does not rely on knowledge distillation, rather it utilizes embedding transformation models. This allows the use of N independently trained and diverse gallery models (e.g., trained on different datasets or having a different architecture) and a single query model. As a result, we improve the overall accuracy beyond that of any single model while maintaining a low computational budget for querying. Additionally, we propose a gallery image rejection method that utilizes the diversity between multiple transformed embeddings to estimate the uncertainty of gallery images.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 836,
+    "label": 16,
+    "text": "Title: MovieChat: From Dense Token to Sparse Memory for Long Video Understanding\nAbstract: Recently, integrating video foundation models and large language models to build a video understanding system overcoming the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection are the remaining challenges. Inspired by Atkinson-Shiffrin memory model, we develop an memory mechanism including a rapidly updated short-term memory and a compact thus sustained long-term memory. We employ tokens in Transformers as the carriers of memory. MovieChat achieves state-of-the-art performace in long video understanding.",
+    "neighbors": [
+      34,
+      392,
+      522,
+      887,
+      1047,
+      1052,
+      1148,
+      1537,
+      1626,
+      1668,
+      1863,
+      2030,
+      2036,
+      2155,
+      2216,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 837,
+    "label": 28,
+    "text": "Title: Superdirectivity-enhanced wireless communications: A multi-user perspective\nAbstract: Superdirective array may achieve an array gain proportional to the square of the number of antennas $M^2$. In the early studies of superdirectivity, little research has been done from wireless communication point of view. To leverage superdirectivity for enhancing the spectral efficiency, this paper investigates multi-user communication systems with superdirective arrays. We first propose a field-coupling-aware (FCA) multi-user channel estimation method, which takes into account the antenna coupling effects. Aiming to maximize the power gain of the target user, we propose multi-user multipath superdirective precoding (SP) as an extension of our prior work on coupling-based superdirective beamforming. Furthermore, to reduce the inter-user interference, we propose interference-nulling superdirective precoding (INSP) as the optimal solution to maximize user power gains while eliminating interference. Then, by taking the ohmic loss into consideration, we further propose a regularized interference-nulling superdirective precoding (RINSP) method. Finally, we discuss the well-known narrow directivity bandwidth issue, and find that it is not a fundamental problem of superdirective arrays in multi-carrier communication systems. Simulation results show our proposed methods outperform the state-of-the-art methods significantly. Interestingly, in the multi-user scenario, an 18-antenna superdirective array can achieve up to a 9-fold increase of spectral efficiency compared to traditional multiple-input multiple-output (MIMO), while simultaneously reducing the array aperture by half.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 838,
+    "label": 16,
+    "text": "Title: Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment\nAbstract: Multi-camera multiple people tracking has become an increasingly important area of research due to the growing demand for accurate and efficient indoor people tracking systems, particularly in settings such as retail, healthcare centers, and transit hubs. We proposed a novel multi-camera multiple people tracking method that uses anchor-guided clustering for cross-camera re-identification and spatio-temporal consistency for geometry-based cross-camera ID reassigning. Our approach aims to improve the accuracy of tracking by identifying key features that are unique to every individual and utilizing the overlap of views between cameras to predict accurate trajectories without needing the actual camera parameters. The method has demonstrated robustness and effectiveness in handling both synthetic and real-world data. The proposed method is evaluated on CVPR AI City Challenge 2023 dataset, achieving IDF1 of 95.36% with the first-place ranking in the challenge. The code is available at: https://github.com/ipl-uw/AIC23_Track1_UWIPL_ETRI.",
+    "neighbors": [
+      642
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 839,
+    "label": 8,
+    "text": "Title: Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks\nAbstract: Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today\u2019s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.",
+    "neighbors": [
+      107
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 840,
+    "label": 31,
+    "text": "Title: How Can Recommender Systems Benefit from Large Language Models: A Survey\nAbstract: Recommender systems (RS) play important roles to match users' information needs for Internet applications. In natural language processing (NLP) domains, large language model (LLM) has shown astonishing emergent abilities (e.g., instruction following, reasoning), thus giving rise to the promising research direction of adapting LLM to RS for performance enhancements and user experience improvements. In this paper, we conduct a comprehensive survey on this research direction from an application-oriented view. We first summarize existing research works from two orthogonal perspectives: where and how to adapt LLM to RS. For the\"WHERE\"question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e., feature engineering, feature encoder, scoring/ranking function, and pipeline controller. For the\"HOW\"question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e., whether to tune LLMs or not, and whether to involve conventional recommendation model (CRM) for inference. Detailed analysis and general development trajectories are provided for both questions, respectively. Then, we highlight key challenges in adapting LLM to RS from three aspects, i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. We also actively maintain a GitHub repository for papers and other related resources in this rising direction: https://github.com/CHIANGEL/Awesome-LLM-for-RecSys.",
+    "neighbors": [
+      3,
+      424,
+      566,
+      1052,
+      1092,
+      1182,
+      1327,
+      1611,
+      1667,
+      1762,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 841,
+    "label": 28,
+    "text": "Title: Near-Field Communications: A Tutorial Review\nAbstract: Extremely large-scale antenna arrays, tremendously high frequencies, and new types of antennas are three clear trends in multi-antenna technology for supporting the sixth-generation (6G) networks. To properly account for the new characteristics introduced by these three trends in communication system design, the near-field spherical-wave propagation model needs to be used, which differs from the classical far-field planar-wave one. As such, near-field communication (NFC) will become essential in 6G networks. In this tutorial, we cover three key aspects of NFC. 1) Channel Modelling: We commence by reviewing near-field spherical-wave-based channel models for spatially-discrete (SPD) antennas. Then, uniform spherical wave (USW) and non-uniform spherical wave (NUSW) models are discussed. Subsequently, we introduce a general near-field channel model for SPD antennas and a Green's function-based channel model for continuous-aperture (CAP) antennas. 2) Beamfocusing and Antenna Architectures: We highlight the properties of near-field beamfocusing and discuss NFC antenna architectures for both SPD and CAP antennas. Moreover, the basic principles of near-field beam training are introduced. 3) Performance Analysis: Finally, we provide a comprehensive performance analysis framework for NFC. For near-field line-of-sight channels, the received signal-to-noise ratio and power-scaling law are derived. For statistical near-field multipath channels, a general analytical framework is proposed, based on which analytical expressions for the outage probability, ergodic channel capacity, and ergodic mutual information are obtained. Finally, for each aspect, topics for future research are discussed.",
+    "neighbors": [
+      606,
+      617,
+      1108
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 842,
+    "label": 11,
+    "text": "Title: Stackelberg Decision Transformer for Asynchronous Action Coordination in Multi-Agent Systems\nAbstract: Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely constrained by network structures or environmental limitations. To address this issue, we propose the Stackelberg Decision Transformer (STEER), a heuristic approach that resolves the difficulties of hierarchical coordination among agents. STEER efficiently manages decision-making processes in both spatial and temporal contexts by incorporating the hierarchical decision structure of SG, the modeling capability of autoregressive sequence models, and the exploratory learning methodology of MARL. Our research contributes to the development of an effective and adaptable asynchronous action coordination method that can be widely applied to various task types and environmental configurations in MAS. Experimental results demonstrate that our method can converge to Stackelberg equilibrium solutions and outperforms other existing methods in complex scenarios.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 843,
+    "label": 24,
+    "text": "Title: Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction\nAbstract: Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.",
+    "neighbors": [
+      890
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 844,
+    "label": 27,
+    "text": "Title: Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning\nAbstract: Sequence modeling approaches have shown promising results in robot imitation learning. Recently, diffusion models have been adopted for behavioral cloning, benefiting from their exceptional capabilities in modeling complex data distribution. In this work, we propose Crossway Diffusion, a method to enhance diffusion-based visuomotor policy learning by using an extra self-supervised learning (SSL) objective. The standard diffusion-based policy generates action sequences from random noise conditioned on visual observations and other low-dimensional states. We further extend this by introducing a new decoder that reconstructs raw image pixels (and other state information) from the intermediate representations of the reverse diffusion process, and train the model jointly using the SSL loss. Our experiments demonstrate the effectiveness of Crossway Diffusion in various simulated and real-world robot tasks, confirming its advantages over the standard diffusion-based policy. We demonstrate that such self-supervised reconstruction enables better representation for policy learning, especially when the demonstrations have different proficiencies.",
+    "neighbors": [
+      787,
+      1908
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 845,
+    "label": 28,
+    "text": "Title: Minimal and Optimal binary codes obtained using CD-construction over the non-unital ring I\nAbstract: In this article, we construct linear codes over the commutative non-unital ring $I$ of size four. We obtain their Lee-weight distributions and study their binary Gray images. Under certain mild conditions, these classes of binary codes are minimal and self-orthogonal. All codes in this article are few-weight codes. Besides, an infinite class of these binary codes consists of distance optimal codes with respect to the Griesmer bound.",
+    "neighbors": [
+      697
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 846,
+    "label": 24,
+    "text": "Title: Web Content Filtering through knowledge distillation of Large Language Models\nAbstract: We introduce a state-of-the-art approach for URL categorization that leverages the power of Large Language Models (LLMs) to address the primary objectives of web content filtering: safeguarding organizations from legal and ethical risks, limiting access to high-risk or suspicious websites, and fostering a secure and professional work environment. Our method utilizes LLMs to generate accurate classifications and then employs established knowledge distillation techniques to create smaller, more specialized student models tailored for web content filtering. Distillation results in a student model with a 9% accuracy rate improvement in classifying websites, sourced from customer telemetry data collected by a large security vendor, into 30 distinct content categories based on their URLs, surpassing the current state-of-the-art approach. Our student model matches the performance of the teacher LLM with 175 times less parameters, allowing the model to be used for in-line scanning of large volumes of URLs, and requires 3 orders of magnitude less manually labeled training data than the current state-of-the-art approach. Depending on the specific use case, the output generated by our approach can either be directly returned or employed as a pre-filter for more resource-intensive operations involving website images or HTML.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 847,
+    "label": 16,
+    "text": "Title: Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?\nAbstract: Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-based distillation methods, (2) low-fidelity synthetic images are better teaching materials, and (3) relatively weak classifiers are better teachers. Code is available at https://github.com/zhengli97/DM-KD.",
+    "neighbors": [
+      736,
+      2007
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 848,
+    "label": 16,
+    "text": "Title: Improving Tuning-Free Real Image Editing with Proximal Guidance\nAbstract: DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstruction quality. To overcome these limitations, we propose proximal guidance and incorporate it to NPI with cross-attention control. We enhance NPI with a regularization term and reconstruction guidance, which reduces artifacts while capitalizing on its training-free nature. Additionally, we extend the concepts to incorporate mutual self-attention control, enabling geometry and layout alterations in the editing process. Our method provides an efficient and straightforward approach, effectively addressing real image editing tasks with minimal computational overhead.",
+    "neighbors": [
+      1008,
+      1173,
+      1279,
+      1420,
+      1582,
+      1710,
+      2161
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 849,
+    "label": 34,
+    "text": "Title: Algorithms Transcending the SAT-Symmetry Interface\nAbstract: Dedicated treatment of symmetries in satisfiability problems (SAT) is indispensable for solving various classes of instances arising in practice. However, the exploitation of symmetries usually takes a black box approach. Typically, off-the-shelf external, general-purpose symmetry detection tools are invoked to compute symmetry groups of a formula. The groups thus generated are a set of permutations passed to a separate tool to perform further analyzes to understand the structure of the groups. The result of this second computation is in turn used for tasks such as static symmetry breaking or dynamic pruning of the search space. Within this pipeline of tools, the detection and analysis of symmetries typically incurs the majority of the time overhead for symmetry exploitation. In this paper we advocate for a more holistic view of what we call the SAT-symmetry interface. We formulate a computational setting, centered around a new concept of joint graph/group pairs, to analyze and improve the detection and analysis of symmetries. Using our methods, no information is lost performing computational tasks lying on the SAT-symmetry interface. Having access to the entire input allows for simpler, yet efficient algorithms. Specifically, we devise algorithms and heuristics for computing finest direct disjoint decompositions, finding equivalent orbits, and finding natural symmetric group actions. Our algorithms run in what we call instance-quasi-linear time, i.e., almost linear time in terms of the input size of the original formula and the description length of the symmetry group returned by symmetry detection tools. Our algorithms improve over both heuristics used in state-of-the-art symmetry exploitation tools, as well as theoretical general-purpose algorithms.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 850,
+    "label": 24,
+    "text": "Title: Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature\nAbstract: Real-world optimisation problems often feature complex combinations of (1) diverse constraints, (2) discrete and mixed spaces, and are (3) highly parallelisable. (4) There are also cases where the objective function cannot be queried if unknown constraints are not satisfied, e.g. in drug discovery, safety on animal experiments (unknown constraints) must be established before human clinical trials (querying objective function) may proceed. However, most existing works target each of the above three problems in isolation and do not consider (4) unknown constraints with query rejection. For problems with diverse constraints and/or unconventional input spaces, it is difficult to apply these techniques as they are often mutually incompatible. We propose cSOBER, a domain-agnostic prudent parallel active sampler for Bayesian optimisation, based on SOBER of Adachi et al. (2023). We consider infeasibility under unknown constraints as a type of integration error that we can estimate. We propose a theoretically-driven approach that propagates such error as a tolerance in the quadrature precision that automatically balances exploitation and exploration with the expected rejection rate. Moreover, our method flexibly accommodates diverse constraints and/or discrete and mixed spaces via adaptive tolerance, including conventional zero-risk cases. We show that cSOBER outperforms competitive baselines on diverse real-world blackbox-constrained problems, including safety-constrained drug discovery, and human-relationship-aware team optimisation over graph-structured space.",
+    "neighbors": [
+      163
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 851,
+    "label": 8,
+    "text": "Title: Wi-BFI: Extracting the IEEE 802.11 Beamforming Feedback Information from Commercial Wi-Fi Devices\nAbstract: Recently, researchers have shown that the beamforming feedback angles (BFAs) used for Wi-Fi multiple-input multiple-output (MIMO) operations can be effectively leveraged as a proxy of the channel frequency response (CFR) for different purposes. Examples are passive human activity recognition and device fingerprinting. However, even though the BFAs report frames are sent in clear text, there is not yet a unified open-source tool to extract and decode the BFAs from the frames. To fill this gap, we developed Wi-BFI, the first tool that allows retrieving Wi-Fi BFAs and reconstructing the beamforming feedback information (BFI) - a compressed representation of the CFR - from the BFAs frames captured over the air. The tool supports BFAs extraction within both IEEE 802.11ac and 802.11ax networks operating on radio channels with 160/80/40/20 MHz bandwidth. Both multi-user and single-user MIMO feedback can be decoded through Wi-BFI. The tool supports real-time and offline extraction and storage of BFAs and BFI. The real-time mode also includes a visual representation of the channel state that continuously updates based on the collected data. Wi-BFI code is open source and the tool is also available as a pip package.",
+    "neighbors": [
+      28,
+      730
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 852,
+    "label": 24,
+    "text": "Title: A supervised generative optimization approach for tabular data\nAbstract: Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation. Many algorithms have been proposed for synthetic data generation but reaching the consensus on which method we should use for the specific data sets and use cases remains challenging. Moreover, the majority of existing approaches are ``unsupervised'' in the sense that they do not take into account the downstream task. To address these issues, this work presents a novel synthetic data generation framework. The framework integrates a supervised component tailored to the specific downstream task and employs a meta-learning approach to learn the optimal mixture distribution of existing synthetic distributions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 853,
+    "label": 39,
+    "text": "Title: Spectrahedral Geometry of Graph Sparsifiers\nAbstract: We propose an approach to graph sparsification based on the idea of preserving the smallest $k$ eigenvalues and eigenvectors of the Graph Laplacian. This is motivated by the fact that small eigenvalues and their associated eigenvectors tend to be more informative of the global structure and geometry of the graph than larger eigenvalues and their eigenvectors. The set of all weighted subgraphs of a graph $G$ that have the same first $k$ eigenvalues (and eigenvectors) as $G$ is the intersection of a polyhedron with a cone of positive semidefinite matrices. We discuss the geometry of these sets and deduce the natural scale of $k$. Various families of graphs illustrate our construction.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 854,
+    "label": 34,
+    "text": "Title: Tight Algorithms for Connectivity Problems Parameterized by Modular-Treewidth\nAbstract: We study connectivity problems from a fine-grained parameterized perspective. Cygan et al. (TALG 2022) obtained algorithms with single-exponential running time $\\alpha^{tw} n^{O(1)}$ for connectivity problems parameterized by treewidth ($tw$) by introducing the cut-and-count-technique, which reduces connectivity problems to locally checkable counting problems. In addition, the bases $\\alpha$ were proven to be optimal assuming the Strong Exponential-Time Hypothesis (SETH). As only sparse graphs may admit small treewidth, these results do not apply to graphs with dense structure. A well-known tool to capture dense structure is the modular decomposition, which recursively partitions the graph into modules whose members have the same neighborhood outside of the module. Contracting the modules yields a quotient graph describing the adjacencies between modules. Measuring the treewidth of the quotient graph yields the parameter modular-treewidth, a natural intermediate step between treewidth and clique-width. We obtain the first tight running times for connectivity problems parameterized by modular-treewidth. For some problems the obtained bounds are the same as relative to treewidth, showing that we can deal with a greater generality in input structure at no cost in complexity. We obtain the following randomized algorithms for graphs of modular-treewidth $k$, given an appropriate decomposition: Steiner Tree can be solved in time $3^k n^{O(1)}$, Connected Dominating Set can be solved in time $4^k n^{O(1)}$, Connected Vertex Cover can be solved in time $5^k n^{O(1)}$, Feedback Vertex Set can be solved in time $5^k n^{O(1)}$. The first two algorithms are tight due to known results and the last two algorithms are complemented by new tight lower bounds under SETH.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 855,
+    "label": 30,
+    "text": "Title: A Comprehensive Overview of Large Language Models\nAbstract: Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations of the underlying neural networks, context length improvements, model alignment, training datasets, benchmarking, efficiency and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides that overview to the research community. It not only focuses on a systematic treatment of the existing literature on a broad range of LLM related concept, but also pays special attention to providing comprehensive summaries with extensive details about the individual existing models, datasets and major insights. We also pay heed to aligning our overview with the emerging outlook of this research direction by accounting for the other recently materializing reviews of the broader research direction of LLMs. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of this research direction. This review article is intended to not only provide a systematic survey, but also a quick comprehensive reference for the researchers and practitioners to draw insights from extensive informative summaries of the existing works to advance the LLM research direction.",
+    "neighbors": [
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+      1548,
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+      2036,
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+      2136,
+      2155,
+      2216,
+      2235,
+      2286
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 856,
+    "label": 16,
+    "text": "Title: Cascaded Zoom-in Detector for High Resolution Aerial Images\nAbstract: Detecting objects in aerial images is challenging because they are typically composed of crowded small objects distributed non-uniformly over high-resolution (in terms of pixel size) images. Density cropping is a widely used method to improve this small object detection where the crowded small object regions are extracted and processed in high image-resolution. However, this is typically accomplished by adding other learnable components, thus complicating the training and inference over a standard detection process. In this paper, we propose an efficient Cascaded Zoom-in (CZ) detector that re-purposes the detector itself for density-guided training and inference. During training, density crops are located, labeled as a new class, and employed to augment the training dataset. During inference, the density crops are first detected along with the base class objects, and then input for a second stage of inference. This approach is easily integrated into any detector, and creates no significant change in the standard detection process, like the uniform cropping approach popular in aerial image detection. Experimental results on the aerial images of the challenging VisDrone and DOTA datasets verify the benefits of the proposed approach. The proposed CZ detector also provides state-of-the-art results over uniform cropping and other density cropping methods on the VisDrone dataset, increasing the detection mAP of small objects by more than 3 percentage points. 1",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 857,
+    "label": 30,
+    "text": "Title: Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation\nAbstract: Large language models (large LMs) are susceptible to producing text with hallucinated content. Self-contradiction, where the LM generates two contradictory sentences within the same context, is an important form of hallucination. In this work, we present a comprehensive analysis on self-contradiction for state-of-the-art, instruction-tuned LMs, including evaluation, detection, and mitigation. To effectively trigger self-contradictions, we design a framework that constrains LMs to generate appropriate sentence pairs. Our evaluation on these sentence pairs reveals that self-contradictions occur frequently across different LMs for both famous and lesser-known topics. Next, we prompt the LMs to detect self-contradictions. Our results indicate that ChatGPT and GPT-4 are able to accurately identify self-contradictions, while Vicuna-13B struggles to do so. For example, with our best prompting method, ChatGPT achieves 91.0% precision and 80.5% recall on the sentence pairs generated by itself. To automatically mitigate self-contradictions, we develop an iterative algorithm that prompts the LMs to remove the detected self-contradictions from the generated text. Our algorithm successfully revises the text such that self-contradictions are significantly reduced, while maintaining its fluency and informativeness. Importantly, our entire pipeline of triggering, detecting, and mitigating self-contradictions is applicable to black-box LMs and does not require any external grounded knowledge.",
+    "neighbors": [
+      377,
+      719,
+      818,
+      975,
+      1052,
+      1133,
+      1863,
+      1952,
+      2013,
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+      2249
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 858,
+    "label": 6,
+    "text": "Title: How Accurate Does It Feel? \u2013 Human Perception of Different Types of Classification Mistakes\nAbstract: Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, which reduces the inter-annotator agreement. This causes noise for the classifier and might affect the user\u2019s perception of the classifier\u2019s performance. In our research, we investigated whether the classification difficulty of a data point influences how strongly a prediction mistake reduces the \u201cperceived accuracy\u201d. In an experimental online study, 225 participants interacted with three fictive classifiers with equal accuracy (73%). The classifiers made prediction mistakes on three different types of data points (easy, difficult, impossible). After the interaction, participants judged the classifier\u2019s accuracy. We found that not all prediction mistakes reduced the perceived accuracy equally. Furthermore, the perceived accuracy differed significantly from the calculated accuracy. To conclude, accuracy and related measures seem unsuitable to represent how users perceive the performance of classifiers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 859,
+    "label": 27,
+    "text": "Title: Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments\nAbstract: We present Orbit, a unified and modular framework for robot learning powered by Nvidia Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit, we provide a suite of benchmark tasks of varying difficulty\u2013 from single-stage cabinet opening and cloth folding to multi-stage tasks such as room reorganization. To support working with diverse observations and action spaces, we include fixed-arm and mobile manipulators with different physically-based sensors and motion generators. Orbit allows training reinforcement learning policies and collecting large demonstration datasets from hand-crafted or expert solutions in a matter of minutes by leveraging GPU-based parallelization. In summary, we offer an open-sourced framework that readily comes with 16 robotic platforms, 4 sensor modalities, 10 motion generators, more than 20 benchmark tasks, and wrappers to 4 learning libraries. With this framework, we aim to support various research areas, including representation learning, reinforcement learning, imitation learning, and task and motion planning. We hope it helps establish interdisciplinary collaborations in these communities, and its modularity makes it easily extensible for more tasks and applications in the future.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 860,
+    "label": 16,
+    "text": "Title: MagicProp: Diffusion-based Video Editing via Motion-aware Appearance Propagation\nAbstract: This paper addresses the issue of modifying the visual appearance of videos while preserving their motion. A novel framework, named MagicProp, is proposed, which disentangles the video editing process into two stages: appearance editing and motion-aware appearance propagation. In the first stage, MagicProp selects a single frame from the input video and applies image-editing techniques to modify the content and/or style of the frame. The flexibility of these techniques enables the editing of arbitrary regions within the frame. In the second stage, MagicProp employs the edited frame as an appearance reference and generates the remaining frames using an autoregressive rendering approach. To achieve this, a diffusion-based conditional generation model, called PropDPM, is developed, which synthesizes the target frame by conditioning on the reference appearance, the target motion, and its previous appearance. The autoregressive editing approach ensures temporal consistency in the resulting videos. Overall, MagicProp combines the flexibility of image-editing techniques with the superior temporal consistency of autoregressive modeling, enabling flexible editing of object types and aesthetic styles in arbitrary regions of input videos while maintaining good temporal consistency across frames. Extensive experiments in various video editing scenarios demonstrate the effectiveness of MagicProp.",
+    "neighbors": [
+      490,
+      955,
+      1020,
+      1251,
+      1420,
+      1539,
+      1902,
+      1935,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 861,
+    "label": 27,
+    "text": "Title: CrazyChoir: Flying Swarms of Crazyflie Quadrotors in ROS 2\nAbstract: This letter introduces CrazyChoir, a modular Python framework based on the Robot Operating System (ROS) 2. The toolbox provides a comprehensive set of functionalities to simulate and run experiments on teams of cooperating Crazyflie nano-quadrotors. Specifically, it allows users to perform realistic simulations over robotic simulators as, e.g., Webots and includes bindings of the firmware control and planning functions. The toolbox also provides libraries to perform radio communication with Crazyflie directly inside ROS 2 scripts. The package can be thus used to design, implement and test planning strategies and control schemes for a Crazyflie nano-quadrotor. Moreover, the modular structure of CrazyChoir allows users to easily implement online distributed optimization and control schemes over multiple quadrotors. The CrazyChoir package is validated via simulations and experiments on a swarm of Crazyflies for formation control, pickup-and-delivery vehicle routing and trajectory tracking tasks.",
+    "neighbors": [
+      2164
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 862,
+    "label": 23,
+    "text": "Title: Taming Android Fragmentation Through Lightweight Crowdsourced Testing\nAbstract: Android fragmentation refers to the overwhelming diversity of Android devices and OS versions. These lead to the impossibility of testing an app on every supported device, leaving a number of compatibility bugs scattered in the community and thereby resulting in poor user experiences. To mitigate this, our fellow researchers have designed various works to automatically detect such compatibility issues. However, the current state-of-the-art tools can only be used to detect specific kinds of compatibility issues (i.e., compatibility issues caused by API signature evolution), i.e., many other essential types of compatibility issues are still unrevealed. For example, customized OS versions on real devices and semantic changes of OS could lead to serious compatibility issues, which are non-trivial to be detected statically. To this end, we propose a novel, lightweight, crowdsourced testing approach, LazyCow, to fill this research gap and enable the possibility of taming Android fragmentation through crowdsourced efforts. Specifically, crowdsourced testing is an emerging alternative to conventional mobile testing mechanisms that allow developers to test their products on real devices to pinpoint platform-specific issues. Experimental results on thousands of test cases on real-world Android devices show that LazyCow is effective in automatically identifying and verifying API-induced compatibility issues. Also, after investigating the user experience through qualitative metrics, users\u2019 satisfaction provides strong evidence that LazyCow is useful and welcome in practice.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 863,
+    "label": 4,
+    "text": "Title: Detecting Unknown Attacks in IoT Environments: An Open Set Classifier for Enhanced Network Intrusion Detection\nAbstract: The widespread integration of Internet of Things (IoT) devices across all facets of life has ushered in an era of interconnectedness, creating new avenues for cybersecurity challenges and underscoring the need for robust intrusion detection systems. However, traditional security systems are designed with a closed-world perspective and often face challenges in dealing with the ever-evolving threat landscape, where new and unfamiliar attacks are constantly emerging. In this paper, we introduce a framework aimed at mitigating the open set recognition (OSR) problem in the realm of Network Intrusion Detection Systems (NIDS) tailored for IoT environments. Our framework capitalizes on image-based representations of packet-level data, extracting spatial and temporal patterns from network traffic. Additionally, we integrate stacking and sub-clustering techniques, enabling the identification of unknown attacks by effectively modeling the complex and diverse nature of benign behavior. The empirical results prominently underscore the framework's efficacy, boasting an impressive 88\\% detection rate for previously unseen attacks when compared against existing approaches and recent advancements. Future work will perform extensive experimentation across various openness levels and attack scenarios, further strengthening the adaptability and performance of our proposed solution in safeguarding IoT environments.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 864,
+    "label": 38,
+    "text": "Title: A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing Highly Cited and Impactful Publications across Six Decades\nAbstract: Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields, thereby driving advancements in other domains of interest. As the field continues to evolve, it is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far. In this paper, we present a comprehensive bibliometric analysis of highly cited ML publications. We collected a dataset consisting of the top-cited papers from reputable ML conferences and journals, covering a period of several years from 1959 to 2022. We employed various bibliometric techniques to analyze the data, including citation analysis, co-authorship analysis, keyword analysis, and publication trends. Our findings reveal the most influential papers, highly cited authors, and collaborative networks within the machine learning community. We identify popular research themes and uncover emerging topics that have recently gained significant attention. Furthermore, we examine the geographical distribution of highly cited publications, highlighting the dominance of certain countries in ML research. By shedding light on the landscape of highly cited ML publications, our study provides valuable insights for researchers, policymakers, and practitioners seeking to understand the key developments and trends in this rapidly evolving field.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 865,
+    "label": 28,
+    "text": "Title: Stacked Intelligent Metasurfaces for Multiuser Beamforming in the Wave Domain\nAbstract: Reconfigurable intelligent surface has recently emerged as a promising technology for shaping the wireless environment by leveraging massive low-cost reconfigurable elements. Prior works mainly focus on a single-layer metasurface that lacks the capability of suppressing multiuser interference. By contrast, we propose a stacked intelligent metasurface (SIM)-enabled transceiver design for multiuser multiple-input single-output downlink communications. Specifically, the SIM is endowed with a multilayer structure and is deployed at the base station to perform transmit beamforming directly in the electromagnetic wave domain. As a result, an SIM-enabled transceiver overcomes the need for digital beamforming and operates with low-resolution digital-to-analog converters and a moderate number of radio-frequency chains, which significantly reduces the hardware cost and energy consumption, while substantially decreasing the precoding delay benefiting from the processing performed in the wave domain. To leverage the benefits of SIM-enabled transceivers, we formulate an optimization problem for maximizing the sum rate of all the users by jointly designing the transmit power allocated to them and the analog beamforming in the wave domain. Numerical results based on a customized alternating optimization algorithm corroborate the effectiveness of the proposed SIM-enabled analog beamforming design as compared with various benchmark schemes. Most notably, the proposed analog beamforming scheme is capable of substantially decreasing the precoding delay compared to its digital counterpart.",
+    "neighbors": [
+      226,
+      745,
+      2269
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 866,
+    "label": 38,
+    "text": "Title: A maturity model for catalogues of semantic artefacts\nAbstract: The work presented in this paper is twofold. On the one hand, we aim to define the concept of semantic artefact catalogue (SAC) by overviewing various definitions used to clarify the meaning of our target of observation, including the meaning of the focal item: semantic artefacts. On the other hand, we aim to identify metrics and dimensions that can be used to assess the maturity of such catalogues. In particular, we define a maturity model to measure, compare and evaluate available semantic artefact catalogues. By setting these dimensions and their metrics, catalogues can be classified by each dimension. So the maturity of both the catalogues and the dimensions as a whole can be expressed. Such a maturity model and its application to 26 semantic artefacts catalogues - from various disciplines and relying on various technologies - are available to be later enriched.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 867,
+    "label": 30,
+    "text": "Title: EM Pre-training for Multi-party Dialogue Response Generation\nAbstract: Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multi-party dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method. The official implementation of this paper is available at https://github.com/EricLee8/MPDRG.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 868,
+    "label": 4,
+    "text": "Title: Quantitative Measurement of Cyber Resilience: Modeling and Experimentation\nAbstract: Cyber resilience is the ability of a system to resist and recover from a cyber attack, thereby restoring the system's functionality. Effective design and development of a cyber resilient system requires experimental methods and tools for quantitative measuring of cyber resilience. This paper describes an experimental method and test bed for obtaining resilience-relevant data as a system (in our case -- a truck) traverses its route, in repeatable, systematic experiments. We model a truck equipped with an autonomous cyber-defense system and which also includes inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e.,\"bonware\") strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious mathematical models to aid in quantifying systems' resilience to cyber attacks. Using the models, we identify quantitative characteristics obtainable from experimental data, and show that these characteristics can serve as useful quantitative measures of cyber resilience.",
+    "neighbors": [
+      2127
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 869,
+    "label": 30,
+    "text": "Title: KEYword based Sampling (KEYS) for Large Language Models\nAbstract: Question answering (Q/A) can be formulated as a generative task (Mitra, 2017) where the task is to generate an answer given the question and the passage (knowledge, if available). Recent advances in QA task is focused a lot on language model advancements and less on other areas such as sampling(Krishna et al., 2021), (Nakano et al., 2021). Keywords play very important role for humans in language generation. (Humans formulate keywords and use grammar to connect those keywords and work). In the research community, very little focus is on how humans generate answers to a question and how this behavior can be incorporated in a language model. In this paper, we want to explore these two areas combined, i.e., how sampling can be to used generate answers which are close to human-like behavior and factually correct. Hence, the type of decoding algorithm we think should be used for Q/A tasks should also depend on the keywords. These keywords can be obtained from the question, passage or internet results. We use knowledge distillation techniques to extract keywords and sample using these extracted keywords on top of vanilla decoding algorithms when formulating the answer to generate a human-like answer. In this paper, we show that our decoding method outperforms most commonly used decoding methods for Q/A task",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 870,
+    "label": 16,
+    "text": "Title: A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking\nAbstract: Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution. Generally speaking, it is challenging to employ these architectures in real-world applications due to many hardware and environmental restrictions, such as processing and computational capabilities. Therefore, this survey investigates the most efficient methodologies to ensure sub-optimal estimation performances. More in detail, four efficient categories will be analyzed: compact architecture, pruning, knowledge distillation, and quantization strategies. Moreover, a new metric called Efficient Error Rate has been introduced in order to normalize and compare models' features that affect hardware devices at inference time, such as the number of parameters, bits, FLOPs, and model size. Summarizing, this paper firstly mathematically defines the strategies used to make Vision Transformer efficient, describes and discusses state-of-the-art methodologies, and analyzes their performances over different application scenarios. Toward the end of this paper, we also discuss open challenges and promising research directions.",
+    "neighbors": [
+      1152
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 871,
+    "label": 37,
+    "text": "Title: Accelerating Relational Database Analytical Processing with Bulk-Bitwise Processing-in-Memory\nAbstract: Online Analytical Processing (OLAP) for relational databases is a business decision support application. The application receives queries about the business database, usually requesting to summarize many database records, and produces few results. Existing OLAP requires transferring a large amount of data between the memory and the CPU, having a few operations per datum, and producing a small output. Hence, OLAP is a good candidate for processing-in-memory (PIM), where computation is performed where the data is stored, thus accelerating applications by reducing data movement between the memory and CPU. In particular, bulk-bitwise PIM, where the memory array is a bitvector processing unit, seems a good match for OLAP. With the extensive inherent parallelism and minimal data movement of bulk-bitwise PIM, OLAP applications can process the entire database in parallel in memory, transferring only the results to the CPU. This paper shows a full stack adaptation of a bulk-bitwise PIM, from compiling SQL to hardware implementation, for supporting OLAP applications. Evaluating the Star Schema Benchmark (SSB), bulk-bitwise PIM achieves a 4.65\u00d7 speedup over Monet-DB, a standard database system.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 872,
+    "label": 24,
+    "text": "Title: Continually learning out-of-distribution spatiotemporal data for robust energy forecasting\nAbstract: Forecasting building energy usage is essential for promoting sustainability and reducing waste, as it enables building managers to optimize energy consumption and reduce costs. This importance is magnified during anomalous periods, such as the COVID-19 pandemic, which have disrupted occupancy patterns and made accurate forecasting more challenging. Forecasting energy usage during anomalous periods is difficult due to changes in occupancy patterns and energy usage behavior. One of the primary reasons for this is the shift in distribution of occupancy patterns, with many people working or learning from home. This has created a need for new forecasting methods that can adapt to changing occupancy patterns. Online learning has emerged as a promising solution to this challenge, as it enables building managers to adapt to changes in occupancy patterns and adjust energy usage accordingly. With online learning, models can be updated incrementally with each new data point, allowing them to learn and adapt in real-time. Another solution is to use human mobility data as a proxy for occupancy, leveraging the prevalence of mobile devices to track movement patterns and infer occupancy levels. Human mobility data can be useful in this context as it provides a way to monitor occupancy patterns without relying on traditional sensors or manual data collection methods. We have conducted extensive experiments using data from six buildings to test the efficacy of these approaches. However, deploying these methods in the real world presents several challenges.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 873,
+    "label": 25,
+    "text": "Title: An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation\nAbstract: Pitch and meter are two fundamental music features for symbolic music generation tasks, where researchers usually choose different encoding methods depending on specific goals. However, the advantages and drawbacks of different encoding methods have not been frequently discussed. This paper presents a integrated analysis of the influence of two low-level feature, pitch and meter, on the performance of a token-based sequential music generation model. First, the commonly used MIDI number encoding and a less used class-octave encoding are compared. Second, an dense intra-bar metric grid is imposed to the encoded sequence as auxiliary features. Different complexity and resolutions of the metric grid are compared. For complexity, the single token approach and the multiple token approach are compared; for grid resolution, 0 (ablation), 1 (bar-level), 4 (downbeat-level) 12, (8th-triplet-level) up to 64 (64th-note-grid-level) are compared; for duration resolution, 4, 8, 12 and 16 subdivisions per beat are compared. All different encodings are tested on separately trained Transformer-XL models for a melody generation task. Regarding distribution similarity of several objective evaluation metrics to the test dataset, results suggest that the class-octave encoding significantly outperforms the taken-for-granted MIDI encoding on pitch-related metrics; finer grids and multiple-token grids improve the rhythmic quality, but also suffer from over-fitting at early training stage. Results display a general phenomenon of over-fitting from two aspects, the pitch embedding space and the test loss of the single-token grid encoding. From a practical perspective, we both demonstrate the feasibility and raise the concern of easy over-fitting problem of using smaller networks and lower embedding dimensions on the generation task. The findings can also contribute to futural models in terms of feature engineering.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 874,
+    "label": 30,
+    "text": "Title: Rethinking Translation Memory Augmented Neural Machine Translation\nAbstract: This paper rethinks translation memory augmented neural machine translation (TM-augmented NMT) from two perspectives, i.e., a probabilistic view of retrieval and the variance-bias decomposition principle. The finding demonstrates that TM-augmented NMT is good at the ability of fitting data (i.e., lower bias) but is more sensitive to the fluctuations in the training data (i.e., higher variance), which provides an explanation to a recently reported contradictory phenomenon on the same translation task: TM-augmented NMT substantially advances vanilla NMT under the high-resource scenario whereas it fails under the low-resource scenario. Then we propose a simple yet effective TM-augmented NMT model to promote the variance and address the contradictory phenomenon. Extensive experiments show that the proposed TM-augmented NMT achieves consistent gains over both conventional NMT and existing TM-augmented NMT under two variance-preferable (low-resource and plug-and-play) scenarios as well as the high-resource scenario.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 875,
+    "label": 34,
+    "text": "Title: Faster 0-1-Knapsack via Near-Convex Min-Plus-Convolution\nAbstract: We revisit the classic 0-1-Knapsack problem, in which we are given $n$ items with their weights and profits as well as a weight budget $W$, and the goal is to find a subset of items of total weight at most $W$ that maximizes the total profit. We study pseudopolynomial-time algorithms parameterized by the largest profit of any item $p_{\\max}$, and the largest weight of any item $w_{\\max}$. Our main result are algorithms for 0-1-Knapsack running in time $\\tilde{O}(n\\,w_\\max\\,p_\\max^{2/3})$ and $\\tilde{O}(n\\,p_\\max\\,w_\\max^{2/3})$, improving upon an algorithm in time $O(n\\,p_\\max\\,w_\\max)$ by Pisinger [J. Algorithms '99]. In the regime $p_\\max \\approx w_\\max \\approx n$ (and $W \\approx \\mathrm{OPT} \\approx n^2$) our algorithms are the first to break the cubic barrier $n^3$. To obtain our result, we give an efficient algorithm to compute the min-plus convolution of near-convex functions. More precisely, we say that a function $f \\colon [n] \\mapsto \\mathbf{Z}$ is $\\Delta$-near convex with $\\Delta \\geq 1$, if there is a convex function $\\breve{f}$ such that $\\breve{f}(i) \\leq f(i) \\leq \\breve{f}(i) + \\Delta$ for every $i$. We design an algorithm computing the min-plus convolution of two $\\Delta$-near convex functions in time $\\tilde{O}(n\\Delta)$. This tool can replace the usage of the prediction technique of Bateni, Hajiaghayi, Seddighin and Stein [STOC '18] in all applications we are aware of, and we believe it has wider applicability.",
+    "neighbors": [
+      948,
+      1511,
+      1518,
+      1734
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 876,
+    "label": 26,
+    "text": "Title: Opinion Optimization in Directed Social Networks\nAbstract: Shifting social opinions has far-reaching implications in various aspects, such as public health campaigns, product marketing, and political candidates. In this paper, we study a problem of opinion optimization based on the popular Friedkin-Johnsen (FJ) model for opinion dynamics in an unweighted directed social network with n nodes and m edges. In the FJ model, the internal opinion of every node lies in the closed interval [0, 1], with 0 and 1 being polar opposites of opinions about a certain issue. Concretely, we focus on the problem of selecting a small number of k",
+    "neighbors": [
+      728
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 877,
+    "label": 31,
+    "text": "Title: Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization\nAbstract: Counterfactual learning to rank (CLTR) relies on exposure-based inverse propensity scoring (IPS), a LTR-specific adaptation of IPS to correct for position bias. While IPS can provide unbiased and consistent estimates, it often suffers from high variance. Especially when little click data is available, this variance can cause CLTR to learn sub-optimal ranking behavior. Consequently, existing CLTR methods bring significant risks with them, as naively deploying their models can result in very negative user experiences. We introduce a novel risk-aware CLTR method with theoretical guarantees for safe deployment. We apply a novel exposure-based concept of risk regularization to IPS estimation for LTR. Our risk regularization penalizes the mismatch between the ranking behavior of a learned model and a given safe model. Thereby, it ensures that learned ranking models stay close to a trusted model, when there is high uncertainty in IPS estimation, which greatly reduces the risks during deployment. Our experimental results demonstrate the efficacy of our proposed method, which is effective at avoiding initial periods of bad performance when little date is available, while also maintaining high performance at convergence. For the CLTR field, our novel exposure-based risk minimization method enables practitioners to adopt CLTR methods in a safer manner that mitigates many of the risks attached to previous methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 878,
+    "label": 27,
+    "text": "Title: The Bridge between Xsens Motion-Capture and Robot Operating System (ROS): Enabling Robots with Online 3D Human Motion Tracking\nAbstract: This document introduces the bridge between the leading inertial motion-capture systems for 3D human tracking and the most used robotics software framework. 3D kinematic data provided by Xsens are translated into ROS messages to make them usable by robots and a Unified Robotics Description Format (URDF) model of the human kinematics is generated, which can be run and displayed in ROS 3D visualizer, RViz. The code to implement the to-ROS-bridge is a ROS package called xsens_mvn_ros and is available on GitHub at https://github.com/hrii-iit/xsens_mvn_ros The main documentation can be found at https://hrii-iit.github.io/xsens_mvn_ros/index.html",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 879,
+    "label": 2,
+    "text": "Title: Multiteam semantics for interventionist counterfactuals: probabilities and causation\nAbstract: In [4], we introduced an extension of team semantics (causal teams) which assigns an interpretation to interventionist counterfactuals and causal notions based on them (as e.g. in Pearl's and Woodward's manipulationist approaches to causation). We now present a further extension of this framework (causal multiteams) which allows us to talk about probabilistic causal statements. We analyze the expressivity resources of two causal-probabilistic languages, one finitary and one infinitary. We show that many causal-probabilistic notions from the field of causal inference can be expressed already in the finitary language, and we prove a normal form theorem that throws new light on Pearl's ``ladder of causation''. On the other hand, we provide an exact semantic characterization of the infinitary language, which shows that this language captures precisely those causal-probabilistic statements that do not commit us to any specific interpretation of probability; and we prove that no usual, countable language is apt for this task.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 880,
+    "label": 16,
+    "text": "Title: EVA-02: A Visual Representation for Neon Genesis\nAbstract: We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling. With an updated plain Transformer architecture as well as extensive pre-training from an open&accessible giant CLIP vision encoder, EVA-02 demonstrates superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets. Notably, using exclusively publicly accessible training data, EVA-02 with only 304M parameters achieves a phenomenal 90.0 fine-tuning top-1 accuracy on ImageNet-1K val set. Additionally, our EVA-02-CLIP can reach up to 80.4 zero-shot top-1 on ImageNet-1K, outperforming the previous largest&best open-sourced CLIP with only ~1/6 parameters and ~1/6 image-text training data. We offer four EVA-02 variants in various model sizes, ranging from 6M to 304M parameters, all with impressive performance. To facilitate open access and open research, we release the complete suite of EVA-02 to the community at https://github.com/baaivision/EVA/tree/master/EVA-02.",
+    "neighbors": [
+      176,
+      719,
+      887,
+      1526,
+      1750,
+      2002,
+      2093
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 881,
+    "label": 23,
+    "text": "Title: Data Architecture for Digital Object Space Management Service (DOSM) using DAT\nAbstract: The Internet of Things (IoT) data and social media data are two of the fastest-growing data segments. Having high-quality data is crucial for making informed business decisions. The strategic process of leveraging insights from data is known as data-driven decision-making. To achieve this, it is necessary to collect, store, analyze, and protect data in the best ways possible. Data architecture is a complex task that involves describing the flow of data from its source to its destination and creating a blueprint for managing the data to meet business needs for information. In this paper, we utilize the Data Architecture Tool (DAT) to model data for Digital Space Management Service, which was developed as part of the VASARI project. This work focuses on describing the movement of data, data formats, data location, data processing (batch or real-time), data storage technologies, and main operations on the data.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 882,
+    "label": 4,
+    "text": "Title: Bug Hunters' Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem\nAbstract: Although researchers have characterized the bug-bounty ecosystem from the point of view of platforms and programs, minimal effort has been made to understand the perspectives of the main workers: bug hunters. To improve bug bounties, it is important to understand hunters' motivating factors, challenges, and overall benefits. We address this research gap with three studies: identifying key factors through a free listing survey (n=56), rating each factor's importance with a larger-scale factor-rating survey (n=159), and conducting semi-structured interviews to uncover details (n=24). Of 54 factors that bug hunters listed, we find that rewards and learning opportunities are the most important benefits. Further, we find scope to be the top differentiator between programs. Surprisingly, we find earning reputation to be one of the least important motivators for hunters. Of the challenges we identify, communication problems, such as unresponsiveness and disputes, are the most substantial. We present recommendations to make the bug-bounty ecosystem accommodating to more bug hunters and ultimately increase participation in an underutilized market.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 883,
+    "label": 30,
+    "text": "Title: Affective Natural Language Generation of Event Descriptions through Fine-grained Appraisal Conditions\nAbstract: Models for affective text generation have shown a remarkable progress, but they commonly rely only on basic emotion theories or valance/arousal values as conditions. This is appropriate when the goal is to create explicit emotion statements (\"The kid is happy.\"). Emotions are, however, commonly communicated implicitly. For instance, the emotional interpretation of an event (\"Their dog died.\") does often not require an explicit emotion statement. In psychology, appraisal theories explain the link between a cognitive evaluation of an event and the potentially developed emotion. They put the assessment of the situation on the spot, for instance regarding the own control or the responsibility for what happens. We hypothesize and subsequently show that including appraisal variables as conditions in a generation framework comes with two advantages. (1) The generation model is informed in greater detail about what makes a specific emotion and what properties it has. This leads to text generation that better fulfills the condition. (2) The variables of appraisal allow a user to perform a more fine-grained control of the generated text, by stating properties of a situation instead of only providing the emotion category. Our Bart and T5-based experiments with 7 emotions (Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame), and 7 appraisals (Attention, Responsibility, Control, Circumstance, Pleasantness, Effort, Certainty) show that (1) adding appraisals during training improves the accurateness of the generated texts by 10 pp in F1. Further, (2) the texts with appraisal variables are longer and contain more details. This exemplifies the greater control for users.",
+    "neighbors": [
+      893
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 884,
+    "label": 27,
+    "text": "Title: Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots\nAbstract: Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. Obtaining such 3D information is non-trivial as agricultural environments are often repetitive and cluttered, and one has to account for the partial observability of fruit and plants. In this paper, we address the problem of jointly estimating complete 3D shapes of fruit and their pose in a 3D multi-resolution map built by a mobile robot. To this end, we propose an online multi-resolution panoptic mapping system where regions of interest are represented with a higher resolution. We exploit data to learn a general fruit shape representation that we use at inference time together with an occlusion-aware differentiable rendering pipeline to complete partial fruit observations and estimate the 7 DoF pose of each fruit in the map. The experiments presented in this paper evaluated both in the controlled environment and in a commercial greenhouse, show that our novel algorithm yields higher completion and pose estimation accuracy than existing methods, with an improvement of 41% in completion accuracy and 52% in pose estimation accuracy while keeping a low inference time of 0.6s in average. Codes are available at: https://github.com/PRBonn/HortiMapping.",
+    "neighbors": [
+      588
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 885,
+    "label": 27,
+    "text": "Title: Vehicle Teleoperation: Performance Assessment of SRPT Approach Under State Estimation Errors\nAbstract: Vehicle teleoperation has numerous potential applications, including serving as a backup solution for autonomous vehicles, facilitating remote delivery services, and enabling hazardous remote operations. However, complex urban scenarios, limited situational awareness, and network delay increase the cognitive workload of human operators and degrade teleoperation performance. To address this, the successive reference pose tracking (SRPT) approach was introduced in earlier work, which transmits successive reference poses to the remote vehicle instead of steering commands. The operator generates reference poses online with the help of a joystick steering and an augmented display, potentially mitigating the detrimental effects of delays. However, it is not clear which minimal set of sensors is essential for the SRPT vehicle teleoperation control loop. This paper tests the robustness of the SRPT approach in the presence of state estimation inaccuracies, environmental disturbances, and measurement noises. The simulation environment, implemented in Simulink, features a 14-dof vehicle model and incorporates difficult maneuvers such as tight corners, double-lane changes, and slalom. Environmental disturbances include low adhesion track regions and strong cross-wind gusts. The results demonstrate that the SRPT approach, using either estimated or actual states, performs similarly under various worst-case scenarios, even without a position sensor requirement. Additionally, the designed state estimator ensures sufficient performance with just an inertial measurement unit, wheel speed encoder, and steer encoder, constituting a minimal set of essential sensors for the SRPT vehicle teleoperation control loop.",
+    "neighbors": [
+      1198
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 886,
+    "label": 16,
+    "text": "Title: Lateralized Learning for Multi-Class Visual Classification Tasks\nAbstract: \u2014The majority of computer vision algorithms fail to \ufb01nd higher-order (abstract) patterns in an image so are not robust against adversarial attacks, unlike human lateralized vision. Deep learning considers each input pixel in a homogeneous manner such that different parts of a \u201clocality-sensitive hashing table\u201d are often not connected, meaning higher-order patterns are not discovered. Hence these systems are not robust against noisy, irrelevant, and redundant data, resulting in the wrong prediction being made with high con\ufb01dence. Conversely, vertebrate brains afford heterogeneous knowledge representation through lateralization, enabling modular learning at different levels of abstrac- tion. This work aims to verify the effectiveness, scalability, and robustness of a lateralized approach to real-world problems that contain noisy, irrelevant, and redundant data. The experimental results of multi-class ( 200 classes) image classi\ufb01cation show that the novel system effectively learns knowledge representation at multiple levels of abstraction making it more robust than other state-of-the-art techniques. Crucially, the novel lateralized system outperformed all the state-of-the-art deep learning-based systems for the classi\ufb01cation of normal and adversarial images by 19 . 05% \u2212 41 . 02% and 1 . 36% \u2212 49 . 22% , respectively. Findings demonstrate the value of heterogeneous and lateralized learning for computer vision applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 887,
+    "label": 16,
+    "text": "Title: BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models\nAbstract: The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. BLIP-2 bridges the modality gap with a lightweight Querying Transformer, which is pre-trained in two stages. The first stage bootstraps vision-language representation learning from a frozen image encoder. The second stage bootstraps vision-to-language generative learning from a frozen language model. BLIP-2 achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. For example, our model outperforms Flamingo80B by 8.7% on zero-shot VQAv2 with 54x fewer trainable parameters. We also demonstrate the model's emerging capabilities of zero-shot image-to-text generation that can follow natural language instructions.",
+    "neighbors": [
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+      1467,
+      1481,
+      1485,
+      1526,
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+      2155,
+      2216,
+      2221,
+      2226,
+      2243,
+      2274
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 888,
+    "label": 10,
+    "text": "Title: A Convex Hull Cheapest Insertion Heuristic for the Non-Euclidean and Precedence Constrained TSPs\nAbstract: The convex hull cheapest insertion heuristic is known to generate good solutions to the Euclidean Traveling Salesperson Problem. This paper presents an adaptation of this heuristic to the non-Euclidean version of the problem and further extends it to the problem with precedence constraints, also known as the Sequential Ordering Problem. To test the proposed algorithm, the well-known TSPLIB benchmark data-set is modified in a replicable manner to create non-Euclidean instances and precedence constraints. The proposed algorithm is shown to outperform the commonly used Nearest Neighbor algorithm in 97% of the cases that do not have precedence constraints. When precedence constraints exist such that the child nodes are centrally located, the algorithm again outperforms the Nearest Neighbor algorithm in 98% of the studied instances. Considering all spatial layouts of precedence constraints, the algorithm outperforms the Nearest Neighbor heuristic 68% of the time.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 889,
+    "label": 26,
+    "text": "Title: Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision\nAbstract: Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals\u2019 stances. We analyze users\u2019 attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 890,
+    "label": 24,
+    "text": "Title: Connectivity Optimized Nested Graph Networks for Crystal Structures\nAbstract: Graph neural networks (GNNs) have been applied to a large variety of applications in materials science and chemistry. Here, we recapitulate the graph construction for crystalline (periodic) materials and investigate its impact on the GNNs model performance. We suggest the asymmetric unit cell as a representation to reduce the number of atoms by using all symmetries of the system. This substantially reduced the computational cost and thus time needed to train large graph neural networks without any loss in accuracy. Furthermore, with a simple but systematically built GNN architecture based on message passing and line graph templates, we introduce a general architecture (Nested Graph Network, NGN) that is applicable to a wide range of tasks. We show that our suggested models systematically improve state-of-the-art results across all tasks within the MatBench benchmark. Further analysis shows that optimized connectivity and deeper message functions are responsible for the improvement. Asymmetric unit cells and connectivity optimization can be generally applied to (crystal) graph networks, while our suggested nested graph framework will open new ways of systematic comparison of GNN architectures.",
+    "neighbors": [
+      843
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 891,
+    "label": 30,
+    "text": "Title: LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles\nAbstract: With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities. But can they think out of the box? Do they possess proficient lateral thinking abilities? Following the setup of Lateral Thinking Puzzles, we propose a novel evaluation benchmark, LatEval, which assesses the model's lateral thinking within an interactive framework. In our benchmark, we challenge LLMs with 2 aspects: the quality of questions posed by the model and the model's capability to integrate information for problem-solving. We find that nearly all LLMs struggle with employing lateral thinking during interactions. For example, even the most advanced model, GPT-4, exhibits the advantage to some extent, yet still maintain a noticeable gap when compared to human. This evaluation benchmark provides LLMs with a highly challenging and distinctive task that is crucial to an effective AI assistant.",
+    "neighbors": [
+      143,
+      1659,
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+      1943,
+      1952,
+      2113,
+      2289
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 892,
+    "label": 16,
+    "text": "Title: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD\nAbstract: In this technical report, we briefly introduce our solution for the Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. For industrial visual inspection, building a single model that can be rapidly adapted to numerous categories without or with only a few normal reference images is a promising research direction. This is primarily because of the vast variety of the product types. For the zero-shot track, we propose a solution based on the CLIP model by adding extra linear layers. These layers are used to map the image features to the joint embedding space, so that they can compare with the text features to generate the anomaly maps. Besides, when the reference images are available, we utilize multiple memory banks to store their features and compare them with the features of the test images during the testing phase. In this challenge, our method achieved first place in the zero-shot track, especially excelling in segmentation with an impressive F1 score improvement of 0.0489 over the second-ranked participant. Furthermore, in the few-shot track, we secured the fourth position overall, with our classification F1 score of 0.8687 ranking first among all participating teams.",
+    "neighbors": [
+      2001,
+      2098
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 893,
+    "label": 30,
+    "text": "Title: Emotion-Conditioned Text Generation through Automatic Prompt Optimization\nAbstract: Conditional natural language generation methods often require either expensive fine-tuning or training a large language model from scratch. Both are unlikely to lead to good results without a substantial amount of data and computational resources. Prompt learning without changing the parameters of a large language model presents a promising alternative. It is a cost-effective approach, while still achieving competitive results. While this procedure is now established for zero- and few-shot text classification and structured prediction, it has received limited attention in conditional text generation. We present the first automatic prompt optimization approach for emotion-conditioned text generation with instruction-fine-tuned models. Our method uses an iterative optimization procedure that changes the prompt by adding, removing, or replacing tokens. As objective function, we only require a text classifier that measures the realization of the conditional variable in the generated text. We evaluate the method on emotion-conditioned text generation with a focus on event reports and compare it to manually designed prompts that also act as the seed for the optimization procedure. The optimized prompts achieve 0.75 macro-average F1 to fulfill the emotion condition in contrast to manually designed seed prompts with only 0.22 macro-average F1.",
+    "neighbors": [
+      883
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 894,
+    "label": 10,
+    "text": "Title: Generalization-based similarity\nAbstract: Detecting and exploiting similarities between seemingly distant objects is at the core of analogical reasoning which itself is at the core of artificial intelligence. This paper develops {\\em from the ground up} an abstract algebraic and {\\em qualitative} notion of similarity based on the observation that sets of generalizations encode important properties of elements. We show that similarity defined in this way has appealing mathematical properties. As we construct our notion of similarity from first principles using only elementary concepts of universal algebra, to convince the reader of its plausibility, we show that it can be naturally embedded into first-order logic via model-theoretic types. In a broader sense, this paper is a further step towards a mathematical theory of analogical reasoning.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 895,
+    "label": 2,
+    "text": "Title: Model Checking Disjoint-Paths Logic on Topological-Minor-Free Graph Classes\nAbstract: Disjoint-paths logic, denoted $\\mathsf{FO}$+$\\mathsf{DP}$, extends first-order logic ($\\mathsf{FO}$) with atomic predicates $\\mathsf{dp}_k[(x_1,y_1),\\ldots,(x_k,y_k)]$, expressing the existence of internally vertex-disjoint paths between $x_i$ and $y_i$, for $1\\leq i\\leq k$. We prove that for every graph class excluding some fixed graph as a topological minor, the model checking problem for $\\mathsf{FO}$+$\\mathsf{DP}$ is fixed-parameter tractable. This essentially settles the question of tractable model checking for this logic on subgraph-closed classes, since the problem is hard on subgraph-closed classes not excluding a topological minor (assuming a further mild condition of efficiency of encoding).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 896,
+    "label": 30,
+    "text": "Title: Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and Vulnerabilities\nAbstract: Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs, including in the context of potentially criminal activities. Specifically, it has been shown that LLMs can be misused for fraud, impersonation, and the generation of malware; while other authors have considered the more general problem of AI alignment. It is important that developers and practitioners alike are aware of security-related problems with such models. In this paper, we provide an overview of existing - predominantly scientific - efforts on identifying and mitigating threats and vulnerabilities arising from LLMs. We present a taxonomy describing the relationship between threats caused by the generative capabilities of LLMs, prevention measures intended to address such threats, and vulnerabilities arising from imperfect prevention measures. With our work, we hope to raise awareness of the limitations of LLMs in light of such security concerns, among both experienced developers and novel users of such technologies.",
+    "neighbors": [
+      27,
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+      899,
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+      1237,
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+      1430,
+      1487,
+      1600,
+      1713,
+      1733,
+      1735,
+      1863,
+      2044,
+      2155,
+      2158,
+      2235,
+      2279,
+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 897,
+    "label": 24,
+    "text": "Title: A Comprehensive Survey of Continual Learning: Theory, Method and Application\nAbstract: To cope with real-world dynamics, an intelligent agent needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance degradation of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.",
+    "neighbors": [
+      634,
+      719,
+      1115,
+      1863,
+      2016,
+      2029
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 898,
+    "label": 16,
+    "text": "Title: Robust Monocular Depth Estimation under Challenging Conditions\nAbstract: While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain. In this paper, we uncover these safety-critical issues and tackle them with md4all: a simple and effective solution that works reliably under both adverse and ideal conditions, as well as for different types of learning supervision. We achieve this by exploiting the efficacy of existing methods under perfect settings. Therefore, we provide valid training signals independently of what is in the input. First, we generate a set of complex samples corresponding to the normal training ones. Then, we train the model by guiding its self- or full-supervision by feeding the generated samples and computing the standard losses on the corresponding original images. Doing so enables a single model to recover information across diverse conditions without modifications at inference time. Extensive experiments on two challenging public datasets, namely nuScenes and Oxford RobotCar, demonstrate the effectiveness of our techniques, outperforming prior works by a large margin in both standard and challenging conditions. Source code and data are available at: https://md4all.github.io.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 899,
+    "label": 24,
+    "text": "Title: Jailbroken: How Does LLM Safety Training Fail?\nAbstract: Large language models trained for safety and harmlessness remain susceptible to adversarial misuse, as evidenced by the prevalence of\"jailbreak\"attacks on early releases of ChatGPT that elicit undesired behavior. Going beyond recognition of the issue, we investigate why such attacks succeed and how they can be created. We hypothesize two failure modes of safety training: competing objectives and mismatched generalization. Competing objectives arise when a model's capabilities and safety goals conflict, while mismatched generalization occurs when safety training fails to generalize to a domain for which capabilities exist. We use these failure modes to guide jailbreak design and then evaluate state-of-the-art models, including OpenAI's GPT-4 and Anthropic's Claude v1.3, against both existing and newly designed attacks. We find that vulnerabilities persist despite the extensive red-teaming and safety-training efforts behind these models. Notably, new attacks utilizing our failure modes succeed on every prompt in a collection of unsafe requests from the models' red-teaming evaluation sets and outperform existing ad hoc jailbreaks. Our analysis emphasizes the need for safety-capability parity -- that safety mechanisms should be as sophisticated as the underlying model -- and argues against the idea that scaling alone can resolve these safety failure modes.",
+    "neighbors": [
+      430,
+      855,
+      896,
+      1237,
+      1305,
+      1307,
+      1863,
+      2158,
+      2305
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 900,
+    "label": 26,
+    "text": "Title: Mixbiotic society measures: Assessment of community well-going as living system\nAbstract: Social isolation is caused by the impoverishment of community (atomism) and fragmentation is caused by the enlargement of in-group (mobism), both of which can be viewed as social problems related to communication. To solve these problems, the philosophical world has proposed the concept of\"mixbiotic society,\"in which individuals with freedom and diverse values mix and mingle to recognize their respective\"fundamental incapability\"each other and sublimate into solidarity. Based on this concept, this study proposes new mixbiotic society measures to evaluate dynamic communication patterns with reference to classification in cellular automata and particle reaction diffusion that simulate living phenomena. Specifically, the hypothesis of measures corresponding to the four classes was formulated, and the hypothesis was validated by simulating the generation and disappearance of communication. As a result, considering communication patterns as multidimensional vectors, it found that the mean of Euclidean distance for\"mobism,\"the variance of the relative change in distance for\"atomism,\"the composite measure that multiplies the mean and variance of cosine similarity for\"mixism,\"which corresponds to the well-going of mixbiotic society, and the almost zero measures for\"nihilism,\"are suitable. Then, evaluating seven real-society datasets using these measures, we showed that the mixism measure is useful for assessing the livingness of communication, and that it is possible to typify communities based on plural measures. The measures established in this study are superior to conventional analysis in that they can evaluate dynamic patterns, they are simple to calculate, and their meanings are easy to interpret. As a future development, the mixbiotic society measures will be used in the fields of digital democracy and platform cooperativism toward a desirable society.",
+    "neighbors": [
+      920
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 901,
+    "label": 34,
+    "text": "Title: Faster Deterministic Distributed MIS and Approximate Matching\nAbstract: We present an \u00d5(log2 n) round deterministic distributed algorithm for the maximal independent set problem. By known reductions, this round complexity extends also to maximal matching, \u0394+1 vertex coloring, and 2\u0394\u22121 edge coloring. These four problems are among the most central problems in distributed graph algorithms and have been studied extensively for the past four decades. This improved round complexity comes closer to the \u03a9(logn) lower bound of maximal independent set and maximal matching [Balliu et al. FOCS \u201919]. The previous best known deterministic complexity for all of these problems was \u0398(log3 n). Via the shattering technique, the improvement permeates also to the corresponding randomized complexities, e.g., the new randomized complexity of \u0394+1 vertex coloring is now \u00d5(log2logn) rounds. Our approach is a novel combination of the previously known (and seemingly orthogonal) two methods for developing fast deterministic algorithms for these problems, namely global derandomization via network decomposition (see e.g., [Rozhon, Ghaffari STOC\u201920; Ghaffari, Grunau, Rozhon SODA\u201921; Ghaffari et al. SODA\u201923]) and local rounding of fractional solutions (see e.g., [Fischer DISC\u201917; Harris FOCS\u201919; Fischer, Ghaffari, Kuhn FOCS\u201917; Ghaffari, Kuhn FOCS\u201921; Faour et al. SODA\u201923]). We consider a relaxation of the classic network decomposition concept, where instead of requiring the clusters in the same block to be non-adjacent, we allow each node to have a small number of neighboring clusters. We also show a deterministic algorithm that computes this relaxed decomposition faster than standard decompositions. We then use this relaxed decomposition to significantly improve the integrality of certain fractional solutions, before handing them to the local rounding procedure that now has to do fewer rounding steps.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 902,
+    "label": 16,
+    "text": "Title: Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions\nAbstract: Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment. Although there have been recent advances, generating detailed and enriched video descriptions continues to be a substantial challenge. In this work, we introduce Video ChatCaptioner, an innovative approach for creating more comprehensive spatiotemporal video descriptions. Our method employs a ChatGPT model as a controller, specifically designed to select frames for posing video content-driven questions. Subsequently, a robust algorithm is utilized to answer these visual queries. This question-answer framework effectively uncovers intricate video details and shows promise as a method for enhancing video content. Following multiple conversational rounds, ChatGPT can summarize enriched video content based on previous conversations. We qualitatively demonstrate that our Video ChatCaptioner can generate captions containing more visual details about the videos. The code is publicly available at https://github.com/Vision-CAIR/ChatCaptioner",
+    "neighbors": [
+      57,
+      173,
+      319,
+      719,
+      818,
+      887,
+      1626,
+      1765,
+      1893,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 903,
+    "label": 24,
+    "text": "Title: Bayesian Exploration Networks\nAbstract: Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an exploration/exploitation dilemma, a major pathology of frequentist methods. A key challenge for Bayesian RL is the computational complexity of learning Bayes-optimal policies, which is only tractable in toy domains. In this paper we propose a novel model-free approach to address this challenge. Rather than modelling uncertainty in high-dimensional state transition distributions as model-based approaches do, we model uncertainty in a one-dimensional Bellman operator. Our theoretical analysis reveals that existing model-free approaches either do not propagate epistemic uncertainty through the MDP or optimise over a set of contextual policies instead of all history-conditioned policies. Both approximations yield policies that can be arbitrarily Bayes-suboptimal. To overcome these issues, we introduce the Bayesian exploration network (BEN) which uses normalising flows to model both the aleatoric uncertainty (via density estimation) and epistemic uncertainty (via variational inference) in the Bellman operator. In the limit of complete optimisation, BEN learns true Bayes-optimal policies, but like in variational expectation-maximisation, partial optimisation renders our approach tractable. Empirical results demonstrate that BEN can learn true Bayes-optimal policies in tasks where existing model-free approaches fail.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 904,
+    "label": 27,
+    "text": "Title: Growing Convex Collision-Free Regions in Configuration Space using Nonlinear Programming\nAbstract: One of the most difficult parts of motion planning in configuration space is ensuring a trajectory does not collide with task-space obstacles in the environment. Generating regions that are convex and collision free in configuration space can separate the computational burden of collision checking from motion planning. To that end, we propose an extension to IRIS (Iterative Regional Inflation by Semidefinite programming) [5] that allows it to operate in configuration space. Our algorithm, IRIS-NP (Iterative Regional Inflation by Semidefinite&Nonlinear Programming), uses nonlinear optimization to add the separating hyperplanes, enabling support for more general nonlinear constraints. Developed in parallel to Amice et al. [1], IRIS-NP trades rigorous certification that regions are collision free for probabilistic certification and the benefit of faster region generation in the configuration-space coordinates. IRIS-NP also provides a solid initialization to C-IRIS to reduce the number of iterations required for certification. We demonstrate that IRIS-NP can scale to a dual-arm manipulator and can handle additional nonlinear constraints using the same machinery. Finally, we show ablations of elements of our implementation to demonstrate their importance.",
+    "neighbors": [
+      1826
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 905,
+    "label": 6,
+    "text": "Title: Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity\nAbstract: Understanding the social context of eating is crucial for promoting healthy eating behaviors. Multimodal smartphone sensor data could provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health apps. However, research on the social context of eating with smartphone sensor data is limited, despite extensive studies in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, our study focuses on a dataset of approximately 24K self-reports on eating events provided by 678 college students in eight countries to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique trends in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings emphasize the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 906,
+    "label": 27,
+    "text": "Title: DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry\nAbstract: With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.",
+    "neighbors": [
+      1829
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 907,
+    "label": 16,
+    "text": "Title: On the Hidden Mystery of OCR in Large Multimodal Models\nAbstract: Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. It remains less explored about their efficacy in text-related visual tasks. We conducted a comprehensive study of existing publicly available multimodal models, evaluating their performance in text recognition (document text, artistic text, handwritten text, scene text), text-based visual question answering (document text, scene text, and bilingual text), key information extraction (receipts, documents, and nutrition facts) and handwritten mathematical expression recognition. Our findings reveal strengths and weaknesses in these models, which primarily rely on semantic understanding for word recognition and exhibit inferior perception of individual character shapes. They also display indifference towards text length and have limited capabilities in detecting finegrained features in images. Consequently, these results demonstrate that even the current most powerful large multimodal models cannot match domain-specific methods in traditional text tasks and face greater challenges in more complex tasks. Most importantly, the baseline results showcased in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal techniques. Evaluation pipeline is available at https://github.com/Yuliang-Liu/MultimodalOCR.",
+    "neighbors": [
+      602,
+      887,
+      983,
+      1007,
+      1027,
+      1039,
+      1047,
+      1052,
+      1344,
+      1520,
+      1755,
+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 908,
+    "label": 16,
+    "text": "Title: Diffusion Self-Guidance for Controllable Image Generation\nAbstract: Large-scale generative models are capable of producing high-quality images from detailed text descriptions. However, many aspects of an image are difficult or impossible to convey through text. We introduce self-guidance, a method that provides greater control over generated images by guiding the internal representations of diffusion models. We demonstrate that properties such as the shape, location, and appearance of objects can be extracted from these representations and used to steer sampling. Self-guidance works similarly to classifier guidance, but uses signals present in the pretrained model itself, requiring no additional models or training. We show how a simple set of properties can be composed to perform challenging image manipulations, such as modifying the position or size of objects, merging the appearance of objects in one image with the layout of another, composing objects from many images into one, and more. We also show that self-guidance can be used to edit real images. For results and an interactive demo, see our project page at https://dave.ml/selfguidance/",
+    "neighbors": [
+      273,
+      736,
+      1902,
+      1978
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 909,
+    "label": 2,
+    "text": "Title: The Produoidal Algebra of Process Decomposition\nAbstract: We introduce the normal produoidal category of monoidal contexts over an arbitrary monoidal category. In the same sense that a monoidal morphism represents a process, a monoidal context represents an incomplete process: a piece of a decomposition, possibly containing missing parts. We characterize monoidal contexts in terms of universal properties. In particular, symmetric monoidal contexts coincide with monoidal lenses, endowing them with a novel universal property. We apply this algebraic structure to the analysis of multi-party interaction protocols in arbitrary theories of processes.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 910,
+    "label": 5,
+    "text": "Title: Backporting RISC-V Vector assembly\nAbstract: Leveraging vectorisation, the ability for a CPU to apply operations to multiple elements of data concurrently, is critical for high performance workloads. However, at the time of writing, commercially available physical RISC-V hardware that provides the RISC-V vector extension (RVV) only supports version 0.7.1, which is incompatible with the latest ratified version 1.0. The challenge is that upstream compiler toolchains, such as Clang, only target the ratified v1.0 and do not support the older v0.7.1. Because v1.0 is not compatible with v0.7.1, the only way to program vectorised code is to use a vendor-provided, older compiler. In this paper we introduce the rvv-rollback tool which translates assembly code generated by the compiler using vector extension v1.0 instructions to v0.7.1. We utilise this tool to compare vectorisation performance of the vendor-provided GNU 8.4 compiler (supports v0.7.1) against LLVM 15.0 (supports only v1.0), where we found that the LLVM compiler is capable of auto-vectorising more computational kernels, and delivers greater performance than GNU in most, but not all, cases. We also tested LLVM vectorisation with vector length agnostic and specific settings, and observed cases with significant difference in performance.",
+    "neighbors": [
+      1134,
+      1885
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 911,
+    "label": 30,
+    "text": "Title: Sentence-to-Label Generation Framework for Multi-task Learning of Japanese Sentence Classification and Named Entity Recognition\nAbstract: nan",
+    "neighbors": [
+      1892
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 912,
+    "label": 30,
+    "text": "Title: A Weakly Supervised Classifier and Dataset of White Supremacist Language\nAbstract: We present a dataset and classifier for detecting the language of white supremacist extremism, a growing issue in online hate speech. Our weakly supervised classifier is trained on large datasets of text from explicitly white supremacist domains paired with neutral and anti-racist data from similar domains. We demonstrate that this approach improves generalization performance to new domains. Incorporating anti-racist texts as counterexamples to white supremacist language mitigates bias.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 913,
+    "label": 38,
+    "text": "Title: Ranking mobility and impact inequality in early academic careers\nAbstract: How difficult is it for an early career academic to climb the ranks of their discipline? We tackle this question with a comprehensive bibliometric analysis of 57 disciplines, examining the publications of more than 5 million authors whose careers started between 1986 and 2008. We calibrate a simple random walk model over historical data of ranking mobility, which we use to 1) identify which strata of academic impact rankings are the most/least mobile and 2) study the temporal evolution of mobility. By focusing our analysis on cohorts of authors starting their careers in the same year, we find that ranking mobility is remarkably low for the top- and bottom-ranked authors and that this excess of stability persists throughout the entire period of our analysis. We further observe that mobility of impact rankings has increased over time, and that such rise has been accompanied by a decline of impact inequality, which is consistent with the negative correlation that we observe between such two quantities. These findings provide clarity on the opportunities of new scholars entering the academic community, with implications for academic policymaking.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 914,
+    "label": 3,
+    "text": "Title: Designing Fiduciary Artificial Intelligence\nAbstract: A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article synthesizes recent work in computer science and law to develop a procedure for designing and auditing Fiduciary AI. The designer of a Fiduciary AI should understand the context of the system, identify its principals, and assess the best interests of those principals. Then the designer must be loyal with respect to those interests, and careful in an contextually appropriate way. We connect the steps in this procedure to dimensions of Trustworthy AI, such as privacy and alignment. Fiduciary AI is a promising means to address the incompleteness of data subject's consent when interacting with complex technical systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 915,
+    "label": 36,
+    "text": "Title: Towards a Switching-Algebraic Theory of Weighted Monotone Voting Systems: The Case of Banzhaf Voting Indices\nAbstract: This paper provides a serious attempt towards constructing a switching-algebraic theory for weighted monotone voting systems, whether they are scalar-weighted or vector-weighted. The paper concentrates on the computation of a prominent index of voting powers, viz., the Banzhaf voting index. This computation involves two distinct operations: (a) either Boolean differencing (Boolean differentiation) or Boolean quotient construction (Boolean restriction), and (b) computation of the weight (the number of true vectors or minterms) of a switching function. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of four voting systems. The paper finally outlines further steps needed towards the establishment of a full-fledged switching-algebraic theory of weighted monotone voting systems. Througout the paper, a tutorial flavour is retained, multiple solutions of consistent results are given, and a liasion is established among game-theoretic voting theory, switching algebra, and sytem reliability analysis.",
+    "neighbors": [
+      1919
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 916,
+    "label": 24,
+    "text": "Title: Differentiable Clustering with Perturbed Spanning Forests\nAbstract: We introduce a differentiable clustering method based on minimum-weight spanning forests, a variant of spanning trees with several connected components. Our method relies on stochastic perturbations of solutions of linear programs, for smoothing and efficient gradient computations. This allows us to include clustering in end-to-end trainable pipelines. We show that our method performs well even in difficult settings, such as datasets with high noise and challenging geometries. We also formulate an ad hoc loss to efficiently learn from partial clustering data using this operation. We demonstrate its performance on several real world datasets for supervised and semi-supervised tasks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 917,
+    "label": 24,
+    "text": "Title: Phase Diagram of Initial Condensation for Two-layer Neural Networks\nAbstract: The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research. In this paper, based on the earlier work by Luo et al.~\\cite{luo2021phase}, we present a phase diagram of initial condensation for two-layer neural networks. Condensation is a phenomenon wherein the weight vectors of neural networks concentrate on isolated orientations during the training process, and it is a feature in non-linear learning process that enables neural networks to possess better generalization abilities. Our phase diagram serves to provide a comprehensive understanding of the dynamical regimes of neural networks and their dependence on the choice of hyperparameters related to initialization. Furthermore, we demonstrate in detail the underlying mechanisms by which small initialization leads to condensation at the initial training stage.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 918,
+    "label": 5,
+    "text": "Title: The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey\nAbstract: Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 919,
+    "label": 23,
+    "text": "Title: Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services\nAbstract: As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to 10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience of deploying CMAnomaly in Huawei Cloud.",
+    "neighbors": [
+      14
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 920,
+    "label": 26,
+    "text": "Title: Mixbiotic society measures: Comparison of organizational structures based on communication simulation\nAbstract: The philosophical world has proposed the concept of\"mixbiotic society,\"in which individuals with freedom and diverse values mix and mingle to recognize their respective\"fundamental incapability\"each other and sublimate into solidarity, toward solving the issues of social isolation and fragmentation. Based on this concept, the mixbiotic society measures have been proposed to evaluate dynamic communication patterns with reference to classification in cellular automata and particle reaction-diffusion that simulate living phenomena. In this paper, we applied these measures to five typologies of organizational structure (Red: impulsive, Amber: adaptive, Orange: achievement, Green: pluralistic, and Teal: evolutionary) and evaluated their features. Specifically, we formed star, tree, tree+jumpers, tree+more jumpers, and small-world type networks corresponding to each of five typologies, conducted communication simulations on these networks, and calculated values for mixbiotic society measures. The results showed that Teal organization has the highest value of the mixism measure among mixbiotic society measures, i.e., it balances similarity (mixing) and dissimilarity (mingling) in communication, and is living and mixbiotic between order and chaos. Measures other than mixism showed that in Teal organization, information is not concentrated in a central leader and that communication takes place among various members. This evaluation of organizational structures shows that the mixbiotic society measures is also useful for assessing organizational change. In the future, these measures will be used not only in business organizations, but also in digital democratic organizations and platform cooperatives in conjunction with information technology.",
+    "neighbors": [
+      900
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 921,
+    "label": 24,
+    "text": "Title: Performance of $\\ell_1$ Regularization for Sparse Convex Optimization\nAbstract: Despite widespread adoption in practice, guarantees for the LASSO and Group LASSO are strikingly lacking in settings beyond statistical problems, and these algorithms are usually considered to be a heuristic in the context of sparse convex optimization on deterministic inputs. We give the first recovery guarantees for the Group LASSO for sparse convex optimization with vector-valued features. We show that if a sufficiently large Group LASSO regularization is applied when minimizing a strictly convex function $l$, then the minimizer is a sparse vector supported on vector-valued features with the largest $\\ell_2$ norm of the gradient. Thus, repeating this procedure selects the same set of features as the Orthogonal Matching Pursuit algorithm, which admits recovery guarantees for any function $l$ with restricted strong convexity and smoothness via weak submodularity arguments. This answers open questions of Tibshirani et al. and Yasuda et al. Our result is the first to theoretically explain the empirical success of the Group LASSO for convex functions under general input instances assuming only restricted strong convexity and smoothness. Our result also generalizes provable guarantees for the Sequential Attention algorithm, which is a feature selection algorithm inspired by the attention mechanism proposed by Yasuda et al. As an application of our result, we give new results for the column subset selection problem, which is well-studied when the loss is the Frobenius norm or other entrywise matrix losses. We give the first result for general loss functions for this problem that requires only restricted strong convexity and smoothness.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 922,
+    "label": 5,
+    "text": "Title: In-depth Analysis On Parallel Processing Patterns for High-Performance Dataframes\nAbstract: The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its e fficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon [1]. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.",
+    "neighbors": [
+      365
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 923,
+    "label": 16,
+    "text": "Title: Iterative Adversarial Attack on Image-guided Story Ending Generation\nAbstract: Multimodal learning involves developing models that can integrate information from various sources like images and texts. In this field, multimodal text generation is a crucial aspect that involves processing data from multiple modalities and outputting text. The image-guided story ending generation (IgSEG) is a particularly significant task, targeting on an understanding of complex relationships between text and image data with a complete story text ending. Unfortunately, deep neural networks, which are the backbone of recent IgSEG models, are vulnerable to adversarial samples. Current adversarial attack methods mainly focus on single-modality data and do not analyze adversarial attacks for multimodal text generation tasks that use cross-modal information. To this end, we propose an iterative adversarial attack method (Iterative-attack) that fuses image and text modality attacks, allowing for an attack search for adversarial text and image in an more effective iterative way. Experimental results demonstrate that the proposed method outperforms existing single-modal and non-iterative multimodal attack methods, indicating the potential for improving the adversarial robustness of multimodal text generation models, such as multimodal machine translation, multimodal question answering, etc.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 924,
+    "label": 23,
+    "text": "Title: Towards an Understanding of Large Language Models in Software Engineering Tasks\nAbstract: Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in tasks such as text generation and reasoning. Derivative products, like ChatGPT, have been extensively deployed and highly sought after. Meanwhile, the evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. However, there is still a lack of systematic research on the application and evaluation of LLMs in the field of software engineering. Therefore, this paper is the first to comprehensively investigate and collate the research and products combining LLMs with software engineering, aiming to answer two questions: (1) What are the current integrations of LLMs with software engineering? (2) Can LLMs effectively handle software engineering tasks? To find the answers, we have collected related literature as extensively as possible from seven mainstream databases, and selected 123 papers for analysis. We have categorized these papers in detail and reviewed the current research status of LLMs from the perspective of seven major software engineering tasks, hoping this will help researchers better grasp the research trends and address the issues when applying LLMs. Meanwhile, we have also organized and presented papers with evaluation content to reveal the performance and effectiveness of LLMs in various software engineering tasks, providing guidance for researchers and developers to optimize.",
+    "neighbors": [
+      121,
+      691,
+      704,
+      1052,
+      1171,
+      1490,
+      1520,
+      1758,
+      1801,
+      1810,
+      1879,
+      2100,
+      2230
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 925,
+    "label": 34,
+    "text": "Title: Dynamic Algorithms for Matroid Submodular Maximization\nAbstract: Submodular maximization under matroid and cardinality constraints are classical problems with a wide range of applications in machine learning, auction theory, and combinatorial optimization. In this paper, we consider these problems in the dynamic setting where (1) we have oracle access to a monotone submodular function $f: 2^{V} \\rightarrow \\mathbb{R}^+$ and (2) we are given a sequence $\\mathcal{S}$ of insertions and deletions of elements of an underlying ground set $V$. We develop the first parameterized (by the rank $k$ of a matroid $\\mathcal{M}$) dynamic $(4+\\epsilon)$-approximation algorithm for the submodular maximization problem under the matroid constraint using an expected worst-case $O(k\\log(k)\\log^3{(k/\\epsilon)})$ query complexity where $0<\\epsilon \\le 1$. Chen and Peng at STOC'22 studied the complexity of this problem in the insertion-only dynamic model (a restricted version of the fully dynamic model where deletion is not allowed), and they raised the following important open question: *\"for fully dynamic streams [sequences of insertions and deletions of elements], there is no known constant-factor approximation algorithm with poly(k) amortized queries for matroid constraints.\"* Our dynamic algorithm answers this question as well as an open problem of Lattanzi et al. (NeurIPS'20) affirmatively. As a byproduct, for the submodular maximization under the cardinality constraint $k$, we propose a parameterized (by the cardinality constraint $k$) dynamic algorithm that maintains a $(2+\\epsilon)$-approximate solution of the sequence $\\mathcal{S}$ at any time $t$ using the expected amortized worst-case complexity $O(k\\epsilon^{-1}\\log^2(k))$. This is the first dynamic algorithm for the problem that has a query complexity independent of the size of ground set $V$.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 926,
+    "label": 4,
+    "text": "Title: Data Protection and Security Issues With Network Error Logging\nAbstract: Network Error Logging helps web server operators detect operational problems in real-time to provide fast and reliable services. This paper analyses Network Error Logging from two angles. Firstly, this paper overviews Network Error Logging from the data protection view. The ePrivacy Directive requires consent for non-essential access to the end devices. Nevertheless, the Network Error Logging design does not allow limiting the tracking to consenting users. Other issues lay in GDPR requirements for transparency and the obligations in the contract between controllers and processors of personal data. Secondly, this paper explains Network Error Logging exploitations to deploy long-time trackers to the victim devices. Even though users should be able to disable Network Error Logging, it is not clear how to do so. Web server operators can mitigate the attack by configuring servers to preventively remove policies that adversaries might have added.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 927,
+    "label": 28,
+    "text": "Title: On the Design of Codes for DNA Computing: Secondary Structure Avoidance Codes\nAbstract: In this work, we investigate a challenging problem, which has been considered to be an important criterion in designing codewords for DNA computing purposes, namely secondary structure avoidance in single-stranded DNA molecules. In short, secondary structure refers to the tendency of a single-stranded DNA sequence to fold back upon itself, thus becoming inactive in the computation process. The main contribution of this work is to provide an explicit construction of DNA codes that completely avoid the formation of secondary structures of arbitrary stem length.Formally, given codeword length n and arbitrary integer m \u2a7e 2, we provide efficient methods to construct DNA codes of length n that avoid secondary structure of any stem length more than or equal to m. Particularly, when m = 3, our constructions yield a family of DNA codes of rate 1.3031 bits/nt, while the highest rate found in the prior art was 1.1609 bits/nt. In addition, for m \u2a7e 3log n+4, we provide an efficient encoder that incurs only one redundant symbol.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 928,
+    "label": 4,
+    "text": "Title: Are Home Security Systems Reliable?\nAbstract: Home security systems have become increasingly popular since they provide an additional layer of protection and peace of mind. These systems typically include battery-powered motion sensors, contact sensors, and smart locks. Z-Wave is a very popular wireless communication technology for these low-power systems. In this paper, we demonstrate two new attacks targeting Z-Wave devices. First, we show how an attacker can remotely attack Z-Wave security devices to increase their power consumption by three orders of magnitude, reducing their battery life from a few years to just a few hours. Second, we show multiple Denial of Service (DoS) attacks which enables an attacker to interrupt the operation of security systems in just a few seconds. Our experiments show that these attacks are effective even when the attacker device is in a car 100 meters away from the targeted house.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 929,
+    "label": 30,
+    "text": "Title: Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models\nAbstract: nan",
+    "neighbors": [
+      106,
+      319,
+      945,
+      971,
+      975,
+      1133,
+      1199,
+      1384,
+      1581,
+      1588,
+      1596,
+      1727,
+      1797,
+      1915,
+      1949,
+      1952,
+      2029,
+      2079,
+      2230,
+      2249,
+      2252,
+      2254,
+      2296
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 930,
+    "label": 25,
+    "text": "Title: SingFake: Singing Voice Deepfake Detection\nAbstract: The rise of singing voice synthesis presents critical challenges to artists and industry stakeholders over unauthorized voice usage. Unlike synthesized speech, synthesized singing voices are typically released in songs containing strong background music that may hide synthesis artifacts. Additionally, singing voices present different acoustic and linguistic characteristics from speech utterances. These unique properties make singing voice deepfake detection a relevant but significantly different problem from synthetic speech detection. In this work, we propose the singing voice deepfake detection task. We first present SingFake, the first curated in-the-wild dataset consisting of 28.93 hours of bonafide and 29.40 hours of deepfake song clips in five languages from 40 singers. We provide a train/val/test split where the test sets include various scenarios. We then use SingFake to evaluate four state-of-the-art speech countermeasure systems trained on speech utterances. We find these systems lag significantly behind their performance on speech test data. When trained on SingFake, either using separated vocal tracks or song mixtures, these systems show substantial improvement. However, our evaluations also identify challenges associated with unseen singers, communication codecs, languages, and musical contexts, calling for dedicated research into singing voice deepfake detection. The SingFake dataset and related resources are available online.",
+    "neighbors": [
+      1393,
+      1454,
+      1863
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 931,
+    "label": 27,
+    "text": "Title: Optimal and Stable Multi-Layer Object Rearrangement on a Tabletop\nAbstract: Object rearrangement is a fundamental sub-task in accomplishing a great many physical tasks. As such, effectively executing rearrangement is an important skill for intelligent robots to master. In this study, we conduct the first algorithmic study on optimally solving the problem of Multi-layer Object Rearrangement on a Tabletop (MORT), in which one object may be relocated at a time, and an object can only be moved if other objects do not block its top surface. In addition, any intermediate structure during the reconfiguration process must be physically stable, i.e., it should stand without external support. To tackle the dual challenges of untangling the dependencies between objects and ensuring structural stability, we develop an algorithm that interleaves the computation of the optimal rearrangement plan and structural stability checking. Using a carefully constructed integer linear programming (ILP) model, our algorithm, Stability-aware Integer Programming-based Planner (SIPP), readily scales to optimally solve complex rearrangement problems of 3D structures with over 60 building blocks, with solution quality significantly outperforming natural greedy best-first approaches. Upon the publication of the manuscript, source code and data will be available at https://github.com/arc-l/mort/",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 932,
+    "label": 2,
+    "text": "Title: Linear-Time-Branching-Time Spectroscopy Accounting for Silent Steps\nAbstract: We provide the first generalized game characterization of van Glabbeek's linear-time--branching-time spectrum with silent steps. Thereby, one multi-dimensional energy game can be used to decide a wide array of behavioral equivalences between stability-respecting branching bisimiarity and weak trace equivalence in one go. To establish correctness, we relate attacker-winning energy budgets and distinguishing sublanguages of Hennessy--Milner logic characterized by eight dimensions of formula expressiveness. We outline how to derive exponential-time algorithms and divergence-preserving variants.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 933,
+    "label": 23,
+    "text": "Title: Software Runtime Monitoring with Adaptive Sampling Rate to Collect Representative Samples of Execution Traces\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 934,
+    "label": 16,
+    "text": "Title: Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures\nAbstract: 3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase. In particular, our loss function is built upon a kernel-based metric over a representation of meshes using geometric measures such as currents and varifolds. The latter allows to implement an efficient dissimilarity measure with many desirable properties such as robustness to resampling of the mesh or point cloud. We demonstrate the efficiency and resilience of our model with a generative learning task of human faces.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 935,
+    "label": 30,
+    "text": "Title: HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding\nAbstract: Natural language understanding (NLU) is integral to various social media applications. However, existing NLU models rely heavily on context for semantic learning, resulting in compromised performance when faced with short and noisy social media content. To address this issue, we leverage in-context learning (ICL), wherein language models learn to make inferences by conditioning on a handful of demonstrations to enrich the context and propose a novel hashtag-driven in-context learning (HICL) framework. Concretely, we pre-train a model #Encoder, which employs #hashtags (user-annotated topic labels) to drive BERT-based pre-training through contrastive learning. Our objective here is to enable #Encoder to gain the ability to incorporate topic-related semantic information, which allows it to retrieve topic-related posts to enrich contexts and enhance social media NLU with noisy contexts. To further integrate the retrieved context with the source text, we employ a gradient-based method to identify trigger terms useful in fusing information from both sources. For empirical studies, we collected 45M tweets to set up an in-context NLU benchmark, and the experimental results on seven downstream tasks show that HICL substantially advances the previous state-of-the-art results. Furthermore, we conducted extensive analyzes and found that: (1) combining source input with a top-retrieved post from #Encoder is more effective than using semantically similar posts; (2) trigger words can largely benefit in merging context from the source and retrieved posts.",
+    "neighbors": [
+      1593
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 936,
+    "label": 16,
+    "text": "Title: Point2SSM: Learning Morphological Variations of Anatomies from Point Cloud\nAbstract: We introduce Point2SSM, a novel unsupervised learning approach that can accurately construct correspondence-based statistical shape models (SSMs) of anatomy directly from point clouds. SSMs are crucial in clinical research for analyzing the population-level morphological variation in bones and organs. However, traditional methods for creating SSMs have limitations that hinder their widespread adoption, such as the need for noise-free surface meshes or binary volumes, reliance on assumptions or predefined templates, and simultaneous optimization of the entire cohort leading to lengthy inference times given new data. Point2SSM overcomes these barriers by providing a data-driven solution that infers SSMs directly from raw point clouds, reducing inference burdens and increasing applicability as point clouds are more easily acquired. Deep learning on 3D point clouds has seen recent success in unsupervised representation learning, point-to-point matching, and shape correspondence; however, their application to constructing SSMs of anatomies is largely unexplored. In this work, we benchmark state-of-the-art point cloud deep networks on the task of SSM and demonstrate that they are not robust to the challenges of anatomical SSM, such as noisy, sparse, or incomplete input and significantly limited training data. Point2SSM addresses these challenges via an attention-based module that provides correspondence mappings from learned point features. We demonstrate that the proposed method significantly outperforms existing networks in terms of both accurate surface sampling and correspondence, better capturing population-level statistics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 937,
+    "label": 10,
+    "text": "Title: Chatbots as Problem Solvers: Playing Twenty Questions with Role Reversals\nAbstract: New chat AI applications like ChatGPT offer an advanced understanding of question context and memory across multi-step tasks, such that experiments can test its deductive reasoning. This paper proposes a multi-role and multi-step challenge, where ChatGPT plays the classic twenty-questions game but innovatively switches roles from the questioner to the answerer. The main empirical result establishes that this generation of chat applications can guess random object names in fewer than twenty questions (average, 12) and correctly guess 94% of the time across sixteen different experimental setups. The research introduces four novel cases where the chatbot fields the questions, asks the questions, both question-answer roles, and finally tries to guess appropriate contextual emotions. One task that humans typically fail but trained chat applications complete involves playing bilingual games of twenty questions (English answers to Spanish questions). Future variations address direct problem-solving using a similar inquisitive format to arrive at novel outcomes deductively, such as patentable inventions or combination thinking. Featured applications of this dialogue format include complex protein designs, neuroscience metadata, and child development educational materials.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 938,
+    "label": 26,
+    "text": "Title: BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 939,
+    "label": 23,
+    "text": "Title: Scalable and Adaptive Log-based Anomaly Detection with Expert in the Loop\nAbstract: System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable it to receive feedback from experts. Interestingly, our findings suggest that contemporary large language models, such as ChatGPT, can provide feedback with a level of consistency comparable to human experts, which can potentially reduce manual verification efforts. We extensively evaluate SeaLog on two public datasets and an industrial dataset. The results show that SeaLog outperforms all baseline methods in terms of effectiveness, runs 2X to 10X faster and only consumes 5% to 41% of the memory resource.",
+    "neighbors": [
+      254
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 940,
+    "label": 10,
+    "text": "Title: Translating SUMO-K to Higher-Order Set Theory\nAbstract: We describe a translation from a fragment of SUMO (SUMO-K) into higher-order set theory. The translation provides a formal semantics for portions of SUMO which are beyond first-order and which have previously only had an informal interpretation. It also for the first time embeds a large common-sense ontology into a very secure interactive theorem proving system. We further extend our previous work in finding contradictions in SUMO from first order constructs to include a portion of SUMO's higher order constructs. Finally, using the translation, we can create problems that can be proven using higher-order interactive and automated theorem provers. This is tested in several systems and can be used to form a corpus of higher-order common-sense reasoning problems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 941,
+    "label": 10,
+    "text": "Title: A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph\nAbstract: The paper presents a study on using language models to automate the construction of executable Knowledge Graph (KG) for compliance. The paper focuses on Abu Dhabi Global Market regulations and taxonomy, involves manual tagging a portion of the regulations, training BERT-based models, which are then applied to the rest of the corpus. Coreference resolution and syntax analysis were used to parse the relationships between the tagged entities and to form KG stored in a Neo4j database. The paper states that the use of machine learning models released by regulators to automate the interpretation of rules is a vital step towards compliance automation, demonstrates the concept querying with Cypher, and states that the produced sub-graphs combined with Graph Neural Networks (GNN) will achieve expandability in judgment automation systems. The graph is open sourced on GitHub to provide structured data for future advancements in the field.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 942,
+    "label": 25,
+    "text": "Title: An Objective Evaluation of Hearing AIDS and DNN-Based Binaural Speech Enhancement in Complex Acoustic Scenes\nAbstract: We investigate the objective performance of five high-end commercially available Hearing Aid (HA) devices compared to DNN-based speech enhancement algorithms in complex acoustic environments. To this end, we measure the HRTFs of a single HA device to synthesize a binaural dataset for training two state-of-the-art causal and non-causal DNN enhancement models. We then generate an evaluation set of realistic speech-in-noise situations using an Ambisonics loudspeaker setup and record with a KU100 dummy head wearing each of the HA devices, both with and without the conventional HA algorithms, applying the DNN enhancers to the latter. We find that the DNN-based enhancement outperforms the HA algorithms in terms of noise suppression and objective intelligibility metrics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 943,
+    "label": 16,
+    "text": "Title: TabIQA: Table Questions Answering on Business Document Images\nAbstract: Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learning techniques 1) to extract table content and structural information from images and 2) to answer various questions related to numerical data, text-based information, and complex queries from structured tables. The evaluation results on VQAonBD 2023 dataset demonstrate the effectiveness of TabIQA in achieving promising performance in answering table-related questions. The TabIQA repository is available at https://github.com/phucty/itabqa.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 944,
+    "label": 26,
+    "text": "Title: Fake News Detection Through Graph-based Neural Networks: A Survey\nAbstract: The popularity of online social networks has enabled rapid dissemination of information. People now can share and consume information much more rapidly than ever before. However, low-quality and/or accidentally/deliberately fake information can also spread rapidly. This can lead to considerable and negative impacts on society. Identifying, labelling and debunking online misinformation as early as possible has become an increasingly urgent problem. Many methods have been proposed to detect fake news including many deep learning and graph-based approaches. In recent years, graph-based methods have yielded strong results, as they can closely model the social context and propagation process of online news. In this paper, we present a systematic review of fake news detection studies based on graph-based and deep learning-based techniques. We classify existing graph-based methods into knowledge-driven methods, propagation-based methods, and heterogeneous social context-based methods, depending on how a graph structure is constructed to model news related information flows. We further discuss the challenges and open problems in graph-based fake news detection and identify future research directions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 945,
+    "label": 30,
+    "text": "Title: Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey\nAbstract: Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.",
+    "neighbors": [
+      57,
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+      1052,
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+      1353,
+      1481,
+      1560,
+      1581,
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+      1893,
+      1940,
+      1952,
+      2013,
+      2113,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 946,
+    "label": 6,
+    "text": "Title: Technical Understanding from IML Hands-on Experience: A Study through a Public Event for Science Museum Visitors\nAbstract: While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 947,
+    "label": 7,
+    "text": "Title: I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)\nAbstract: We propose an integrated finite element neural network (I-FENN) framework to expedite the solution of coupled multiphysics problems. A physics-informed temporal convolutional network (PI-TCN) is embedded within the finite element framework to leverage the fast inference of neural networks (NNs). The PI-TCN model captures some of the fields in the multiphysics problem, and their derivatives are calculated via automatic differentiation available in most machine learning platforms. The other fields of interest are computed using the finite element method. We introduce I-FENN for the solution of transient thermoelasticity, where the thermo-mechanical fields are fully coupled. We establish a framework that computationally decouples the energy equation from the linear momentum equation. We first develop a PI-TCN model to predict the temperature field based on the energy equation and available strain data. The PI-TCN model is integrated into the finite element framework, where the PI-TCN output (temperature) is used to introduce the temperature effect to the linear momentum equation. The finite element problem is solved using the implicit Euler time discretization scheme, resulting in a computational cost comparable to that of a weakly-coupled thermoelasticity problem but with the ability to solve fully-coupled problems. Finally, we demonstrate the computational efficiency and generalization capability of I-FENN in thermoelasticity through several numerical examples.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 948,
+    "label": 34,
+    "text": "Title: Faster Algorithms for Bounded Knapsack and Bounded Subset Sum Via Fine-Grained Proximity Results\nAbstract: We investigate pseudopolynomial-time algorithms for Bounded Knapsack and Bounded Subset Sum. Recent years have seen a growing interest in settling their fine-grained complexity with respect to various parameters. For Bounded Knapsack, the number of items $n$ and the maximum item weight $w_{\\max}$ are two of the most natural parameters that have been studied extensively in the literature. The previous best running time in terms of $n$ and $w_{\\max}$ is $O(n + w^3_{\\max})$ [Polak, Rohwedder, Wegrzycki '21]. There is a conditional lower bound of $O((n + w_{\\max})^{2-o(1)})$ based on $(\\min,+)$-convolution hypothesis [Cygan, Mucha, Wegrzycki, Wlodarczyk '17]. We narrow the gap significantly by proposing a $\\tilde{O}(n + w^{12/5}_{\\max})$-time algorithm. Note that in the regime where $w_{\\max} \\approx n$, our algorithm runs in $\\tilde{O}(n^{12/5})$ time, while all the previous algorithms require $\\Omega(n^3)$ time in the worst case. For Bounded Subset Sum, we give two algorithms running in $\\tilde{O}(nw_{\\max})$ and $\\tilde{O}(n + w^{3/2}_{\\max})$ time, respectively. These results match the currently best running time for 0-1 Subset Sum. Prior to our work, the best running times (in terms of $n$ and $w_{\\max}$) for Bounded Subset Sum is $\\tilde{O}(n + w^{5/3}_{\\max})$ [Polak, Rohwedder, Wegrzycki '21] and $\\tilde{O}(n + \\mu_{\\max}^{1/2}w_{\\max}^{3/2})$ [implied by Bringmann '19 and Bringmann, Wellnitz '21], where $\\mu_{\\max}$ refers to the maximum multiplicity of item weights.",
+    "neighbors": [
+      875,
+      1511,
+      1518
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 949,
+    "label": 30,
+    "text": "Title: Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca\nAbstract: Large Language Models (LLMs), such as ChatGPT and GPT-4, have dramatically transformed natural language processing research and shown promising strides towards Artificial General Intelligence (AGI). Nonetheless, the high costs associated with training and deploying LLMs present substantial obstacles to transparent, accessible academic research. While several large language models, such as LLaMA, have been open-sourced by the community, these predominantly focus on English corpora, limiting their usefulness for other languages. In this paper, we propose a method to augment LLaMA with capabilities for understanding and generating Chinese text and its ability to follow instructions. We achieve this by extending LLaMA's existing vocabulary with an additional 20,000 Chinese tokens, thereby improving its encoding efficiency and semantic understanding of Chinese. We further incorporate secondary pre-training using Chinese data and fine-tune the model with Chinese instruction datasets, significantly enhancing the model's ability to comprehend and execute instructions. Our experimental results indicate that the newly proposed model markedly enhances the original LLaMA's proficiency in understanding and generating Chinese content. Additionally, the results on the C-Eval dataset yield competitive performance among the models with several times the size of ours. We have made our pre-trained models, training scripts, and other resources available through GitHub, fostering open research for our community. GitHub repository: https://github.com/ymcui/Chinese-LLaMA-Alpaca",
+    "neighbors": [
+      143,
+      529,
+      1052,
+      1546,
+      1655,
+      1863,
+      1972,
+      2166
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 950,
+    "label": 16,
+    "text": "Title: Shape complexity estimation using VAE\nAbstract: In this paper, we compare methods for estimating the complexity of two-dimensional shapes and introduce a method that exploits reconstruction loss of Variational Autoencoders with different sizes of latent vectors. Although complexity of a shape is not a well defined attribute, different aspects of it can be estimated. We demonstrate that our methods captures some aspects of shape complexity. Code and training details will be publicly available.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 951,
+    "label": 16,
+    "text": "Title: What Can Human Sketches Do for Object Detection?\nAbstract: Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result is a sketch-enabled object detection framework that detects based on what you sketch \u2013 that \u201czebra\u201d (e.g., one that is eating the grass) in a herd of zebras (instance-aware detection), and only the part (e.g., \u201chead\u201d of a \u201czebra\u201d) that you desire (part-aware detection). We further dictate that our model works without (i) knowing which category to expect at testing (zero-shot) and (ii) not requiring additional bounding boxes (as per fully supervised) and class labels (as per weakly supervised). Instead of devising a model from the ground up, we show an intuitive synergy between foundation models (e.g., CLIP) and existing sketch models build for sketch-based image retrieval (SBIR), which can already elegantly solve the task \u2013 CLIP to provide model generalisation, and SBIR to bridge the (sketch\u2192photo) gap. In particular, we first perform independent prompting on both sketch and photo branches of an SBIR model to build highly generalisable sketch and photo encoders on the back of the generalisation ability of CLIP. We then devise a training paradigm to adapt the learned encoders for object detection, such that the region embeddings of detected boxes are aligned with the sketch and photo embeddings from SBIR. Evaluating our framework on standard object detection datasets like PASCAL-VOC and MS-COCO outperforms both supervised (SOD) and weakly-supervised object detectors (WSOD) on zero-shot setups. Project Page: https://pinakinathc.github.io/sketch-detect",
+    "neighbors": [
+      2223
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 952,
+    "label": 9,
+    "text": "Title: Characterizing Direct Product Testing via Coboundary Expansion\nAbstract: A $d$-dimensional simplicial complex $X$ is said to support a direct product tester if any locally consistent function defined on its $k$-faces (where $k\\ll d$) necessarily come from a function over its vertices. More precisely, a direct product tester has a distribution $\\mu$ over pairs of $k$-faces $(A,A')$, and given query access to $F\\colon X(k)\\to\\{0,1\\}^k$ it samples $(A,A')\\sim \\mu$ and checks that $F[A]|_{A\\cap A'} = F[A']|_{A\\cap A'}$. The tester should have (1) the\"completeness property\", meaning that any assignment $F$ which is a direct product assignment passes the test with probability $1$, and (2) the\"soundness property\", meaning that if $F$ passes the test with probability $s$, then $F$ must be correlated with a direct product function. Dinur and Kaufman showed that a sufficiently good spectral expanding complex $X$ admits a direct product tester in the\"high soundness\"regime where $s$ is close to $1$. They asked whether there are high dimensional expanders that support direct product tests in the\"low soundness\", when $s$ is close to $0$. We give a characterization of high-dimensional expanders that support a direct product tester in the low soundness regime. We show that spectral expansion is insufficient, and the complex must additionally satisfy a variant of coboundary expansion, which we refer to as Unique-Games coboundary expanders. This property can be seen as a high-dimensional generalization of the standard notion of coboundary expansion over non-Abelian groups for 2-dimensional complexes. It asserts that any locally consistent Unique-Games instance obtained using the low-level faces of the complex, must admit a good global solution.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 953,
+    "label": 30,
+    "text": "Title: Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction\nAbstract: Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews. Previous studies commonly employ fully-connected neural networks (FCNNs) as the final score predictor and pairwise loss as the training objective. However, FCNNs have been shown to perform inefficient splitting for review features, making the model difficult to clearly differentiate helpful from unhelpful reviews. Furthermore, pairwise objective, which works on review pairs, may not completely capture the MRHP goal to produce the ranking for the entire review list, and possibly induces low generalization during testing. To address these issues, we propose a listwise attention network that clearly captures the MRHP ranking context and a listwise optimization objective that enhances model generalization. We further propose gradient-boosted decision tree as the score predictor to efficaciously partition product reviews' representations. Extensive experiments demonstrate that our method achieves state-of-the-art results and polished generalization performance on two large-scale MRHP benchmark datasets.",
+    "neighbors": [
+      354,
+      1184
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 954,
+    "label": 6,
+    "text": "Title: My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning\nAbstract: Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer\"Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?\"Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 955,
+    "label": 16,
+    "text": "Title: Composer: Creative and Controllable Image Synthesis with Composable Conditions\nAbstract: Recent large-scale generative models learned on big data are capable of synthesizing incredible images yet suffer from limited controllability. This work offers a new generation paradigm that allows flexible control of the output image, such as spatial layout and palette, while maintaining the synthesis quality and model creativity. With compositionality as the core idea, we first decompose an image into representative factors, and then train a diffusion model with all these factors as the conditions to recompose the input. At the inference stage, the rich intermediate representations work as composable elements, leading to a huge design space (i.e., exponentially proportional to the number of decomposed factors) for customizable content creation. It is noteworthy that our approach, which we call Composer, supports various levels of conditions, such as text description as the global information, depth map and sketch as the local guidance, color histogram for low-level details, etc. Besides improving controllability, we confirm that Composer serves as a general framework and facilitates a wide range of classical generative tasks without retraining. Code and models will be made available.",
+    "neighbors": [
+      260,
+      273,
+      860,
+      1173,
+      1582,
+      1710,
+      1902,
+      2161
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 956,
+    "label": 30,
+    "text": "Title: YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus\nAbstract: Machine learning for sign languages is bottlenecked by data. In this paper, we present YouTube-ASL, a large-scale, open-domain corpus of American Sign Language (ASL) videos and accompanying English captions drawn from YouTube. With ~1000 hours of videos and>2500 unique signers, YouTube-ASL is ~3x as large and has ~10x as many unique signers as the largest prior ASL dataset. We train baseline models for ASL to English translation on YouTube-ASL and evaluate them on How2Sign, where we achieve a new finetuned state of the art of 12.39 BLEU and, for the first time, report zero-shot results.",
+    "neighbors": [
+      1728,
+      2090
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 957,
+    "label": 16,
+    "text": "Title: Edit-A-Video: Single Video Editing with Object-Aware Consistency\nAbstract: Despite the fact that text-to-video (TTV) model has recently achieved remarkable success, there have been few approaches on TTV for its extension to video editing. Motivated by approaches on TTV models adapting from diffusion-based text-to-image (TTI) models, we suggest the video editing framework given only a pretrained TTI model and a singlepair, which we term Edit-A-Video. The framework consists of two stages: (1) inflating the 2D model into the 3D model by appending temporal modules and tuning on the source video (2) inverting the source video into the noise and editing with target text prompt and attention map injection. Each stage enables the temporal modeling and preservation of semantic attributes of the source video. One of the key challenges for video editing include a background inconsistency problem, where the regions not included for the edit suffer from undesirable and inconsistent temporal alterations. To mitigate this issue, we also introduce a novel mask blending method, termed as sparse-causal blending (SC Blending). We improve previous mask blending methods to reflect the temporal consistency so that the area where the editing is applied exhibits smooth transition while also achieving spatio-temporal consistency of the unedited regions. We present extensive experimental results over various types of text and videos, and demonstrate the superiority of the proposed method compared to baselines in terms of background consistency, text alignment, and video editing quality.",
+    "neighbors": [
+      1020,
+      1042,
+      1582,
+      1935,
+      2135,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 958,
+    "label": 28,
+    "text": "Title: Distributed Precoding for Satellite-Terrestrial Integrated Networks Without Sharing CSIT: A Rate-Splitting Approach\nAbstract: Satellite-terrestrial integrated networks (STINs) are promising architecture for providing global coverage. In STINs, full frequency reuse between a satellite and a terrestrial base station (BS) is encouraged for enhancing spectral efficiency, which accounts for non-negligible amount of interference. To address the interference management problem in STINs, this paper proposes a novel distributed precoding method. Key features of our method are: i) a rate-splitting (RS) strategy is incorporated for efficient interference management, ii) precoders are designed in a distributed way without sharing channel state information between a satellite and a terrestrial BS. Specifically, to design precoders in a distributed fashion, we put forth a spectral efficiency decoupling technique. This technique disentangles the total spectral efficiency into two distinct terms, each dependent solely on the satellite's precoder and the terrestrial BS's precoder, respectively. Then, to resolve the non-smoothness raised by adopting the RS strategy, we approximate the spectral efficiency expression as a smooth function; thereafter we develop a generalized power iteration inspired optimization algorithm built based on the first-order optimality condition. Simulation results demonstrate that the proposed method improves the spectral efficiency (around 20~29%) compared to existing distributed precoding schemes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 959,
+    "label": 16,
+    "text": "Title: MOSE: A New Dataset for Video Object Segmentation in Complex Scenes\nAbstract: Video object segmentation (VOS) aims at segmenting a particular object throughout the entire video clip sequence. The state-of-the-art VOS methods have achieved excellent performance (e.g., 90+% J&F) on existing datasets. However, since the target objects in these existing datasets are usually relatively salient, dominant, and isolated, VOS under complex scenes has rarely been studied. To revisit VOS and make it more applicable in the real world, we collect a new VOS dataset called coMplex video Object SEgmentation (MOSE) to study the tracking and segmenting objects in complex environments. MOSE contains 2,149 video clips and 5,200 objects from 36 categories, with 431,725 high-quality object segmentation masks. The most notable feature of MOSE dataset is complex scenes with crowded and occluded objects. The target objects in the videos are commonly occluded by others and disappear in some frames. To analyze the proposed MOSE dataset, we benchmark 18 existing VOS methods under 4 different settings on the proposed MOSE dataset and conduct comprehensive comparisons. The experiments show that current VOS algorithms cannot well perceive objects in complex scenes. For example, under the semi-supervised VOS setting, the highest J&F by existing state-of-the-art VOS methods is only 59.4% on MOSE, much lower than their ~90% J&F performance on DAVIS. The results reveal that although excellent performance has been achieved on existing benchmarks, there are unresolved challenges under complex scenes and more efforts are desired to explore these challenges in the future. The proposed MOSE dataset has been released at https://henghuiding.github.io/MOSE.",
+    "neighbors": [
+      1714,
+      2156,
+      2203
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 960,
+    "label": 32,
+    "text": "Title: PyOED: An Extensible Suite for Data Assimilation and Model-Constrained Optimal Design of Experiments\nAbstract: This paper describes the first version (v1.0) of PyOED, a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, the current version of PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to ``enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.'' This paper provides a brief description of the PyOED layout and philosophy and provides a set of exemplary test cases and tutorials to demonstrate how the package can be utilized.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 961,
+    "label": 30,
+    "text": "Title: RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank\nAbstract: Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative. In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks. Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 962,
+    "label": 3,
+    "text": "Title: Envisioning an Inclusive Metaverse: Student Perspectives on Accessible and Empowering Metaverse-Enabled Learning\nAbstract: The emergence of the metaverse is being widely viewed as a revolutionary technology owing to a myriad of factors, particularly the potential to increase the accessibility of learning for students with disabilities. However, not much is yet known about the views and expectations of disabled students in this regard. The fact that the metaverse is still in its nascent stage exemplifies the need for such timely discourse. To bridge this important gap, we conducted a series of semi-structured interviews with 56 university students with disabilities in the United States and Hong Kong to understand their views and expectations concerning the future of metaverse-driven education. We have distilled student expectations into five thematic categories, referred to as the REEPS framework: Recognition, Empowerment, Engagement, Privacy, and Safety. Additionally, we have summarized the main design considerations in eight concise points. This paper is aimed at helping technology developers and policymakers plan ahead of time and improving the experiences of students with disabilities.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 963,
+    "label": 8,
+    "text": "Title: Collie: Finding Performance Anomalies in RDMA Subsystems\nAbstract: High-speed RDMA networks are getting rapidly adopted in the industry for their low latency and reduced CPU overheads. To verify that RDMA can be used in production, system administrators need to understand the set of application workloads that can potentially trigger abnormal performance behaviors (e.g., unexpected low throughput, PFC pause frame storm). We design and implement Collie, a tool for users to systematically uncover performance anomalies in RDMA subsystems without the need to access hardware internal designs. Instead of individually testing each hardware device (e.g., NIC, memory, PCIe), Collie is holistic, constructing a comprehensive search space for application workloads. Collie then uses simulated annealing to drive RDMA-related performance and diagnostic counters to extreme value regions to find workloads that can trigger performance anomalies. We evaluate Collie on combinations of various RDMA NIC, CPU, and other hardware components. Collie found 15 new performance anomalies. All of them are acknowledged by the hardware vendors. 7 of them are already fixed after we reported them. We also present our experience in using Collie to avoid performance anomalies for an RDMA RPC library and an RDMA distributed machine learning framework.",
+    "neighbors": [
+      633,
+      822,
+      1167
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 964,
+    "label": 27,
+    "text": "Title: Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition\nAbstract: We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sampling-based robot planners (e.g. motion or grasp samplers) for generating diverse and rich manipulation trajectories. To robustify this data-collection process, the LLM also infers a code-snippet for the success condition of each task, simultaneously enabling the data-collection process to detect failure and retry as well as the automatic labeling of trajectories with success/failure. For (2), we extend the diffusion policy single-task behavior-cloning approach to multi-task settings with language conditioning. Finally, we propose a new multi-task benchmark with 18 tasks across five domains to test long-horizon behavior, common-sense reasoning, tool-use, and intuitive physics. We find that our distilled policy successfully learned the robust retrying behavior in its data collection policy, while improving absolute success rates by 34.8% on average across five domains. The benchmark, code, and qualitative results are on our website https://www.cs.columbia.edu/~huy/scalingup/",
+    "neighbors": [
+      787,
+      999,
+      1047,
+      1339
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 965,
+    "label": 26,
+    "text": "Title: A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things\nAbstract: The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 966,
+    "label": 10,
+    "text": "Title: Cultivated Wildness: Technodiversity and Wildness in Machines\nAbstract: This paper investigates the idea of cultivated wildness at the intersection of landscape design and artificial intelligence. The paper posits that contemporary landscape practices should overcome the potentially single understanding on wilderness, and instead explore landscape strategies to cultivate new forms of wild places via ideas and concerns in contemporary Environmental Humanities, Science and Technology Studies, Ecological Sciences, and Landscape Architecture. Drawing cases in environmental engineering, computer science, and landscape architecture research, this paper explores a framework to construct wild places with intelligent machines. In this framework, machines are not understood as a layer of\"digital infrastructure\"that is used to extend localized human intelligence and agency. Rather machines are conceptualized as active agents who can participate in the intelligence of co-production. Recent developments in cybernetic technologies such as sensing networks, artificial intelligence, and cyberphysical systems can also contribute to establishing the framework. At the heart of this framework is\"technodiversity,\"in parallel with biodiversity, since a singular vision on technological development driven by optimization and efficiency reinforces a monocultural approach that eliminates other possible relationships to construct with the environment. Thus, cultivated wildness is also about recognizing\"wildness\"in machines.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 967,
+    "label": 25,
+    "text": "Title: Knowledge-based Multimodal Music Similarity\nAbstract: Music similarity is an essential aspect of music retrieval, recommendation systems, and music analysis. Moreover, similarity is of vital interest for music experts, as it allows studying analogies and influences among composers and historical periods. Current approaches to musical similarity rely mainly on symbolic content, which can be expensive to produce and is not always readily available. Conversely, approaches using audio signals typically fail to provide any insight about the reasons behind the observed similarity. This research addresses the limitations of current approaches by focusing on the study of musical similarity using both symbolic and audio content. The aim of this research is to develop a fully explainable and interpretable system that can provide end-users with more control and understanding of music similarity and classification systems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 968,
+    "label": 5,
+    "text": "Title: MoDeST: Bridging the Gap between Federated and Decentralized Learning with Decentralized Sampling\nAbstract: Federated and decentralized machine learning leverage end-user devices for privacy-preserving training of models at lower operating costs than within a data center. In a round of Federated Learning (FL), a random sample of participants trains locally, then a central server aggregates the local models to produce a single model for the next round. In a round of Decentralized Learning (DL), all participants train locally and then aggregate with their immediate neighbors, resulting in many local models with residual variance between them. On the one hand, FL's sampling and lower model variance provides lower communication costs and faster convergence. On the other hand, DL removes the need for a central server and distributes the communication costs more evenly amongst nodes, albeit at a larger total communication cost and slower convergence. In this paper, we present MoDeST: Mostly-Consistent Decentralized Sampling Training. MoDeST implements decentralized sampling in which a random subset of nodes is responsible for training and aggregation every round: this provides the benefits of both FL and DL without their traditional drawbacks. Our evaluation of MoDeST on four common learning tasks: (i) confirms convergence as fast as FL, (ii) shows a 3x-14x reduction in communication costs compared to DL, and (iii) demonstrates that MoDeST quickly adapts to nodes joining, leaving, or failing, even when 80% of all nodes become unresponsive.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 969,
+    "label": 27,
+    "text": "Title: Loosely Coupled Odometry, UWB Ranging, and Cooperative Spatial Detection for Relative Monte-Carlo Multi-Robot Localization\nAbstract: As mobile robots become more ubiquitous, their deployments grow across use cases where GNSS positioning is either unavailable or unreliable. This has led to increased interest in multi-modal relative localization methods. Complementing onboard odometry, ranging allows for relative state estimation, with ultra-wideband (UWB) ranging having gained widespread recognition due to its low cost and centimeter-level out-of-box accuracy. Infrastructure-free localization methods allow for more dynamic, ad-hoc, and flexible deployments, yet they have received less attention from the research community. In this work, we propose a cooperative relative multi-robot localization where we leverage inter-robot ranging and simultaneous spatial detections of objects in the environment. To achieve this, we equip robots with a single UWB transceiver and a stereo camera. We propose a novel Monte-Carlo approach to estimate relative states by either employing only UWB ranges or dynamically integrating simultaneous spatial detections from the stereo cameras. We also address the challenges for UWB ranging error mitigation, especially in non-line-of-sight, with a study on different LSTM networks to estimate the ranging error. The proposed approach has multiple benefits. First, we show that a single range is enough to estimate the accurate relative states of two robots when fusing odometry measurements. Second, our experiments also demonstrate that our approach surpasses traditional methods such as multilateration in terms of accuracy. Third, to increase accuracy even further, we allow for the integration of cooperative spatial detections. Finally, we show how ROS 2 and Zenoh can be integrated to build a scalable wireless communication solution for multi-robot systems. The experimental validation includes real-time deployment and autonomous navigation based on the relative positioning method.",
+    "neighbors": [
+      1316,
+      1443
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 970,
+    "label": 8,
+    "text": "Title: Deployment of Real-Time Network Traffic Analysis using GraphBLAS Hypersparse Matrices and D4M Associative Arrays\nAbstract: Matrix/array analysis of networks can provide significant insight into their behavior and aid in their operation and protection. Prior work has demonstrated the analytic, performance, and compression capabilities of GraphBLAS (graphblas.org) hypersparse matrices and D4M (d4m.mit.edu) associative arrays (a mathematical superset of matrices). Obtaining the benefits of these capabilities requires integrating them into operational systems, which comes with its own unique challenges. This paper describes two examples of real-time operational implementations. First, is an operational GraphBLAS implementation that constructs anonymized hypersparse matrices on a high-bandwidth network tap. Second, is an operational D4M implementation that analyzes daily cloud gateway logs. The architectures of these implementations are presented. Detailed measurements of the resources and the performance are collected and analyzed. The implementations are capable of meeting their operational requirements using modest computational resources (a couple of processing cores). GraphBLAS is well-suited for low-level analysis of high-bandwidth connections with relatively structured network data. D4M is well-suited for higher-level analysis of more unstructured data. This work demonstrates that these technologies can be implemented in operational settings.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 971,
+    "label": 30,
+    "text": "Title: Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization\nAbstract: Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \\cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation.",
+    "neighbors": [
+      485,
+      519,
+      735,
+      929,
+      975,
+      1436,
+      1694,
+      1949,
+      1952,
+      2062,
+      2249,
+      2289
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 972,
+    "label": 6,
+    "text": "Title: \u201cWe need to do more... I need to do more\u201d: Augmenting Digital Media Consumption via Critical Reflection to Increase Compassion and Promote Prosocial Attitudes and Behaviors\nAbstract: Much HCI research on prompting prosocial behaviors focuses on methods for increasing empathy. However, increased empathy may have unintended negative consequences. Our work offers an alternative solution that encourages critical reflection for nurturing compassion, which involves motivation and action to help others. In a between-subject experiment, participants (N=60) viewed a climate change documentary while receiving no prompts (CON), reflective prompts to focus on their emotions (RE) or surprises (RS). State compassion, critical reflection, and motivation to act or learn were measured at the end of the session (post-video) and two weeks later (follow-up). Despite participants\u2019 condition not affecting compassion, critical reflection was positively correlated with post-video state compassion. RE and RS participants demonstrated deeper reflection and reported higher motivation to learn post-video, and more prosocial behavioral changes during follow-up. RS participants reported better follow-up recall than RE participants. We conclude by discussing implications on designing technology to support compassion and longer-term critical reflection.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 973,
+    "label": 24,
+    "text": "Title: FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout\nAbstract: Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 974,
+    "label": 16,
+    "text": "Title: DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition\nAbstract: As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20K semantic segmentation task.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 975,
+    "label": 30,
+    "text": "Title: Is ChatGPT a General-Purpose Natural Language Processing Task Solver?\nAbstract: Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies.",
+    "neighbors": [
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+      1036,
+      1133,
+      1199,
+      1520,
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+      1940,
+      1949,
+      2016,
+      2062,
+      2081,
+      2113,
+      2215,
+      2244,
+      2249,
+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 976,
+    "label": 16,
+    "text": "Title: Template-free Articulated Neural Point Clouds for Reposable View Synthesis\nAbstract: Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformation fields makes reanimation of the captured object poses challenging. Moreover, the state of the art dynamic models are often limited by low visual fidelity, long reconstruction time or specificity to narrow application domains. In this paper, we present a novel method utilizing a point-based representation and Linear Blend Skinning (LBS) to jointly learn a Dynamic NeRF and an associated skeletal model from even sparse multi-view video. Our forward-warping approach achieves state-of-the-art visual fidelity when synthesizing novel views and poses while significantly reducing the necessary learning time when compared to existing work. We demonstrate the versatility of our representation on a variety of articulated objects from common datasets and obtain reposable 3D reconstructions without the need of object-specific skeletal templates. Code will be made available at https://github.com/lukasuz/Articulated-Point-NeRF.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 977,
+    "label": 24,
+    "text": "Title: GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations\nAbstract: Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifi-cations, particularly for safety-critical applications. Existing detection methods usually only focus on adversarial attacks or out-of-distribution samples as reasons for false predictions. However, generalization errors occur due to diverse reasons often related to poorly learning relevant invariances. We therefore propose GIT, a holistic approach for the detection of generalization errors that combines the usage of gradient information and invariance transformations. The invariance transformations are designed to shift misclassified samples back into the generalization area of the neural network, while the gradient information measures the contradiction between the initial prediction and the corre-sponding inherent computations of the neural network using the transformed sample. Our experiments demonstrate the superior performance of GIT compared to the state-of-the-art on a variety of network architectures, problem setups and perturbation types.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 978,
+    "label": 30,
+    "text": "Title: TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement\nAbstract: Speech enhancement models have greatly progressed in recent years, but still show limits in perceptual quality of their speech outputs. We propose an objective for perceptual quality based on temporal acoustic parameters. These are fundamental speech features that play an essential role in various applications, including speaker recognition and paralinguistic analysis. We provide a differentiable estimator for four categories of low-level acoustic descriptors involving: frequency-related parameters, energy or amplitude-related parameters, spectral balance parameters, and temporal features. Unlike prior work that looks at aggregated acoustic parameters or a few categories of acoustic parameters, our temporal acoustic parameter (TAP) loss enables auxiliary optimization and improvement of many fine-grain speech characteristics in enhancement workflows. We show that adding TAPLoss as an auxiliary objective in speech enhancement produces speech with improved perceptual quality and intelligibility. We use data from the Deep Noise Suppression 2020 Challenge to demonstrate that both time-domain models and time-frequency domain models can benefit from our method.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 979,
+    "label": 16,
+    "text": "Title: Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models\nAbstract: Vision-language models (VLMs) pre-trained on large- scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still limited. We believe that the greatest value of pre-trained VLMs lies in building a bridge between visual and textual domains. In this paper, we propose a novel framework called BIKE, which utilizes the cross-modal bridge to explore bidirectional knowledge: i) We introduce the Video Attribute Association mechanism, which leverages the Video-to-Text knowledge to generate textual auxiliary attributes for complementing video recognition. ii) We also present a Temporal Concept Spotting mechanism that uses the Text-to-Video expertise to capture temporal saliency in a parameter-free manner, leading to enhanced video representation. Extensive studies on six popular video datasets, including Kinetics-400 & 600, UCF-101, HMDB-51, ActivityNet and Charades, show that our method achieves state-of-the-art performance in various recognition scenarios, such as general, zero-shot, and few-shot video recognition. Our best model achieves a state-of-the-art accuracy of 88.6% on the challenging Kinetics-400 using the released CLIP model. The code is available at https://github.com/whwu95/BIKE.",
+    "neighbors": [
+      1787
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 980,
+    "label": 24,
+    "text": "Title: Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities\nAbstract: Decision-focused learning (DFL) is an emerging paradigm in machine learning which trains a model to optimize decisions, integrating prediction and optimization in an end-to-end system. This paradigm holds the promise to revolutionize decision-making in many real-world applications which operate under uncertainty, where the estimation of unknown parameters within these decision models often becomes a substantial roadblock. This paper presents a comprehensive review of DFL. It provides an in-depth analysis of the various techniques devised to integrate machine learning and optimization models, introduces a taxonomy of DFL methods distinguished by their unique characteristics, and conducts an extensive empirical evaluation of these methods proposing suitable benchmark dataset and tasks for DFL. Finally, the study provides valuable insights into current and potential future avenues in DFL research.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 981,
+    "label": 24,
+    "text": "Title: Accelerating Exact Combinatorial Optimization via RL-based Initialization - A Case Study in Scheduling\nAbstract: Scheduling on dataflow graphs (also known as computation graphs) is an NP-hard problem. The traditional exact methods are limited by runtime complexity, while reinforcement learning (RL) and heuristic-based approaches struggle with determinism and solution quality. This research aims to develop an innovative approach that employs machine learning (ML) for addressing combinatorial optimization problems, using scheduling as a case study. The goal is to provide guarantees in optimality and determinism while maintaining the runtime cost of heuristic methods. Specifically, we introduce a novel two-phase RL-to-ILP scheduling framework, which includes three steps: 1) RL solver acts as coarse-grain scheduler, 2) solution relaxation and 3) exact solving via ILP. Our framework demonstrates the same scheduling performance compared with using exact scheduling methods while achieving up to 128 $\\times$ speed improvements. This was conducted on actual EdgeTPU platforms, utilizing ImageNet DNN computation graphs as input. Additionally, the framework offers improved on-chip inference runtime and acceleration compared to the commercially available EdgeTPU compiler.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 982,
+    "label": 16,
+    "text": "Title: Deep Neural Networks Fused with Textures for Image Classification\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 983,
+    "label": 16,
+    "text": "Title: BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions\nAbstract: Vision Language Models (VLMs), which extend Large Language Models (LLM) by incorporating visual understanding capability, have demonstrated significant advancements in addressing open-ended visual question-answering (VQA) tasks. However, these models cannot accurately interpret images infused with text, a common occurrence in real-world scenarios. Standard procedures for extracting information from images often involve learning a fixed set of query embeddings. These embeddings are designed to encapsulate image contexts and are later used as soft prompt inputs in LLMs. Yet, this process is limited to the token count, potentially curtailing the recognition of scenes with text-rich context. To improve upon them, the present study introduces BLIVA: an augmented version of InstructBLIP with Visual Assistant. BLIVA incorporates the query embeddings from InstructBLIP and also directly projects encoded patch embeddings into the LLM, a technique inspired by LLaVA. This approach assists the model to capture intricate details potentially missed during the query decoding process. Empirical evidence demonstrates that our model, BLIVA, significantly enhances performance in processing text-rich VQA benchmarks (up to 17.76\\% in OCR-VQA benchmark) and in undertaking typical VQA benchmarks (up to 7.9\\% in Visual Spatial Reasoning benchmark), comparing to our baseline InstructBLIP. BLIVA demonstrates significant capability in decoding real-world images, irrespective of text presence. To demonstrate the broad industry applications enabled by BLIVA, we evaluate the model using a new dataset comprising YouTube thumbnails paired with question-answer sets across 13 diverse categories. For researchers interested in further exploration, our code and models are freely accessible at https://github.com/mlpc-ucsd/BLIVA.git",
+    "neighbors": [
+      522,
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+      1007,
+      1052,
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+      1537,
+      1863,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 984,
+    "label": 27,
+    "text": "Title: Improved Trust in Human-Robot Collaboration With ChatGPT\nAbstract: Human-robot collaboration is becoming increasingly important as robots become more involved in various aspects of human life in the era of Artificial Intelligence. However, the issue of human operators\u2019 trust in robots remains a significant concern, primarily due to the lack of adequate semantic understanding and communication between humans and robots. The emergence of Large Language Models (LLMs), such as ChatGPT, provides an opportunity to develop an interactive, communicative, and robust human-robot collaboration approach. This paper explores the impact of ChatGPT on trust in a human-robot collaboration assembly task. This study designs a robot control system called RoboGPT using ChatGPT to control a 7-degree-of-freedom robot arm to help human operators fetch, and place tools, while human operators can communicate with and control the robot arm using natural language. A human-subject experiment showed that incorporating ChatGPT in robots significantly increased trust in human-robot collaboration, which can be attributed to the robot\u2019s ability to communicate more effectively with humans. Furthermore, ChatGPT\u2019s ability to understand the nuances of human language and respond appropriately helps to build a more natural and intuitive human-robot interaction. The findings of this study have significant implications for the development of trustworthy human-robot collaboration systems.",
+    "neighbors": [
+      855,
+      1822
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 985,
+    "label": 24,
+    "text": "Title: A Machine Learning Approach for Player and Position Adjusted Expected Goals in Football (Soccer)\nAbstract: Football is a very result-driven industry, with goals being rarer than in most sports, so having further parameters to judge the performance of teams and individuals is key. Expected Goals (xG) allow further insight than just a scoreline. To tackle the need for further analysis in football, this paper uses machine learning applications that are developed and applied to Football Event data. From the concept, a Binary Classification problem is created whereby a probabilistic valuation is outputted using Logistic Regression and Gradient Boosting based approaches. The model successfully predicts xGs probability values for football players based on 15,575 shots. The proposed solution utilises StatsBomb as the data provider and an industry benchmark to tune the models in the right direction. The proposed ML solution for xG is further used to tackle the age-old cliche of: 'the ball has fallen to the wrong guy there'. The development of the model is used to adjust and gain more realistic values of expected goals than the general models show. To achieve this, this paper tackles Positional Adjusted xG, splitting the training data into Forward, Midfield, and Defence with the aim of providing insight into player qualities based on their positional sub-group. Positional Adjusted xG successfully predicts and proves that more attacking players are better at accumulating xG. The highest value belonged to Forwards followed by Midfielders and Defenders. Finally, this study has further developments into Player Adjusted xG with the aim of proving that Messi is statistically at a higher efficiency level than the average footballer. This is achieved by using Messi subset samples to quantify his qualities in comparison to the average xG models finding that Messi xG performs 347 xG higher than the general model outcome.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 986,
+    "label": 30,
+    "text": "Title: Product Question Answering in E-Commerce: A Survey\nAbstract: Product question answering (PQA), aiming to automatically provide instant responses to customer\u2019s questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing datasets and evaluation protocols for each setting. We further summarize the most significant challenges that characterize PQA from general QA applications and discuss their corresponding solutions. Finally, we conclude this paper by providing the prospect on several future directions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 987,
+    "label": 24,
+    "text": "Title: On the Expressive Power of Geometric Graph Neural Networks\nAbstract: The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and the WL framework are inapplicable for geometric graphs embedded in Euclidean space, such as biomolecules, materials, and other physical systems. In this work, we propose a geometric version of the WL test (GWL) for discriminating geometric graphs while respecting the underlying physical symmetries: permutations, rotation, reflection, and translation. We use GWL to characterise the expressive power of geometric GNNs that are invariant or equivariant to physical symmetries in terms of distinguishing geometric graphs. GWL unpacks how key design choices influence geometric GNN expressivity: (1) Invariant layers have limited expressivity as they cannot distinguish one-hop identical geometric graphs; (2) Equivariant layers distinguish a larger class of graphs by propagating geometric information beyond local neighbourhoods; (3) Higher order tensors and scalarisation enable maximally powerful geometric GNNs; and (4) GWL's discrimination-based perspective is equivalent to universal approximation. Synthetic experiments supplementing our results are available at \\url{https://github.com/chaitjo/geometric-gnn-dojo}",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 988,
+    "label": 16,
+    "text": "Title: HRDFuse: Monocular 360\u00b0 Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions\nAbstract: Depth estimation from a monocular 360\u00b0 image is a burgeoning problem owing to its holistic sensing of a scene. Recently, some methods, e.g., OmniFusion, have applied the tangent projection (TP) to represent a 360\u00b0 image and predicted depth values via patch-wise regressions, which are merged to get a depth map with equirectangular projection (ERP) format. However, these methods suffer from 1) non-trivial process of merging plenty of patches; 2) capturing less holistic-with-regional contextual information by directly regressing the depth value of each pixel. In this paper, we propose a novel framework, HRDFuse, that subtly combines the potential of convolutional neural networks (CNNs) and transformers by collaboratively learning the holistic contextual information from the ERP and the regional structural information from the TP. Firstly, we propose a spatial feature alignment (SFA) module that learns feature similarities between the TP and ERP to aggregate the TP features into a complete ERP feature map in a pixelwise manner. Secondly, we propose a collaborative depth distribution classification (CDDC) module that learns the holistic-with-regional histograms capturing the ERP and TP depth distributions. As such, the final depth values can be predicted as a linear combination of histogram bin centers. Lastly, we adaptively combine the depth predictions from ERP and TP to obtain the final depth map. Extensive experiments show that our method predicts more smooth and accurate depth results while achieving favorably better results than the SOTA methods.",
+    "neighbors": [
+      1005
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 989,
+    "label": 30,
+    "text": "Title: AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents\nAbstract: Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \\framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \\framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \\framework will soon be released at \\url{https://github.com/OpenBMB/AgentVerse}.",
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+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 990,
+    "label": 30,
+    "text": "Title: Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data\nAbstract: Background: Speech and language pathologists (SLPs) often relyon judgements of speech fluency for diagnosing or monitoringpatients with aphasia. However, such subjective methods havebeen criticised for their lack of reliability and their clinical cost interms of time. Aims: This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency in people with aphasia (PWA). Methods&Procedures: Twenty-nine PWA and five control participantswere recruited via non-profit organizations and SLP networks. All participants were recorded while reading out loud a set ofsentences taken from the French version of the Boston Diagnostic Aphasia Examination. Three trained SLPs assessed the fluency of each sentence on a five-point qualitative scale. A forward-backward divergence segmentation and a clustering algorithm were used to compute, for each sentence, four automatic predictors of speech fluency: pseudo-syllable rate, speech ratio, rate of silent breaks, and standard deviation of pseudo-syllable length. The four predictors were finally combined into multivariate regression models (a multiplelinear regression - MLR, and two non-linear models) to predict the average SLP ratings of speech fluency, using a leave-one speaker-out validation scheme. Outcomes&Results: All models achieved accurate predictions of speech fluency ratings, with average root-mean-square errors as low as 0.5. The MLR yielded a correlation coefficient of 0.87 with reference ratings at the sentence level, and of 0.93 when aggregating the data for each participant. The inclusion of an additional predictor sensitive to repetitions improved further the predictions with a correlation coefficient of 0.91 at the sentence level, and of 0.96 at the participant level. Conclusions: The algorithms used in this study can constitute a cost-effective and reliable tool for the assessment of the speech fluency of patients with aphasia in read-aloud tasks. Perspectives for the assessment of spontaneous speech are discussed.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 991,
+    "label": 5,
+    "text": "Title: Correct orchestration of Federated Learning generic algorithms: formalisation and verification in CSP\nAbstract: Federated learning (FL) is a machine learning setting where clients keep the training data decentralised and collaboratively train a model either under the coordination of a central server (centralised FL) or in a peer-to-peer network (decentralised FL). Correct orchestration is one of the main challenges. In this paper, we formally verify the correctness of two generic FL algorithms, a centralised and a decentralised one, using the CSP process calculus and the PAT model checker. The CSP models consist of CSP processes corresponding to generic FL algorithm instances. PAT automatically proves the correctness of the two generic FL algorithms by proving their deadlock freeness (safety property) and successful termination (liveness property). The CSP models are constructed bottom-up by hand as a faithful representation of the real Python code and is automatically checked top-down by PAT.",
+    "neighbors": [
+      395,
+      1161
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 992,
+    "label": 23,
+    "text": "Title: Developers\u2019 Perception of GitHub Actions: A Survey Analysis\nAbstract: GitHub Actions is a powerful tool for automating workflows on GitHub repositories, with thousands of Actions currently available on the GitHub Marketplace. So far, the research community has conducted mining studies on Actions, with much of the focus on CI/CD. However, the motivation and best practices of developers for using, developing, and debugging Actions are unknown. To address this gap, we conducted a survey study with 90 Action users and developers. Our findings indicate that developers prefer Actions with verified creators and more stars when choosing between similar Actions, and often switch to alternative Actions when faced with bugs or a lack of documentation. We also found that developers find the composition of YAML files, which are essential for Action integration, challenging and error-prone. They primarily rely on Q&A forums to fix issues with these YAML files. Finally, we observed that developers would not likely adopt Actions when there are concerns around complexity and security risks. Our study summarizes developers\u2019 perceptions, decision-making process, and challenges in using, developing, and debugging Actions. We provide recommendations for improving the visibility, re-usability, documentation, and support surrounding GitHub Actions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 993,
+    "label": 34,
+    "text": "Title: Superpolynomial Lower Bounds for Learning Monotone Classes\nAbstract: Koch, Strassle, and Tan [SODA 2023], show that, under the randomized exponential time hypothesis, there is no distribution-free PAC-learning algorithm that runs in time $n^{\\tilde O(\\log\\log s)}$ for the classes of $n$-variable size-$s$ DNF, size-$s$ Decision Tree, and $\\log s$-Junta by DNF (that returns a DNF hypothesis). Assuming a natural conjecture on the hardness of set cover, they give the lower bound $n^{\\Omega(\\log s)}$. This matches the best known upper bound for $n$-variable size-$s$ Decision Tree, and $\\log s$-Junta. In this paper, we give the same lower bounds for PAC-learning of $n$-variable size-$s$ Monotone DNF, size-$s$ Monotone Decision Tree, and Monotone $\\log s$-Junta by~DNF. This solves the open problem proposed by Koch, Strassle, and Tan and subsumes the above results. The lower bound holds, even if the learner knows the distribution, can draw a sample according to the distribution in polynomial time, and can compute the target function on all the points of the support of the distribution in polynomial time.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 994,
+    "label": 16,
+    "text": "Title: Object Recognition System on a Tactile Device for Visually Impaired\nAbstract: People with visual impairments face numerous challenges when interacting with their environment. Our objective is to develop a device that facilitates communication between individuals with visual impairments and their surroundings. The device will convert visual information into auditory feedback, enabling users to understand their environment in a way that suits their sensory needs. Initially, an object detection model is selected from existing machine learning models based on its accuracy and cost considerations, including time and power consumption. The chosen model is then implemented on a Raspberry Pi, which is connected to a specifically designed tactile device. When the device is touched at a specific position, it provides an audio signal that communicates the identification of the object present in the scene at that corresponding position to the visually impaired individual. Conducted tests have demonstrated the effectiveness of this device in scene understanding, encompassing static or dynamic objects, as well as screen contents such as TVs, computers, and mobile phones.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 995,
+    "label": 24,
+    "text": "Title: Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces\nAbstract: As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there remains a critical gap in simultaneously exploring and integrating these properties. In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes. We use causal structural models and sensitive attributes to create a fair metric and apply it to measure semantic similarity among individuals. By introducing a novel causal adversarial perturbation and applying adversarial training, we create a new regularizer that combines individual fairness, causality, and robustness in the classifier. Our method is evaluated on both real-world and synthetic datasets, demonstrating its effectiveness in achieving an accurate classifier that simultaneously exhibits fairness, adversarial robustness, and causal awareness.",
+    "neighbors": [
+      1780
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 996,
+    "label": 16,
+    "text": "Title: Efficient Diffusion Training via Min-SNR Weighting Strategy\nAbstract: Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence. In this paper, we discovered that the slow convergence is partly due to conflicting optimization directions between timesteps. To address this issue, we treat the diffusion training as a multi-task learning problem, and introduce a simple yet effective approach referred to as Min-SNR-$\\gamma$. This method adapts loss weights of timesteps based on clamped signal-to-noise ratios, which effectively balances the conflicts among timesteps. Our results demonstrate a significant improvement in converging speed, 3.4$\\times$ faster than previous weighting strategies. It is also more effective, achieving a new record FID score of 2.06 on the ImageNet $256\\times256$ benchmark using smaller architectures than that employed in previous state-of-the-art. The code is available at https://github.com/TiankaiHang/Min-SNR-Diffusion-Training.",
+    "neighbors": [
+      800,
+      1033,
+      1179
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 997,
+    "label": 10,
+    "text": "Title: Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints\nAbstract: Online decision making under uncertainty in partially observable domains, also known as Belief Space Planning, is a fundamental problem in robotics and Artificial Intelligence. Due to an abundance of plausible future unravelings, calculating an optimal course of action inflicts an enormous computational burden on the agent. Moreover, in many scenarios, e.g., information gathering, it is required to introduce a belief-dependent constraint. Prompted by this demand, in this paper, we consider a recently introduced probabilistic belief-dependent constrained POMDP. We present a technique to adaptively accept or discard a candidate action sequence with respect to a probabilistic belief-dependent constraint, before expanding a complete set of future observations samples and without any loss in accuracy. Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e.g., information gain) in terms of Value at Risk for the candidate action sequence with substantial acceleration. On top of that, we introduce an adaptive simplification technique for a probabilistically constrained setting. Such an approach provably returns an identical-quality solution while dramatically accelerating online decision making. Our universal framework applies to any belief-dependent constrained continuous POMDP with parametric beliefs, as well as nonparametric beliefs represented by particles. In the context of an information-theoretic constraint, our presented framework stochastically quantifies if a cumulative information gain along the planning horizon is sufficiently significant (e.g. for, information gathering, active SLAM). We apply our method to active SLAM, a highly challenging problem of high dimensional Belief Space Planning. Extensive realistic simulations corroborate the superiority of our proposed ideas.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 998,
+    "label": 6,
+    "text": "Title: On the Augmentation of Cognitive Accuracy and Cognitive Precision in Human/Cog Ensembles\nAbstract: Whenever humans use tools human performance is enhanced. Cognitive systems are a new kind of tool continually increasing in cognitive capability and are now performing high level cognitive tasks previously thought to be explicitly human. Usage of such tools, known as cogs, are expected to result in ever increasing levels of human cognitive augmentation. In a human cog ensemble, a cooperative, peer to peer, and collaborative dialog between a human and a cognitive system, human cognitive capability is augmented as a result of the interaction. The human cog ensemble is therefore able to achieve more than just the human or the cog working alone. This article presents results from two studies designed to measure the effect information supplied by a cog has on cognitive accuracy, the ability to produce the correct result, and cognitive precision, the propensity to produce only the correct result. Both cognitive accuracy and cognitive precision are shown to be increased by information of different types (policies and rules, examples, and suggestions) and with different kinds of problems (inventive problem solving and puzzles). Similar effects shown in other studies are compared.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 999,
+    "label": 27,
+    "text": "Title: Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware\nAbstract: Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations. Project website: https://tonyzhaozh.github.io/aloha/",
+    "neighbors": [
+      964
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1000,
+    "label": 2,
+    "text": "Title: The Alternating-Time \u03bc-Calculus with Disjunctive Explicit Strategies\nAbstract: Alternating-time temporal logic (ATL) and its extensions, including the alternating-time $\\mu$-calculus (AMC), serve the specification of the strategic abilities of coalitions of agents in concurrent game structures. The key ingredient of the logic are path quantifiers specifying that some coalition of agents has a joint strategy to enforce a given goal. This basic setup has been extended to let some of the agents (revocably) commit to using certain named strategies, as in ATL with explicit strategies (ATLES). In the present work, we extend ATLES with fixpoint operators and strategy disjunction, arriving at the alternating-time $\\mu$-calculus with disjunctive explicit strategies (AMCDES), which allows for a more flexible formulation of temporal properties (e.g. fairness) and, through strategy disjunction, a form of controlled nondeterminism in commitments. Our main result is an ExpTime upper bound for satisfiability checking (which is thus ExpTime-complete). We also prove upper bounds QP (quasipolynomial time) and NP $\\cap$ coNP for model checking under fixed interpretations of explicit strategies, and NP under open interpretation. Our key technical tool is a treatment of the AMCDES within the generic framework of coalgebraic logic, which in particular reduces the analysis of most reasoning tasks to the treatment of a very simple one-step logic featuring only propositional operators and next-step operators without nesting; we give a new model construction principle for this one-step logic that relies on a set-valued variant of first-order resolution.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1001,
+    "label": 30,
+    "text": "Title: A Survey on Evaluation of Large Language Models\nAbstract: Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, educations, natural and social sciences, agent applications, and other areas. Secondly, we answer the `where' and `how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey.",
+    "neighbors": [
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+      2013,
+      2062,
+      2113,
+      2230,
+      2254,
+      2265,
+      2281
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1002,
+    "label": 30,
+    "text": "Title: How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources\nAbstract: In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied by limited evaluation, making it difficult to compare models across the board and determine the utility of various resources. We provide a large set of instruction-tuned models from 6.7B to 65B parameters in size, trained on 12 instruction datasets ranging from manually curated (e.g., OpenAssistant) to synthetic and distilled (e.g., Alpaca) and systematically evaluate them on their factual knowledge, reasoning, multilinguality, coding, and open-ended instruction following abilities through a collection of automatic, model-based, and human-based metrics. We further introduce T\\\"ulu, our best performing instruction-tuned model suite finetuned on a combination of high-quality open resources. Our experiments show that different instruction-tuning datasets can uncover or enhance specific skills, while no single dataset (or combination) provides the best performance across all evaluations. Interestingly, we find that model and human preference-based evaluations fail to reflect differences in model capabilities exposed by benchmark-based evaluations, suggesting the need for the type of systemic evaluation performed in this work. Our evaluations show that the best model in any given evaluation reaches on average 83% of ChatGPT performance, and 68% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap. We release our instruction-tuned models, including a fully finetuned 65B T\\\"ulu, along with our code, data, and evaluation framework at https://github.com/allenai/open-instruct to facilitate future research.",
+    "neighbors": [
+      126,
+      505,
+      529,
+      566,
+      602,
+      644,
+      664,
+      811,
+      1039,
+      1052,
+      1112,
+      1114,
+      1346,
+      1617,
+      1733,
+      1907,
+      1950,
+      1972
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1003,
+    "label": 38,
+    "text": "Title: Beyond the Library Collections: Proceedings of the 2022 Erasmus Staff Training Week at ULi\u00e8ge Library\nAbstract: No library can buy or hold everything its patrons need. At a certain point, librarians need to pool their resources and collaborate to provide access to what they don't have: Collaboration and partnership, centralized and shared collection storage, digitization projects, interlibrary loan and resource sharing, purchase on demand, PDA and EBA are notably key to success. The 2022 edition of the Erasmus Mobility Staff Training week organized at the University of Li\\`ege Library focused on services, projects and policies that libraries can deploy and promote to increase and ease access to materials that do not belong to their print or electronic holdings. More than 20 librarians, managers, and researchers in library science share their experiences and visions in this book. Keywords: Collaboration between libraries; Collection Development; Academic Libraries; Research Libraries; Interlibrary Loan; ILL; Resource Sharing; Purchase on Demand; Patron-Driven Acquisition; PDA; Evidence-Based Acquisition; EBA; Alma; Koha; RapidILL; Impala; Subito; Library systems; Digitalization; Workflows",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1004,
+    "label": 8,
+    "text": "Title: Reliability Analysis of Multi-hop Routing in Multi-tier LEO Satellite Networks\nAbstract: This article studies the reliability of multi-hop routing in a multi-tier hybrid satellite-terrestrial relay network (HSTRN). We evaluate the reliability of multi-hop routing by introducing interruption probability, which is the probability that no relay device (ground gateway or satellite) is available during a hop. The single-hop interruption probability is derived and extended to the multi-hop interruption probability using a stochastic geometry-based approach. Since the interruption probability in HSTRN highly depends on the priority of selecting communication devices at different tiers, we propose three priority strategies: (i) stationary optimal priority strategy, (ii) single-hop interruption probability inspired strategy, and (iii) density inspired strategy. Among them, the interruption probability under the stationary optimal priority strategy can approach the ideal lower bound. However, when analyzing an HSTRN with a large number of tiers, the stationary optimal priority strategy is computationally expensive. The single-hop interruption probability inspired strategy is expected to be a low-complexity but less reliable alternative to the stationary optimal priority strategy. In numerical results, we study the complementarity between terrestrial devices and satellites. Furthermore, analytical results for reliability are also applicable to the analysis of satellite availability, coverage probability, and ultra-reliable and low latency communications (URLLC) rate. Finally, we extend our original routing strategy into a multi-flow one with dynamic priority strategy.",
+    "neighbors": [
+      1522,
+      2084
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1005,
+    "label": 16,
+    "text": "Title: 360$^\\circ$ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer\nAbstract: Recently, omnidirectional images (ODIs) have become increasingly popular; however, their angular resolution tends to be lower than that of perspective images.This leads to degraded structural details such as edges, causing difficulty in learning 3D scene understanding tasks, especially monocular depth estimation. Existing methods typically leverage high-resolution (HR) ODI as the input, so as to recover the structural details via fully-supervised learning. However, the HR depth ground truth (GT) maps may be arduous or expensive to be collected due to resource-constrained devices in practice. Therefore, in this paper, we explore for the first time to estimate the HR omnidirectional depth directly from a low-resolution (LR) ODI, when no HR depth GT map is available. Our key idea is to transfer the scene structural knowledge from the readily available HR image modality and the corresponding LR depth maps to achieve the goal of HR depth estimation without extra inference cost. Specifically, we introduce ODI super-resolution (SR) as an auxiliary task and train both tasks collaboratively in a weakly supervised manner to boost the performance of HR depth estimation. The ODI SR task takes an LR ODI as the input to predict an HR image, enabling us to extract the scene structural knowledge via uncertainty estimation. Buttressed by this, a scene structural knowledge transfer (SSKT) module is proposed with two key components. First, we employ a cylindrical implicit interpolation function (CIIF) to learn cylindrical neural interpolation weights for feature up-sampling and share the parameters of CIIFs between the two tasks. Then, we propose a feature distillation (FD) loss that provides extra structural regularization to help the HR depth estimation task learn more scene structural knowledge.",
+    "neighbors": [
+      988
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1006,
+    "label": 16,
+    "text": "Title: Industrial Segment Anything - a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul\nAbstract: Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge.",
+    "neighbors": [
+      341,
+      535,
+      584,
+      1052,
+      1068,
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+      1365,
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+      2233,
+      2272
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1007,
+    "label": 16,
+    "text": "Title: LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding\nAbstract: Instruction tuning unlocks the superior capability of Large Language Models (LLM) to interact with humans. Furthermore, recent instruction-following datasets include images as visual inputs, collecting responses for image-based instructions. However, visual instruction-tuned models cannot comprehend textual details within images well. This work enhances the current visual instruction tuning pipeline with text-rich images (e.g., movie posters, book covers, etc.). Specifically, we first use publicly available OCR tools to collect results on 422K text-rich images from the LAION dataset. Moreover, we prompt text-only GPT-4 with recognized texts and image captions to generate 16K conversations, each containing question-answer pairs for text-rich images. By combining our collected data with previous multi-modal instruction-following data, our model, LLaVAR, substantially improves the LLaVA model's capability on text-based VQA datasets (up to 20% accuracy improvement) while achieving an accuracy of 91.42% on ScienceQA. The GPT-4-based instruction-following evaluation also demonstrates the improvement of our model on both natural images and text-rich images. Through qualitative analysis, LLaVAR shows promising interaction (e.g., reasoning, writing, and elaboration) skills with humans based on the latest real-world online content that combines text and images. We make our code/data/models publicly available at https://llavar.github.io/.",
+    "neighbors": [
+      126,
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+      1668,
+      1899,
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1008,
+    "label": 16,
+    "text": "Title: HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention\nAbstract: The success of large-scale contrastive vision-language pretraining (CLIP) has benefited both visual recognition and multimodal content understanding. The concise design brings CLIP the advantage in inference efficiency against other vision-language models with heavier cross-attention fusion layers, making it a popular choice for a wide spectrum of downstream tasks. However, CLIP does not explicitly capture the hierarchical nature of high-level and fine-grained semantics conveyed in images and texts, which is arguably critical to vision-language understanding and reasoning. To this end, we equip both the visual and language branches in CLIP with hierarchy-aware attentions, namely Hierarchy-aware CLIP (HiCLIP), to progressively discover semantic hierarchies layer-by-layer from both images and texts in an unsupervised manner. As a result, such hierarchical aggregation significantly improves the cross-modal alignment. To demonstrate the advantages of HiCLIP, we conduct qualitative analysis on its unsupervised hierarchy induction during inference, as well as extensive quantitative experiments on both visual recognition and vision-language downstream tasks.",
+    "neighbors": [
+      848
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1009,
+    "label": 3,
+    "text": "Title: Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and A Case Study\nAbstract: Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain's role in sustainability, we propose a three-layers structure encapsulating four green utilities: Recording and Tracking, Wide Verification, Value Trading, and Concept Disseminating. Nori, a decentralized voluntary carbon offset project, serves as our case, illuminating these utilities. Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset. We argue that blockchain's contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1010,
+    "label": 8,
+    "text": "Title: Is 30 MHz Enough for C-V2X?\nAbstract: Connected vehicles are no longer a futuristic dream coming out of a science fiction, but they are swiftly taking a bigger part of one's everyday life. One of the key technologies actualizing the connected vehicles is vehicle-to-everything communications (V2X). Nonetheless, the United States (U.S.) federal government decided to reallocate the spectrum band that used to be dedicated to V2X uses (namely, the ``5.9 GHz band'') and to leave only 40\\% of the original chunk (i.e., 30 MHz of bandwidth) for V2X. It ignited concern of whether the 30-MHz spectrum suffices key V2X safety messages and the respective applications. We lay out an extensive study on the safety message types and their latency requirements. Then, we present our simulation results examining whether they can be supported in the 30-MHz spectrum setup.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1011,
+    "label": 30,
+    "text": "Title: Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes\nAbstract: Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset. We release the code at: https://github.com/google-research/distilling-step-by-step .",
+    "neighbors": [
+      570,
+      619,
+      945,
+      1613
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1012,
+    "label": 24,
+    "text": "Title: Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation\nAbstract: Data valuation, a critical aspect of data-centric ML research, aims to quantify the usefulness of individual data sources in training machine learning (ML) models. However, data valuation faces significant yet frequently overlooked privacy challenges despite its importance. This paper studies these challenges with a focus on KNN-Shapley, one of the most practical data valuation methods nowadays. We first emphasize the inherent privacy risks of KNN-Shapley, and demonstrate the significant technical difficulties in adapting KNN-Shapley to accommodate differential privacy (DP). To overcome these challenges, we introduce TKNN-Shapley, a refined variant of KNN-Shapley that is privacy-friendly, allowing for straightforward modifications to incorporate DP guarantee (DP-TKNN-Shapley). We show that DP-TKNN-Shapley has several advantages and offers a superior privacy-utility tradeoff compared to naively privatized KNN-Shapley in discerning data quality. Moreover, even non-private TKNN-Shapley achieves comparable performance as KNN-Shapley. Overall, our findings suggest that TKNN-Shapley is a promising alternative to KNN-Shapley, particularly for real-world applications involving sensitive data.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1013,
+    "label": 2,
+    "text": "Title: Model Checking Time Window Temporal Logic for Hyperproperties\nAbstract: Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties in domains such as cyber-physical systems, smart grids, and automotive. This paper introduces a model checking algorithm for a new formalism, HyperTWTL, which extends Time Window Temporal Logic (TWTL) -- a domain-specific formal specification language for robotics, by allowing explicit and simultaneous quantification over multiple execution traces. We present HyperTWTL with both \\emph{synchronous} and \\emph{asynchronous} semantics, based on the alignment of the timestamps in the traces. Consequently, we demonstrate the application of HyperTWTL in formalizing important information-flow security policies and concurrency for robotics applications. Finally, we propose a model checking algorithm for verifying fragments of HyperTWTL by reducing the problem to a TWTL model checking problem.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1014,
+    "label": 23,
+    "text": "Title: Repairing DNN Architecture: Are We There Yet?\nAbstract: As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems, software developers are increasingly required to design, train, and deploy such models into the systems they develop. Consequently, testing and improving the robustness of these models have received a lot of attention lately. However, relatively little effort has been made to address the difficulties developers experience when designing and training such models: if the evaluation of a model shows poor performance after the initial training, what should the developer change? We survey and evaluate existing state-of-the-art techniques that can be used to repair model performance, using a benchmark of both real-world mistakes developers made while designing DNN models and artificial faulty models generated by mutating the model code. The empirical evaluation shows that random baseline is comparable with or sometimes outperforms existing state-of-the-art techniques. However, for larger and more complicated models, all repair techniques fail to find fixes. Our findings call for further research to develop more sophisticated techniques for Deep Learning repair.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1015,
+    "label": 3,
+    "text": "Title: The Language Labyrinth: Constructive Critique on the Terminology Used in the AI Discourse\nAbstract: In the interdisciplinary field of artificial intelligence (AI) the problem of clear terminology is especially momentous. This paper claims, that AI debates are still characterised by a lack of critical distance to metaphors like \u2018training\u2019, \u2018learning\u2019 or \u2018deciding\u2019. As consequence, reflections regarding responsibility or potential use-cases are greatly distorted. Yet, if relevant decision-makers are convinced that AI can develop an \u2018understanding\u2019 or properly \u2018interpret\u2019 issues, its regular use for sensitive tasks like deciding about social benefits or judging court cases looms. The chapter argues its claim by analysing central notions of the AI debate and tries to contribute by proposing more fitting terminology and hereby enabling more fruitful debates. It is a conceptual work at the intersection of critical computer science and philosophy of language.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1016,
+    "label": 17,
+    "text": "Title: Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-Number Field\nAbstract: Estimating normals with globally consistent orientations for a raw point cloud has many downstream geometry processing applications. Despite tremendous efforts in the past decades, it remains challenging to deal with an unoriented point cloud with various imperfections, particularly in the presence of data sparsity coupled with nearby gaps or thin-walled structures. In this paper, we propose a smooth objective function to characterize the requirements of an acceptable winding-number field, which allows one to find the globally consistent normal orientations starting from a set of completely random normals. By taking the vertices of the Voronoi diagram of the point cloud as examination points, we consider the following three requirements: (1) the winding number is either 0 or 1, (2) the occurrences of 1 and the occurrences of 0 are balanced around the point cloud, and (3) the normals align with the outside Voronoi poles as much as possible. Extensive experimental results show that our method outperforms the existing approaches, especially in handling sparse and noisy point clouds, as well as shapes with complex geometry/topology.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1017,
+    "label": 13,
+    "text": "Title: Particularity\nAbstract: We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges. We trace the development of this\"particularity\"approach from the use of lexicase selection in genetic programming to\"particularist\"approaches to other forms of machine learning and to the design of adaptive systems more generally.",
+    "neighbors": [
+      157,
+      1850
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1018,
+    "label": 6,
+    "text": "Title: Modelling communication-enabled traffic interactions\nAbstract: A major challenge for autonomous vehicles is handling interactions with human-driven vehicles\u2014for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver\u2019s behaviours. We demonstrate our model\u2019s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model\u2019s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1019,
+    "label": 16,
+    "text": "Title: Burstormer: Burst Image Restoration and Enhancement Transformer\nAbstract: On a shutter press, modern handheld cameras capture multiple images in rapid succession and merge them to gen-erate a single image. However, individual frames in a burst are misaligned due to inevitable motions and contain mul-tiple degradations. The challenge is to properly align the successive image shots and merge their complimentary in-formation to achieve high-quality outputs. Towards this direction, we propose Burstormer: a novel transformer-based architecture for burst image restoration and enhancement. In comparison to existing works, our approach exploits multi-scale local and non-local features to achieve improved alignment and feature fusion. Our key idea is to enable inter-frame communication in the burst neighborhoods for information aggregation and progressive fusion while modeling the burst-wide context. However, the input burst frames need to be properly aligned before fusing their information. Therefore, we propose an enhanced de-formable alignment module for aligning burst features with regards to the reference frame. Unlike existing methods, the proposed alignment module not only aligns burst features but also exchanges fea-ture information and maintains focused communication with the reference frame through the proposed reference-based feature enrichment mechanism, which facilitates handling complex motions. After multi-level alignment and enrichment, we re-emphasize on inter-frame communication within burst using a cyclic burst sampling module. Finally, the inter-frame information is aggre-gated using the proposed burst feature fusion module followed by progressive upsampling. Our Burstormer outperforms state-of-the-art methods on burst super-resolution, burst denoising and burst low-light enhance-ment. Our codes and pre-trained models are available at https://github.com/akshaydudhane16/Burstormer.",
+    "neighbors": [
+      394
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1020,
+    "label": 16,
+    "text": "Title: Dreamix: Video Diffusion Models are General Video Editors\nAbstract: Text-driven image and video diffusion models have recently achieved unprecedented generation realism. While diffusion models have been successfully applied for image editing, very few works have done so for video editing. We present the first diffusion-based method that is able to perform text-based motion and appearance editing of general videos. Our approach uses a video diffusion model to combine, at inference time, the low-resolution spatio-temporal information from the original video with new, high resolution information that it synthesized to align with the guiding text prompt. As obtaining high-fidelity to the original video requires retaining some of its high-resolution information, we add a preliminary stage of finetuning the model on the original video, significantly boosting fidelity. We propose to improve motion editability by a new, mixed objective that jointly finetunes with full temporal attention and with temporal attention masking. We further introduce a new framework for image animation. We first transform the image into a coarse video by simple image processing operations such as replication and perspective geometric projections, and then use our general video editor to animate it. As a further application, we can use our method for subject-driven video generation. Extensive qualitative and numerical experiments showcase the remarkable editing ability of our method and establish its superior performance compared to baseline methods.",
+    "neighbors": [
+      330,
+      860,
+      957,
+      1251,
+      1420,
+      1539,
+      1590,
+      1707,
+      1935,
+      2085,
+      2135
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1021,
+    "label": 24,
+    "text": "Title: SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot\nAbstract: We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time. SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible with weight quantization approaches. The code is available at: https://github.com/IST-DASLab/sparsegpt.",
+    "neighbors": [
+      619,
+      2184
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1022,
+    "label": 23,
+    "text": "Title: Transfer learning for conflict and duplicate detection in software requirement pairs\nAbstract: Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as a requirement pair classification task. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task. Furthermore, we apply supervised multi-stage fine-tuning to the pre-trained transformer models. We test the performance of different transfer models using four different datasets. We find that sequentially trained and fine-tuned transformer models perform well across the datasets with SR-BERT achieving the best performance for larger datasets. We also explore the cross-domain performance of conflict detection models and adopt a rule-based filtering approach to validate the model classifications. Our analysis indicates that the sentence pair classification approach and the proposed transformer-based natural language processing strategies can contribute significantly to achieving automation in conflict and duplicate detection",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1023,
+    "label": 24,
+    "text": "Title: Bayesian Self-Supervised Contrastive Learning\nAbstract: Recent years have witnessed many successful applications of contrastive learning in diverse domains, yet its self-supervised version still remains many exciting challenges. As the negative samples are drawn from unlabeled datasets, a randomly selected sample may be actually a false negative to an anchor, leading to incorrect encoder training. This paper proposes a new self-supervised contrastive loss called the BCL loss that still uses random samples from the unlabeled data while correcting the resulting bias with importance weights. The key idea is to design the desired sampling distribution for sampling hard true negative samples under the Bayesian framework. The prominent advantage lies in that the desired sampling distribution is a parametric structure, with a location parameter for debiasing false negative and concentration parameter for mining hard negative, respectively. Experiments validate the effectiveness and superiority of the BCL loss.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1024,
+    "label": 6,
+    "text": "Title: About Engaging and Governing Strategies: A Thematic Analysis of Dark Patterns in Social Networking Services\nAbstract: Research in HCI has shown a growing interest in unethical design practices across numerous domains, often referred to as \u201cdark patterns\u201d. There is, however, a gap in related literature regarding social networking services (SNSs). In this context, studies emphasise a lack of users\u2019 self-determination regarding control over personal data and time spent on SNSs. We collected over 16 hours of screen recordings from Facebook\u2019s, Instagram\u2019s, TikTok\u2019s, and Twitter\u2019s mobile applications to understand how dark patterns manifest in these SNSs. For this task, we turned towards HCI experts to mitigate possible difficulties of non-expert participants in recognising dark patterns, as prior studies have noticed. Supported by the recordings, two authors of this paper conducted a thematic analysis based on previously described taxonomies, manually classifying the recorded material while delivering two key findings: We observed which instances occur in SNSs and identified two strategies \u2014 engaging and governing \u2014 with five dark patterns undiscovered before.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1025,
+    "label": 16,
+    "text": "Title: Auditing Gender Presentation Differences in Text-to-Image Models\nAbstract: Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide range of downstream applications, the distributions of these generated images are still not fully understood, especially when it comes to the potential stereotypical attributes of different genders. In this work, we propose a paradigm (Gender Presentation Differences) that utilizes fine-grained self-presentation attributes to study how gender is presented differently in text-to-image models. By probing gender indicators in the input text (e.g.,\"a woman\"or\"a man\"), we quantify the frequency differences of presentation-centric attributes (e.g.,\"a shirt\"and\"a dress\") through human annotation and introduce a novel metric: GEP. Furthermore, we propose an automatic method to estimate such differences. The automatic GEP metric based on our approach yields a higher correlation with human annotations than that based on existing CLIP scores, consistently across three state-of-the-art text-to-image models. Finally, we demonstrate the generalization ability of our metrics in the context of gender stereotypes related to occupations.",
+    "neighbors": [
+      1710
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1026,
+    "label": 30,
+    "text": "Title: Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models\nAbstract: Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage. Unfortunately, demonstrations are hard to acquire, and can result in undesirable biased usage if the wrong demonstration is chosen. Even in the rare scenario that demonstrations are readily available, there is no principled selection protocol to determine how many and which ones to provide. As tasks grow more complex, the selection search grows combinatorially and invariably becomes intractable. Our work provides an alternative to demonstrations: tool documentation. We advocate the use of tool documentation, descriptions for the individual tool usage, over demonstrations. We substantiate our claim through three main empirical findings on 6 tasks across both vision and language modalities. First, on existing benchmarks, zero-shot prompts with only tool documentation are sufficient for eliciting proper tool usage, achieving performance on par with few-shot prompts. Second, on a newly collected realistic tool-use dataset with hundreds of available tool APIs, we show that tool documentation is significantly more valuable than demonstrations, with zero-shot documentation significantly outperforming few-shot without documentation. Third, we highlight the benefits of tool documentations by tackling image generation and video tracking using just-released unseen state-of-the-art models as tools. Finally, we highlight the possibility of using tool documentation to automatically enable new applications: by using nothing more than the documentation of GroundingDino, Stable Diffusion, XMem, and SAM, LLMs can re-invent the functionalities of the just-released Grounded-SAM and Track Anything models.",
+    "neighbors": [
+      57,
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+      1047,
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+      1084,
+      1128,
+      1467,
+      1659,
+      1668,
+      1863,
+      1893,
+      2286
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1027,
+    "label": 30,
+    "text": "Title: Towards Expert-Level Medical Question Answering with Large Language Models\nAbstract: Recent artificial intelligence (AI) systems have reached milestones in\"grand challenges\"ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long been viewed as one such grand challenge. Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a\"passing\"score in US Medical Licensing Examination (USMLE) style questions with a score of 67.2% on the MedQA dataset. However, this and other prior work suggested significant room for improvement, especially when models' answers were compared to clinicians' answers. Here we present Med-PaLM 2, which bridges these gaps by leveraging a combination of base LLM improvements (PaLM 2), medical domain finetuning, and prompting strategies including a novel ensemble refinement approach. Med-PaLM 2 scored up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19% and setting a new state-of-the-art. We also observed performance approaching or exceeding state-of-the-art across MedMCQA, PubMedQA, and MMLU clinical topics datasets. We performed detailed human evaluations on long-form questions along multiple axes relevant to clinical applications. In pairwise comparative ranking of 1066 consumer medical questions, physicians preferred Med-PaLM 2 answers to those produced by physicians on eight of nine axes pertaining to clinical utility (p<0.001). We also observed significant improvements compared to Med-PaLM on every evaluation axis (p<0.001) on newly introduced datasets of 240 long-form\"adversarial\"questions to probe LLM limitations. While further studies are necessary to validate the efficacy of these models in real-world settings, these results highlight rapid progress towards physician-level performance in medical question answering.",
+    "neighbors": [
+      907,
+      1142,
+      1267,
+      1651
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1028,
+    "label": 30,
+    "text": "Title: Information Extraction from Documents: Question Answering vs Token Classification in real-world setups\nAbstract: Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in both natural language processing (NLP) and computer vision helped building document-focused pre-training methods, leveraging a multimodal understanding of the document text, layout and image modalities. However, these breakthroughs also led to the emergence of a new DocKIE subtask of extractive document Question Answering (DocQA), as part of the Machine Reading Comprehension (MRC) research field. In this work, we compare the Question Answering approach with the classical token classification approach for document key information extraction. We designed experiments to benchmark five different experimental setups : raw performances, robustness to noisy environment, capacity to extract long entities, fine-tuning speed on Few-Shot Learning and finally Zero-Shot Learning. Our research showed that when dealing with clean and relatively short entities, it is still best to use token classification-based approach, while the QA approach could be a good alternative for noisy environment or long entities use-cases.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1029,
+    "label": 37,
+    "text": "Title: Desbordante: from benchmarking suite to high-performance science-intensive data profiler (preprint)\nAbstract: Pioneering data profiling",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1030,
+    "label": 25,
+    "text": "Title: Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features\nAbstract: Roman Numeral analysis is the important task of identifying chords and their functional context in pieces of tonal music. This paper presents a new approach to automatic Roman Numeral analysis in symbolic music. While existing techniques rely on an intermediate lossy representation of the score, we propose a new method based on Graph Neural Networks (GNNs) that enable the direct description and processing of each individual note in the score. The proposed architecture can leverage notewise features and interdependencies between notes but yield onset-wise representation by virtue of our novel edge contraction algorithm. Our results demonstrate that ChordGNN outperforms existing state-of-the-art models, achieving higher accuracy in Roman Numeral analysis on the reference datasets. In addition, we investigate variants of our model using proposed techniques such as NADE, and post-processing of the chord predictions. The full source code for this work is available at https://github.com/manoskary/chordgnn",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1031,
+    "label": 37,
+    "text": "Title: SMARTFEAT: Efficient Feature Construction through Feature-Level Foundation Model Interactions\nAbstract: Before applying data analytics or machine learning to a data set, a vital step is usually the construction of an informative set of features from the data. In this paper, we present SMARTFEAT, an efficient automated feature engineering tool to assist data users, even non-experts, in constructing useful features. Leveraging the power of Foundation Models (FMs), our approach enables the creation of new features from the data, based on contextual information and open-world knowledge. To achieve this, our method incorporates an intelligent operator selector that discerns a subset of operators, effectively avoiding exhaustive combinations of original features, as is typically observed in traditional automated feature engineering tools. Moreover, we address the limitations of performing data tasks through row-level interactions with FMs, which could lead to significant delays and costs due to excessive API calls. To tackle this, we introduce a function generator that facilitates the acquisition of efficient data transformations, such as dataframe built-in methods or lambda functions, ensuring the applicability of SMARTFEAT to generate new features for large datasets. With SMARTFEAT, dataset users can efficiently search for and apply transformations to obtain new features, leading to improvements in the AUC of downstream ML classification by up to 29.8%.",
+    "neighbors": [
+      1304,
+      2136
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1032,
+    "label": 25,
+    "text": "Title: In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1033,
+    "label": 25,
+    "text": "Title: Noise2Music: Text-conditioned Music Generation with Diffusion Models\nAbstract: We introduce Noise2Music, where a series of diffusion models is trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one using a spectrogram and the other using audio with lower fidelity. We find that the generated audio is not only able to faithfully reflect key elements of the text prompt such as genre, tempo, instruments, mood, and era, but goes beyond to ground fine-grained semantics of the prompt. Pretrained large language models play a key role in this story -- they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. Generated examples: https://google-research.github.io/noise2music",
+    "neighbors": [
+      736,
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+      1380,
+      1536,
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+      2205
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1034,
+    "label": 30,
+    "text": "Title: M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models\nAbstract: Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligence for large language models (LLMs), as they inherently demand a much wider range of abilities such as language understanding, domain knowledge, and problem-solving skills. To this end, we introduce M3Exam, a novel benchmark sourced from real and official human exam questions for evaluating LLMs in a multilingual, multimodal, and multilevel context. M3Exam exhibits three unique characteristics: (1) multilingualism, encompassing questions from multiple countries that require strong multilingual proficiency and cultural knowledge; (2) multimodality, accounting for the multimodal nature of many exam questions to test the model's multimodal understanding capability; and (3) multilevel structure, featuring exams from three critical educational periods to comprehensively assess a model's proficiency at different levels. In total, M3Exam contains 12,317 questions in 9 diverse languages with three educational levels, where about 23\\% of the questions require processing images for successful solving. We assess the performance of top-performing LLMs on M3Exam and find that current models, including GPT-4, still struggle with multilingual text, particularly in low-resource and non-Latin script languages. Multimodal LLMs also perform poorly with complex multimodal questions. We believe that M3Exam can be a valuable resource for comprehensively evaluating LLMs by examining their multilingual and multimodal abilities and tracking their development. Data and evaluation code is available at \\url{https://github.com/DAMO-NLP-SG/M3Exam}.",
+    "neighbors": [
+      143,
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+      1735,
+      1863,
+      1952,
+      2062
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1035,
+    "label": 10,
+    "text": "Title: On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective\nAbstract: ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1036,
+    "label": 30,
+    "text": "Title: Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions\nAbstract: nan",
+    "neighbors": [
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1037,
+    "label": 30,
+    "text": "Title: Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes\nAbstract: Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82.",
+    "neighbors": [
+      2099
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1038,
+    "label": 8,
+    "text": "Title: Metro Access Network with Convergence of Coherent and Analog RoF Data Services\nAbstract: Efficient use of spectral resources will be an important aspect of converged access network deployment. This work analyzes the performance of variable bandwidth Analog Radio-over-Fiber signals transmitted in the unfilled spectral spaces of telecom-grade ROADM channels dedicated for coherent signals transmission over the OpenIreland testbed.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1039,
+    "label": 30,
+    "text": "Title: Instruction Tuning with GPT-4\nAbstract: Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instruction-following data for LLM finetuning. Our early experiments on instruction-tuned LLaMA models show that the 52K English and Chinese instruction-following data generated by GPT-4 leads to superior zero-shot performance on new tasks to the instruction-following data generated by previous state-of-the-art models. We also collect feedback and comparison data from GPT-4 to enable a comprehensive evaluation and reward model training. We make our data generated using GPT-4 as well as our codebase publicly available.",
+    "neighbors": [
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1040,
+    "label": 34,
+    "text": "Title: Distributed Symmetry Breaking on Power Graphs via Sparsification\nAbstract: In this paper we present efficient distributed algorithms for classical symmetry breaking problems, maximal independent sets (MIS) and ruling sets, in power graphs. We work in the standard CONGEST model of distributed message passing, where the communication network is abstracted as a graph G. Typically, the problem instance in CONGEST is identical to the communication network G, that is, we perform the symmetry breaking in G. In this work, we consider a setting where the problem instance corresponds to a power graph Gk, where each node of the communication network G is connected to all of its k-hop neighbors. A \u03b2-ruling set is a set of non-adjacent nodes such that each node in G has a ruling neighbor within \u03b2 hops; a natural generalization of an MIS. On top of being a natural family of problems, ruling sets (in power graphs) are well-motivated through their applications in the powerful shattering framework [BEPS JACM'16, Ghaffari SODA'19] (and others). We present randomized algorithms for computing maximal independent sets and ruling sets of Gk in essentially the same time as they can be computed in G. Our main contribution is a deterministic poly(k, log n) time algorithm for computing k-ruling sets of Gk, which (for k > 1) improves exponentially on the current state-of-the-art runtimes. Our main technical ingredient for this result is a deterministic sparsification procedure which may be of independent interest. We also revisit the shattering algorithm for MIS [BEPS J'ACM'16] and present different approaches for the post-shattering phase. Our solutions are algorithmically and analytically simpler (also in the LOCAL model) than existing solutions and obtain the same runtime as [Ghaffari SODA'16].",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1041,
+    "label": 24,
+    "text": "Title: Meta Temporal Point Processes\nAbstract: A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs). We introduce context sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce local history matching to help learn more informative features. We demonstrate the potential of the proposed method on popular public benchmark datasets and tasks, and compare with state-of-the-art TPP methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1042,
+    "label": 16,
+    "text": "Title: MagicEdit: High-Fidelity and Temporally Coherent Video Editing\nAbstract: In this report, we present MagicEdit, a surprisingly simple yet effective solution to the text-guided video editing task. We found that high-fidelity and temporally coherent video-to-video translation can be achieved by explicitly disentangling the learning of content, structure and motion signals during training. This is in contradict to most existing methods which attempt to jointly model both the appearance and temporal representation within a single framework, which we argue, would lead to degradation in per-frame quality. Despite its simplicity, we show that MagicEdit supports various downstream video editing tasks, including video stylization, local editing, video-MagicMix and video outpainting.",
+    "neighbors": [
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+      1902,
+      1935,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1043,
+    "label": 13,
+    "text": "Title: Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference\nAbstract: Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1044,
+    "label": 30,
+    "text": "Title: Sparks of Artificial General Intelligence: Early experiments with GPT-4\nAbstract: Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1045,
+    "label": 11,
+    "text": "Title: SICO: Simulation for Infection Control Operations\nAbstract: In response to the COVID-19 pandemic and the potential threat of future epidemics caused by novel viruses, we developed a flexible framework for modeling disease intervention effects. This tool is intended to aid decision makers at multiple levels as they compare possible responses to emerging epidemiological threats for optimal control and reduction of harm. The framework is specifically designed to be both scalable and modular, allowing it to model a variety of population levels, viruses, testing methods and strategies--including pooled testing--and intervention strategies. In this paper, we provide an overview of this framework and examine the impact of different intervention strategies and their impact on infection dynamics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1046,
+    "label": 16,
+    "text": "Title: Attentional Graph Convolutional Network for Structure-Aware Audiovisual Scene Classification\nAbstract: Audiovisual scene understanding is a challenging problem due to the unstructured spatial\u2013temporal relations that exist in the audio signals and spatial layouts of different objects in the visual images. Recently, many studies have focused on abstracting features from convolutional neural networks, while the learning of explicit semantically relevant frames of sound signals and visual images has been overlooked. To this end, we present an end-to-end framework, namely, attentional graph convolutional network (AGCN), for structure-aware audiovisual scene representation. First, the spectrogram of sound and input image is processed by a backbone network for feature extraction. Then, to build multiscale hierarchical information of input signals, we utilize an attention fusion mechanism to aggregate features from multiple layers of the backbone network. Notably, to well represent the salient regions and contextual information, the salient acoustic graph (SAG) and contextual acoustic graph (CAG), salient visual graph (SVG), and contextual visual graph (CVG) are constructed for the audiovisual scene representation. Finally, the constructed graphs pass through a graph convolutional network for structure-aware audiovisual scene recognition. Extensive experimental results on the audio, visual, and audiovisual scene recognition datasets show that promising results have been achieved by the AGCN methods. We have achieved 90.6 precision on the ADVANCE dataset, showing a 20.5% improvement over the previous method. Visualizing graphs on the spectrograms and images have been presented to show the effectiveness of the proposed CAG/SAG and CVG/SVG that could focus on the salient and semantic relevant regions.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1047,
+    "label": 24,
+    "text": "Title: PaLM-E: An Embodied Multimodal Language Model\nAbstract: Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1048,
+    "label": 13,
+    "text": "Title: A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning\nAbstract: Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model\u2019s rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model\u2019s dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC\u2019s recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain\u2019s mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1049,
+    "label": 16,
+    "text": "Title: Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model\nAbstract: This paper investigates the problem of the current HOI detection methods and introduces DiffHOI, a novel HOI detection scheme grounded on a pre-trained text-image diffusion model, which enhances the detector's performance via improved data diversity and HOI representation. We demonstrate that the internal representation space of a frozen text-to-image diffusion model is highly relevant to verb concepts and their corresponding context. Accordingly, we propose an adapter-style tuning method to extract the various semantic associated representation from a frozen diffusion model and CLIP model to enhance the human and object representations from the pre-trained detector, further reducing the ambiguity in interaction prediction. Moreover, to fill in the gaps of HOI datasets, we propose SynHOI, a class-balance, large-scale, and high-diversity synthetic dataset containing over 140K HOI images with fully triplet annotations. It is built using an automatic and scalable pipeline designed to scale up the generation of diverse and high-precision HOI-annotated data. SynHOI could effectively relieve the long-tail issue in existing datasets and facilitate learning interaction representations. Extensive experiments demonstrate that DiffHOI significantly outperforms the state-of-the-art in regular detection (i.e., 41.50 mAP) and zero-shot detection. Furthermore, SynHOI can improve the performance of model-agnostic and backbone-agnostic HOI detection, particularly exhibiting an outstanding 11.55% mAP improvement in rare classes.",
+    "neighbors": [
+      616,
+      1835,
+      2009
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1050,
+    "label": 16,
+    "text": "Title: Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models\nAbstract: The AI community has been pursuing algorithms known as artificial general intelligence (AGI) that apply to any kind of real-world problem. Recently, chat systems powered by large language models (LLMs) emerge and rapidly become a promising direction to achieve AGI in natural language processing (NLP), but the path towards AGI in computer vision (CV) remains unclear. One may owe the dilemma to the fact that visual signals are more complex than language signals, yet we are interested in finding concrete reasons, as well as absorbing experiences from GPT and LLMs to solve the problem. In this paper, we start with a conceptual definition of AGI and briefly review how NLP solves a wide range of tasks via a chat system. The analysis inspires us that unification is the next important goal of CV. But, despite various efforts in this direction, CV is still far from a system like GPT that naturally integrates all tasks. We point out that the essential weakness of CV lies in lacking a paradigm to learn from environments, yet NLP has accomplished the task in the text world. We then imagine a pipeline that puts a CV algorithm (i.e., an agent) in world-scale, interactable environments, pre-trains it to predict future frames with respect to its action, and then fine-tunes it with instruction to accomplish various tasks. We expect substantial research and engineering efforts to push the idea forward and scale it up, for which we share our perspectives on future research directions.",
+    "neighbors": [
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1051,
+    "label": 23,
+    "text": "Title: A Framework for Successful Corporate Cloud Transformation\nAbstract: Corporate Cloud transformation is expected to continue to grow double-digit each of the next few years. This growth is augmented by digital transformation, which itself is gaining huge momentum due to the recent consumer behavior trends and especially the COVID pandemic. It is also estimated that globally billions of dollars are wasted due to efficiencies in the way cloud migrations are launched and handled. This paper discusses a framework using which organizations can successfully execute cloud transformation.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1052,
+    "label": 30,
+    "text": "Title: LLaMA: Open and Efficient Foundation Language Models\nAbstract: We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.",
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1053,
+    "label": 4,
+    "text": "Title: Icicle: A Re-designed Emulator for Grey-Box Firmware Fuzzing\nAbstract: Emulation-based fuzzers enable testing binaries without source code and facilitate testing embedded applications where automated execution on the target hardware architecture is difficult and slow. The instrumentation techniques added to extract feedback and guide input mutations towards generating effective test cases is at the core of modern fuzzers. But, modern emulation-based fuzzers have evolved by re-purposing general-purpose emulators; consequently, developing and integrating fuzzing techniques, such as instrumentation methods, is difficult and often added in an ad-hoc manner, specific to an instruction set architecture (ISA). This limits state-of-the-art fuzzing techniques to a few ISAs such as x86/x86-64 or ARM/AArch64; a significant problem for firmware fuzzing of diverse ISAs. This study presents our efforts to re-think emulation for fuzzing. We design and implement a fuzzing-specific, multi-architecture emulation framework\u2014Icicle. We demonstrate the capability to add instrumentation once, in an architecture agnostic manner, with low execution overhead. We employ Icicle as the emulator for a state-of-the-art ARM firmware fuzzer\u2014Fuzzware\u2014and replicate results. Significantly, we demonstrate the availability of new instrumentation in Icicle enabled the discovery of new bugs. We demonstrate the fidelity of Icicle and efficacy of architecture agnostic instrumentation by discovering bugs in benchmarks that require a known and specific operational capability of instrumentation techniques, across a diverse set of instruction set architectures (x86-64, ARM/AArch64, RISC-V, MIPS). Further, to demonstrate the effectiveness of Icicle to discover bugs in a currently unsupported architecture in emulation-based fuzzers, we perform a fuzzing campaign with real-world firmware binaries for Texas Instruments\u2019 MSP430 ISA and discovered 7 new bugs.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1054,
+    "label": 10,
+    "text": "Title: Learning Assumption-based Argumentation Frameworks\nAbstract: We propose a novel approach to logic-based learning which generates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. These ABA frameworks can be mapped onto logic programs with negation as failure that may be non-stratified. Whereas existing argumentation-based methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformation rules, including some adapted from logic program transformation rules (notably folding) as well as others, such as rote learning and assumption introduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we also propose a variant that handles the non-stratified case. We illustrate the benefits of our approach with a number of examples, which show that, on one hand, we are able to easily reconstruct other logic-based learning approaches and, on the other hand, we can work out in a very simple and natural way problems that seem to be hard for existing techniques.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1055,
+    "label": 30,
+    "text": "Title: Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA\nAbstract: Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. \u2018Selective prediction\u2019 partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question \u2018what to do after abstention\u2019. To this end, we present an explorative study on \u2018Post-Abstention\u2019, a task that allows re-attempting the abstained instances with the aim of increasing **coverage** of the system without significantly sacrificing its **accuracy**. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements \u2013performance metric of the Post-Abstention task\u2013 both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.",
+    "neighbors": [
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1056,
+    "label": 24,
+    "text": "Title: On the Privacy-Robustness-Utility Trilemma in Distributed Learning\nAbstract: The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied independently in distributed ML, their synthesis remains poorly understood. We present the first tight analysis of the error incurred by any algorithm ensuring robustness against a fraction of adversarial machines, as well as differential privacy (DP) for honest machines' data against any other curious entity. Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility. To prove our lower bound, we consider the case of mean estimation, subject to distributed DP and robustness constraints, and devise reductions to centralized estimation of one-way marginals. We prove our matching upper bound by presenting a new distributed ML algorithm using a high-dimensional robust aggregation rule. The latter amortizes the dependence on the dimension in the error (caused by adversarial workers and DP), while being agnostic to the statistical properties of the data.",
+    "neighbors": [
+      675,
+      1448
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1057,
+    "label": 24,
+    "text": "Title: Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain\nAbstract: Real-world applications with multiple sensors observing an event are expected to make continuously-available predictions, even in cases where information may be intermittently missing. We explore methods in ensemble learning and sensor fusion to make use of redundancy and information shared between four camera views, applied to the task of hand activity classi\ufb01cation for autonomous driving. In particular, we show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model. To enable this approach, we propose a scheme for handling missing information, and then provide comparative analysis of this late-fusion approach to additional methods such as weighted majority voting and model combination schemes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1058,
+    "label": 4,
+    "text": "Title: A Post Quantum Key Agreement Protocol Based on a Modified Matrix Power Function over a Rectangular Matrices Semiring\nAbstract: We present an improved post quantum version of Sakalauskas matrix power function key agreement protocol, using rectangular matrices instead of the original square ones. Sakalauskas matrix power function is an efficient and secure way to generate a shared secret key, and using rectangular matrices provides additional flexibility and security. This method reduces the computational burden by allowing smaller random integer matrices while maintaining equal security. Another advantage of using the rank deficient rectangular matrices over key agreement protocols is that it blocks linearization attacks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1059,
+    "label": 23,
+    "text": "Title: Applying and Extending the Delta Debugging Algorithm for Elevator Dispatching Algorithms (Experience Paper)\nAbstract: Elevator systems are one kind of Cyber-Physical Systems (CPSs), and as such, test cases are usually complex and long in time. This is mainly because realistic test scenarios are employed (e.g., for testing elevator dispatching algorithms, typically a full day of passengers traveling through a system of elevators is used). However, in such a context, when needing to reproduce a failure, it is of high benefit to provide the minimal test input to the software developers. This way, analyzing and trying to localize the root-cause of the failure is easier and more agile. Delta debugging has been found to be an efficient technique to reduce failure-inducing test inputs. In this paper, we enhance this technique by first monitoring the environment at which the CPS operates as well as its physical states. With the monitored information, we search for stable states of the CPS during the execution of the simulation. In a second step, we use such identified stable states to help the delta debugging algorithm isolate the failure-inducing test inputs more efficiently. We report our experience of applying our approach into an industrial elevator dispatching algorithm. An empirical evaluation carried out with real operational data from a real installation of elevators suggests that the proposed environment-wise delta debugging algorithm is between 1.3 to 1.8 times faster than the traditional delta debugging, while producing a larger reduction in the failure-inducing test inputs. The results provided by the different implemented delta debugging algorithm versions are qualitatively assessed with domain experts. This assessment provides new insights and lessons learned, such as, potential applications of the delta debugging algorithm beyond debugging.",
+    "neighbors": [
+      1404
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1060,
+    "label": 16,
+    "text": "Title: Visual Tuning\nAbstract: Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.",
+    "neighbors": [
+      25,
+      319,
+      613,
+      1047,
+      2002
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1061,
+    "label": 24,
+    "text": "Title: DNNShifter: An Efficient DNN Pruning System for Edge Computing\nAbstract: Deep neural networks (DNNs) underpin many machine learning applications. Production quality DNN models achieve high inference accuracy by training millions of DNN parameters which has a significant resource footprint. This presents a challenge for resources operating at the extreme edge of the network, such as mobile and embedded devices that have limited computational and memory resources. To address this, models are pruned to create lightweight, more suitable variants for these devices. Existing pruning methods are unable to provide similar quality models compared to their unpruned counterparts without significant time costs and overheads or are limited to offline use cases. Our work rapidly derives suitable model variants while maintaining the accuracy of the original model. The model variants can be swapped quickly when system and network conditions change to match workload demand. This paper presents DNNShifter, an end-to-end DNN training, spatial pruning, and model switching system that addresses the challenges mentioned above. At the heart of DNNShifter is a novel methodology that prunes sparse models using structured pruning. The pruned model variants generated by DNNShifter are smaller in size and thus faster than dense and sparse model predecessors, making them suitable for inference at the edge while retaining near similar accuracy as of the original dense model. DNNShifter generates a portfolio of model variants that can be swiftly interchanged depending on operational conditions. DNNShifter produces pruned model variants up to 93x faster than conventional training methods. Compared to sparse models, the pruned model variants are up to 5.14x smaller and have a 1.67x inference latency speedup, with no compromise to sparse model accuracy. In addition, DNNShifter has up to 11.9x lower overhead for switching models and up to 3.8x lower memory utilisation than existing approaches.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1062,
+    "label": 1,
+    "text": "Title: FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI\nAbstract: Light detection and ranging (LiDAR) sensors are becoming available on modern mobile devices and provide a 3D sensing capability. This new capability is beneficial for perceptions in various use cases, but it is challenging for resource-constrained mobile devices to use the perceptions in real-time because of their high computational complexity. In this context, edge computing can be used to enable LiDAR online perceptions, but offloading the perceptions on the edge server requires a low-latency, lightweight, and efficient compression due to the large volume of LiDAR point clouds data. This paper presents FLiCR, a fast and lightweight LiDAR point cloud compression method for enabling edge-assisted online perceptions. FLiCR is based on range images (RI) as an intermediate representation (IR), and dictionary coding for compressing RIs. FLiCR achieves its benefits by leveraging lossy RIs, and we show the efficiency of bytestream compression is largely improved with quantization and subsampling. In addition, we identify the limitation of current quality metrics for presenting the entropy of a point cloud, and introduce a new metric that reflects both point-wise and entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more suitable for edge-assisted real-time perceptions than the existing LiDAR compressions, and we demonstrate the effectiveness of our compression and metric with the evaluations on 3D object detection and LiDAR SLAM.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1063,
+    "label": 4,
+    "text": "Title: Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards\nAbstract: Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1064,
+    "label": 30,
+    "text": "Title: NBIAS: A Natural Language Processing Framework for Bias Identification in Text\nAbstract: Bias in textual data can lead to skewed interpretations and outcomes when the data is used. These biases could perpetuate stereotypes, discrimination, or other forms of unfair treatment. An algorithm trained on biased data may end up making decisions that disproportionately impact a certain group of people. Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data. To this end, we develop a comprehensive and robust framework NBIAS that consists of four main layers: data, corpus construction, model development and an evaluation layer. The dataset is constructed by collecting diverse data from various domains, including social media, healthcare, and job hiring portals. As such, we applied a transformer-based token classification model that is able to identify bias words/ phrases through a unique named entity BIAS. In the evaluation procedure, we incorporate a blend of quantitative and qualitative measures to gauge the effectiveness of our models. We achieve accuracy improvements ranging from 1% to 8% compared to baselines. We are also able to generate a robust understanding of the model functioning. The proposed approach is applicable to a variety of biases and contributes to the fair and ethical use of textual data.",
+    "neighbors": [
+      2225
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1065,
+    "label": 24,
+    "text": "Title: Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization\nAbstract: Adversarial contrastive learning (ACL), without requiring labels, incorporates adversarial data with standard contrastive learning (SCL) and outputs a robust representation which is generalizable and resistant to adversarial attacks and common corruptions. The style-independence property of representations has been validated to be beneficial in improving robustness transferability. Standard invariant regularization (SIR) has been proposed to make the learned representations via SCL to be independent of the style factors. However, how to equip robust representations learned via ACL with the style-independence property is still unclear so far. To this end, we leverage the technique of causal reasoning to propose an adversarial invariant regularization (AIR) that enforces robust representations learned via ACL to be style-independent. Then, we enhance ACL using invariant regularization (IR), which is a weighted sum of SIR and AIR. Theoretically, we show that AIR implicitly encourages the prediction of adversarial data and consistency between adversarial and natural data to be independent of data augmentations. We also theoretically demonstrate that the style-independence property of robust representation learned via ACL still holds in downstream tasks, providing generalization guarantees. Empirically, our comprehensive experimental results corroborate that IR can significantly improve the performance of ACL and its variants on various datasets.",
+    "neighbors": [
+      502,
+      2256
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1066,
+    "label": 31,
+    "text": "Title: KuaiSAR: A Unified Search And Recommendation Dataset\nAbstract: The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in joint optimization using user behavior data from both S&R services. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 350 million daily active users. Previous research in this field has predominantly employed publicly available semi-synthetic datasets and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR contains genuine user behaviors, including the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and utilizing search data for recommender systems (and recommendation data for search engines). Furthermore, due to the various feedback labels associated with user-video interactions, KuaiSAR also supports a broad range of tasks, including intent recommendation, multi-task learning, and modeling of long sequential multi-behavioral patterns. We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.",
+    "neighbors": [
+      2143
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1067,
+    "label": 34,
+    "text": "Title: Logarithmic Weisfeiler-Leman Identifies All Graphs of Bounded Rank Width\nAbstract: In this paper, we extend the work of Grohe&Neuen (ACM T. Comput. Log., 2023) to show that the $(6k+3)$-dimensional Weisfeiler--Leman (WL) algorithm can identify graphs of rank width $k$ using only $O(\\log n)$ rounds. As a consequence, we obtain that graphs of bounded rank width are identified by $\\textsf{FO} + \\textsf{C}$ formulas with $6k+4$ variables and quantifier depth $O(\\log n)$. Furthermore, in light of the parallel WL implementation due to Grohe&Verbitsky (ICALP 2006), we obtain $\\textsf{TC}^{1}$ upper bounds for isomorphism testing of graphs of bounded rank width. Prior to this paper, isomorphism testing for graphs of bounded rank width was not known to be in $\\textsf{NC}$.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1068,
+    "label": 16,
+    "text": "Title: RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation\nAbstract: The Segment Anything Model (SAM) has gained significant attention for its impressive performance in image segmentation. However, it lacks proficiency in referring video object segmentation (RVOS) due to the need for precise user-interactive prompts and limited understanding of different modalities, such as language and vision. This paper presents the RefSAM model, which for the first time explores the potential of SAM for RVOS by incorporating multi-view information from diverse modalities and successive frames at different timestamps. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-Modal MLP that projects the text embedding of the referring expression into sparse and dense embeddings, serving as user-interactive prompts. Subsequently, a parameter-efficient tuning strategy is employed to effectively align and fuse the language and vision features. Through comprehensive ablation studies, we demonstrate the practical and effective design choices of our strategy. Extensive experiments conducted on Ref-Youtu-VOS and Ref-DAVIS17 datasets validate the superiority and effectiveness of our RefSAM model over existing methods. The code and models will be made publicly at \\href{https://github.com/LancasterLi/RefSAM}{github.com/LancasterLi/RefSAM}.",
+    "neighbors": [
+      584,
+      719,
+      1006,
+      1084,
+      1365,
+      1932,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1069,
+    "label": 27,
+    "text": "Title: Large Language Models as Zero-Shot Human Models for Human-Robot Interaction\nAbstract: Human models play a crucial role in human-robot interaction (HRI), enabling robots to consider the impact of their actions on people and plan their behavior accordingly. However, crafting good human models is challenging; capturing context-dependent human behavior requires significant prior knowledge and/or large amounts of interaction data, both of which are difficult to obtain. In this work, we explore the potential of large-language models (LLMs) -- which have consumed vast amounts of human-generated text data -- to act as zero-shot human models for HRI. Our experiments on three social datasets yield promising results; the LLMs are able to achieve performance comparable to purpose-built models. That said, we also discuss current limitations, such as sensitivity to prompts and spatial/numerical reasoning mishaps. Based on our findings, we demonstrate how LLM-based human models can be integrated into a social robot's planning process and applied in HRI scenarios. Specifically, we present one case study on a simulated trust-based table-clearing task and replicate past results that relied on custom models. Next, we conduct a new robot utensil-passing experiment (n = 65) where preliminary results show that planning with a LLM-based human model can achieve gains over a basic myopic plan. In summary, our results show that LLMs offer a promising (but incomplete) approach to human modeling for HRI.",
+    "neighbors": [
+      855,
+      1983
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1070,
+    "label": 23,
+    "text": "Title: Triple Graph Grammars for Multi-version Models\nAbstract: Like conventional software projects, projects in model-driven software engineering require adequate management of multiple versions of development artifacts, importantly allowing living with temporary inconsistencies. In the case of model-driven software engineering, employed versioning approaches also have to handle situations where different artifacts, that is, different models, are linked via automatic model transformations. In this report, we propose a technique for jointly handling the transformation of multiple versions of a source model into corresponding versions of a target model, which enables the use of a more compact representation that may afford improved execution time of both the transformation and further analysis operations. Our approach is based on the well-known formalism of triple graph grammars and a previously introduced encoding of model version histories called multi-version models. In addition to showing the correctness of our approach with respect to the standard semantics of triple graph grammars, we conduct an empirical evaluation that demonstrates the potential benefit regarding execution time performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1071,
+    "label": 16,
+    "text": "Title: Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic\nAbstract: In human conversations, individuals can indicate relevant regions within a scene while addressing others. In turn, the other person can then respond by referring to specific regions if necessary. This natural referential ability in dialogue remains absent in current Multimodal Large Language Models (MLLMs). To fill this gap, this paper proposes an MLLM called Shikra, which can handle spatial coordinate inputs and outputs in natural language. Its architecture consists of a vision encoder, an alignment layer, and a LLM. It is designed to be straightforward and simple, without the need for extra vocabularies, position encoder, pre-/post-detection modules, or external plug-in models. All inputs and outputs are in natural language form. Referential dialogue is a superset of various vision-language (VL) tasks. Shikra can naturally handle location-related tasks like REC and PointQA, as well as conventional VL tasks such as Image Captioning and VQA. Experimental results showcase Shikra's promising performance. Furthermore, it enables numerous exciting applications, like providing mentioned objects' coordinates in chains of thoughts and comparing user-pointed regions similarities. Our code, model and dataset are accessed at https://github.com/shikras/shikra.",
+    "neighbors": [
+      0,
+      34,
+      754,
+      887,
+      1047,
+      1129,
+      1148,
+      1344,
+      1459,
+      1485,
+      1540,
+      1765,
+      2036,
+      2095,
+      2155,
+      2286
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1072,
+    "label": 3,
+    "text": "Title: Redistricting for Proportionality\nAbstract: Abstract American democracy is currently heavily reliant on plurality in single-member districts, or PSMD, as a system of election. But public perceptions of fairness are often keyed to partisan proportionality, or the degree of congruence between each party\u2019s share of the vote and its share of representation. PSMD has not tended to secure proportional outcomes historically, partially due to gerrymandering, where line-drawers intentionally extract more advantage for their side. But it is now increasingly clear that even blind PSMD is frequently disproportional, and in unpredictable ways that depend on local political geography. In this paper we consider whether it is feasible to bring PSMD into alignment with a proportionality norm by targeting proportional outcomes in the design and selection of districts. We do this mainly through a close examination of the \u201cFreedom to Vote Test,\u201d a redistricting reform proposed in draft legislation in 2021. We find that applying the test with a proportionality target makes for sound policy: it performs well in legal battleground states and has a workable exception to handle edge cases where proportionality is out of reach.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1073,
+    "label": 3,
+    "text": "Title: Identifying shifts in multi-modal travel patterns during special events using mobile data: Celebrating Vappu in Helsinki\nAbstract: Large urban special events significantly contribute to a city's vibrancy and economic growth but concurrently impose challenges on transportation systems due to alterations in mobility patterns. This study aims to shed light on mobility patterns by utilizing a unique, comprehensive dataset collected from the Helsinki public transport mobile application and Bluetooth beacons. Earlier methods, relying on mobile phone records or focusing on single traffic modes, do not fully grasp the intricacies of travel behavior during such events. We focus on the Vappu festivities (May 1st) in the Helsinki Metropolitan Area, a national holiday characterized by mass gatherings and outdoor activities. We examine and compare multi-modal mobility patterns during the event with those during typical non-working days in May 2022. Through this case study, we find that people tend to favor public transport over private cars and are prepared to walk longer distances to participate in the event. The study underscores the value of using comprehensive multi-modal data to better understand and manage transportation during large-scale events.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1074,
+    "label": 24,
+    "text": "Title: Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla\nAbstract: \\emph{Circuit analysis} is a promising technique for understanding the internal mechanisms of language models. However, existing analyses are done in small models far from the state of the art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. In particular, we study multiple-choice question answering, and investigate Chinchilla's capability to identify the correct answer \\emph{label} given knowledge of the correct answer \\emph{text}. We find that the existing techniques of logit attribution, attention pattern visualization, and activation patching naturally scale to Chinchilla, allowing us to identify and categorize a small set of `output nodes' (attention heads and MLPs). We further study the `correct letter' category of attention heads aiming to understand the semantics of their features, with mixed results. For normal multiple-choice question answers, we significantly compress the query, key and value subspaces of the head without loss of performance when operating on the answer labels for multiple-choice questions, and we show that the query and key subspaces represent an `Nth item in an enumeration' feature to at least some extent. However, when we attempt to use this explanation to understand the heads' behaviour on a more general distribution including randomized answer labels, we find that it is only a partial explanation, suggesting there is more to learn about the operation of `correct letter' heads on multiple choice question answering.",
+    "neighbors": [
+      1863,
+      2060
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1075,
+    "label": 10,
+    "text": "Title: Safe Policy Improvement for POMDPs via Finite-State Controllers\nAbstract: We study safe policy improvement (SPI) for partially observable Markov decision processes (POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1) historical data about an environment, and (2) the so-called behavior policy that previously generated this data by interacting with the environment. SPI methods neither require access to a model nor the environment itself, and aim to reliably improve upon the behavior policy in an offline manner. Existing methods make the strong assumption that the environment is fully observable. In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies. This assumption allows us to map the POMDP to a finite-state fully observable MDP, the history MDP. We estimate this MDP by combining the historical data and the memory of the FSC, and compute an improved policy using an off-the-shelf SPI algorithm. The underlying SPI method constrains the policy space according to the available data, such that the newly computed policy only differs from the behavior policy when sufficient data is available. We show that this new policy, converted into a new FSC for the (unknown) POMDP, outperforms the behavior policy with high probability. Experimental results on several well-established benchmarks show the applicability of the approach, even in cases where finite memory is not sufficient.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1076,
+    "label": 30,
+    "text": "Title: Do PLMs Know and Understand Ontological Knowledge?\nAbstract: Ontological knowledge, which comprises classes and properties and their relationships, is integral to world knowledge. It is significant to explore whether Pretrained Language Models (PLMs) know and understand such knowledge. However, existing PLM-probing studies focus mainly on factual knowledge, lacking a system- atic probing of ontological knowledge. In this paper, we focus on probing whether PLMs store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form. To probe whether PLMs know ontological knowledge, we investigate how well PLMs memorize: (1) types of entities; (2) hierarchical relationships among classes and properties, e.g., Person is a subclass of Animal and Member of Sports Team is a subproperty of Member of ; (3) domain and range constraints of properties, e.g., the subject of Member of Sports Team should be a Person and the object should be a Sports Team. To further probe whether PLMs truly understand ontological knowledge beyond memorization, we comprehensively study whether they can reliably perform logical reasoning with given knowledge according to ontological entailment rules. Our probing results show that PLMs can memorize certain ontological knowledge and utilize implicit knowledge in reasoning. How- ever, both the memorizing and reasoning per- formances are less than perfect, indicating in- complete knowledge and understanding.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1077,
+    "label": 26,
+    "text": "Title: Identifying and analysing toxic actors and communities on Facebook by employing network analysis\nAbstract: There has been an increasingly widespread agreement among both academic circles and the general public that the Social Media Platforms (SMPs) play a central role in the dissemination of harmful and negative sentiment content in a coordinated manner. A substantial body of recent scholarly research has demonstrated the ways in which hateful content, political propaganda, and targeted messaging on SMPs have contributed to serious real-world consequences. Adopting inspirations from graph theory, in this paper we apply novel network and community finding algorithms over a representative Facebook dataset (n=608,417) which we have scrapped through 630 pages. By applying Girvan-Newman algorithm over the historical dataset our analysis finds five communities of coordinated networks of actors, within the contexts of Indian far-right Hindutva discourse. This work further paves the path for future potentials of applying such novel network analysis algorithms to SMPs, in order to automatically identify toxic coordinated communities and sub-communities, and to possibly resist real-world threats emerging from information dissemination in the SMPs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1078,
+    "label": 23,
+    "text": "Title: A context-aware multiple Blockchain architecture for managing low memory devices\nAbstract: Blockchain technology constitutes a paradigm shift in the way we conceive distributed architectures. A Blockchain system lets us build platforms where data are immutable and tamper-proof, with some constraints on the throughput and the amount of memory required to store the ledger. This paper aims to solve the issue of memory and performance requirements developing a multiple Blockchain architecture that mixes the benefits deriving from a public and a private Blockchain. This kind of approach enables small sensors - with memory and performance constraints - to join the network without worrying about the amount of data to store. The development is proposed following a context-aware approach, to make the architecture scalable and easy to use in different scenarios.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1079,
+    "label": 16,
+    "text": "Title: A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions\nAbstract: Recently, there has been significant progress in the development of large models. Following the success of ChatGPT, numerous language models have been introduced, demonstrating remarkable performance. Similar advancements have also been observed in image generation models, such as Google's Imagen model, OpenAI's DALL-E 2, and stable diffusion models, which have exhibited impressive capabilities in generating images. However, similar to large language models, these models still encounter unresolved challenges. Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework. This survey aims to examine the existing issues and the current solutions pertaining to image generation models.",
+    "neighbors": [
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+      2183,
+      2242,
+      2306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1080,
+    "label": 16,
+    "text": "Title: ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation\nAbstract: Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous-domain knowledge can be hardly directly deployed to a new domain without additional costs. In this paper, we provide a new perspective and approach of alleviating the domain shifts, by proposing a Reconstruction-Simulation-Perception (ReSimAD) scheme. Specifically, the implicit reconstruction process is based on the knowledge from the previous old domain, aiming to convert the domain-related knowledge into domain-invariant representations, \\textit{e.g.}, 3D scene-level meshes. Besides, the point clouds simulation process of multiple new domains is conditioned on the above reconstructed 3D meshes, where the target-domain-like simulation samples can be obtained, thus reducing the cost of collecting and annotating new-domain data for the subsequent perception process. For experiments, we consider different cross-domain situations such as Waymo-to-KITTI, Waymo-to-nuScenes, Waymo-to-ONCE, \\textit{etc}, to verify the \\textbf{zero-shot} target-domain perception using ReSimAD. Results demonstrate that our method is beneficial to boost the domain generalization ability, even promising for 3D pre-training.",
+    "neighbors": [
+      1358,
+      1628,
+      1901
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1081,
+    "label": 24,
+    "text": "Title: Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories\nAbstract: In this paper, we define, evaluate, and improve the ``relay-generalization'' performance of reinforcement learning (RL) agents on the out-of-distribution ``controllable'' states. Ideally, an RL agent that generally masters a task should reach its goal starting from any controllable state of the environment instead of memorizing a small set of trajectories. For example, a self-driving system should be able to take over the control from humans in the middle of driving and must continue to drive the car safely. To practically evaluate this type of generalization, we start the test agent from the middle of other independently well-trained \\emph{stranger} agents' trajectories. With extensive experimental evaluation, we show the prevalence of \\emph{generalization failure} on controllable states from stranger agents. For example, in the Humanoid environment, we observed that a well-trained Proximal Policy Optimization (PPO) agent, with only 3.9\\% failure rate during regular testing, failed on 81.6\\% of the states generated by well-trained stranger PPO agents. To improve\"relay generalization,\"we propose a novel method called Self-Trajectory Augmentation (STA), which will reset the environment to the agent's old states according to the Q function during training. After applying STA to the Soft Actor Critic's (SAC) training procedure, we reduced the failure rate of SAC under relay-evaluation by more than three times in most settings without impacting agent performance and increasing the needed number of environment interactions. Our code is available at https://github.com/lan-lc/STA.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1082,
+    "label": 28,
+    "text": "Title: Unsourced Massive Access-Based Digital Over-the-Air Computation for Efficient Federated Edge Learning\nAbstract: Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in OBDA can result in a serious information loss and slower convergence of FEEL. To overcome this limitation, this paper proposes an unsourced massive access (UMA)-based generalized digital OAC (GD-OAC) scheme. Specifically, at the transmitter, all the devices share the same non-orthogonal UMA codebook for uplink transmission. The local model update of each device is quantized based on the same quantization codebook. Then, each device transmits a sequence selected from the UMA codebook based on the quantized elements of its model update. At the receiver, we propose an approximate message passing-based algorithm for efficient UMA detection and model aggregation. Simulation results show that the proposed GD-OAC scheme significantly accelerates the FEEL convergences compared with the state-of-the-art OBDA scheme while using the same uplink communication resources.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1083,
+    "label": 6,
+    "text": "Title: Mysterious and Manipulative Black Boxes: A Qualitative Analysis of Perceptions on Recommender Systems\nAbstract: Recommender systems are used to provide relevant suggestions on various matters. Although these systems are a classical research topic, knowledge is still limited regarding the public opinion about these systems. Public opinion is also important because the systems are known to cause various problems. To this end, this paper presents a qualitative analysis of the perceptions of ordinary citizens, civil society groups, businesses, and others on recommender systems in Europe. The dataset examined is based on the answers submitted to a consultation about the Digital Services Act (DSA) recently enacted in the European Union (EU). Therefore, not only does the paper contribute to the pressing question about regulating new technologies and online platforms, but it also reveals insights about the policy-making of the DSA. According to the qualitative results, Europeans have generally negative opinions about recommender systems and the quality of their recommendations. The systems are widely seen to violate privacy and other fundamental rights. According to many Europeans, these also cause various societal problems, including even threats to democracy. Furthermore, existing regulations in the EU are commonly seen to have failed due to a lack of proper enforcement. Numerous suggestions were made by the respondents to the consultation for improving the situation, but only a few of these ended up to the DSA.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1084,
+    "label": 16,
+    "text": "Title: Track Anything: Segment Anything Meets Videos\nAbstract: Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model (TAM), which achieves high-performance interactive tracking and segmentation in videos. To be detailed, given a video sequence, only with very little human participation, i.e., several clicks, people can track anything they are interested in, and get satisfactory results in one-pass inference. Without additional training, such an interactive design performs impressively on video object tracking and segmentation. All resources are available on {https://github.com/gaomingqi/Track-Anything}. We hope this work can facilitate related research.",
+    "neighbors": [
+      106,
+      341,
+      719,
+      1006,
+      1026,
+      1050,
+      1068,
+      1207,
+      1262,
+      1932,
+      2233
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1085,
+    "label": 2,
+    "text": "Title: The 19th International Workshop on Termination (WST 2023): Preface, Invited Talk Abstract, and Tool Descriptions\nAbstract: This report contains the proceedings of the 19th International Workshop on Termination (WST 2023), which was held in Obergurgl during August 24--25 as part of Obergurgl Summer on Rewriting (OSR 2023).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1086,
+    "label": 24,
+    "text": "Title: Backpropagation-free Training of Deep Physical Neural Networks\nAbstract: Recent years have witnessed the outstanding success of deep learning in various fields such as vision and natural language processing. This success is largely indebted to the massive size of deep learning models that is expected to increase unceasingly. This growth of the deep learning models is accompanied by issues related to their considerable energy consumption, both during the training and inference phases, as well as their scalability. Although a number of work based on unconventional physical systems have been proposed which addresses the issue of energy efficiency in the inference phase, efficient training of deep learning models has remained unaddressed. So far, training of digital deep learning models mainly relies on backpropagation, which is not suitable for physical implementation as it requires perfect knowledge of the computation performed in the so-called forward pass of the neural network. Here, we tackle this issue by proposing a simple deep neural network architecture augmented by a biologically plausible learning algorithm, referred to as\"model-free forward-forward training\". The proposed architecture enables training deep physical neural networks consisting of layers of physical nonlinear systems, without requiring detailed knowledge of the nonlinear physical layers' properties. We show that our method outperforms state-of-the-art hardware-aware training methods by improving training speed, decreasing digital computations, and reducing power consumption in physical systems. We demonstrate the adaptability of the proposed method, even in systems exposed to dynamic or unpredictable external perturbations. To showcase the universality of our approach, we train diverse wave-based physical neural networks that vary in the underlying wave phenomenon and the type of non-linearity they use, to perform vowel and image classification tasks experimentally.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1087,
+    "label": 16,
+    "text": "Title: Critical Points ++: An Agile Point Cloud Importance Measure for Robust Classification, Adversarial Defense and Explainable AI\nAbstract: The ability to cope accurately and fast with Out-Of-Distribution (OOD) samples is crucial in real-world safety demanding applications. In this work we first study the interplay between critical points of 3D point clouds and OOD samples. Our findings are that common corruptions and outliers are often interpreted as critical points. We generalize the notion of critical points into importance measures. We show that training a classification network based only on less important points dramatically improves robustness, at a cost of minor performance loss on the clean set. We observe that normalized entropy is highly informative for corruption analysis. An adaptive threshold based on normalized entropy is suggested for selecting the set of uncritical points. Our proposed importance measure is extremely fast to compute. We show it can be used for a variety of applications, such as Explainable AI (XAI), Outlier Removal, Uncertainty Estimation, Robust Classification and Adversarial Defense. We reach SOTA results on the two latter tasks. Code is available at: https://github.com/yossilevii100/critical_points2",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1088,
+    "label": 24,
+    "text": "Title: Monitoring and Adapting ML Models on Mobile Devices\nAbstract: ML models are increasingly being pushed to mobile devices, for low-latency inference and offline operation. However, once the models are deployed, it is hard for ML operators to track their accuracy, which can degrade unpredictably (e.g., due to data drift). We design the first end-to-end system for continuously monitoring and adapting models on mobile devices without requiring feedback from users. Our key observation is that often model degradation is due to a specific root cause, which may affect a large group of devices. Therefore, once the system detects a consistent degradation across a large number of devices, it employs a root cause analysis to determine the origin of the problem and applies a cause-specific adaptation. We evaluate the system on two computer vision datasets, and show it consistently boosts accuracy compared to existing approaches. On a dataset containing photos collected from driving cars, our system improves the accuracy on average by 15%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1089,
+    "label": 24,
+    "text": "Title: Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation\nAbstract: In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN\u2013Flux with PCA augmentation, achieves AUROC and AUPR scores exceeding 0.94 for each classification on average. In addition to predicting eventual failures with high accuracy, ANN\u2013Flux achieves comparable remaining useful life RMSE for the same test split of the dataset when benchmarked against past work, with significantly less computational cost.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1090,
+    "label": 34,
+    "text": "Title: Light, Reliable Spanners\nAbstract: A \\emph{$\\nu$-reliable spanner} of a metric space $(X,d)$, is a (dominating) graph $H$, such that for any possible failure set $B\\subseteq X$, there is a set $B^+$ just slightly larger $|B^+|\\le(1+\\nu)\\cdot|B|$, and all distances between pairs in $X\\setminus B^+$ are (approximately) preserved in $H\\setminus B$. Recently, there have been several works on sparse reliable spanners in various settings, but so far, the weight of such spanners has not been analyzed at all. In this work, we initiate the study of \\emph{light} reliable spanners, whose weight is proportional to that of the Minimum Spanning Tree (MST) of $X$. We first observe that unlike sparsity, the lightness of any deterministic reliable spanner is huge, even for the metric of the simple path graph. Therefore, randomness must be used: an \\emph{oblivious} reliable spanner is a distribution over spanners, and the bound on $|B^+|$ holds in expectation. We devise an oblivious $\\nu$-reliable $(2+\\frac{2}{k-1})$-spanner for any $k$-HST, whose lightness is $\\approx \\nu^{-2}$. We demonstrate a matching $\\Omega(\\nu^{-2})$ lower bound on the lightness (for any finite stretch). We also note that any stretch below 2 must incur linear lightness. For general metrics, doubling metrics, and metrics arising from minor-free graphs, we construct {\\em light} tree covers, in which every tree is a $k$-HST of low weight. Combining these covers with our results for $k$-HSTs, we obtain oblivious reliable light spanners for these metric spaces, with nearly optimal parameters. In particular, for doubling metrics we get an oblivious $\\nu$-reliable $(1+\\varepsilon)$-spanner with lightness $\\varepsilon^{-O({\\rm ddim})}\\cdot\\tilde{O}(\\nu^{-2}\\cdot\\log n)$, which is best possible (up to lower order terms).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1091,
+    "label": 4,
+    "text": "Title: Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework\nAbstract: We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a framework with a dynamic server selection and study convergence and security conditions. The implementation and results are publicly available1.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1092,
+    "label": 31,
+    "text": "Title: ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation\nAbstract: With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.",
+    "neighbors": [
+      3,
+      840,
+      1052,
+      1182,
+      1611,
+      1667,
+      1762
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1093,
+    "label": 5,
+    "text": "Title: Advising OpenMP Parallelization via a Graph-Based Approach with Transformers\nAbstract: There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing this need today is OpenMP. Nevertheless, writing parallel code manually is complex and effort-intensive. Thus, many deterministic source-to-source (S2S) compilers have emerged, intending to automate the process of translating serial to parallel code. However, recent studies have shown that these compilers are impractical in many scenarios. In this work, we combine the latest advancements in the field of AI and natural language processing (NLP) with the vast amount of open-source code to address the problem of automatic parallelization. Specifically, we propose a novel approach, called OMPify, to detect and predict the OpenMP pragmas and shared-memory attributes in parallel code, given its serial version. OMPify is based on a Transformer-based model that leverages a graph-based representation of source code that exploits the inherent structure of code. We evaluated our tool by predicting the parallelization pragmas and attributes of a large corpus of (over 54,000) snippets of serial code written in C and C++ languages (Open-OMP-Plus). Our results demonstrate that OMPify outperforms existing approaches, the general-purposed and popular ChatGPT and targeted PragFormer models, in terms of F1 score and accuracy. Specifically, OMPify achieves up to 90% accuracy on commonly-used OpenMP benchmark tests such as NAS, SPEC, and PolyBench. Additionally, we performed an ablation study to assess the impact of different model components and present interesting insights derived from the study. Lastly, we also explored the potential of using data augmentation and curriculum learning techniques to improve the model's robustness and generalization capabilities.",
+    "neighbors": [
+      1562,
+      1956
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1094,
+    "label": 3,
+    "text": "Title: Tackling Hate Speech in Low-resource Languages with Context Experts\nAbstract: Given Myanmar\u2019s historical and socio-political context, hate speech spread on social media have escalated into offline unrest and violence. This paper presents findings from our remote study on the automatic detection of hate speech online in Myanmar. We argue that effectively addressing this problem will require community-based approaches that combine the knowledge of context experts with machine learning tools that can analyze the vast amount of data produced. To this end, we develop a systematic process to facilitate this collaboration covering key aspects of data collection, annotation, and model validation strategies. We highlight challenges in this area stemming from small and imbalanced datasets, the need to balance non-glamorous data work and stakeholder priorities, and closed data-sharing practices. Stemming from these findings, we discuss avenues for further work in developing and deploying hate speech detection systems for low-resource languages.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1095,
+    "label": 4,
+    "text": "Title: A Highly Accurate Query-Recovery Attack against Searchable Encryption using Non-Indexed Documents\nAbstract: Cloud data storage solutions offer customers cost-effective and reduced data management. While attractive, data security issues remain to be a core concern. Traditional encryption protects stored documents, but hinders simple functionalities such as keyword search. Therefore, searchable encryption schemes have been proposed to allow for the search on encrypted data. Efficient schemes leak at least the access pattern (the accessed documents per keyword search), which is known to be exploitable in query recovery attacks assuming the attacker has a significant amount of background knowledge on the stored documents. Existing attacks can only achieve decent results with strong adversary models (e.g. at least 20% of previously known documents or require additional knowledge such as on query frequencies) and they give no metric to evaluate the certainty of recovered queries. This hampers their practical utility and questions their relevance in the real-world. We propose a refined score attack which achieves query recovery rates of around 85% without requiring exact background knowledge on stored documents; a distributionally similar, but otherwise different (i.e., non-indexed), dataset suffices. The attack starts with very few known queries (around 10 known queries in our experiments over different datasets of varying size) and then iteratively recovers further queries with confidence scores by adding previously recovered queries that had high confidence scores to the set of known queries. Additional to high recovery rates, our approach yields interpretable results in terms of confidence scores.",
+    "neighbors": [
+      751,
+      2301
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1096,
+    "label": 16,
+    "text": "Title: FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration\nAbstract: We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters. Our method consists of two parts: a Quick Image Rectification Module developed with a Pix2Pix GAN and Wasserstein GAN (W-Pix2PixGAN), and a Calibration Module with a CNN architecture. Our Quick Rectification Network performs robust rectification with good resolution, making it suitable for constant calibration in camera-based surveillance equipment. To achieve highquality calibration, we use the straightened output from the Quick Rectification Module as a guidance-like semantic feature map for the Calibration Module to learn the geometric relationship between the straightened feature and the distorted feature. We train and validate our method with a large synthesized dataset labeled with well-simulated parameters applied to a perspective image dataset. Our solution has achieved robust performance in high-resolution with a significant PSNR value of 22.343. 1",
+    "neighbors": [
+      1856,
+      2241
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1097,
+    "label": 34,
+    "text": "Title: Beyond the Quadratic Time Barrier for Network Unreliability\nAbstract: Karger (STOC 1995) gave the first FPTAS for the network (un)reliability problem, setting in motion research over the next three decades that obtained increasingly faster running times, eventually leading to a $\\tilde{O}(n^2)$-time algorithm (Karger, STOC 2020). This represented a natural culmination of this line of work because the algorithmic techniques used can enumerate $\\Theta(n^2)$ (near)-minimum cuts. In this paper, we go beyond this quadratic barrier and obtain a faster FPTAS for the network unreliability problem. Our algorithm runs in $m^{1+o(1)} + \\tilde{O}(n^{1.5})$ time. Our main contribution is a new estimator for network unreliability in very reliable graphs. These graphs are usually the bottleneck for network unreliability since the disconnection event is elusive. Our estimator is obtained by defining an appropriate importance sampling subroutine on a dual spanning tree packing of the graph. To complement this estimator for very reliable graphs, we use recursive contraction for moderately reliable graphs. We show that an interleaving of sparsification and contraction can be used to obtain a better parametrization of the recursive contraction algorithm that yields a faster running time matching the one obtained for the very reliable case.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1098,
+    "label": 30,
+    "text": "Title: Adaptive Machine Translation with Large Language Models\nAbstract: Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specific projects. Machine translation (MT) has achieved significant progress in the area of domain adaptation. However, real-time adaptation remains challenging. Large-scale language models (LLMs) have recently shown interesting capabilities of in-context learning, where they learn to replicate certain input-output text generation patterns, without further fine-tuning. By feeding an LLM at inference time with a prompt that consists of a list of translation pairs, it can then simulate the domain and style characteristics. This work aims to investigate how we can utilize in-context learning to improve real-time adaptive MT. Our extensive experiments show promising results at translation time. For example, GPT-3.5 can adapt to a set of in-domain sentence pairs and/or terminology while translating a new sentence. We observe that the translation quality with few-shot in-context learning can surpass that of strong encoder-decoder MT systems, especially for high-resource languages. Moreover, we investigate whether we can combine MT from strong encoder-decoder models with fuzzy matches, which can further improve translation quality, especially for less supported languages. We conduct our experiments across five diverse language pairs, namely English-to-Arabic (EN-AR), English-to-Chinese (EN-ZH), English-to-French (EN-FR), English-to-Kinyarwanda (EN-RW), and English-to-Spanish (EN-ES).",
+    "neighbors": [
+      1052,
+      1146,
+      1546,
+      1671,
+      2226
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1099,
+    "label": 24,
+    "text": "Title: Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning\nAbstract: Lately, differential privacy (DP) has been introduced in cooperative multiagent reinforcement learning (CMARL) to safeguard the agents' privacy against adversarial inference during knowledge sharing. Nevertheless, we argue that the noise introduced by DP mechanisms may inadvertently give rise to a novel poisoning threat, specifically in the context of private knowledge sharing during CMARL, which remains unexplored in the literature. To address this shortcoming, we present an adaptive, privacy-exploiting, and evasion-resilient localized poisoning attack (PeLPA) that capitalizes on the inherent DP-noise to circumvent anomaly detection systems and hinder the optimal convergence of the CMARL model. We rigorously evaluate our proposed PeLPA attack in diverse environments, encompassing both non-adversarial and multiple-adversarial contexts. Our findings reveal that, in a medium-scale environment, the PeLPA attack with attacker ratios of 20% and 40% can lead to an increase in average steps to goal by 50.69% and 64.41%, respectively. Furthermore, under similar conditions, PeLPA can result in a 1.4x and 1.6x computational time increase in optimal reward attainment and a 1.18x and 1.38x slower convergence for attacker ratios of 20% and 40%, respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1100,
+    "label": 26,
+    "text": "Title: Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions\nAbstract: Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various applications, including identifying emerging trends, monitoring network security, understanding social dynamics, tracking disease outbreaks, and understanding financial dynamics. In this paper, we present a comprehensive benchmarking study that compares 12 data-driven methods for anomaly detection in temporal graphs. We conduct experiments on two temporal graphs extracted from Twitter and Facebook, aiming to identify anomalies in group interactions. Surprisingly, our study reveals an unclear pattern regarding the best method for such tasks, highlighting the complexity and challenges involved in anomaly emergence detection in large and dynamic systems. The results underscore the need for further research and innovative approaches to effectively detect emerging anomalies in dynamic systems represented as temporal graphs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1101,
+    "label": 30,
+    "text": "Title: Specification of MiniDemographicABM.jl: A simplified agent-based demographic model of the UK\nAbstract: This document presents adequate formal terminology for the mathematical specification of a simplified non-calibrated agent-based demographic model of the UK. Individuals of an initial population are subject to ageing, deaths, births, divorces and marriages. The main purpose of the model is to explore and exploit capabilities of the state-of-the-art Agents.jl Julia package [1]. Additionally, the model can serve as a base model to be adjusted to realistic large-scale socio-economics, pandemics or social interactions-based studies mainly within a demographic context. A specific simulation is progressed with a user-defined simulation fixed step size on a hourly, daily, weekly, monthly basis or even an arbitrary user-defined clock rate.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1102,
+    "label": 24,
+    "text": "Title: Unearthing InSights into Mars: unsupervised source separation with limited data\nAbstract: Source separation involves the ill-posed problem of retrieving a set of source signals that have been observed through a mixing operator. Solving this problem requires prior knowledge, which is commonly incorporated by imposing regularity conditions on the source signals, or implicitly learned through supervised or unsupervised methods from existing data. While data-driven methods have shown great promise in source separation, they often require large amounts of data, which rarely exists in planetary space missions. To address this challenge, we propose an unsupervised source separation scheme for domains with limited data access that involves solving an optimization problem in the wavelet scattering covariance representation space$\\unicode{x2014}$an interpretable, low-dimensional representation of stationary processes. We present a real-data example in which we remove transient, thermally-induced microtilts$\\unicode{x2014}$known as glitches$\\unicode{x2014}$from data recorded by a seismometer during NASA's InSight mission on Mars. Thanks to the wavelet scattering covariances' ability to capture non-Gaussian properties of stochastic processes, we are able to separate glitches using only a few glitch-free data snippets.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1103,
+    "label": 16,
+    "text": "Title: Semantic Image Segmentation: Two Decades of Research\nAbstract: Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. This survey is an effort to summarize two decades of research in the field of SiS, where we propose a literature review of solutions starting from early historical methods followed by an overview of more recent deep learning methods including the latest trend of using transformers. We complement the review by discussing particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation such as curriculum, incremental or self-supervised learning. State-of-the-art SiS models rely on a large amount of annotated samples, which are more expensive to obtain than labels for tasks such as image classification. Since unlabeled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community. Therefore, a second core contribution of this book is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS) which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. In addition to providing a comprehensive survey on DASiS techniques, we unveil also newer trends such as multi-domain learning, domain generalization, domain incremental learning, test-time adaptation and source-free domain adaptation. Finally, we conclude this survey by describing datasets and benchmarks most widely used in SiS and DASiS and briefly discuss related tasks such as instance and panoptic image segmentation, as well as applications such as medical image segmentation.",
+    "neighbors": [
+      1804
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1104,
+    "label": 24,
+    "text": "Title: Black-Box Batch Active Learning for Regression\nAbstract: Batch active learning is a popular approach for efficiently training machine learning models on large, initially unlabelled datasets by repeatedly acquiring labels for batches of data points. However, many recent batch active learning methods are white-box approaches and are often limited to differentiable parametric models: they score unlabeled points using acquisition functions based on model embeddings or first- and second-order derivatives. In this paper, we propose black-box batch active learning for regression tasks as an extension of white-box approaches. Crucially, our method only relies on model predictions. This approach is compatible with a wide range of machine learning models, including regular and Bayesian deep learning models and non-differentiable models such as random forests. It is rooted in Bayesian principles and utilizes recent kernel-based approaches. This allows us to extend a wide range of existing state-of-the-art white-box batch active learning methods (BADGE, BAIT, LCMD) to black-box models. We demonstrate the effectiveness of our approach through extensive experimental evaluations on regression datasets, achieving surprisingly strong performance compared to white-box approaches for deep learning models.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1105,
+    "label": 26,
+    "text": "Title: A simple statistic for determining the dimensionality of complex networks\nAbstract: Detecting the dimensionality of graphs is a central topic in machine learning. While the problem has been tackled empirically as well as theoretically, existing methods have several drawbacks. On the one hand, empirical tools are computationally heavy and lack theoretical foundation. On the other hand, theoretical approaches do not apply to graphs with heterogeneous degree distributions, which is often the case for complex real-world networks. To address these drawbacks, we consider geometric inhomogeneous random graphs (GIRGs) as a random graph model, which captures a variety of properties observed in practice. These include a heterogeneous degree distribution and non-vanishing clustering coefficient, which is the probability that two random neighbours of a vertex are adjacent. In GIRGs, $n$ vertices are distributed on a $d$-dimensional torus and weights are assigned to the vertices according to a power-law distribution. Two vertices are then connected with a probability that depends on their distance and their weights. Our first result shows that the clustering coefficient of GIRGs scales inverse exponentially with respect to the number of dimensions, when the latter is at most logarithmic in $n$. This gives a first theoretical explanation for the low dimensionality of real-world networks observed by Almagro et. al. [Nature '22]. Our second result is a linear-time algorithm for determining the dimensionality of a given GIRG. We prove that our algorithm returns the correct number of dimensions with high probability when the input is a GIRG. As a result, our algorithm bridges the gap between theory and practice, as it not only comes with a rigorous proof of correctness but also yields results comparable to that of prior empirical approaches, as indicated by our experiments on real-world instances.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1106,
+    "label": 27,
+    "text": "Title: HuNavSim: A ROS 2 Human Navigation Simulator for Benchmarking Human-Aware Robot Navigation\nAbstract: This work presents the Human Navigation Simulator (HuNavSim), a novel open-source tool for the simulation of different human-agent navigation behaviors in scenarios with mobile robots. The tool, the first programmed under the ROS 2 framework, can be employed along with different well-known robotics simulators like Gazebo. The main goal is to ease the development and evaluation of human-aware robot navigation systems in simulation. Besides a general human-navigation model, HuNavSim includes, as a novelty, a rich set of individual and realistic human navigation behaviors and a complete set of metrics for social navigation benchmarking.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1107,
+    "label": 3,
+    "text": "Title: Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions\nAbstract: We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset. Score functions from the multiwinner voting literature have been used to aggregate rankings into quality scores for subsets. We study this setting of subset selection problems when, in addition, rankings may contain systemic or unconscious biases toward a group of items. For a general model of input rankings and biases, we show that requiring the selected subset to satisfy group fairness constraints can improve the quality of the selection with respect to unbiased rankings. Importantly, we show that for fairness constraints to be effective, different multiwinner score functions may require a drastically different number of rankings: While for some functions, fairness constraints need an exponential number of rankings to recover a close-to-optimal solution, for others, this dependency is only polynomial. This result relies on a novel notion of ``smoothness'' of submodular functions in this setting that quantifies how well a function can ``correctly'' assess the quality of items in the presence of bias. The results in this paper can be used to guide the choice of multiwinner score functions for the subset selection setting considered here; we additionally provide a tool to empirically enable this.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1108,
+    "label": 28,
+    "text": "Title: TTD Configurations for Near-Field Beamforming: Parallel, Serial, or Hybrid?\nAbstract: True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. A serial and a hybrid serial-parallel TTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay through multiple TTDs, which potentially alleviates the maximum delay requirements on the TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. In this context, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. Moreover, the wideband near-field beamforming design for different configurations is studied for maximizing the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for each TTD configuration. In addition, a mixed-forward-and-backward (MFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the TTDs and ii) the hybrid configuration excels in single-user systems, while the serial configuration is preferred in multi-user systems.",
+    "neighbors": [
+      606,
+      617,
+      841,
+      1289
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1109,
+    "label": 6,
+    "text": "Title: Evaluating the Possibility of Integrating Augmented Reality and Internet of Things Technologies to Help Patients with Alzheimer\u2019s Disease\nAbstract: The independent life of the individuals suffering from Alzheimer\u2019s disease (AD) is compromised due to their memory loss, and as a result they always depend on someone by their side to help them lead their daily life. In this situation, generally, either the family members or the caregivers extend their helping hands; they attach notes on every single object or take out the contents of a drawer, the family might need to make those visible when they leave the patient alone at home. The goal of this study is to provide some helping means for the patient and the family members through existing science and technology. This study reports preliminary results on an Ambient Assisted Living (AAL) real-time system, achieved through Internet of Things (IoT) and Augmented Reality (AR) concepts, to fulfil this goal. The system has two main sections: the first one is the smartphone or windows application that allows caregivers/family-members to monitor a patient\u2019s status at home and be notified if patient are at risk. The second part allows patient to use smart glasses to recognize QR codes in the environment and receive information related to the tags in the form of audio, text or three-dimensional image. This work presents preliminary results and investigates the possibility of implementing such a system.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1110,
+    "label": 16,
+    "text": "Title: Toward Realistic Evaluation of Deep Active Learning Algorithms in Image Classification\nAbstract: Active Learning (AL) aims to reduce the labeling burden by interactively querying the most informative observations from a data pool. Despite extensive research on improving AL query methods in the past years, recent studies have ques-tioned the advantages of AL, especially in the light of emerg-ing alternative training paradigms such as semi-supervised (Semi-SL) and self-supervised learning (Self-SL). Thus, to-day\u2019s AL literature paints an inconsistent picture and leaves practitioners wondering whether and how to employ AL in their tasks. We argue that this heterogeneous landscape is caused by a lack of a systematic and realistic evaluation of AL algorithms, including key parameters such as complex and imbalanced datasets, realistic labeling scenarios, systematic method con\ufb01guration, and integration of Semi-SL and Self-SL. To this end, we present an AL benchmarking suite and run extensive experiments on \ufb01ve datasets shedding light on the questions: when and how to apply AL?",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1111,
+    "label": 27,
+    "text": "Title: TacGNN: Learning Tactile-Based In-Hand Manipulation With a Blind Robot Using Hierarchical Graph Neural Network\nAbstract: In this letter, we propose a novel framework for tactile-based dexterous manipulation learning with a blind anthropomorphic robotic hand, i.e. without visual sensing. First, object-related states were extracted from the raw tactile signals by a graph-based perception model - TacGNN. The resulting tactile features were then utilized in the policy learning of an in-hand manipulation task in the second stage. This method was examined by a Baoding ball task - simultaneously manipulating two spheres around each other by 180 degrees in hand. We conducted experiments on object states prediction and in-hand manipulation using a reinforcement learning algorithm (PPO). Results show that TacGNN is effective in predicting object-related states during manipulation by decreasing the RMSE of prediction to 0.096 cm comparing to other methods, such as MLP, CNN, and GCN. Finally, the robot hand could finish an in-hand manipulation task solely relying on the robotic own perception - tactile sensing and proprioception. In addition, our methods are tested on three tasks with different difficulty levels and transferred to the real robot without further training.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1112,
+    "label": 30,
+    "text": "Title: WizardLM: Empowering Large Language Models to Follow Complex Instructions\nAbstract: Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed and Vicuna's testset show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM are preferred to outputs from OpenAI ChatGPT. In GPT-4 automatic evaluation, WizardLM achieves more than 90\\% capacity of ChatGPT on 17 out of 29 skills. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing LLMs. Our code and data are public at https://github.com/nlpxucan/WizardLM",
+    "neighbors": [
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+      2113,
+      2155
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1113,
+    "label": 24,
+    "text": "Title: Graph Contrastive Learning with Generative Adversarial Network\nAbstract: Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1114,
+    "label": 30,
+    "text": "Title: FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets\nAbstract: Evaluation of Large Language Models (LLMs) is challenging because aligning to human values requires the composition of multiple skills and the required set of skills varies depending on the instruction. Recent studies have evaluated the performance of LLMs in two ways, (1) automatic evaluation on several independent benchmarks and (2) human or machined-based evaluation giving an overall score to the response. However, both settings are coarse-grained evaluations, not considering the nature of user instructions that require instance-wise skill composition, which limits the interpretation of the true capabilities of LLMs. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment SKill Sets), a fine-grained evaluation protocol that can be used for both model-based and human-based evaluation which decomposes coarse-level scoring to an instance-wise skill set-level. Specifically, we define 12 fine-grained skills needed for LLMs to follow open-ended user instructions and construct an evaluation set by allocating a set of skills for each instance. Additionally, by annotating the target domains and difficulty level for each instance, FLASK provides a holistic view with a comprehensive analysis of a model's performance depending on skill, domain, and difficulty. Through using FLASK, we compare multiple open-sourced and proprietary LLMs and observe highly-correlated findings between model-based and human-based evaluations. FLASK enables developers to more accurately measure the model performance and how it can be improved by analyzing factors that make LLMs proficient in particular skills. For practitioners, FLASK can be used to recommend suitable models for particular situations through comprehensive comparison among various LLMs. We release the evaluation data and code implementation at https://github.com/kaistAI/FLASK.",
+    "neighbors": [
+      126,
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+      1613,
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+      1636,
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+      1972,
+      2087
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1115,
+    "label": 24,
+    "text": "Title: A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning\nAbstract: Forgetting refers to the loss or deterioration of previously acquired information or knowledge. While the existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new tasks, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we aim to present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, in future work, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \\url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.",
+    "neighbors": [
+      634,
+      897,
+      1052,
+      1305,
+      1661,
+      2232,
+      2291
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1116,
+    "label": 11,
+    "text": "Title: Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems\nAbstract: This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmanned aerial vehicles (UAVs). In the context of facilitating collaboration among multiple unmanned aerial vehicles (UAVs), the application of multi-agent reinforcement learning (MARL) techniques is regarded as a promising approach. These methods enable UAVs to learn collectively, optimizing their actions within a shared environment, ultimately leading to more efficient cooperative behavior. Furthermore, the principles of a quantum computing (QC) are employed in our study to enhance the training process and inference capabilities of the UAVs involved. By leveraging the unique computational advantages of quantum computing, our approach aims to boost the overall effectiveness of the UAV system. However, employing a QC introduces scalability challenges due to the near intermediate-scale quantum (NISQ) limitation associated with qubit usage. The proposed algorithm addresses this issue by implementing a quantum centralized critic, effectively mitigating the constraints imposed by NISQ limitations. Additionally, the advantages of the QMACN with performance improvements in terms of training speed and wireless service quality are verified via various data-intensive evaluations. Furthermore, this paper validates that a noise injection scheme can be used for handling environmental uncertainties in order to realize robust mobile access.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1117,
+    "label": 10,
+    "text": "Title: Exploring XAI for the Arts: Explaining Latent Space in Generative Music\nAbstract: Explainable AI has the potential to support more interactive and fluid co-creative AI systems which can creatively collaborate with people. To do this, creative AI models need to be amenable to debugging by offering eXplainable AI (XAI) features which are inspectable, understandable, and modifiable. However, currently there is very little XAI for the arts. In this work, we demonstrate how a latent variable model for music generation can be made more explainable; specifically we extend MeasureVAE which generates measures of music. We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions. We suggest that in doing so we bridge the gap between the latent space and the generated musical outcomes in a meaningful way which makes the model and its outputs more explainable and more debuggable.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1118,
+    "label": 24,
+    "text": "Title: Lecture Notes: Neural Network Architectures\nAbstract: These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent Neural Network.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1119,
+    "label": 22,
+    "text": "Title: Type Theory as a Language Workbench\nAbstract: Language Workbenches offer language designers an expressive environment in which to create their DSLs. Similarly, research into mechanised meta-theory has shown how dependently typed languages provide expressive environments to formalise and study DSLs and their meta-theoretical properties. But can we claim that dependently typed languages qualify as language workbenches? We argue yes! We have developed an exemplar DSL called Velo that showcases not only dependently typed techniques to realise and manipulate IRs, but that dependently typed languages make fine language workbenches. Velo is a simple verified language with well-typed holes and comes with a complete compiler pipeline: parser, elaborator, REPL, evaluator, and compiler passes. Specifically, we describe our design choices for well-typed IRs design that includes support for well-typed holes, how CSE is achieved in a well-typed setting, and how the mechanised type-soundness proof for Velo is the source of the evaluator.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1120,
+    "label": 24,
+    "text": "Title: COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1121,
+    "label": 16,
+    "text": "Title: Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models\nAbstract: In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.",
+    "neighbors": [
+      1939
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1122,
+    "label": 31,
+    "text": "Title: PowerMat: context-aware recommender system without user item rating values that solves the cold-start problem\nAbstract: Recommender systems serves as an important technical asset in many modern companies. With the increasing demand for higher precision of the technology, more and more research and investment has been allocated to the field. One important sub-field of recommender systems that has been stagnating is context-aware recommender systems. Due to the difficulty of collecting input dataset, the amount of research on context-aware recommender systems is much less than other sub-fields of recommender systems. In this paper, we propose a new algorithm named PowerMat to tackle the context-aware recommendation problem. We build our theory on matrix factorization and Zipf\u2019s law, and also more recent research work such as DotMat. We prove by experiments that our method achieves superior results to the classic matrix factorization algorithm and other context-aware recommender systems such as MovieMat+. In addition, by theoretical analysis, we show that our algorithm solves the cold-start problem for context-aware recommendation.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1123,
+    "label": 34,
+    "text": "Title: High-Accuracy Multicommodity Flows via Iterative Refinement\nAbstract: The multicommodity flow problem is a classic problem in network flow and combinatorial optimization, with applications in transportation, communication, logistics, and supply chain management, etc. Existing algorithms often focus on low-accuracy approximate solutions, while high-accuracy algorithms typically rely on general linear program solvers. In this paper, we present efficient high-accuracy algorithms for a broad family of multicommodity flow problems on undirected graphs, demonstrating improved running times compared to general linear program solvers. Our main result shows that we can solve the $\\ell_{q, p}$-norm multicommodity flow problem to a $(1 + \\varepsilon)$ approximation in time $O_{q, p}(m^{1+o(1)} k^2 \\log(1 / \\varepsilon))$, where $k$ is the number of commodities, and $O_{q, p}(\\cdot)$ hides constants depending only on $q$ or $p$. As $q$ and $p$ approach to $1$ and infinity respectively, $\\ell_{q, p}$-norm flow tends to maximum concurrent flow. We introduce the first iterative refinement framework for $\\ell_{q, p}$-norm minimization problems, which reduces the problem to solving a series of decomposable residual problems. In the case of $k$-commodity flow, each residual problem can be decomposed into $k$ single commodity convex flow problems, each of which can be solved in almost-linear time. As many classical variants of multicommodity flows were shown to be complete for linear programs in the high-accuracy regime [Ding-Kyng-Zhang, ICALP'22], our result provides new directions for studying more efficient high-accuracy multicommodity flow algorithms.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1124,
+    "label": 24,
+    "text": "Title: A Huber Loss Minimization Approach to Byzantine Robust Federated Learning\nAbstract: Federated learning systems are susceptible to adversarial attacks. To combat this, we introduce a novel aggregator based on Huber loss minimization, and provide a comprehensive theoretical analysis. Under independent and identically distributed (i.i.d) assumption, our approach has several advantages compared to existing methods. Firstly, it has optimal dependence on $\\epsilon$, which stands for the ratio of attacked clients. Secondly, our approach does not need precise knowledge of $\\epsilon$. Thirdly, it allows different clients to have unequal data sizes. We then broaden our analysis to include non-i.i.d data, such that clients have slightly different distributions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1125,
+    "label": 16,
+    "text": "Title: Zero-1-to-3: Zero-shot One Image to 3D Object\nAbstract: We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained setting, we capitalize on the geometric priors that large-scale diffusion models learn about natural images. Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint, which allow new images to be generated of the same object under a specified camera transformation. Even though it is trained on a synthetic dataset, our model retains a strong zero-shot generalization ability to out-of-distribution datasets as well as in-the-wild images, including impressionist paintings. Our viewpoint-conditioned diffusion approach can further be used for the task of 3D reconstruction from a single image. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.",
+    "neighbors": [
+      260,
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+      2190
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1126,
+    "label": 4,
+    "text": "Title: Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency\nAbstract: Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)11https://github.com/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1127,
+    "label": 4,
+    "text": "Title: On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities\nAbstract: nan",
+    "neighbors": [
+      434
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1128,
+    "label": 30,
+    "text": "Title: Gorilla: Large Language Model Connected with Massive APIs\nAbstract: Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of-the-art LLMs such as GPT-4, largely due to their inability to generate accurate input arguments and their tendency to hallucinate the wrong usage of an API call. We release Gorilla, a finetuned LLaMA-based model that surpasses the performance of GPT-4 on writing API calls. When combined with a document retriever, Gorilla demonstrates a strong capability to adapt to test-time document changes, enabling flexible user updates or version changes. It also substantially mitigates the issue of hallucination, commonly encountered when prompting LLMs directly. To evaluate the model's ability, we introduce APIBench, a comprehensive dataset consisting of HuggingFace, TorchHub, and TensorHub APIs. The successful integration of the retrieval system with Gorilla demonstrates the potential for LLMs to use tools more accurately, keep up with frequently updated documentation, and consequently increase the reliability and applicability of their outputs. Gorilla's code, model, data, and demo are available at https://gorilla.cs.berkeley.edu",
+    "neighbors": [
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+      2166
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1129,
+    "label": 16,
+    "text": "Title: Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models\nAbstract: Recently, Multimodal Large Language Models (MLLMs) that enable Large Language Models (LLMs) to interpret images through visual instruction tuning have achieved significant success. However, existing visual instruction tuning methods only utilize image-language instruction data to align the language and image modalities, lacking a more fine-grained cross-modal alignment. In this paper, we propose Position-enhanced Visual Instruction Tuning (PVIT), which extends the functionality of MLLMs by integrating an additional region-level vision encoder. This integration promotes a more detailed comprehension of images for the MLLM. In addition, to efficiently achieve a fine-grained alignment between the vision modules and the LLM, we design multiple data generation strategies to construct an image-region-language instruction dataset. Finally, we present both quantitative experiments and qualitative analysis that demonstrate the superiority of the proposed model. Code and data will be released at https://github.com/PVIT-official/PVIT.",
+    "neighbors": [
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+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1130,
+    "label": 3,
+    "text": "Title: Drivers of Mobile Payment Acceptance: The Impact of Network Externalities in Nigeria\nAbstract: The rising popularity of mobile payments can be attributed to the widespread use of smartphones and their applications. Despite its potential to simplify our lives, its adoption in African countries has been limited. This paper aims to enhance our understanding of the critical factors that influence the acceptance of mobile payments in Nigeria by exploring the impact of network externalities in addition to conventional technology acceptance factors. It posits that performance expectancy, effort expectancy, social influence, trust, and network externality are the key drivers of mobile payment acceptance. The research findings indicate that while traditional drivers still have an impact on customer's willingness to adopt mobile payment, network externalities have the strongest influence. The paper provides recommendations for future research, although the results did not support the impact of effort expectancy.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1131,
+    "label": 23,
+    "text": "Title: Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention\nAbstract: Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose Hades, the first end-to-end semi-supervised approach to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from heterogeneous data via a cross-modal attention module, trained in a semi-supervised manner. We evaluate Hades extensively on large-scale simulated data and datasets from Huawei Cloud. The experimental results present the effectiveness of our model in detecting system anomalies. We also release the code and the annotated dataset for replication and future research.",
+    "neighbors": [
+      14,
+      2173
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1132,
+    "label": 31,
+    "text": "Title: Methods and advancement of content-based fashion image retrieval: A Review\nAbstract: Content-based fashion image retrieval (CBFIR) has been widely used in our daily life for searching fashion images or items from online platforms. In e-commerce purchasing, the CBFIR system can retrieve fashion items or products with the same or comparable features when a consumer uploads a reference image, image with text, sketch or visual stream from their daily life. This lowers the CBFIR system reliance on text and allows for a more accurate and direct searching of the desired fashion product. Considering recent developments, CBFIR still has limits when it comes to visual searching in the real world due to the simultaneous availability of multiple fashion items, occlusion of fashion products, and shape deformation. This paper focuses on CBFIR methods with the guidance of images, images with text, sketches, and videos. Accordingly, we categorized CBFIR methods into four main categories, i.e., image-guided CBFIR (with the addition of attributes and styles), image and text-guided, sketch-guided, and video-guided CBFIR methods. The baseline methodologies have been thoroughly analyzed, and the most recent developments in CBFIR over the past six years (2017 to 2022) have been thoroughly examined. Finally, key issues are highlighted for CBFIR with promising directions for future research.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1133,
+    "label": 30,
+    "text": "Title: Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT\nAbstract: Recently, ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. Several prior studies have shown that ChatGPT attains remarkable generation ability compared with existing models. However, the quantitative analysis of ChatGPT's understanding ability has been given little attention. In this report, we explore the understanding ability of ChatGPT by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question-answering tasks. Additionally, by combining some advanced prompting strategies, we show that the understanding ability of ChatGPT can be further improved.",
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+      1940,
+      1949,
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1134,
+    "label": 5,
+    "text": "Title: Test-driving RISC-V Vector hardware for HPC\nAbstract: Whilst the RISC-V Vector extension (RVV) has been ratified, at the time of writing both hardware implementations and open source software support are still limited for vectorisation on RISC-V. This is important because vectorisation is crucial to obtaining good performance for High Performance Computing (HPC) workloads and, as of April 2023, the Allwinner D1 SoC, containing the XuanTie C906 processor, is the only mass-produced and commercially available hardware supporting RVV. This paper surveys the current state of RISC-V vectorisation as of 2023, reporting the landscape of both the hardware and software ecosystem. Driving our discussion from experiences in setting up the Allwinner D1 as part of the EPCC RISC-V testbed, we report the results of benchmarking the Allwinner D1 using the RAJA Performance Suite, which demonstrated reasonable vectorisation speedup using vendor-provided compiler, as well as favourable performance compared to the StarFive VisionFive V2 with SiFive's U74 processor.",
+    "neighbors": [
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+      1405,
+      1885
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1135,
+    "label": 31,
+    "text": "Title: Framework to Automatically Determine the Quality of Open Data Catalogs\nAbstract: Data catalogs play a crucial role in modern data-driven organizations by facilitating the discovery, understanding, and utilization of diverse data assets. However, ensuring their quality and reliability is complex, especially in open and large-scale data environments. This paper proposes a framework to automatically determine the quality of open data catalogs, addressing the need for efficient and reliable quality assessment mechanisms. Our framework can analyze various core quality dimensions, such as accuracy, completeness, consistency, scalability, and timeliness, offer several alternatives for the assessment of compatibility and similarity across such catalogs as well as the implementation of a set of non-core quality dimensions such as provenance, readability, and licensing. The goal is to empower data-driven organizations to make informed decisions based on trustworthy and well-curated data assets. The source code that illustrates our approach can be downloaded from https://www.github.com/jorge-martinez-gil/dataq/.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1136,
+    "label": 6,
+    "text": "Title: Implications of Edge Computing for Static Site Generation\nAbstract: Static site generation (SSG) is a common technique in the web development space to create performant websites that are easy to host. Numerous SSG tools exist, and the approach has been complemented by newer approaches, such as Jamstack, that extend its usability. Edge computing represents a new option to extend the usefulness of SSG further by allowing the creation of dynamic sites on top of a static backdrop, providing dynamic resources close to the user. In this paper, we explore the impact of the recent developments in the edge computing space and consider its implications for SSG.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1137,
+    "label": 24,
+    "text": "Title: Efficient Online Reinforcement Learning with Offline Data\nAbstract: Sample efficiency and exploration remain major challenges in online reinforcement learning (RL). A powerful approach that can be applied to address these issues is the inclusion of offline data, such as prior trajectories from a human expert or a sub-optimal exploration policy. Previous methods have relied on extensive modifications and additional complexity to ensure the effective use of this data. Instead, we ask: can we simply apply existing off-policy methods to leverage offline data when learning online? In this work, we demonstrate that the answer is yes; however, a set of minimal but important changes to existing off-policy RL algorithms are required to achieve reliable performance. We extensively ablate these design choices, demonstrating the key factors that most affect performance, and arrive at a set of recommendations that practitioners can readily apply, whether their data comprise a small number of expert demonstrations or large volumes of sub-optimal trajectories. We see that correct application of these simple recommendations can provide a $\\mathbf{2.5\\times}$ improvement over existing approaches across a diverse set of competitive benchmarks, with no additional computational overhead. We have released our code at https://github.com/ikostrikov/rlpd.",
+    "neighbors": [
+      1186,
+      2260
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1138,
+    "label": 24,
+    "text": "Title: Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits\nAbstract: In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs between the number of rounds of communication among the agents and the regret of the collaborative learning process.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1139,
+    "label": 23,
+    "text": "Title: Compatible Remediation on Vulnerabilities from Third-Party Libraries for Java Projects\nAbstract: With the increasing disclosure of vulnerabilities in open-source software, software composition analysis (SCA) has been widely applied to reveal third-party libraries and the associated vulnerabilities in software projects. Beyond the revelation, SCA tools adopt various remediation strategies to fix vulnerabilities, the quality of which varies substantially. However, ineffective remediation could induce side effects, such as compi-lation failures, which impede acceptance by users. According to our studies, existing SCA tools could not correctly handle the concerns of users regarding the compatibility of remediated projects. To this end, we propose Compatible Remediation of Third-party libraries (CORAL) for Maven projects to fix vulnerabilities without breaking the projects. The evaluation proved that Coralnot only fixed 87.56% of vulnerabilities which outperformed other tools (best 75.32%) and achieved a 98.67% successful compilation rate and a 92.96% successful unit test rate. Furthermore, we found that 78.45% of vulnerabilities in popular Maven projects could be fixed without breaking the compilation, and the rest of the vulnerabilities (21.55%) could either be fixed by upgrades that break the compilations or even be impossible to fix by upgrading.",
+    "neighbors": [
+      242,
+      1607
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1140,
+    "label": 27,
+    "text": "Title: Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors\nAbstract: Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing method or Finite Element Method (FEM). However, elastomer deformation physics is not considered in the former method, whereas the latter requires a massive amount of computational resources like a computer cluster. In this work, we propose a pluggable and low computational cost simulator using the Taichi programming language for simulating optical tactile sensors, named as Tacchi. It reconstructs elastomer deformation using particles, which allows deformed elastomer surfaces to be rendered into tactile images and reveals contact information without suffering from high computational costs. Tacchi facilitates creating realistic tactile images in simulation, e.g., ones that capture wear-and-tear defects on object surfaces. In addition, the proposed Tacchi can be integrated with robotics simulators for a robot system simulation. Experiment results showed that Tacchi can produce images with better similarity to real images and achieved higher Sim2Real accuracy compared to the existing methods. Moreover, it can be connected with MuJoCo and Gazebo with only the requirement of 1G memory space in GPU compared to a computer cluster applied for FEM. With Tacchi, physical robot simulation with optical tactile sensors becomes possible.",
+    "neighbors": [
+      715
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1141,
+    "label": 27,
+    "text": "Title: CoDEPS: Online Continual Learning for Depth Estimation and Panoptic Segmentation\nAbstract: Operating a robot in the open world requires a high level of robustness with respect to previously unseen environments. Optimally, the robot is able to adapt by itself to new conditions without human supervision, e.g., automatically adjusting its perception system to changing lighting conditions. In this work, we address the task of continual learning for deep learning-based monocular depth estimation and panoptic segmentation in new environments in an online manner. We introduce CoDEPS to perform continual learning involving multiple real-world domains while mitigating catastrophic forgetting by leveraging experience replay. In particular, we propose a novel domain-mixing strategy to generate pseudo-labels to adapt panoptic segmentation. Furthermore, we explicitly address the limited storage capacity of robotic systems by leveraging sampling strategies for constructing a fixed-size replay buffer based on rare semantic class sampling and image diversity. We perform extensive evaluations of CoDEPS on various real-world datasets demonstrating that it successfully adapts to unseen environments without sacrificing performance on previous domains while achieving state-of-the-art results. The code of our work is publicly available at http://codeps.cs.uni-freiburg.de.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1142,
+    "label": 30,
+    "text": "Title: Capabilities of GPT-4 on Medical Challenge Problems\nAbstract: Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the USMLE, a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond measuring model performance, experiments were conducted to investigate the influence of test questions containing both text and images on model performance, probe for memorization of content during training, and study probability calibration, which is of critical importance in high-stakes applications like medicine. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). In addition, GPT-4 is significantly better calibrated than GPT-3.5, demonstrating a much-improved ability to predict the likelihood that its answers are correct. We also explore the behavior of the model qualitatively through a case study that shows the ability of GPT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case. Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.",
+    "neighbors": [
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1143,
+    "label": 28,
+    "text": "Title: Optimization of Rate-Splitting Multiple Access in Beyond Diagonal RIS-assisted URLLC Systems\nAbstract: This paper proposes a general optimization framework for rate splitting multiple access (RSMA) in beyond diagonal (BD) reconfigurable intelligent surface (RIS) assisted ultra-reliable low-latency communications (URLLC) systems. This framework can solve a large family of optimization problems in which the objective and/or constraints are linear functions of the rates and/or energy efficiency (EE) of users. Using this framework, we show that RSMA and RIS can be mutually beneficial tools when the system is overloaded, i.e., when the number of users per cell is higher than the number of base station (BS) antennas. Additionally, we show that the benefits of RSMA increase when the packets are shorter and/or the reliability constraint is more stringent. Furthermore, we show that the RSMA benefits increase with the number of users per cell and decrease with the number of BS antennas. Finally, we show that RIS (either diagonal or BD) can highly improve the system performance, and BD-RIS outperforms regular RIS.",
+    "neighbors": [
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+      1530,
+      1823
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1144,
+    "label": 28,
+    "text": "Title: A proof that Reed-Muller codes achieve Shannon capacity on symmetric channels\nAbstract: Reed-Muller codes were introduced in 1954, with a simple explicit construction based on polynomial evaluations, and have long been conjectured to achieve Shannon capacity on symmetric channels. Major progress was made towards a proof over the last decades; using combinatorial weight enumerator bounds, a breakthrough on the erasure channel from sharp thresholds, hypercontractivity arguments, and polarization theory. Another major progress recently established that the bit error probability vanishes slowly below capacity. However, when channels allow for errors, the results of Bourgain-Kalai do not apply for converting a vanishing bit to a vanishing block error probability, neither do the known weight enumerator bounds. The conjecture that RM codes achieve Shannon capacity on symmetric channels, with high probability of recovering the codewords, has thus remained open. This paper closes the conjecture's proof. It uses a new recursive boosting framework, which aggregates the decoding of codeword restrictions on `subspace-sunflowers', handling their dependencies via an $L_p$ Boolean Fourier analysis, and using a list-decoding argument with a weight enumerator bound from Sberlo-Shpilka. The proof does not require a vanishing bit error probability for the base case, but only a non-trivial probability, obtained here for general symmetric codes. This gives in particular a shortened and tightened argument for the vanishing bit error probability result of Reeves-Pfister, and with prior works, it implies the strong wire-tap secrecy of RM codes on pure-state classical-quantum channels.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1145,
+    "label": 16,
+    "text": "Title: Implicit Neural Representation for Cooperative Low-light Image Enhancement\nAbstract: The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data. To address these limitations, we propose an implicit Neural Representation method for Cooperative low-light image enhancement, dubbed NeRCo. It robustly recovers perceptual-friendly results in an unsupervised manner. Concretely, NeRCo unifies the diverse degradation factors of real-world scenes with a controllable fitting function, leading to better robustness. In addition, for the output results, we introduce semantic-orientated supervision with priors from the pre-trained vision-language model. Instead of merely following reference images, it encourages results to meet subjective expectations, finding more visual-friendly solutions. Further, to ease the reliance on paired data and reduce solution space, we develop a dual-closed-loop constrained enhancement module. It is trained cooperatively with other affiliated modules in a self-supervised manner. Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo. Our code is available at https://github.com/Ysz2022/NeRCo.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1146,
+    "label": 30,
+    "text": "Title: Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis\nAbstract: Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions: 1) How well do LLMs perform in translating a massive number of languages? 2) Which factors affect LLMs' performance in translation? We evaluate popular LLMs, including XGLM, OPT, BLOOMZ, and ChatGPT, on 102 languages. Our empirical results show that even the best model ChatGPT still lags behind the supervised baseline NLLB in 83.33% of translation directions. Through further analysis, we discover that LLMs exhibit new working patterns when used for MMT. First, prompt semantics can surprisingly be ignored when given in-context exemplars, where LLMs still show strong performance even with unreasonable prompts. Second, cross-lingual exemplars can provide better task instruction for low-resource translation than exemplars in the same language pairs. Third, we observe the overestimated performance of BLOOMZ on dataset Flores-101, indicating the potential risk when using public datasets for evaluation.",
+    "neighbors": [
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1147,
+    "label": 6,
+    "text": "Title: Towards Valid and Reliable Privacy Concern Scales: The Example of IUIPC-8\nAbstract: Valid and reliable measurement instruments are crucial for human factors in privacy research. We expect them to measure what they purport to measure, yielding validity, and to measure this consistently, offering us reliability. While there is a range of privacy concern instruments available in the field and their investigation continues unabated, we shall focus on a brief form of the scale Internet Users? Information Privacy Concerns (IUIPC-8) as an example. We not only present IUIPC-8 itself, but also consider methods for the evaluation of valid and reliable measurement instruments. In this, confirmatory factor analysis (CFA) serves us as a valuable tool. Our inquiry takes into account the ordinal and non-normal data yielded by the IUIPC questionnaire, compares multiple models to confirm the three-dimensionality of the scale, examines global and local fit and, finally, estimates construct validity and internal consistency reliability metrics. We offer a comparison between IUIPC-10 and IUIPC-8 drawing on two independent samples. In conclusion, we highlight properties of the scale and considerations for its use in practice.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1148,
+    "label": 16,
+    "text": "Title: Otter: A Multi-Modal Model with In-Context Instruction Tuning\nAbstract: Large language models (LLMs) have demonstrated significant universal capabilities as few/zero-shot learners in various tasks due to their pre-training on vast amounts of text data, as exemplified by GPT-3, which boosted to InstrctGPT and ChatGPT, effectively following natural language instructions to accomplish real-world tasks. In this paper, we propose to introduce instruction tuning into multi-modal models, motivated by the Flamingo model's upstream interleaved format pretraining dataset. We adopt a similar approach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT) dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following ability and in-context learning. We also optimize OpenFlamingo's implementation for researchers, democratizing the required training resources from 1$\\times$ A100 GPU to 4$\\times$ RTX-3090 GPUs, and integrate both OpenFlamingo and Otter into Huggingface Transformers for more researchers to incorporate the models into their customized training and inference pipelines.",
+    "neighbors": [
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+      1950,
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+      2064,
+      2155,
+      2216,
+      2226,
+      2274
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1149,
+    "label": 24,
+    "text": "Title: A theory of continuous generative flow networks\nAbstract: Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.",
+    "neighbors": [
+      2236
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1150,
+    "label": 24,
+    "text": "Title: Correcting sampling biases via importance reweighting for spatial modeling\nAbstract: In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introduce an approach based on the ideas of importance sampling to obtain an unbiased estimate of the target error. By taking into account difference between desirable error and available data, our method reweights errors at each sample point and neutralizes the shift. Importance sampling technique and kernel density estimation were used for reweighteing. We validate the effectiveness of our approach using artificial data that resemble real-world spatial datasets. Our findings demonstrate advantages of the proposed approach for the estimation of the target error, offering a solution to a distribution shift problem. Overall error of predictions dropped from 7% to just 2% and it gets smaller for larger samples.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1151,
+    "label": 16,
+    "text": "Title: Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning\nAbstract: Contrastive learning-based video-language representation learning approaches, e.g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging shell-breaking interactions for fine-grained cross-modal learning. In this paper, we creatively model video-text as game players with multivariate cooperative game theory to wisely handle the uncertainty during fine-grained semantic interaction with diverse granularity, flexible combination, and vague intensity. Concretely, we propose Hierarchical Banzhaf Interaction (HBI) to value possible correspondence between video frames and text words for sensitive and explainable cross-modal contrast. To efficiently realize the cooperative game of multiple video frames and multiple text words, the proposed method clusters the original video frames (text words) and computes the Banzhaf Interaction between the merged tokens. By stacking token merge modules, we achieve cooperative games at different semantic levels. Extensive experiments on commonly used text-video retrieval and video-question answering bench-marks with superior performances justify the efficacy of our HBI. More encouragingly, it can also serve as a visualization tool to promote the understanding of cross-modal interaction, which have a far-reaching impact on the community. Project page is available at https://jpthu17.github.io/HBI/.",
+    "neighbors": [
+      758,
+      1787
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1152,
+    "label": 16,
+    "text": "Title: Supervised Masked Knowledge Distillation for Few-Shot Transformers\nAbstract: Vision Transformers (ViTs) emerge to achieve impressive performance on many data-abundant computer vision tasks by capturing long-range dependencies among local features. However, under few-shot learning (FSL) settings on small datasets with only a few labeled data, ViT tends to overfit and suffers from severe performance degradation due to its absence of CNN-alike inductive bias. Previous works in FSL avoid such problem either through the help of self-supervised auxiliary losses, or through the dextile uses of label information under supervised settings. But the gap between self-supervised and supervised few-shot Transformers is still unfilled. Inspired by recent advances in self-supervised knowledge distillation and masked image modeling (MIM), we propose a novel Supervised Masked Knowledge Distillation model (SMKD) for few-shot Transformers which incorporates label information into self-distillation frameworks. Compared with previous self-supervised methods, we allow intra-class knowledge distillation on both class and patch tokens, and introduce the challenging task of masked patch tokens reconstruction across intra-class images. Experimental results on four few-shot classification benchmark datasets show that our method with simple design outperforms previous methods by a large margin and achieves a new start-of-the-art. Detailed ablation studies confirm the effectiveness of each component of our model. Code for this paper is available here: https://github.com/HL-hanlin/SMKD.",
+    "neighbors": [
+      870,
+      1595
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1153,
+    "label": 24,
+    "text": "Title: IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL\nAbstract: We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications. In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition. Specifically, each agent plans inspections and repairs for a specific system component, aiming to minimise maintenance costs while cooperating to minimise system failure risk. With IMP-MARL, we release several environments including one related to offshore wind structural systems, in an effort to meet today's needs to improve management strategies to support sustainable and reliable energy systems. Supported by IMP practical engineering environments featuring up to 100 agents, we conduct a benchmark campaign, where the scalability and performance of state-of-the-art cooperative MARL methods are compared against expert-based heuristic policies. The results reveal that centralised training with decentralised execution methods scale better with the number of agents than fully centralised or decentralised RL approaches, while also outperforming expert-based heuristic policies in most IMP environments. Based on our findings, we additionally outline remaining cooperation and scalability challenges that future MARL methods should still address. Through IMP-MARL, we encourage the implementation of new environments and the further development of MARL methods.",
+    "neighbors": [
+      1509
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1154,
+    "label": 30,
+    "text": "Title: A Computational Evaluation Framework for Singable Lyric Translation\nAbstract: Lyric translation plays a pivotal role in amplifying the global resonance of music, bridging cultural divides, and fostering universal connections. Translating lyrics, unlike conventional translation tasks, requires a delicate balance between singability and semantics. In this paper, we present a computational framework for the quantitative evaluation of singable lyric translation, which seamlessly integrates musical, linguistic, and cultural dimensions of lyrics. Our comprehensive framework consists of four metrics that measure syllable count distance, phoneme repetition similarity, musical structure distance, and semantic similarity. To substantiate the efficacy of our framework, we collected a singable lyrics dataset, which precisely aligns English, Japanese, and Korean lyrics on a line-by-line and section-by-section basis, and conducted a comparative analysis between singable and non-singable lyrics. Our multidisciplinary approach provides insights into the key components that underlie the art of lyric translation and establishes a solid groundwork for the future of computational lyric translation assessment.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1155,
+    "label": 30,
+    "text": "Title: The Geometry of Multilingual Language Models: An Equality Lens\nAbstract: Understanding the representations of different languages in multilingual language models is essential for comprehending their cross-lingual properties, predicting their performance on downstream tasks, and identifying any biases across languages. In our study, we analyze the geometry of three multilingual language models in Euclidean space and find that all languages are represented by unique geometries. Using a geometric separability index we find that although languages tend to be closer according to their linguistic family, they are almost separable with languages from other families. We also introduce a Cross-Lingual Similarity Index to measure the distance of languages with each other in the semantic space. Our findings indicate that the low-resource languages are not represented as good as high resource languages in any of the models",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1156,
+    "label": 25,
+    "text": "Title: Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models\nAbstract: Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with\"No Modality Left Behind\", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.github.io",
+    "neighbors": [
+      414,
+      1536,
+      1958,
+      2103
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1157,
+    "label": 37,
+    "text": "Title: VerifAI: Verified Generative AI\nAbstract: Generative AI has made significant strides, yet concerns about the accuracy and reliability of its outputs continue to grow. Such inaccuracies can have serious consequences such as inaccurate decision-making, the spread of false information, privacy violations, legal liabilities, and more. Although efforts to address these risks are underway, including explainable AI and responsible AI practices such as transparency, privacy protection, bias mitigation, and social and environmental responsibility, misinformation caused by generative AI will remain a significant challenge. We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI. This involves analyzing the underlying data from multi-modal data lakes, including text files, tables, and knowledge graphs, and assessing its quality and consistency. By doing so, we can establish a stronger foundation for evaluating the outputs of generative AI models. Such an approach can ensure the correctness of generative AI, promote transparency, and enable decision-making with greater confidence. Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.",
+    "neighbors": [
+      251,
+      1304
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1158,
+    "label": 31,
+    "text": "Title: Keyword decisions in sponsored search advertising: A literature review and research agenda\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1159,
+    "label": 27,
+    "text": "Title: Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects\nAbstract: Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories. Few-shot learning is a promising solution for alleviating this issue by allowing robots to perform a few interactions with unseen objects. However, extant approaches often necessitate costly and inefficient test-time interactions with each unseen instance. Recognizing this limitation, we observe that despite their distinct shapes, different categories often share similar local geometries essential for manipulation, such as pullable handles and graspable edges - a factor typically underutilized in previous few-shot learning works. To harness this commonality, we introduce 'Where2Explore', an affordance learning framework that effectively explores novel categories with minimal interactions on a limited number of instances. Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects. Extensive experiments in simulated and real-world environments demonstrate our framework's capacity for efficient few-shot exploration and generalization.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1160,
+    "label": 31,
+    "text": "Title: Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems\nAbstract: Recommender system is a widely adopted technology in a diversified class of product lines. Modern day recommender system approaches include matrix factorization, learning to rank and deep learning paradigms, etc. Unlike many other approaches, learning to rank builds recommendation results based on maximization of the probability of ranking orders. There are intrinsic issues related to recommender systems such as selection bias, exposure bias and popularity bias. In this paper, we propose a fair recommender system algorithm that uses Poisson process and Skellam distribution. We demonstrate in our experiments that our algorithm is competitive in accuracy metrics and far more superior than other modern algorithms in fairness metrics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1161,
+    "label": 5,
+    "text": "Title: Behavioural Types for Local-First Software\nAbstract: Peer-to-peer systems are the most resilient form of distributed computing, but the design of robust protocols for their coordination is difficult. This makes it hard to specify and reason about global behaviour of such systems. This paper presents swarm protocols to specify such systems from a global viewpoint. Swarm protocols are projected to machines, that is local specifications of peers. We take inspiration from behavioural types with a key difference: peers communicate through an event notification mechanism rather than through point-to-point message passing. Our goal is to adhere to the principles of local-first software where network devices collaborate on a common task while retaining full autonomy: every participating device can locally make progress at all times, not encumbered by unavailability of other devices or network connections. This coordination-free approach leads to inconsistencies that may emerge during computations. Our main result shows that under suitable well-formedness conditions for swarm protocols consistency is eventually recovered and the locally observable behaviour of conforming machines will eventually match the global specification. The model we propose elaborates on an existing industrial platform and provides the basis for tool support (sketched here and fully described in a companion artifact paper), wherefore we consider this work to be a viable step towards reasoning about local-first and peer-to-peer software systems.",
+    "neighbors": [
+      991
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1162,
+    "label": 24,
+    "text": "Title: Learning Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning\nAbstract: In modern communication systems, efficient and reliable information dissemination is crucial for supporting critical operations across domains like disaster response, autonomous vehicles, and sensor networks. This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach as a significant step forward in achieving more decentralized, efficient, and collaborative solutions. We propose a Decentralized-POMDP formulation for information dissemination, empowering each agent to independently decide on message forwarding. This constitutes a significant paradigm shift from traditional heuristics based on Multi-Point Relay (MPR) selection. Our approach harnesses Graph Convolutional Reinforcement Learning, employing Graph Attention Networks (GAT) with dynamic attention to capture essential network features. We propose two approaches, L-DGN and HL-DGN, which differ in the information that is exchanged among agents. We evaluate the performance of our decentralized approaches, by comparing them with a widely-used MPR heuristic, and we show that our trained policies are able to efficiently cover the network while bypassing the MPR set selection process. Our approach promises a first step toward bolstering the resilience of real-world broadcast communication infrastructures via learned, collaborative information dissemination.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1163,
+    "label": 24,
+    "text": "Title: Mastering Strategy Card Game (Legends of Code and Magic) via End-to-End Policy and Optimistic Smooth Fictitious Play\nAbstract: Deep Reinforcement Learning combined with Fictitious Play shows impressive results on many benchmark games, most of which are, however, single-stage. In contrast, real-world decision making problems may consist of multiple stages, where the observation spaces and the action spaces can be completely different across stages. We study a two-stage strategy card game Legends of Code and Magic and propose an end-to-end policy to address the difficulties that arise in multi-stage game. We also propose an optimistic smooth fictitious play algorithm to find the Nash Equilibrium for the two-player game. Our approach wins double championships of COG2022 competition. Extensive studies verify and show the advancement of our approach.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1164,
+    "label": 13,
+    "text": "Title: Investigating Normalization in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point\nAbstract: Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on the performance of EMO algorithms has been investigated in the literature, that of preference-based EMO (PBEMO) algorithms is poorly understood. Since PBEMO aims to approximate a region of interest, its population generally does not cover the Pareto front in the objective space. This property may make normalization of objectives in PBEMO difficult. This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. We present a bounded archive-based method for approximating the nadir point. First, we demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. Then, we show that PBEMO requires normalization of objectives on problems with differently scaled objectives. Our results show that there is no clear\"best normalization method\"in PBEMO, but an external archive-based method performs relatively well.",
+    "neighbors": [
+      210,
+      614
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1165,
+    "label": 16,
+    "text": "Title: HIVE: Harnessing Human Feedback for Instructional Visual Editing\nAbstract: Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional image editing models, where outputs are generated based on an input image and an editing instruction, could similarly benefit from human feedback, as their outputs may not adhere to the correct instructions and preferences of users. In this paper, we present a novel framework to harness human feedback for instructional visual editing (HIVE). Specifically, we collect human feedback on the edited images and learn a reward function to capture the underlying user preferences. We then introduce scalable diffusion model fine-tuning methods that can incorporate human preferences based on the estimated reward. Besides, to mitigate the bias brought by the limitation of data, we contribute a new 1M training dataset, a 3.6K reward dataset for rewards learning, and a 1K evaluation dataset to boost the performance of instructional image editing. We conduct extensive empirical experiments quantitatively and qualitatively, showing that HIVE is favored over previous state-of-the-art instructional image editing approaches by a large margin.",
+    "neighbors": [
+      1969,
+      1989,
+      2161,
+      2210,
+      2258
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1166,
+    "label": 3,
+    "text": "Title: Building a Credible Case for Safety: Waymo's Approach for the Determination of Absence of Unreasonable Risk\nAbstract: This paper presents an overview of Waymo's approach to building a reliable case for safety - a novel and thorough blueprint for use by any company building fully autonomous driving systems. A safety case for fully autonomous operations is a formal way to explain how a company determines that an AV system is safe enough to be deployed on public roads without a human driver, and it includes evidence to support that determination. It involves an explanation of the system, the methodologies used to develop it, the metrics used to validate it and the actual results of validation tests. Yet, in order to develop a worthwhile safety case, it is first important to understand what makes one credible and well crafted, and align on evaluation criteria. This paper helps enabling such alignment by providing foundational thinking into not only how a system is determined to be ready for deployment but also into justifying that the set of acceptance criteria employed in such determination is sufficient and that their evaluation (and associated methods) is credible. The publication is structured around three complementary perspectives on safety that build upon content published by Waymo since 2020: a layered approach to safety; a dynamic approach to safety; and a credible approach to safety. The proposed approach is methodology-agnostic, so that anyone in the space could employ portions or all of it.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1167,
+    "label": 8,
+    "text": "Title: TSoR: TCP Socket over RDMA Container Network for Cloud Native Computing\nAbstract: Cloud-native containerized applications constantly seek high-performance and easy-to-operate container network solutions. RDMA network is a potential enabler with higher throughput and lower latency than the standard TCP/IP network stack. However, several challenges remain in equipping containerized applications with RDMA network: 1) How to deliver transparent improvements without modifying application code; 2) How to integrate RDMA-based network solutions with container orchestration systems; 3) How to efficiently utilize RDMA for container networks. In this paper, we present an RDMA-based container network solution, TCP Socket over RDMA (TSoR), which addresses all the above challenges. To transparently accelerate applications using POSIX socket interfaces without modifications, we integrate TSoR with a container runtime that can intercept system calls for socket interfaces. To be compatible with orchestration systems like Kubernetes, TSoR implements a container network following the Kubernetes network model and satisfies all requirements of the model. To leverage RDMA benefits, TSoR designs a high-performance network stack that efficiently transfers TCP traffic using RDMA network. Thus, TSoR provides a turn-key solution for existing Kubernetes clusters to adopt the high-performance RDMA network with minimal effort. Our evaluation results show that TSoR provides up to 2.3x higher throughput and 64\\% lower latency for existing containerized applications, such as Redis key-value store and Node.js web server, with no code changes. TSoR code will be open-sourced.",
+    "neighbors": [
+      963
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1168,
+    "label": 6,
+    "text": "Title: Disappearing frameworks explained\nAbstract: The web is the most prominent application platform globally, thanks to its vast user base. It started as a site platform in the 90s and morphed into an application one over time as interactive web applications became a reality. So-called single-page applications (SPAs) represent the current mainstream way of developing web applications but they come with their drawbacks. Although SPAs provide good developer experience, there is a cost for the users due to the underlying technical assumptions. Disappearing frameworks question these assumptions and provide a fresh start. The purpose of this short book is to give a quick introduction to disappearing frameworks and show their meaning as an emerging topic within the space of web application development.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1169,
+    "label": 30,
+    "text": "Title: Data Augmentation for Conversational AI\nAbstract: Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1170,
+    "label": 34,
+    "text": "Title: Fast Algorithms for Energy Games in Special Cases\nAbstract: In this paper, we study algorithms for special cases of energy games, a class of turn-based games on graphs that show up in the quantitative analysis of reactive systems. In an energy game, the vertices of a weighted directed graph belong either to Alice or to Bob. A token is moved to a next vertex by the player controlling its current location, and its energy is changed by the weight of the edge. Given a fixed starting vertex and initial energy, Alice wins the game if the energy of the token remains nonnegative at every moment. If the energy goes below zero at some point, then Bob wins. The problem of determining the winner in an energy game lies in $\\mathsf{NP} \\cap \\mathsf{coNP}$. It is a long standing open problem whether a polynomial time algorithm for this problem exists. We devise new algorithms for three special cases of the problem. The first two results focus on the single-player version, where either Alice or Bob controls the whole game graph. We develop an $\\tilde{O}(n^\\omega W^\\omega)$ time algorithm for a game graph controlled by Alice, by providing a reduction to the All-Pairs Nonnegative Prefix Paths problem (APNP), where $W$ is the maximum weight and $\\omega$ is the best exponent for matrix multiplication. Thus we study the APNP problem separately, for which we develop an $\\tilde{O}(n^\\omega W^\\omega)$ time algorithm. For both problems, we improve over the state of the art of $\\tilde O(mn)$ for small $W$. For the APNP problem, we also provide a conditional lower bound, which states that there is no $O(n^{3-\\epsilon})$ time algorithm for any $\\epsilon>0$, unless the APSP Hypothesis fails. For a game graph controlled by Bob, we obtain a near-linear time algorithm. Regarding our third result, we present a variant of the value iteration algorithm, and we prove that it gives an $O(mn)$ time algorithm for game graphs without negative cycles.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1171,
+    "label": 24,
+    "text": "Title: CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X\nAbstract: Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer. In this paper, we introduce CodeGeeX, a multilingual model with 13 billion parameters for code generation. CodeGeeX is pre-trained on 850 billion tokens of 23 programming languages as of June 2022. Our extensive experiments suggest that CodeGeeX outperforms multilingual code models of similar scale for both the tasks of code generation and translation on HumanEval-X. Building upon HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript, and Go. In addition, we build CodeGeeX-based extensions on Visual Studio Code, JetBrains, and Cloud Studio, generating 4.7 billion tokens for tens of thousands of active users per week. Our user study demonstrates that CodeGeeX can help to increase coding efficiency for 83.4% of its users. Finally, CodeGeeX is publicly accessible and in Sep. 2022, we open-sourced its code, model weights (the version of 850B tokens), API, extensions, and HumanEval-X at https://github.com/THUDM/CodeGeeX.",
+    "neighbors": [
+      268,
+      924,
+      1249,
+      1606,
+      1840,
+      1879,
+      1907
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1172,
+    "label": 30,
+    "text": "Title: Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary tasks\nAbstract: Effectively leveraging multimodal information from social media posts is essential to various downstream tasks such as sentiment analysis, sarcasm detection and hate speech classification. However, combining text and image information is challenging because of the idiosyncratic cross-modal semantics with hidden or complementary information present in matching image-text pairs. In this work, we aim to directly model this by proposing the use of two auxiliary losses jointly with the main task when fine-tuning any pre-trained multimodal model. Image-Text Contrastive (ITC) brings image-text representations of a post closer together and separates them from different posts, capturing underlying dependencies. Image-Text Matching (ITM) facilitates the understanding of semantic correspondence between images and text by penalizing unrelated pairs. We combine these objectives with five multimodal models, demonstrating consistent improvements across four popular social media datasets. Furthermore, through detailed analysis, we shed light on the specific scenarios and cases where each auxiliary task proves to be most effective.",
+    "neighbors": [
+      1593
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1173,
+    "label": 16,
+    "text": "Title: T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models\nAbstract: The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to ``dig out\"the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.",
+    "neighbors": [
+      43,
+      260,
+      706,
+      736,
+      848,
+      955,
+      1079,
+      1251,
+      1303,
+      1420,
+      1582,
+      1588,
+      1590,
+      1707,
+      1789,
+      1902
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1174,
+    "label": 31,
+    "text": "Title: DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations\nAbstract: In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for the similarity and diversity of recommendation results are dynamic and vary under different conditions. Moreover, individual item-level diversity is too coarse-grained since all recommended items are related to the trigger item. Thus, the two main challenges are to learn fine-grained representations of similarity and diversity and capture users' dynamic preferences for them under different conditions. To address these challenges, we propose a novel method called the Dynamic Preference-based and Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in relevant recommendations. Specifically, based on Attribute-aware Activation Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR) is designed to obtain similarity and diversity representations of user interests and item information. Then Shallow and Deep Union-based Fusion (SDUF) is proposed to capture users' dynamic preferences for the diverse degree of recommendation results according to various conditions. DPAN has demonstrated its effectiveness through extensive offline experiments and online A/B testing, resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been successfully deployed on our e-commerce platform serving the primary traffic for relevant recommendations. The code of DPAN has been made publicly available.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1175,
+    "label": 16,
+    "text": "Title: Object Segmentation by Mining Cross-Modal Semantics\nAbstract: Multi-sensor clues have shown promise for object segmentation, but inherent noise in each sensor, as well as the calibration error in practice, may bias the segmentation accuracy. In this paper, we propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features, with the aim of controlling the modal contribution based on relative entropy. We explore semantics among the multimodal inputs in two aspects: the modality-shared consistency and the modality-specific variation. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision. On the one hand, the AF block explicitly dissociates the shared and specific representation and learns to weight the modal contribution by adjusting the \\textit{proportion, region,} and \\textit{pattern}, depending upon the quality. On the other hand, our CFD initially decodes the shared feature and then refines the output through specificity-aware querying. Further, we enforce semantic consistency across the decoding layers to enable interaction across network hierarchies, improving feature discriminability. Exhaustive comparison on eleven datasets with depth or thermal clues, and on two challenging tasks, namely salient and camouflage object segmentation, validate our effectiveness in terms of both performance and robustness. The source code is publicly available at https://github.com/Zongwei97/XMSNet.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1176,
+    "label": 24,
+    "text": "Title: Learning from A Single Graph is All You Need for Near-Shortest Path Routing in Wireless Networks\nAbstract: We propose a learning algorithm for local routing policies that needs only a few data samples obtained from a single graph while generalizing to all random graphs in a standard model of wireless networks. We thus solve the all-pairs near-shortest path problem by training deep neural networks (DNNs) that efficiently and scalably learn routing policies that are local, i.e., they only consider node states and the states of neighboring nodes. Remarkably, one of these DNNs we train learns a policy that exactly matches the performance of greedy forwarding; another generally outperforms greedy forwarding. Our algorithm design exploits network domain knowledge in several ways: First, in the selection of input features and, second, in the selection of a ``seed graph'' and subsamples from its shortest paths. The leverage of domain knowledge provides theoretical explainability of why the seed graph and node subsampling suffice for learning that is efficient, scalable, and generalizable. Simulation-based results on uniform random graphs with diverse sizes and densities empirically corroborate that using samples generated from a few routing paths in a modest-sized seed graph quickly learns a model that is generalizable across (almost) all random graphs in the wireless network model.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1177,
+    "label": 24,
+    "text": "Title: A Deep Learning Approach for Generating Soft Range Information from RF Data\nAbstract: Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate of distance. We propose a deep learning approach to generate accurate SRI from RF measurements. In particular, the proposed approach is implemented by a network with two neural modules and conducts the generation directly from raw data. Extensive experiments on a case study with two public datasets are conducted to quantify the efficiency in different indoor localization tasks. The results show that the proposed approach can generate highly accurate SRI, and significantly outperforms conventional techniques in both non-line-of-sight (NLOS) detection and ranging error mitigation.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1178,
+    "label": 24,
+    "text": "Title: Sifer: Overcoming simplicity bias in deep networks using a feature sieve\nAbstract: Simplicity bias is the concerning tendency of deep networks to over-depend on simple, weakly predictive features, to the exclusion of stronger, more complex features. This is exacerbated in real-world applications by limited training data and spurious feature-label correlations, leading to biased, incorrect predictions. We propose a direct, interventional method for addressing simplicity bias in DNNs, which we call the feature sieve. We aim to automatically identify and suppress easily-computable spurious features in lower layers of the network, thereby allowing the higher network levels to extract and utilize richer, more meaningful representations. We provide concrete evidence of this differential suppression&enhancement of relevant features on both controlled datasets and real-world images, and report substantial gains on many real-world debiasing benchmarks (11.4% relative gain on Imagenet-A; 3.2% on BAR, etc). Crucially, we do not depend on prior knowledge of spurious attributes or features, and in fact outperform many baselines that explicitly incorporate such information. We believe that our feature sieve work opens up exciting new research directions in automated adversarial feature extraction and representation learning for deep networks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1179,
+    "label": 16,
+    "text": "Title: Zero-shot Image-to-Image Translation\nAbstract: Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse, high-quality images. However, directly applying these models for real image editing remains challenging for two reasons. First, it is hard for users to craft a perfect text prompt depicting every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we introduce pix2pix-zero, an image-to-image translation method that can preserve the original image\u2019s content without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the content structure, we propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. Finally, to enable interactive editing, we distill the diffusion model into a fast conditional GAN. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model.",
+    "neighbors": [
+      706,
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+      1420,
+      1582,
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+      1902,
+      2021,
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+      2243,
+      2251
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1180,
+    "label": 13,
+    "text": "Title: RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults\nAbstract: To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation. In this manner, the embedded systems that employ RescueSNN-enhanced chips can efficiently ensure reliable executions against permanent faults during their operational lifetime.",
+    "neighbors": [
+      1254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1181,
+    "label": 5,
+    "text": "Title: Recent Latest Message Driven GHOST: Balancing Dynamic Availability With Asynchrony Resilience\nAbstract: Dynamic participation has recently become a crucial requirement for devising permissionless consensus protocols. This notion, originally formalized by Pass and Shi (ASIACRYPT 2017) through their\"sleepy model\", captures the essence of a system's ability to handle participants joining or leaving during a protocol execution. A dynamically available consensus protocol preserves safety and liveness while allowing dynamic participation. Blockchain protocols, such as Bitcoin's consensus protocol, have implicitly adopted this concept. In the context of Ethereum's consensus protocol, Gasper, Neu, Tas, and Tse (S&P 2021) presented an attack against LMD-GHOST -- the component of Gasper designed to ensure dynamic availability. Consequently, LMD-GHOST results unable to fulfill its intended function of providing dynamic availability for the protocol. Despite attempts to mitigate this issue, the modified protocol still does not achieve dynamic availability, highlighting the need for more secure dynamically available protocols. In this work, we present RLMD-GHOST, a synchronous consensus protocol that not only ensures dynamic availability but also maintains safety during bounded periods of asynchrony. This protocol is particularly appealing for practical systems where strict synchrony assumptions may not always hold, contrary to general assumptions in standard synchronous protocols. Additionally, we present the\"generalized sleepy model\", within which our results are proven. Building upon the original sleepy model proposed by Pass and Shi, our model extends it with more generalized and stronger constraints on the corruption and sleepiness power of the adversary. This approach allows us to explore a wide range of dynamic participation regimes, spanning from complete dynamic participation to no dynamic participation, i.e., with every participant online.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1182,
+    "label": 31,
+    "text": "Title: TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation\nAbstract: Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems. Initial attempts have leveraged the exceptional capabilities of LLMs, such as rich knowledge and strong generalization through In-context Learning, which involves phrasing the recommendation task as prompts. Nevertheless, the performance of LLMs in recommendation tasks remains suboptimal due to a substantial disparity between the training tasks for LLMs and recommendation tasks, as well as inadequate recommendation data during pre-training. To bridge the gap, we consider building a Large Recommendation Language Model by tunning LLMs with recommendation data. To this end, we propose an efficient and effective Tuning framework for Aligning LLMs with Recommendations, namely TALLRec. We have demonstrated that the proposed TALLRec framework can significantly enhance the recommendation capabilities of LLMs in the movie and book domains, even with a limited dataset of fewer than 100 samples. Additionally, the proposed framework is highly efficient and can be executed on a single RTX 3090 with LLaMA-7B. Furthermore, the fine-tuned LLM exhibits robust cross-domain generalization. Our code and data are available at https://github.com/SAI990323/TALLRec.",
+    "neighbors": [
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+      1611,
+      1667,
+      1915,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1183,
+    "label": 24,
+    "text": "Title: Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic\nAbstract: Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mixes exponentially fast with a rate parameter that appears in the step-size selection. Unfortunately, this assumption is violated for large state spaces or settings with sparse rewards, and the mixing time is unknown, making the step size inoperable. In this work, we propose an RL methodology attuned to the mixing time by employing a multi-level Monte Carlo estimator for the critic, the actor, and the average reward embedded within an actor-critic (AC) algorithm. This method, which we call \\textbf{M}ulti-level \\textbf{A}ctor-\\textbf{C}ritic (MAC), is developed especially for infinite-horizon average-reward settings and neither relies on oracle knowledge of the mixing time in its parameter selection nor assumes its exponential decay; it, therefore, is readily applicable to applications with slower mixing times. Nonetheless, it achieves a convergence rate comparable to the state-of-the-art AC algorithms. We experimentally show that these alleviated restrictions on the technical conditions required for stability translate to superior performance in practice for RL problems with sparse rewards.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1184,
+    "label": 30,
+    "text": "Title: Effective Neural Topic Modeling with Embedding Clustering Regularization\nAbstract: Topic models have been prevalent for decades with various applications. However, existing topic models commonly suffer from the notorious topic collapsing: discovered topics semantically collapse towards each other, leading to highly repetitive topics, insufficient topic discovery, and damaged model interpretability. In this paper, we propose a new neural topic model, Embedding Clustering Regularization Topic Model (ECRTM). Besides the existing reconstruction error, we propose a novel Embedding Clustering Regularization (ECR), which forces each topic embedding to be the center of a separately aggregated word embedding cluster in the semantic space. This enables each produced topic to contain distinct word semantics, which alleviates topic collapsing. Regularized by ECR, our ECRTM generates diverse and coherent topics together with high-quality topic distributions of documents. Extensive experiments on benchmark datasets demonstrate that ECRTM effectively addresses the topic collapsing issue and consistently surpasses state-of-the-art baselines in terms of topic quality, topic distributions of documents, and downstream classification tasks.",
+    "neighbors": [
+      354,
+      953
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1185,
+    "label": 36,
+    "text": "Title: Best of Both Distortion Worlds\nAbstract: We study the problem of designing voting rules that take as input the ordinal preferences of n agents over a set of n alternatives and output a single alternative, aiming to optimize the overall happiness of the agents. The input to the voting rule is each agent's ranking of the alternatives from most to least preferred, yet the agents have more refined (cardinal) preferences that capture the intensity with which they prefer one alternative over another. To quantify the extent to which voting rules can optimize over the cardinal preferences given access only to the ordinal ones, prior work has used the distortion measure, i.e., the worst-case approximation ratio between a voting rule's performance and the best performance achievable given the cardinal preferences. The work on the distortion of voting rules has been largely divided into two \"worlds\": utilitarian distortion and metric distortion. In the former, the cardinal preferences of the agents correspond to general utilities and the goal is to maximize a normalized social welfare. In the latter, the agents' cardinal preferences correspond to costs given by distances in an underlying metric space and the goal is to minimize the (unnormalized) social cost. Several deterministic and randomized voting rules have been proposed and evaluated for each of these worlds separately, gradually improving the achievable distortion bounds, but none of the known voting rules perform well in both worlds simultaneously. In this work, we prove that one can in fact achieve the \"best of both worlds\" by designing new voting rules, both deterministic and randomized, that simultaneously achieve near-optimal distortion guarantees in both distortion worlds. We also prove that this positive result does not generalize to the case where the voting rule is provided with the rankings of only the top-t alternatives of each agent, for t < m, and study the extent to which such best-of-both-worlds guarantees can be achieved.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1186,
+    "label": 24,
+    "text": "Title: IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies\nAbstract: Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions through a modified Bellman backup. However, it is unclear which policy actually attains the values represented by this implicitly trained Q-function. In this paper, we reinterpret IQL as an actor-critic method by generalizing the critic objective and connecting it to a behavior-regularized implicit actor. This generalization shows how the induced actor balances reward maximization and divergence from the behavior policy, with the specific loss choice determining the nature of this tradeoff. Notably, this actor can exhibit complex and multimodal characteristics, suggesting issues with the conditional Gaussian actor fit with advantage weighted regression (AWR) used in prior methods. Instead, we propose using samples from a diffusion parameterized behavior policy and weights computed from the critic to then importance sampled our intended policy. We introduce Implicit Diffusion Q-learning (IDQL), combining our general IQL critic with the policy extraction method. IDQL maintains the ease of implementation of IQL while outperforming prior offline RL methods and demonstrating robustness to hyperparameters. Code is available at https://github.com/philippe-eecs/IDQL.",
+    "neighbors": [
+      787,
+      1137,
+      1908,
+      2245,
+      2260
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1187,
+    "label": 24,
+    "text": "Title: On the Convergence of No-Regret Learning Dynamics in Time-Varying Games\nAbstract: Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the convergence of no-regret learning algorithms in dynamic multiagent settings. In this paper, we characterize the convergence of optimistic gradient descent (OGD) in time-varying games. Our framework yields sharp convergence bounds for the equilibrium gap of OGD in zero-sum games parameterized on natural variation measures of the sequence of games, subsuming known results for static games. Furthermore, we establish improved second-order variation bounds under strong convexity-concavity, as long as each game is repeated multiple times. Our results also apply to time-varying general-sum multi-player games via a bilinear formulation of correlated equilibria, which has novel implications for meta-learning and for obtaining refined variation-dependent regret bounds, addressing questions left open in prior papers. Finally, we leverage our framework to also provide new insights on dynamic regret guarantees in static games.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1188,
+    "label": 6,
+    "text": "Title: Advances in Automatically Rating the Trustworthiness of Text Processing Services\nAbstract: AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1189,
+    "label": 24,
+    "text": "Title: Causal Interpretable Progression Trajectory Analysis of Chronic Disease\nAbstract: Chronic disease is the leading cause of death, emphasizing the need for accurate prediction of disease progression trajectories and informed clinical decision-making. Machine learning (ML) models have shown promise in this domain by capturing non-linear patterns within patient features. However, existing ML-based models lack the ability to provide causal interpretable predictions and estimate treatment effects, limiting their decision-assisting perspective. In this study, we propose a novel model called causal trajectory prediction (CTP) to tackle the limitation. The CTP model combines trajectory prediction and causal discovery to enable accurate prediction of disease progression trajectories and uncovering causal relationships between features. By incorporating a causal graph into the prediction process, CTP ensures that ancestor features are not influenced by treatment on descendant features, thereby enhancing the interpretability of the model. By estimating the bounds of treatment effects, even in the presence of unmeasured confounders, the CTP provides valuable insights for clinical decision-making. We evaluate the performance of the CTP using simulated and real medical datasets. Experimental results demonstrate that our model achieves satisfactory performance, highlighting its potential to assist clinical decisions.",
+    "neighbors": [
+      423
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1190,
+    "label": 24,
+    "text": "Title: Refined Value-Based Offline RL under Realizability and Partial Coverage\nAbstract: In offline reinforcement learning (RL) we have no opportunity to explore so we must make assumptions that the data is sufficient to guide picking a good policy, taking the form of assuming some coverage, realizability, Bellman completeness, and/or hard margin (gap). In this work we propose value-based algorithms for offline RL with PAC guarantees under just partial coverage, specifically, coverage of just a single comparator policy, and realizability of soft (entropy-regularized) Q-function of the single policy and a related function defined as a saddle point of certain minimax optimization problem. This offers refined and generally more lax conditions for offline RL. We further show an analogous result for vanilla Q-functions under a soft margin condition. To attain these guarantees, we leverage novel minimax learning algorithms to accurately estimate soft or vanilla Q-functions with $L^2$-convergence guarantees. Our algorithms' loss functions arise from casting the estimation problems as nonlinear convex optimization problems and Lagrangifying.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1191,
+    "label": 16,
+    "text": "Title: Larger is not Better: A Survey on the Robustness of Computer Vision Models against Common Corruptions\nAbstract: The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e.g. noise, blur, illumination changes, etc.), that can hinder their reliability when deployed in real scenarios. These corruptions are not always considered to test model generalization and robustness. In this survey, we present a comprehensive overview of methods that improve the robustness of computer vision models against common corruptions. We categorize methods into four groups based on the model part and training method addressed: data augmentation, representation learning, knowledge distillation, and network components. We also cover indirect methods for generalization and mitigation of shortcut learning, potentially useful for corruption robustness. We release a unified benchmark framework to compare robustness performance on several datasets, and address the inconsistencies of evaluation in the literature. We provide an experimental overview of the base corruption robustness of popular vision backbones, and show that corruption robustness does not necessarily scale with model size. The very large models (above 100M parameters) gain negligible robustness, considering the increased computational requirements. To achieve generalizable and robust computer vision models, we foresee the need of developing new learning strategies to efficiently exploit limited data and mitigate unwanted or unreliable learning behaviors.",
+    "neighbors": [
+      1963,
+      2033
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1192,
+    "label": 25,
+    "text": "Title: FunCodec: A Fundamental, Reproducible and Integrable Open-source Toolkit for Neural Speech Codec\nAbstract: This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an extension of the open-source speech processing toolkit FunASR. FunCodec provides reproducible training recipes and inference scripts for the latest neural speech codec models, such as SoundStream and Encodec. Thanks to the unified design with FunASR, FunCodec can be easily integrated into downstream tasks, such as speech recognition. Along with FunCodec, pre-trained models are also provided, which can be used for academic or generalized purposes. Based on the toolkit, we further propose the frequency-domain codec models, FreqCodec, which can achieve comparable speech quality with much lower computation and parameter complexity. Experimental results show that, under the same compression ratio, FunCodec can achieve better reconstruction quality compared with other toolkits and released models. We also demonstrate that the pre-trained models are suitable for downstream tasks, including automatic speech recognition and personalized text-to-speech synthesis. This toolkit is publicly available at https://github.com/alibaba-damo-academy/FunCodec.",
+    "neighbors": [
+      1450
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1193,
+    "label": 16,
+    "text": "Title: Object-Centric Multiple Object Tracking\nAbstract: Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines. Unfortunately, they lack two key properties: objects are often split into parts and are not consistently tracked over time. In fact, state-of-the-art models achieve pixel-level accuracy and temporal consistency by relying on supervised object detection with additional ID labels for the association through time. This paper proposes a video object-centric model for MOT. It consists of an index-merge module that adapts the object-centric slots into detection outputs and an object memory module that builds complete object prototypes to handle occlusions. Benefited from object-centric learning, we only require sparse detection labels (0%-6.25%) for object localization and feature binding. Relying on our self-supervised Expectation-Maximization-inspired loss for object association, our approach requires no ID labels. Our experiments significantly narrow the gap between the existing object-centric model and the fully supervised state-of-the-art and outperform several unsupervised trackers.",
+    "neighbors": [
+      1262
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1194,
+    "label": 30,
+    "text": "Title: HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models\nAbstract: Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, \\ie content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, \\ie sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (\\ie about $11.4\\%$ user queries). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. While, our experiments also prove that the hallucination recognition can be improved by providing external knowledge or adding reasoning steps. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.",
+    "neighbors": [
+      36,
+      1485,
+      1713,
+      1952,
+      2013,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1195,
+    "label": 30,
+    "text": "Title: Improving Machine Translation with Phrase Pair Injection and Corpus Filtering\nAbstract: In this paper, we show that the combination of Phrase Pair Injection and Corpus Filtering boosts the performance of Neural Machine Translation (NMT) systems. We extract parallel phrases and sentences from the pseudo-parallel corpus and augment it with the parallel corpus to train the NMT models. With the proposed approach, we observe an improvement in the Machine Translation (MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi, and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on the FLORES test data. These BLEU score improvements are over the models trained using the whole pseudo-parallel corpus augmented with the parallel corpus.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1196,
+    "label": 4,
+    "text": "Title: Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferability\nAbstract: Evasion attacks are a threat to machine learning models, where adversaries attempt to affect classifiers by injecting malicious samples. An alarming side-effect of evasion attacks is their ability to transfer among different models: this property is called transferability. Therefore, an attacker can produce adversarial samples on a custom model (surrogate) to conduct the attack on a victim's organization later. Although literature widely discusses how adversaries can transfer their attacks, their experimental settings are limited and far from reality. For instance, many experiments consider both attacker and defender sharing the same dataset, balance level (i.e., how the ground truth is distributed), and model architecture. In this work, we propose the DUMB attacker model. This framework allows analyzing if evasion attacks fail to transfer when the training conditions of surrogate and victim models differ. DUMB considers the following conditions: Dataset soUrces, Model architecture, and the Balance of the ground truth. We then propose a novel testbed to evaluate many state-of-the-art evasion attacks with DUMB; the testbed consists of three computer vision tasks with two distinct datasets each, four types of balance levels, and three model architectures. Our analysis, which generated 13K tests over 14 distinct attacks, led to numerous novel findings in the scope of transferable attacks with surrogate models. In particular, mismatches between attackers and victims in terms of dataset source, balance levels, and model architecture lead to non-negligible loss of attack performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1197,
+    "label": 30,
+    "text": "Title: Explaining Legal Concepts with Augmented Large Language Models (GPT-4)\nAbstract: Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike.",
+    "neighbors": [
+      57,
+      704,
+      2035
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1198,
+    "label": 27,
+    "text": "Title: Vehicle State Estimation through Modular Factor Graph-based Fusion of Multiple Sensors\nAbstract: This study focuses on the critical aspect of robust state estimation for the safe navigation of an Autonomous Vehicle (AV). Existing literature primarily employs two prevalent techniques for state estimation, namely filtering-based and graph-based approaches. Factor Graph (FG) is a graph-based approach, constructed using Values and Factors for Maximum Aposteriori (MAP) estimation, that offers a modular architecture that facilitates the integration of inputs from diverse sensors. However, most FG-based architectures in current use require explicit knowledge of sensor parameters and are designed for single setups. To address these limitations, this research introduces a novel plug-and-play FG-based state estimator capable of operating without predefined sensor parameters. This estimator is suitable for deployment in multiple sensor setups, offering convenience and providing comprehensive state estimation at a high frequency, including mean and covariances. The proposed algorithm undergoes rigorous validation using various sensor setups on two different vehicles: a quadricycle and a shuttle bus. The algorithm provides accurate and robust state estimation across diverse scenarios, even when faced with degraded Global Navigation Satellite System (GNSS) measurements or complete outages. These findings highlight the efficacy and reliability of the algorithm in real-world AV applications.",
+    "neighbors": [
+      885
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1199,
+    "label": 30,
+    "text": "Title: DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4\nAbstract: The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are highly needed. While various computer-assisted de-identification methods, including both rule-based and learning-based, have been developed and used in prior practice, such solutions still lack generalizability or need to be fine-tuned according to different scenarios, significantly imposing restrictions in wider use. The advancement of large language models (LLM), such as ChatGPT and GPT-4, have shown great potential in processing text data in the medical domain with zero-shot in-context learning, especially in the task of privacy protection, as these models can identify confidential information by their powerful named entity recognition (NER) capability. In this work, we developed a novel GPT4-enabled de-identification framework (\"DeID-GPT\") to automatically identify and remove the identifying information. Compared to existing commonly used medical text data de-identification methods, our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text while preserving the original structure and meaning of the text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification, which provides insights for further research and solution development on the use of LLMs such as ChatGPT/GPT-4 in healthcare. Codes and benchmarking data information are available at https://github.com/yhydhx/ChatGPT-API.",
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+      1952,
+      2230,
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+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1200,
+    "label": 24,
+    "text": "Title: Adaptive Data Analysis in a Balanced Adversarial Model\nAbstract: In adaptive data analysis, a mechanism gets $n$ i.i.d. samples from an unknown distribution $D$, and is required to provide accurate estimations to a sequence of adaptively chosen statistical queries with respect to $D$. Hardt and Ullman (FOCS 2014) and Steinke and Ullman (COLT 2015) showed that in general, it is computationally hard to answer more than $\\Theta(n^2)$ adaptive queries, assuming the existence of one-way functions. However, these negative results strongly rely on an adversarial model that significantly advantages the adversarial analyst over the mechanism, as the analyst, who chooses the adaptive queries, also chooses the underlying distribution $D$. This imbalance raises questions with respect to the applicability of the obtained hardness results -- an analyst who has complete knowledge of the underlying distribution $D$ would have little need, if at all, to issue statistical queries to a mechanism which only holds a finite number of samples from $D$. We consider more restricted adversaries, called \\emph{balanced}, where each such adversary consists of two separated algorithms: The \\emph{sampler} who is the entity that chooses the distribution and provides the samples to the mechanism, and the \\emph{analyst} who chooses the adaptive queries, but does not have a prior knowledge of the underlying distribution. We improve the quality of previous lower bounds by revisiting them using an efficient \\emph{balanced} adversary, under standard public-key cryptography assumptions. We show that these stronger hardness assumptions are unavoidable in the sense that any computationally bounded \\emph{balanced} adversary that has the structure of all known attacks, implies the existence of public-key cryptography.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1201,
+    "label": 27,
+    "text": "Title: Energy-Constrained Active Exploration Under Incremental-Resolution Symbolic Perception\nAbstract: In this work, we consider the problem of autonomous exploration in search of targets while respecting a fixed energy budget. The robot is equipped with an incremental-resolution symbolic perception module wherein the perception of targets in the environment improves as the robot's distance from targets decreases. We assume no prior information about the total number of targets, their locations as well as their possible distribution within the environment. This work proposes a novel decision-making framework for the resulting constrained sequential decision-making problem by first converting it into a reward maximization problem on a product graph computed offline. It is then solved online as a Mixed-Integer Linear Program (MILP) where the knowledge about the environment is updated at each step, combining automata-based and MILP-based techniques. We demonstrate the efficacy of our approach with the help of a case study and present empirical evaluation in terms of expected regret. Furthermore, the runtime performance shows that online planning can be efficiently performed for moderately-sized grid environments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1202,
+    "label": 23,
+    "text": "Title: Improving Students With Rubric-Based Self-Assessment and Oral Feedback\nAbstract: Rubrics and oral feedback are approaches to help students improve performance and meet learning outcomes. However, their effect on the actual improvement achieved is inconclusive. This paper evaluates the effect of rubrics and oral feedback on student learning outcomes. An experiment was conducted in a software engineering course on requirements engineering, using the two approaches in course assignments. Both approaches led to statistically significant improvements, though no material improvement (i.e., a change by more than one grade) was achieved. The rubrics led to a significant decrease in the number of complaints and questions regarding grades.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1203,
+    "label": 6,
+    "text": "Title: Generative Agents: Interactive Simulacra of Human Behavior\nAbstract: Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools. In this paper, we introduce generative agents--computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversations; they remember and reflect on days past as they plan the next day. To enable generative agents, we describe an architecture that extends a large language model to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior. We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine's Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time. We demonstrate through ablation that the components of our agent architecture--observation, planning, and reflection--each contribute critically to the believability of agent behavior. By fusing large language models with computational, interactive agents, this work introduces architectural and interaction patterns for enabling believable simulations of human behavior.",
+    "neighbors": [
+      36,
+      126,
+      240,
+      247,
+      945,
+      989,
+      1044,
+      1339,
+      1659,
+      1878,
+      2029,
+      2265
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1204,
+    "label": 24,
+    "text": "Title: Machine Learning Applications In Healthcare: The State Of Knowledge and Future Directions\nAbstract: Detection of easily missed hidden patterns with fast processing power makes machine learning (ML) indispensable to today's healthcare system. Though many ML applications have already been discovered and many are still under investigation, only a few have been adopted by current healthcare systems. As a result, there exists an enormous opportunity in healthcare system for ML but distributed information, scarcity of properly arranged and easily explainable documentation in related sector are major impede which are making ML applications difficult to healthcare professionals. This study aimed to gather ML applications in different areas of healthcare concisely and more effectively so that necessary information can be accessed immediately with relevant references. We divided our study into five major groups: community level work, risk management/ preventive care, healthcare operation management, remote care, and early detection. Dividing these groups into subgroups, we provided relevant references with description in tabular form for quick access. Our objective is to inform people about ML applicability in healthcare industry, reduce the knowledge gap of clinicians about the ML applications and motivate healthcare professionals towards more machine learning based healthcare system.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1205,
+    "label": 16,
+    "text": "Title: Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era\nAbstract: Generative AI (AIGC, a.k.a. AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC. Due to the development in text-to-image as well 3D modeling technologies (like NeRF), text-to-3D has become a newly emerging yet highly active research field. Our work conducts the first yet comprehensive survey on text-to-3D to help readers interested in this direction quickly catch up with its fast development. First, we introduce 3D data representations, including both Euclidean data and non-Euclidean data. On top of that, we introduce various foundation technologies as well as summarize how recent works combine those foundation technologies to realize satisfactory text-to-3D. Moreover, we summarize how text-to-3D technology is used in various applications, including avatar generation, texture generation, shape transformation, and scene generation.",
+    "neighbors": [
+      48,
+      63,
+      1207,
+      1573,
+      1601,
+      1902,
+      1905
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1206,
+    "label": 16,
+    "text": "Title: MO-VLN: A Multi-Task Benchmark for Open-set Zero-Shot Vision-and-Language Navigation\nAbstract: Given a natural language, a general robot has to comprehend the instruction and find the target object or location based on visual observations even in unexplored environments. Most agents rely on massive diverse training data to achieve better generalization, which requires expensive labor. These agents often focus on common objects and fewer tasks, thus are not intelligent enough to handle different types of instructions. To facilitate research in open-set vision-and-language navigation, we propose a benchmark named MO-VLN, aiming at testing the effectiveness and generalization of the agent in the multi-task setting. First, we develop a 3D simulator rendered by realistic scenarios using Unreal Engine 5, containing more realistic lights and details. The simulator contains three scenes, i.e., cafe, restaurant, and nursing house, of high value in the industry. Besides, our simulator involves multiple uncommon objects, such as takeaway cup and medical adhesive tape, which are more complicated compared with existing environments. Inspired by the recent success of large language models (e.g., ChatGPT, Vicuna), we construct diverse high-quality data of instruction type without human annotation. Our benchmark MO-VLN provides four tasks: 1) goal-conditioned navigation given a specific object category (e.g.,\"fork\"); 2) goal-conditioned navigation given simple instructions (e.g.,\"Search for and move towards a tennis ball\"); 3) step-by-step instruction following; 4) finding abstract object based on high-level instruction (e.g.,\"I am thirsty\").",
+    "neighbors": [
+      1052,
+      1267,
+      1863,
+      2136
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1207,
+    "label": 16,
+    "text": "Title: Robustness of SAM: Segment Anything Under Corruptions and Beyond\nAbstract: Segment anything model (SAM), as the name suggests, is claimed to be capable of cutting out any object and demonstrates impressive zero-shot transfer performance with the guidance of prompts. However, there is currently a lack of comprehensive evaluation regarding its robustness under various corruptions. Understanding the robustness of SAM across different corruption scenarios is crucial for its real-world deployment. Prior works show that SAM is biased towards texture (style) rather than shape, motivated by which we start by investigating its robustness against style transfer, which is synthetic corruption. Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption. These corruptions mainly fall into categories such as digital, noise, weather, and blur, and within each corruption category, we explore 5 severity levels to simulate real-world corruption scenarios. Beyond the corruptions, we further assess the robustness of SAM against local occlusion and local adversarial patch attacks. To the best of our knowledge, our work is the first of its kind to evaluate the robustness of SAM under style change, local occlusion, and local adversarial patch attacks. Given that patch attacks visible to human eyes are easily detectable, we further assess its robustness against global adversarial attacks that are imperceptible to human eyes. Overall, this work provides a comprehensive empirical study of the robustness of SAM, evaluating its performance under various corruptions and extending the assessment to critical aspects such as local occlusion, local adversarial patch attacks, and global adversarial attacks. These evaluations yield valuable insights into the practical applicability and effectiveness of SAM in addressing real-world challenges.",
+    "neighbors": [
+      341,
+      584,
+      1084,
+      1205,
+      1601,
+      1663,
+      1690,
+      1932
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1208,
+    "label": 24,
+    "text": "Title: Uncertainty Estimation by Fisher Information-based Evidential Deep Learning\nAbstract: Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different uncertainties by treating the network's outputs as evidence to parameterize the Dirichlet distribution, and achieve impressive performance in uncertainty estimation. However, for high data uncertainty samples but annotated with the one-hot label, the evidence-learning process for those mislabeled classes is over-penalized and remains hindered. To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\\mathcal{I}$-EDL). In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes. The generalization ability of our network is further improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our proposed method consistently outperforms traditional EDL-related algorithms in multiple uncertainty estimation tasks, especially in the more challenging few-shot classification settings.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1209,
+    "label": 27,
+    "text": "Title: Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment\nAbstract: In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1210,
+    "label": 30,
+    "text": "Title: ChatGPT: A Meta-Analysis after 2.5 Months\nAbstract: ChatGPT, a chatbot developed by OpenAI, has gained widespread popularity and media attention since its release in November 2022. However, little hard evidence is available regarding its perception in various sources. In this paper, we analyze over 300,000 tweets and more than 150 scientific papers to investigate how ChatGPT is perceived and discussed. Our findings show that ChatGPT is generally viewed as of high quality, with positive sentiment and emotions of joy dominating in social media. Its perception has slightly decreased since its debut, however, with joy decreasing and (negative) surprise on the rise, and it is perceived more negatively in languages other than English. In recent scientific papers, ChatGPT is characterized as a great opportunity across various fields including the medical domain, but also as a threat concerning ethics and receives mixed assessments for education. Our comprehensive meta-analysis of ChatGPT's current perception after 2.5 months since its release can contribute to shaping the public debate and informing its future development. We make our data available.",
+    "neighbors": [
+      1611,
+      1636,
+      2249,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1211,
+    "label": 16,
+    "text": "Title: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture\nAbstract: This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.",
+    "neighbors": [
+      427,
+      1637,
+      2051
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1212,
+    "label": 30,
+    "text": "Title: Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment\nAbstract: Recently, speech-text pre-training methods have shown remarkable success in many speech and natural language processing tasks. However, most previous pre-trained models are usually tailored for one or two specific tasks, but fail to conquer a wide range of speech-text tasks. In addition, existing speech-text pre-training methods fail to explore the contextual information within a dialogue to enrich utterance representations. In this paper, we propose Speech-text Pre-training for spoken dialog understanding with ExpliCiT cRoss-Modal Alignment (SPECTRA), which is the first-ever speech-text dialog pre-training model. Concretely, to consider the temporality of speech modality, we design a novel temporal position prediction task to capture the speech-text alignment. This pre-training task aims to predict the start and end time of each textual word in the corresponding speech waveform. In addition, to learn the characteristics of spoken dialogs, we generalize a response selection task from textual dialog pre-training to speech-text dialog pre-training scenarios. Experimental results on four different downstream speech-text tasks demonstrate the superiority of SPECTRA in learning speech-text alignment and multi-turn dialog context.",
+    "neighbors": [
+      819,
+      1378
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1213,
+    "label": 24,
+    "text": "Title: Stability Is Stable: Connections between Replicability, Privacy, and Adaptive Generalization\nAbstract: The notion of replicable algorithms was introduced by Impagliazzo, Lei, Pitassi, and Sorrell (STOC\u201922) to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with high probability when its randomness is fixed and it is run on a new i.i.d. sample drawn from the same distribution. Using replicable algorithms for data analysis can facilitate the verification of published results by ensuring that the results of an analysis will be the same with high probability, even when that analysis is performed on a new data set. In this work, we establish new connections and separations between replicability and standard notions of algorithmic stability. In particular, we give sample-efficient algorithmic reductions between perfect generalization, approximate differential privacy, and replicability for a broad class of statistical problems. Conversely, we show any such equivalence must break down computationally: there exist statistical problems that are easy under differential privacy, but that cannot be solved replicably without breaking public-key cryptography. Furthermore, these results are tight: our reductions are statistically optimal, and we show that any computational separation between DP and replicability must imply the existence of one-way functions. Our statistical reductions give a new algorithmic framework for translating between notions of stability, which we instantiate to answer several open questions in replicability and privacy. This includes giving sample-efficient replicable algorithms for various PAC learning, distribution estimation, and distribution testing problems, algorithmic amplification of \u03b4 in approximate DP, conversions from item-level to user-level privacy, and the existence of private agnostic-to-realizable learning reductions under structured distributions.",
+    "neighbors": [
+      677,
+      1471
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1214,
+    "label": 16,
+    "text": "Title: LITAR: Visually Coherent Lighting for Mobile Augmented Reality\nAbstract: An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support reflective rendering, existing methods have leveraged deep learning models to estimate or have used physical light probes to capture physical lighting, typically represented in the form of an environment map. However, these methods often fail to provide visually coherent details or require additional setups. For example, the commercial framework ARKit uses a convolutional neural network that can generate realistic environment maps; however the corresponding reflective rendering might not match the physical environments. In this work, we present the design and implementation of a lighting reconstruction framework called LitAR that enables realistic and visually-coherent rendering. LitAR addresses several challenges of supporting lighting information for mobile AR. First, to address the spatial variance problem, LitAR uses two-field lighting reconstruction to divide the lighting reconstruction task into the spatial variance-aware near-field reconstruction and the directional-aware far-field reconstruction. The corresponding environment map allows reflective rendering with correct color tones. Second, LitAR uses two noise-tolerant data capturing policies to ensure data quality, namely guided bootstrapped movement and motion-based automatic capturing. Third, to handle the mismatch between the mobile computation capability and the high computation requirement of lighting reconstruction, LitAR employs two novel real-time environment map rendering techniques called multi-resolution projection and anchor extrapolation. These two techniques effectively remove the need of time-consuming mesh reconstruction while maintaining visual quality.",
+    "neighbors": [
+      769
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1215,
+    "label": 30,
+    "text": "Title: Tagged End-to-End Simultaneous Speech Translation Training Using Simultaneous Interpretation Data\nAbstract: Simultaneous speech translation (SimulST) translates partial speech inputs incrementally. Although the monotonic correspondence between input and output is preferable for smaller latency, it is not the case for distant language pairs such as English and Japanese. A prospective approach to this problem is to mimic simultaneous interpretation (SI) using SI data to train a SimulST model. However, the size of such SI data is limited, so the SI data should be used together with ordinary bilingual data whose translations are given in offline. In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline. The proposed method trains a single model using the mixed data with style tags that tell the model to generate SI- or offline-style outputs. Experiment results show improvements of BLEURT in different latency ranges, and our analyses revealed the proposed model generates SI-style outputs more than the baseline.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1216,
+    "label": 24,
+    "text": "Title: Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning\nAbstract: In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion, neural networks are known to suffer from catastrophic forgetting, where the model's performances on old tasks drop dramatically after being optimized for a new task. Since then, the continual learning (CL) community has proposed several solutions aiming to equip the neural network with the ability to learn the current task (plasticity) while still achieving high accuracy on the previous tasks (stability). Despite remarkable improvements, the plasticity-stability trade-off is still far from being solved and its underlying mechanism is poorly understood. In this work, we propose Auxiliary Network Continual Learning (ANCL), a novel method that applies an additional auxiliary network which promotes plasticity to the continually learned model which mainly focuses on stability. More concretely, the proposed framework materializes in a regularizer that naturally interpolates between plasticity and stability, surpassing strong baselines on task incremental and class incremental scenarios. Through extensive analyses on ANCL solutions, we identify some essential principles beneath the stability-plasticity tradeoff. The code implementation of our work is available at https://github.com/kim-sanghwan/ANCL.",
+    "neighbors": [
+      1625,
+      1996
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1217,
+    "label": 6,
+    "text": "Title: Towards Building Child-Centered Machine Learning Pipelines: Use Cases from K-12 and Higher-Education\nAbstract: Researchers and policy-makers have started creating frameworks and guidelines for building machine-learning (ML) pipelines with a human-centered lens. Machine Learning pipelines stand for all the necessary steps to develop ML systems (e.g., developing a predictive keyboard). On the other hand, a child-centered focus in developing ML systems has been recently gaining interest as children are becoming users of these products. These efforts dominantly focus on children's interaction with ML-based systems. However, from our experience, ML pipelines are yet to be adapted using a child-centered lens. In this paper, we list the questions we ask ourselves in adapting human-centered ML pipelines to child-centered ones. We also summarize two case studies of building end-to-end ML pipelines for children's products.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1218,
+    "label": 31,
+    "text": "Title: Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction\nAbstract: Conversion rate (CVR) prediction is an essential task for large-scale e-commerce platforms. However, refund behaviors frequently occur after conversion in online shopping systems, which drives us to pay attention to effective conversion for building healthier shopping services. This paper defines the probability of item purchasing without any subsequent refund as an effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to decompose it into two sub-tasks: CVR prediction and post-conversion refund rate (RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and sample selection bias (SSB) issues, as the refund behaviors are only available after user purchase. Furthermore, there is delayed feedback in both conversion and refund events and they are sequentially dependent, named cascade delayed feedback (CDF), which significantly harms data freshness for model training. Previous studies mainly focus on tackling DS and SSB or delayed feedback for a single event. To jointly tackle these issues in ECVR prediction, we propose an Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically, ECAD deals with DS and SSB by constructing two tasks including CVR prediction and conversion \\&refund rate (CVRFR) prediction using the entire space modeling framework. In addition, it carefully schedules auxiliary tasks to leverage both conversion and refund time within data to alleviate CDF. Experimental results on the offline industrial dataset and online A/B testing demonstrate the effectiveness of ECAD. In addition, ECAD has been deployed in one of the recommender systems in Alibaba, contributing to a significant improvement of ECVR.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1219,
+    "label": 30,
+    "text": "Title: SemEval-2023 Task 10: Explainable Detection of Online Sexism\nAbstract: Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.",
+    "neighbors": [
+      216,
+      1683,
+      1965
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1220,
+    "label": 24,
+    "text": "Title: Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks\nAbstract: Air Quality Monitoring and Forecasting has been a popular research topic in recent years. Recently, data-driven approaches for air quality forecasting have garnered significant attention, owing to the availability of well-established data collection facilities in urban areas. Fixed infrastructures, typically deployed by national institutes or tech giants, often fall short in meeting the requirements of diverse personalized scenarios, e.g., forecasting in areas without any existing infrastructure. Consequently, smaller institutes or companies with limited budgets are compelled to seek tailored solutions by introducing more flexible infrastructures for data collection. In this paper, we propose an expandable graph attention network (EGAT) model, which digests data collected from existing and newly-added infrastructures, with different spatial structures. Additionally, our proposal can be embedded into any air quality forecasting models, to apply to the scenarios with evolving spatial structures. The proposal is validated over real air quality data from PurpleAir.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1221,
+    "label": 6,
+    "text": "Title: CrowDC: A Divide-and-Conquer Approach for Paired Comparisons in Crowdsourcing\nAbstract: Ranking a set of samples based on subjectivity, such as the experience quality of streaming video or the happiness of images, has been a typical crowdsourcing task. Numerous studies have employed paired comparison analysis to solve challenges since it reduces the workload for participants by allowing them to select a single solution. Nonetheless, to thoroughly compare all target combinations, the number of tasks increases quadratically. This paper presents ``CrowDC'', a divide-and-conquer algorithm for paired comparisons. Simulation results show that when ranking more than 100 items, CrowDC can reduce 40-50% in the number of tasks while maintaining 90-95% accuracy compared to the baseline approach.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1222,
+    "label": 5,
+    "text": "Title: MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment\nAbstract: Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between various parts of an image frame. On another front, the rising popularity of deep vision-based applications at the edge has been facilitated by the recent advancements in heterogeneous multi-processor Systems on Chips (MPSoCs) that enable inference under real-time, stringent execution requirements. By extension, GNNs employed for vision-based applications must adhere to the same execution requirements. Yet contrary to typical deep neural networks, the irregular flow of graph learning operations poses a challenge to running GNNs on such heterogeneous MPSoC platforms. In this paper, we propose a novel unified design-mapping approach for efficient processing of vision GNN workloads on heterogeneous MPSoC platforms. Particularly, we develop MaGNAS, a mapping-aware Graph Neural Architecture Search framework. MaGNAS proposes a GNN architectural design space coupled with prospective mapping options on a heterogeneous SoC to identify model architectures that maximize on-device resource efficiency. To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs. Through designing a supernet derived from the recent Vision GNN (ViG) architecture, we conducted experiments on four (04) state-of-the-art vision datasets using both (i) a real hardware SoC platform (NVIDIA Xavier AGX) and (ii) a performance/cost model simulator for DNN accelerators. Our experimental results demonstrate that MaGNAS is able to provide 1.57\u00d7 latency speedup and is 3.38\u00d7 more energy-efficient for several vision datasets executed on the Xavier MPSoC vs. the GPU-only deployment while sustaining an average 0.11% accuracy reduction from the baseline.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1223,
+    "label": 4,
+    "text": "Title: Split-State Non-Malleable Codes and Secret Sharing Schemes for Quantum Messages\nAbstract: Non-malleable codes are fundamental objects at the intersection of cryptography and coding theory. These codes provide security guarantees even in settings where error correction and detection are impossible, and have found applications to several other cryptographic tasks. Roughly speaking, a non-malleable code for a family of tampering functions guarantees that no adversary can tamper (using functions from this family) the encoding of a given message into the encoding of a related distinct message. Non-malleable secret sharing schemes are a strengthening of non-malleable codes which satisfy additional privacy and reconstruction properties. We first focus on the $2$-split-state tampering model, one of the strongest and most well-studied adversarial tampering models. Here, a codeword is split into two parts which are stored in physically distant servers, and the adversary can then independently tamper with each part using arbitrary functions. This model can be naturally extended to the secret sharing setting with several parties by having the adversary independently tamper with each share. Previous works on non-malleable coding and secret sharing in the split-state tampering model only considered the encoding of \\emph{classical} messages. Furthermore, until the recent work by Aggarwal, Boddu, and Jain (arXiv 2022), adversaries with quantum capabilities and \\emph{shared entanglement} had not been considered, and it is a priori not clear whether previous schemes remain secure in this model. In this work, we introduce the notions of split-state non-malleable codes and secret sharing schemes for quantum messages secure against quantum adversaries with shared entanglement. We also present explicit constructions of such schemes that achieve low-error non-malleability.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1224,
+    "label": 27,
+    "text": "Title: Toward Globally Optimal State Estimation Using Automatically Tightened Semidefinite Relaxations\nAbstract: In recent years, semidefinite relaxations of common optimization problems in robotics have attracted growing attention due to their ability to provide globally optimal solutions. In many cases, it was shown that specific handcrafted redundant constraints are required to obtain tight relaxations and thus global optimality. These constraints are formulation-dependent and typically require a lengthy manual process to find. Instead, the present paper suggests an automatic method to find a set of sufficient redundant constraints to obtain tightness, if they exist. We first propose an efficient feasibility check to determine if a given set of variables can lead to a tight formulation. Secondly, we show how to scale the method to problems of bigger size. At no point of the process do we have to manually find redundant constraints. We showcase the effectiveness of the approach, in simulation and on real datasets, for range-based localization and stereo-based pose estimation. Finally, we reproduce semidefinite relaxations presented in recent literature and show that our automatic method finds a smaller set of constraints sufficient for tightness than previously considered.",
+    "neighbors": [
+      1443,
+      1848,
+      1973
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1225,
+    "label": 32,
+    "text": "Title: Massively Distributed Finite-Volume Flux Computation\nAbstract: Designing large-scale geological carbon capture and storage projects and ensuring safe long-term CO2 containment - as a climate change mitigation strategy - requires fast and accurate numerical simulations. These simulations involve solving complex PDEs governing subsurface fluid flow using implicit finite-volume schemes widely based on Two-Point Flux Approximation (TPFA). This task is computationally and memory expensive, especially when performed on highly detailed geomodels. In most current HPC architectures, memory hierarchy and data management mechanism are insufficient to overcome the challenges of large scale numerical simulations. Therefore, it is crucial to design algorithms that can exploit alternative and more balanced paradigms, such as dataflow and in-memory computing. This work introduces an algorithm for TPFA computations that leverages effectively a dataflow architecture, such as Cerebras CS2, which helps to significantly minimize memory bottlenecks. Our implementation achieves two orders of magnitude speedup compared to multiple reference implementations running on NVIDIA A100 GPUs.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1226,
+    "label": 16,
+    "text": "Title: DreamStone: Image as Stepping Stone for Text-Guided 3D Shape Generation\nAbstract: In this paper, we present a new text-guided 3D shape generation approach DreamStone that uses images as a stepping stone to bridge the gap between text and shape modalities for generating 3D shapes without requiring paired text and 3D data. The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text. Besides, to extend beyond the generative capability of the SVR model, we design a text-guided 3D shape stylization module that can enhance the output shapes with novel structures and textures. Further, we exploit pre-trained text-to-image diffusion models to enhance the generative diversity, fidelity, and stylization capability. Our approach is generic, flexible, and scalable, and it can be easily integrated with various SVR models to expand the generative space and improve the generative fidelity. Extensive experimental results demonstrate that our approach outperforms the state-of-the-art methods in terms of generative quality and consistency with the input text. Codes and models are released at https://github.com/liuzhengzhe/DreamStone-ISS.",
+    "neighbors": [
+      1573,
+      1898
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1227,
+    "label": 30,
+    "text": "Title: Can Large Language Models Be an Alternative to Human Evaluations?\nAbstract: Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans. However, human evaluation is very difficult to reproduce and its quality is notoriously unstable, hindering fair comparisons among different natural language processing (NLP) models and algorithms.Recently, large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided.In this paper, we explore if such an ability of the LLMs can be used as an alternative to human evaluation.We present the LLMs with the exact same instructions, samples to be evaluated, and questions used to conduct human evaluation, and then ask the LLMs to generate responses to those questions; we dub this LLM evaluation.We use human evaluation and LLM evaluation to evaluate the texts in two NLP tasks: open-ended story generation and adversarial attacks.We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation: the texts rated higher by human experts are also rated higher by the LLMs.We also find that the results of LLM evaluation are stable over different formatting of the task instructions and the sampling algorithm used to generate the answer.We are the first to show the potential of using LLMs to assess the quality of texts and discuss the limitations and ethical considerations of LLM evaluation.",
+    "neighbors": [
+      126,
+      247,
+      652,
+      811,
+      1114,
+      1727,
+      1949,
+      2094
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1228,
+    "label": 23,
+    "text": "Title: Which Requirements Artifact Quality Defects are Automatically Detectable? A Case Study\nAbstract: [Context:] The quality of requirements engineering artifacts, e.g. requirements specifications, is acknowledged to be an important success factor for projects. Therefore, many companies spend significant amounts of money to control the quality of their RE artifacts. To reduce spending and improve the RE artifact quality, methods were proposed that combine manual quality control, i.e. reviews, with automated approaches. [Problem:] So far, we have seen various approaches to automatically detect certain aspects in RE artifacts. However, we still lack an overview what can and cannot be automatically detected. [Approach:] Starting from an industry guideline for RE artifacts, we classify 166 existing rules for RE artifacts along various categories to discuss the share and the characteristics of those rules that can be automated. For those rules, that cannot be automated, we discuss the main reasons. [Contribution:] We estimate that 53% of the 166 rules can be checked automatically either perfectly or with a good heuristic. Most rules need only simple techniques for checking. The main reason why some rules resist automation is due to imprecise definition. [Impact:] By giving first estimates and analyses of automatically detectable and not automatically detectable rule violations, we aim to provide an overview of the potential of automated methods in requirements quality control.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1229,
+    "label": 31,
+    "text": "Title: Multi-domain Recommendation with Embedding Disentangling and Domain Alignment\nAbstract: Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as Amazon, Facebook, and LinkedIn that host multiple services. Existing MDR models face two challenges: First, it is difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars). Second, they have limited ability to transfer knowledge across domains with small overlaps. We propose a new MDR method named EDDA with two key components, i.e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively. In particular, the embedding disentangling recommender separates both the model and embedding for the inter-domain part and the intra-domain part, while most existing MDR methods only focus on model-level disentangling. The domain alignment leverages random walks from graph processing to identify similar user/item pairs from different domains and encourages similar user/item pairs to have similar embeddings, enhancing knowledge transfer. We compare EDDA with 12 state-of-the-art baselines on 3 real datasets. The results show that EDDA consistently outperforms the baselines on all datasets and domains. All datasets and codes are available at https://github.com/Stevenn9981/EDDA.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1230,
+    "label": 28,
+    "text": "Title: A Framework for Control Channels Applied to Reconfigurable Intelligent Surfaces\nAbstract: The research on Reconfigurable Intelligent Surfaces (RISs) has dominantly been focused on physical-layer aspects and analyses of the achievable adaptation of the propagation environment. Compared to that, the questions related to link/MAC protocol and system-level integration of RISs have received much less attention. This paper addresses the problem of designing and analyzing control/signaling procedures, which are necessary for the integration of RISs as a new type of network element within the overall wireless infrastructure. We build a general model for designing control channels along two dimensions: i) allocated bandwidth (in-band and out-of band) and ii) rate selection (multiplexing or diversity). Specifically, the second dimension results in two transmission schemes, one based on channel estimation and the subsequent adapted RIS configuration, while the other is based on sweeping through predefined RIS phase profiles. The paper analyzes the performance of the control channel in multiple communication setups, obtained as combinations of the aforementioned dimensions. While necessarily simplified, our analysis reveals the basic trade-offs in designing control channels and the associated communication algorithms. Perhaps the main value of this work is to serve as a framework for subsequent design and analysis of various system-level aspects related to the RIS technology.",
+    "neighbors": [
+      1847
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1231,
+    "label": 4,
+    "text": "Title: Your Identity is Your Behavior - Continuous User Authentication based on Machine Learning and Touch Dynamics\nAbstract: The aim of this research paper is to look into the use of continuous authentication with mobile touch dynamics, using three different algorithms: Neural Network, Extreme Gradient Boosting, and Support Vector Machine. Mobile devices are constantly increasing in popularity in the world, today smartphone subscriptions have surpassed 6 billion. Mobile touch dynamics refer to the distinct patterns of how a user interacts with their mobile device, this includes factors such as touch pressure, swipe speed, and touch duration. Continuous authentication refers to the process of continuously verifying a user's identity while they are using a device, rather than just at the initial login. This research used a dataset of touch dynamics collected from 40 subjects using the LG V30+. The participants played four mobile games, PUBG, Diep.io, Slither, and Minecraft, for 10 minutes each game. The three algorithms were trained and tested on the extracted dataset, and their performance was evaluated based on metrics such as accuracy, precision, false negative rate, and false positive rate. The results of the research showed that all three algorithms were able to effectively classify users based on their individual touch dynamics, with accuracy ranging from 80% to 95%. The Neural Network algorithm performed the best, achieving the highest accuracy and precision scores, followed closely by XGBoost and SVC. The data shows that continuous authentication using mobile touch dynamics has the potential to be a useful method for enhancing security and reducing the risk of unauthorized access to personal devices. This research also notes the importance of choosing the correct algorithm for a given dataset and use case, as different algorithms may have varying levels of performance depending on the specific task.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1232,
+    "label": 22,
+    "text": "Title: Context-Aware Separation Logic\nAbstract: Separation logic is often praised for its ability to closely mimic the locality of state updates when reasoning about them at the level of assertions. The prover only needs to concern themselves with the footprint of the computation at hand, i.e., the part of the state that is actually being accessed and manipulated. Modern concurrent separation logics lift this local reasoning principle from the physical state to abstract ghost state. For instance, these logics allow one to abstract the state of a fine-grained concurrent data structure by a predicate that provides a client the illusion of atomic access to the underlying state. However, these abstractions inadvertently increase the footprint of a computation: when reasoning about a local low-level state update, one needs to account for its effect on the abstraction, which encompasses a possibly unbounded portion of the low-level state. Often this gives the reasoning a global character. We present context-aware separation logic (CASL) to provide new opportunities for local reasoning in the presence of rich ghost state abstractions. CASL introduces the notion of a context of a computation, the part of the concrete state that is only affected on the abstract level. Contexts give rise to a new proof rule that allows one to reduce the footprint by the context, provided the computation preserves the context as an invariant. The context rule complements the frame rule of separation logic by enabling more local reasoning in cases where the predicate to be framed is known in advance. We instantiate our developed theory for the flow framework, which enables local reasoning about global properties of heap graphs. We then use the instantiation to obtain a fully local proof of functional correctness for a sequential binary search tree implementation that is inspired by fine-grained concurrent search structures.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1233,
+    "label": 24,
+    "text": "Title: Reward Design with Language Models\nAbstract: Reward design in reinforcement learning (RL) is challenging since specifying human notions of desired behavior may be difficult via reward functions or require many expert demonstrations. Can we instead cheaply design rewards using a natural language interface? This paper explores how to simplify reward design by prompting a large language model (LLM) such as GPT-3 as a proxy reward function, where the user provides a textual prompt containing a few examples (few-shot) or a description (zero-shot) of the desired behavior. Our approach leverages this proxy reward function in an RL framework. Specifically, users specify a prompt once at the beginning of training. During training, the LLM evaluates an RL agent's behavior against the desired behavior described by the prompt and outputs a corresponding reward signal. The RL agent then uses this reward to update its behavior. We evaluate whether our approach can train agents aligned with user objectives in the Ultimatum Game, matrix games, and the DealOrNoDeal negotiation task. In all three tasks, we show that RL agents trained with our framework are well-aligned with the user's objectives and outperform RL agents trained with reward functions learned via supervised learning",
+    "neighbors": [
+      1430,
+      1451,
+      1933,
+      2122,
+      2258
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1234,
+    "label": 16,
+    "text": "Title: Improving Masked Autoencoders by Learning Where to Mask\nAbstract: Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them. The question is: Is there a better masking strategy than random sampling and how can we learn it? We empirically study this problem and initially find that introducing object-centric priors in mask sampling can significantly improve the learned representations. Inspired by this observation, we present AutoMAE, a fully differentiable framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In this way, our approach can adaptively find patches with higher information density for different images, and further strike a balance between the information gain obtained from image reconstruction and its practical training difficulty. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.",
+    "neighbors": [
+      1637,
+      2116
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1235,
+    "label": 24,
+    "text": "Title: A Survey on Physics Informed Reinforcement Learning: Review and Open Problems\nAbstract: The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. In this work we explore their utility for reinforcement learning applications. We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL). We introduce a novel taxonomy with the reinforcement learning pipeline as the backbone to classify existing works, compare and contrast them, and derive crucial insights. Existing works are analyzed with regard to the representation/ form of the governing physics modeled for integration, their specific contribution to the typical reinforcement learning architecture, and their connection to the underlying reinforcement learning pipeline stages. We also identify core learning architectures and physics incorporation biases (i.e., observational, inductive and learning) of existing PIRL approaches and use them to further categorize the works for better understanding and adaptation. By providing a comprehensive perspective on the implementation of the physics-informed capability, the taxonomy presents a cohesive approach to PIRL. It identifies the areas where this approach has been applied, as well as the gaps and opportunities that exist. Additionally, the taxonomy sheds light on unresolved issues and challenges, which can guide future research. This nascent field holds great potential for enhancing reinforcement learning algorithms by increasing their physical plausibility, precision, data efficiency, and applicability in real-world scenarios.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1236,
+    "label": 25,
+    "text": "Title: Spiking Structured State Space Model for Monaural Speech Enhancement\nAbstract: Speech enhancement seeks to extract clean speech from noisy signals. Traditional deep learning methods face two challenges: efficiently using information in long speech sequences and high computational costs. To address these, we introduce the Spiking Structured State Space Model (Spiking-S4). This approach merges the energy efficiency of Spiking Neural Networks (SNN) with the long-range sequence modeling capabilities of Structured State Space Models (S4), offering a compelling solution. Evaluation on the DNS Challenge and VoiceBank+Demand Datasets confirms that Spiking-S4 rivals existing Artificial Neural Network (ANN) methods but with fewer computational resources, as evidenced by reduced parameters and Floating Point Operations (FLOPs).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1237,
+    "label": 30,
+    "text": "Title: Universal and Transferable Adversarial Attacks on Aligned Language Models\nAbstract: Because\"out-of-the-box\"large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called\"jailbreaks\"against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probability that the model produces an affirmative response (rather than refusing to answer). However, instead of relying on manual engineering, our approach automatically produces these adversarial suffixes by a combination of greedy and gradient-based search techniques, and also improves over past automatic prompt generation methods. Surprisingly, we find that the adversarial prompts generated by our approach are quite transferable, including to black-box, publicly released LLMs. Specifically, we train an adversarial attack suffix on multiple prompts (i.e., queries asking for many different types of objectionable content), as well as multiple models (in our case, Vicuna-7B and 13B). When doing so, the resulting attack suffix is able to induce objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others. In total, this work significantly advances the state-of-the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information. Code is available at github.com/llm-attacks/llm-attacks.",
+    "neighbors": [
+      11,
+      428,
+      430,
+      529,
+      682,
+      811,
+      896,
+      899,
+      1733,
+      2235,
+      2257
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1238,
+    "label": 24,
+    "text": "Title: Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs\nAbstract: Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at https://github.com/CurryTang/Graph-LLM.",
+    "neighbors": [
+      57,
+      118,
+      424,
+      1044,
+      1052,
+      1148,
+      1544,
+      1667,
+      1735,
+      1762,
+      1863,
+      2109,
+      2113
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1239,
+    "label": 16,
+    "text": "Title: Federated Learning for Computer Vision\nAbstract: Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associated with storing and processing data on central cloud servers put these algorithms under severe strain. To address these issues, federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors' knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It provides an overview of current FL applications in various CV tasks, emphasizing the advantages of FL and the challenges of implementing it in CV. To facilitate this, the paper proposes a taxonomy of FL techniques in CV, outlining their applications and security threats. It also discusses privacy concerns related to implementing blockchain in FL schemes for CV tasks and summarizes existing privacy preservation methods. Moving on, the paper identifies open research challenges and potential future research directions to further exploit the potential of FL and blockchain in CV applications.",
+    "neighbors": [
+      1036
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1240,
+    "label": 6,
+    "text": "Title: Visualization of AI Systems in Virtual Reality: A Comprehensive Review\nAbstract: This study provides a comprehensive review of the utilization of Virtual Reality (VR) for visualizing Artificial Intelligence (AI) systems, drawing on 18 selected studies. The results illuminate a complex interplay of tools, methods, and approaches, notably the prominence of VR engines like Unreal Engine and Unity. However, despite these tools, a universal solution for effective AI visualization remains elusive, reflecting the unique strengths and limitations of each technique. We observed the application of VR for AI visualization across multiple domains, despite challenges such as high data complexity and cognitive load. Moreover, it briefly discusses the emerging ethical considerations pertaining to the broad integration of these technologies. Despite these challenges, the field shows significant potential, emphasizing the need for dedicated research efforts to unlock the full potential of these immersive technologies. This review, therefore, outlines a roadmap for future research, encouraging innovation in visualization techniques, addressing identified challenges, and considering the ethical implications of VR and AI convergence.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1241,
+    "label": 16,
+    "text": "Title: Multi-modal Facial Affective Analysis based on Masked Autoencoder\nAbstract: Human affective behavior analysis focuses on analyzing human expressions or other behaviors to enhance the understanding of human psychology. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Affwild2 for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories (EXPR), and Valence-Arousal (VA). The competition is committed to making significant strides in improving the accuracy and practicality of affective analysis research in real-world scenarios. In this paper, we introduce our submission to the CVPR 2023: ABAW5. Our approach involves several key components. First, we utilize the visual information from a Masked Autoencoder (MAE) model that has been pre-trained on a large-scale face image dataset in a self-supervised manner. Next, we finetune the MAE encoder on the image frames from the Aff-wild2 for AU, EXPR and VA tasks, which can be regarded as a static and uni-modal training. Additionally, we leverage the multi-modal and temporal information from the videos and implement a transformer-based framework to fuse the multimodal features. Our approach achieves impressive results in the ABAW5 competition, with an average F1 score of 55.49% and 41.21% in the AU and EXPR tracks, respectively, and an average CCC of 0.6372 in the VA track. Our approach ranks first in the EXPR and AU tracks, and second in the VA track. Extensive quantitative experiments and ablation studies demonstrate the effectiveness of our proposed method.",
+    "neighbors": [
+      220,
+      1265,
+      1533,
+      1541
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1242,
+    "label": 36,
+    "text": "Title: On the Optimal Fixed-Price Mechanism in Bilateral Trade\nAbstract: We study the problem of social welfare maximization in bilateral trade, where two agents, a buyer and a seller, trade an indivisible item. The seminal result of Myerson and Satterthwaite shows that no incentive compatible and budget balanced (i.e., the mechanism does not run a deficit) mechanism can achieve the optimal social welfare in bilateral trade. Motivated by this impossibility result, we focus on approximating the optimal social welfare. We consider arguably the simplest form of mechanisms \u2013 the fixed-price mechanisms, where the designer offers trade at a fixed price to the seller and buyer. Besides the simple form, fixed-price mechanisms are also the only dominant strategy incentive compatible and budget balanced mechanisms in bilateral trade. We obtain improved approximation ratios of fixed-price mechanisms in both (i) the setting where the designer has the full prior information, that is, the value distributions of both the seller and buyer; and (ii) the setting where the designer only has access to limited information of the prior. In the full prior information setting, we show that the optimal fixed-price mechanism can achieve at least 0.72 of the optimal welfare, and no fixed-price mechanism can achieve more than 0.7381 of the optimal welfare. Prior to our result the state of the art approximation ratio was 1 \u2212 1/e + 0.0001\u2248 0.632. Interestingly, we further show that the optimal approximation ratio achievable with full prior information is identical to the optimal approximation ratio obtainable with only one-sided prior information, i.e., the buyer\u2019s or the seller\u2019s value distribution. As a simple corollary, our upper and lower bounds in the full prior information setting also apply to the case when the designer only has access to one-sided prior information. We further consider two limited information settings. In the first one, the designer is only given the mean of the buyer\u2019s value (or the mean of the seller\u2019s value). We show that with such minimal information, one can already design a fixed-price mechanism that achieves 2/3 of the optimal social welfare, which surpasses the previous state of the art ratio even when the designer has access to the full prior information. In the second limited information setting, we assume that the designer has access to finitely many samples from the value distributions. Recent results show that one can already obtain a constant factor approximation to the optimal welfare using a single sample from the seller\u2019s distribution. Our goal is to understand what approximation ratios are possible if the designer has more than one but still finitely many samples. This is usually a technically more challenging regime and requires tools different from the single-sample analysis. We propose a new family of sample-based fixed-price mechanisms that we refer to as the order statistic mechanisms and provide a complete characterization of their approximation ratios for any fixed number of samples. Using the characterization, we provide the optimal approximation ratios obtainable by order statistic mechanism for small sample sizes (no more than 10 samples) and observe that they significantly outperform the single sample mechanism.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1243,
+    "label": 24,
+    "text": "Title: Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression\nAbstract: The origin of adversarial examples is still inexplicable in research fields, and it arouses arguments from various view-points, albeit comprehensive investigations. In this paper, we propose a way of delving into the unexpected vulnerability in adversarially trained networks from a causal perspective, namely adversarial instrumental variable (IV) regression. By deploying it, we estimate the causal relation of adversarial prediction under an unbiased environment dissociated from unknown confounders. Our approach aims to demystify inherent causal features on adversarial examples by leveraging a zero-sum optimization game between a casual feature estimator (i.e., hypothesis model) and worst-case counterfactuals (i.e., test function) disturbing to find causal features. Through extensive analyses, we demonstrate that the estimated causal features are highly related to the correct prediction for adversarial robustness, and the counterfactuals exhibit extreme features significantly deviating from the correct prediction. In addition, we present how to effectively inoculate CAusal FEatures (CAFE) into defense networks for improving adversarial robustness.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1244,
+    "label": 20,
+    "text": "Title: Economical Convex Coverings and Applications\nAbstract: Coverings of convex bodies have emerged as a central component in the design of efficient solutions to approximation problems involving convex bodies. Intuitively, given a convex body $K$ and $\\epsilon>0$, a covering is a collection of convex bodies whose union covers $K$ such that a constant factor expansion of each body lies within an $\\epsilon$ expansion of $K$. Coverings have been employed in many applications, such as approximations for diameter, width, and $\\epsilon$-kernels of point sets, approximate nearest neighbor searching, polytope approximations, and approximations to the Closest Vector Problem (CVP). It is known how to construct coverings of size $n^{O(n)} / \\epsilon^{(n-1)/2}$ for general convex bodies in $\\textbf{R}^n$. In special cases, such as when the convex body is the $\\ell_p$ unit ball, this bound has been improved to $2^{O(n)} / \\epsilon^{(n-1)/2}$. This raises the question of whether such a bound generally holds. In this paper we answer the question in the affirmative. We demonstrate the power and versatility of our coverings by applying them to the problem of approximating a convex body by a polytope, under the Banach-Mazur metric. Given a well-centered convex body $K$ and an approximation parameter $\\epsilon>0$, we show that there exists a polytope $P$ consisting of $2^{O(n)} / \\epsilon^{(n-1)/2}$ vertices (facets) such that $K \\subset P \\subset K(1+\\epsilon)$. This bound is optimal in the worst case up to factors of $2^{O(n)}$. As an additional consequence, we obtain the fastest $(1+\\epsilon)$-approximate CVP algorithm that works in any norm, with a running time of $2^{O(n)} / \\epsilon ^{(n-1)/2}$ up to polynomial factors in the input size, and we obtain the fastest $(1+\\epsilon)$-approximation algorithm for integer programming. We also present a framework for constructing coverings of optimal size for any convex body (up to factors of $2^{O(n)}$).",
+    "neighbors": [
+      1438,
+      1615
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1245,
+    "label": 26,
+    "text": "Title: Human-Centric Resource Allocation in the Metaverse over Wireless Communications\nAbstract: The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimizing wireless communications to enable the human-centric Metaverse is important to satisfy the demands of mobile users. In this paper, we formulate the optimization of the system utility-cost ratio (UCR) for the Metaverse over wireless networks. Our human-centric utility measure for virtual reality (VR) applications of the Metaverse represents users' perceptual assessment of the VR video quality as a function of the data rate and the video resolution, and is learnt from real datasets. The variables jointly optimized in our problem include the allocation of both communication and computation resources as well as VR video resolutions. The system cost in our problem comprises the energy consumption and delay, and is non-convex with respect to the optimization variables due to fractions in the mathematical expressions. To solve the non-convex optimization, we develop a novel fractional programming technique, which contributes to optimization theory and has broad applicability beyond our paper. Our proposed algorithm for the system UCR optimization is computationally efficient and finds a stationary point to the constrained optimization. Through extensive simulations, our algorithm is demonstrated to outperform other approaches.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1246,
+    "label": 27,
+    "text": "Title: Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot\nAbstract: Task automation of surgical robot has the potentials to improve surgical efficiency. Recent reinforcement learning (RL) based approaches provide scalable solutions to surgical automation, but typically require extensive data collection to solve a task if no prior knowledge is given. This issue is known as the exploration challenge, which can be alleviated by providing expert demonstrations to an RL agent. Yet, how to make effective use of demonstration data to improve exploration efficiency still remains an open challenge. In this work, we introduce Demonstration-guided EXploration (DEX), an efficient reinforcement learning algorithm that aims to overcome the exploration problem with expert demonstrations for surgical automation. To effectively exploit demonstrations, our method estimates expert-like behaviors with higher values to facilitate productive interactions, and adopts non-parametric regression to enable such guidance at states unobserved in demonstration data. Extensive experiments on 10 surgical manipulation tasks from SurRoL, a comprehensive surgical simulation platform, demonstrate significant improvements in the exploration efficiency and task success rates of our method. Moreover, we also deploy the learned policies to the da Vinci Research Kit (dVRK) platform to show the effectiveness on the real robot. Code is available at https://github.com/med-air/DEX.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1247,
+    "label": 37,
+    "text": "Title: SpotLess: Concurrent Rotational Consensus Made Practical through Rapid View Synchronization\nAbstract: The emergence of blockchain technology has renewed the interest in consensus-based data management systems that are resilient to failures. To maximize the throughput of these systems, we have recently seen several prototype consensus solutions that optimize for throughput at the expense of overall implementation complexity, high costs, and reliability. Due to this, it remains unclear how these prototypes will perform in real-world environments. In this paper, we present SpotLess, a novel concurrent rotational consensus protocol made practical. Central to SpotLess is the combination of (1) a chained consensus design for replicating requests with a reduced message cost; (2) the novel Rapid View Synchronization protocol that enables robust and continuous low-cost failure recovery that eliminates the traditional complex, error-prone view-change protocol; and (3) a high-performance concurrent consensus architecture in which independent instances of the chained consensus operate concurrently to process requests with high throughput and without single-replica bottlenecks. Due to the concurrent consensus architecture, SpotLess greatly outperforms traditional primary-backup consensus protocols such as PBFT (by up to 430%), Narwhal-HS (by up to 137%), and HotStuff (by up to 3803%). Due to its reduced message cost, SpotLess is even able to outperform RCC, a state-of-the-art high-throughput concurrent consensus protocol, by up to 23%. Furthermore, SpotLess is able to maintain a stable and low latency and consistently high throughput even during failures.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1248,
+    "label": 37,
+    "text": "Title: Data Processing with FPGAs on Modern Architectures\nAbstract: Trends in hardware, the prevalence of the cloud, and the rise of highly demanding applications have ushered an era of specialization that is quickly changing the way data is processed at scale. These changes are likely to continue and accelerate in the next years as new technologies are adopted and deployed: smart NICs, smart storage, smart memory, disaggregated storage, disaggregated memory, specialized accelerators (GPUS, TPUs, FPGAs), as well as a wealth of ASICS specifically created to deal with computationally expensive tasks (e.g., cryptography or compression). In this tutorial we focus on data processing on FPGAs, a technology that has received less attention than, e.g., TPUs or GPUs but that is, however, increasingly being deployed in the cloud for data processing tasks due to the architectural flexibility of FPGAs and their ability to process data at line rate, something not possible with other type of processors or accelerators. In the tutorial we will cover what are FPGAs, their characteristics, their advantages and disadvantages over other design options, as well as examples from deployments in industry and how they are used in a variety of data processing tasks. Then we will provide a brief introduction to FPGA programming with High Level Synthesis (HLS) tools as well as briefly describe resources available to researchers in the form of academic clusters and open source systems that simplify the first steps. The tutorial will also include several case studies borrowed from research done in collaboration with companies that illustrate both the potential of FPGAs in data processing but also how software and hardware architectures are evolving to take advantage of the possibilities offered by FPGAs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1249,
+    "label": 30,
+    "text": "Title: Textbooks Are All You Need\nAbstract: We introduce phi-1, a new large language model for code, with significantly smaller size than competing models: phi-1 is a Transformer-based model with 1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook quality\"data from the web (6B tokens) and synthetically generated textbooks and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays surprising emergent properties compared to phi-1-base, our model before our finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller model with 350M parameters trained with the same pipeline as phi-1 that still achieves 45% on HumanEval.",
+    "neighbors": [
+      126,
+      268,
+      1044,
+      1171,
+      1613,
+      1735,
+      1840,
+      1879,
+      1906
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1250,
+    "label": 10,
+    "text": "Title: Safe Reinforcement Learning via Probabilistic Logic Shields\nAbstract: Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1251,
+    "label": 16,
+    "text": "Title: Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators\nAbstract: Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets. In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain. Our key modifications include (i) enriching the latent codes of the generated frames with motion dynamics to keep the global scene and the background time consistent; and (ii) reprogramming frame-level self-attention using a new cross-frame attention of each frame on the first frame, to preserve the context, appearance, and identity of the foreground object. Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation. Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instruct-Pix2Pix, i.e., instruction-guided video editing. As experiments show, our method performs comparably or sometimes better than recent approaches, despite not being trained on additional video data. Our code will be open sourced at: https://github.com/Picsart-AI-Research/Text2Video-Zero .",
+    "neighbors": [
+      330,
+      860,
+      1020,
+      1173,
+      1303,
+      1380,
+      1590,
+      1707,
+      1902,
+      1935,
+      2135,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1252,
+    "label": 16,
+    "text": "Title: Diffusion Action Segmentation\nAbstract: Temporal action segmentation is crucial for understanding long-form videos. Previous works on this task commonly adopt an iterative refinement paradigm by using multi-stage models. We propose a novel framework via denoising diffusion models, which nonetheless shares the same inherent spirit of such iterative refinement. In this framework, action predictions are iteratively generated from random noise with input video features as conditions. To enhance the modeling of three striking characteristics of human actions, including the position prior, the boundary ambiguity, and the relational dependency, we devise a unified masking strategy for the conditioning inputs in our framework. Extensive experiments on three benchmark datasets, i.e., GTEA, 50Salads, and Breakfast, are performed and the proposed method achieves superior or comparable results to state-of-the-art methods, showing the effectiveness of a generative approach for action segmentation.",
+    "neighbors": [
+      1457
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1253,
+    "label": 13,
+    "text": "Title: A Proof that Using Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation\nAbstract: Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multiobjective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are still not fully understood.\n\nWe provide a theoretical analysis of well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover. We propose a class of problems on which these EMO algorithms using crossover find the Pareto set in expected polynomial time. In sharp contrast, they and many other EMO algorithms without crossover require exponential time to even find a single Pareto-optimal point. This is the first example of an exponential performance gap through the use of crossover for the widely used NSGA-II algorithm.",
+    "neighbors": [
+      1700
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1254,
+    "label": 13,
+    "text": "Title: TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents\nAbstract: Autonomous mobile agents require low-power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks while adapting to diverse environments, as mobile agents are usually powered by batteries. These requirements can be fulfilled by Spiking Neural Networks (SNNs) as they offer low power/energy processing due to their sparse computations and efficient online learning with bio-inspired learning mechanisms for adapting to different environments. Recent works studied that the energy consumption of SNNs can be optimized by reducing the computation time of each neuron for processing a sequence of spikes (timestep). However, state-of-the-art techniques rely on intensive design searches to determine fixed timestep settings for only inference, thereby hindering the SNNs from achieving further energy efficiency gains in both training and inference. These techniques also restrict the SNNs from performing efficient online learning at run time. Toward this, we propose TopSpark, a novel methodology that leverages adaptive timestep reduction to enable energy-efficient SNN processing in both training and inference, while keeping its accuracy close to the accuracy of SNNs without timestep reduction. The ideas of TopSpark include: analyzing the impact of different timesteps on the accuracy; identifying neuron parameters that have a significant impact on accuracy in different timesteps; employing parameter enhancements that make SNNs effectively perform learning and inference using less spiking activity; and developing a strategy to trade-off accuracy, latency, and energy to meet the design requirements. The results show that, TopSpark saves the SNN latency by 3.9x as well as energy consumption by 3.5x (training) and 3.3x (inference) on average, across different network sizes, learning rules, and workloads, while maintaining the accuracy within 2% of SNNs without timestep reduction.",
+    "neighbors": [
+      1180
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1255,
+    "label": 16,
+    "text": "Title: IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing\nAbstract: Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but their performance deviates greatly. We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications. As far as we know, IAD methods are not evaluated systematically. As a result, this makes it difficult for researchers to analyze them because they are designed for different or special cases. To solve this problem, we first propose a uniform IM setting to assess how well these algorithms perform, which includes several aspects, i.e., various levels of supervision (unsupervised vs. semi-supervised), few-shot learning, continual learning, noisy labels, memory usage, and inference speed. Moreover, we skillfully build a comprehensive image anomaly detection benchmark (IM-IAD) that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our extensive experiments (17,017 in total) provide in-depth insights for IAD algorithm redesign or selection under the IM setting. Next, the proposed benchmark IM-IAD gives challenges as well as directions for the future. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD.",
+    "neighbors": [
+      1618,
+      1903
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1256,
+    "label": 24,
+    "text": "Title: WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings\nAbstract: Machine learning models often learn latent embedding representations that capture the domain semantics of their training data. These embedding representations are valuable for interpreting trained models, building new models, and analyzing new datasets. However, interpreting and using embeddings can be challenging due to their opaqueness, high dimensionality, and the large size of modern datasets. To tackle these challenges, we present WizMap, an interactive visualization tool to help researchers and practitioners easily explore large embeddings. With a novel multi-resolution embedding summarization method and a familiar map-like interaction design, WizMap enables users to navigate and interpret embedding spaces with ease. Leveraging modern web technologies such as WebGL and Web Workers, WizMap scales to millions of embedding points directly in users\u2019 web browsers and computational notebooks without the need for dedicated backend servers. WizMap is open-source and available at the following public demo link: https://poloclub.github.io/wizmap.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1257,
+    "label": 16,
+    "text": "Title: Improving Viewpoint Robustness for Visual Recognition via Adversarial Training\nAbstract: Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. Motivated by the success of adversarial training in enhancing model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve the viewpoint robustness of image classifiers. Regarding viewpoint transformation as an attack, we formulate VIAT as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on the proposed attack method GMVFool. The outer minimization obtains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case viewpoint distributions that can share the same one for different objects within the same category. Based on GMVFool, we contribute a large-scale dataset called ImageNet-V+ to benchmark viewpoint robustness. Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool. Furthermore, we propose ViewRS, a certified viewpoint robustness method that provides a certified radius and accuracy to demonstrate the effectiveness of VIAT from the theoretical perspective.",
+    "neighbors": [
+      887
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1258,
+    "label": 10,
+    "text": "Title: Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education\nAbstract: The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological information are valuable for developing Artificial Intelligence (AI) algorithms for Digital Pathology (DP). Transferring medical data\"as open as possible\"enhances the usability of the data for secondary purposes but poses a risk to patient privacy. At the same time, existing regulations push towards keeping medical data\"as closed as necessary\"to avoid re-identification risks. Generally, these legal regulations require the removal of sensitive data but do not consider the possibility of data linkage attacks due to modern image-matching algorithms. In addition, the lack of standardization in DP makes it harder to establish a single solution for all formats of WSIs. These challenges raise problems for bio-informatics researchers in balancing privacy and progress while developing AI algorithms. This paper explores the legal regulations and terminologies for medical data-sharing. We review existing approaches and highlight challenges from the histopathological perspective. We also present a data-sharing guideline for histological data to foster multidisciplinary research and education.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1259,
+    "label": 30,
+    "text": "Title: How is ChatGPT's behavior changing over time?\nAbstract: GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on several diverse tasks: 1) math problems, 2) sensitive/dangerous questions, 3) opinion surveys, 4) multi-hop knowledge-intensive questions, 5) generating code, 6) US Medical License tests, and 7) visual reasoning. We find that the performance and behavior of both GPT-3.5 and GPT-4 can vary greatly over time. For example, GPT-4 (March 2023) was reasonable at identifying prime vs. composite numbers (84% accuracy) but GPT-4 (June 2023) was poor on these same questions (51% accuracy). This is partly explained by a drop in GPT-4's amenity to follow chain-of-thought prompting. Interestingly, GPT-3.5 was much better in June than in March in this task. GPT-4 became less willing to answer sensitive questions and opinion survey questions in June than in March. GPT-4 performed better at multi-hop questions in June than in March, while GPT-3.5's performance dropped on this task. Both GPT-4 and GPT-3.5 had more formatting mistakes in code generation in June than in March. Overall, our findings show that the behavior of the\"same\"LLM service can change substantially in a relatively short amount of time, highlighting the need for continuous monitoring of LLMs.",
+    "neighbors": [
+      75,
+      377,
+      1142,
+      1713,
+      1907,
+      1952
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1260,
+    "label": 16,
+    "text": "Title: VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion\nAbstract: Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. Our framework adopts a two-stage design where we start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the fea-turization and prediction of the visible structures is more reliable. Once we obtain the set of sparse queries, we apply a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training to less than 16GB. Our code is available on https://github.com/NV1abs/VoxFormer.",
+    "neighbors": [
+      71,
+      1491,
+      2198
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1261,
+    "label": 7,
+    "text": "Title: Physics-informed neural network for friction-involved nonsmooth dynamics problems\nAbstract: Friction-induced vibration (FIV) is very common in engineering areas. Analysing the dynamic behaviour of systems containing a multiple-contact point frictional interface is an important topic. However, accurately simulating nonsmooth/discontinuous dynamic behaviour due to friction is challenging. This paper presents a new physics-informed neural network approach for solving nonsmooth friction-induced vibration or friction-involved vibration problems. Compared with schemes of the conventional time-stepping methodology, in this new computational framework, the theoretical formulations of nonsmooth multibody dynamics are transformed and embedded in the training process of the neural network. Major findings include that the new framework not only can perform accurate simulation of nonsmooth dynamic behaviour, but also eliminate the need for extremely small time steps typically associated with the conventional time-stepping methodology for multibody systems, thus saving much computation work while maintaining high accuracy. Specifically, four kinds of high-accuracy PINN-based methods are proposed: (1) single PINN; (2) dual PINN; (3) advanced single PINN; (4) advanced dual PINN. Two typical dynamics problems with nonsmooth contact are tested: one is a 1-dimensional contact problem with stick-slip, and the other is a 2-dimensional contact problem considering separation-reattachment and stick-slip oscillation. Both single and dual PINN methods show their advantages in dealing with the 1-dimensional stick-slip problem, which outperforms conventional methods across friction models that are difficult to simulate by the conventional time-stepping method. For the 2-dimensional problem, the capability of the advanced single and advanced dual PINN on accuracy improvement is shown, and they provide good results even in the cases when conventional methods fail.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1262,
+    "label": 16,
+    "text": "Title: Segment Everything Everywhere All at Once\nAbstract: In this work, we present SEEM, a promptable and interactive model for segmenting everything everywhere all at once in an image, as shown in Fig.1. In SEEM, we propose a novel decoding mechanism that enables diverse prompting for all types of segmentation tasks, aiming at a universal segmentation interface that behaves like large language models (LLMs). More specifically, SEEM is designed with four desiderata: i) Versatility. We introduce a new visual prompt to unify different spatial queries including points, boxes, scribbles and masks, which can further generalize to a different referring image; ii) Compositionality. We learn a joint visual-semantic space between text and visual prompts, which facilitates the dynamic composition of two prompt types required for various segmentation tasks; iii) Interactivity. We further incorporate learnable memory prompts into the decoder to retain segmentation history through mask-guided cross-attention from decoder to image features; and iv) Semantic-awareness. We use a text encoder to encode text queries and mask labels into the same semantic space for open-vocabulary segmentation. We conduct a comprehensive empirical study to validate the effectiveness of SEEM across diverse segmentation tasks. Notably, our single SEEM model achieves competitive performance across interactive segmentation, generic segmentation, referring segmentation, and video object segmentation on 9 datasets with minimum 1/100 supervision. Furthermore, SEEM showcases a remarkable capacity for generalization to novel prompts or their combinations, rendering it a readily universal image segmentation interface.",
+    "neighbors": [
+      57,
+      106,
+      269,
+      319,
+      719,
+      1044,
+      1050,
+      1084,
+      1193,
+      1485,
+      1540,
+      1902,
+      1913,
+      1932,
+      2009,
+      2161,
+      2203,
+      2272
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1263,
+    "label": 4,
+    "text": "Title: Designing a Visual Cryptography Curriculum for K-12 Education\nAbstract: We have designed and developed a simple, visual, and narrative K-12 cybersecurity curriculum leveraging the Scratch programming platform to demonstrate and teach fundamental cybersecurity concepts such as confidentiality, integrity protection, and authentication. The visual curriculum simulates a real-world scenario of a user and a bank performing a bank transaction and an adversary attempting to attack the transaction. We have designed six visual scenarios, the curriculum first introduces students to three visual scenarios demonstrating attacks that exist when systems do not integrate concepts such as confidentiality, integrity protection, and authentication. Then, it introduces them to three visual scenarios that build on the attacks to demonstrate and teach how these fundamental concepts can be used to defend against them. We conducted an evaluation of our curriculum through a study with 18 middle and high school students. To evaluate the student's comprehension of these concepts we distributed a technical survey, where overall average of students answering these questions related to the demonstrated concepts is 9.28 out of 10. Furthermore, the survey results revealed that 66.7% found the system extremely easy and the remaining 27.8% found it easy to use and understand.",
+    "neighbors": [
+      1469
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1264,
+    "label": 5,
+    "text": "Title: Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks\nAbstract: Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely neglected the potential for inter-network cooperation, treating edge/fog devices and other infrastructure participating in ML as separate processing elements. Consequently, FL has been vulnerable to several dimensions of network heterogeneity, such as varying computation capabilities, communication resources, data qualities, and privacy demands. We advocate for cooperative federated learning (CFL), a cooperative edge/fog ML paradigm built on device-to-device (D2D) and device-to-server (D2S) interactions. Through D2D and D2S cooperation, CFL counteracts network heterogeneity in edge/fog networks through enabling a model/data/resource pooling mechanism, which will yield substantial improvements in ML model training quality and network resource consumption. We propose a set of core methodologies that form the foundation of D2D and D2S cooperation and present preliminary experiments that demonstrate their benefits. We also discuss new FL functionalities enabled by this cooperative framework such as the integration of unlabeled data and heterogeneous device privacy into ML model training. Finally, we describe some open research directions at the intersection of cooperative edge/fog and FL.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1265,
+    "label": 16,
+    "text": "Title: POSTER V2: A simpler and stronger facial expression recognition network\nAbstract: Facial expression recognition (FER) plays an important role in a variety of real-world applications such as human-computer interaction. POSTER achieves the state-of-the-art (SOTA) performance in FER by effectively combining facial landmark and image features through two-stream pyramid cross-fusion design. However, the architecture of POSTER is undoubtedly complex. It causes expensive computational costs. In order to relieve the computational pressure of POSTER, in this paper, we propose POSTER++. It improves POSTER in three directions: cross-fusion, two-stream, and multi-scale feature extraction. In cross-fusion, we use window-based cross-attention mechanism replacing vanilla cross-attention mechanism. We remove the image-to-landmark branch in the two-stream design. For multi-scale feature extraction, POSTER++ combines images with landmark's multi-scale features to replace POSTER's pyramid design. Extensive experiments on several standard datasets show that our POSTER++ achieves the SOTA FER performance with the minimum computational cost. For example, POSTER++ reached 92.21% on RAF-DB, 67.49% on AffectNet (7 cls) and 63.77% on AffectNet (8 cls), respectively, using only 8.4G floating point operations (FLOPs) and 43.7M parameters (Param). This demonstrates the effectiveness of our improvements.",
+    "neighbors": [
+      142,
+      1241,
+      1872
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1266,
+    "label": 14,
+    "text": "Title: Data Augmentation for Mathematical Objects\nAbstract: This paper discusses and evaluates ideas of data balancing and data augmentation in the context of mathematical objects: an important topic for both the symbolic computation and satisfiability checking communities, when they are making use of machine learning techniques to optimise their tools. We consider a dataset of non-linear polynomial problems and the problem of selecting a variable ordering for cylindrical algebraic decomposition to tackle these with. By swapping the variable names in already labelled problems, we generate new problem instances that do not require any further labelling when viewing the selection as a classification problem. We find this augmentation increases the accuracy of ML models by 63% on average. We study what part of this improvement is due to the balancing of the dataset and what is achieved thanks to further increasing the size of the dataset, concluding that both have a very significant effect. We finish the paper by reflecting on how this idea could be applied in other uses of machine learning in mathematics.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1267,
+    "label": 30,
+    "text": "Title: Self-Refine: Iterative Refinement with Self-Feedback\nAbstract: Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides feedback for its output and uses it to refine itself, iteratively. Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner, and feedback provider. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5, ChatGPT, and GPT-4) LLMs. Across all evaluated tasks, outputs generated with Self-Refine are preferred by humans and automatic metrics over those generated with the same LLM using conventional one-step generation, improving by ~20% absolute on average in task performance. Our work demonstrates that even state-of-the-art LLMs like GPT-4 can be further improved at test time using our simple, standalone approach.",
+    "neighbors": [
+      126,
+      127,
+      240,
+      505,
+      618,
+      652,
+      818,
+      989,
+      1027,
+      1039,
+      1052,
+      1206,
+      1306,
+      1490,
+      1659,
+      1878,
+      1907,
+      2016,
+      2038,
+      2042,
+      2092,
+      2136,
+      2235,
+      2258,
+      2305
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1268,
+    "label": 27,
+    "text": "Title: REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction\nAbstract: The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong reasoning abilities on textual inputs. To leverage the power of LLM for robot failure explanation, we introduce a framework REFLECT, which queries LLM to identify and explain robot failures given a hierarchical summary of robot past experiences generated from multi-sensory data. Conditioned on the explanation, a task planner will generate an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset with a variety of tasks and failure scenarios. We demonstrate that the LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Videos and code available at: https://roboreflect.github.io/.",
+    "neighbors": [
+      887,
+      1044,
+      1052,
+      1339
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1269,
+    "label": 24,
+    "text": "Title: Approximation-Generalization Trade-offs under (Approximate) Group Equivariance\nAbstract: The explicit incorporation of task-specific inductive biases through symmetry has emerged as a general design precept in the development of high-performance machine learning models. For example, group equivariant neural networks have demonstrated impressive performance across various domains and applications such as protein and drug design. A prevalent intuition about such models is that the integration of relevant symmetry results in enhanced generalization. Moreover, it is posited that when the data and/or the model may only exhibit $\\textit{approximate}$ or $\\textit{partial}$ symmetry, the optimal or best-performing model is one where the model symmetry aligns with the data symmetry. In this paper, we conduct a formal unified investigation of these intuitions. To begin, we present general quantitative bounds that demonstrate how models capturing task-specific symmetries lead to improved generalization. In fact, our results do not require the transformations to be finite or even form a group and can work with partial or approximate equivariance. Utilizing this quantification, we examine the more general question of model mis-specification i.e. when the model symmetries don't align with the data symmetries. We establish, for a given symmetry group, a quantitative comparison between the approximate/partial equivariance of the model and that of the data distribution, precisely connecting model equivariance error and data equivariance error. Our result delineates conditions under which the model equivariance error is optimal, thereby yielding the best-performing model for the given task and data.",
+    "neighbors": [
+      1400
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1270,
+    "label": 7,
+    "text": "Title: Solving the Discretised Boltzmann Transport Equations using Neural Networks: Applications in Neutron Transport\nAbstract: In this paper we solve the Boltzmann transport equation using AI libraries. The reason why this is attractive is because it enables one to use the highly optimised software within AI libraries, enabling one to run on different computer architectures and enables one to tap into the vast quantity of community based software that has been developed for AI and ML applications e.g. mixed arithmetic precision or model parallelism. Here we take the first steps towards developing this approach for the Boltzmann transport equation and develop the necessary methods in order to do that effectively. This includes: 1) A space-angle multigrid solution method that can extract the level of parallelism necessary to run efficiently on GPUs or new AI computers. 2) A new Convolutional Finite Element Method (ConvFEM) that greatly simplifies the implementation of high order finite elements (quadratic to quintic, say). 3) A new non-linear Petrov-Galerkin method that introduces dissipation anisotropically.",
+    "neighbors": [
+      1724
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1271,
+    "label": 23,
+    "text": "Title: Is unsafe an Achilles' Heel? A Comprehensive Study of Safety Requirements in Unsafe Rust Programming\nAbstract: Rust is an emerging, strongly-typed programming language focusing on efficiency and memory safety. With increasing projects adopting Rust, knowing how to use Unsafe Rust is crucial for Rust security. We observed that the description of safety requirements needs to be unified in Unsafe Rust programming. Current unsafe API documents in the standard library exhibited variations, including inconsistency and insufficiency. To enhance Rust security, we suggest unsafe API documents to list systematic descriptions of safety requirements for users to follow. In this paper, we conducted the first comprehensive empirical study on safety requirements across unsafe boundaries. We studied unsafe API documents in the standard library and defined 19 safety properties (SP). We then completed the data labeling on 416 unsafe APIs while analyzing their correlation to find interpretable results. To validate the practical usability and SP coverage, we categorized existing Rust CVEs until 2023-07-08 and performed a statistical analysis of std unsafe API usage toward the crates.io ecosystem. In addition, we conducted a user survey to gain insights into four aspects from experienced Rust programmers. We finally received 50 valid responses and confirmed our classification with statistical significance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1272,
+    "label": 28,
+    "text": "Title: Delay-Doppler Domain Tomlinson-Harashima Precoding for OTFS-Based Downlink MU-MIMO Transmissions: Linear Complexity Implementation and Scaling Law Analysis\nAbstract: Orthogonal time frequency space (OTFS) modulation is a recently proposed delay-Doppler (DD) domain communication scheme, which has shown promising performance in general wireless communications, especially over high-mobility channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding (THP) for downlink multiuser multiple-input and multiple-output OTFS (MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the huge equivalent channel matrix, we propose a simple implementation of THP that does not require any matrix decomposition or inversion. Such a simple implementation is enabled by the DD domain channel property, i.e., different resolvable paths do not share the same delay and Doppler shifts, which makes it possible to pre-cancel all the DD domain interference in a symbol-by-symbol manner. We also study the achievable rate performance for the proposed scheme by leveraging the information-theoretical equivalent models. In particular, we show that the proposed scheme can achieve a near optimal performance in the high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for achievable rates with respect to number of antennas and users are derived, which indicate that the achievable rate increases logarithmically with the number of antennas and linearly with the number of users. Our numerical results align well with our findings and also demonstrate a significant improvement compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division multiplexing (OFDM).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1273,
+    "label": 16,
+    "text": "Title: Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models\nAbstract: Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generate harmful or copyrighted content. The biases and harmfulness arise throughout the entire training process and are hard to completely remove, which have become significant hurdles to the safe deployment of these models. In this paper, we propose a method called SDD to prevent problematic content generation in text-to-image diffusion models. We self-distill the diffusion model to guide the noise estimate conditioned on the target removal concept to match the unconditional one. Compared to the previous methods, our method eliminates a much greater proportion of harmful content from the generated images without degrading the overall image quality. Furthermore, our method allows the removal of multiple concepts at once, whereas previous works are limited to removing a single concept at a time.",
+    "neighbors": [
+      1669,
+      2251,
+      2267
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1274,
+    "label": 28,
+    "text": "Title: Modular Polynomial Codes for Secure and Robust Distributed Matrix Multiplication\nAbstract: We present Modular Polynomial (MP) Codes for Secure Distributed Matrix Multiplication (SDMM). The construction is based on the observation that one can decode certain proper subsets of the coefficients of a polynomial with fewer evaluations than is necessary to interpolate the entire polynomial. We also present Generalized Gap Additive Secure Polynomial (GGASP) codes. Both MP and GGASP codes are shown experimentally to perform favorably in terms of recovery threshold when compared to other comparable polynomials codes for SDMM which use the grid partition. Both MP and GGASP codes achieve the recovery threshold of Entangled Polynomial Codes for robustness against stragglers, but MP codes can decode below this recovery threshold depending on the set of worker nodes which fails. The decoding complexity of MP codes is shown to be lower than other approaches in the literature, due to the user not being tasked with interpolating an entire polynomial.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1275,
+    "label": 16,
+    "text": "Title: Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models\nAbstract: State-of-the-art machine learning models often learn spurious correlations embedded in the training data. This poses risks when deploying these models for high-stake decision-making, such as in medical applications like skin cancer detection. To tackle this problem, we propose Reveal to Revise (R2R), a framework entailing the entire eXplainable Artificial Intelligence (XAI) life cycle, enabling practitioners to iteratively identify, mitigate, and (re-)evaluate spurious model behavior with a minimal amount of human interaction. In the first step (1), R2R reveals model weaknesses by finding outliers in attributions or through inspection of latent concepts learned by the model. Secondly (2), the responsible artifacts are detected and spatially localized in the input data, which is then leveraged to (3) revise the model behavior. Concretely, we apply the methods of RRR, CDEP and ClArC for model correction, and (4) (re-)evaluate the model's performance and remaining sensitivity towards the artifact. Using two medical benchmark datasets for Melanoma detection and bone age estimation, we apply our R2R framework to VGG, ResNet and EfficientNet architectures and thereby reveal and correct real dataset-intrinsic artifacts, as well as synthetic variants in a controlled setting. Completing the XAI life cycle, we demonstrate multiple R2R iterations to mitigate different biases. Code is available on https://github.com/maxdreyer/Reveal2Revise.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1276,
+    "label": 24,
+    "text": "Title: Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities\nAbstract: We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraints. The objective and constraints are described by {\\it general utilities}, i.e., nonlinear functions of the long-term state-action occupancy measure, which encompass broader decision-making goals such as risk, exploration, or imitations. The exponential growth of the state-action space size with the number of agents presents challenges for global observability, further exacerbated by the global coupling arising from agents' safety constraints. To tackle this issue, we propose a primal-dual method utilizing shadow reward and $\\kappa$-hop neighbor truncation under a form of correlation decay property, where $\\kappa$ is the communication radius. In the exact setting, our algorithm converges to a first-order stationary point (FOSP) at the rate of $\\mathcal{O}\\left(T^{-2/3}\\right)$. In the sample-based setting, we demonstrate that, with high probability, our algorithm requires $\\widetilde{\\mathcal{O}}\\left(\\epsilon^{-3.5}\\right)$ samples to achieve an $\\epsilon$-FOSP with an approximation error of $\\mathcal{O}(\\phi_0^{2\\kappa})$, where $\\phi_0\\in (0,1)$. Finally, we demonstrate the effectiveness of our model through extensive numerical experiments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1277,
+    "label": 23,
+    "text": "Title: An Empirical Study on Software Bill of Materials: Where We Stand and the Road Ahead\nAbstract: The rapid growth of software supply chain attacks has attracted considerable attention to software bill of materials (SBOM). SBOMs are a crucial building block to ensure the transparency of software supply chains that helps improve software supply chain security. Although there are significant efforts from academia and industry to facilitate SBOM development, it is still unclear how practitioners perceive SBOMs and what are the challenges of adopting SBOMs in practice. Furthermore, existing SBOM-related studies tend to be ad-hoc and lack software engineering focuses. To bridge this gap, we conducted the first empirical study to interview and survey SBOM practitioners. We applied a mixed qualitative and quantitative method for gathering data from 17 interviewees and 65 survey respondents from 15 countries across five continents to understand how practitioners perceive the SBOM field. We summarized 26 statements and grouped them into three topics on SBOM's states of practice. Based on the study results, we derived a goal model and highlighted future directions where practitioners can put in their effort.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1278,
+    "label": 23,
+    "text": "Title: Locating Buggy Segments in Quantum Program Debugging\nAbstract: When a bug is detected by testing a quantum program on a quantum computer, we want to determine its detailed location to fix it. To locate the bug, the quantum program is divided into several segments and each segment is tested. However, to prepare a quantum state that is input to a segment, it is necessary to execute all the segments ahead of that segment in a quantum computer. This means that the cost of testing each segment depends on its location. We can also locate a buggy segment only if it is confirmed that there are no bugs in all segments ahead of that buggy segment. Since a quantum program is tested statistically on the basis of measurement results, there is a tradeoff between testing accuracy and cost. Although these characteristics are unique to quantum programs and complicate locating bugs, they have not been investigated. We suggest for the first time that these characteristics should be considered to efficiently locate bugs. We are also the first to propose a bug-locating method that takes these characteristics into account. The results from experiments indicate that the bug-locating cost that is represented as the number of executed quantum gates can be reduced with the proposed method compared with naive methods.",
+    "neighbors": [
+      829
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1279,
+    "label": 16,
+    "text": "Title: InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning\nAbstract: Recent advances in personalized image generation allow a pre-trained text-to-image model to learn a new concept from a set of images. However, existing personalization approaches usually require heavy test-time finetuning for each concept, which is time-consuming and difficult to scale. We propose InstantBooth, a novel approach built upon pre-trained text-to-image models that enables instant text-guided image personalization without any test-time finetuning. We achieve this with several major components. First, we learn the general concept of the input images by converting them to a textual token with a learnable image encoder. Second, to keep the fine details of the identity, we learn rich visual feature representation by introducing a few adapter layers to the pre-trained model. We train our components only on text-image pairs without using paired images of the same concept. Compared to test-time finetuning-based methods like DreamBooth and Textual-Inversion, our model can generate competitive results on unseen concepts concerning language-image alignment, image fidelity, and identity preservation while being 100 times faster.",
+    "neighbors": [
+      330,
+      848,
+      1079,
+      1582,
+      1902,
+      2085,
+      2161,
+      2243
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1280,
+    "label": 24,
+    "text": "Title: A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games\nAbstract: Many model-based reinforcement learning (RL) algorithms can be viewed as having two phases that are iteratively implemented: a learning phase where the model is approximately learned and a planning phase where the learned model is used to derive a policy. In the case of standard MDPs, the learning problem can be solved using either value iteration or policy iteration. However, in the case of zero-sum Markov games, there is no efficient policy iteration algorithm; e.g., it has been shown in Hansen et al. (2013) that one has to solve Omega(1/(1-alpha)) MDPs, where alpha is the discount factor, to implement the only known convergent version of policy iteration. Another algorithm for Markov zero-sum games, called naive policy iteration, is easy to implement but is only provably convergent under very restrictive assumptions. Prior attempts to fix naive policy iteration algorithm have several limitations. Here, we show that a simple variant of naive policy iteration for games converges, and converges exponentially fast. The only addition we propose to naive policy iteration is the use of lookahead in the policy improvement phase. This is appealing because lookahead is anyway often used in RL for games. We further show that lookahead can be implemented efficiently in linear Markov games, which are the counterpart of the linear MDPs and have been the subject of much attention recently. We then consider multi-agent reinforcement learning which uses our algorithm in the planning phases, and provide sample and time complexity bounds for such an algorithm.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1281,
+    "label": 5,
+    "text": "Title: Deterministic Rendezvous Algorithms\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1282,
+    "label": 24,
+    "text": "Title: Neural Graph Revealers\nAbstract: Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adoption to only identifying connections among the input variables. On the other hand, the Probabilistic Graphical Models (PGMs) assume an underlying base graph between variables and learns a distribution over them. PGM design choices are carefully made such that the inference \\&sampling algorithms are efficient. This brings in certain restrictions and often simplifying assumptions. In this work, we propose Neural Graph Revealers (NGRs), that are an attempt to efficiently merge the sparse graph recovery methods with PGMs into a single flow. The problem setting consists of an input data X with D features and M samples and the task is to recover a sparse graph showing connection between the features and learn a probability distribution over the D at the same time. NGRs view the neural networks as a `glass box' or more specifically as a multitask learning framework. We introduce `Graph-constrained path norm' that NGRs leverage to learn a graphical model that captures complex non-linear functional dependencies between the features in the form of an undirected sparse graph. Furthermore, NGRs can handle multimodal inputs like images, text, categorical data, embeddings etc. which is not straightforward to incorporate in the existing methods. We show experimental results of doing sparse graph recovery and probabilistic inference on data from Gaussian graphical models and a multimodal infant mortality dataset by Centers for Disease Control and Prevention.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1283,
+    "label": 16,
+    "text": "Title: Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting\nAbstract: We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for\"scored actors\"in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.",
+    "neighbors": [
+      470,
+      1491,
+      2138,
+      2278
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1284,
+    "label": 30,
+    "text": "Title: Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals\nAbstract: Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios. While unlike the general dialogue system which emphasizes the semantic performance, the task-oriented dialogue (ToD) systems aim to achieve the dialogue goal efficiently and successfully in multiple turns. Unfortunately, existing LLM-induced ToD systems lack the direct reward toward the final goal and do not take account of the dialogue proactivity that can strengthen the dialogue efficiency. To fill these gaps, we introduce the ProToD (Proactively Goal-Driven LLM-Induced ToD) approach, which anticipates the future dialogue actions and incorporates the goal-oriented reward signal to enhance ToD systems. Additionally, we present a novel evaluation method that assesses ToD systems based on goal-driven dialogue simulations. This method allows us to gauge user satisfaction, system efficiency and successful rate while overcoming the limitations of current Information and Success metrics. Empirical experiments conducted on the MultiWoZ 2.1 dataset demonstrate that our model can achieve superior performance using only 10% of the data compared to previous end-to-end fully supervised models. This improvement is accompanied by enhanced user satisfaction and efficiency.",
+    "neighbors": [
+      1462,
+      1507,
+      1543,
+      2231
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1285,
+    "label": 24,
+    "text": "Title: Differentially Private Heavy Hitter Detection using Federated Analytics\nAbstract: In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each user has multiple data points and the goal is to learn as many of the most frequent data points as possible across all users' data with aggregate and local differential privacy. We propose an adaptive hyperparameter tuning algorithm that improves the performance of the algorithm while satisfying computational, communication and privacy constraints. We explore the impact of different data-selection schemes as well as the impact of introducing deny lists during multiple runs of the algorithm. We test these improvements using extensive experimentation on the Reddit dataset~\\cite{caldas2018leaf} on the task of learning the most frequent words.",
+    "neighbors": [
+      1955
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1286,
+    "label": 6,
+    "text": "Title: Co-Design with Myself: A Brain-Computer Interface Design Tool that Predicts Live Emotion to Enhance Metacognitive Monitoring of Designers\nAbstract: Modulating the feelings of confidence toward design options and metacognitive monitoring are important components of design intuition. We find not many creativity support tools explored the potential of biofeedback to aid design processes in this regard. In the current study, we present \u201cMulti-Self,\u201d a BCI-VR design tool that aims to enhance metacognitive monitoring during architectural design. It evaluates designers\u2019 own valence and arousal to their design work and presents designers with their biofeedback visually in real time. A pilot proof-of-concept study with 24 participants was conducted to evaluate the feasibility of the tool. Interview responses regarding the accuracy of the feedback were mixed. The majority of the participants found the feedback mechanism useful and the tool elicited metacognitive feelings and stimulated explorations of the design space while modulating subjective feelings of uncertainty.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1287,
+    "label": 16,
+    "text": "Title: A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking\nAbstract: Deep neural networks (DNNs) have found widespread applications in interpreting remote sensing (RS) imagery. However, it has been demonstrated in previous works that DNNs are vulnerable to different types of noises, particularly adversarial noises. Surprisingly, there has been a lack of comprehensive studies on the robustness of RS tasks, prompting us to undertake a thorough survey and benchmark on the robustness of image classification and object detection in RS. To our best knowledge, this study represents the first comprehensive examination of both natural robustness and adversarial robustness in RS tasks. Specifically, we have curated and made publicly available datasets that contain natural and adversarial noises. These datasets serve as valuable resources for evaluating the robustness of DNNs-based models. To provide a comprehensive assessment of model robustness, we conducted meticulous experiments with numerous different classifiers and detectors, encompassing a wide range of mainstream methods. Through rigorous evaluation, we have uncovered insightful and intriguing findings, which shed light on the relationship between adversarial noise crafting and model training, yielding a deeper understanding of the susceptibility and limitations of various models, and providing guidance for the development of more resilient and robust models",
+    "neighbors": [
+      331,
+      759,
+      1646
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1288,
+    "label": 24,
+    "text": "Title: A Brief Survey on the Approximation Theory for Sequence Modelling\nAbstract: We survey current developments in the approximation theory of sequence modelling in machine learning. Particular emphasis is placed on classifying existing results for various model architectures through the lens of classical approximation paradigms, and the insights one can gain from these results. We also outline some future research directions towards building a theory of sequence modelling.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1289,
+    "label": 28,
+    "text": "Title: Near-Field Integrated Sensing and Communications\nAbstract: A near-field integrated sensing and communications (ISAC) framework is proposed, which introduces an additional distance dimension for both sensing and communications compared to the conventional far-field system. In particular, the Cram\u00e9r-Rao bound for the near-field joint distance and angle sensing is derived, which is minimized subject to the minimum communication rate requirement of each user. Both fully digital antennas and hybrid digital and analog antennas are investigated. For fully digital antennas, a globally optimal solution of the ISAC waveform is obtained via semidefinite relaxation. For hybrid antennas, a high-quality solution is obtained through two-stage optimization. Numerical results demonstrate the performance gain introduced by the additional distance dimension of the near-field ISAC over the far-field ISAC.",
+    "neighbors": [
+      617,
+      1108
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1290,
+    "label": 24,
+    "text": "Title: A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction\nAbstract: Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1291,
+    "label": 13,
+    "text": "Title: Competitive plasticity to reduce the energetic costs of learning\nAbstract: The brain is not only constrained by energy needed to fuel computation, but it is also constrained by energy needed to form memories. Experiments have shown that learning simple conditioning tasks already carries a significant metabolic cost. Yet, learning a task like MNIST to 95% accuracy appears to require at least 10^{8} synaptic updates. Therefore the brain has likely evolved to be able to learn using as little energy as possible. We explored the energy required for learning in feedforward neural networks. Based on a parsimonious energy model, we propose two plasticity restricting algorithms that save energy: 1) only modify synapses with large updates, and 2) restrict plasticity to subsets of synapses that form a path through the network. Combining these two methods leads to substantial energy savings while only incurring a small increase in learning time. In biology networks are often much larger than the task requires. In particular in that case, large savings can be achieved. Thus competitively restricting plasticity helps to save metabolic energy associated to synaptic plasticity. The results might lead to a better understanding of biological plasticity and a better match between artificial and biological learning. Moreover, the algorithms might also benefit hardware because in electronics memory storage is energetically costly as well.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1292,
+    "label": 4,
+    "text": "Title: Mitigating Speculation-based Attacks through Configurable Hardware/Software Co-design\nAbstract: New speculation-based attacks that affect large numbers of modern systems are disclosed regularly. Currently, CPU vendors regularly fall back to heavy-handed mitigations like using barriers or enforcing strict programming guidelines resulting in significant performance overhead. What is missing is a solution that allows for efficient mitigation and is flexible enough to address both current and future speculation vulnerabilities, without additional hardware changes. In this work, we present SpecControl, a novel hardware/software co-design, that enables new levels of security while reducing the performance overhead that has been demonstrated by state-of-the-art methodologies. SpecControl introduces a communication interface that allows compilers and application developers to inform the hardware about true branch dependencies, confidential control-flow instructions, and fine-grained instruction constraints in order to apply restrictions only when necessary. We evaluate SpecControl against known speculative execution attacks and in addition, present a new speculative fetch attack variant on the Pattern History Table (PHT) in branch predictors that shows how similar previously reported vulnerabilities are more dangerous by enabling unprivileged attacks, especially with the state-of-the-art branch predictors. SpecControl provides stronger security guarantees compared to the existing defenses while reducing the performance overhead of two state-of-the-art defenses from 51% and 43% to just 23%.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1293,
+    "label": 4,
+    "text": "Title: ApproxABFT: Approximate Algorithm-Based Fault Tolerance for Vision Transformers\nAbstract: Vision Transformers (ViTs) with outstanding performance becomes a popular backbone of deep learning models for the main-stream vision tasks including classification, object detection, and segmentation. Other than the performance, reliability is also a critical metric for the adoption of ViTs in safety-critical applications such as autonomous driving and robotics. With the observation that the major computing blocks in ViTs such as multi-head attention and feed forward are usually performed with general matrix multiplication (GEMM), we propose a classical algorithm-based fault tolerance (ABFT) strategy originally developed for GEMM to protect ViTs against soft errors in the underlying computing engines. Unlike classical ABFT that will invoke the expensive error recovery procedure whenever computing errors are detected, we leverage the inherent fault-tolerance of ViTs and propose an approximate ABFT, namely ApproxABFT, to invoke the error recovery procedure only when the computing errors are significant enough, which skips many useless error recovery procedures and simplifies the overall GEMM error recovery. Meanwhile, it also relaxes the error threshold in error recovery procedure and ignores minor computing errors, which reduces the error recovery complexity and improves the error recovery quality. In addition, we also apply a fine-grained blocking strategy to ApproxABFT and split GEMM with distinct sizes into smaller sub blocks such that it can smooth the error thresholds across ViTs and further improve the error recovery quality. According to our experiments, the ApproxABFT reduces the computing overhead by 25.92\\% to 81.62\\% and improves the model accuracy by 2.63\\% to 72.56\\% compared to the baseline ABFT while the blocking optimization further reduces the computing overhead by 6.56\\% to 73.5\\% with comparable accuracy.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1294,
+    "label": 24,
+    "text": "Title: RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series\nAbstract: An open-ended time series refers to a series of data points indexed in time order without an end. Such a time series can be found everywhere due to the prevalence of Internet of Things. Providing lightweight and real-time anomaly detection for open-ended time series is highly desirable to industry and organizations since it allows immediate response and avoids potential financial loss. In the last few years, several real-time time series anomaly detection approaches have been introduced. However, they might exhaust system resources when they are applied to open-ended time series for a long time. To address this issue, in this paper we propose RePAD2, a lightweight real-time anomaly detection approach for open-ended time series by improving its predecessor RePAD, which is one of the state-of-the-art anomaly detection approaches. We conducted a series of experiments to compare RePAD2 with RePAD and another similar detection approach based on real-world time series datasets, and demonstrated that RePAD2 can address the mentioned resource exhaustion issue while offering comparable detection accuracy and slightly less time consumption.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1295,
+    "label": 6,
+    "text": "Title: Beyond English: Centering Multilingualism in Data Visualization\nAbstract: Information visualization and natural language are intricately linked. However, the majority of research and relevant work in information and data visualization (and human-computer interaction) involve English-speaking populations as both researchers and participants, are published in English, and are presented predominantly at English-speaking venues. Although several solutions can be proposed such as translating English texts in visualization to other languages, there is little research that looks at the intersection of data visualization and different languages, and the implications that current visualization practices have on non-English speaking communities. In this position paper, we argue that linguistically diverse communities abound beyond the English-speaking world and offer a richness of experiences for the visualization research community to engage with. Through a case study of how two non-English languages interplay with data visualization reasoning in Madagascar, we describe how monolingualism in data visualization impacts the experiences of underrepresented populations and emphasize potential harm to these communities. Lastly, we raise several questions towards advocating for more inclusive visualization practices that center the diverse experiences of linguistically underrepresented populations.",
+    "neighbors": [
+      1320
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1296,
+    "label": 20,
+    "text": "Title: Comparing Morse Complexes Using Optimal Transport: An Experimental Study\nAbstract: Morse complexes and Morse-Smale complexes are topological descriptors popular in topology-based visualization. Comparing these complexes plays an important role in their applications in feature correspondences, feature tracking, symmetry detection, and uncertainty visualization. Leveraging recent advances in optimal transport, we apply a class of optimal transport distances to the comparative analysis of Morse complexes. Contrasting with existing comparative measures, such distances are easy and efficient to compute, and naturally provide structural matching between Morse complexes. We perform an experimental study involving scientific simulation datasets and discuss the effectiveness of these distances as comparative measures for Morse complexes. We also provide an initial guideline for choosing the optimal transport distances under various data assumptions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1297,
+    "label": 24,
+    "text": "Title: Set Learning for Accurate and Calibrated Models\nAbstract: Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a novel method to alleviate these problems that we call odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets rather than for single examples. This naturally allows the model to capture correlations across data examples and achieves both better accuracy and calibration, especially in limited training data and class-imbalanced regimes. Perhaps surprisingly, OKO often yields better calibration even when training with hard labels and dropping any additional calibration parameter tuning, such as temperature scaling. We provide theoretical justification, establishing that OKO naturally yields better calibration, and provide extensive experimental analyses that corroborate our theoretical findings. We emphasize that OKO is a general framework that can be easily adapted to many settings and the trained model can be applied to single examples at inference time, without introducing significant run-time overhead or architecture changes.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1298,
+    "label": 4,
+    "text": "Title: This is How You Lose the Transient Execution War\nAbstract: A new class of vulnerabilities related to speculative and out-of-order execution, fault-injection, and microarchitectural side channels rose to attention in 2018. The techniques behind the transient execution vulnerabilities were not new, but the combined application of the techniques was more sophisticated, and the security impact more severe, than previously considered possible. Numerous mitigations have been proposed and implemented for variants of the transient execution vulnerabilities. While Meltdown-type exception-based transient execution vulnerabilities have proven to be tractable, Spectre-type vulnerabilities and other speculation-based transient execution vulnerabilities have been far more resistant to countermeasures. A few proposed mitigations have been widely adopted by hardware vendors and software developers, but combining those commonly deployed mitigations does not produce an effective and comprehensive solution, it only protects against a small subset of the variants. Over the years, newly proposed mitigations have been trending towards more effective and comprehensive approaches with better performance, and yet, older mitigations remain the most popular despite limited security benefits and prohibitive performance penalties. If we continue this way, we can look forward to many generations of hardware debilitated by performance penalties from increasing layers of mitigations as new variants are discovered, and yet still vulnerable to both known and future variants.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1299,
+    "label": 38,
+    "text": "Title: Right HTML, Wrong JSON: Challenges in Replaying Archived Webpages Built with Client-Side Rendering\nAbstract: Many web sites are transitioning how they construct their pages. The conventional model is where the content is embedded server-side in the HTML and returned to the client in an HTTP response. Increasingly, sites are moving to a model where the initial HTTP response contains only an HTML skeleton plus JavaScript that makes API calls to a variety of servers for the content (typically in JSON format), and then builds out the DOM client-side, more easily allowing for periodically refreshing the content in a page and allowing dynamic modification of the content. This client-side rendering, now predominant in social media platforms such as Twitter and Instagram, is also being adopted by news outlets, such as CNN.com. When conventional web archiving techniques, such as crawling with Heritrix, are applied to pages that render their content client-side, the JSON responses can become out of sync with the HTML page in which it is to be embedded, resulting in temporal violations on replay. Because the violative JSON is not directly observable in the page (i.e., in the same manner a violative embedded image is), the temporal violations can be difficult to detect. We describe how the top level CNN.com page has used client-side rendering since April 2015 and the impact this has had on web archives. Between April 24, 2015 and July 21, 2016, we found almost 15,000 mementos with a temporal violation of more than 2 days between the base CNN.com HTML and the JSON responses used to deliver the content under the main story. One way to mitigate this problem is to use browser-based crawling instead of conventional crawlers like Heritrix, but browser-based crawling is currently much slower than non-browser-based tools such as Heritrix.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1300,
+    "label": 6,
+    "text": "Title: Teachable Reality: Prototyping Tangible Augmented Reality with Everyday Objects by Leveraging Interactive Machine Teaching\nAbstract: This paper introduces Teachable Reality, an augmented reality (AR) prototyping tool for creating interactive tangible AR applications with arbitrary everyday objects. Teachable Reality leverages vision-based interactive machine teaching (e.g., Teachable Machine), which captures real-world interactions for AR prototyping. It identifies the user-defined tangible and gestural interactions using an on-demand computer vision model. Based on this, the user can easily create functional AR prototypes without programming, enabled by a trigger-action authoring interface. Therefore, our approach allows the flexibility, customizability, and generalizability of tangible AR applications that can address the limitation of current marker-based approaches. We explore the design space and demonstrate various AR prototypes, which include tangible and deformable interfaces, context-aware assistants, and body-driven AR applications. The results of our user study and expert interviews confirm that our approach can lower the barrier to creating functional AR prototypes while also allowing flexible and general-purpose prototyping experiences.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1301,
+    "label": 15,
+    "text": "Title: Quark: An Integer RISC-V Vector Processor for Sub-Byte Quantized DNN Inference\nAbstract: In this paper, we present Quark, an integer RISC-V vector processor specifically tailored for sub-byte DNN inference. Quark is implemented in GlobalFoundries' 22FDX FD-SOI technology. It is designed on top of Ara, an open-source 64-bit RISC-V vector processor. To accommodate sub-byte DNN inference, Quark extends Ara by adding specialized vector instructions to perform sub-byte quantized operations. We also remove the floating-point unit from Quarks' lanes and use the CVA6 RISC-V scalar core for the re-scaling operations that are required in quantized neural network inference. This makes each lane of Quark 2 times smaller and 1.9 times more power efficient compared to the ones of Ara. In this paper we show that Quark can run quantized models at sub-byte precision. Notably we show that for 1-bit and 2-bit quantized models, Quark can accelerate computation of Conv2d over various ranges of inputs and kernel sizes.",
+    "neighbors": [
+      123,
+      479
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1302,
+    "label": 30,
+    "text": "Title: Findings of Factify 2: Multimodal Fake News Detection\nAbstract: With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.",
+    "neighbors": [
+      1363,
+      1553,
+      2099
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1303,
+    "label": 16,
+    "text": "Title: ControlVideo: Training-free Controllable Text-to-Video Generation\nAbstract: Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especially in long video synthesis. To address these challenges, we design a \\emph{training-free} framework called \\textbf{ControlVideo} to enable natural and efficient text-to-video generation. ControlVideo, adapted from ControlNet, leverages coarsely structural consistency from input motion sequences, and introduces three modules to improve video generation. Firstly, to ensure appearance coherence between frames, ControlVideo adds fully cross-frame interaction in self-attention modules. Secondly, to mitigate the flicker effect, it introduces an interleaved-frame smoother that employs frame interpolation on alternated frames. Finally, to produce long videos efficiently, it utilizes a hierarchical sampler that separately synthesizes each short clip with holistic coherency. Empowered with these modules, ControlVideo outperforms the state-of-the-arts on extensive motion-prompt pairs quantitatively and qualitatively. Notably, thanks to the efficient designs, it generates both short and long videos within several minutes using one NVIDIA 2080Ti. Code is available at https://github.com/YBYBZhang/ControlVideo.",
+    "neighbors": [
+      1173,
+      1251,
+      1710,
+      1768,
+      1902,
+      1978,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1304,
+    "label": 37,
+    "text": "Title: RetClean: Retrieval-Based Data Cleaning Using Foundation Models and Data Lakes\nAbstract: Can foundation models (such as ChatGPT) clean your data? In this proposal, we demonstrate that indeed ChatGPT can assist in data cleaning by suggesting corrections for specific cells in a data table (scenario 1). However, ChatGPT may struggle with datasets it has never encountered before (e.g., local enterprise data) or when the user requires an explanation of the source of the suggested clean values. To address these issues, we developed a retrieval-based method that complements ChatGPT's power with a user-provided data lake. The data lake is first indexed, we then retrieve the top-k relevant tuples to the user's query tuple and finally leverage ChatGPT to infer the correct value (scenario 2). Nevertheless, sharing enterprise data with ChatGPT, an externally hosted model, might not be feasible for privacy reasons. To assist with this scenario, we developed a custom RoBERTa-based foundation model that can be locally deployed. By fine-tuning it on a small number of examples, it can effectively make value inferences based on the retrieved tuples (scenario 3). Our proposed system, RetClean, seamlessly supports all three scenarios and provides a user-friendly GUI that enables the VLDB audience to explore and experiment with the system.",
+    "neighbors": [
+      1031,
+      1157
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1305,
+    "label": 24,
+    "text": "Title: Analyzing Leakage of Personally Identifiable Information in Language Models\nAbstract: Language Models (LMs) have been shown to leak information about training data through sentence-level membership inference and reconstruction attacks. Understanding the risk of LMs leaking Personally Identifiable Information (PII) has received less attention, which can be attributed to the false assumption that dataset curation techniques such as scrubbing are sufficient to prevent PII leakage. Scrubbing techniques reduce but do not prevent the risk of PII leakage: in practice scrubbing is imperfect and must balance the trade-off between minimizing disclosure and preserving the utility of the dataset. On the other hand, it is unclear to which extent algorithmic defenses such as differential privacy, designed to guarantee sentence-or user-level privacy, prevent PII disclosure. In this work, we introduce rigorous game-based definitions for three types of PII leakage via black-box extraction, inference, and reconstruction attacks with only API access to an LM. We empirically evaluate the attacks against GPT-2 models fine-tuned with and without defenses in three domains: case law, health care, and e-mails. Our main contributions are (i) novel attacks that can extract up to 10\u00d7 more PII sequences than existing attacks, (ii) showing that sentence-level differential privacy reduces the risk of PII disclosure but still leaks about 3% of PII sequences, and (iii) a subtle connection between record-level membership inference and PII reconstruction. Code to reproduce all experiments in the paper is available at https://github.com/microsoft/analysing_pii_leakage.",
+    "neighbors": [
+      896,
+      899,
+      1115,
+      1307,
+      2158
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1306,
+    "label": 24,
+    "text": "Title: Large Language Models as Optimizers\nAbstract: Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.",
+    "neighbors": [
+      57,
+      127,
+      342,
+      505,
+      1112,
+      1267,
+      1490,
+      1515,
+      1735,
+      2029
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1307,
+    "label": 25,
+    "text": "Title: Sparks of Large Audio Models: A Survey and Outlook\nAbstract: This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio processing, with its diverse signal representations and a wide range of sources--from human voices to musical instruments and environmental sounds--poses challenges distinct from those found in traditional Natural Language Processing scenarios. Nevertheless, \\textit{Large Audio Models}, epitomized by transformer-based architectures, have shown marked efficacy in this sphere. By leveraging massive amount of data, these models have demonstrated prowess in a variety of audio tasks, spanning from Automatic Speech Recognition and Text-To-Speech to Music Generation, among others. Notably, recently these Foundational Audio Models, like SeamlessM4T, have started showing abilities to act as universal translators, supporting multiple speech tasks for up to 100 languages without any reliance on separate task-specific systems. This paper presents an in-depth analysis of state-of-the-art methodologies regarding \\textit{Foundational Large Audio Models}, their performance benchmarks, and their applicability to real-world scenarios. We also highlight current limitations and provide insights into potential future research directions in the realm of \\textit{Large Audio Models} with the intent to spark further discussion, thereby fostering innovation in the next generation of audio-processing systems. Furthermore, to cope with the rapid development in this area, we will consistently update the relevant repository with relevant recent articles and their open-source implementations at https://github.com/EmulationAI/awesome-large-audio-models.",
+    "neighbors": [
+      344,
+      414,
+      602,
+      691,
+      704,
+      719,
+      899,
+      1001,
+      1033,
+      1044,
+      1052,
+      1305,
+      1450,
+      1560,
+      1659,
+      1735,
+      1863,
+      1879,
+      1958,
+      2013,
+      2047,
+      2113,
+      2158
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1308,
+    "label": 28,
+    "text": "Title: Effectiveness of Reconfigurable Intelligent Surfaces to Enhance Connectivity in UAV Networks\nAbstract: Reconfigurable intelligent surfaces (RISs) are expected to make future 6G networks more connected and resilient against node failures, due to their ability to introduce controllable phase-shifts onto impinging electromagnetic waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs) are prone to failure due to limited energy, random failures, or targeted failures, which causes network disintegration that results in information delivery loss. In this paper, we show that the integration between UAVs and RISs for improving network connectivity is crucial. We utilize RISs to provide path diversity and alternative connectivity options for information flow from user equipments (UEs) to less critical UAVs by adding more links to the network, thereby making the network more resilient and connected. To that end, we first define the criticality of UAV nodes, which reflects the importance of some nodes over other nodes. We then employ the algebraic connectivity metric, which is adjusted by the reflected links of the RISs and their criticality weights, to formulate the problem of maximizing the network connectivity. Such problem is a computationally expensive combinatorial optimization. To tackle this problem, we propose a relaxation method such that the discrete scheduling constraint of the problem is relaxed and becomes continuous. Leveraging this, we propose two efficient solutions, namely semi-definite programming (SDP) optimization and perturbation heuristic, which both solve the problem in polynomial time. For the perturbation heuristic, we derive the lower and upper bounds of the algebraic connectivity obtained by adding new links to the network. Finally, we corroborate the effectiveness of the proposed solutions through extensive simulation experiments.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1309,
+    "label": 16,
+    "text": "Title: Rethinking interpretation: Input-agnostic saliency mapping of deep visual classifiers\nAbstract: Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use `general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1310,
+    "label": 5,
+    "text": "Title: Performance Optimization using Multimodal Modeling and Heterogeneous GNN\nAbstract: Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose search strategies, which often might not identify the best configurations or their time to convergence is a significant barrier. There is, thus, a need for a general purpose and efficient tuning approach that can be easily scaled and adapted to various tuning tasks. We propose a technique for tuning parallel code regions that is general enough to be adapted to multiple tasks. In this paper, we analyze IR-based programming models to make task-specific performance optimizations. To this end, we propose the Multimodal Graph Neural Network and Autoencoder (MGA) tuner, a multimodal deep learning based approach that adapts Heterogeneous Graph Neural Networks and Denoising Autoencoders for modeling IR-based code representations that serve as separate modalities. This approach is used as part of our pipeline to model a syntax, semantics, and structure-aware IR-based code representation for tuning parallel code regions/kernels. We extensively experiment on OpenMP and OpenCL code regions/kernels obtained from PolyBench, Rodinia, STREAM, DataRaceBench, AMD SDK, NPB, NVIDIA SDK, Parboil, SHOC, LULESH, XSBench, RSBench, miniFE, miniAMR, and Quicksilver benchmarks and applications. We apply our multimodal learning techniques to the tasks of (i) optimizing the number of threads, scheduling policy and chunk size in OpenMP loops and, (ii) identifying the best device for heterogeneous device mapping of OpenCL kernels. Our experiments show that this multimodal learning based approach outperforms the state-of-the-art in almost all experiments.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1311,
+    "label": 16,
+    "text": "Title: YOLOv6 v3.0: A Full-Scale Reloading\nAbstract: The YOLO community has been in high spirits since our first two releases! By the advent of Chinese New Year 2023, which sees the Year of the Rabbit, we refurnish YOLOv6 with numerous novel enhancements on the network architecture and the training scheme. This release is identified as YOLOv6 v3.0. For a glimpse of performance, our YOLOv6-N hits 37.5% AP on the COCO dataset at a throughput of 1187 FPS tested with an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 45.0% AP at 484 FPS, outperforming other mainstream detectors at the same scale (YOLOv5-S, YOLOv8-S, YOLOX-S and PPYOLOE-S). Whereas, YOLOv6-M/L also achieve better accuracy performance (50.0%/52.8% respectively) than other detectors at a similar inference speed. Additionally, with an extended backbone and neck design, our YOLOv6-L6 achieves the state-of-the-art accuracy in real-time. Extensive experiments are carefully conducted to validate the effectiveness of each improving component. Our code is made available at https://github.com/meituan/YOLOv6.",
+    "neighbors": [
+      2133
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1312,
+    "label": 24,
+    "text": "Title: Federated Learning under Heterogeneous and Correlated Client Availability\nAbstract: The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm\u2019s convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1313,
+    "label": 6,
+    "text": "Title: Designing Situated Dashboards: Challenges and Opportunities\nAbstract: Situated Visualization is an emerging field that unites several areas - visualization, augmented reality, human-computer interaction, and internet-of-things, to support human data activities within the ubiquitous world. Likewise, dashboards are broadly used to simplify complex data through multiple views. However, dashboards are only adapted for desktop settings, and requires visual strategies to support situatedness. We propose the concept of AR-based situated dashboards and present design considerations and challenges developed over interviews with experts. These challenges aim to propose directions and opportunities for facilitating the effective designing and authoring of situated dashboards.",
+    "neighbors": [
+      270
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1314,
+    "label": 16,
+    "text": "Title: Visual Prompt Flexible-Modal Face Anti-Spoofing\nAbstract: Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS~\\cite{yu2023flexible} has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose \\textbf{V}isual \\textbf{P}rompt flexible-modal \\textbf{FAS} (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4\\% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training.",
+    "neighbors": [
+      419,
+      1330
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1315,
+    "label": 16,
+    "text": "Title: PointLLM: Empowering Large Language Models to Understand Point Clouds\nAbstract: The unprecedented advancements in Large Language Models (LLMs) have created a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding. This paper introduces PointLLM, a preliminary effort to fill this gap, thereby enabling LLMs to understand point clouds and offering a new avenue beyond 2D visual data. PointLLM processes colored object point clouds with human instructions and generates contextually appropriate responses, illustrating its grasp of point clouds and common sense. Specifically, it leverages a point cloud encoder with a powerful LLM to effectively fuse geometric, appearance, and linguistic information. We collect a novel dataset comprising 660K simple and 70K complex point-text instruction pairs to enable a two-stage training strategy: initially aligning latent spaces and subsequently instruction-tuning the unified model. To rigorously evaluate our model's perceptual abilities and its generalization capabilities, we establish two benchmarks: Generative 3D Object Classification and 3D Object Captioning, assessed through three different methods, including human evaluation, GPT-4/ChatGPT evaluation, and traditional metrics. Experiment results show that PointLLM demonstrates superior performance over existing 2D baselines. Remarkably, in human-evaluated object captioning tasks, PointLLM outperforms human annotators in over 50% of the samples. Codes, datasets, and benchmarks are available at https://github.com/OpenRobotLab/PointLLM .",
+    "neighbors": [
+      34,
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+      1052,
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+      1863,
+      1893,
+      2036,
+      2155,
+      2205,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1316,
+    "label": 27,
+    "text": "Title: Is Alice Really in Wonderland? UWB-Based Proof of Location for UAVs with Hyperledger Fabric Blockchain\nAbstract: Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations.",
+    "neighbors": [
+      969
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1317,
+    "label": 24,
+    "text": "Title: Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach\nAbstract: The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-approximates the true posterior. This naturally motivates an expectation propagation approach to federated learning (FedEP), where approximations to the global posterior are iteratively refined through probabilistic message-passing between the central server and the clients. We conduct an extensive empirical study across various algorithmic considerations and describe practical strategies for scaling up expectation propagation to the modern federated setting. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1318,
+    "label": 34,
+    "text": "Title: Optimal Algorithms for Bounded Weighted Edit Distance\nAbstract: The edit distance of two strings is the minimum number of insertions, deletions, and substitutions of characters needed to transform one string into the other. The textbook dynamic-programming algorithm computes the edit distance of two length-$n$ strings in $O(n^2)$ time, which is optimal up to subpolynomial factors under SETH. An established way of circumventing this hardness is to consider the bounded setting, where the running time is parameterized by the edit distance $k$. A celebrated algorithm by Landau and Vishkin (JCSS '88) achieves time $O(n + k^2)$, which is optimal as a function of $n$ and $k$. Most practical applications rely on a more general weighted edit distance, where each edit has a weight depending on its type and the involved characters from the alphabet $\\Sigma$. This is formalized through a weight function $w : \\Sigma\\cup\\{\\varepsilon\\}\\times\\Sigma\\cup\\{\\varepsilon\\}\\to\\mathbb{R}$ normalized so that $w(a,a)=0$ and $w(a,b)\\geq 1$ for all $a,b \\in \\Sigma\\cup\\{\\varepsilon\\}$ with $a \\neq b$; the goal is to find an alignment of the two strings minimizing the total weight of edits. The $O(n^2)$-time algorithm supports this setting seamlessly, but only very recently, Das, Gilbert, Hajiaghayi, Kociumaka, and Saha (STOC '23) gave the first non-trivial algorithm for the bounded version, achieving time $O(n + k^5)$. While this running time is linear for $k\\le n^{1/5}$, it is still very far from the bound $O(n+k^2)$ achievable in the unweighted setting. In this paper, we essentially close this gap by showing both an improved $\\tilde O(n+\\sqrt{nk^3})$-time algorithm and, more surprisingly, a matching lower bound: Conditioned on the All-Pairs Shortest Paths (APSP) hypothesis, our running time is optimal for $\\sqrt{n}\\le k\\le n$ (up to subpolynomial factors). This is the first separation between the complexity of the weighted and unweighted edit distance problems.",
+    "neighbors": [
+      468
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1319,
+    "label": 24,
+    "text": "Title: Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution\nAbstract: Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. Though one could lower the complexity of Transformers by inducing the sparsity in point-wise self-attentions for LTTF, the limited information utilization prohibits the model from exploring the complex dependencies comprehensively. To this end, we propose an efficient Transformer-based model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects: (i) an encoder-decoder architecture incorporating a linear complexity without sacrificing information utilization is proposed on top of sliding-window attention and Stationary and Instant Recurrent Network (SIRN); (ii) a module derived from the normalizing flow is devised to further improve the information utilization by inferring the outputs with the latent variables in SIRN directly; (iii) the inter-series correlation and temporal dynamics in time-series data are modeled explicitly to fuel the downstream self-attention mechanism. Extensive experiments on seven real-world datasets demonstrate that Conformer outperforms the state-of-the-art methods on LTTF and generates reliable prediction results with uncertainty quantification.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1320,
+    "label": 6,
+    "text": "Title: Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load Perspective\nAbstract: Investigations into using visualization to improve Bayesian reasoning and advance risk communication have produced mixed results, suggesting that cognitive ability might affect how users perform with different presentation formats. Our work examines the cognitive load elicited when solving Bayesian problems using icon arrays, text, and a juxtaposition of text and icon arrays. We used a three-pronged approach to capture a nuanced picture of cognitive demand and measure differences in working memory capacity, performance under divided attention using a dual-task paradigm, and subjective ratings of self-reported effort. We found that individuals with low working memory capacity made fewer errors and experienced less subjective workload when the problem contained an icon array compared to text alone, showing that visualization improves accuracy while exerting less cognitive demand. We believe these findings can considerably impact accessible risk communication, especially for individuals with low working memory capacity.",
+    "neighbors": [
+      1295
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1321,
+    "label": 24,
+    "text": "Title: Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models\nAbstract: The Gaussian process state-space model (GPSSM) has attracted extensive attention for modeling complex nonlinear dynamical systems. However, the existing GPSSM employs separate Gaussian processes (GPs) for each latent state dimension, leading to escalating computational complexity and parameter proliferation, thus posing challenges for modeling dynamical systems with high-dimensional latent states. To surmount this obstacle, we propose to integrate the efficient transformed Gaussian process (ETGP) into the GPSSM, which involves pushing a shared GP through multiple normalizing flows to efficiently model the transition function in high-dimensional latent state space. Additionally, we develop a corresponding variational inference algorithm that surpasses existing methods in terms of parameter count and computational complexity. Experimental results on diverse synthetic and real-world datasets corroborate the efficiency of the proposed method, while also demonstrating its ability to achieve similar inference performance compared to existing methods. Code is available at \\url{https://github.com/zhidilin/gpssmProj}.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1322,
+    "label": 30,
+    "text": "Title: MEGA: Multilingual Evaluation of Generative AI\nAbstract: Generative AI models have impressive performance on many Natural Language Processing tasks such as language understanding, reasoning and language generation. One of the most important questions that is being asked by the AI community today is about the capabilities and limits of these models, and it is clear that evaluating generative AI is very challenging. Most studies on generative Large Language Models (LLMs) are restricted to English and it is unclear how capable these models are at understanding and generating other languages. We present the first comprehensive benchmarking of generative LLMs - MEGA, which evaluates models on standard NLP benchmarks, covering 8 diverse tasks and 33 typologically diverse languages. We also compare the performance of generative LLMs to State of the Art (SOTA) non-autoregressive models on these tasks to determine how well generative models perform compared to the previous generation of LLMs. We present a thorough analysis of the performance of models across languages and discuss some of the reasons why generative LLMs are currently not optimal for all languages. We create a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.",
+    "neighbors": [
+      652,
+      748,
+      1001,
+      1034,
+      1636,
+      1655,
+      1735,
+      1952
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1323,
+    "label": 25,
+    "text": "Title: Music Source Separation with Band-Split RoPE Transformer\nAbstract: Music source separation (MSS) aims to separate a music recording into multiple musically distinct stems, such as vocals, bass, drums, and more. Recently, deep learning approaches such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used, but the improvement is still limited. In this paper, we propose a novel frequency-domain approach based on a Band-Split RoPE Transformer (called BS-RoFormer). BS-RoFormer relies on a band-split module to project the input complex spectrogram into subband-level representations, and then arranges a stack of hierarchical Transformers to model the inner-band as well as inter-band sequences for multi-band mask estimation. To facilitate training the model for MSS, we propose to use the Rotary Position Embedding (RoPE). The BS-RoFormer system trained on MUSDB18HQ and 500 extra songs ranked the first place in the MSS track of Sound Demixing Challenge (SDX23). Benchmarking a smaller version of BS-RoFormer on MUSDB18HQ, we achieve state-of-the-art result without extra training data, with 9.80 dB of average SDR.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1324,
+    "label": 10,
+    "text": "Title: Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions\nAbstract: An abstraction can be used to relate two structural causal models representing the same system at different levels of resolution. Learning abstractions which guarantee consistency with respect to interventional distributions would allow one to jointly reason about evidence across multiple levels of granularity while respecting the underlying cause-effect relationships. In this paper, we introduce a first framework for causal abstraction learning between SCMs based on the formalization of abstraction recently proposed by Rischel (2020). Based on that, we propose a differentiable programming solution that jointly solves a number of combinatorial sub-problems, and we study its performance and benefits against independent and sequential approaches on synthetic settings and on a challenging real-world problem related to electric vehicle battery manufacturing.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1325,
+    "label": 32,
+    "text": "Title: Collective-Optimized FFTs\nAbstract: This paper measures the impact of the various alltoallv methods. Results are analyzed within Beatnik, a Z-model solver that is bottlenecked by HeFFTe and representative of applications that rely on FFTs.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1326,
+    "label": 16,
+    "text": "Title: One-class Damage Detector Using Deeper Fully Convolutional Data Descriptions for Civil Application\nAbstract: Infrastructure managers must maintain high standards to ensure user satisfaction during the lifecycle of infrastructures. Surveillance cameras and visual inspections have enabled progress in automating the detection of anomalous features and assessing the occurrence of deterioration. However, collecting damage data is typically time consuming and requires repeated inspections. The one-class damage detection approach has an advantage in that normal images can be used to optimize model parameters. Additionally, visual evaluation of heatmaps enables us to understand localized anomalous features. The authors highlight damage vision applications utilized in the robust property and localized damage explainability. First, we propose a civil-purpose application for automating one-class damage detection reproducing a Fully Convolutional Data Description (FCDD) as a baseline model. We have obtained accurate and explainable results demonstrating experimental studies on concrete damage and steel corrosion in civil engineering. Additionally, to develop a more robust application, we applied our method to another outdoor domain that contains complex and noisy backgrounds using natural disaster datasets collected using various devices. Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets. The key results indicate that the deeper FCDDs outperformed the baseline FCDD on datasets representing natural disaster damage caused by hurricanes, typhoons, earthquakes, and fourevent disasters.",
+    "neighbors": [
+      2178
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1327,
+    "label": 31,
+    "text": "Title: When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities\nAbstract: The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has been dramatically improved, leading to human-like performances in understanding, language synthesizing, and common-sense reasoning, etc. Such a major leap-forward in general AI capacity will change the pattern of how personalization is conducted. For one thing, it will reform the way of interaction between humans and personalization systems. Instead of being a passive medium of information filtering, large language models present the foundation for active user engagement. On top of such a new foundation, user requests can be proactively explored, and user's required information can be delivered in a natural and explainable way. For another thing, it will also considerably expand the scope of personalization, making it grow from the sole function of collecting personalized information to the compound function of providing personalized services. By leveraging large language models as general-purpose interface, the personalization systems may compile user requests into plans, calls the functions of external tools to execute the plans, and integrate the tools' outputs to complete the end-to-end personalization tasks. Today, large language models are still being developed, whereas the application in personalization is largely unexplored. Therefore, we consider it to be the right time to review the challenges in personalization and the opportunities to address them with LLMs. In particular, we dedicate this perspective paper to the discussion of the following aspects: the development and challenges for the existing personalization system, the newly emerged capabilities of large language models, and the potential ways of making use of large language models for personalization.",
+    "neighbors": [
+      57,
+      319,
+      424,
+      505,
+      529,
+      704,
+      840,
+      1035,
+      1052,
+      1182,
+      1353,
+      1411,
+      1481,
+      1659,
+      1667,
+      1762,
+      1799,
+      2113,
+      2136,
+      2249
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1328,
+    "label": 23,
+    "text": "Title: Performance of Genetic Algorithms in the Context of Software Model Refactoring\nAbstract: Software systems continuously evolve due to new functionalities, requirements, or maintenance activities. In the context of software evolution, software refactoring has gained a strategic relevance. The space of possible software refactoring is usually very large, as it is given by the combinations of different refactoring actions that can produce software system alternatives. Multi-objective algorithms have shown the ability to discover alternatives by pursuing different objectives simultaneously. Performance of such algorithms in the context of software model refactoring is of paramount importance. Therefore, in this paper, we conduct a performance analysis of three genetic algorithms to compare them in terms of performance and quality of solutions. Our results show that there are significant differences in performance among the algorithms (e.g., PESA2 seems to be the fastest one, while NSGA-II shows the least memory usage).",
+    "neighbors": [
+      403,
+      1875
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1329,
+    "label": 24,
+    "text": "Title: Explainable Deep Learning-based Solar Flare Prediction with post hoc Attention for Operational Forecasting\nAbstract: This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of $\\geq$M1.0-class flares within 24 hours. We leveraged custom data augmentation and sample weighting to counter the inherent class-imbalance problem and used true skill statistic and Heidke skill score as evaluation metrics. Recent advancements in gradient-based attention methods allow us to interpret models by sending gradient signals to assign the burden of the decision on the input features. We interpret our model using three post hoc attention methods: (i) Guided Gradient-weighted Class Activation Mapping, (ii) Deep Shapley Additive Explanations, and (iii) Integrated Gradients. Our analysis shows that full-disk predictions of solar flares align with characteristics related to the active regions. The key findings of this study are: (1) We demonstrate that our full disk model can tangibly locate and predict near-limb solar flares, which is a critical feature for operational flare forecasting, (2) Our candidate model achieves an average TSS=0.51$\\pm$0.05 and HSS=0.38$\\pm$0.08, and (3) Our evaluation suggests that these models can learn conspicuous features corresponding to active regions from full-disk magnetograms.",
+    "neighbors": [
+      1846
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1330,
+    "label": 16,
+    "text": "Title: MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing\nAbstract: The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent with the training input, which seriously limits its deployment scenarios. And the latter is built on multiple branches to process different modalities independently, which limits their use in applications with low memory or fast execution requirements. In this work, we present a single branch based Transformer framework, namely Modality-Agnostic Vision Transformer (MA-ViT), which aims to improve the performance of arbitrary modal attacks with the help of multi-modal data. Specifically, MA-ViT adopts the early fusion to aggregate all the available training modalities\u2019 data and enables flexible testing of any given modal samples. Further, we develop the Modality-Agnostic Transformer Block (MATB) in MA-ViT, which consists of two stacked attentions named Modal-Disentangle Attention (MDA) and Cross-Modal Attention (CMA), to eliminate modality-related information for each modal sequences and supplement modality-agnostic liveness features from another modal sequences, respectively. Experiments demonstrate that the single model trained based on MA-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin, and approaches the multi-modal frameworks introduced with smaller FLOPs and model parameters.",
+    "neighbors": [
+      1314
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1331,
+    "label": 4,
+    "text": "Title: VetIoT: On Vetting IoT Defenses Enforcing Policies at Runtime\nAbstract: Smart homes are powered by numerous programmable IoT platforms. Despite tremendous innovations, these platforms often suffer from safety and security issues. One class of defense solutions dynamically enforces safety and security policies, which essentially capture the expected behavior of the IoT system. While many proposed works were built on this runtime approach, they all are under-vetted. The primary reason lies in their evaluation approach. They are mostly self-evaluated in isolation using a virtual testbed combined with manually orchestrated test scenarios that rely on user interactions with the platform's UI. Such hand-crafted and non-uniform evaluation setups are limiting not only the reproducibility but also a comparative analysis of their efficacy results. Closing this gap in the traditional way requires a huge upfront manual effort, which causes the researchers turn away from any large-scale comparative empirical evaluation. Therefore, in this paper, we propose a highly-automated uniform evaluation platform, dubbed VetIoT, to vet the defense solutions that hinge on runtime policy enforcement. Given a defense solution, VetIoT easily instantiates a virtual testbed inside which the solution is empirically evaluated. VetIoT replaces manual UI-based interactions with an automated event simulator and manual inspection of test outcomes with an automated comparator. We developed a fully-functional prototype of VetIoT and applied it on three runtime policy enforcement solutions: Expat, Patriot, and IoTguard. VetIoT reproduced their individual prior results and assessed their efficacy results via stress testing and differential testing. We believe VetIoT can foster future research/evaluation.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1332,
+    "label": 24,
+    "text": "Title: Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS\nAbstract: Causal modeling provides us with powerful counterfactual reasoning and interventional mechanism to generate predictions and reason under various what-if scenarios. However, causal discovery using observation data remains a nontrivial task due to unobserved confounding factors, finite sampling, and changes in the data distribution. These can lead to spurious cause-effect relationships. To mitigate these challenges in practice, researchers augment causal learning with known causal relations. The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS. We provide a comprehensive set of comparative analyses of biasing the model using different types of knowledge. We found that (i) knowledge that corrects the mistakes of the NOTEARS model can lead to statistically significant improvements, (ii) constraints on active edges have a larger positive impact on causal discovery than inactive edges, and surprisingly, (iii) the induced knowledge does not correct on average more incorrect active and/or inactive edges than expected. We also demonstrate the behavior of the model and the effectiveness of domain knowledge on a real-world dataset.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1333,
+    "label": 27,
+    "text": "Title: Soft Continuum Actuator Tip Position and Contact Force Prediction, Using Electrical Impedance Tomography and Recurrent Neural Networks\nAbstract: Enabling dexterous manipulation and safe human-robot interaction, soft robots are widely used in numerous surgical applications. One of the complications associated with using soft robots in surgical applications is reconstructing their shape and the external force exerted on them. Several sensor-based and model-based approaches have been proposed to address the issue. In this paper, a shape sensing technique based on Electrical Impedance Tomography (EIT) is proposed. The performance of this sensing technique in predicting the tip position and contact force of a soft bending actuator is highlighted by conducting a series of empirical tests. The predictions were performed based on a data-driven approach using a Long Short-Term Memory (LSTM) recurrent neural network. The tip position predictions indicate the importance of using EIT data along with pressure inputs. Changing the number of EIT channels, we evaluated the effect of the number of EIT inputs on the accuracy of the predictions. The least RMSE values for the tip position are 3.6 and 4.6 mm in Y and Z coordinates, respectively, which are 7.36% and 6.07% of the actuator's total range of motion. Contact force predictions were conducted in three different bending angles and by varying the number of EIT channels. The results of the predictions illustrated that increasing the number of channels contributes to higher accuracy of the force estimation. The mean errors of using 8 channels are 7.69%, 2.13%, and 2.96% of the total force range in three different bending angles.",
+    "neighbors": [
+      660
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1334,
+    "label": 24,
+    "text": "Title: FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation\nAbstract: Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of prediction fairness, which can result in underestimation of mobility flows across regions with vulnerable population groups, potentially resulting in inequitable resource distribution and infrastructure development. To overcome this limitation, our study presents a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. The FairMobi-Net model uniquely incorporates fairness loss into the loss function and employs a hybrid approach, merging binary classification and numerical regression techniques for human flow prediction. We validate the FairMobi-Net model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. Our findings reveal that the FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity model) in producing more accurate and equitable human flow predictions across a variety of region pairs, regardless of regional income differences. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern. Further analysis of feature importance elucidates the impact of physical distances and road network structures on human flows across regions. With fairness as its touchstone, the model and results provide researchers and practitioners across the fields of urban sciences, transportation engineering, and computing with an effective tool for accurate generation of human mobility flows across regions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1335,
+    "label": 15,
+    "text": "Title: Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration\nAbstract: Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and intermediate data in GNN computations. For dynamic sparsity exploitation, we leverage the heterogeneous computing capabilities of AMD Versal ACAP architecture to accelerate GNN inference. We develop a custom hardware module that executes the sparse primitives of the computation kernel on the Programmable Logic (PL) and efficiently computes the dense primitives using the AI Engine (AIE). To exploit data sparsity during inference, we devise a runtime kernel mapping strategy that dynamically assigns computation tasks to the PL and AIE based on data sparsity. Our implementation on the VCK5000 ACAP platform leads to superior performance compared with the state-of-the-art implementations on CPU, GPU, ACAP, and other custom GNN accelerators. Compared with these implementations, we achieve significant average runtime speedup across various models and datasets of 162.42x, 17.01x, 9.90x, and 27.23x, respectively. Furthermore, for Graph Convolutional Network (GCN) inference, our approach leads to a speedup of 3.9-96.7x compared to designs using PL only on the same ACAP device.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1336,
+    "label": 24,
+    "text": "Title: Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation\nAbstract: Sparse Gaussian Processes are a key component of high-throughput Bayesian Optimisation (BO) loops; however, we show that existing methods for allocating their inducing points severely hamper optimisation performance. By exploiting the quality-diversity decomposition of Determinantal Point Processes, we propose the first inducing point allocation strategy designed specifically for use in BO. Unlike existing methods which seek only to reduce global uncertainty in the objective function, our approach provides the local high-fidelity modelling of promising regions required for precise optimisation. More generally, we demonstrate that our proposed framework provides a flexible way to allocate modelling capacity in sparse models and so is suitable broad range of downstream sequential decision making tasks.",
+    "neighbors": [
+      2075
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1337,
+    "label": 16,
+    "text": "Title: Active Teacher for Semi-Supervised Object Detection\nAbstract: In this paper, we study teacher-student learning from the perspective of data initialization and propose a novel algorithm called Active Teacher11Source code are available at: https://github.com/HunterJLin/ActiveTeacher for semi-supervised object detection (SSOD). Active Teacher extends the teacher-student framework to an iterative version, where the label set is partially initialized and gradually augmented by evaluating three key factors of unlabeled examples, including difficulty, information and diversity. With this design, Active Teacher can maximize the effect of limited label information while improving the quality of pseudo-labels. To validate our approach, we conduct extensive experiments on the MS-COCO benchmark and compare Active Teacher with a set of recently proposed SSOD methods. The experimental results not only validate the superior performance gain of Active Teacher over the compared methods, but also show that it enables the baseline network, i.e., Faster-RCNN, to achieve 100% supervised performance with much less label expenditure, i.e. 40% labeled examples on MS-COCO. More importantly, we believe that the experimental analyses in this paper can provide useful empirical knowledge for data annotation in practical applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1338,
+    "label": 10,
+    "text": "Title: RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games\nAbstract: The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1339,
+    "label": 24,
+    "text": "Title: ExpeL: LLM Agents Are Experiential Learners\nAbstract: The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is resource-intensive and may diminish the model's generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates. To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions. Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.",
+    "neighbors": [
+      173,
+      964,
+      1052,
+      1203,
+      1268,
+      1863,
+      1878,
+      2095,
+      2136,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1340,
+    "label": 31,
+    "text": "Title: MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation\nAbstract: The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items. While many existing studies address the long-tailed problem in SRS, they only focus on either the user or item perspective. However, we discover that the long-tailed user and item problems exist at the same time, and considering only either one of them leads to sub-optimal performance of the other one. In this paper, we propose a novel framework for SRS, called Mutual Enhancement of Long-Tailed user and item (MELT), that jointly alleviates the long-tailed problem in the perspectives of both users and items. MELT consists of bilateral branches each of which is responsible for long-tailed users and items, respectively, and the branches are trained to mutually enhance each other, which is trained effectively by a curriculum learning-based training. MELT is model-agnostic in that it can be seamlessly integrated with existing SRS models. Extensive experiments on eight datasets demonstrate the benefit of alleviating the long-tailed problems in terms of both users and items even without sacrificing the performance of head users and items, which has not been achieved by existing methods. To the best of our knowledge, MELT is the first work that jointly alleviates the long-tailed user and item problems in SRS.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1341,
+    "label": 24,
+    "text": "Title: Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning\nAbstract: Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios where centralized training is either not possible or not practical. One of the critical challenges in decentralized deep MARL is the non-stationarity of the learning environment when multiple agents are learning concurrently. A commonly used and efficient scheme for decentralized MARL is independent learning in which agents concurrently update their policies independently of each other. We first show that independent learning does not always converge, while sequential learning where agents update their policies one after another in a sequence is guaranteed to converge to an agent-by-agent optimal solution. In sequential learning, when one agent updates its policy, all other agent's policies are kept fixed, alleviating the challenge of non-stationarity due to simultaneous updates in other agents' policies. However, it can be slow because only one agent is learning at any time. Therefore it might also not always be practical. In this work, we propose a decentralized cooperative MARL algorithm based on multi-timescale learning. In multi-timescale learning, all agents learn simultaneously, but at different learning rates. In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate. This speeds up sequential learning, while also minimizing non-stationarity caused by other agents updating concurrently. Multi-timescale learning outperforms state-of-the-art decentralized learning methods on a set of challenging multi-agent cooperative tasks in the epymarl(Papoudakis et al., 2020) benchmark. This can be seen as a first step towards more general decentralized cooperative deep MARL methods based on multi-timescale learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1342,
+    "label": 24,
+    "text": "Title: A Deep Learning Model for Heterogeneous Dataset Analysis - Application to Winter Wheat Crop Yield Prediction\nAbstract: Western countries rely heavily on wheat, and yield prediction is crucial. Time-series deep learning models, such as Long Short Term Memory (LSTM), have already been explored and applied to yield prediction. Existing literature reported that they perform better than traditional Machine Learning (ML) models. However, the existing LSTM cannot handle heterogeneous datasets (a combination of data which varies and remains static with time). In this paper, we propose an efficient deep learning model that can deal with heterogeneous datasets. We developed the system architecture and applied it to the real-world dataset in the digital agriculture area. We showed that it outperforms the existing ML models.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1343,
+    "label": 15,
+    "text": "Title: Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications\nAbstract: The challenging deployment of compute-intensive applications from domains such Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of computing systems to explore new design approaches. Approximate Computing appears as an emerging solution, allowing to tune the quality of results in the design of a system in order to improve the energy efficiency and/or performance. This radical paradigm shift has attracted interest from both academia and industry, resulting in significant research on approximation techniques and methodologies at different design layers (from system down to integrated circuits). Motivated by the wide appeal of Approximate Computing over the last 10 years, we conduct a two-part survey to cover key aspects (e.g., terminology and applications) and review the state-of-the art approximation techniques from all layers of the traditional computing stack. In Part II of our survey, we classify and present the technical details of application-specific and architectural approximation techniques, which both target the design of resource-efficient processors/accelerators&systems. Moreover, we present a detailed analysis of the application spectrum of Approximate Computing and discuss open challenges and future directions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1344,
+    "label": 16,
+    "text": "Title: LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models\nAbstract: Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their efficacy. This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (LVLM-eHub). Our LVLM-eHub consists of $8$ representative LVLMs such as InstructBLIP and MiniGPT-4, which are thoroughly evaluated by a quantitative capability evaluation and an online arena platform. The former evaluates $6$ categories of multimodal capabilities of LVLMs such as visual question answering and embodied artificial intelligence on $47$ standard text-related visual benchmarks, while the latter provides the user-level evaluation of LVLMs in an open-world question-answering scenario. The study reveals several innovative findings. First, instruction-tuned LVLM with massive in-domain data such as InstructBLIP heavily overfits many existing tasks, generalizing poorly in the open-world scenario. Second, instruction-tuned LVLM with moderate instruction-following data may result in object hallucination issues (i.e., generate objects that are inconsistent with target images in the descriptions). It either makes the current evaluation metric such as CIDEr for image captioning ineffective or generates wrong answers. Third, employing a multi-turn reasoning evaluation framework can mitigate the issue of object hallucination, shedding light on developing an effective pipeline for LVLM evaluation. The findings provide a foundational framework for the conception and assessment of innovative strategies aimed at enhancing zero-shot multimodal techniques. Our LVLM-eHub will be available at https://github.com/OpenGVLab/Multi-Modality-Arena",
+    "neighbors": [
+      176,
+      392,
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+      754,
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+      907,
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+      1052,
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+      1485,
+      1755,
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+      1990,
+      2036,
+      2095,
+      2155,
+      2216
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1345,
+    "label": 33,
+    "text": "Title: Remarks on Parikh-recognizable omega-languages\nAbstract: Several variants of Parikh automata on infinite words were recently introduced by Guha et al. [FSTTCS, 2022]. We show that one of these variants coincides with blind counter machine as introduced by Fernau and Stiebe [Fundamenta Informaticae, 2008]. Fernau and Stiebe showed that every $\\omega$-language recognized by a blind counter machine is of the form $\\bigcup_iU_iV_i^\\omega$ for Parikh recognizable languages $U_i, V_i$, but blind counter machines fall short of characterizing this class of $\\omega$-languages. They posed as an open problem to find a suitable automata-based characterization. We introduce several additional variants of Parikh automata on infinite words that yield automata characterizations of classes of $\\omega$-language of the form $\\bigcup_iU_iV_i^\\omega$ for all combinations of languages $U_i, V_i$ being regular or Parikh-recognizable. When both $U_i$ and $V_i$ are regular, this coincides with B\\\"uchi's classical theorem. We study the effect of $\\varepsilon$-transitions in all variants of Parikh automata and show that almost all of them admit $\\varepsilon$-elimination. Finally we study the classical decision problems with applications to model checking.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1346,
+    "label": 30,
+    "text": "Title: Large Language Models are not Fair Evaluators\nAbstract: In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context. This manipulation allows us to skew the evaluation result, making one model appear considerably superior to the other, e.g., Vicuna-13B could beat ChatGPT on 66 over 80 tested queries with ChatGPT as an evaluator. To address this issue, we propose a calibration framework with three simple yet effective strategies: 1) Multiple Evidence Calibration, which requires the evaluator model to generate multiple evaluation evidence before assigning ratings; 2) Balanced Position Calibration, which aggregates results across various orders to determine the final score; 3) Human-in-the-Loop Calibration, which introduces a balanced position diversity entropy to measure the difficulty of each example and seeks human assistance when needed. We also manually annotate the\"win/tie/lose\"outcomes of responses from ChatGPT and Vicuna-13B in the Vicuna Benchmark's question prompt, and extensive experiments demonstrate that our approach successfully mitigates evaluation bias, resulting in closer alignment with human judgments. We release our code and human annotation at \\url{https://github.com/i-Eval/FairEval} to facilitate future research.",
+    "neighbors": [
+      126,
+      247,
+      566,
+      652,
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+      1001,
+      1002,
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+      1039,
+      1114,
+      1411,
+      1430,
+      1566,
+      1863,
+      2038
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1347,
+    "label": 24,
+    "text": "Title: Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression\nAbstract: Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communication compression. We propose PORTER, which considers two variants of gradient clipping added before or after taking a mini-batch of stochastic gradients, where the former variant PORTER-DP allows local differential privacy analysis with additional Gaussian perturbation, and the latter variant PORTER-GC helps to stabilize training. We develop a novel analysis framework that establishes their convergence guarantees without assuming the stringent bounded gradient assumption. To the best of our knowledge, our work provides the first convergence analysis for decentralized nonconvex optimization with gradient clipping and communication compression, highlighting the trade-offs between convergence rate, compression ratio, network connectivity, and privacy.",
+    "neighbors": [
+      376,
+      391
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1348,
+    "label": 16,
+    "text": "Title: NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models\nAbstract: Trained with an unprecedented scale of data, large language models (LLMs) like ChatGPT and GPT-4 exhibit the emergence of significant reasoning abilities from model scaling. Such a trend underscored the potential of training LLMs with unlimited language data, advancing the development of a universal embodied agent. In this work, we introduce the NavGPT, a purely LLM-based instruction-following navigation agent, to reveal the reasoning capability of GPT models in complex embodied scenes by performing zero-shot sequential action prediction for vision-and-language navigation (VLN). At each step, NavGPT takes the textual descriptions of visual observations, navigation history, and future explorable directions as inputs to reason the agent's current status, and makes the decision to approach the target. Through comprehensive experiments, we demonstrate NavGPT can explicitly perform high-level planning for navigation, including decomposing instruction into sub-goal, integrating commonsense knowledge relevant to navigation task resolution, identifying landmarks from observed scenes, tracking navigation progress, and adapting to exceptions with plan adjustment. Furthermore, we show that LLMs is capable of generating high-quality navigational instructions from observations and actions along a path, as well as drawing accurate top-down metric trajectory given the agent's navigation history. Despite the performance of using NavGPT to zero-shot R2R tasks still falling short of trained models, we suggest adapting multi-modality inputs for LLMs to use as visual navigation agents and applying the explicit reasoning of LLMs to benefit learning-based models.",
+    "neighbors": [
+      25,
+      57,
+      319,
+      513,
+      818,
+      887,
+      1044,
+      1047,
+      1052,
+      1863
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1349,
+    "label": 30,
+    "text": "Title: Dissociating language and thought in large language models: a cognitive perspective\nAbstract: Today's large language models (LLMs) routinely generate coherent, grammatical and seemingly meaningful paragraphs of text. This achievement has led to speculation that these networks are -- or will soon become --\"thinking machines\", capable of performing tasks that require abstract knowledge and reasoning. Here, we review the capabilities of LLMs by considering their performance on two different aspects of language use: 'formal linguistic competence', which includes knowledge of rules and patterns of a given language, and 'functional linguistic competence', a host of cognitive abilities required for language understanding and use in the real world. Drawing on evidence from cognitive neuroscience, we show that formal competence in humans relies on specialized language processing mechanisms, whereas functional competence recruits multiple extralinguistic capacities that comprise human thought, such as formal reasoning, world knowledge, situation modeling, and social cognition. In line with this distinction, LLMs show impressive (although imperfect) performance on tasks requiring formal linguistic competence, but fail on many tests requiring functional competence. Based on this evidence, we argue that (1) contemporary LLMs should be taken seriously as models of formal linguistic skills; (2) models that master real-life language use would need to incorporate or develop not only a core language module, but also multiple non-language-specific cognitive capacities required for modeling thought. Overall, a distinction between formal and functional linguistic competence helps clarify the discourse surrounding LLMs' potential and provides a path toward building models that understand and use language in human-like ways.",
+    "neighbors": [
+      644,
+      667,
+      1520,
+      1877,
+      1906,
+      2062,
+      2184,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1350,
+    "label": 24,
+    "text": "Title: Adaptive Graph Convolutional Subspace Clustering\nAbstract: Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that, by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC11We present the codes of AGCSC and the evaluated algorithms on https://github.com/weilyshmtu/AGCSC. outperforms some related methods as well as some deep models.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1351,
+    "label": 24,
+    "text": "Title: A Unified Framework for Discovering Discrete Symmetries\nAbstract: We consider the problem of learning a function respecting a symmetry from among a class of symmetries. We develop a unified framework that enables symmetry discovery across a broad range of subgroups including locally symmetric, dihedral and cyclic subgroups. At the core of the framework is a novel architecture composed of linear and tensor-valued functions that expresses functions invariant to these subgroups in a principled manner. The structure of the architecture enables us to leverage multi-armed bandit algorithms and gradient descent to efficiently optimize over the linear and the tensor-valued functions, respectively, and to infer the symmetry that is ultimately learnt. We also discuss the necessity of the tensor-valued functions in the architecture. Experiments on image-digit sum and polynomial regression tasks demonstrate the effectiveness of our approach.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1352,
+    "label": 24,
+    "text": "Title: Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction\nAbstract: Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1353,
+    "label": 10,
+    "text": "Title: TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs\nAbstract: Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next.",
+    "neighbors": [
+      25,
+      57,
+      173,
+      319,
+      363,
+      505,
+      704,
+      945,
+      1047,
+      1128,
+      1327,
+      1626,
+      1659,
+      1720,
+      1810,
+      1878,
+      1906,
+      2030,
+      2166
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1354,
+    "label": 30,
+    "text": "Title: Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations\nAbstract: Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles for conversation partners. These datasets also do not explore depth or breadth of topical coverage with transitions in conversations. We introduce Topical-Chat, a knowledge-grounded human-human conversation dataset where the underlying knowledge spans 8 broad topics and conversation partners don't have explicitly defined roles, to help further research in open-domain conversational AI. We also train several state-of-the-art encoder-decoder conversational models on Topical-Chat and perform automated and human evaluation for benchmarking.",
+    "neighbors": [
+      247
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1355,
+    "label": 16,
+    "text": "Title: Nerfstudio: A Modular Framework for Neural Radiance Field Development\nAbstract: Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing.",
+    "neighbors": [
+      48,
+      286,
+      1771
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1356,
+    "label": 16,
+    "text": "Title: What Do Self-Supervised Vision Transformers Learn?\nAbstract: We present a comparative study on how and why contrastive learning (CL) and masked image modeling (MIM) differ in their representations and in their performance of downstream tasks. In particular, we demonstrate that self-supervised Vision Transformers (ViTs) have the following properties: (1) CL trains self-attentions to capture longer-range global patterns than MIM, such as the shape of an object, especially in the later layers of the ViT architecture. This CL property helps ViTs linearly separate images in their representation spaces. However, it also makes the self-attentions collapse into homogeneity for all query tokens and heads. Such homogeneity of self-attention reduces the diversity of representations, worsening scalability and dense prediction performance. (2) CL utilizes the low-frequency signals of the representations, but MIM utilizes high-frequencies. Since low- and high-frequency information respectively represent shapes and textures, CL is more shape-oriented and MIM more texture-oriented. (3) CL plays a crucial role in the later layers, while MIM mainly focuses on the early layers. Upon these analyses, we find that CL and MIM can complement each other and observe that even the simplest harmonization can help leverage the advantages of both methods. The code is available at https://github.com/naver-ai/cl-vs-mim.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1357,
+    "label": 24,
+    "text": "Title: Towards the Characterization of Representations Learned via Capsule-based Network Architectures\nAbstract: Capsule Networks (CapsNets) have been re-introduced as a more compact and interpretable alternative to standard deep neural networks. While recent efforts have proved their compression capabilities, to date, their interpretability properties have not been fully assessed. Here, we conduct a systematic and principled study towards assessing the interpretability of these types of networks. Moreover, we pay special attention towards analyzing the level to which part-whole relationships are indeed encoded within the learned representation. Our analysis in the MNIST, SVHN, PASCAL-part and CelebA datasets suggest that the representations encoded in CapsNets might not be as disentangled nor strictly related to parts-whole relationships as is commonly stated in the literature.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1358,
+    "label": 16,
+    "text": "Title: AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset\nAbstract: It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, meaning that they perform the pre-training and fine-tuning on the same benchmark, which is difficult to attain the performance scalability and cross-dataset application for the pre-training checkpoint. In this paper, for the first time, we are committed to building a large-scale pre-training point-cloud dataset with diverse data distribution, and meanwhile learning generalizable representations from such a diverse pre-training dataset. We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to generate the unified backbone representations that can be directly applied to many baseline models and benchmarks, decoupling the AD-related pre-training process and downstream fine-tuning task. During the period of backbone pre-training, by enhancing the scene- and instance-level distribution diversity and exploiting the backbone's ability to learn from unknown instances, we achieve significant performance gains on a series of downstream perception benchmarks including Waymo, nuScenes, and KITTI, under different baseline models like PV-RCNN++, SECOND, CenterPoint.",
+    "neighbors": [
+      1080,
+      1628,
+      2150
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1359,
+    "label": 24,
+    "text": "Title: Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics\nAbstract: nan",
+    "neighbors": [
+      2152,
+      2290
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1360,
+    "label": 16,
+    "text": "Title: Temporal Sentence Grounding in Streaming Videos\nAbstract: This paper aims to tackle a novel task - Temporal Sentence Grounding in Streaming Videos (TSGSV). The goal of TSGSV is to evaluate the relevance between a video stream and a given sentence query. Unlike regular videos, streaming videos are acquired continuously from a particular source, and are always desired to be processed on-the-fly in many applications such as surveillance and live-stream analysis. Thus, TSGSV is challenging since it requires the model to infer without future frames and process long historical frames effectively, which is untouched in the early methods. To specifically address the above challenges, we propose two novel methods: (1) a TwinNet structure that enables the model to learn about upcoming events; and (2) a language-guided feature compressor that eliminates redundant visual frames and reinforces the frames that are relevant to the query. We conduct extensive experiments using ActivityNet Captions, TACoS, and MAD datasets. The results demonstrate the superiority of our proposed methods. A systematic ablation study also confirms their effectiveness.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1361,
+    "label": 23,
+    "text": "Title: On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem\nAbstract: Reusing software libraries is a pillar of modern software engineering. In 2022, the average Java application depends on 40 third-party libraries. Relying on such libraries exposes a project to potential vulnerabilities and may put an application and its users at risk. Unfortunately, research on software ecosystems has shown that the number of projects that are affected by such vulnerabilities is rising. Previous investigations usually reason about dependencies on the dependency level, but we believe that this highly inflates the actual number of affected projects. In this work, we study the effect of transitivity and granularity on vulnerability propagation in the Maven ecosystem. In our research methodology, we gather a large dataset of 3M recent Maven packages. We obtain the full transitive set of dependencies for this dataset, construct whole-program call graphs, and perform reachability analysis. This approach allows us to identify Maven packages that are actually affected by using vulnerable dependencies. Our empirical results show that: (1) about 1/3 of packages in our dataset are identified as vulnerable if and only if all the transitive dependencies are considered. (2) less than 1% of packages have a reachable call path to vulnerable code in their dependencies, which is far lower than that of a naive dependency-based analysis. (3) limiting the depth of the resolved dependency tree might be a useful technique to reduce computation time for expensive fine-grained (vulnerability) analysis. We discuss the implications of our work and provide actionable insights for researchers and practitioners.",
+    "neighbors": [
+      1607
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1362,
+    "label": 37,
+    "text": "Title: Discovering Sound Free-Choice Workflow Nets with Non-block Structures\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1363,
+    "label": 30,
+    "text": "Title: Factify 2: A Multimodal Fake News and Satire News Dataset\nAbstract: The internet gives the world an open platform to express their views and share their stories. While this is very valuable, it makes fake news one of our society's most pressing problems. Manual fact checking process is time consuming, which makes it challenging to disprove misleading assertions before they cause significant harm. This is he driving interest in automatic fact or claim verification. Some of the existing datasets aim to support development of automating fact-checking techniques, however, most of them are text based. Multi-modal fact verification has received relatively scant attention. In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles. Factify 2 has 50,000 new data instances. Similar to FACTIFY 1.0, we have three broad categories - support, no-evidence, and refute, with sub-categories based on the entailment of visual and textual data. We also provide a BERT and Vison Transformer based baseline, which acheives 65% F1 score in the test set. The baseline codes and the dataset will be made available at https://github.com/surya1701/Factify-2.0.",
+    "neighbors": [
+      1302,
+      1553
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1364,
+    "label": 4,
+    "text": "Title: Dynamic, Private, Anonymous, Collateralizable Commitments vs. MEV\nAbstract: We introduce DPACCs, a generalized commitment scheme based on smart contract wallets and non-interactive zero knowledge proofs. DPACCs allow any smart contract wallet holder to collateralize a claim, request, or commitment in general, in a private and anonymous manner. DPACCs can prove arbitrarily much or little about the wallet generating the commitment, and/or the transaction which is being committed. This can be used to convince a prospective block builder or relayer that the wallet generating the DPACC has enough funds to pay required fees, that the wallet is committed to performing certain actions, and importantly, that the wallet loses some amount of collateral if this commitment is broken. DPACCs delegate typically expensive zero knowledge operations off-chain, only requiring an additional one or two mapping checks when compared to transactions being sent from basic externally owned accounts. We demonstrate that DPACCs can be applied to effectively eliminate MEV in DeFi where it currently occurs, shifting MEV instead to censorship. Although still a concern, censorship can be made prohibitively expensive, making DPACCs a viable solution to most sources of MEV.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1365,
+    "label": 16,
+    "text": "Title: Customized Segment Anything Model for Medical Image Segmentation\nAbstract: We propose SAMed, a general solution for medical image segmentation. Different from the previous methods, SAMed is built upon the large-scale image segmentation model, Segment Anything Model (SAM), to explore the new research paradigm of customizing large-scale models for medical image segmentation. SAMed applies the low-rank-based (LoRA) finetuning strategy to the SAM image encoder and finetunes it together with the prompt encoder and the mask decoder on labeled medical image segmentation datasets. We also observe the warmup finetuning strategy and the AdamW optimizer lead SAMed to successful convergence and lower loss. Different from SAM, SAMed could perform semantic segmentation on medical images. Our trained SAMed model achieves 81.88 DSC and 20.64 HD on the Synapse multi-organ segmentation dataset, which is on par with the state-of-the-art methods. We conduct extensive experiments to validate the effectiveness of our design. Since SAMed only updates a small fraction of the SAM parameters, its deployment cost and storage cost are quite marginal in practical usage. The code of SAMed is available at https://github.com/hitachinsk/SAMed.",
+    "neighbors": [
+      1006,
+      1052,
+      1068,
+      1932,
+      2151,
+      2203
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1366,
+    "label": 28,
+    "text": "Title: Reliable and Unreliable Sources in Age-Based Gossiping\nAbstract: We consider a network consisting of n nodes that aim to track a continually updating process or event. To disseminate updates about the event to the network, two sources are available, such that information obtained from one source is considered more reliable than the other source. The nodes wish to have access to information about the event that is not only latest but also more reliable, and prefer a reliable packet over an unreliable packet even when the former is a bit outdated with respect to the latter. We study how such preference affects the fraction of users with reliable information in the network and their version age of information. We derive the analytical equations to characterize the two quantities, long-term expected fraction of nodes with reliable packets and their long-term expected version age using stochastic hybrid systems (SHS) modelling and study their properties. We also compare these results with the case where nodes give more preference to freshness of information than its reliability. Finally we show simulation results to verify the theoretical results and shed further light on behavior of above quantities with respect to dependent variables.",
+    "neighbors": [
+      2111
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1367,
+    "label": 27,
+    "text": "Title: Sim-to-Real Vision-depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopter\nAbstract: Nano-quadcopters are versatile platforms attracting the interest of both academia and industry. Their tiny form factor, i.e., $\\,$10 cm diameter, makes them particularly useful in narrow scenarios and harmless in human proximity. However, these advantages come at the price of ultra-constrained onboard computational and sensorial resources for autonomous operations. This work addresses the task of estimating human pose aboard nano-drones by fusing depth and images in a novel CNN exclusively trained in simulation yet capable of robust predictions in the real world. We extend a commercial off-the-shelf (COTS) Crazyflie nano-drone -- equipped with a 320$\\times$240 px camera and an ultra-low-power System-on-Chip -- with a novel multi-zone (8$\\times$8) depth sensor. We design and compare different deep-learning models that fuse depth and image inputs. Our models are trained exclusively on simulated data for both inputs, and transfer well to the real world: field testing shows an improvement of 58% and 51% of our depth+camera system w.r.t. a camera-only State-of-the-Art baseline on the horizontal and angular mean pose errors, respectively. Our prototype is based on COTS components, which facilitates reproducibility and adoption of this novel class of systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1368,
+    "label": 24,
+    "text": "Title: Homological Neural Networks: A Sparse Architecture for Multivariate Complexity\nAbstract: The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability. In this study, we apply advanced network-based information filtering techniques to design a novel deep neural network unit characterized by a sparse higher-order graphical architecture built over the homological structure of underlying data. We demonstrate its effectiveness in two application domains which are traditionally challenging for deep learning: tabular data and time series regression problems. Results demonstrate the advantages of this novel design which can tie or overcome the results of state-of-the-art machine learning and deep learning models using only a fraction of parameters.",
+    "neighbors": [
+      253,
+      671
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1369,
+    "label": 27,
+    "text": "Title: Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors\nAbstract: Picking cluttered general objects is a challenging task due to the complex geometries and various stacking configurations. Many prior works utilize pose estimation for picking, but pose estimation is difficult on cluttered objects. In this paper, we propose Cluttered Objects Descriptors (CODs), a dense cluttered objects descriptor which can represent rich object structures, and use the pre-trained CODs network along with its intermediate outputs to train a picking policy. Additionally, we train the policy with reinforcement learning, which enable the policy to learn picking without supervision. We conduct experiments to demonstrate that our CODs is able to consistently represent seen and unseen cluttered objects, which allowed for the picking policy to robustly pick cluttered general objects. The resulting policy can pick 96.69% of unseen objects in our experimental environment that are twice as cluttered as the training scenarios.",
+    "neighbors": [
+      1828,
+      2268
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1370,
+    "label": 24,
+    "text": "Title: Assessment of Reinforcement Learning for Macro Placement\nAbstract: We provide open, transparent implementation and assessment of Google Brain's deep reinforcement learning approach to macro placement (Nature) and its Circuit Training (CT) implementation in GitHub. We implement in open-source key \"blackbox\" elements of CT, and clarify discrepancies between CT and Nature. New testcases on open enablements are developed and released. We assess CT alongside multiple alternative macro placers, with all evaluation flows and related scripts public in GitHub. Our experiments also encompass academic mixed-size placement benchmarks, as well as ablation and stability studies. We comment on the impact of Nature and CT, as well as directions for future research.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1371,
+    "label": 31,
+    "text": "Title: MobileRec: A Large Scale Dataset for Mobile Apps Recommendation\nAbstract: Recommender systems have become ubiquitous in our digital lives, from recommending products on e-commerce websites to suggesting movies and music on streaming platforms. Existing recommendation datasets, such as Amazon Product Reviews and MovieLens, greatly facilitated the research and development of recommender systems in their respective domains. While the number of mobile users and applications (aka apps) has increased exponentially over the past decade, research in mobile app recommender systems has been significantly constrained, primarily due to the lack of high-quality benchmark datasets, as opposed to recommendations for products, movies, and news. To facilitate research for app recommendation systems, we introduce a large-scale dataset, called MobileRec. We constructed MobileRec from users' activity on the Google play store. MobileRec contains 19.3 million user interactions (i.e., user reviews on apps) with over 10K unique apps across 48 categories. MobileRec records the sequential activity of a total of 0.7 million distinct users. Each of these users has interacted with no fewer than five distinct apps, which stands in contrast to previous datasets on mobile apps that recorded only a single interaction per user. Furthermore, MobileRec presents users' ratings as well as sentiments on installed apps, and each app contains rich metadata such as app name, category, description, and overall rating, among others. We demonstrate that MobileRec can serve as an excellent testbed for app recommendation through a comparative study of several state-of-the-art recommendation approaches. The MobileRec dataset is available at https://huggingface.co/datasets/recmeapp/mobilerec.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1372,
+    "label": 4,
+    "text": "Title: A First Order Meta Stackelberg Method for Robust Federated Learning (Technical Report)\nAbstract: Previous research has shown that federated learning (FL) systems are exposed to an array of security risks. Despite the proposal of several defensive strategies, they tend to be non-adaptive and specific to certain types of attacks, rendering them ineffective against unpredictable or adaptive threats. This work models adversarial federated learning as a Bayesian Stackelberg Markov game (BSMG) to capture the defender's incomplete information of various attack types. We propose meta-Stackelberg learning (meta-SL), a provably efficient meta-learning algorithm, to solve the equilibrium strategy in BSMG, leading to an adaptable FL defense. We demonstrate that meta-SL converges to the first-order $\\varepsilon$-equilibrium point in $O(\\varepsilon^{-2})$ gradient iterations, with $O(\\varepsilon^{-4})$ samples needed per iteration, matching the state of the art. Empirical evidence indicates that our meta-Stackelberg framework performs exceptionally well against potent model poisoning and backdoor attacks of an uncertain nature.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1373,
+    "label": 24,
+    "text": "Title: Recent advances in artificial intelligence for retrosynthesis\nAbstract: Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by artificial intelligence have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI-based retrosynthesis. For single-step and multi-step retrosynthesis both, we first list their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1374,
+    "label": 24,
+    "text": "Title: Resilient Binary Neural Network\nAbstract: Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is a severe performance drop compared with {real-valued} networks, due to its intrinsic frequent weight oscillation during training. In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training. We identify that the weight oscillation mainly stems from the non-parametric scaling factor. To address this issue, we propose to parameterize the scaling factor and introduce a weighted reconstruction loss to build an adaptive training objective. For the first time, we show that the weight oscillation is controlled by the balanced parameter attached to the reconstruction loss, which provides a theoretical foundation to parameterize it in back propagation. Based on this, we learn our ReBNN by calculating the balanced parameter based on its maximum magnitude, which can effectively mitigate the weight oscillation with a resilient training process. Extensive experiments are conducted upon various network models, such as ResNet and Faster-RCNN for computer vision, as well as BERT for natural language processing. The results demonstrate the overwhelming performance of our ReBNN over prior arts. For example, our ReBNN achieves 66.9% Top-1 accuracy with ResNet-18 backbone on the ImageNet dataset, surpassing existing state-of-the-arts by a significant margin. Our code is open-sourced at https://github.com/SteveTsui/ReBNN.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1375,
+    "label": 16,
+    "text": "Title: Curriculum Learning for ab initio Deep Learned Refractive Optics\nAbstract: Deep lens optimization has recently emerged as a new paradigm for designing computational imaging systems, however it has been limited to either simple optical systems consisting of a single DOE or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a deep lens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces, therefore overcoming the need for a good initial design. We demonstrate this approach with the fully-automatic design of an extended depth-of-field computational camera in a cellphone-style form factor, highly aspherical surfaces, and a short back focal length.",
+    "neighbors": [
+      1652
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1376,
+    "label": 23,
+    "text": "Title: Read It, Don't Watch It: Captioning Bug Recordings Automatically\nAbstract: Screen recordings of mobile applications are easy to capture and include a wealth of information, making them a popular mechanism for users to inform developers of the problems encountered in the bug reports. However, watching the bug recordings and efficiently understanding the semantics of user actions can be time-consuming and tedious for developers. Inspired by the conception of the video subtitle in movie industry, we present a lightweight approach CAPdroid to caption bug recordings automatically. CAPdroid is a purely image-based and non-intrusive approach by using image processing and convolutional deep learning models to segment bug recordings, infer user action attributes, and generate subtitle descriptions. The automated experiments demonstrate the good performance of CAPdroid in inferring user actions from the recordings, and a user study confirms the usefulness of our generated step descriptions in assisting developers with bug replay.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1377,
+    "label": 24,
+    "text": "Title: Spatial Implicit Neural Representations for Global-Scale Species Mapping\nAbstract: Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of records for hundreds of thousands of species. In this work, we use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously. We find that our approach scales gracefully, making increasingly better predictions as we increase the number of species and the amount of data per species when training. To make this problem accessible to machine learning researchers, we provide four new benchmarks that measure different aspects of species range estimation and spatial representation learning. Using these benchmarks, we demonstrate that noisy and biased crowdsourced data can be combined with implicit neural representations to approximate expert-developed range maps for many species.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1378,
+    "label": 30,
+    "text": "Title: UniSA: Unified Generative Framework for Sentiment Analysis\nAbstract: Sentiment analysis is a crucial task that aims to understand people's emotional states and predict emotional categories based on multimodal information. It consists of several subtasks, such as emotion recognition in conversation (ERC), aspect-based sentiment analysis (ABSA), and multimodal sentiment analysis (MSA). However, unifying all subtasks in sentiment analysis presents numerous challenges, including modality alignment, unified input/output forms, and dataset bias. To address these challenges, we propose a Task-Specific Prompt method to jointly model subtasks and introduce a multimodal generative framework called UniSA. Additionally, we organize the benchmark datasets of main subtasks into a new Sentiment Analysis Evaluation benchmark, SAEval. We design novel pre-training tasks and training methods to enable the model to learn generic sentiment knowledge among subtasks to improve the model's multimodal sentiment perception ability. Our experimental results show that UniSA performs comparably to the state-of-the-art on all subtasks and generalizes well to various subtasks in sentiment analysis.",
+    "neighbors": [
+      819,
+      1212,
+      1384,
+      1892
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1379,
+    "label": 16,
+    "text": "Title: Exploring Invariant Representation for Visible-Infrared Person Re-Identification\nAbstract: Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this paper, we address the problem from both image-level and feature-level in an end-to-end hybrid learning framework named robust feature mining network (RFM). In particular, we observe that the reflective intensity of the same surface in photos shot in different wavelengths could be transformed using a linear model. Besides, we show the variable linear factor across the different surfaces is the main culprit which initiates the modality discrepancy. We integrate such a reflection observation into an image-level data augmentation by proposing the linear transformation generator (LTG). Moreover, at the feature level, we introduce a cross-center loss to explore a more compact intra-class distribution and modality-aware spatial attention to take advantage of textured regions more efficiently. Experiment results on two standard cross-spectral person re-identification datasets, i.e., RegDB and SYSU-MM01, have demonstrated state-of-the-art performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1380,
+    "label": 16,
+    "text": "Title: In-Context Learning Unlocked for Diffusion Models\nAbstract: We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prompts. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation on the trained tasks and generalizes effectively to new, unseen vision tasks with their respective prompts. Our model also shows compelling text-guided image editing results. Our framework, with code publicly available at https://github.com/Zhendong-Wang/Prompt-Diffusion, aims to facilitate research into in-context learning for computer vision.",
+    "neighbors": [
+      1033,
+      1179,
+      1251,
+      1902,
+      2203,
+      2226
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1381,
+    "label": 6,
+    "text": "Title: Student Teacher Interaction While Learning Computer Science: Early Results from an Experiment on Undergraduates\nAbstract: The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the same teacher at a difference of two weeks between lectures). We tried to minimize external factors' impact by carefully selecting the dates (close ones), having the courses in the same classroom and on the same day of the week, at the same hour, and checking the number and the complexity of the introduced items to be comparable. We asked for students' feedback and we define a set of countable body signs for their involvement in the course. The results were comparable by both metrics (body language) and text analysis results, students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1382,
+    "label": 24,
+    "text": "Title: Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics\nAbstract: We propose to study and promote the robustness of a model as per its performance through the interpolation of training data distributions. Specifically, (1) we augment the data by finding the worst-case Wasserstein barycenter on the geodesic connecting subpopulation distributions of different categories. (2) We regularize the model for smoother performance on the continuous geodesic path connecting subpopulation distributions. (3) Additionally, we provide a theoretical guarantee of robustness improvement and investigate how the geodesic location and the sample size contribute, respectively. Experimental validations of the proposed strategy on \\textit{four} datasets, including CIFAR-100 and ImageNet, establish the efficacy of our method, e.g., our method improves the baselines' certifiable robustness on CIFAR10 up to $7.7\\%$, with $16.8\\%$ on empirical robustness on CIFAR-100. Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.",
+    "neighbors": [
+      1453
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1383,
+    "label": 30,
+    "text": "Title: AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages\nAbstract: Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\\`ub\\'a) from four language families annotated by native speakers. The data is used in SemEval 2023 Task 12, the first Afro-centric SemEval shared task. We describe the data collection methodology, annotation process, and related challenges when curating each of the datasets. We conduct experiments with different sentiment classification baselines and discuss their usefulness. We hope AfriSenti enables new work on under-represented languages. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be loaded as a huggingface datasets (https://huggingface.co/datasets/shmuhammad/AfriSenti).",
+    "neighbors": [
+      1585
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1384,
+    "label": 30,
+    "text": "Title: ChatGPT: Jack of all trades, master of none\nAbstract: nan",
+    "neighbors": [
+      352,
+      549,
+      551,
+      929,
+      1035,
+      1142,
+      1378,
+      1636,
+      1949,
+      1952,
+      1983,
+      2113,
+      2230,
+      2244,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1385,
+    "label": 24,
+    "text": "Title: Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics\nAbstract: Discovering a suitable coordinate transformation for nonlinear systems enables the construction of simpler models, facilitating prediction, control, and optimization for complex nonlinear systems. To that end, Koopman operator theory offers a framework for global linearization for nonlinear systems, thereby allowing the usage of linear tools for design studies. In this work, we focus on the identification of global linearized embeddings for canonical nonlinear Hamiltonian systems through a symplectic transformation. While this task is often challenging, we leverage the power of deep learning to discover the desired embeddings. Furthermore, to overcome the shortcomings of Koopman operators for systems with continuous spectra, we apply the lifting principle and learn global cubicized embeddings. Additionally, a key emphasis is paid to enforce the bounded stability for the dynamics of the discovered embeddings. We demonstrate the capabilities of deep learning in acquiring compact symplectic coordinate transformation and the corresponding simple dynamical models, fostering data-driven learning of nonlinear canonical Hamiltonian systems, even those with continuous spectra.",
+    "neighbors": [
+      348,
+      473
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1386,
+    "label": 15,
+    "text": "Title: ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation\nAbstract: The edge processing of deep neural networks (DNNs) is becoming increasingly important due to its ability to extract valuable information directly at the data source to minimize latency and energy consumption. Frequency-domain model compression, such as with the Walsh-Hadamard transform (WHT), has been identified as an efficient alternative. However, the benefits of frequency-domain processing are often offset by the increased multiply-accumulate (MAC) operations required. This paper proposes a novel approach to an energy-efficient acceleration of frequency-domain neural networks by utilizing analog-domain frequency-based tensor transformations. Our approach offers unique opportunities to enhance computational efficiency, resulting in several high-level advantages, including array micro-architecture with parallelism, ADC/DAC-free analog computations, and increased output sparsity. Our approach achieves more compact cells by eliminating the need for trainable parameters in the transformation matrix. Moreover, our novel array micro-architecture enables adaptive stitching of cells column-wise and row-wise, thereby facilitating perfect parallelism in computations. Additionally, our scheme enables ADC/DAC-free computations by training against highly quantized matrix-vector products, leveraging the parameter-free nature of matrix multiplications. Another crucial aspect of our design is its ability to handle signed-bit processing for frequency-based transformations. This leads to increased output sparsity and reduced digitization workload. On a 16$\\times$16 crossbars, for 8-bit input processing, the proposed approach achieves the energy efficiency of 1602 tera operations per second per Watt (TOPS/W) without early termination strategy and 5311 TOPS/W with early termination strategy at VDD = 0.8 V.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1387,
+    "label": 24,
+    "text": "Title: Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing\nAbstract: Bayesian Optimization (BO) is used to find the global optima of black box functions. In this work, we propose a practical BO method of function compositions where the form of the composition is known but the constituent functions are expensive to evaluate. By assuming an independent Gaussian process (GP) model for each of the constituent black-box function, we propose Expected Improvement (EI) and Upper Confidence Bound (UCB) based BO algorithms and demonstrate their ability to outperform not just vanilla BO but also the current state-of-art algorithms. We demonstrate a novel application of the proposed methods to dynamic pricing in revenue management when the underlying demand function is expensive to evaluate.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1388,
+    "label": 24,
+    "text": "Title: QUBO-inspired Molecular Fingerprint for Chemical Property Prediction\nAbstract: Molecular fingerprints are widely used for predicting chemical properties, and selecting appropriate fingerprints is important. We generate new fingerprints based on the assumption that a performance of prediction using a more effective fingerprint is better. We generate effective interaction fingerprints that are the product of multiple base fingerprints. It is difficult to evaluate all combinations of interaction fingerprints because of computational limitations. Against this problem, we transform a problem of searching more effective interaction fingerprints into a quadratic unconstrained binary optimization problem. In this study, we found effective interaction fingerprints using QM9 dataset.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1389,
+    "label": 24,
+    "text": "Title: fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks\nAbstract: To design with AI models, user experience (UX) designers must assess the fit between the model and user needs. Based on user research, they need to contextualize the model\u2019s behavior and potential failures within their product-specific data instances and user scenarios. However, our formative interviews with ten UX professionals revealed that such a proactive discovery of model limitations is challenging and time-intensive. Furthermore, designers often lack technical knowledge of AI and accessible exploration tools, which challenges their understanding of model capabilities and limitations. In this work, we introduced a failure-driven design approach to AI, a workflow that encourages designers to explore model behavior and failure patterns early in the design process. The implementation of fAIlureNotes, a designer-centered failure exploration and analysis tool, supports designers in evaluating models and identifying failures across diverse user groups and scenarios. Our evaluation with UX practitioners shows that fAIlureNotes outperforms today\u2019s interactive model cards in assessing context-specific model performance.",
+    "neighbors": [
+      1897
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1390,
+    "label": 4,
+    "text": "Title: ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications\nAbstract: Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmetic computations directly on encrypted data without decrypting it. Thus, even when processed by untrusted systems, confidential data is never exposed. In this work, we develop new techniques for accelerated encrypted execution and demonstrate the significant performance advantages of our approach. Our current focus is the Fully Homomorphic Encryption over the Torus (CGGI) scheme, which is a current state-of-the-art method for evaluating arbitrary functions in the encrypted domain. CGGI represents a computation as a graph of homomorphic logic gates and each individual bit of the plaintext is transformed into a polynomial in the encrypted domain. Arithmetic on such data becomes very expensive: operations on bits become operations on entire polynomials. Therefore, evaluating even relatively simple nonlinear functions, such as a sigmoid, can take thousands of seconds on a single CPU thread. Using our novel framework for end-to-end accelerated encrypted execution called ArctyrEX, developers with no knowledge of complex FHE libraries can simply describe their computation as a C program that is evaluated over $40\\times$ faster on an NVIDIA DGX A100 and $6\\times$ faster with a single A100 relative to a 256-threaded CPU baseline.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1391,
+    "label": 24,
+    "text": "Title: Interpreting and generalizing deep learning in physics-based problems with functional linear models\nAbstract: Although deep learning has achieved remarkable success in various scientific machine learning applications, its black-box nature poses concerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems. Moreover, acquiring extensive datasets that encompass the entire range of input features is challenging in many physics-based learning tasks, leading to increased errors when encountering out-of-distribution (OOD) data. In this work, motivated by the field of functional data analysis (FDA), we propose generalized functional linear models as an interpretable surrogate for a trained deep learning model. We demonstrate that our model could be trained either based on a trained neural network (post-hoc interpretation) or directly from training data (interpretable operator learning). A library of generalized functional linear models with different kernel functions is considered and sparse regression is used to discover an interpretable surrogate model that could be analytically presented. We present test cases in solid mechanics, fluid mechanics, and transport. Our results demonstrate that our model can achieve comparable accuracy to deep learning and can improve OOD generalization while providing more transparency and interpretability. Our study underscores the significance of interpretability in scientific machine learning and showcases the potential of functional linear models as a tool for interpreting and generalizing deep learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1392,
+    "label": 16,
+    "text": "Title: UniverSeg: Universal Medical Image Segmentation\nAbstract: While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu",
+    "neighbors": [
+      1685
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1393,
+    "label": 25,
+    "text": "Title: FSD: An Initial Chinese Dataset for Fake Song Detection\nAbstract: Singing voice synthesis and singing voice conversion have significantly advanced, revolutionizing musical experiences. However, the rise of\"Deepfake Songs\"generated by these technologies raises concerns about authenticity. Unlike Audio DeepFake Detection (ADD), the field of song deepfake detection lacks specialized datasets or methods for song authenticity verification. In this paper, we initially construct a Chinese Fake Song Detection (FSD) dataset to investigate the field of song deepfake detection. The fake songs in the FSD dataset are generated by five state-of-the-art singing voice synthesis and singing voice conversion methods. Our initial experiments on FSD revealed the ineffectiveness of existing speech-trained ADD models for the task of song deepFake detection. Thus, we employ the FSD dataset for the training of ADD models. We subsequently evaluate these models under two scenarios: one with the original songs and another with separated vocal tracks. Experiment results show that song-trained ADD models exhibit a 38.58% reduction in average equal error rate compared to speech-trained ADD models on the FSD test set.",
+    "neighbors": [
+      930
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1394,
+    "label": 24,
+    "text": "Title: Predicting Early Dropouts of an Active and Healthy Ageing App\nAbstract: In this work, we present a machine learning approach for predicting early dropouts of an active and healthy ageing app. The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022. We have processed the given database and generated seven datasets. We used pre-processing techniques to construct classification models that predict the adherence of users using dynamic and static features. We submitted 11 official runs and our results show that machine learning algorithms can provide high-quality adherence predictions. Based on the results, the dynamic features positively influence a model's classification performance. Due to the imbalanced nature of the dataset, we employed oversampling methods such as SMOTE and ADASYN to improve the classification performance. The oversampling approaches led to a remarkable improvement of 10\\%. Our methods won first place in the IFMBE Scientific Challenge 2022.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1395,
+    "label": 10,
+    "text": "Title: Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving\nAbstract: Risk assessment is a central element for the development and validation of autonomous vehicles. It comprises a combination of occurrence probability and severity of future critical events. Time headway (TH) as well as time-to-contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g.,\u00a0longitudinal following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal, and behavioral uncertainties as they arise in real-world scenarios. The resulting risk spot detector (RSD) is applied and tested on naturalistic driving data of a multilane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.",
+    "neighbors": [
+      1931,
+      2011
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1396,
+    "label": 33,
+    "text": "Title: History-deterministic Vector Addition Systems\nAbstract: We consider history-determinism, a restricted form of non-determinism, for Vector Addition Systems with States (VASS) when used as acceptors to recognise languages of finite words. History-determinism requires that the non-deterministic choices can be resolved on-the-fly; based on the past and without jeopardising acceptance of any possible continuation of the input word. Our results show that the history-deterministic (HD) VASS sit strictly between deterministic and non-deterministic VASS regardless of the number of counters. We compare the relative expressiveness of HD systems, and closure-properties of the induced language classes, with coverability and reachability semantics, and with and without $\\varepsilon$-labelled transitions. Whereas in dimension 1, inclusion and regularity remain decidable, from dimension two onwards, HD-VASS with suitable resolver strategies, are essentially able to simulate 2-counter Minsky machines, leading to several undecidability results: It is undecidable whether a VASS is history-deterministic, or if a language equivalent history-deterministic VASS exists. Checking language inclusion between history-deterministic 2-VASS is also undecidable.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1397,
+    "label": 8,
+    "text": "Title: Wi-Fi Multi-Link Operation: An Experimental Study of Latency and Throughput\nAbstract: In this article, we investigate the real-world capability of the multi-link operation (MLO) framework -- one of the key MAC-layer features included in the IEEE 802.11be amendment -- by using a large dataset containing 5 GHz spectrum occupancy measurements on multiple channels. Our results show that when both available links are often busy, as is the case in ultra-dense and crowded scenarios, MLO attains the highest throughput gains over single-link operation (SLO) since it is able to leverage multiple intermittent transmission opportunities. As for latency, if the two links exhibit statistically the same level of occupancy, MLO can outperform SLO by one order of magnitude. In contrast, in asymmetrically occupied links, MLO can sometimes be detrimental and even increase latency. We study this somewhat unexpected phenomenon, and find its origins to be packets suboptimally mapped to either link before carrying out the backoff, with the latter likely to be interrupted on the busier link. We cross validate our study with real-time traffic generated by a cloud gaming application and quantify MLO's benefits for latency-sensitive applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1398,
+    "label": 25,
+    "text": "Title: Two Stage Contextual Word Filtering for Context bias in Unified Streaming and Non-streaming Transducer\nAbstract: It is difficult for an E2E ASR system to recognize words such as entities appearing infrequently in the training data. A widely used method to mitigate this issue is feeding contextual information into the acoustic model. Previous works have proven that a compact and accurate contextual list can boost the performance significantly. In this paper, we propose an efficient approach to obtain a high quality contextual list for a unified streaming/non-streaming based E2E model. Specifically, we make use of the phone-level streaming output to first filter the predefined contextual word list then fuse it into non-casual encoder and decoder to generate the final recognition results. Our approach improve the accuracy of the contextual ASR system and speed up the inference process. Experiments on two datasets demonstrates over 20% CER reduction comparing to the baseline system. Meanwhile, the RTF of our system can be stabilized within 0.15 when the size of the contextual word list grows over 6,000.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1399,
+    "label": 24,
+    "text": "Title: Federated Learning with Regularized Client Participation\nAbstract: Federated Learning (FL) is a distributed machine learning approach where multiple clients work together to solve a machine learning task. One of the key challenges in FL is the issue of partial participation, which occurs when a large number of clients are involved in the training process. The traditional method to address this problem is randomly selecting a subset of clients at each communication round. In our research, we propose a new technique and design a novel regularized client participation scheme. Under this scheme, each client joins the learning process every $R$ communication rounds, which we refer to as a meta epoch. We have found that this participation scheme leads to a reduction in the variance caused by client sampling. Combined with the popular FedAvg algorithm (McMahan et al., 2017), it results in superior rates under standard assumptions. For instance, the optimization term in our main convergence bound decreases linearly with the product of the number of communication rounds and the size of the local dataset of each client, and the statistical term scales with step size quadratically instead of linearly (the case for client sampling with replacement), leading to better convergence rate $\\mathcal{O}\\left(\\frac{1}{T^2}\\right)$ compared to $\\mathcal{O}\\left(\\frac{1}{T}\\right)$, where $T$ is the total number of communication rounds. Furthermore, our results permit arbitrary client availability as long as each client is available for training once per each meta epoch.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1400,
+    "label": 24,
+    "text": "Title: The Exact Sample Complexity Gain from Invariances for Kernel Regression on Manifolds\nAbstract: In practice, encoding invariances into models helps sample complexity. In this work, we tighten and generalize theoretical results on how invariances improve sample complexity. In particular, we provide minimax optimal rates for kernel ridge regression on any manifold, with a target function that is invariant to an arbitrary group action on the manifold. Our results hold for (almost) any group action, even groups of positive dimension. For a finite group, the gain increases the\"effective\"number of samples by the group size. For groups of positive dimension, the gain is observed by a reduction in the manifold's dimension, in addition to a factor proportional to the volume of the quotient space. Our proof takes the viewpoint of differential geometry, in contrast to the more common strategy of using invariant polynomials. Hence, this new geometric viewpoint on learning with invariances may be of independent interest.",
+    "neighbors": [
+      1269
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1401,
+    "label": 28,
+    "text": "Title: Near-Field Beam Focusing Pattern and Grating Lobe Characterization for Modular XL-Array\nAbstract: In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1402,
+    "label": 23,
+    "text": "Title: Software Entity Recognition with Noise-Robust Learning\nAbstract: Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models, data, and code for future research.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1403,
+    "label": 10,
+    "text": "Title: Disproving XAI Myths with Formal Methods - Initial Results\nAbstract: The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety-critical domains. eXplainable artificial intelligence (XAI) is at the core of ongoing efforts for delivering trustworthy AI. Unfortunately, XAI is riddled with critical misconceptions, that foster distrust instead of building trust. This paper details some of the most visible misconceptions in XAI, and shows how formal methods have been used, both to disprove those misconceptions, but also to devise practically effective alternatives.",
+    "neighbors": [
+      1845
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1404,
+    "label": 23,
+    "text": "Title: Automated Misconfiguration Repair of Configurable Cyber-Physical Systems with Search: an Industrial Case Study on Elevator Dispatching Algorithms\nAbstract: Real-world Cyber-Physical Systems (CPSs) are usually configurable. Through parameters, it is possible to configure, select or unselect different system functionalities. While this provides high flexibility, it also becomes a source for failures due to misconfigurations. The large number of parameters these systems have and the long test execution time in this context due to the use of simulation-based testing make the manual repair process a cumbersome activity. Subsequently, in this context, automated repairing methods are paramount. In this paper, we propose an approach to automatically repair CPSs\u2019 misconfigurations. Our approach is evaluated with an industrial CPS case study from the elevation domain. Experiments with a real building and data obtained from operation suggests that our approach outperforms a baseline algorithm as well as the state of the practice (i.e., manual repair carried out by domain experts).",
+    "neighbors": [
+      1059,
+      2288
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1405,
+    "label": 5,
+    "text": "Title: Short reasons for long vectors in HPC CPUs: a study based on RISC-V\nAbstract: For years, SIMD/vector units have enhanced the capabilities of modern CPUs in High-Performance Computing (HPC) and mobile technology. Typical commercially-available SIMD units process up to 8 double-precision elements with one instruction. The optimal vector width and its impact on CPU throughput due to memory latency and bandwidth remain challenging research areas. This study examines the behavior of four computational kernels on a RISC-V core connected to a customizable vector unit, capable of operating up to 256 double precision elements per instruction. The four codes have been purposefully selected to represent non-dense workloads: SpMV, BFS, PageRank, FFT. The experimental setup allows us to measure their performance while varying the vector length, the memory latency, and bandwidth. Our results not only show that larger vector lengths allow for better tolerance of limitations in the memory subsystem but also offer hope to code developers beyond dense linear algebra.",
+    "neighbors": [
+      1134
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1406,
+    "label": 35,
+    "text": "Title: From MMU to MPU: adaptation of the Pip kernel to constrained devices\nAbstract: This article presents a hardware-based memory isolation solution for constrained devices. Existing solutions target high-end embedded systems (typically ARM Cortex-A with a Memory Management Unit, MMU) such as seL4 or Pip (formally verified kernels) or target low-end devices such as ACES, MINION, TrustLite, EwoK but with limited flexibility by proposing a single level of isolation. Our approach consists in adapting Pip to inherit its flexibility (multiple levels of isolation) but using the Memory Protection Unit (MPU) instead of the MMU since the MPU is commonly available on constrained embedded systems (typically ARMv7 Cortex-M4 or ARMv8 Cortex-M33 and similar devices). This paper describes our design of Pip-MPU (Pip\u2019s variant based on the MPU) and the rationale behind our choices. We validate our proposal with an implementation on an nRF52840 development kit and we perform various evaluations such as memory footprint, CPU cycles and energy consumption. We demonstrate that although our prototyped Pip-MPU causes a 16% overhead on both performance and energy consumption, it can reduce the attack surface of the accessible application memory from 100% down to 2% and the privileged operations by 99%. Pip-MPU takes less than 10 kB of Flash (6 kB for its core components) and 550 B of RAM.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1407,
+    "label": 26,
+    "text": "Title: Heuristic Modularity Maximization Algorithms for Community Detection Rarely Return an Optimal Partition or Anything Similar\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1408,
+    "label": 24,
+    "text": "Title: DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning\nAbstract: Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving well-performing meta-learning model requires a large amount of training tasks with high-quality meta-data representing the underlying task generalization goal, which is sometimes difficult and expensive to obtain for real applications. Current meta-data-driven meta-learning approaches, however, are fairly hard to train satisfactory meta-models with imperfect training tasks. To address this issue, we suggest a meta-knowledge informed meta-learning (MKIML) framework to improve meta-learning by additionally integrating compensated meta-knowledge into meta-learning process. We preliminarily integrate meta-knowledge into meta-objective via using an appropriate meta-regularization (MR) objective to regularize capacity complexity of the meta-model function class to facilitate better generalization on unseen tasks. As a practical implementation, we introduce data augmentation consistency to encode invariance as meta-knowledge for instantiating MR objective, denoted by DAC-MR. The proposed DAC-MR is hopeful to learn well-performing meta-models from training tasks with noisy, sparse or unavailable meta-data. We theoretically demonstrate that DAC-MR can be treated as a proxy meta-objective used to evaluate meta-model without high-quality meta-data. Besides, meta-data-driven meta-loss objective combined with DAC-MR is capable of achieving better meta-level generalization. 10 meta-learning tasks with different network architectures and benchmarks substantiate the capability of our DAC-MR on aiding meta-model learning. Fine performance of DAC-MR are obtained across all settings, and are well-aligned with our theoretical insights. This implies that our DAC-MR is problem-agnostic, and hopeful to be readily applied to extensive meta-learning problems and tasks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1409,
+    "label": 4,
+    "text": "Title: PrivGraph: Differentially Private Graph Data Publication by Exploiting Community Information\nAbstract: Graph data is used in a wide range of applications, while analyzing graph data without protection is prone to privacy breach risks. To mitigate the privacy risks, we resort to the standard technique of differential privacy to publish a synthetic graph. However, existing differentially private graph synthesis approaches either introduce excessive noise by directly perturbing the adjacency matrix, or suffer significant information loss during the graph encoding process. In this paper, we propose an effective graph synthesis algorithm PrivGraph by exploiting the community information. Concretely, PrivGraph differentially privately partitions the private graph into communities, extracts intra-community and inter-community information, and reconstructs the graph from the extracted graph information. We validate the effectiveness of PrivGraph on six real-world graph datasets and seven commonly used graph metrics.",
+    "neighbors": [
+      230
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1410,
+    "label": 28,
+    "text": "Title: Energy-efficient Rate Splitting for MIMO STAR-RIS-assisted Broadcast Channels with I/Q Imbalance\nAbstract: This paper proposes an energy-efficient scheme for multicell multiple-input, multiple-output (MIMO) simultaneous transmit and reflect (STAR) reconfigurable intelligent surfaces (RIS)-assisted broadcast channels by employing rate splitting (RS) and improper Gaussian signaling (IGS). Regular RISs can only reflect signals. Thus, a regular RIS can assist only when the transmitter and receiver are in the reflection space of the RIS. However, a STAR-RIS can simultaneously transmit and reflect, thus providing a 360-degrees coverage. In this paper, we assume that transceivers may suffer from I/Q imbalance (IQI). To compensate for IQI, we employ IGS. Moreover, we employ RS to manage intracell interference. We show that RIS can significantly improve the energy efficiency (EE) of the system when RIS components are carefully optimized. Additionally, we show that STAR-RIS can significantly outperform a regular RIS when the regular RIS cannot cover all the users. We also show that RS can highly increase the EE comparing to treating interference as noise.",
+    "neighbors": [
+      238,
+      1530,
+      2253
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1411,
+    "label": 30,
+    "text": "Title: Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting\nAbstract: Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output, often referred to as chain-of-thought reasoning (CoT). It is tempting to interpret these CoT explanations as the LLM's process for solving a task. However, we find that CoT explanations can systematically misrepresent the true reason for a model's prediction. We demonstrate that CoT explanations can be heavily influenced by adding biasing features to model inputs -- e.g., by reordering the multiple-choice options in a few-shot prompt to make the answer always\"(A)\"-- which models systematically fail to mention in their explanations. When we bias models toward incorrect answers, they frequently generate CoT explanations supporting those answers. This causes accuracy to drop by as much as 36% on a suite of 13 tasks from BIG-Bench Hard, when testing with GPT-3.5 from OpenAI and Claude 1.0 from Anthropic. On a social-bias task, model explanations justify giving answers in line with stereotypes without mentioning the influence of these social biases. Our findings indicate that CoT explanations can be plausible yet misleading, which risks increasing our trust in LLMs without guaranteeing their safety. CoT is promising for explainability, but our results highlight the need for targeted efforts to evaluate and improve explanation faithfulness.",
+    "neighbors": [
+      1327,
+      1346
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1412,
+    "label": 33,
+    "text": "Title: Model Checking Strategies from Synthesis Over Finite Traces\nAbstract: The innovations in reactive synthesis from {\\em Linear Temporal Logics over finite traces} (LTLf) will be amplified by the ability to verify the correctness of the strategies generated by LTLf synthesis tools. This motivates our work on {\\em LTLf model checking}. LTLf model checking, however, is not straightforward. The strategies generated by LTLf synthesis may be represented using {\\em terminating} transducers or {\\em non-terminating} transducers where executions are of finite-but-unbounded length or infinite length, respectively. For synthesis, there is no evidence that one type of transducer is better than the other since they both demonstrate the same complexity and similar algorithms. In this work, we show that for model checking, the two types of transducers are fundamentally different. Our central result is that LTLf model checking of non-terminating transducers is \\emph{exponentially harder} than that of terminating transducers. We show that the problems are EXPSPACE-complete and PSPACE-complete, respectively. Hence, considering the feasibility of verification, LTLf synthesis tools should synthesize terminating transducers. This is, to the best of our knowledge, the \\emph{first} evidence to use one transducer over the other in LTLf synthesis.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1413,
+    "label": 31,
+    "text": "Title: EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments\nAbstract: EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.",
+    "neighbors": [
+      100
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1414,
+    "label": 24,
+    "text": "Title: A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints\nAbstract: In many applications of Reinforcement Learning (RL), it is critically important that the algorithm performs safely, such that instantaneous hard constraints are satisfied at each step, and unsafe states and actions are avoided. However, existing algorithms for ''safe'' RL are often designed under constraints that either require expected cumulative costs to be bounded or assume all states are safe. Thus, such algorithms could violate instantaneous hard constraints and traverse unsafe states (and actions) in practice. Therefore, in this paper, we develop the first near-optimal safe RL algorithm for episodic Markov Decision Processes with unsafe states and actions under instantaneous hard constraints and the linear mixture model. It not only achieves a regret $\\tilde{O}(\\frac{d H^3 \\sqrt{dK}}{\\Delta_c})$ that tightly matches the state-of-the-art regret in the setting with only unsafe actions and nearly matches that in the unconstrained setting, but is also safe at each step, where $d$ is the feature-mapping dimension, $K$ is the number of episodes, $H$ is the number of steps in each episode, and $\\Delta_c$ is a safety-related parameter. We also provide a lower bound $\\tilde{\\Omega}(\\max\\{dH \\sqrt{K}, \\frac{H}{\\Delta_c^2}\\})$, which indicates that the dependency on $\\Delta_c$ is necessary. Further, both our algorithm design and regret analysis involve several novel ideas, which may be of independent interest.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1415,
+    "label": 5,
+    "text": "Title: Morpheus unleashed: Fast cross-platform SpMV on emerging architectures\nAbstract: Sparse matrices and linear algebra are at the heart of scientific simulations. Over the years, more than 70 sparse matrix storage formats have been developed, targeting a wide range of hardware architectures and matrix types, each of which exploit the particular strengths of an architecture, or the specific sparsity patterns of the matrices. In this work, we explore the suitability of storage formats such as COO, CSR and DIA for emerging architectures such as AArch64 CPUs and FPGAs. In addition, we detail hardware-specific optimisations to these targets and evaluate the potential of each contribution to be integrated into Morpheus, a modern library that provides an abstraction of sparse matrices (currently) across x86 CPUs and NVIDIA/AMD GPUs. Finally, we validate our work by comparing the performance of the Morpheus-enabled HPCG benchmark against vendor-optimised implementations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1416,
+    "label": 17,
+    "text": "Title: Deep Dynamic Cloud Lighting\nAbstract: Sky illumination is a core source of lighting in rendering, and a substantial amount of work has been developed to simulate lighting from clear skies. However, in reality, clouds substantially alter the appearance of the sky and subsequently change the scene's illumination. While there have been recent advances in developing sky models which include clouds, these all neglect cloud movement which is a crucial component of cloudy sky appearance. In any sort of video or interactive environment, it can be expected that clouds will move, sometimes quite substantially in a short period of time. Our work proposes a solution to this which enables whole-sky dynamic cloud synthesis for the first time. We achieve this by proposing a multi-timescale sky appearance model which learns to predict the sky illumination over various timescales, and can be used to add dynamism to previous static, cloudy sky lighting approaches.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1417,
+    "label": 6,
+    "text": "Title: Computationally efficient human body modelling for real time motion comfort assessment\nAbstract: Due to the complexity of the human body and its neuromuscular stabilization, it has been challenging to efficiently and accurately predict human motion and capture posture while being driven. Existing simple models of the seated human body are mostly two-dimensional and developed in the mid-sagittal plane ex-posed to in-plane excitation. Such models capture fore-aft and vertical motion but not the more complex 3D motions due to lateral loading. Advanced 3D full-body active human models (AHMs), such as in MADYMO, can be used for comfort analysis and to investigate how vibrations influence the human body while being driven. However, such AHMs are very time-consuming due to their complexity. To effectively analyze motion comfort, a computationally efficient and accurate three dimensional (3D) human model, which runs faster than real-time, is presented. The model's postural stabilization parameters are tuned using available 3D vibration data for head, trunk and pelvis translation and rotation. A comparison between AHM and EHM is conducted regarding human body kinematics. According to the results, the EHM model configuration with two neck joints, two torso bending joints, and a spinal compression joint accurately predicts body kinematics.",
+    "neighbors": [
+      1599
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1418,
+    "label": 16,
+    "text": "Title: MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation\nAbstract: Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes. Project webpage: https://multidiffusion.github.io",
+    "neighbors": [
+      357,
+      706,
+      1588,
+      1789,
+      1902,
+      2221,
+      2306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1419,
+    "label": 24,
+    "text": "Title: Learning Capacity: A Measure of the Effective Dimensionality of a Model\nAbstract: We exploit a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to define a\"learning capacity'' which is a measure of the effective dimensionality of a model. We show that the learning capacity is a tiny fraction of the number of parameters for many deep networks trained on typical datasets, depends upon the number of samples used for training, and is numerically consistent with notions of capacity obtained from the PAC-Bayesian framework. The test error as a function of the learning capacity does not exhibit double descent. We show that the learning capacity of a model saturates at very small and very large sample sizes; this provides guidelines, as to whether one should procure more data or whether one should search for new architectures, to improve performance. We show how the learning capacity can be used to understand the effective dimensionality, even for non-parametric models such as random forests and $k$-nearest neighbor classifiers.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1420,
+    "label": 16,
+    "text": "Title: FateZero: Fusing Attentions for Zero-shot Text-based Video Editing\nAbstract: The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. To edit videos consistently, we propose several techniques based on the pre-trained models. Firstly, in contrast to the straightforward DDIM inversion technique, our approach captures intermediate attention maps during inversion, which effectively retain both structural and motion information. These maps are directly fused in the editing process rather than generated during denoising. To further minimize semantic leakage of the source video, we then fuse self-attentions with a blending mask obtained by cross-attention features from the source prompt. Furthermore, we have implemented a reform of the self-attention mechanism in denoising UNet by introducing spatial-temporal attention to ensure frame consistency. Yet succinct, our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model. We also have a better zero-shot shape-aware editing ability based on the text-to-video model. Extensive experiments demonstrate our superior temporal consistency and editing capability than previous works.",
+    "neighbors": [
+      848,
+      860,
+      1020,
+      1173,
+      1179,
+      1590,
+      1707,
+      1902,
+      1935,
+      2135,
+      2190
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1421,
+    "label": 10,
+    "text": "Title: Monitoring Algorithmic Fairness under Partial Observations\nAbstract: As AI and machine-learned software are used increasingly for making decisions that affect humans, it is imperative that they remain fair and unbiased in their decisions. To complement design-time bias mitigation measures, runtime verification techniques have been introduced recently to monitor the algorithmic fairness of deployed systems. Previous monitoring techniques assume full observability of the states of the (unknown) monitored system. Moreover, they can monitor only fairness properties that are specified as arithmetic expressions over the probabilities of different events. In this work, we extend fairness monitoring to systems modeled as partially observed Markov chains (POMC), and to specifications containing arithmetic expressions over the expected values of numerical functions on event sequences. The only assumptions we make are that the underlying POMC is aperiodic and starts in the stationary distribution, with a bound on its mixing time being known. These assumptions enable us to estimate a given property for the entire distribution of possible executions of the monitored POMC, by observing only a single execution. Our monitors observe a long run of the system and, after each new observation, output updated PAC-estimates of how fair or biased the system is. The monitors are computationally lightweight and, using a prototype implementation, we demonstrate their effectiveness on several real-world examples.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1422,
+    "label": 4,
+    "text": "Title: Impacts and Risk of Generative AI Technology on Cyber Defense\nAbstract: Generative Artificial Intelligence (GenAI) has emerged as a powerful technology capable of autonomously producing highly realistic content in various domains, such as text, images, audio, and videos. With its potential for positive applications in creative arts, content generation, virtual assistants, and data synthesis, GenAI has garnered significant attention and adoption. However, the increasing adoption of GenAI raises concerns about its potential misuse for crafting convincing phishing emails, generating disinformation through deepfake videos, and spreading misinformation via authentic-looking social media posts, posing a new set of challenges and risks in the realm of cybersecurity. To combat the threats posed by GenAI, we propose leveraging the Cyber Kill Chain (CKC) to understand the lifecycle of cyberattacks, as a foundational model for cyber defense. This paper aims to provide a comprehensive analysis of the risk areas introduced by the offensive use of GenAI techniques in each phase of the CKC framework. We also analyze the strategies employed by threat actors and examine their utilization throughout different phases of the CKC, highlighting the implications for cyber defense. Additionally, we propose GenAI-enabled defense strategies that are both attack-aware and adaptive. These strategies encompass various techniques such as detection, deception, and adversarial training, among others, aiming to effectively mitigate the risks posed by GenAI-induced cyber threats.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1423,
+    "label": 16,
+    "text": "Title: Learning Context-aware Classifier for Semantic Segmentation\nAbstract: Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing. Different from the mainstream literature where the efficacy of strong backbones and effective decoder heads has been well studied, in this paper, additional contextual hints are instead exploited via learning a context-aware classifier whose content is data-conditioned, decently adapting to different latent distributions. Since only the classifier is dynamically altered, our method is model-agnostic and can be easily applied to generic segmentation models. Notably, with only negligible additional parameters and +2\\% inference time, decent performance gain has been achieved on both small and large models with challenging benchmarks, manifesting substantial practical merits brought by our simple yet effective method. The implementation is available at https://github.com/tianzhuotao/CAC.",
+    "neighbors": [
+      1913
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1424,
+    "label": 30,
+    "text": "Title: A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery\nAbstract: Intent Detection is one of the tasks of the Natural Language Understanding (NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of Domain (OOD) inputs may run these systems into a problem. On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue systems. The creation of a labeled dataset is time-consuming and needs human resources. The purpose of this article is to address mentioned problems. The task of identifying OOD/OOS inputs is named OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of OOD inputs is well known by Intent Discovery. In OOD intent detection part, we make use of a Variational Autoencoder to distinguish between known and unknown intents independent of input data distribution. After that, an unsupervised clustering method is used to discover different unknown intents underlying OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS representations to make distances between representations more meaning full for clustering. Our results show that the proposed model for both OOD/OOS Intent Detection and Intent Discovery achieves great results and passes baselines in English and Persian languages.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1425,
+    "label": 28,
+    "text": "Title: Goal-oriented Tensor: Beyond Age of Information Towards Semantics-Empowered Goal-Oriented Communications\nAbstract: Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system's decision-making utility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-making utility. Leveraging the GoT, we consider a sampler&decision-maker pair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent the curse of dimensionality in obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposed sampler&decision-maker co-design surpasses the current literature on AoI and its variants in terms of both goal achievement utility and sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1426,
+    "label": 24,
+    "text": "Title: Consistency Models\nAbstract: Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by directly mapping noise to data. They support fast one-step generation by design, while still allowing multistep sampling to trade compute for sample quality. They also support zero-shot data editing, such as image inpainting, colorization, and super-resolution, without requiring explicit training on these tasks. Consistency models can be trained either by distilling pre-trained diffusion models, or as standalone generative models altogether. Through extensive experiments, we demonstrate that they outperform existing distillation techniques for diffusion models in one- and few-step sampling, achieving the new state-of-the-art FID of 3.55 on CIFAR-10 and 6.20 on ImageNet 64x64 for one-step generation. When trained in isolation, consistency models become a new family of generative models that can outperform existing one-step, non-adversarial generative models on standard benchmarks such as CIFAR-10, ImageNet 64x64 and LSUN 256x256.",
+    "neighbors": [
+      736,
+      1582,
+      1776,
+      2049,
+      2085
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1427,
+    "label": 30,
+    "text": "Title: Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction\nAbstract: Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1428,
+    "label": 29,
+    "text": "Title: Splitter Orderings for Probabilistic Bisimulation\nAbstract: Model checking has been proposed as a formal verification approach for analyzing computer-based and cyber-physical systems. The state space explosion problem is the main obstacle for applying this approach for sophisticated systems. Bisimulation minimization is a prominent method for reducing the number of states in a labeled transition system and is used to alleviate the challenges of the state space explosion problem. For systems with stochastic behaviors, probabilistic bisimulation is used to reduce a given model to its minimized equivalent one. In recent years, several techniques have been proposed to reduce the time complexity of the iterative methods for computing probabilistic bisimulation of stochastic systems with nondeterministic behaviors. In this paper, we propose several techniques to accelerate iterative processes to partition the state space of a given probabilistic model to its bisimulation classes. The first technique applies two ordering heuristics for choosing splitter blocks. The second technique uses hash tables to reduce the running time and the average time complexity of the standard iterative method. The proposed approaches are implemented and run on several conventional case studies and reduce the running time by one order of magnitude on average.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1429,
+    "label": 10,
+    "text": "Title: Pure Monte Carlo Counterfactual Regret Minimization\nAbstract: Counterfactual Regret Minimization (CFR) and its variants are the best algorithms so far for solving large-scale incomplete information games. Building upon CFR, this paper proposes a new algorithm named Pure CFR (PCFR) for achieving better performance. PCFR can be seen as a combination of CFR and Fictitious Play (FP), inheriting the concept of counterfactual regret (value) from CFR, and using the best response strategy instead of the regret matching strategy for the next iteration. Our theoretical proof that PCFR can achieve Blackwell approachability enables PCFR's ability to combine with any CFR variant including Monte Carlo CFR (MCCFR). The resultant Pure MCCFR (PMCCFR) can significantly reduce time and space complexity. Particularly, the convergence speed of PMCCFR is at least three times more than that of MCCFR. In addition, since PMCCFR does not pass through the path of strictly dominated strategies, we developed a new warm-start algorithm inspired by the strictly dominated strategies elimination method. Consequently, the PMCCFR with new warm start algorithm can converge by two orders of magnitude faster than the CFR+ algorithm.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1430,
+    "label": 30,
+    "text": "Title: Preference Ranking Optimization for Human Alignment\nAbstract: Large language models (LLMs) often contain misleading content, emphasizing the need to align them with human values to ensure secur AI systems. Reinforcement learning from human feedback (RLHF) has been employed to achieve this alignment by combining a reward model, typically based on Bradley-Terry paired comparison, with an RL algorithm such as Proximal Policy Optimization (PPO) to optimize LLM responses. However, RLHF exhibits complexity, instability, and sensitivity to hyperparameters. In this paper, we propose Preference Ranking Optimization (PRO) as an alternative to PPO for directly aligning LLMs with the Bradley-Terry comparison. PRO extends the pairwise Bradley-Terry comparison to accommodate preference rankings of any length. By iteratively contrasting the likelihood of generating responses, PRO instructs the LLM to prioritize the best response while progressively ranking the remaining responses. In this manner, PRO effectively transforms human alignment into aligning the probability ranking of $n$ responses generated by LLM with the preference ranking of humans towards these responses. Experiments have shown that PRO outperforms existing alignment algorithms, achieving comparable results to ChatGPT and human responses through automatic-based, reward-based, GPT-4, and human evaluations. Furthermore, we demonstrate that longer, more diverse, and higher-quality preference ranking sequences can consistently enhance the performance of human alignment.",
+    "neighbors": [
+      363,
+      566,
+      811,
+      855,
+      896,
+      1039,
+      1044,
+      1052,
+      1233,
+      1346,
+      1548,
+      1647,
+      1863
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1431,
+    "label": 23,
+    "text": "Title: A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges\nAbstract: Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1432,
+    "label": 3,
+    "text": "Title: Silence Speaks Volumes: Re-weighting Techniques for Under-Represented Users in Fake News Detection\nAbstract: Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform. However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. We propose to leverage re-weighting techniques to make the silent majority heard, and in turn, investigate whether the cues from these users can improve the performance of the current models for the downstream task of fake news detection.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1433,
+    "label": 6,
+    "text": "Title: Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces\nAbstract: User Experience (UX) professionals need to be able to analyze large amounts of usage data on their own to make evidence-based design decisions. However, the design process for In-Vehicle Information Systems (IVISs) lacks data-driven support and effective tools for visualizing and analyzing user interaction data. Therefore, we propose ICEBOAT1, an interactive visualization tool tailored to the needs of automotive UX experts to effectively and efficiently evaluate driver interactions with IVISs. ICEBOAT visualizes telematics data collected from production line vehicles, allowing UX experts to perform task-specific analyses. Following a mixed methods User-Centered Design (UCD) approach, we conducted an interview study (N=4) to extract the domain specific information and interaction needs of automotive UX experts and used a co-design approach (N=4) to develop an interactive analysis tool. Our evaluation (N=12) shows that ICEBOAT enables UX experts to efficiently generate knowledge that facilitates data-driven design decisions.",
+    "neighbors": [
+      1842
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1434,
+    "label": 16,
+    "text": "Title: NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching\nAbstract: We present Neural Correspondence Prior (NCP), a new paradigm for computing correspondences between 3D shapes. Our approach is fully unsupervised and can lead to high-quality correspondences even in challenging cases such as sparse point clouds or non-isometric meshes, where current methods fail. Our first key observation is that, in line with neural priors observed in other domains, recent network architectures on 3D data, even without training, tend to produce pointwise features that induce plausible maps between rigid or non-rigid shapes. Secondly, we show that given a noisy map as input, training a feature extraction network with the input map as supervision tends to remove artifacts from the input and can act as a powerful correspondence denoising mechanism, both between individual pairs and within a collection. With these observations in hand, we propose a two-stage unsupervised paradigm for shape matching by (i) performing unsupervised training by adapting an existing approach to obtain an initial set of noisy matches, and (ii) using these matches to train a network in a supervised manner. We demonstrate that this approach significantly improves the accuracy of the maps, especially when trained within a collection. We show that NCP is data-efficient, fast, and achieves state-of-the-art results on many tasks. Our code can be found online: https://github.com/pvnieo/NCP.",
+    "neighbors": [
+      2020
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1435,
+    "label": 24,
+    "text": "Title: Improving physics-informed neural networks with meta-learned optimization\nAbstract: We show that the error achievable using physics-informed neural networks for solving systems of differential equations can be substantially reduced when these networks are trained using meta-learned optimization methods rather than to using fixed, hand-crafted optimizers as traditionally done. We choose a learnable optimization method based on a shallow multi-layer perceptron that is meta-trained for specific classes of differential equations. We illustrate meta-trained optimizers for several equations of practical relevance in mathematical physics, including the linear advection equation, Poisson's equation, the Korteweg--de Vries equation and Burgers' equation. We also illustrate that meta-learned optimizers exhibit transfer learning abilities, in that a meta-trained optimizer on one differential equation can also be successfully deployed on another differential equation.",
+    "neighbors": [
+      323
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1436,
+    "label": 30,
+    "text": "Title: ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey\nAbstract: ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing (NLP) and has pushed the boundaries of LLM capabilities. ChatGPT has played a pivotal role in enabling widespread public interaction with generative artificial intelligence (GAI) on a large scale. It has also sparked research interest in developing similar technologies and investigating their applications and implications. In this paper, our primary goal is to provide a concise survey on the current lines of research on ChatGPT and its evolution. We considered both the glass box and black box views of ChatGPT, encompassing the components and foundational elements of the technology, as well as its applications, impacts, and implications. The glass box approach focuses on understanding the inner workings of the technology, and the black box approach embraces it as a complex system, and thus examines its inputs, outputs, and effects. This paves the way for a comprehensive exploration of the technology and provides a road map for further research and experimentation. We also lay out essential foundational literature on LLMs and GAI in general and their connection with ChatGPT. This overview sheds light on existing and missing research lines in the emerging field of LLMs, benefiting both public users and developers. Furthermore, the paper delves into the broad spectrum of applications and significant concerns in fields such as education, research, healthcare, finance, etc.",
+    "neighbors": [
+      42,
+      971,
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+      1133,
+      1142,
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+      1487,
+      1663,
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+      1727,
+      1758,
+      1765,
+      2113,
+      2114,
+      2184,
+      2244,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1437,
+    "label": 24,
+    "text": "Title: Simplifying Subgraph Representation Learning for Scalable Link Prediction\nAbstract: Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally expensive, and not scalable to large-scale graphs due to expensive subgraph-level operations. To unlock the scalability of SGRLs, we propose a new class of SGRLs, that we call Scalable Simplified SGRL (S3GRL). Aimed at faster training and inference, S3GRL simplifies the message passing and aggregation operations in each link's subgraph. S3GRL, as a scalability framework, accommodates various subgraph sampling strategies and diffusion operators to emulate computationally-expensive SGRLs. We propose multiple instances of S3GRL and empirically study them on small to large-scale graphs. Our extensive experiments demonstrate that the proposed S3GRL models scale up SGRLs without significant performance compromise (even with considerable gains in some cases), while offering substantially lower computational footprints (e.g., multi-fold inference and training speedup).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1438,
+    "label": 20,
+    "text": "Title: Optimal area-sensitive bounds for polytope approximation\nAbstract: Approximating convex bodies is a fundamental question in geometry and has applications to a wide variety of optimization problems. Given a convex body K in REd for fixed d, the objective is to minimize the number of vertices or facets of an approximating polytope for a given Hausdorff error \u03b5. The best known uniform bound, due to Dudley (1974), shows that O((diam(K)/\u03b5)(d-1)/2) facets suffice. While this bound is optimal in the case of a Euclidean ball, it is far from optimal for skinny convex bodies. We show that, under the assumption that the width of the body in any direction is at least \u03b5, it is possible to approximate a convex body using O(\u221aarea(K)/\u03b5(d-1)/2) facets, where area(K) is the surface area of the body. This bound is never worse than the previous bound and may be significantly better for skinny bodies. This bound is provably optimal in the worst case and improves upon our earlier result (which appeared in SODA 2012).\n Our improved bound arises from a novel approach to sampling points on the boundary of a convex body in order to stab all (dual) caps of a given width. This approach involves the application of an elegant concept from the theory of convex bodies, called Macbeath regions. While Macbeath regions are defined in terms of volume considerations, we show that by applying them to both the original body and its dual, and then combining this with known bounds on the Mahler volume, it is possible to achieve the desired width-based sampling.",
+    "neighbors": [
+      1244,
+      1615
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1439,
+    "label": 24,
+    "text": "Title: Continuous Indeterminate Probability Neural Network\nAbstract: This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables. Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem can be solved. Our contributions are Four-fold. First, we derive the analytical solution of the posterior calculation of continuous latent random variables and propose a general classification model (CIPNN). Second, we propose a general auto-encoder called CIPAE - Continuous Indeterminate Probability Auto-Encoder, the decoder part is not a neural network and uses a fully probabilistic inference model for the first time. Third, we propose a new method to visualize the latent random variables, we use one of N dimensional latent variables as a decoder to reconstruct the input image, which can work even for classification tasks, in this way, we can see what each latent variable has learned. Fourth, IPNN has shown great classification capability, CIPNN has pushed this classification capability to infinity. Theoretical advantages are reflected in experimental results.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1440,
+    "label": 22,
+    "text": "Title: An Evaluation Algorithm for Datalog with Equality\nAbstract: We describe an evaluation algorithm for relational Horn logic (RHL). RHL extends Datalog with quantification over sorts, existential quantification in conclusions and, crucially, the ability to infer equalities. These capabilities allow RHL evaluation to subsume and expand applications of Datalog and congruence closure algorithms. We explain how aspects of a fast congruence closure algorithm can be incorporated into Datalog evaluation to obtain an efficient RHL evaluation algorithm. We then sketch how Steensgaard's points-to analysis and type inference can be implemented using RHL evaluation. RHL and the evaluation algorithm described here are the foundation of the Eqlog Datalog engine.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1441,
+    "label": 3,
+    "text": "Title: Agency and legibility for artists through Experiential AI\nAbstract: Experiential AI is an emerging research field that addresses the challenge of making AI tangible and explicit, both to fuel cultural experiences for audiences, and to make AI systems more accessible to human understanding. The central theme is how artists, scientists and other interdisciplinary actors can come together to understand and communicate the functionality of AI, ML and intelligent robots, their limitations, and consequences, through informative and compelling experiences. It provides an approach and methodology for the arts and tangible experiences to mediate between impenetrable computer code and human understanding, making not just AI systems but also their values and implications more transparent, and therefore accountable. In this paper, we report on an empirical case study of an experiential AI system designed for creative data exploration of a user-defined dimension, to enable creators to gain more creative control over the AI process. We discuss how experiential AI can increase legibility and agency for artists, and how the arts can provide creative strategies and methods which can add to the toolbox for human-centred XAI.",
+    "neighbors": [
+      1623
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1442,
+    "label": 4,
+    "text": "Title: A Survey on Malware Detection with Graph Representation Learning\nAbstract: Malware detection has become a major concern due to the increasing number and complexity of malware. Traditional detection methods based on signatures and heuristics are used for malware detection, but unfortunately, they suffer from poor generalization to unknown attacks and can be easily circumvented using obfuscation techniques. In recent years, Machine Learning (ML) and notably Deep Learning (DL) achieved impressive results in malware detection by learning useful representations from data and have become a solution preferred over traditional methods. More recently, the application of such techniques on graph-structured data has achieved state-of-the-art performance in various domains and demonstrates promising results in learning more robust representations from malware. Yet, no literature review focusing on graph-based deep learning for malware detection exists. In this survey, we provide an in-depth literature review to summarize and unify existing works under the common approaches and architectures. We notably demonstrate that Graph Neural Networks (GNNs) reach competitive results in learning robust embeddings from malware represented as expressive graph structures, leading to an efficient detection by downstream classifiers. This paper also reviews adversarial attacks that are utilized to fool graph-based detection methods. Challenges and future research directions are discussed at the end of the paper.",
+    "neighbors": [
+      366
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1443,
+    "label": 27,
+    "text": "Title: Certifiably Correct Range-Aided SLAM\nAbstract: We present the first algorithm to efficiently compute certifiably optimal solutions to range-aided simultaneous localization and mapping (RA-SLAM) problems. Robotic navigation systems increasingly incorporate point-to-point ranging sensors, leading to state estimation problems in the form of RA-SLAM. However, the RA-SLAM problem is significantly more difficult to solve than traditional pose-graph SLAM: ranging sensor models introduce non-convexity and single range measurements do not uniquely determine the transform between the involved sensors. As a result, RA-SLAM inference is sensitive to initial estimates yet lacks reliable initialization techniques. Our approach, certifiably correct RA-SLAM (CORA), leverages a novel quadratically constrained quadratic programming (QCQP) formulation of RA-SLAM to relax the RA-SLAM problem to a semidefinite program (SDP). CORA solves the SDP efficiently using the Riemannian Staircase methodology; the SDP solution provides both (i) a lower bound on the RA-SLAM problem's optimal value, and (ii) an approximate solution of the RA-SLAM problem, which can be subsequently refined using local optimization. CORA applies to problems with arbitrary pose-pose, pose-landmark, and ranging measurements and, due to using convex relaxation, is insensitive to initialization. We evaluate CORA on several real-world problems. In contrast to state-of-the-art approaches, CORA is able to obtain high-quality solutions on all problems despite being initialized with random values. Additionally, we study the tightness of the SDP relaxation with respect to important problem parameters: the number of (i) robots, (ii) landmarks, and (iii) range measurements. These experiments demonstrate that the SDP relaxation is often tight and reveal relationships between graph rigidity and the tightness of the SDP relaxation.",
+    "neighbors": [
+      969,
+      1224,
+      1848,
+      1973
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1444,
+    "label": 11,
+    "text": "Title: Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories\nAbstract: In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and trajectory of each agent may be unavailable to the other agents. Prior work on using partial observations to infer the costs in dynamic games assumes an open-loop information pattern. In this work, we demonstrate that the feedback Nash equilibrium concept is more expressive and encodes more complex behavior. It is desirable to develop specific tools for inferring players' objectives in feedback games. Therefore, we consider the dynamic game cost inference problem under the feedback information pattern, using only partial state observations and incomplete trajectory data. To this end, we first propose an inverse feedback game loss function, whose minimizer yields a feedback Nash equilibrium state trajectory closest to the observation data. We characterize the landscape and differentiability of the loss function. Given the difficulty of obtaining the exact gradient, our main contribution is an efficient gradient approximator, which enables a novel inverse feedback game solver that minimizes the loss using first-order optimization. In thorough empirical evaluations, we demonstrate that our algorithm converges reliably and has better robustness and generalization performance than the open-loop baseline method when the observation data reflects a group of players acting in a feedback Nash game.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1445,
+    "label": 5,
+    "text": "Title: Flow-Based Energy Services Composition\nAbstract: We propose a novel spatio-temporal service composition framework for crowdsourcing multiple IoT energy services to cater to multiple energy requests. We define a new energy service model to leverage the wearable-based energy and wireless power transfer technologies. We reformulate the problem of spatio-temporal service composition to provision multiple energy requests as a matching problem. We leverage the fragmented nature of energy to offer partial services to maximize the utilization of energy services. We propose EnergyFlowComp, a modified Maximum Flow matching algorithm that efficiently provisions IoT energy services to accommodate multiple energy requests. Moreover, we propose PartialFlowComp, an extension of the EnergyFlowComp approach that considers the partial-temporal overlap between services and requests in provisioning. We conduct an extensive set of experiments to assess the effectiveness and efficiency of the proposed framework.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1446,
+    "label": 31,
+    "text": "Title: A Survey on Causal Inference for Recommendation\nAbstract: Recently, causal inference has attracted increasing attention from researchers of recommender systems (RS), which analyzes the relationship between a cause and its effect and has a wide range of real-world applications in multiple fields. Causal inference can model the causality in recommender systems like confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, these surveys introduce approaches in a relatively isolated way and lack theoretical analysis of existing methods. Due to the unfamiliarity with causality to RS researchers, it is both necessary and challenging to comprehensively review the relevant studies from the perspective of causal theory, which might be instructive for the readers to propose new approaches in practice. This survey attempts to provide a systematic review of up-to-date papers in this area from a theoretical standpoint. Firstly, we introduce the fundamental concepts of causal inference as the basis of the following review. Then we propose a new taxonomy from the perspective of causal techniques and further discuss technical details about how existing methods apply causal inference to address specific recommender issues. Finally, we highlight some promising directions for future research in this field.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1447,
+    "label": 30,
+    "text": "Title: Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus\nAbstract: The emotion detection technology to enhance human decision-making is an important research issue for real-world applications, but real-life emotion datasets are relatively rare and small. The experiments conducted in this paper use the CEMO, which was collected in a French emergency call center. Two pre-trained models based on speech and text were fine-tuned for speech emotion recognition. Using pre-trained Transformer encoders mitigates our data's limited and sparse nature. This paper explores the different fusion strategies of these modality-specific models. In particular, fusions with and without cross-attention mechanisms were tested to gather the most relevant information from both the speech and text encoders. We show that multimodal fusion brings an absolute gain of 4-9% with respect to either single modality and that the Symmetric multi-headed cross-attention mechanism performed better than late classical fusion approaches. Our experiments also suggest that for the real-life CEMO corpus, the audio component encodes more emotive information than the textual one.",
+    "neighbors": [
+      827
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1448,
+    "label": 24,
+    "text": "Title: Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity\nAbstract: Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homogeneous, which is seldom true in practical settings. Data heterogeneity makes Byzantine ML considerably more challenging, since a Byzantine machine can hardly be distinguished from a non-Byzantine outlier. A few solutions have been proposed to tackle this issue, but these provide suboptimal probabilistic guarantees and fare poorly in practice. This paper closes the theoretical gap, achieving optimality and inducing good empirical results. In fact, we show how to automatically adapt existing solutions for (homogeneous) Byzantine ML to the heterogeneous setting through a powerful mechanism, we call nearest neighbor mixing (NNM), which boosts any standard robust distributed gradient descent variant to yield optimal Byzantine resilience under heterogeneity. We obtain similar guarantees (in expectation) by plugging NNM in the distributed stochastic heavy ball method, a practical substitute to distributed gradient descent. We obtain empirical results that significantly outperform state-of-the-art Byzantine ML solutions.",
+    "neighbors": [
+      675,
+      1056
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1449,
+    "label": 16,
+    "text": "Title: K-Planes: Explicit Radiance Fields in Space, Time, and Appearance\nAbstract: We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses planes to represent a d-dimensional scene, providing a seamless way to go from static (d = 3) to dynamic (d= 4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smoothness and multi-resolution spatial structure, and induces a natural decomposition of static and dynamic components of a scene. We use a linear feature decoder with a learned color basis that yields similar performance as a nonlinear black-box MLP decoder. Across a range of synthetic and real, static and dynamic, fixed and varying appearance scenes, k-planes yields competitive and often state-of-the-art recon- struction fidelity with low memory usage, achieving 1000x compression over a full 4D grid, and fast optimization with a pure PyTorch implementation. For video results and code, please see sarafridov.github.io/K-Planes.",
+    "neighbors": [
+      330
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1450,
+    "label": 30,
+    "text": "Title: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers\nAbstract: We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that Vall-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. See https://aka.ms/valle for demos of our work.",
+    "neighbors": [
+      170,
+      1192,
+      1307,
+      1765,
+      1958,
+      2226
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1451,
+    "label": 27,
+    "text": "Title: VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models\nAbstract: Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to synthesize robot trajectories, i.e., a dense sequence of 6-DoF end-effector waypoints, for a large variety of manipulation tasks given an open-set of instructions and an open-set of objects. We achieve this by first observing that LLMs excel at inferring affordances and constraints given a free-form language instruction. More importantly, by leveraging their code-writing capabilities, they can interact with a visual-language model (VLM) to compose 3D value maps to ground the knowledge into the observation space of the agent. The composed value maps are then used in a model-based planning framework to zero-shot synthesize closed-loop robot trajectories with robustness to dynamic perturbations. We further demonstrate how the proposed framework can benefit from online experiences by efficiently learning a dynamics model for scenes that involve contact-rich interactions. We present a large-scale study of the proposed method in both simulated and real-robot environments, showcasing the ability to perform a large variety of everyday manipulation tasks specified in free-form natural language. Project website: https://voxposer.github.io",
+    "neighbors": [
+      25,
+      638,
+      887,
+      1047,
+      1233,
+      1863,
+      1877,
+      1893,
+      1933,
+      2029,
+      2136
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1452,
+    "label": 36,
+    "text": "Title: Learning in Repeated Multi-Unit Pay-As-Bid Auctions\nAbstract: Motivated by Carbon Emissions Trading Schemes, Treasury Auctions, and Procurement Auctions, which all involve the auctioning of homogeneous multiple units, we consider the problem of learning how to bid in repeated multi-unit pay-as-bid auctions. In each of these auctions, a large number of (identical) items are to be allocated to the largest submitted bids, where the price of each of the winning bids is equal to the bid itself. The problem of learning how to bid in pay-as-bid auctions is challenging due to the combinatorial nature of the action space. We overcome this challenge by focusing on the offline setting, where the bidder optimizes their vector of bids while only having access to the past submitted bids by other bidders. We show that the optimal solution to the offline problem can be obtained using a polynomial time dynamic programming (DP) scheme. We leverage the structure of the DP scheme to design online learning algorithms with polynomial time and space complexity under full information and bandit feedback settings. We achieve an upper bound on regret of $O(M\\sqrt{T\\log |\\mathcal{B}|})$ and $O(M\\sqrt{|\\mathcal{B}|T\\log |\\mathcal{B}|})$ respectively, where $M$ is the number of units demanded by the bidder, $T$ is the total number of auctions, and $|\\mathcal{B}|$ is the size of the discretized bid space. We accompany these results with a regret lower bound, which match the linear dependency in $M$. Our numerical results suggest that when all agents behave according to our proposed no regret learning algorithms, the resulting market dynamics mainly converge to a welfare maximizing equilibrium where bidders submit uniform bids. Lastly, our experiments demonstrate that the pay-as-bid auction consistently generates significantly higher revenue compared to its popular alternative, the uniform price auction.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1453,
+    "label": 24,
+    "text": "Title: Generating Synthetic Datasets by Interpolating along Generalized Geodesics\nAbstract: Data for pretraining machine learning models often consists of collections of heterogeneous datasets. Although training on their union is reasonable in agnostic settings, it might be suboptimal when the target domain -- where the model will ultimately be used -- is known in advance. In that case, one would ideally pretrain only on the dataset(s) most similar to the target one. Instead of limiting this choice to those datasets already present in the pretraining collection, here we explore extending this search to all datasets that can be synthesized as `combinations' of them. We define such combinations as multi-dataset interpolations, formalized through the notion of generalized geodesics from optimal transport (OT) theory. We compute these geodesics using a recent notion of distance between labeled datasets, and derive alternative interpolation schemes based on it: using either barycentric projections or optimal transport maps, the latter computed using recent neural OT methods. These methods are scalable, efficient, and -- notably -- can be used to interpolate even between datasets with distinct and unrelated label sets. Through various experiments in transfer learning in computer vision, we demonstrate this is a promising new approach for targeted on-demand dataset synthesis.",
+    "neighbors": [
+      1382
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1454,
+    "label": 25,
+    "text": "Title: Learning From Yourself: A Self-Distillation Method for Fake Speech Detection\nAbstract: In this paper, we propose a novel self-distillation method for fake speech detection (FSD), which can significantly improve the performance of FSD without increasing the model complexity. For FSD, some fine-grained information is very important, such as spectrogram defects, mute segments, and so on, which are often perceived by shallow networks. However, shallow networks have much noise, which can not capture this very well. To address this problem, we propose using the deepest network instruct shallow network for enhancing shallow networks. Specifically, the networks of FSD are divided into several segments, the deepest network being used as the teacher model, and all shallow networks become multiple student models by adding classifiers. Meanwhile, the distillation path between the deepest network feature and shallow network features is used to reduce the feature difference. A series of experimental results on the ASVspoof 2019 LA and PA datasets show the effectiveness of the proposed method, with significant improvements compared to the baseline.",
+    "neighbors": [
+      930
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1455,
+    "label": 30,
+    "text": "Title: CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation\nAbstract: End-to-end speech translation (ST) is the task of translating speech signals in the source language into text in the target language. As a cross-modal task, end-to-end ST is difficult to train with limited data. Existing methods often try to transfer knowledge from machine translation (MT), but their performances are restricted by the modality gap between speech and text. In this paper, we propose Cross-modal Mixup via Optimal Transport (CMOT) to overcome the modality gap. We find the alignment between speech and text sequences via optimal transport and then mix up the sequences from different modalities at a token level using the alignment. Experiments on the MuST-C ST benchmark demonstrate that CMOT achieves an average BLEU of 30.0 in 8 translation directions, outperforming previous methods. Further analysis shows CMOT can adaptively find the alignment between modalities, which helps alleviate the modality gap between speech and text.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1456,
+    "label": 9,
+    "text": "Title: Sum-of-Squares Lower Bounds for Densest k-Subgraph\nAbstract: Given a graph and an integer k, Densest k-Subgraph is the algorithmic task of finding the subgraph on k vertices with the maximum number of edges. This is a fundamental problem that has been subject to intense study for decades, with applications spanning a wide variety of fields. The state-of-the-art algorithm is an O(n1/4 + )-factor approximation (for any > 0) due to Bhaskara et al. [STOC \u201910]. Moreover, the so-called log-density framework predicts that this is optimal, i.e. it is impossible for an efficient algorithm to achieve an O(n1/4 \u2212 )-factor approximation. In the average case, Densest k-Subgraph is a prototypical noisy inference task which is conjectured to exhibit a statistical-computational gap. In this work, we provide the strongest evidence yet of hardness for Densest k-Subgraph by showing matching lower bounds against the powerful Sum-of-Squares (SoS) algorithm, a meta-algorithm based on convex programming that achieves state-of-art algorithmic guarantees for many optimization and inference problems. For k \u2264 n1/2, we obtain a degree n\u03b4 SoS lower bound for the hard regime as predicted by the log-density framework. To show this, we utilize the modern framework for proving SoS lower bounds on average-case problems pioneered by Barak et al. [FOCS \u201916]. A key issue is that small denser-than-average subgraphs in the input will greatly affect the value of the candidate pseudoexpectation operator around the subgraph. To handle this challenge, we devise a novel matrix factorization scheme based on the positive minimum vertex separator. We then prove an intersection tradeoff lemma to show that the error terms when using this separator are indeed small.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1457,
+    "label": 16,
+    "text": "Title: A Survey on Deep Learning-based Spatio-temporal Action Detection\nAbstract: Spatio-temporal action detection (STAD) aims to classify the actions present in a video and localize them in space and time. It has become a particularly active area of research in computer vision because of its explosively emerging real-world applications, such as autonomous driving, visual surveillance, entertainment, etc. Many efforts have been devoted in recent years to building a robust and effective framework for STAD. This paper provides a comprehensive review of the state-of-the-art deep learning-based methods for STAD. Firstly, a taxonomy is developed to organize these methods. Next, the linking algorithms, which aim to associate the frame- or clip-level detection results together to form action tubes, are reviewed. Then, the commonly used benchmark datasets and evaluation metrics are introduced, and the performance of state-of-the-art models is compared. At last, this paper is concluded, and a set of potential research directions of STAD are discussed.",
+    "neighbors": [
+      1252,
+      1616
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1458,
+    "label": 10,
+    "text": "Title: UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge Graphs\nAbstract: Irregular data in real-world are usually organized as heterogeneous graphs (HGs) consisting of multiple types of nodes and edges. To explore useful knowledge from real-world data, both the large-scale encyclopedic HG datasets and corresponding effective learning methods are crucial, but haven't been well investigated. In this paper, we construct a large-scale HG benchmark dataset named UniKG from Wikidata to facilitate knowledge mining and heterogeneous graph representation learning. Overall, UniKG contains more than 77 million multi-attribute entities and 2000 diverse association types, which significantly surpasses the scale of existing HG datasets. To perform effective learning on the large-scale UniKG, two key measures are taken, including (i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes into a common embedding space to facilitate node aggregation in a large receptive field; (ii) proposing a novel plug-and-play anisotropy propagation module (APM) to learn effective multi-hop anisotropy propagation kernels, which extends methods of large-scale homogeneous graphs to heterogeneous graphs. These two strategies enable efficient information propagation among a tremendous number of multi-attribute entities and meantimes adaptively mine multi-attribute association through the multi-hop aggregation in large-scale HGs. We set up a node classification task on our UniKG dataset, and evaluate multiple baseline methods which are constructed by embedding our APM into large-scale homogenous graph learning methods. Our UniKG dataset and the baseline codes have been released at https://github.com/Yide-Qiu/UniKG.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1459,
+    "label": 16,
+    "text": "Title: HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving\nAbstract: Autonomous driving systems generally employ separate models for different tasks resulting in intricate designs. For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from videos, i.e., the Risk Object Localization and Intention and Suggestion Prediction (ROLISP) task. ROLISP uses natural language to simultaneously identify and interpret risk objects, understand ego-vehicle intentions, and provide motion suggestions, eliminating the necessity for task-specific architectures. However, lacking high-resolution (HR) information, existing MLLMs often miss small objects (e.g., traffic cones) and overly focus on salient ones (e.g., large trucks) when applied to ROLISP. We propose HiLM-D (Towards High-Resolution Understanding in MLLMs for Autonomous Driving), an efficient method to incorporate HR information into MLLMs for the ROLISP task. Especially, HiLM-D integrates two branches: (i) the low-resolution reasoning branch, can be any MLLMs, processes low-resolution videos to caption risk objects and discern ego-vehicle intentions/suggestions; (ii) the high-resolution perception branch (HR-PB), prominent to HiLM-D,, ingests HR images to enhance detection by capturing vision-specific HR feature maps and prioritizing all potential risks over merely salient objects. Our HR-PB serves as a plug-and-play module, seamlessly fitting into current MLLMs. Experiments on the ROLISP benchmark reveal HiLM-D's notable advantage over leading MLLMs, with improvements of 4.8% in BLEU-4 for captioning and 17.2% in mIoU for detection.",
+    "neighbors": [
+      34,
+      887,
+      1052,
+      1071,
+      1574,
+      1863,
+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1460,
+    "label": 6,
+    "text": "Title: Filtering Eye-Tracking Data From an EyeLink 1000: Comparing Heuristic, Savitzky-Golay, IIR and FIR Digital Filters\nAbstract: In a previous report (Raju et al.,2023) we concluded that, if the goal was to preserve events such as saccades, microsaccades, and smooth pursuit in eye-tracking recordings, data with sine wave frequencies less than 100 Hz (-3db) were the signal and data above 100 Hz were noise. We compare 5 filters in their ability to preserve signal and remove noise. Specifically, we compared the proprietary STD and EXTRA heuristic filters provided by our EyeLink 1000 (SR-Research, Ottawa, Canada), a Savitzky-Golay (SG) filter, an infinite impulse response (IIR) filter (low-pass Butterworth), and a finite impulse filter (FIR). For each of the non-heuristic filters, we systematically searched for optimal parameters. Both the IIR and the FIR filters were zero-phase filters. Mean frequency response profiles and amplitude spectra for all 5 filters are provided. In addition, we examined the effect of our filters on a noisy recording. Our FIR filter had the sharpest roll-off of any filter. Therefore, it maintained the signal and removed noise more effectively than any other filter. On this basis, we recommend the use of our FIR filter. Several reports have shown that filtering increased the temporal autocorrelation of a signal. To address this, the present filters were also evaluated in terms of autocorrelation (specifically the first 3 lags). Of all our filters, the STD filter introduced the least amount of autocorrelation.",
+    "neighbors": [
+      1711
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1461,
+    "label": 30,
+    "text": "Title: BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models\nAbstract: Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction tuning plays a vital role in aligning LLMs to human preferences. However, the existing LLMs are usually focused on English, leading to inferior performance in non-English languages. In order to improve the performance for non-English languages, it is necessary to collect language-specific training data for foundation LLMs and construct language-specific instructions for instruction tuning, both of which are heavy loads. To minimize human workload, we propose to transfer the capabilities of language generation and instruction following from English to other languages through an interactive translation task. We have developed BayLing, an instruction-following LLM by utilizing LLaMA as the foundation LLM and automatically constructing interactive translation instructions for instructing tuning. Extensive assessments demonstrate that BayLing achieves comparable performance to GPT-3.5-turbo, despite utilizing a considerably smaller parameter size of only 13 billion. Experimental results on translation tasks show that BayLing achieves 95% of single-turn translation capability compared to GPT-4 with automatic evaluation and 96% of interactive translation capability compared to GPT-3.5-turbo with human evaluation. To estimate the performance on general tasks, we created a multi-turn instruction test set called BayLing-80. The experimental results on BayLing-80 indicate that BayLing achieves 89% of performance compared to GPT-3.5-turbo. BayLing also demonstrates outstanding performance on knowledge assessment of Chinese GaoKao and English SAT, second only to GPT-3.5-turbo among a multitude of instruction-following LLMs. Demo, homepage, code and models of BayLing are available.",
+    "neighbors": [
+      685,
+      1052,
+      1146,
+      1546,
+      1972,
+      2087
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1462,
+    "label": 30,
+    "text": "Title: Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System\nAbstract: Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found to underperform compared to smaller task-specific models. Nonetheless, it is crucial to acknowledge the significant potential of LLMs and explore improved approaches for leveraging their impressive abilities. Motivated by the goal of leveraging LLMs, we propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller TOD model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. By utilizing the satisfaction feedback generated by LLMs, UGRO further optimizes the supervised fine-tuned TOD model. Specifically, the TOD model takes the dialogue history as input and, with the assistance of the user simulator's feedback, generates high-satisfaction responses that meet the user's requirements. Through empirical experiments on two TOD benchmarks, we validate the effectiveness of our method. The results demonstrate that our approach outperforms previous state-of-the-art (SOTA) results.",
+    "neighbors": [
+      1284,
+      1507,
+      1706,
+      2231
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1463,
+    "label": 24,
+    "text": "Title: Adversarial Agents For Attacking Inaudible Voice Activated Devices\nAbstract: The paper applies reinforcement learning to novel Internet of Thing configurations. Our analysis of inaudible attacks on voice-activated devices confirms the alarming risk factor of 7.6 out of 10, underlining significant security vulnerabilities scored independently by NIST National Vulnerability Database (NVD). Our baseline network model showcases a scenario in which an attacker uses inaudible voice commands to gain unauthorized access to confidential information on a secured laptop. We simulated many attack scenarios on this baseline network model, revealing the potential for mass exploitation of interconnected devices to discover and own privileged information through physical access without adding new hardware or amplifying device skills. Using Microsoft's CyberBattleSim framework, we evaluated six reinforcement learning algorithms and found that Deep-Q learning with exploitation proved optimal, leading to rapid ownership of all nodes in fewer steps. Our findings underscore the critical need for understanding non-conventional networks and new cybersecurity measures in an ever-expanding digital landscape, particularly those characterized by mobile devices, voice activation, and non-linear microphones susceptible to malicious actors operating stealth attacks in the near-ultrasound or inaudible ranges. By 2024, this new attack surface might encompass more digital voice assistants than people on the planet yet offer fewer remedies than conventional patching or firmware fixes since the inaudible attacks arise inherently from the microphone design and digital signal processing.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1464,
+    "label": 27,
+    "text": "Title: Geometric Mechanics of Simultaneous Nonslip Contact in a Planar Quadruped\nAbstract: In this paper, we develop a geometric framework for generating non-slip quadrupedal two-beat gaits. We consider a four-bar mechanism as a surrogate model for a contact state and develop the geometric tools such as shape-change basis to aid in gait generation, local connection as the matrix-equation of motion, and stratified panels to model net locomotion in line with previous work\\cite{prasad2023contactswitch}. Standard two-beat gaits in quadrupedal systems like trot divide the shape space into two equal, decoupled subspaces. The subgaits generated in each subspace space are designed independently and when combined with appropriate phasing generate a two-beat gait where the displacements add up due to the geometric nature of the system. By adding ``scaling\"and ``sliding\"control knobs to subgaits defined as flows over the shape-change basis, we continuously steer an arbitrary, planar quadrupedal system. This exhibits translational anisotropy when modulated using the scaling inputs. To characterize the steering induced by sliding inputs, we define an average path curvature function analytically and show that the steering gaits can be generated using a geometric nonslip contact modeling framework.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1465,
+    "label": 16,
+    "text": "Title: RemovalNet: DNN Fingerprint Removal Attacks\nAbstract: With the performance of deep neural networks (DNNs) remarkably improving, DNNs have been widely used in many areas. Consequently, the DNN model has become a valuable asset, and its intellectual property is safeguarded by ownership verification techniques (e.g., DNN fingerprinting). However, the feasibility of the DNN fingerprint removal attack and its potential influence remains an open problem. In this paper, we perform the first comprehensive investigation of DNN fingerprint removal attacks. Generally, the knowledge contained in a DNN model can be categorized into general semantic and fingerprint-specific knowledge. To this end, we propose a min-max bilevel optimization-based DNN fingerprint removal attack named RemovalNet, to evade model ownership verification. The lower-level optimization is designed to remove fingerprint-specific knowledge. While in the upper-level optimization, we distill the victim model's general semantic knowledge to maintain the surrogate model's performance. We conduct extensive experiments to evaluate the fidelity, effectiveness, and efficiency of the RemovalNet against four advanced defense methods on six metrics. The empirical results demonstrate that (1) the RemovalNet is effective. After our DNN fingerprint removal attack, the model distance between the target and surrogate models is x100 times higher than that of the baseline attacks, (2) the RemovalNet is efficient. It uses only 0.2% (400 samples) of the substitute dataset and 1,000 iterations to conduct our attack. Besides, compared with advanced model stealing attacks, the RemovalNet saves nearly 85% of computational resources at most, (3) the RemovalNet achieves high fidelity that the created surrogate model maintains high accuracy after the DNN fingerprint removal process. Our code is available at: https://github.com/grasses/RemovalNet.",
+    "neighbors": [
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1466,
+    "label": 4,
+    "text": "Title: Formal-Guided Fuzz Testing: Targeting Security Assurance from Specification to Implementation for 5G and Beyond\nAbstract: Softwarization and virtualization in 5G and beyond necessitate thorough testing to ensure the security of critical infrastructure and networks, requiring the identification of vulnerabilities and unintended emergent behaviors from protocol designs to their software stack implementation. To provide an efficient and comprehensive solution, we propose a novel and first-of-its-kind approach that connects the strengths and coverage of formal and fuzzing methods to efficiently detect vulnerabilities across protocol logic and implementation stacks in a hierarchical manner. We design and implement formal verification to detect attack traces in critical protocols, which are used to guide subsequent fuzz testing and incorporate feedback from fuzz testing to broaden the scope of formal verification. This innovative approach significantly improves efficiency and enables the auto-discovery of vulnerabilities and unintended emergent behaviors from the 3GPP protocols to software stacks. Following this approach, we discover one identifier leakage model, one DoS attack model, and two eavesdrop attack models due to the absence of rudimentary MITM protection within the protocol, despite the existence of a Transport Layer Security (TLS) solution to this issue for over a decade. More remarkably, guided by the identified formal analysis and attack models, we exploit 61 vulnerabilities using fuzz testing demonstrated on srsRAN platforms. These identified vulnerabilities contribute to fortifying protocol-level assumptions and refining the search space. Compared to state-of-the-art fuzz testing, our united formal and fuzzing methodology enables auto-assurance by systematically discovering vulnerabilities. It significantly reduces computational complexity, transforming the non-practical exponential growth in computational cost into linear growth.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1467,
+    "label": 16,
+    "text": "Title: GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction\nAbstract: This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly inaccessible data. To address these challenges, we propose the GPT4Tools based on self-instruct to enable open-source LLMs, such as LLaMA and OPT, to use tools. It generates an instruction-following dataset by prompting an advanced teacher with various multi-modal contexts. By using the Low-Rank Adaptation (LoRA) optimization, our approach facilitates the open-source LLMs to solve a range of visual problems, including visual comprehension and image generation. Moreover, we provide a benchmark to evaluate the ability of LLMs to use tools, which is performed in both zero-shot and fine-tuning ways. Extensive experiments demonstrate the effectiveness of our method on various language models, which not only significantly improves the accuracy of invoking seen tools, but also enables the zero-shot capacity for unseen tools. The code and demo are available at https://github.com/StevenGrove/GPT4Tools.",
+    "neighbors": [
+      57,
+      173,
+      319,
+      522,
+      855,
+      887,
+      1026,
+      1039,
+      1047,
+      1052,
+      1863,
+      1899,
+      1902,
+      1913,
+      2094,
+      2155,
+      2205,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1468,
+    "label": 24,
+    "text": "Title: DAG Learning on the Permutahedron\nAbstract: We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutation vectors, the so-called Permutahedron, to learn a topological ordering. Edges can be optimized jointly, or learned conditional on the ordering via a non-differentiable subroutine. Compared to existing continuous optimization approaches our formulation has a number of advantages including: 1. validity: optimizes over exact DAGs as opposed to other relaxations optimizing approximate DAGs; 2. modularity: accommodates any edge-optimization procedure, edge structural parameterization, and optimization loss; 3. end-to-end: either alternately iterates between node-ordering and edge-optimization, or optimizes them jointly. We demonstrate, on real-world data problems in protein-signaling and transcriptional network discovery, that our approach lies on the Pareto frontier of two key metrics, the SID and SHD.",
+    "neighbors": [
+      160,
+      1482
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1469,
+    "label": 4,
+    "text": "Title: CryptoScratch: Developing and evaluating a block-based programming tool for teaching K-12 cryptography education using Scratch\nAbstract: The world continues to experience a shortage of skilled cybersecurity personnel. The widely accepted solution for reducing this gap is raising awareness about cybersecurity. In response, many schools are integrating cybersecurity into the K-12 curriculum. Also, educational initiatives from the National Initiative for Cybersecurity Education, GenCyber, and the CryptoClub project enable universities to provide enrichment to middle- and high-school students regarding the importance of cybersecurity. Unfortunately, there is currently an absence of visual and straightforward tools that limits the feasibility of hands-on practice during these initiatives. This paper presents the design, implementation, and evaluation of a new framework called CryptoScratch, which extends the Scratch programming environment with modern cryptographic algorithms (e.g., AES, RSA, SHA-256) implemented as visual blocks. Using the simple interface of CryptoScratch, K-12 students can study how to use cryptographic algorithms for services like confidentiality, authentication, and integrity protection; and then use these blocks to build complex modern cryptographic schemes (e.g., Pretty Good Privacy, Digital Signatures). In addition, we present the design and implementation of a Task Block that provides students instruction on various cryptography problems and verifies that they have successfully completed the problem. The task block also generates feedback, nudging learners to implement more secure solutions for cryptographic problems. An initial usability study was performed with 16 middle-school students where students were taught basic cryptographic concepts and then asked to complete tasks using those concepts. Once students had knowledge of a variety of basic cryptographic algorithms, they were asked to use those algorithms to implement complex cryptographic schemes such as Pretty Good Privacy and Digital Signatures. Using the successful implementation of the cryptographic and task blocks in Scratch, the initial testing indicated that $\\approx 60\\%$ of the students could quickly grasp and implement complex cryptography concepts using CryptoScratch, while $\\approx 90\\%$ showed comfort with cryptography concepts and use-cases. Based on the positive results from the initial testing, a larger study of students is being developed to investigate the effectiveness across the socioeconomic spectrum.",
+    "neighbors": [
+      1263
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1470,
+    "label": 6,
+    "text": "Title: Improving Surgical Situational Awareness with Signed Distance Field: A Pilot Study in Virtual Reality\nAbstract: The introduction of image-guided surgical navigation (IGSN) has greatly benefited technically demanding surgical procedures by providing real-time support and guidance to the surgeon during surgery. \\hi{To develop effective IGSN, a careful selection of the surgical information and the medium to present this information to the surgeon is needed. However, this is not a trivial task due to the broad array of available options.} To address this problem, we have developed an open-source library that facilitates the development of multimodal navigation systems in a wide range of surgical procedures relying on medical imaging data. To provide guidance, our system calculates the minimum distance between the surgical instrument and the anatomy and then presents this information to the user through different mechanisms. The real-time performance of our approach is achieved by calculating Signed Distance Fields at initialization from segmented anatomical volumes. Using this framework, we developed a multimodal surgical navigation system to help surgeons navigate anatomical variability in a skull base surgery simulation environment. Three different feedback modalities were explored: visual, auditory, and haptic. To evaluate the proposed system, a pilot user study was conducted in which four clinicians performed mastoidectomy procedures with and without guidance. Each condition was assessed using objective performance and subjective workload metrics. This pilot user study showed improvements in procedural safety without additional time or workload. These results demonstrate our pipeline's successful use case in the context of mastoidectomy.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1471,
+    "label": 24,
+    "text": "Title: Replicability and stability in learning\nAbstract: Replicability is essential in science as it allows us to validate and verify research findings. Impagliazzo, Lei, Pitassi and Sorrell (`22) recently initiated the study of replicability in machine learning. A learning algorithm is replicable if it typically produces the same output when applied on two i.i.d. inputs using the same internal randomness. We study a variant of replicability that does not involve fixing the randomness. An algorithm satisfies this form of replicability if it typically produces the same output when applied on two i.i.d. inputs (without fixing the internal randomness). This variant is called global stability and was introduced by Bun, Livni and Moran ('20) in the context of differential privacy. Impagliazzo et al. showed how to boost any replicable algorithm so that it produces the same output with probability arbitrarily close to 1. In contrast, we demonstrate that for numerous learning tasks, global stability can only be accomplished weakly, where the same output is produced only with probability bounded away from 1. To overcome this limitation, we introduce the concept of list replicability, which is equivalent to global stability. Moreover, we prove that list replicability can be boosted so that it is achieved with probability arbitrarily close to 1. We also describe basic relations between standard learning-theoretic complexity measures and list replicable numbers. Our results, in addition, imply that besides trivial cases, replicable algorithms (in the sense of Impagliazzo et al.) must be randomized. The proof of the impossibility result is based on a topological fixed-point theorem. For every algorithm, we are able to locate a\"hard input distribution\"by applying the Poincar\\'{e}-Miranda theorem in a related topological setting. The equivalence between global stability and list replicability is algorithmic.",
+    "neighbors": [
+      677,
+      1213
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1472,
+    "label": 24,
+    "text": "Title: A Survey of Meta-Reinforcement Learning\nAbstract: While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1473,
+    "label": 11,
+    "text": "Title: Set-Membership Filtering-Based Cooperative State Estimation for Multi-Agent Systems\nAbstract: In this article, we focus on the cooperative state estimation problem of a multi-agent system. Each agent is equipped with absolute and relative measurements. The purpose of this research is to make each agent generate its own state estimation with only local measurement information and local communication with neighborhood agents using Set Membership Filter(SMF). To handle this problem, we analyzed centralized SMF framework as a benchmark of distributed SMF and propose a finite-horizon method called OIT-Inspired centralized constrained zonotopic algorithm. Moreover, we put forward a distributed Set Membership Filtering(SMFing) framework and develop a distributed constained zonotopic algorithm. Finally, simulation verified our theoretical results, that our proposed algorithms can effectively estimate the state of each agent.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1474,
+    "label": 30,
+    "text": "Title: On the Robustness of Text Vectorizers\nAbstract: A fundamental issue in machine learning is the robustness of the model with respect to changes in the input. In natural language processing, models typically contain a first embedding layer, transforming a sequence of tokens into vector representations. While the robustness with respect to changes of continuous inputs is well-understood, the situation is less clear when considering discrete changes, for instance replacing a word by another in an input sentence. Our work formally proves that popular embedding schemes, such as concatenation, TF-IDF, and Paragraph Vector (a.k.a. doc2vec), exhibit robustness in the H\\\"older or Lipschitz sense with respect to the Hamming distance. We provide quantitative bounds for these schemes and demonstrate how the constants involved are affected by the length of the document. These findings are exemplified through a series of numerical examples.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1475,
+    "label": 24,
+    "text": "Title: RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment\nAbstract: Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcomes, and unfairness, with potentially serious consequences. Consequently, aligning these models with human ethics and preferences is an essential step toward ensuring their responsible and effective deployment in real-world applications. Prior research has primarily employed Reinforcement Learning from Human Feedback (RLHF) to address this problem, where generative models are fine-tuned with RL algorithms guided by a human-feedback-informed reward model. However, the inefficiencies and instabilities associated with RL algorithms frequently present substantial obstacles to the successful alignment, necessitating the development of a more robust and streamlined approach. To this end, we introduce a new framework, Reward rAnked FineTuning (RAFT), designed to align generative models effectively. Utilizing a reward model and a sufficient number of samples, our approach selects the high-quality samples, discarding those that exhibit undesired behavior, and subsequently enhancing the model by fine-tuning on these filtered samples. Our studies show that RAFT can effectively improve the model performance in both reward learning and other automated metrics in both large language models and diffusion models.",
+    "neighbors": [
+      855,
+      1052,
+      1969,
+      1994,
+      2007,
+      2122,
+      2258,
+      2305
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1476,
+    "label": 24,
+    "text": "Title: Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution\nAbstract: Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros. We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing. The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph, then learn structural graph properties to recover the function from its values on certain vertices -- the problem of graph signal sampling. We propose a class of regularization functionals that takes into account discrete random label noise in the graph vertex domain, then develop the GS-IMC approach which biases the reconstruction towards functions that vary little between adjacent vertices for noise reduction. Theoretical result shows that accurate reconstructions can be achieved under mild conditions. For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain and builds upon a prediction-correction update algorithm to obtain the unbiased and minimum-variance reconstruction. Both GS-IMC and BGS-IMC have closed-form solutions and thus are highly scalable in large data. Experiments show that our methods achieve state-of-the-art performance on public benchmarks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1477,
+    "label": 16,
+    "text": "Title: ConceptFusion: Open-set Multimodal 3D Mapping\nAbstract: Building 3D maps of the environment is central to robot navigation, planning, and interaction with objects in a scene. Most existing approaches that integrate semantic concepts with 3D maps largely remain confined to the closed-set setting: they can only reason about a finite set of concepts, pre-defined at training time. Further, these maps can only be queried using class labels, or in recent work, using text prompts. We address both these issues with ConceptFusion, a scene representation that is (1) fundamentally open-set, enabling reasoning beyond a closed set of concepts and (ii) inherently multimodal, enabling a diverse range of possible queries to the 3D map, from language, to images, to audio, to 3D geometry, all working in concert. ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio. We demonstrate that pixel-aligned open-set features can be fused into 3D maps via traditional SLAM and multi-view fusion approaches. This enables effective zero-shot spatial reasoning, not needing any additional training or finetuning, and retains long-tailed concepts better than supervised approaches, outperforming them by more than 40% margin on 3D IoU. We extensively evaluate ConceptFusion on a number of real-world datasets, simulated home environments, a real-world tabletop manipulation task, and an autonomous driving platform. We showcase new avenues for blending foundation models with 3D open-set multimodal mapping. For more information, visit our project page https://concept-fusion.github.io or watch our 5-minute explainer video https://www.youtube.com/watch?v=rkXgws8fiDs",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1478,
+    "label": 24,
+    "text": "Title: MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization\nAbstract: Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space remains a challenging task. In this paper, we propose MO-DEHB, an effective and flexible multi-objective (MO) optimizer that extends the recent evolutionary Hyperband method DEHB. We validate the performance of MO-DEHB using a comprehensive suite of 15 benchmarks consisting of diverse and challenging MO problems, including HPO, neural architecture search (NAS), and joint NAS and HPO, with objectives including accuracy, latency and algorithmic fairness. A comparative study against state-of-the-art MO optimizers demonstrates that MO-DEHB clearly achieves the best performance across our 15 benchmarks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1479,
+    "label": 30,
+    "text": "Title: Named Entity Recognition Based Automatic Generation of Research Highlights\nAbstract: A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers. In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1480,
+    "label": 3,
+    "text": "Title: Intersectionality and Testimonial Injustice in Medical Records\nAbstract: Detecting testimonial injustice is an essential element of addressing inequities and promoting inclusive healthcare practices, many of which are life-critical. However, using a single demographic factor to detect testimonial injustice does not fully encompass the nuanced identities that contribute to a patient\u2019s experience. Further, some injustices may only be evident when examining the nuances that arise through the lens of intersectionality. Ignoring such injustices can result in poor quality of care or life-endangering events. Thus, considering intersectionality could result in more accurate classifications and just decisions. To illustrate this, we use real-world medical data to determine whether medical records exhibit words that could lead to testimonial injustice, employ fairness metrics (e.g. demographic parity, differential intersectional fairness, and subgroup fairness) to assess the severity to which subgroups are experiencing testimonial injustice, and analyze how the intersectionality of demographic features (e.g. gender and race) make a difference in uncovering testimonial injustice. From our analysis we found that with intersectionality we can better see disparities in how subgroups are treated and there are differences in how someone is treated based on the intersection of their demographic attributes. This has not been previously studied in clinical records, nor has it been proven through empirical study.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1481,
+    "label": 10,
+    "text": "Title: A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT\nAbstract: Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant attention from society. As a result, many individuals have become interested in related resources and are seeking to uncover the background and secrets behind its impressive performance. In fact, ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC), which involves the creation of digital content, such as images, music, and natural language, through AI models. The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace. AIGC is achieved by extracting and understanding intent information from instructions provided by human, and generating the content according to its knowledge and the intent information. In recent years, large-scale models have become increasingly important in AIGC as they provide better intent extraction and thus, improved generation results. With the growth of data and the size of the models, the distribution that the model can learn becomes more comprehensive and closer to reality, leading to more realistic and high-quality content generation. This survey provides a comprehensive review on the history of generative models, and basic components, recent advances in AIGC from unimodal interaction and multimodal interaction. From the perspective of unimodality, we introduce the generation tasks and relative models of text and image. From the perspective of multimodality, we introduce the cross-application between the modalities mentioned above. Finally, we discuss the existing open problems and future challenges in AIGC.",
+    "neighbors": [
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+      1436,
+      1651,
+      1985,
+      2085,
+      2113,
+      2249,
+      2279,
+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1482,
+    "label": 24,
+    "text": "Title: BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery\nAbstract: Bayesian causal discovery aims to infer the posterior distribution over causal models from observed data, quantifying epistemic uncertainty and benefiting downstream tasks. However, computational challenges arise due to joint inference over combinatorial space of Directed Acyclic Graphs (DAGs) and nonlinear functions. Despite recent progress towards efficient posterior inference over DAGs, existing methods are either limited to variational inference on node permutation matrices for linear causal models, leading to compromised inference accuracy, or continuous relaxation of adjacency matrices constrained by a DAG regularizer, which cannot ensure resulting graphs are DAGs. In this work, we introduce a scalable Bayesian causal discovery framework based on stochastic gradient Markov Chain Monte Carlo (SG-MCMC) that overcomes these limitations. Our approach directly samples DAGs from the posterior without requiring any DAG regularization, simultaneously draws function parameter samples and is applicable to both linear and nonlinear causal models. To enable our approach, we derive a novel equivalence to the permutation-based DAG learning, which opens up possibilities of using any relaxed gradient estimator defined over permutations. To our knowledge, this is the first framework applying gradient-based MCMC sampling for causal discovery. Empirical evaluations on synthetic and real-world datasets demonstrate our approach's effectiveness compared to state-of-the-art baselines.",
+    "neighbors": [
+      1468
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1483,
+    "label": 27,
+    "text": "Title: Interactive Trajectory Planner for Mandatory Lane Changing in Dense Non-Cooperative Traffic\nAbstract: When the traffic stream is extremely congested and surrounding vehicles are not cooperative, the mandatory lane changing can be significantly difficult. In this work, we propose an interactive trajectory planner, which will firstly attempt to change lanes as long as safety is ensured. Based on receding horizon planning, the ego vehicle can abort or continue changing lanes according to surrounding vehicles' reactions. We demonstrate the performance of our planner in extensive simulations with eight surrounding vehicles, initial velocity ranging from 0.5 to 5 meters per second, and bumper to bumper gap ranging from 4 to 10 meters. The ego vehicle with our planner can change lanes safely and smoothly. The computation time of the planner at every step is within 10 milliseconds in most cases on a laptop with 1.8GHz Intel Core i7-10610U.",
+    "neighbors": [
+      1722,
+      1909
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1484,
+    "label": 30,
+    "text": "Title: Knowledge-preserving Pruning for Pre-trained Language Models without Retraining\nAbstract: Given a pre-trained language model, how can we efficiently compress it without retraining? Retraining-free structured pruning algorithms are crucial in pre-trained language model compression due to their significantly reduced pruning cost and capability to prune large language models. However, existing retraining-free algorithms encounter severe accuracy degradation, as they fail to preserve the useful knowledge of pre-trained models. In this paper, we propose K-pruning (Knowledge-preserving pruning), an accurate retraining-free structured pruning algorithm for pre-trained language models. K-pruning identifies and prunes attention heads and neurons deemed to be superfluous, based on the amount of their inherent knowledge. K-pruning applies an iterative process of pruning followed by knowledge reconstruction for each sub-layer to preserve the knowledge of the pre-trained models. Consequently, K-pruning shows up to 58.02%p higher F1 score than existing retraining-free pruning algorithms under a high compression rate of 80% on the SQuAD benchmark.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1485,
+    "label": 16,
+    "text": "Title: Evaluating Object Hallucination in Large Vision-Language Models\nAbstract: Inspired by the superior language abilities of large language models (LLM), large vision-language models (LVLM) have been recently explored by integrating powerful LLMs for improving the performance on complex multimodal tasks. Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i.e. they tend to generate objects that are inconsistent with the target images in the descriptions. To investigate it, this work presents the first systematic study on object hallucination of LVLMs. We conduct the evaluation experiments on several representative LVLMs, and show that they mostly suffer from severe object hallucination issue. We further discuss that the visual instructions may influence the hallucination, and find that: objects that frequently occur in the visual instructions or co-occur with the image objects, are obviously prone to be hallucinated by LVLMs. Besides, we find that existing evaluation methods might be affected by the input instructions and generation styles of LVLMs. Thus, we further design an improved evaluation method for object hallucination by proposing a polling-based query method called POPE. Experiment results demonstrate that our POPE can evaluate the object hallucination in a more stable and flexible way. Our codes and data are publicly available at https://github.com/RUCAIBox/POPE.",
+    "neighbors": [
+      136,
+      392,
+      602,
+      719,
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+      1071,
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+      1194,
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+      1344,
+      1755,
+      1863,
+      1952,
+      2036,
+      2038,
+      2113,
+      2155,
+      2216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1486,
+    "label": 27,
+    "text": "Title: Conformal Predictive Safety Filter for RL Controllers in Dynamic Environments\nAbstract: The interest in using reinforcement learning (RL) controllers in safety-critical applications such as robot navigation around pedestrians motivates the development of additional safety mechanisms. Running RL-enabled systems among uncertain dynamic agents may result in high counts of collisions and failures to reach the goal. The system could be safer if the pre-trained RL policy was uncertainty-informed. For that reason, we propose conformal predictive safety filters that: 1) predict the other agents' trajectories, 2) use statistical techniques to provide uncertainty intervals around these predictions, and 3) learn an additional safety filter that closely follows the RL controller but avoids the uncertainty intervals. We use conformal prediction to learn uncertainty-informed predictive safety filters, which make no assumptions about the agents' distribution. The framework is modular and outperforms the existing controllers in simulation. We demonstrate our approach with multiple experiments in a collision avoidance gym environment and show that our approach minimizes the number of collisions without making overly-conservative predictions.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1487,
+    "label": 24,
+    "text": "Title: A Watermark for Large Language Models\nAbstract: Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models. The watermark can be embedded with negligible impact on text quality, and can be detected using an efficient open-source algorithm without access to the language model API or parameters. The watermark works by selecting a randomized set of\"green\"tokens before a word is generated, and then softly promoting use of green tokens during sampling. We propose a statistical test for detecting the watermark with interpretable p-values, and derive an information-theoretic framework for analyzing the sensitivity of the watermark. We test the watermark using a multi-billion parameter model from the Open Pretrained Transformer (OPT) family, and discuss robustness and security.",
+    "neighbors": [
+      27,
+      42,
+      352,
+      580,
+      679,
+      691,
+      896,
+      1436,
+      1600,
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+      2044,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1488,
+    "label": 16,
+    "text": "Title: Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions\nAbstract: Deep learning has seen rapid growth in recent years and achieved state-of-the-art performance in a wide range of applications. However, training models typically requires expensive and time-consuming collection of large quantities of labeled data. This is particularly true within the scope of medical imaging analysis (MIA), where data are limited and labels are expensive to be acquired. Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data. In this survey, we extensively investigated over 300 recent papers to provide a comprehensive overview of recent progress on label-efficient learning strategies in MIA. We first present the background of label-efficient learning and categorize the approaches into different schemes. Next, we examine the current state-of-the-art methods in detail through each scheme. Specifically, we provide an in-depth investigation, covering not only canonical semi-supervised, self-supervised, and multi-instance learning schemes, but also recently emerged active and annotation-efficient learning strategies. Moreover, as a comprehensive contribution to the field, this survey not only elucidates the commonalities and unique features of the surveyed methods but also presents a detailed analysis of the current challenges in the field and suggests potential avenues for future research.",
+    "neighbors": [
+      356,
+      1698
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1489,
+    "label": 16,
+    "text": "Title: Balancing Logit Variation for Long-Tailed Semantic Segmentation\nAbstract: Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get squeezed into a narrow area in the feature space. Towards a balanced feature distribution, we introduce category-wise variation into the network predictions in the training phase such that an instance is no longer projected to a feature point, but a small region instead. Such a perturbation is highly dependent on the category scale, which appears as assigning smaller variation to head classes and larger variation to tail classes. In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation. It is note-worthy that the introduced variation is discarded at the inference stage to facilitate a confident prediction. Although with an embarrassingly simple implementation, our method manifests itself in strong generalizability to various datasets and task settings. Extensive experiments suggest that our plug-in design lends itself well to a range of state-of-the-art approaches and boosts the performance on top of them.11Code: https://github.com/grantword8/BLV.",
+    "neighbors": [
+      1637,
+      2048
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1490,
+    "label": 30,
+    "text": "Title: Teaching Large Language Models to Self-Debug\nAbstract: Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose Self-Debugging, which teaches a large language model to debug its predicted program via few-shot demonstrations. In particular, we demonstrate that Self-Debugging can teach the large language model to perform rubber duck debugging; i.e., without any feedback on the code correctness or error messages, the model is able to identify its mistakes by explaining the generated code in natural language. Self-Debugging achieves the state-of-the-art performance on several code generation benchmarks, including the Spider dataset for text-to-SQL generation, TransCoder for C++-to-Python translation, and MBPP for text-to-Python generation. On the Spider benchmark where there are no unit tests to verify the correctness of predictions, Self-Debugging with code explanation consistently improves the baseline by 2-3%, and improves the prediction accuracy on problems of the hardest label by 9%. On TransCoder and MBPP where unit tests are available, Self-Debugging improves the baseline accuracy by up to 12%. Meanwhile, by leveraging feedback messages and reusing failed predictions, Self-Debugging notably improves sample efficiency, and can match or outperform baseline models that generate more than 10x candidate programs.",
+    "neighbors": [
+      127,
+      240,
+      430,
+      505,
+      924,
+      1128,
+      1267,
+      1306,
+      1606,
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+      1840,
+      1907,
+      2016,
+      2085,
+      2092,
+      2100,
+      2136
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1491,
+    "label": 16,
+    "text": "Title: Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review\nAbstract: Grid-centric perception is a crucial field for mobile robot perception and navigation. Nonetheless, grid-centric perception is less prevalent than object-centric perception for autonomous driving as autonomous vehicles need to accurately perceive highly dynamic, large-scale outdoor traffic scenarios and the complexity and computational costs of grid-centric perception are high. The rapid development of deep learning techniques and hardware gives fresh insights into the evolution of grid-centric perception and enables the deployment of many real-time algorithms. Current industrial and academic research demonstrates the great advantages of grid-centric perception, such as comprehensive fine-grained environmental representation, greater robustness to occlusion, more efficient sensor fusion, and safer planning policies. Given the lack of current surveys for this rapidly expanding field, we present a hierarchically-structured review of grid-centric perception for autonomous vehicles. We organize previous and current knowledge of occupancy grid techniques and provide a systematic in-depth analysis of algorithms in terms of three aspects: feature representation, data utility, and applications in autonomous driving systems. Lastly, we present a summary of the current research trend and provide some probable future outlooks.",
+    "neighbors": [
+      513,
+      1260,
+      1283,
+      1571,
+      2198
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1492,
+    "label": 27,
+    "text": "Title: Autonomous Reflectance Transformation Imaging by a Team of Unmanned Aerial Vehicles\nAbstract: A Reflectance Transformation Imaging technique (RTI) realized by multi-rotor Unmanned Aerial Vehicles (UAVs) with a focus on deployment in difficult to access buildings is presented in this letter. RTI is a computational photographic method that captures a surface shape and color of a subject and enables its interactive re-lighting from any direction in a software viewer, revealing details that are not visible with the naked eye. The input of RTI is a set of images captured by a static camera, each one under illumination from a different known direction. We present an innovative approach applying two multi-rotor UAVs to perform this scanning procedure in locations that are hardly accessible or even inaccessible for people. The proposed system is designed for its safe deployment within real-world scenarios in historical buildings with priceless historical value.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1493,
+    "label": 24,
+    "text": "Title: Optimality of Message-Passing Architectures for Sparse Graphs\nAbstract: We study the node classification problem on feature-decorated graphs in the sparse setting, i.e., when the expected degree of a node is $O(1)$ in the number of nodes. Such graphs are typically known to be locally tree-like. We introduce a notion of Bayes optimality for node classification tasks, called asymptotic local Bayes optimality, and compute the optimal classifier according to this criterion for a fairly general statistical data model with arbitrary distributions of the node features and edge connectivity. The optimal classifier is implementable using a message-passing graph neural network architecture. We then compute the generalization error of this classifier and compare its performance against existing learning methods theoretically on a well-studied statistical model with naturally identifiable signal-to-noise ratios (SNRs) in the data. We find that the optimal message-passing architecture interpolates between a standard MLP in the regime of low graph signal and a typical convolution in the regime of high graph signal. Furthermore, we prove a corresponding non-asymptotic result.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1494,
+    "label": 27,
+    "text": "Title: Adaptive Coverage Path Planning for Efficient Exploration of Unknown Environments\nAbstract: We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked with planning a path over a horizon such that the accumulated area swept out by its sensor footprint is maximized. Because this problem exhibits a diminishing returns property known as submodularity, we choose to formulate it as a tree-based sequential decision making process. This formulation allows us to evaluate the effects of the robot's actions on future world coverage states, while simultaneously accounting for traversability risk and the dynamic constraints of the robot. To quickly find near-optimal solutions, we propose an effective approximation to the coverage sensor model which adapts to the local environment. Our method was extensively tested across various complex environments and served as the local exploration algorithm for a competing entry in the DARPA Subterranean Challenge.",
+    "neighbors": [
+      454
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1495,
+    "label": 38,
+    "text": "Title: The Digitization of Historical Astrophysical Literature with Highly-Localized Figures and Figure Captions\nAbstract: Scientific articles published prior to the\"age of digitization\"in the late 1990s contain figures which are\"trapped\"within their scanned pages. While progress to extract figures and their captions has been made, there is currently no robust method for this process. We present a YOLO-based method for use on scanned pages, after they have been processed with Optical Character Recognition (OCR), which uses both grayscale and OCR-features. We focus our efforts on translating the intersection-over-union (IOU) metric from the field of object detection to document layout analysis and quantify\"high localization\"levels as an IOU of 0.9. When applied to the astrophysics literature holdings of the NASA Astrophysics Data System (ADS), we find F1 scores of 90.9% (92.2%) for figures (figure captions) with the IOU cut-off of 0.9 which is a significant improvement over other state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1496,
+    "label": 5,
+    "text": "Title: Ethereum Proof-of-Stake Consensus Layer: Participation and Decentralization\nAbstract: In September 2022, Ethereum transitioned from Proof-of-Work (PoW) to Proof-of-Stake (PoS) during 'the merge' - making it the largest PoS cryptocurrency in terms of market capitalization. With this work, we present a comprehensive measurement study of the current state of the Ethereum PoS consensus layer on the beacon chain. We perform a longitudinal study over the entire history of the beacon chain, which ranges from 1 December 2020 until 15 May 2023. Our work finds that all dips in network participation, unrelated to network upgrades, are caused by issues with major consensus clients or service operators controlling a large number of validators. Thus, we analyze the decentralization of staking power over time by clustering validators to entities. We find that the staking power is concentrated in the hands of a few large entities. Further, we also analyze the consensus client landscape, given that bugs in a consensus client pose a security risk to the consensus layer. While the consensus client landscape exhibits significant concentration, with a single client accounting for one-third of the market share throughout the entire history of the beacon chain, we observe an improving trend.",
+    "neighbors": [
+      1837
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1497,
+    "label": 30,
+    "text": "Title: Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM\nAbstract: Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and BART-Large model performances were evaluated on the open source dataset (Xsum, CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared resume dataset. This resume dataset consists of many information such as language, education, experience, personal information, skills, and this data includes 75 resumes. The primary objective of this research was to classify resume text. Various techniques such as LSTM, pre-trained models, and fine-tuned models were assessed using a dataset of resumes. The BART-Large model fine-tuned with the resume dataset gave the best performance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1498,
+    "label": 16,
+    "text": "Title: Ensemble of Anchor-Free Models for Robust Bangla Document Layout Segmentation\nAbstract: In this research paper, we introduce a novel approach designed for the purpose of segmenting the layout of Bangla documents. Our methodology involves the utilization of a sophisticated ensemble of YOLOv8 models, which were trained for the DL Sprint 2.0 - BUET CSE Fest 2023 Competition focused on Bangla document layout segmentation. Our primary emphasis lies in enhancing various aspects of the task, including techniques such as image augmentation, model architecture, and the incorporation of model ensembles. We deliberately reduce the quality of a subset of document images to enhance the resilience of model training, thereby resulting in an improvement in our cross-validation score. By employing Bayesian optimization, we determine the optimal confidence and Intersection over Union (IoU) thresholds for our model ensemble. Through our approach, we successfully demonstrate the effectiveness of anchor-free models in achieving robust layout segmentation in Bangla documents.",
+    "neighbors": [
+      1928
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1499,
+    "label": 24,
+    "text": "Title: Input margins can predict generalization too\nAbstract: Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to the decision boundary for a given sample or its representation internal to the network. While margins have been shown to be correlated with the generalization ability of a model when measured at its hidden representations (hidden margins), no such link between large margins and generalization has been established for input margins. We show that while input margins are not generally predictive of generalization, they can be if the search space is appropriately constrained. We develop such a measure based on input margins, which we refer to as `constrained margins'. The predictive power of this new measure is demonstrated on the 'Predicting Generalization in Deep Learning' (PGDL) dataset and contrasted with hidden representation margins. We find that constrained margins achieve highly competitive scores and outperform other margin measurements in general. This provides a novel insight on the relationship between generalization and classification margins, and highlights the importance of considering the data manifold for investigations of generalization in DNNs.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1500,
+    "label": 33,
+    "text": "Title: Regular Separability in B\u00fcchi VASS\nAbstract: We study the ($\\omega$-)regular separability problem for B\\\"uchi VASS languages: Given two B\\\"uchi VASS with languages $L_1$ and $L_2$, check whether there is a regular language that fully contains $L_1$ while remaining disjoint from $L_2$. We show that the problem is decidable in general and PSPACE-complete in the 1-dimensional case, assuming succinct counter updates. The results rely on several arguments. We characterize the set of all regular languages disjoint from $L_2$. Based on this, we derive a (sound and complete) notion of inseparability witnesses, non-regular subsets of $L_1$. Finally, we show how to symbolically represent inseparability witnesses and how to check their existence.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1501,
+    "label": 16,
+    "text": "Title: CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP\nAbstract: Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the learning in 3D scene understanding has yet to be explored. In this paper, we make the first attempt to investigate how CLIP knowledge benefits 3D scene understanding. We propose CLIP2Scene, a simple yet effective framework that transfers CLIP knowledge from 2D image-text pre-trained models to a 3D point cloud network. We show that the pre-trained 3D network yields impressive performance on various downstream tasks, i.e., annotation-free and fine-tuning with labelled data for semantic segmentation. Specifically, built upon CLIP, we design a Semantic-driven Cross-modal Contrastive Learning framework that pre-trains a 3D network via semantic and spatial-temporal consistency regularization. For the former, we first leverage CLIP's text semantics to select the positive and negative point samples and then employ the contrastive loss to train the 3D network. In terms of the latter, we force the consistency between the temporally coherent point cloud features and their corresponding image features. We conduct experiments on SemanticKITTI, nuScenes, and ScanNet. For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20.8% and 25.08% mIoU on nuScenes and ScanNet, respectively. When fine-tuned with 1% or 100% labelled data, our method significantly outperforms other self-supervised methods, with improvements of 8% and 1% mIoU, respectively. Furthermore, we demonstrate the generalizability for handling cross-domain datasets. Code is publicly available11https://github.com/runnanchen/CLIP2Scene..",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1502,
+    "label": 10,
+    "text": "Title: Mastering Diverse Domains through World Models\nAbstract: General intelligence requires solving tasks across many domains. Current reinforcement learning algorithms carry this potential but are held back by the resources and knowledge required to tune them for new tasks. We present DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches across a wide range of domains with fixed hyperparameters. These domains include continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies, and reward scales. We observe favorable scaling properties of DreamerV3, with larger models directly translating to higher data-efficiency and final performance. Applied out of the box, DreamerV3 is the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in artificial intelligence. Our general algorithm makes reinforcement learning broadly applicable and allows scaling to hard decision-making problems.",
+    "neighbors": [
+      2029
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1503,
+    "label": 16,
+    "text": "Title: Synthetic Data for Face Recognition: Current State and Future Prospects\nAbstract: Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition.",
+    "neighbors": [
+      1744
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1504,
+    "label": 2,
+    "text": "Title: Normalization for Multimodal Type Theory\nAbstract: We prove normalization for MTT, a general multimodal dependent type theory capable of expressing modal type theories for guarded recursion, internalized parametricity, and various other prototypical modal situations. We prove that deciding type checking and conversion in MTT can be reduced to deciding the equality of modalities in the underlying modal situation, immediately yielding a type checking algorithm for all instantiations of MTT in the literature. This proof follows from a generalization of synthetic Tait computability\u2014an abstract approach to gluing proofs\u2014to account for modalities. This extension is based on MTT itself, so that this proof also constitutes a significant case study of MTT.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1505,
+    "label": 31,
+    "text": "Title: Inference-time Re-ranker Relevance Feedback for Neural Information Retrieval\nAbstract: Neural information retrieval often adopts a retrieve-and-rerank framework: a bi-encoder network first retrieves K (e.g., 100) candidates that are then re-ranked using a more powerful cross-encoder model to rank the better candidates higher. The re-ranker generally produces better candidate scores than the retriever, but is limited to seeing only the top K retrieved candidates, thus providing no improvements in retrieval performance as measured by Recall@K. In this work, we leverage the re-ranker to also improve retrieval by providing inference-time relevance feedback to the retriever. Concretely, we update the retriever's query representation for a test instance using a lightweight inference-time distillation of the re-ranker's prediction for that instance. The distillation loss is designed to bring the retriever's candidate scores closer to those of the re-ranker. A second retrieval step is then performed with the updated query vector. We empirically show that our approach, which can serve arbitrary retrieve-and-rerank pipelines, significantly improves retrieval recall in multiple domains, languages, and modalities.",
+    "neighbors": [
+      1547
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1506,
+    "label": 16,
+    "text": "Title: Leveraging Model Fusion for Improved License Plate Recognition\nAbstract: License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, there has been a noticeable lack of studies exploring the potential improvements in results by fusing the outputs from multiple recognition models. This research aims to fill this gap by investigating the combination of up to 12 different models using straightforward approaches, such as selecting the most confident prediction or employing majority vote-based strategies. Our experiments encompass a wide range of datasets, revealing substantial benefits of fusion approaches in both intra- and cross-dataset setups. Essentially, fusing multiple models reduces considerably the likelihood of obtaining subpar performance on a particular dataset/scenario. We also found that combining models based on their speed is an appealing approach. Specifically, for applications where the recognition task can tolerate some additional time, though not excessively, an effective strategy is to combine 4-6 models. These models may not be the most accurate individually, but their fusion strikes an optimal balance between accuracy and speed.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1507,
+    "label": 30,
+    "text": "Title: Are LLMs All You Need for Task-Oriented Dialogue?\nAbstract: Instructions-tuned Large Language Models (LLMs) gained recently huge popularity thanks to their ability to interact with users through conversation. In this work we aim to evaluate their ability to complete multi-turn tasks and interact with external databases in the context of established task-oriented dialogue benchmarks. We show that for explicit belief state tracking, LLMs underperform compared to specialized task-specific models. Nevertheless, they show ability to guide the dialogue to successful ending if given correct slot values. Furthermore this ability improves with access to true belief state distribution or in-domain examples.",
+    "neighbors": [
+      1052,
+      1284,
+      1462,
+      1543,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1508,
+    "label": 18,
+    "text": "Title: A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms\nAbstract: The transistor celebrated its 75th birthday in 2022. The continued scaling of the transistor defined by Moore\u2019s law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning (ML) and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.",
+    "neighbors": [
+      1583,
+      1987,
+      2200
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1509,
+    "label": 24,
+    "text": "Title: TorchRL: A data-driven decision-making library for PyTorch\nAbstract: Striking a balance between integration and modularity is crucial for a machine learning library to be versatile and user-friendly, especially in handling decision and control tasks that involve large development teams and complex, real-world data, and environments. To address this issue, we propose TorchRL, a generalistic control library for PyTorch that provides well-integrated, yet standalone components. With a versatile and robust primitive design, TorchRL facilitates streamlined algorithm development across the many branches of Reinforcement Learning (RL) and control. We introduce a new PyTorch primitive, TensorDict, as a flexible data carrier that empowers the integration of the library's components while preserving their modularity. Hence replay buffers, datasets, distributed data collectors, environments, transforms and objectives can be effortlessly used in isolation or combined. We provide a detailed description of the building blocks, supporting code examples and an extensive overview of the library across domains and tasks. Finally, we show comparative benchmarks to demonstrate its computational efficiency. TorchRL fosters long-term support and is publicly available on GitHub for greater reproducibility and collaboration within the research community. The code is opensourced on https://github.com/pytorch/rl.",
+    "neighbors": [
+      1153
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1510,
+    "label": 8,
+    "text": "Title: Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning\nAbstract: In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple radio access technology (RAT). An intelligent traffic steering mechanism can play an important role to maintain seamless user experience by choosing appropriate RAT (5G or LTE) dynamically for a specific user traffic flow with certain QoS requirements. In this paper, we propose a novel traffic steering mechanism based on Deep Q-learning that can automate traffic steering decisions in a dynamic environment having multiple RATs, and maintain diverse QoS requirements for different traffic classes. The proposed method is compared with two baseline algorithms: a heuristic-based algorithm and Q-learning-based traffic steering. Compared to the Q-learning and heuristic baselines, our results show that the proposed algorithm achieves better performance in terms of 6% and 10% higher average system throughput, and 23% and 33% lower network delay, respectively.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1511,
+    "label": 34,
+    "text": "Title: Knapsack with Small Items in Near-Quadratic Time\nAbstract: The Bounded Knapsack problem is one of the most fundamental NP-complete problems at the intersection of computer science, optimization, and operations research. A recent line of research worked towards understanding the complexity of pseudopolynomial-time algorithms for Bounded Knapsack parameterized by the maximum item weight $w_{\\mathrm{max}}$ and the number of items $n$. A conditional lower bound rules out that Bounded Knapsack can be solved in time $O((n+w_{\\mathrm{max}})^{2-\\delta})$ for any $\\delta>0$ [Cygan, Mucha, Wegrzycki, Wlodarczyk'17, K\\\"unnemann, Paturi, Schneider'17]. This raised the question whether Bounded Knapsack can be solved in time $\\tilde O((n+w_{\\mathrm{max}})^2)$. The quest of resolving this question lead to algorithms that run in time $\\tilde O(n^3 w_{\\mathrm{max}}^2)$ [Tamir'09], $\\tilde O(n^2 w_{\\mathrm{max}}^2)$ and $\\tilde O(n w_{\\mathrm{max}}^3)$ [Bateni, Hajiaghayi, Seddighin, Stein'18], $O(n^2 w_{\\mathrm{max}}^2)$ and $\\tilde O(n w_{\\mathrm{max}}^2)$ [Eisenbrand and Weismantel'18], $O(n + w_{\\mathrm{max}}^3)$ [Polak, Rohwedder, Wegrzycki'21], and very recently $\\tilde O(n + w_{\\mathrm{max}}^{12/5})$ [Chen, Lian, Mao, Zhang'23]. In this paper we resolve this question by designing an algorithm for Bounded Knapsack with running time $\\tilde O(n + w_{\\mathrm{max}}^2)$, which is conditionally near-optimal.",
+    "neighbors": [
+      875,
+      948,
+      1518,
+      1734
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1512,
+    "label": 4,
+    "text": "Title: On the Impact of the Hardware Warm-Up Time on Deep Learning-Based RF Fingerprinting\nAbstract: Deep learning-based RF fingerprinting offers great potential for improving the security robustness of various emerging wireless networks. Although much progress has been done in enhancing fingerprinting methods, the impact of device hardware stabilization and warm-up time on the achievable fingerprinting performances has not received adequate attention. As such, this paper focuses on addressing this gap by investigating and shedding light on what could go wrong if the hardware stabilization aspects are overlooked. Specifically, our experimental results show that when the deep learning models are trained with data samples captured after the hardware stabilizes but tested with data captured right after powering on the devices, the device classification accuracy drops below 37%. However, when both the training and testing data are captured after the stabilization period, the achievable average accuracy exceeds 99%, when the model is trained and tested on the same day, and achieves 88% and 96% when the model is trained on one day but tested on another day, for the wireless and wired scenarios, respectively. Additionally, in this work, we leverage simulation and testbed experimentation to explain the cause behind the I/Q signal behavior observed during the device hardware warm-up time that led to the RF fingerprinting performance degradation. Furthermore, we release a large WiFi dataset, containing both unstable (collected during the warm-up period) and stable (collected after the warm-up period) captures across multiple days. Our work contributes datasets, explanations, and guidelines to enhance the robustness of RF fingerprinting in securing emerging wireless networks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1513,
+    "label": 3,
+    "text": "Title: Finding differences in perspectives between designers and engineers to develop trustworthy AI for autonomous cars\nAbstract: In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1514,
+    "label": 31,
+    "text": "Title: Effective Visualization and Analysis of Recommender Systems\nAbstract: Recommender system exists everywhere in the business world. From Goodreads to TikTok, customers of internet products become more addicted to the products thanks to the technology. Industrial practitioners focus on increasing the technical accuracy of recommender systems while at same time balancing other factors such as diversity and serendipity. In spite of the length of the research and development history of recommender systems, there has been little discussion on how to take advantage of visualization techniques to facilitate the algorithmic design of the technology. In this paper, we use a series of data analysis and visualization techniques such as Takens Embedding, Determinantal Point Process and Social Network Analysis to help people develop effective recommender systems by predicting intermediate computational cost and output performance. Our work is pioneering in the field, as to our limited knowledge, there have been few publications (if any) on visualization of recommender systems.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1515,
+    "label": 16,
+    "text": "Title: Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement\nAbstract: While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent research suggests that this replication may stem from the insufficient generalization of training data captions and duplication of training images, effective mitigation strategies remain elusive. To address this gap, our paper first introduces a generality score that measures the caption generality and employ large language model (LLM) to generalize training captions. Subsequently, we leverage generalized captions and propose a novel dual fusion enhancement approach to mitigate the replication of diffusion models. Our empirical results demonstrate that our proposed methods can significantly reduce replication by 43.5% compared to the original diffusion model while maintaining the diversity and quality of generations.",
+    "neighbors": [
+      268,
+      1112,
+      1306,
+      1863,
+      1902,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1516,
+    "label": 30,
+    "text": "Title: SSD-2: Scaling and Inference-time Fusion of Diffusion Language Models\nAbstract: Diffusion-based language models (LMs) have been shown to be competent generative models that are easy to control at inference and are a promising alternative to autoregressive LMs. While autoregressive LMs have benefited immensely from scaling and instruction-based learning, existing studies on diffusion LMs have been conducted on a relatively smaller scale. Starting with a recently proposed diffusion model SSD-LM, in this work we explore methods to scale it from 0.4B to 13B parameters, proposing several techniques to improve its training and inference efficiency. We call the new model SSD-2. We further show that this model can be easily finetuned to follow instructions. Finally, leveraging diffusion models' capability at inference-time control, we show that SSD-2 facilitates novel ensembles with 100x smaller models that can be customized and deployed by individual users. We find that compared to autoregressive models, the collaboration between diffusion models is more effective, leading to higher-quality and more relevant model responses due to their ability to incorporate bi-directional contexts.",
+    "neighbors": [
+      566,
+      1617,
+      1863,
+      2087
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1517,
+    "label": 31,
+    "text": "Title: Evaluating Temporal Persistence Using Replicability Measures\nAbstract: In real-world Information Retrieval (IR) experiments, the Evaluation Environment (EE) is exposed to constant change. Documents are added, removed, or updated, and the information need and the search behavior of users is evolving. Simultaneously, IR systems are expected to retain a consistent quality. The LongEval Lab seeks to investigate the longitudinal persistence of IR systems, and in this work, we describe our participation. We submitted runs of five advanced retrieval systems, namely a Reciprocal Rank Fusion (RRF) approach, ColBERT, monoT5, Doc2Query, and E5, to both sub-tasks. Further, we cast the longitudinal evaluation as a replicability study to better understand the temporal change observed. As a result, we quantify the persistence of the submitted runs and see great potential in this evaluation method.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1518,
+    "label": 34,
+    "text": "Title: Approximating Knapsack and Partition via Dense Subset Sums\nAbstract: Knapsack and Partition are two important additive problems whose fine-grained complexities in the $(1-\\varepsilon)$-approximation setting are not yet settled. In this work, we make progress on both problems by giving improved algorithms. - Knapsack can be $(1 - \\varepsilon)$-approximated in $\\tilde O(n + (1/\\varepsilon) ^ {2.2} )$ time, improving the previous $\\tilde O(n + (1/\\varepsilon) ^ {2.25} )$ by Jin (ICALP'19). There is a known conditional lower bound of $(n+\\varepsilon)^{2-o(1)}$ based on $(\\min,+)$-convolution hypothesis. - Partition can be $(1 - \\varepsilon)$-approximated in $\\tilde O(n + (1/\\varepsilon) ^ {1.25} )$ time, improving the previous $\\tilde O(n + (1/\\varepsilon) ^ {1.5} )$ by Bringmann and Nakos (SODA'21). There is a known conditional lower bound of $(1/\\varepsilon)^{1-o(1)}$ based on Strong Exponential Time Hypothesis. Both of our new algorithms apply the additive combinatorial results on dense subset sums by Galil and Margalit (SICOMP'91), Bringmann and Wellnitz (SODA'21). Such techniques have not been explored in the context of Knapsack prior to our work. In addition, we design several new methods to speed up the divide-and-conquer steps which naturally arise in solving additive problems.",
+    "neighbors": [
+      875,
+      948,
+      1511,
+      1734,
+      1753
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1519,
+    "label": 27,
+    "text": "Title: RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning\nAbstract: We introduce RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. RAMP consists of beams that a robot must assemble into specified goal configurations using pegs as fasteners. As such it assesses planning and execution capabilities, and poses challenges in perception, reasoning, manipulation, diagnostics, fault recovery and goal parsing. RAMP has been designed to be accessible and extensible. Parts are either 3D printed or otherwise constructed from materials that are readily obtainable. The part design and detailed instructions are publicly available. In order to broaden community engagement, RAMP incorporates fixtures such as April Tags which enable researchers to focus on individual sub-tasks of the assembly challenge if desired. We provide a full digital twin as well as rudimentary baselines to enable rapid progress. Our vision is for RAMP to form the substrate for a community-driven endeavour that evolves as capability matures.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1520,
+    "label": 30,
+    "text": "Title: Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness\nAbstract: The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we focus on assessing the overall ability of ChatGPT using 7 fine-grained information extraction (IE) tasks. Specially, we present the systematically analysis by measuring ChatGPT's performance, explainability, calibration, and faithfulness, and resulting in 15 keys from either the ChatGPT or domain experts. Our findings reveal that ChatGPT's performance in Standard-IE setting is poor, but it surprisingly exhibits excellent performance in the OpenIE setting, as evidenced by human evaluation. In addition, our research indicates that ChatGPT provides high-quality and trustworthy explanations for its decisions. However, there is an issue of ChatGPT being overconfident in its predictions, which resulting in low calibration. Furthermore, ChatGPT demonstrates a high level of faithfulness to the original text in the majority of cases. We manually annotate and release the test sets of 7 fine-grained IE tasks contains 14 datasets to further promote the research. The datasets and code are available at https://github.com/pkuserc/ChatGPT_for_IE.",
+    "neighbors": [
+      69,
+      75,
+      719,
+      818,
+      834,
+      907,
+      924,
+      975,
+      1035,
+      1133,
+      1349,
+      1636,
+      1727,
+      1797,
+      1943,
+      1950,
+      1952,
+      2244,
+      2252,
+      2254,
+      2265,
+      2281
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1521,
+    "label": 24,
+    "text": "Title: Stabilized Neural Differential Equations for Learning Constrained Dynamics\nAbstract: Many successful methods to learn dynamical systems from data have recently been introduced. However, assuring that the inferred dynamics preserve known constraints, such as conservation laws or restrictions on the allowed system states, remains challenging. We propose stabilized neural differential equations (SNDEs), a method to enforce arbitrary manifold constraints for neural differential equations. Our approach is based on a stabilization term that, when added to the original dynamics, renders the constraint manifold provably asymptotically stable. Due to its simplicity, our method is compatible with all common neural ordinary differential equation (NODE) models and broadly applicable. In extensive empirical evaluations, we demonstrate that SNDEs outperform existing methods while extending the scope of which types of constraints can be incorporated into NODE training.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1522,
+    "label": 8,
+    "text": "Title: Democratizing LEO Satellite Network Measurement\nAbstract: Low Earth Orbit (LEO) satellite networks are quickly gaining traction with promises of impressively low latency, high bandwidth, and global reach. However, the research community knows relatively little about their operation and performance in practice. The obscurity is largely due to the high barrier of entry for measuring LEO networks, which requires deploying specialized hardware or recruiting large numbers of satellite Internet customers. In this paper, we introduce LEO HitchHiking, a new methodology that democratizes global visibility into LEO satellite networks. HitchHiking builds on the observation that Internet exposed services that use LEO Internet can reveal satellite network architecture and performance, without needing physical hardware. We evaluate HitchHiking against ground truth measurements and prior methods, showing that it provides more coverage, speed, and accuracy. With HitchHiking, we complete the largest study to date of Starlink network latency, measuring over 1,600 users across 13~countries. We uncover unexpected patterns in latency that surface how LEO routing is more complex than previously understood. Finally, we conclude with recommendations for future research on LEO networks.",
+    "neighbors": [
+      1004
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1523,
+    "label": 16,
+    "text": "Title: Graph-based Asynchronous Event Processing for Rapid Object Recognition\nAbstract: Different from traditional video cameras, event cam- eras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. In this paper, we introduce a novel graph-based framework for event cameras, namely SlideGCN. Unlike some recent graph-based methods that use groups of events as input, our approach can efficiently process data event-by-event, unlock the low latency nature of events data while still maintaining the graph\u2019s structure internally. For fast graph construction, we develop a radius search algorithm, which better exploits the partial regular structure of event cloud against k-d tree based generic methods. Experiments show that our method reduces the computational complexity up to 100 times with respect to current graph-based methods while keeping state-of-the-art performance on object recognition. Moreover, we verify the superiority of event-wise processing with our method. When the state becomes stable, we can give a prediction with high confidence, thus making an early recognition.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1524,
+    "label": 27,
+    "text": "Title: Nonlinear MPC for Full-Pose Manipulation of a Cable-Suspended Load using Multiple UAVs\nAbstract: In this work, we propose a centralized control method based on nonlinear model predictive control to let multiple UAVs manipulate the full pose of an object via cables. At the best of the authors knowledge this is the first method that takes into account the full nonlinear model of the load-UAV system, and ensures all the feasibility constraints concerning the UAV maximumum and minimum thrusts, the collision avoidance between the UAVs, cables and load, and the tautness and maximum tension of the cables. By taking into account the above factors, the proposed control algorithm can fully exploit the performance of UAVs and facilitate the speed of operation. Simulations are conducted to validate the algorithm to achieve fast and safe manipulation of the pose of a rigid-body payload using multiple UAVs. We demonstrate that the computational time of the proposed method is sufficiently small (<100 ms) for UAV teams composed by up to 10 units, which makes it suitable for a huge variety of future industrial applications, such as autonomous building construction and heavy-load transportation.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1525,
+    "label": 16,
+    "text": "Title: A Unified Visual Information Preservation Framework for Self-supervised Pre-Training in Medical Image Analysis\nAbstract: Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations. Codes and models are available at https://github.com/RL4M/PCRLv2.",
+    "neighbors": [
+      356,
+      1685,
+      1698
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1526,
+    "label": 16,
+    "text": "Title: EVA-CLIP: Improved Training Techniques for CLIP at Scale\nAbstract: Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models that significantly improve the efficiency and effectiveness of CLIP training. Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs. Notably, our largest 5.0B-parameter EVA-02-CLIP-E/14+ with only 9 billion seen samples achieves 82.0 zero-shot top-1 accuracy on ImageNet-1K val. A smaller EVA-02-CLIP-L/14+ with only 430 million parameters and 6 billion seen samples achieves 80.4 zero-shot top-1 accuracy on ImageNet-1K val. To facilitate open access and open research, we release the complete suite of EVA-CLIP to the community at https://github.com/baaivision/EVA/tree/master/EVA-CLIP.",
+    "neighbors": [
+      719,
+      880,
+      887,
+      983,
+      1050,
+      2030,
+      2064
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1527,
+    "label": 4,
+    "text": "Title: Detecting software vulnerabilities using Language Models\nAbstract: Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately. However, current state-of-the-art deep learning models, such as Convolutional Neural Networks (CNN), and Long Short-Term Memories (LSTMs) require substantial computational resources. This results in a level of overhead that makes their implementation unfeasible for deployment in realtime settings. This study presents a novel transformer-based vulnerability detection framework, referred to as VulDetect, which is achieved through the fine-tuning of a pre-trained large language model, (GPT) on various benchmark datasets of vulnerable code. Our empirical findings indicate that our framework is capable of identifying vulnerable software code with an accuracy of up to 92.65%. Our proposed technique outperforms SyseVR and VulDeBERT, two state-of-the-art vulnerability detection techniques",
+    "neighbors": [
+      62
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1528,
+    "label": 25,
+    "text": "Title: A Unified Audio-Visual Learning Framework for Localization, Separation, and Recognition\nAbstract: The ability to accurately recognize, localize and separate sound sources is fundamental to any audio-visual perception task. Historically, these abilities were tackled separately, with several methods developed independently for each task. However, given the interconnected nature of source localization, separation, and recognition, independent models are likely to yield suboptimal performance as they fail to capture the interdependence between these tasks. To address this problem, we propose a unified audio-visual learning framework (dubbed OneAVM) that integrates audio and visual cues for joint localization, separation, and recognition. OneAVM comprises a shared audio-visual encoder and task-specific decoders trained with three objectives. The first objective aligns audio and visual representations through a localized audio-visual correspondence loss. The second tackles visual source separation using a traditional mix-and-separate framework. Finally, the third objective reinforces visual feature separation and localization by mixing images in pixel space and aligning their representations with those of all corresponding sound sources. Extensive experiments on MUSIC, VGG-Instruments, VGG-Music, and VGGSound datasets demonstrate the effectiveness of OneAVM for all three tasks, audio-visual source localization, separation, and nearest neighbor recognition, and empirically demonstrate a strong positive transfer between them.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1529,
+    "label": 9,
+    "text": "Title: Uniform Tests and Algorithmic Thermodynamic Entropy\nAbstract: We prove that given a computable metric space and two computable measures, the set of points that have high universal uniform test scores with respect to the first measure will have a lower bound with respect to the second measure. This result is transferred to thermodynamics, showing that algorithmic thermodynamic entropy must oscillate in the presence of dynamics. Another application is that outliers will become emergent in computable dynamics of computable metric spaces.",
+    "neighbors": [
+      1657
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1530,
+    "label": 28,
+    "text": "Title: Rate Region of MIMO RIS-assisted Broadcast Channels with Rate Splitting and Improper Signaling\nAbstract: In this paper, we study the achievable rate region of 1-layer rate splitting (RS) in the presence of hardware impairment (HWI) and improper Gaussian signaling (IGS) for a single-cell reconfigurable intelligent surface (RIS) assisted broadcast channel (BC). We assume that the transceivers may suffer from an imbalance in in-band and quadrature signals, which is known as I/Q imbalance (IQI). The received signal and noise can be improper when there exists IQI. Therefore, we employ IGS to compensate for IQI as well as to manage interference. Our results show that RS and RIS can significantly enlarge the rate region, where the role of RS is to manage interference while RIS mainly improves the coverage.",
+    "neighbors": [
+      1143,
+      1410,
+      2253
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1531,
+    "label": 12,
+    "text": "Title: ChatGPT believes it is conscious\nAbstract: The development of advanced generative chat models, such as ChatGPT, has raised questions about the potential consciousness of these tools and the extent of their general artificial intelligence. ChatGPT consistent avoidance of passing the test is here overcome by asking ChatGPT to apply the Turing test to itself. This explores the possibility of the model recognizing its own sentience. In its own eyes, it passes this test. ChatGPT's self-assessment makes serious implications about our understanding of the Turing test and the nature of consciousness. This investigation concludes by considering the existence of distinct types of consciousness and the possibility that the Turing test is only effective when applied between consciousnesses of the same kind. This study also raises intriguing questions about the nature of AI consciousness and the validity of the Turing test as a means of verifying such consciousness.",
+    "neighbors": [
+      1044,
+      1713,
+      1983
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1532,
+    "label": 16,
+    "text": "Title: The 2023 Video Similarity Dataset and Challenge\nAbstract: This work introduces a dataset, benchmark, and challenge for the problem of video copy detection and localization. The problem comprises two distinct but related tasks: determining whether a query video shares content with a reference video (\"detection\"), and additionally temporally localizing the shared content within each video (\"localization\"). The benchmark is designed to evaluate methods on these two tasks, and simulates a realistic needle-in-haystack setting, where the majority of both query and reference videos are\"distractors\"containing no copied content. We propose a metric that reflects both detection and localization accuracy. The associated challenge consists of two corresponding tracks, each with restrictions that reflect real-world settings. We provide implementation code for evaluation and baselines. We also analyze the results and methods of the top submissions to the challenge. The dataset, baseline methods and evaluation code is publicly available and will be discussed at a dedicated CVPR'23 workshop.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1533,
+    "label": 16,
+    "text": "Title: Local Region Perception and Relationship Learning Combined with Feature Fusion for Facial Action Unit Detection\nAbstract: Human affective behavior analysis plays a vital role in human-computer interaction (HCI) systems. In this paper, we introduce our submission to the CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW). We propose a single-stage trained AU detection framework. Specifically, in order to effectively extract facial local region features related to AU detection, we use a local region perception module to effectively extract features of different AUs. Meanwhile, we use a graph neural network-based relational learning module to capture the relationship between AUs. In addition, considering the role of the overall feature of the target face on AU detection, we also use the feature fusion module to fuse the feature information extracted by the backbone network and the AU feature information extracted by the relationship learning module. We also adopted some sampling methods, data augmentation techniques and post-processing strategies to further improve the performance of the model. On the official test set, our method ranks third in the AU detection track. This result and subsequent ablation experiments prove the effectiveness of our proposed method.",
+    "neighbors": [
+      220,
+      1241,
+      1541,
+      1916,
+      2101
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1534,
+    "label": 24,
+    "text": "Title: Enriching Disentanglement: Definitions to Metrics\nAbstract: Disentangled representation learning is a challenging task that involves separating multiple factors of variation in complex data. Although various metrics for learning and evaluating disentangled representations have been proposed, it remains unclear what these metrics truly quantify and how to compare them. In this work, we study the definitions of disentanglement given by first-order equational predicates and introduce a systematic approach for transforming an equational definition into a compatible quantitative metric based on enriched category theory. Specifically, we show how to replace (i) equality with metric or divergence, (ii) logical connectives with order operations, (iii) universal quantifier with aggregation, and (iv) existential quantifier with the best approximation. Using this approach, we derive metrics for measuring the desired properties of a disentangled representation extractor and demonstrate their effectiveness on synthetic data. Our proposed approach provides practical guidance for researchers in selecting appropriate evaluation metrics and designing effective learning algorithms for disentangled representation learning.",
+    "neighbors": [
+      567
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1535,
+    "label": 4,
+    "text": "Title: Password-Stealing without Hacking: Wi-Fi Enabled Practical Keystroke Eavesdropping\nAbstract: The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches, yet existing attacks relying on Wi-Fi CSI (channel state information) demand hacking Wi-Fi hardware to obtain desired CSIs. Since such hacking has proven prohibitively hard due to compact hardware, its feasibility in keeping up with fast-developing Wi-Fi technology becomes very questionable. To this end, we propose WiKI-Eve to eavesdrop keystrokes on smartphones without the need for hacking. WiKI-Eve exploits a new feature, BFI (beamforming feedback information), offered by latest Wi-Fi hardware: since BFI is transmitted from a smartphone to an AP in clear-text, it can be overheard (hence eavesdropped) by any other Wi-Fi devices switching to monitor mode. As existing keystroke inference methods offer very limited generalizability, WiKI-Eve further innovates in an adversarial learning scheme to enable its inference generalizable towards unseen scenarios. We implement WiKI-Eve and conduct extensive evaluation on it; the results demonstrate that WiKI-Eve achieves 88.9% inference accuracy for individual keystrokes and up to 65.8% top-10 accuracy for stealing passwords of mobile applications (e.g., WeChat).",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1536,
+    "label": 25,
+    "text": "Title: A Survey of AI Music Generation Tools and Models\nAbstract: In this work, we provide a comprehensive survey of AI music generation tools, including both research projects and commercialized applications. To conduct our analysis, we classified music generation approaches into three categories: parameter-based, text-based, and visual-based classes. Our survey highlights the diverse possibilities and functional features of these tools, which cater to a wide range of users, from regular listeners to professional musicians. We observed that each tool has its own set of advantages and limitations. As a result, we have compiled a comprehensive list of these factors that should be considered during the tool selection process. Moreover, our survey offers critical insights into the underlying mechanisms and challenges of AI music generation.",
+    "neighbors": [
+      1033,
+      1156,
+      1958
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1537,
+    "label": 16,
+    "text": "Title: ImageBind One Embedding Space to Bind Them All\nAbstract: We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. ImageBind can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications \u2018out-of-the-box\u2019 including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, we show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks.",
+    "neighbors": [
+      176,
+      392,
+      513,
+      602,
+      719,
+      779,
+      836,
+      983,
+      1799,
+      1950,
+      2178
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1538,
+    "label": 16,
+    "text": "Title: Gradient Domain Diffusion Models for Image Synthesis\nAbstract: Diffusion models are getting popular in generative image and video synthesis. However, due to the diffusion process, they require a large number of steps to converge. To tackle this issue, in this paper, we propose to perform the diffusion process in the gradient domain, where the convergence becomes faster. There are two reasons. First, thanks to the Poisson equation, the gradient domain is mathematically equivalent to the original image domain. Therefore, each diffusion step in the image domain has a unique corresponding gradient domain representation. Second, the gradient domain is much sparser than the image domain. As a result, gradient domain diffusion models converge faster. Several numerical experiments confirm that the gradient domain diffusion models are more efficient than the original diffusion models. The proposed method can be applied in a wide range of applications such as image processing, computer vision and machine learning tasks.",
+    "neighbors": [
+      831,
+      1668,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1539,
+    "label": 16,
+    "text": "Title: Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models\nAbstract: Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and finetuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution $512 \\times 1024$, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pretrained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to $1280 \\times 2048$. We show that the temporal layers trained in this way generalize to different finetuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://nv-tlabs.github.io/VideoLDM/",
+    "neighbors": [
+      43,
+      860,
+      1020,
+      1590,
+      1978,
+      2085,
+      2190
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1540,
+    "label": 16,
+    "text": "Title: Universal Instance Perception as Object Discovery and Retrieval\nAbstract: All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field has been split into multiple independent sub-tasks. In this work, we present a universal instance perception model of the next generation, termed UNINEXT. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts. This unified formulation brings the following benefits: (1) enormous data from different tasks and label vocabularies can be exploited for jointly training general instance-level representations, which is especially beneficial for tasks lacking in training data. (2) the unified model is parameter-efficient and can save redundant computation when handling multiple tasks simultaneously. UNINEXT shows superior performance on 20 challenging benchmarks from 10 instance-level tasks including classical image-level tasks (object detection and instance segmentation), vision-and-language tasks (referring expression comprehension and segmentation), and six video-level object tracking tasks. Code is available at https://github.com/MasterBin-IIAU/UNINEXT.",
+    "neighbors": [
+      34,
+      1071,
+      1262,
+      1626,
+      1642,
+      1714
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1541,
+    "label": 16,
+    "text": "Title: ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges\nAbstract: The 5th ABAW Competition is part of the respective Workshop held in conjunction with IEEE CVPR 2023 and is a continuation of the Competitions held at ECCV 2022, IEEE CVPR 2022, ICCV 2021, IEEE FG 2020 and CVPR 2017 Conferences. It is dedicated at automatically analyzing affect. For this year\u2019s Competition, we feature two corpora: i) an extended version of the Aff-Wild2 database and ii) the Hume-Reaction dataset. The former database is an audiovisual (A/V) one of around 600 videos of around 3M frames and is annotated for: a) two continuous affect dimensions, valence (how positive/negative a person is) and arousal (how active/passive a person is); b) basic expressions (e.g. happiness, neutral); and c) action units (i.e., facial muscle actions). The latter dataset is A/V in which reactions of individuals to emotional stimuli have been annotated for seven emotional expression intensities. Thus the 5th ABAW Competition encompasses four Challenges: i) Valence-Arousal Estimation, ii) Expression Classification, iii) Action Unit Detection, and iv) Emotional Reaction Intensity Estimation. In this paper, we present these Challenges and their corpora, we outline the evaluation metrics and present the baseline systems and top performing teams\u2019 per Challenge along with their obtained performance. More information for the Competition can be found in: https://ibug.doc.ic.ac.uk/resources/ cvpr-2023-5th-abaw.",
+    "neighbors": [
+      142,
+      220,
+      1241,
+      1533,
+      1832,
+      1872,
+      1916,
+      2101
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1542,
+    "label": 3,
+    "text": "Title: SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education. 2nd Teacher Training, January 23-27, 2023, Pula, Croatia. Revised lecture notes\nAbstract: This volume exhibits the revised lecture notes of the 2nd teacher training organized as part of the project Promoting Sustainability as a Fundamental Driver in Software Development Training and Education, held at the Juraj Dobrila University of Pula, Croatia, in the week January 23-27, 2023. It is the Erasmus+ project No. 2020-1-PT01-KA203-078646 - Sustrainable. More details can be found at the project web site https://sustrainable.github.io/ One of the most important contributions of the project are two summer schools. The 2nd SusTrainable Summer School (SusTrainable - 23) will be organized at the University of Coimbra, Portugal, in the week July 10-14, 2023. The summer school will consist of lectures and practical work for master and PhD students in computing science and closely related fields. There will be contributions from Babe\\c{s}-Bolyai University, E\\\"{o}tv\\\"{o}s Lor\\'{a}nd University, Juraj Dobrila University of Pula, Radboud University Nijmegen, Roskilde University, Technical University of Ko\\v{s}ice, University of Amsterdam, University of Coimbra, University of Minho, University of Plovdiv, University of Porto, University of Rijeka. To prepare and streamline the summer school, the consortium organized a teacher training in Pula, Croatia. This was an event of five full days, organized by Tihana Galinac Grbac and Neven Grbac. The Juraj Dobrila University of Pula is very concerned with the sustainability issues. The education, research and management are conducted with sustainability goals in mind. The contributions in the proceedings were reviewed and provide a good overview of the range of topics that will be covered at the summer school. The papers in the proceedings, as well as the very constructive and cooperative teacher training, guarantee the highest quality and beneficial summer school for all participants.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1543,
+    "label": 30,
+    "text": "Title: Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems\nAbstract: When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. Prior works mainly focus on adopting advanced RL techniques to train the ToD agents, while the design of the reward function is not well studied. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents. Specifically, we introduce two generalized objectives for reward-function learning, inspired by the classical learning-to-rank literature. Further, we utilize the learned reward function to guide the training of the E2E ToD agent. With the proposed techniques, we achieve competitive results on the E2E response-generation task on the Multiwoz 2.0 dataset. Source code and checkpoints are publicly released at https://github.com/Shentao-YANG/Fantastic_Reward_ICLR2023.",
+    "neighbors": [
+      1284,
+      1507
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1544,
+    "label": 24,
+    "text": "Title: Large Graph Models: A Perspective\nAbstract: Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large models have not achieved the same level of success as in other fields, such as natural language processing and computer vision. In order to promote applying large models for graphs forward, we present a perspective paper to discuss the challenges and opportunities associated with developing large graph models. First, we discuss the desired characteristics of large graph models. Then, we present detailed discussions from three key perspectives: representation basis, graph data, and graph models. In each category, we provide a brief overview of recent advances and highlight the remaining challenges together with our visions. Finally, we discuss valuable applications of large graph models. We believe this perspective paper is able to encourage further investigations into large graph models, ultimately pushing us one step closer towards artificial general intelligence (AGI).",
+    "neighbors": [
+      118,
+      619,
+      1238,
+      1560,
+      1799,
+      1851,
+      1863,
+      2083,
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+      2113,
+      2281
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1545,
+    "label": 24,
+    "text": "Title: Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics\nAbstract: In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets, such as population genetics, RNA or protein sequences data. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affects the diversity of synthetic data and increases generation times. To address these issues, we use a novel training algorithm that exploits non-equilibrium effects. This approach, applied on the Restricted Boltzmann Machine, improves the model's ability to correctly classify samples and generate high-quality synthetic data in only a few sampling steps. The effectiveness of this method is demonstrated by its successful application to four different types of data: handwritten digits, mutations of human genomes classified by continental origin, functionally characterized sequences of an enzyme protein family, and homologous RNA sequences from specific taxonomies.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1546,
+    "label": 30,
+    "text": "Title: Empowering Cross-lingual Abilities of Instruction-tuned Large Language Models by Translation-following demonstrations\nAbstract: The language ability of Large Language Models (LLMs) is often unbalanced towards English because of the imbalance in the distribution of the pre-training data. This disparity is demanded in further fine-tuning and affecting the cross-lingual abilities of LLMs. In this paper, we propose to empower Instructiontuned LLMs (It-LLMs) in languages other than English by building semantic alignment between them. Hence, we propose CrossAlpaca, an It-LLM with cross-lingual instruction-following and Translation-following demonstrations to improve semantic alignment between languages. We validate our approach on the multilingual Question Answering (QA) benchmarks XQUAD and MLQA and adapted versions of MMLU and BBH. Our models, tested over six different languages, outperform the It-LLMs tuned on monolingual data. The final results show that instruction tuning on non-English data is not enough and that semantic alignment can be further improved by Translation-following demonstrations.",
+    "neighbors": [
+      11,
+      949,
+      1052,
+      1098,
+      1146,
+      1461,
+      1952
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1547,
+    "label": 31,
+    "text": "Title: Online Distillation for Pseudo-Relevance Feedback\nAbstract: Model distillation has emerged as a prominent technique to improve neural search models. To date, distillation taken an offline approach, wherein a new neural model is trained to predict relevance scores between arbitrary queries and documents. In this paper, we explore a departure from this offline distillation strategy by investigating whether a model for a specific query can be effectively distilled from neural re-ranking results (i.e., distilling in an online setting). Indeed, we find that a lexical model distilled online can reasonably replicate the re-ranking of a neural model. More importantly, these models can be used as queries that execute efficiently on indexes. This second retrieval stage can enrich the pool of documents for re-ranking by identifying documents that were missed in the first retrieval stage. Empirically, we show that this approach performs favourably when compared with established pseudo relevance feedback techniques, dense retrieval methods, and sparse-dense ensemble\"hybrid\"approaches.",
+    "neighbors": [
+      1505
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1548,
+    "label": 30,
+    "text": "Title: The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset\nAbstract: As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.",
+    "neighbors": [
+      11,
+      855,
+      1199,
+      1430,
+      1556,
+      1617,
+      1733,
+      1907,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1549,
+    "label": 16,
+    "text": "Title: Improved Region Proposal Network for Enhanced Few-Shot Object Detection\nAbstract: Despite significant success of deep learning in object detection tasks, the standard training of deep neural networks requires access to a substantial quantity of annotated images across all classes. Data annotation is an arduous and time-consuming endeavor, particularly when dealing with infrequent objects. Few-shot object detection (FSOD) methods have emerged as a solution to the limitations of classic object detection approaches based on deep learning. FSOD methods demonstrate remarkable performance by achieving robust object detection using a significantly smaller amount of training data. A challenge for FSOD is that instances from novel classes that do not belong to the fixed set of training classes appear in the background and the base model may pick them up as potential objects. These objects behave similarly to label noise because they are classified as one of the training dataset classes, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during the FSOD training stage to improve FSOD performance. Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. We test our approach and COCO and PASCAL VOC baselines that are commonly used in FSOD literature. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods. Our implementation is provided as a supplement to support reproducibility of the results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1550,
+    "label": 2,
+    "text": "Title: Lemmas: Generation, Selection, Application\nAbstract: Noting that lemmas are a key feature of mathematics, we engage in an investigation of the role of lemmas in automated theorem proving. The paper describes experiments with a combined system involving learning technology that generates useful lemmas for automated theorem provers, demonstrating improvement for several representative systems and solving a hard problem not solved by any system for twenty years. By focusing on condensed detachment problems we simplify the setting considerably, allowing us to get at the essence of lemmas and their role in proof search.",
+    "neighbors": [
+      571
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1551,
+    "label": 10,
+    "text": "Title: GPU-accelerated matrix cover algorithm for multiple patterning layout decomposition\nAbstract: Multiple patterning lithography (MPL) is regarded as one of the most promising ways of overcoming the resolution limitations of conventional optical lithography due to the delay of next-generation lithography technology. As the feature size continues to decrease, layout decomposition for multiple patterning lithography (MPLD) technology is becoming increasingly crucial for improving the manufacturability in advanced nodes. The decomposition process refers to assigning the layout features to different mask layers according to the design rules and density requirements. When the number of masks k \u2265 3, the MPLD problems are N P-hard and thus may suffer from runtime overhead for practical designs. However, the number of layout patterns is increasing exponentially in industrial layouts, which hinders the runtime performance of MPLD models. In this research, we substitute the CPU\u2019s dance link data structure with parallel GPU matrix operations to accelerate the solution for exact coverbased MPLD algorithms. Experimental results demonstrate that our system is capable of full-scale, lightningfast layout decomposition, which can achieve more than 10\u00d7 speed-up without quality degradation compared to state-of-the-art layout decomposition methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1552,
+    "label": 4,
+    "text": "Title: Automated Machine Learning for Deep Learning based Malware Detection\nAbstract: Deep learning (DL) has proven to be effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture search (NAS) and the model's optimal set of hyper-parameters, remains a challenge that requires domain expertise. In addition, many of the proposed state-of-the-art models are very complex and may not be the best fit for different datasets. A promising approach, known as Automated Machine Learning (AutoML), can reduce the domain expertise required to implement a custom DL model. AutoML reduces the amount of human trial-and-error involved in designing DL models, and in more recent implementations can find new model architectures with relatively low computational overhead. This work provides a comprehensive analysis and insights on using AutoML for static and online malware detection. For static, our analysis is performed on two widely used malware datasets: SOREL-20M to demonstrate efficacy on large datasets; and EMBER-2018, a smaller dataset specifically curated to hinder the performance of machine learning models. In addition, we show the effects of tuning the NAS process parameters on finding a more optimal malware detection model on these static analysis datasets. Further, we also demonstrate that AutoML is performant in online malware detection scenarios using Convolutional Neural Networks (CNNs) for cloud IaaS. We compare an AutoML technique to six existing state-of-the-art CNNs using a newly generated online malware dataset with and without other applications running in the background during malware execution.In general, our experimental results show that the performance of AutoML based static and online malware detection models are on par or even better than state-of-the-art models or hand-designed models presented in literature.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1553,
+    "label": 30,
+    "text": "Title: Logically at Factify 2023: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture\nAbstract: In this paper, we present the Logically submissions to De-Factify 2 challenge (DE-FACTIFY 2023) on the task 1 of Multi-Modal Fact Checking. We describes our submissions to this challenge including explored evidence retrieval and selection techniques, pre-trained cross-modal and unimodal models, and a cross-modal veracity model based on the well established Transformer Encoder (TE) architecture which is heavily relies on the concept of self-attention. Exploratory analysis is also conducted on this Factify 2 data set that uncovers the salient multi-modal patterns and hypothesis motivating the architecture proposed in this work. A series of preliminary experiments were done to investigate and benchmarking different pre-trained embedding models, evidence retrieval settings and thresholds. The final system, a standard two-stage evidence based veracity detection system, yields weighted avg. 0.79 on both val set and final blind test set on the task 1, which achieves 3rd place with a small margin to the top performing system on the leaderboard among 9 participants.",
+    "neighbors": [
+      1302,
+      1363
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1554,
+    "label": 30,
+    "text": "Title: Towards Bridging the Digital Language Divide\nAbstract: It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages.' The goal of our paper is to bring attention to a phenomenon that we call linguistic bias: multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. Linguistic bias is manifested in uneven per-language performance even in the case of similar test conditions. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented, and that can even become ethically problematic as they disregard valuable aspects of diversity as well as the needs of the language communities themselves. As our attempt at building diversity-aware language resources, we present a new initiative that aims at reducing linguistic bias through both technological design and methodology, based on an eye-level collaboration with local communities.",
+    "neighbors": [
+      511,
+      2086
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1555,
+    "label": 23,
+    "text": "Title: What Makes a Code Review Useful to OpenDev Developers? An Empirical Investigation\nAbstract: Context: Due to the association of significant efforts, even a minor improvement in the effectiveness of Code Reviews(CR) can incur significant savings for a software development organization. Aim: This study aims to develop a finer grain understanding of what makes a code review comment useful to OSS developers, to what extent a code review comment is considered useful to them, and how various contextual and participant-related factors influence its usefulness level. Method: On this goal, we have conducted a three-stage mixed-method study. We randomly selected 2,500 CR comments from the OpenDev Nova project and manually categorized the comments. We designed a survey of OpenDev developers to better understand their perspectives on useful CRs. Combining our survey-obtained scores with our manually labeled dataset, we trained two regression models - one to identify factors that influence the usefulness of CR comments and the other to identify factors that improve the odds of `Functional' defect identification over the others. Key findings: The results of our study suggest that a CR comment's usefulness is dictated not only by its technical contributions such as defect findings or quality improvement tips but also by its linguistic characteristics such as comprehensibility and politeness. While a reviewer's coding experience positively associates with CR usefulness, the number of mutual reviews, comment volume in a file, the total number of lines added /modified, and CR interval has the opposite associations. While authorship and reviewership experiences for the files under review have been the most popular attributes for reviewer recommendation systems, we do not find any significant association of those attributes with CR usefulness.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1556,
+    "label": 24,
+    "text": "Title: Data-Juicer: A One-Stop Data Processing System for Large Language Models\nAbstract: The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, diverse, and high-quality data. Despite this, existing open-source tools for LLM data processing remain limited and mostly tailored to specific datasets, with an emphasis on the reproducibility of released data over adaptability and usability, inhibiting potential applications. In response, we propose a one-stop, powerful yet flexible and user-friendly LLM data processing system named Data-Juicer. Our system offers over 50 built-in versatile operators and pluggable tools, which synergize modularity, composability, and extensibility dedicated to diverse LLM data processing needs. By incorporating visualized and automatic evaluation capabilities, Data-Juicer enables a timely feedback loop to accelerate data processing and gain data insights. To enhance usability, Data-Juicer provides out-of-the-box components for users with various backgrounds, and fruitful data recipes for LLM pre-training and post-tuning usages. Further, we employ multi-facet system optimization and seamlessly integrate Data-Juicer with both LLM and distributed computing ecosystems, to enable efficient and scalable data processing. Empirical validation of the generated data recipes reveals considerable improvements in LLaMA performance for various pre-training and post-tuning cases, demonstrating up to 7.45% relative improvement of averaged score across 16 LLM benchmarks and 16.25% higher win rate using pair-wise GPT-4 evaluation. The system's efficiency and scalability are also validated, supported by up to 88.7% reduction in single-machine processing time, 77.1% and 73.1% less memory and CPU usage respectively, and 7.91x processing acceleration when utilizing distributed computing ecosystems. Our system, data recipes, and multiple tutorial demos are released, calling for broader research centered on LLM data.",
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+      1800,
+      1840,
+      1972,
+      2087,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1557,
+    "label": 17,
+    "text": "Title: Walk on Stars: A Grid-Free Monte Carlo Method for PDEs with Neumann Boundary Conditions\nAbstract: Grid-free Monte Carlo methods based on the walk on spheres (WoS) algorithm solve fundamental partial differential equations (PDEs) like the Poisson equation without discretizing the problem domain or approximating functions in a finite basis. Such methods hence avoid aliasing in the solution, and evade the many challenges of mesh generation. Yet for problems with complex geometry, practical grid-free methods have been largely limited to basic Dirichlet boundary conditions. We introduce the walk on stars (WoSt) algorithm, which solves linear elliptic PDEs with arbitrary mixed Neumann and Dirichlet boundary conditions. The key insight is that one can efficiently simulate reflecting Brownian motion (which models Neumann conditions) by replacing the balls used by WoS with star-shaped domains. We identify such domains via the closest point on the visibility silhouette, by simply augmenting a standard bounding volume hierarchy with normal information. Overall, WoSt is an easy modification of WoS, and retains the many attractive features of grid-free Monte Carlo methods such as progressive and view-dependent evaluation, trivial parallelization, and sublinear scaling to increasing geometric detail.",
+    "neighbors": [
+      2144
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1558,
+    "label": 24,
+    "text": "Title: Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts\nAbstract: Recurrent Neural Networks (RNNs) have become an integral part of modeling and forecasting frameworks in areas like natural language processing and high-dimensional dynamical systems such as turbulent fluid flows. To improve the accuracy of predictions, RNNs are trained using the Backpropagation Through Time (BPTT) method to minimize prediction loss. During testing, RNNs are often used in autoregressive scenarios where the output of the network is fed back into the input. However, this can lead to the exposure bias effect, as the network was trained to receive ground-truth data instead of its own predictions. This mismatch between training and testing is compounded when the state distributions are different, and the train and test losses are measured. To address this, previous studies have proposed solutions for language processing networks with probabilistic predictions. Building on these advances, we propose the Scheduled Autoregressive BPTT (BPTT-SA) algorithm for predicting complex systems. Our results show that BPTT-SA effectively reduces iterative error propagation in Convolutional RNNs and Convolutional Autoencoder RNNs, and demonstrate its capabilities in long-term prediction of high-dimensional fluid flows.",
+    "neighbors": [
+      1723
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1559,
+    "label": 24,
+    "text": "Title: Federated Empirical Risk Minimization via Second-Order Method\nAbstract: Many convex optimization problems with important applications in machine learning are formulated as empirical risk minimization (ERM). There are several examples: linear and logistic regression, LASSO, kernel regression, quantile regression, $p$-norm regression, support vector machines (SVM), and mean-field variational inference. To improve data privacy, federated learning is proposed in machine learning as a framework for training deep learning models on the network edge without sharing data between participating nodes. In this work, we present an interior point method (IPM) to solve a general ERM problem under the federated learning setting. We show that the communication complexity of each iteration of our IPM is $\\tilde{O}(d^{3/2})$, where $d$ is the dimension (i.e., number of features) of the dataset.",
+    "neighbors": [
+      450,
+      748
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1560,
+    "label": 24,
+    "text": "Title: BloombergGPT: A Large Language Model for Finance\nAbstract: The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has been reported in literature. In this work, we present BloombergGPT, a 50 billion parameter language model that is trained on a wide range of financial data. We construct a 363 billion token dataset based on Bloomberg's extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345 billion tokens from general purpose datasets. We validate BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most accurately reflect our intended usage. Our mixed dataset training leads to a model that outperforms existing models on financial tasks by significant margins without sacrificing performance on general LLM benchmarks. Additionally, we explain our modeling choices, training process, and evaluation methodology. We release Training Chronicles (Appendix C) detailing our experience in training BloombergGPT.",
+    "neighbors": [
+      36,
+      424,
+      834,
+      855,
+      945,
+      1034,
+      1052,
+      1307,
+      1544,
+      1556,
+      1713,
+      1906,
+      1940
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1561,
+    "label": 16,
+    "text": "Title: Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters\nAbstract: Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery. A common benchmark case is to evaluate SSL pre-trained model embeddings on datasets of remotely sensed imagery with small patch sizes, e.g., 32x32 pixels, whereas standard SSL pre-training takes place with larger patch sizes, e.g., 224x224. Furthermore, pre-training methods tend to use different image normalization preprocessing steps depending on the dataset. In this paper, we show, across seven satellite and aerial imagery datasets of varying resolution, that by simply following the preprocessing steps used in pre-training (precisely, image sizing and normalization methods), one can achieve significant performance improvements when evaluating the extracted features on downstream tasks -- an important detail overlooked in previous work in this space. We show that by following these steps, ImageNet pre-training remains a competitive baseline for satellite imagery based transfer learning tasks -- for example we find that these steps give +32.28 to overall accuracy on the So2Sat random split dataset and +11.16 on the EuroSAT dataset. Finally, we report comprehensive benchmark results with a variety of simple baseline methods for each of the seven datasets, forming an initial benchmark suite for remote sensing imagery.",
+    "neighbors": [
+      306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1562,
+    "label": 5,
+    "text": "Title: MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers\nAbstract: Message Passing Interface (MPI) plays a crucial role in distributed memory parallelization across multiple nodes. However, parallelizing MPI code manually, and specifically, performing domain decomposition, is a challenging, error-prone task. In this paper, we address this problem by developing MPI-RICAL, a novel data-driven, programming-assistance tool that assists programmers in writing domain decomposition based distributed memory parallelization code. Specifically, we train a supervised language model to suggest MPI functions and their proper locations in the code on the fly. We also introduce MPICodeCorpus, the first publicly available corpus of MPI-based parallel programs that is created by mining more than 15,000 open-source repositories on GitHub. Experimental results have been done on MPICodeCorpus and more importantly, on a compiled benchmark of MPI-based parallel programs for numerical computations that represent real-world scientific applications. MPI-RICAL achieves F1 scores between 0.87-0.91 on these programs, demonstrating its accuracy in suggesting correct MPI functions at appropriate code locations.. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCN/MPI-rical",
+    "neighbors": [
+      1093,
+      1956
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1563,
+    "label": 27,
+    "text": "Title: Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning\nAbstract: Lane change in dense traffic is considered a challenging problem that typically requires the recognition of an opportune and appropriate opportunity for maneuvers. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). The embodied MPC in our framework is parameterized with augmented decision variables, where full-state references and regulatory factors concerning their relative importance are introduced. Furthermore, to improve the convergence speed and ensure a high-quality policy, effective curriculum design is integrated into the reinforcement learning (RL) framework with policy transfer and enhancement. Then the proposed framework is deployed to numerical simulations towards dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1564,
+    "label": 23,
+    "text": "Title: Equivalence, Identity, and Unitarity Checking in Black-Box Testing of Quantum Programs\nAbstract: Quantum programs exhibit inherent non-deterministic behavior, which poses more significant challenges for error discovery compared to classical programs. While several testing methods have been proposed for quantum programs, they often overlook fundamental questions in black-box testing. In this paper, we bridge this gap by presenting three novel algorithms specifically designed to address the challenges of equivalence, identity, and unitarity checking in black-box testing of quantum programs. We also explore optimization techniques for these algorithms, including specialized versions for equivalence and unitarity checking, and provide valuable insights into parameter selection to maximize performance and effectiveness. To evaluate the effectiveness of our proposed methods, we conducted comprehensive experimental evaluations, which demonstrate that our methods can rigorously perform equivalence, identity, and unitarity checking, offering robust support for black-box testing of quantum programs.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1565,
+    "label": 4,
+    "text": "Title: SeePrivacy: Automated Contextual Privacy Policy Generation for Mobile Applications\nAbstract: Privacy policies have become the most critical approach to safeguarding individuals' privacy and digital security. To enhance their presentation and readability, researchers propose the concept of contextual privacy policies (CPPs), aiming to fragment policies into shorter snippets and display them only in corresponding contexts. In this paper, we propose a novel multi-modal framework, namely SeePrivacy, designed to automatically generate contextual privacy policies for mobile apps. Our method synergistically combines mobile GUI understanding and privacy policy document analysis, yielding an impressive overall 83.6% coverage rate for privacy-related context detection and an accuracy of 0.92 in extracting corresponding policy segments. Remarkably, 96% of the retrieved policy segments can be correctly matched with their contexts. The user study shows SeePrivacy demonstrates excellent functionality and usability (4.5/5). Specifically, participants exhibit a greater willingness to read CPPs (4.1/5) compared to original privacy policies (2/5). Our solution effectively assists users in comprehending privacy notices, and this research establishes a solid foundation for further advancements and exploration.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1566,
+    "label": 30,
+    "text": "Title: PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization\nAbstract: Instruction tuning large language models (LLMs) remains a challenging task, owing to the complexity of hyperparameter selection and the difficulty involved in evaluating the tuned models. To determine the optimal hyperparameters, an automatic, robust, and reliable evaluation benchmark is essential. However, establishing such a benchmark is not a trivial task due to the challenges associated with evaluation accuracy and privacy protection. In response to these challenges, we introduce a judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets. It addresses vital subjective factors such as relative conciseness, clarity, adherence to instructions, comprehensiveness, and formality. To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences. Our results indicate that PandaLM-7B achieves 93.75% of GPT-3.5's evaluation ability and 88.28% of GPT-4's in terms of F1-score on our test dataset. PandaLM enables the evaluation of LLM to be fairer but with less cost, evidenced by significant improvements achieved by models tuned through PandaLM compared to their counterparts trained with default Alpaca's hyperparameters. In addition, PandaLM does not depend on API-based evaluations, thus avoiding potential data leakage. All resources of PandaLM are released at https://github.com/WeOpenML/PandaLM.",
+    "neighbors": [
+      247,
+      811,
+      1001,
+      1039,
+      1052,
+      1133,
+      1346,
+      1733,
+      1735,
+      1863
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1567,
+    "label": 14,
+    "text": "Title: A Poly-algorithmic Approach to Quantifier Elimination\nAbstract: Cylindrical Algebraic Decomposition (CAD) was the first practical means for doing real quantifier elimination (QE), and is still a major method, with many improvements since Collins' original method. Nevertheless, its complexity is inherently doubly exponential in the number of variables. Where applicable, virtual term substitution (VTS) is more effective, turning a QE problem in $n$ variables to one in $n-1$ variables in one application, and so on. Hence there is scope for hybrid methods: doing VTS where possible then using CAD. This paper describes such a poly-algorithmic implementation, based on the second author's Ph.D. thesis. The version of CAD used is based on a new implementation of Lazard's recently-justified method, with some improvements to handle equational constraints.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1568,
+    "label": 27,
+    "text": "Title: A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem\nAbstract: This paper proposes a novel Decentralized Spike-based Learning (DSL) framework for the discrete Perimeter Defense Problem (d-PDP). A team of defenders is operating on the perimeter to protect the circular territory from radially incoming intruders. At first, the d-PDP is formulated as a spatio-temporal multi-task assignment problem (STMTA). The problem of STMTA is then converted into a multi-label learning problem to obtain labels of segments that defenders have to visit in order to protect the perimeter. The DSL framework uses a Multi-Label Classifier using Synaptic Efficacy Function spiking neuRON (MLC-SEFRON) network for deterministic multi-label learning. Each defender contains a single MLC-SEFRON network. Each MLC-SEFRON network is trained independently using input from its own perspective for decentralized operations. The input spikes to the MLC-SEFRON network can be directly obtained from the spatio-temporal information of defenders and intruders without any extra pre-processing step. The output of MLC-SEFRON contains the labels of segments that a defender has to visit in order to protect the perimeter. Based on the multi-label output from the MLC-SEFRON a trajectory is generated for a defender using a Consensus-Based Bundle Algorithm (CBBA) in order to capture the intruders. The target multi-label output for training MLC-SEFRON is obtained from an expert policy. Also, the MLC-SEFRON trained for a defender can be directly used for obtaining labels of segments assigned to another defender without any retraining. The performance of MLC-SEFRON has been evaluated for full observation and partial observation scenarios of the defender. The overall performance of the DSL framework is then compared with expert policy along with other existing learning algorithms. The scalability of the DSL has been evaluated using an increasing number of defenders.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1569,
+    "label": 27,
+    "text": "Title: Optimality Principles in Spacecraft Neural Guidance and Control\nAbstract: Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, utilizing consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred onboard where controllers have the task of tracking the uploaded guidance profile. Here we argue that end-to-end neural guidance and control architectures (here called G&CNets) allow transferring onboard the burden of acting upon these optimality principles. In this way, the sensor information is transformed in real time into optimal plans thus increasing the mission autonomy and robustness. We discuss the main results obtained in training such neural architectures in simulation for interplanetary transfers, landings and close proximity operations, highlighting the successful learning of optimality principles by the neural model. We then suggest drone racing as an ideal gym environment to test these architectures on real robotic platforms, thus increasing confidence in their utilization on future space exploration missions. Drone racing shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought, but it also entails different levels of uncertainties and unmodelled effects. Furthermore, the success of G&CNets on extremely resource-restricted drones illustrates their potential to bring real-time optimal control within reach of a wider variety of robotic systems, both in space and on Earth.",
+    "neighbors": [
+      573
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1570,
+    "label": 30,
+    "text": "Title: A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution\nAbstract: State-of-the-art natural language processing models have been shown to achieve remarkable performance in 'closed-world' settings where all the labels in the evaluation set are known at training time. However, in real-world settings, 'novel' instances that do not belong to any known class are often observed. This renders the ability to deal with novelties crucial. To initiate a systematic research in this important area of 'dealing with novelties', we introduce 'NoveltyTask', a multi-stage task to evaluate a system's performance on pipelined novelty 'detection' and 'accommodation' tasks. We provide mathematical formulation of NoveltyTask and instantiate it with the authorship attribution task that pertains to identifying the correct author of a given text. We use Amazon reviews corpus and compile a large dataset (consisting of 250k instances across 200 authors/labels) for NoveltyTask. We conduct comprehensive experiments and explore several baseline methods for the task. Our results show that the methods achieve considerably low performance making the task challenging and leaving sufficient room for improvement. Finally, we believe our work will encourage research in this underexplored area of dealing with novelties, an important step en route to developing robust systems.",
+    "neighbors": [
+      1055
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1571,
+    "label": 16,
+    "text": "Title: Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction\nAbstract: Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://github.com/wzzheng/TPVFormer.",
+    "neighbors": [
+      47,
+      71,
+      1491,
+      2198,
+      2308
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1572,
+    "label": 30,
+    "text": "Title: What do self-supervised speech models know about words?\nAbstract: Many self-supervised speech models (S3Ms) have been introduced over the last few years, producing performance and data efficiency improvements for a variety of speech tasks. Evidence is emerging that different S3Ms encode linguistic information in different layers, and also that some S3Ms appear to learn phone-like sub-word units. However, the extent to which these models capture larger linguistic units, such as words, and where word-related information is encoded, remains unclear. In this study, we conduct several analyses of word segment representations extracted from different layers of three S3Ms: wav2vec2, HuBERT, and WavLM. We employ canonical correlation analysis (CCA), a lightweight analysis tool, to measure the similarity between these representations and word-level linguistic properties. We find that the maximal word-level linguistic content tends to be found in intermediate model layers, while some lower-level information like pronunciation is also retained in higher layers of HuBERT and WavLM. Syntactic and semantic word attributes have similar layer-wise behavior. We also find that, for all of the models tested, word identity information is concentrated near the center of each word segment. We then test the layer-wise performance of the same models, when used directly with no additional learned parameters, on several tasks: acoustic word discrimination, word segmentation, and semantic sentence similarity. We find similar layer-wise trends in performance, and furthermore, find that when using the best-performing layer of HuBERT or WavLM, it is possible to achieve performance on word segmentation and sentence similarity that rivals more complex existing approaches.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1573,
+    "label": 16,
+    "text": "Title: X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance\nAbstract: Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent architecture lacks textual guidance during predicting attributes, thus leading to unsatisfactory stylization and slow convergence. To address these limitations, we present X-Mesh, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed. Furthermore, existing works lack standard benchmarks and automated metrics for evaluation, often relying on subjective and non-reproducible user studies to assess the quality of stylized 3D assets. To overcome this limitation, we introduce a new standard text-mesh benchmark, namely MIT-30, and two automated metrics, which will enable future research to achieve fair and objective comparisons. Our extensive qualitative and quantitative experiments demonstrate that X-Mesh outperforms previous state-of-the-art methods.",
+    "neighbors": [
+      1205,
+      1226,
+      1905,
+      2118
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1574,
+    "label": 16,
+    "text": "Title: Contextual Object Detection with Multimodal Large Language Models\nAbstract: Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection -- understanding visible objects within different human-AI interactive contexts. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction. Our ContextDET involves three key submodels: (i) a visual encoder for extracting visual representations, (ii) a pre-trained LLM for multimodal context decoding, and (iii) a visual decoder for predicting bounding boxes given contextual object words. The new generate-then-detect framework enables us to detect object words within human vocabulary. Extensive experiments show the advantages of ContextDET on our proposed CODE benchmark, open-vocabulary detection, and referring image segmentation. Github: https://github.com/yuhangzang/ContextDET.",
+    "neighbors": [
+      42,
+      173,
+      176,
+      319,
+      602,
+      887,
+      1047,
+      1052,
+      1459,
+      1765,
+      1863,
+      2036,
+      2044
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1575,
+    "label": 26,
+    "text": "Title: Towards Detecting Inauthentic Coordination in Twitter Likes Data\nAbstract: Social media feeds typically favor posts according to user engagement. The most ubiquitous type of engagement (and the type we study) is *likes*. Users customarily take engagement metrics such as likes as a neutral proxy for quality and authority. This incentivizes like manipulation to influence public opinion through *coordinated inauthentic behavior* (CIB). CIB targeted at likes is largely unstudied as collecting suitable data about users' liking behavior is non-trivial. This paper contributes a scripted algorithm to collect suitable liking data from Twitter and a collected 30 day dataset of liking data from the Danish political Twittersphere #dkpol, over which we analyze the script's performance. Using only the binary matrix of users and the tweets they liked, we identify large clusters of perfectly correlated users, and discuss our findings in relation to CIB.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1576,
+    "label": 4,
+    "text": "Title: Assessing Cyclostationary Malware Detection via Feature Selection and Classification\nAbstract: Cyclostationarity involves periodic statistical variations in signals and processes, commonly used in signal analysis and network security. In the context of attacks, cyclostationarity helps detect malicious behaviors within network traffic, such as traffic patterns in Distributed Denial of Service (DDoS) attacks or hidden communication channels in malware. This approach enhances security by identifying abnormal patterns and informing Network Intrusion Detection Systems (NIDSs) to recognize potential attacks, enhancing protection against both known and novel threats. This research focuses on identifying cyclostationary malware behavior and its detection. The main goal is to pinpoint essential cyclostationary features used in NIDSs. These features are extracted using algorithms such as Boruta and Principal Component Analysis (PCA), and then categorized to find the most significant cyclostationary patterns. The aim of this article is to reveal periodically changing malware behaviors through cyclostationarity. The study highlights the importance of spotting cyclostationary malware in NIDSs by using established datasets like KDD99, NSL-KDD, and the UGRansome dataset. The UGRansome dataset is designed for anomaly detection research and includes both normal and abnormal network threat categories of zero-day attacks. A comparison is made using the Random Forest (RF) and Support Vector Machine (SVM) algorithms, while also evaluating the effectiveness of Boruta and PCA. The findings show that PCA is more promising than using Boruta alone for extracting cyclostationary network feature patterns. Additionally, the analysis identifies the internet protocol as the most noticeable cyclostationary feature pattern used by malware. Notably, the UGRansome dataset outperforms the KDD99 and NSL-KDD, achieving 99% accuracy in signature malware detection using the RF algorithm and 98% with the SVM.",
+    "neighbors": [
+      2041,
+      2162,
+      2280
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1577,
+    "label": 30,
+    "text": "Title: Efficient Encoders for Streaming Sequence Tagging\nAbstract: A naive application of state-of-the-art bidirectional encoders for streaming sequence tagging would require encoding each token from scratch for each new token in an incremental streaming input (like transcribed speech). The lack of re-usability of previous computation leads to a higher number of Floating Point Operations (or FLOPs) and higher number of unnecessary label flips. Increased FLOPs consequently lead to higher wall-clock time and increased label flipping leads to poorer streaming performance. In this work, we present a Hybrid Encoder with Adaptive Restart (HEAR) that addresses these issues while maintaining the performance of bidirectional encoders over the offline (or complete) and improving streaming (or incomplete) inputs. HEAR has a Hybrid unidirectional-bidirectional encoder architecture to perform sequence tagging, along with an Adaptive Restart Module (ARM) to selectively guide the restart of bidirectional portion of the encoder. Across four sequence tagging tasks, HEAR offers FLOP savings in streaming settings upto 71.1% and also outperforms bidirectional encoders for streaming predictions by upto +10% streaming exact match.",
+    "neighbors": [
+      555
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1578,
+    "label": 24,
+    "text": "Title: On the Lipschitz Constant of Deep Networks and Double Descent\nAbstract: Existing bounds on the generalization error of deep networks assume some form of smooth or bounded dependence on the input variable, falling short of investigating the mechanisms controlling such factors in practice. In this work, we present an extensive experimental study of the empirical Lipschitz constant of deep networks undergoing double descent, and highlight non-monotonic trends strongly correlating with the test error. Building a connection between parameter-space and input-space gradients for SGD around a critical point, we isolate two important factors -- namely loss landscape curvature and distance of parameters from initialization -- respectively controlling optimization dynamics around a critical point and bounding model function complexity, even beyond the training data. Our study presents novels insights on implicit regularization via overparameterization, and effective model complexity for networks trained in practice.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1579,
+    "label": 24,
+    "text": "Title: Robust Average-Reward Markov Decision Processes\nAbstract: In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor goes to 1, and moreover when it is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1580,
+    "label": 24,
+    "text": "Title: Benchmarking sparse system identification with low-dimensional chaos\nAbstract: nan",
+    "neighbors": [
+      1723,
+      2065
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1581,
+    "label": 16,
+    "text": "Title: ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models\nAbstract: Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM like ChatGPT has successfully passed part of the US medical licensing exam. However, LLMs currently have difficulty processing images, making it challenging to interpret information from medical images, which are rich in information that supports clinical decisions. On the other hand, computer-aided diagnosis (CAD) networks for medical images have seen significant success in the medical field by using advanced deep-learning algorithms to support clinical decision-making. This paper presents a method for integrating LLMs into medical-image CAD networks. The proposed framework uses LLMs to enhance the output of multiple CAD networks, such as diagnosis networks, lesion segmentation networks, and report generation networks, by summarizing and reorganizing the information presented in natural language text format. The goal is to merge the strengths of LLMs' medical domain knowledge and logical reasoning with the vision understanding capability of existing medical-image CAD models to create a more user-friendly and understandable system for patients compared to conventional CAD systems. In the future, LLM's medical knowledge can be also used to improve the performance of vision-based medical-image CAD models.",
+    "neighbors": [
+      929,
+      945,
+      1199
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1582,
+    "label": 16,
+    "text": "Title: SVDiff: Compact Parameter Space for Diffusion Fine-Tuning\nAbstract: Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities. However, existing methods for customizing these models are limited by handling multiple personalized subjects and the risk of overfitting. Moreover, their large number of parameters is inefficient for model storage. In this paper, we propose a novel approach to address these limitations in existing text-to-image diffusion models for personalization. Our method involves fine-tuning the singular values of the weight matrices, leading to a compact and efficient parameter space that reduces the risk of overfitting and language drifting. We also propose a Cut-Mix-Unmix data-augmentation technique to enhance the quality of multi-subject image generation and a simple text-based image editing framework. Our proposed SVDiff method has a significantly smaller model size compared to existing methods (approximately 2,200 times fewer parameters compared with vanilla DreamBooth), making it more practical for real-world applications.",
+    "neighbors": [
+      436,
+      848,
+      955,
+      957,
+      1173,
+      1179,
+      1279,
+      1426,
+      1710,
+      1902,
+      2242
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1583,
+    "label": 18,
+    "text": "Title: Roadmap for Unconventional Computing with Nanotechnology\nAbstract: In the Beyond Moore Law era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50x faster than Moore law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges.",
+    "neighbors": [
+      1508
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1584,
+    "label": 24,
+    "text": "Title: An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects\nAbstract: We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.",
+    "neighbors": [
+      82,
+      1860
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1585,
+    "label": 30,
+    "text": "Title: SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)\nAbstract: We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.",
+    "neighbors": [
+      1383
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1586,
+    "label": 24,
+    "text": "Title: Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations\nAbstract: Tensor network (TN) is a powerful framework in machine learning, but selecting a good TN model, known as TN structure search (TN-SS), is a challenging and computationally intensive task. The recent approach TNLS~\\cite{li2022permutation} showed promising results for this task, however, its computational efficiency is still unaffordable, requiring too many evaluations of the objective function. We propose TnALE, a new algorithm that updates each structure-related variable alternately by local enumeration, \\emph{greatly} reducing the number of evaluations compared to TNLS. We theoretically investigate the descent steps for TNLS and TnALE, proving that both algorithms can achieve linear convergence up to a constant if a sufficient reduction of the objective is \\emph{reached} in each neighborhood. We also compare the evaluation efficiency of TNLS and TnALE, revealing that $\\Omega(2^N)$ evaluations are typically required in TNLS for \\emph{reaching} the objective reduction in the neighborhood, while ideally $O(N^2R)$ evaluations are sufficient in TnALE, where $N$ denotes the tensor order and $R$ reflects the \\emph{``low-rankness''} of the neighborhood. Experimental results verify that TnALE can find practically good TN-ranks and permutations with vastly fewer evaluations than the state-of-the-art algorithms.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1587,
+    "label": 31,
+    "text": "Title: Implementation of a noisy hyperlink removal system: A semantic and relatedness approach\nAbstract: As the volume of data on the web grows, the web structure graph, which is a graph representation of the web, continues to evolve. The structure of this graph has gradually shifted from content-based to non-content-based. Furthermore, spam data, such as noisy hyperlinks, in the web structure graph adversely affect the speed and efficiency of information retrieval and link mining algorithms. Previous works in this area have focused on removing noisy hyperlinks using structural and string approaches. However, these approaches may incorrectly remove useful links or be unable to detect noisy hyperlinks in certain circumstances. In this paper, a data collection of hyperlinks is initially constructed using an interactive crawler. The semantic and relatedness structure of the hyperlinks is then studied through semantic web approaches and tools such as the DBpedia ontology. Finally, the removal process of noisy hyperlinks is carried out using a reasoner on the DBpedia ontology. Our experiments demonstrate the accuracy and ability of semantic web technologies to remove noisy hyperlinks",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1588,
+    "label": 16,
+    "text": "Title: Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback\nAbstract: The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While remarkable, as the complexity of given text input increases, the state-of-the-art diffusion models may still fail in generating images which accurately convey the semantics of the given prompt. Furthermore, it has been observed that such misalignments are often left undetected by pretrained multi-modal models such as CLIP. To address these problems, in this paper we explore a simple yet effective decompositional approach towards both evaluation and improvement of text-to-image alignment. In particular, we first introduce a Decompositional-Alignment-Score which given a complex prompt decomposes it into a set of disjoint assertions. The alignment of each assertion with generated images is then measured using a VQA model. Finally, alignment scores for different assertions are combined aposteriori to give the final text-to-image alignment score. Experimental analysis reveals that the proposed alignment metric shows significantly higher correlation with human ratings as opposed to traditional CLIP, BLIP scores. Furthermore, we also find that the assertion level alignment scores provide a useful feedback which can then be used in a simple iterative procedure to gradually increase the expression of different assertions in the final image outputs. Human user studies indicate that the proposed approach surpasses previous state-of-the-art by 8.7% in overall text-to-image alignment accuracy. Project page for our paper is available at https://1jsingh.github.io/divide-evaluate-and-refine",
+    "neighbors": [
+      887,
+      929,
+      1052,
+      1173,
+      1418,
+      1601,
+      1902,
+      1969,
+      2242
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1589,
+    "label": 2,
+    "text": "Title: A Practitioner's Guide to MDP Model Checking Algorithms\nAbstract: Model checking undiscounted reachability and expected-reward properties on Markov decision processes (MDPs) is key for the verification of systems that act under uncertainty. Popular algorithms are policy iteration and variants of value iteration; in tool competitions, most participants rely on the latter. These algorithms generally need worst-case exponential time. However the problem can equally be formulated as a linear program, solvable in polynomial time. In this paper, we give a detailed overview of today's state-of-the-art algorithms for MDP model checking with a focus on performance and correctness. We highlight their fundamental differences, and describe various optimisations and implementation variants. We experimentally compare floating-point and exact-arithmetic implementations of all algorithms on three benchmark sets using two probabilistic model checkers. Our results show that (optimistic) value iteration is a sensible default, but other algorithms are preferable in specific settings. This paper thereby provides a guide for MDP verification practitioners -- tool builders and users alike.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1590,
+    "label": 16,
+    "text": "Title: Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance\nAbstract: Creating a vivid video from the event or scenario in our imagination is a truly fascinating experience. Recent advancements in text-to-video synthesis have unveiled the potential to achieve this with prompts only. While text is convenient in conveying the overall scene context, it may be insufficient to control precisely. In this paper, we explore customized video generation by utilizing text as context description and motion structure (e.g. frame-wise depth) as concrete guidance. Our method, dubbed Make-Your-Video, involves joint-conditional video generation using a Latent Diffusion Model that is pre-trained for still image synthesis and then promoted for video generation with the introduction of temporal modules. This two-stage learning scheme not only reduces the computing resources required, but also improves the performance by transferring the rich concepts available in image datasets solely into video generation. Moreover, we use a simple yet effective causal attention mask strategy to enable longer video synthesis, which mitigates the potential quality degradation effectively. Experimental results show the superiority of our method over existing baselines, particularly in terms of temporal coherence and fidelity to users' guidance. In addition, our model enables several intriguing applications that demonstrate potential for practical usage.",
+    "neighbors": [
+      1020,
+      1173,
+      1251,
+      1420,
+      1539,
+      1902,
+      2161,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1591,
+    "label": 30,
+    "text": "Title: Multilingual Content Moderation: A Case Study on Reddit\nAbstract: Content moderation is the process of flagging content based on pre-defined platform rules. There has been a growing need for AI moderators to safeguard users as well as protect the mental health of human moderators from traumatic content. While prior works have focused on identifying hateful/offensive language, they are not adequate for meeting the challenges of content moderation since 1) moderation decisions are based on violation of rules, which subsumes detection of offensive speech, and 2) such rules often differ across communities which entails an adaptive solution. We propose to study the challenges of content moderation by introducing a multilingual dataset of 1.8 Million Reddit comments spanning 56 subreddits in English, German, Spanish and French1. We perform extensive experimental analysis to highlight the underlying challenges and suggest related research problems such as cross-lingual transfer, learning under label noise (human biases), transfer of moderation models, and predicting the violated rule. Our dataset and analysis can help better prepare for the challenges and opportunities of auto moderation.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1592,
+    "label": 16,
+    "text": "Title: Principlism Guided Responsible Data Curation\nAbstract: Human-centric computer vision (HCCV) data curation practices often neglect privacy and bias concerns, leading to dataset retractions and unfair models. Further, HCCV datasets constructed through nonconsensual web scraping lack the necessary metadata for comprehensive fairness and robustness evaluations. Current remedies address issues post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations for curating HCCV datasets, addressing privacy and bias. We adopt an ante hoc reflective perspective and draw from current practices and guidelines, guided by the ethical framework of principlism.",
+    "neighbors": [
+      783
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1593,
+    "label": 30,
+    "text": "Title: Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification\nAbstract: Social media is daily creating massive multimedia content with paired image and text, presenting the pressing need to automate the vision and language understanding for various multimodal classification tasks. Compared to the commonly researched visual-lingual data, social media posts tend to exhibit more implicit image-text relations. To better glue the cross-modal semantics therein, we capture hinting features from user comments, which are retrieved via jointly leveraging visual and lingual similarity. Afterwards, the classification tasks are explored via self-training in a teacher-student framework, motivated by the usually limited labeled data scales in existing benchmarks. Substantial experiments are conducted on four multimodal social media benchmarks for image-text relation classification, sarcasm detection, sentiment classification, and hate speech detection. The results show that our method further advances the performance of previous state-of-the-art models, which do not employ comment modeling or self-training.",
+    "neighbors": [
+      935,
+      1172
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1594,
+    "label": 36,
+    "text": "Title: Breaking the 3/4 Barrier for Approximate Maximin Share\nAbstract: We study the fundamental problem of fairly allocating a set of indivisible goods among $n$ agents with additive valuations using the desirable fairness notion of maximin share (MMS). MMS is the most popular share-based notion, in which an agent finds an allocation fair to her if she receives goods worth at least her MMS value. An allocation is called MMS if all agents receive at least their MMS value. Since MMS allocations need not exist when $n>2$, a series of works showed the existence of approximate MMS allocations with the current best factor of $\\frac34 + O(\\frac{1}{n})$. However, a simple example in [DFL82, BEF21, AGST23] showed the limitations of existing approaches and proved that they cannot improve this factor to $3/4 + \\Omega(1)$. In this paper, we bypass these barriers to show the existence of $(\\frac{3}{4} + \\frac{3}{3836})$-MMS allocations by developing new reduction rules and analysis techniques.",
+    "neighbors": [
+      2061
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1595,
+    "label": 16,
+    "text": "Title: Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work\nAbstract: Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field.",
+    "neighbors": [
+      726,
+      1152
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1596,
+    "label": 23,
+    "text": "Title: An Analysis of the Automatic Bug Fixing Performance of ChatGPT\nAbstract: To support software developers in finding and fixing software bugs, several automated program repair techniques have been introduced. Given a test suite, standard methods usually either synthesize a repair, or navigate a search space of software edits to find test-suite passing variants. Recent program repair methods are based on deep learning approaches. One of these novel methods, which is not primarily intended for automated program repair, but is still suitable for it, is ChatGPT. The bug fixing performance of ChatGPT, however, is so far unclear. Therefore, in this paper we evaluate ChatGPT on the standard bug fixing benchmark set, QuixBugs, and compare the performance with the results of several other approaches reported in the literature. We find that ChatGPT's bug fixing performance is competitive to the common deep learning approaches CoCoNut and Codex and notably better than the results reported for the standard program repair approaches. In contrast to previous approaches, ChatGPT offers a dialogue system through which further information, e.g., the expected output for a certain input or an observed error message, can be entered. By providing such hints to ChatGPT, its success rate can be further increased, fixing 31 out of 40 bugs, outperforming state-of-the-art.",
+    "neighbors": [
+      485,
+      644,
+      929,
+      1036,
+      1713,
+      1727,
+      1907,
+      2249,
+      2252
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1597,
+    "label": 39,
+    "text": "Title: New Record-Breaking Condorcet Domains on 10 and 11 Alternatives\nAbstract: We report on discovering new record-breaking Condorcet domains on $n=10$ and n=11 alternatives, challenging long-standing voting theory results. Our work presents new records with sizes of 1082 (previous record 1069) for n=10 and 2349 (previous record 2324) for $n=11$, which appear sporadic and do not fit into the existing alternating schema discovered in 1996. While the method used to discover these domains was inspired by the application of value functions in reinforcement learning, a subcategory of artificial intelligence, the current version of the method is somewhat ad-hoc and unstable. Therefore, we will not expound on the search method in this paper. Instead, we outline the key components that contribute to the success of our approach. We will also discuss the theoretical implications of our findings and explore the structure of the new Condorcet domains, raising several open questions related to them. Our results contribute to the ongoing investigation of Condorcet domains and their mathematical properties, potentially demonstrating the power of artificial intelligence-inspired problem-solving methods in advancing mathematical research.",
+    "neighbors": [
+      1701
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1598,
+    "label": 24,
+    "text": "Title: Building the Bridge of Schr\u00f6dinger: A Continuous Entropic Optimal Transport Benchmark\nAbstract: Over the last several years, there has been a significant progress in developing neural solvers for the Schr\\\"odinger Bridge (SB) problem and applying them to generative modeling. This new research field is justifiably fruitful as it is interconnected with the practically well-performing diffusion models and theoretically-grounded entropic optimal transport (EOT). Still the area lacks non-trivial tests allowing a researcher to understand how well do the methods solve SB or its equivalent continuous EOT problem. We fill this gap and propose a novel way to create pairs of probability distributions for which the ground truth OT solution in known by the construction. Our methodology is generic and works for a wide range of OT formulations, in particular, it covers the EOT which is equivalent to SB (the main interest of our study). This development allows us to create continuous benchmark distributions with the known EOT and SB solution on high-dimensional spaces such as spaces of images. As an illustration, we use these benchmark pairs to test how well do existing neural EOT/SB solvers actually compute the EOT solution. The benchmark is available via the link: https://github.com/ngushchin/EntropicOTBenchmark.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1599,
+    "label": 6,
+    "text": "Title: Modelling human seat contact interaction for vibration comfort\nAbstract: The seat to head vibration transmissibility depends on various characteristics of the seat and the human body. One of these, is the contact interaction, which transmits vibrational energy from the seat to the body. To enhance ride comfort, seat designers should be able to accurately simulate seat contact without the need for extensive experiments. Here, the contact area, pressure, friction and seat and body deformation in compression and shear play a significant role. To address these challenges, the aim of this paper is to define appropriate contact models to improve the prediction capabilities of a seated human body model with regards to experimental data. A computationally efficient multibody (MB) model is evaluated interacting with finite element (FE) and MB backrest models, using several contact models. Outcomes are evaluated in the frequency domain for 3D vibration transmission from seat to pelvis, trunk, head and knees. Results illustrate that both FE and MB backrest models allowing compression and shear provide realistic results.",
+    "neighbors": [
+      1417
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1600,
+    "label": 30,
+    "text": "Title: Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense\nAbstract: To detect the deployment of large language models for malicious use cases (e.g., fake content creation or academic plagiarism), several approaches have recently been proposed for identifying AI-generated text via watermarks or statistical irregularities. How robust are these detection algorithms to paraphrases of AI-generated text? To stress test these detectors, we first train an 11B parameter paraphrase generation model (DIPPER) that can paraphrase paragraphs, optionally leveraging surrounding text (e.g., user-written prompts) as context. DIPPER also uses scalar knobs to control the amount of lexical diversity and reordering in the paraphrases. Paraphrasing text generated by three large language models (including GPT3.5-davinci-003) with DIPPER successfully evades several detectors, including watermarking, GPTZero, DetectGPT, and OpenAI's text classifier. For example, DIPPER drops the detection accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of 1%), without appreciably modifying the input semantics. To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider. Given a candidate text, our algorithm searches a database of sequences previously generated by the API, looking for sequences that match the candidate text within a certain threshold. We empirically verify our defense using a database of 15M generations from a fine-tuned T5-XXL model and find that it can detect 80% to 97% of paraphrased generations across different settings, while only classifying 1% of human-written sequences as AI-generated. We will open source our code, model and data for future research.",
+    "neighbors": [
+      27,
+      42,
+      580,
+      896,
+      1487,
+      1805,
+      2044
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1601,
+    "label": 16,
+    "text": "Title: Text-to-image Diffusion Models in Generative AI: A Survey\nAbstract: This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e., text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.",
+    "neighbors": [
+      146,
+      170,
+      719,
+      736,
+      1079,
+      1205,
+      1207,
+      1588,
+      1651,
+      1663,
+      1789,
+      2277
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1602,
+    "label": 16,
+    "text": "Title: RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks\nAbstract: Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently. It can significantly reduce energy consumption since they quantize the real-valued membrane potentials to 0/1 spikes to transmit information thus the multiplications of activations and weights can be replaced by additions when implemented on hardware. However, this quantization mechanism will inevitably introduce quantization error, thus causing catastrophic information loss. To address the quantization error problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust the distribution which is directly related to quantization error to a range close to the spikes. Our method is extremely simple to implement and straightforward to train an SNN. Furthermore, it is shown to consistently outperform previous state-of-the-art methods over different network architectures and datasets.",
+    "neighbors": [
+      1677,
+      2014
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1603,
+    "label": 24,
+    "text": "Title: Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1604,
+    "label": 30,
+    "text": "Title: Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT\nAbstract: With ChatGPT under the spotlight, utilizing large language models (LLMs) for academic writing has drawn a significant amount of discussions and concerns in the community. While substantial research efforts have been stimulated for detecting LLM-Generated Content (LLM-content), most of the attempts are still in the early stage of exploration. In this paper, we present a holistic investigation of detecting LLM-generate academic writing, by providing a dataset, evidence, and algorithms, in order to inspire more community effort to address the concern of LLM academic misuse. We first present GPABenchmark, a benchmarking dataset of 600,000 samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of research papers in CS, physics, and humanities and social sciences (HSS). We show that existing open-source and commercial GPT detectors provide unsatisfactory performance on GPABenchmark, especially for GPT-polished text. Moreover, through a user study of 150+ participants, we show that it is highly challenging for human users, including experienced faculty members and researchers, to identify GPT-generated abstracts. We then present CheckGPT, a novel LLM-content detector consisting of a general representation module and an attentive-BiLSTM classification module, which is accurate, transferable, and interpretable. Experimental results show that CheckGPT achieves an average classification accuracy of 98% to 99% for the task-specific discipline-specific detectors and the unified detectors. CheckGPT is also highly transferable that, without tuning, it achieves ~90% accuracy in new domains, such as news articles, while a model tuned with approximately 2,000 samples in the target domain achieves ~98% accuracy. Finally, we demonstrate the explainability insights obtained from CheckGPT to reveal the key behaviors of how LLM generates texts.",
+    "neighbors": [
+      691,
+      2044,
+      2158,
+      2230,
+      2244
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1605,
+    "label": 16,
+    "text": "Title: Implicit Temporal Modeling with Learnable Alignment for Video Recognition\nAbstract: Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straight through tube is widely adopted in literature, we find that simple frame alignment already provides enough essence without temporal attention. To this end, in this paper, we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes the temporal modeling effort while achieving incredibly high performance. Specifically, for a frame pair, an interactive point is predicted in each frame, serving as a mutual information rich region. By enhancing the features around the interactive point, two frames are implicitly aligned. The aligned features are then pooled into a single token, which is leveraged in the subsequent spatial self-attention. Our method allows eliminating the costly or insufficient temporal self-attention in video. Extensive experiments on benchmarks demonstrate the superiority and generality of our module. Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is released at https://github.com/Francis-Rings/ILA .",
+    "neighbors": [
+      158
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1606,
+    "label": 24,
+    "text": "Title: BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge\nAbstract: Pre-trained language models like ChatGPT have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks. Moreover, in bioinformatics, generating functional programs poses additional notable challenges due to the amount of domain knowledge, the need for complicated data operations, and intricate functional dependencies between the operations. Here, we present BioCoder, a benchmark developed to evaluate existing pre-trained models in generating bioinformatics code. In relation to function-code generation, BioCoder covers potential package dependencies, class declarations, and global variables. It incorporates 1026 functions and 1243 methods in Python and Java from GitHub and 253 examples from the Rosalind Project. BioCoder incorporates a fuzz-testing framework for evaluation, and we have applied it to evaluate many models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, and ChatGPT. Our detailed analysis of these models emphasizes the importance of domain knowledge, pragmatic code generation, and contextual understanding. Our dataset, benchmark, Docker images, and scripts required for testing are all available at https://github.com/gersteinlab/biocoder.",
+    "neighbors": [
+      268,
+      817,
+      1052,
+      1128,
+      1171,
+      1490,
+      1733,
+      1840,
+      1863,
+      1879,
+      1907
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1607,
+    "label": 23,
+    "text": "Title: Mitigating Persistence of Open-Source Vulnerabilities in Maven Ecosystem\nAbstract: Vulnerabilities from third-party libraries (TPLs) have been unveiled to threaten the Maven ecosystem. Despite patches being released promptly after vulnerabilities are disclosed, the libraries and applications in the community still use the vulnerable versions, which makes the vulnerabilities persistent in the Maven ecosystem (e.g., the notorious Log4Shell still greatly influences the Maven ecosystem nowadays from 2021). Both academic and industrial researchers have proposed user-oriented standards and solutions to address vulnerabilities, while such solutions fail to tackle the ecosystem-wide persistent vulnerabilities because it requires a collective effort from the community to timely adopt patches without introducing breaking issues. To seek an ecosystem-wide solution, we first carried out an empirical study to examine the prevalence of persistent vulnerabilities in the Maven ecosystem. Then, we identified affected libraries for alerts by implementing an algorithm monitoring downstream dependents of vulnerabilities based on an up-to-date dependency graph. Based on them, we further quantitatively revealed that patches blocked by upstream libraries caused the persistence of vulnerabilities. After reviewing the drawbacks of existing countermeasures, to address them, we proposed a solution for range restoration (Ranger) to automatically restore the compatible and secure version ranges of dependencies for downstream dependents. The automatic restoration requires no manual effort from the community, and the code-centric compatibility assurance ensures smooth upgrades to patched versions. Moreover, Ranger along with the ecosystem monitoring can timely alert developers of blocking libraries and suggest flexible version ranges to rapidly unblock patch versions. By evaluation, Ranger could restore 75.64% of ranges which automatically remediated 90.32% of vulnerable downstream projects.",
+    "neighbors": [
+      1139,
+      1361
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1608,
+    "label": 24,
+    "text": "Title: TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training\nAbstract: Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to fully exploit the underlying heterogeneous network environments. In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology. Based on communication modeling, we abstract the dispatch problem into an optimization objective and obtain the approximate dispatch pattern under different topologies. On top of that, we design a topology-aware auxiliary loss, which can adaptively route the data to fit in the underlying topology without sacrificing the model accuracy. Experiments show that TA-MoE can substantially outperform its counterparts on various hardware and model configurations, with roughly 1.01x-1.61x, 1.01x-4.77x, 1.25x-1.54x improvements over the popular DeepSpeed-MoE, FastMoE and FasterMoE.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1609,
+    "label": 16,
+    "text": "Title: Hyperbolic Contrastive Learning\nAbstract: Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches have been proposed. Among them, contrastive learning has shown competitive performance on several benchmark datasets. The embeddings of contrastive learning are arranged on a hypersphere that results in using the inner (dot) product as a distance measurement in Euclidean space. However, the underlying structure of many scientific fields like social networks, brain imaging, and computer graphics data exhibit highly non-Euclidean latent geometry. We propose a novel contrastive learning framework to learn semantic relationships in the hyperbolic space. Hyperbolic space is a continuous version of trees that naturally owns the ability to model hierarchical structures and is thus beneficial for efficient contrastive representation learning. We also extend the proposed Hyperbolic Contrastive Learning (HCL) to the supervised domain and studied the adversarial robustness of HCL. The comprehensive experiments show that our proposed method achieves better results on self-supervised pretraining, supervised classification, and higher robust accuracy than baseline methods.",
+    "neighbors": [
+      2051
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1610,
+    "label": 13,
+    "text": "Title: How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs\nAbstract: In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal cliff benchmark with remarkable efficiency. With its O (n3) runtime, for almost all cliff widths d, the MAHH massively outperforms the \u0398(nd) runtime of simple elitist evolutionary algorithms (EAs). For the most prominent multimodal benchmark, the jump functions, the given runtime estimates of O(n2mm-\u0398(m)) and \u03a9(2\u03a9(m)), for gap size m \u2265 2, are far apart and the real performance of MAHH is still an open question. In this work, we resolve this question. We prove that for any choice of the MAHH selection parameter p, the expected runtime of the MAHH on a jump function with gap size m = o(n1/2) is at least \u03a9(n2m-1/(2m - 1)!). This renders the MAHH much slower than simple elitist evolutionary algorithms with their typical O(nm) runtime. We also show that the MAHH with the global bit-wise mutation operator instead of the local one-bit operator optimizes jump functions in time [EQUATION], essentially the minimum of the optimization times of the (1 + 1) EA and the MAHH. This suggests that combining several ways to cope with local optima can be a fruitful approach.",
+    "neighbors": [
+      1700,
+      2074
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1611,
+    "label": 31,
+    "text": "Title: Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation\nAbstract: The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm \u2014 Recommendation via LLM (RecLLM). Nevertheless, it is important to note that LLMs may contain social prejudices, and therefore, the fairness of recommendations made by RecLLM requires further investigation. To avoid the potential risks of RecLLM, it is imperative to evaluate the fairness of RecLLM with respect to various sensitive attributes on the user side. Due to the differences between the RecLLM paradigm and the traditional recommendation paradigm, it is problematic to directly use the fairness benchmark of traditional recommendation. To address the dilemma, we propose a novel benchmark called Fairness of Recommendation via LLM (FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset that accounts for eight sensitive attributes1 in two recommendation scenarios: music and movies. By utilizing our FaiRLLM benchmark, we conducted an evaluation of ChatGPT and discovered that it still exhibits unfairness to some sensitive attributes when generating recommendations. Our code and dataset can be found at https://github.com/jizhi-zhang/FaiRLLM.",
+    "neighbors": [
+      424,
+      840,
+      1001,
+      1052,
+      1092,
+      1182,
+      1210,
+      1667,
+      1915,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1612,
+    "label": 25,
+    "text": "Title: Attention-based Encoder-Decoder End-to-End Neural Diarization with Embedding Enhancer\nAbstract: Deep neural network-based systems have significantly improved the performance of speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often struggle to generalize to scenarios with an unseen number of speakers, while target speaker voice activity detection (TS-VAD) systems tend to be overly complex. In this paper, we propose a simple attention-based encoder-decoder network for end-to-end neural diarization (AED-EEND). In our training process, we introduce a teacher-forcing strategy to address the speaker permutation problem, leading to faster model convergence. For evaluation, we propose an iterative decoding method that outputs diarization results for each speaker sequentially. Additionally, we propose an Enhancer module to enhance the frame-level speaker embeddings, enabling the model to handle scenarios with an unseen number of speakers. We also explore replacing the transformer encoder with a Conformer architecture, which better models local information. Furthermore, we discovered that commonly used simulation datasets for speaker diarization have a much higher overlap ratio compared to real data. We found that using simulated training data that is more consistent with real data can achieve an improvement in consistency. Extensive experimental validation demonstrates the effectiveness of our proposed methodologies. Our best system achieved a new state-of-the-art diarization error rate (DER) performance on all the CALLHOME (10.08%), DIHARD II (24.64%), and AMI (13.00%) evaluation benchmarks, when no oracle voice activity detection (VAD) is used. Beyond speaker diarization, our AED-EEND system also shows remarkable competitiveness as a speech type detection model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1613,
+    "label": 30,
+    "text": "Title: The False Promise of Imitating Proprietary LLMs\nAbstract: An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others). This approach looks to cheaply imitate the proprietary model's capabilities using a weaker open-source model. In this work, we critically analyze this approach. We first finetune a series of LMs that imitate ChatGPT using varying base model sizes (1.5B--13B), data sources, and imitation data amounts (0.3M--150M tokens). We then evaluate the models using crowd raters and canonical NLP benchmarks. Initially, we were surprised by the output quality of our imitation models -- they appear far better at following instructions, and crowd workers rate their outputs as competitive with ChatGPT. However, when conducting more targeted automatic evaluations, we find that imitation models close little to none of the gap from the base LM to ChatGPT on tasks that are not heavily supported in the imitation data. We show that these performance discrepancies may slip past human raters because imitation models are adept at mimicking ChatGPT's style but not its factuality. Overall, we conclude that model imitation is a false promise: there exists a substantial capabilities gap between open and closed LMs that, with current methods, can only be bridged using an unwieldy amount of imitation data or by using more capable base LMs. In turn, we argue that the highest leverage action for improving open-source models is to tackle the difficult challenge of developing better base LMs, rather than taking the shortcut of imitating proprietary systems.",
+    "neighbors": [
+      3,
+      602,
+      664,
+      811,
+      1011,
+      1039,
+      1052,
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+      1249,
+      1907,
+      1950,
+      2036,
+      2155,
+      2235,
+      2244
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1614,
+    "label": 24,
+    "text": "Title: TAPS: Connecting Certified and Adversarial Training\nAbstract: Training certifiably robust neural networks remains a notoriously hard problem. On one side, adversarial training optimizes under-approximations of the worst-case loss, which leads to insufficient regularization for certification, while on the other, sound certified training methods optimize loose over-approximations, leading to over-regularization and poor (standard) accuracy. In this work we propose TAPS, an (unsound) certified training method that combines IBP and PGD training to yield precise, although not necessarily sound, worst-case loss approximations, reducing over-regularization and increasing certified and standard accuracies. Empirically, TAPS achieves a new state-of-the-art in many settings, e.g., reaching a certified accuracy of $22\\%$ on TinyImageNet for $\\ell_\\infty$-perturbations with radius $\\epsilon=1/255$.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1615,
+    "label": 20,
+    "text": "Title: Optimal Volume-Sensitive Bounds for Polytope Approximation\nAbstract: Approximating convex bodies is a fundamental question in geometry and has a wide variety of applications. Consider a convex body $K$ of diameter $\\Delta$ in $\\textbf{R}^d$ for fixed $d$. The objective is to minimize the number of vertices (alternatively, the number of facets) of an approximating polytope for a given Hausdorff error $\\varepsilon$. It is known from classical results of Dudley (1974) and Bronshteyn and Ivanov (1976) that $\\Theta((\\Delta/\\varepsilon)^{(d-1)/2})$ vertices (alternatively, facets) are both necessary and sufficient. While this bound is tight in the worst case, that of Euclidean balls, it is far from optimal for skinny convex bodies. A natural way to characterize a convex object's skinniness is in terms of its relationship to the Euclidean ball. Given a convex body $K$, define its \\emph{volume diameter} $\\Delta_d$ to be the diameter of a Euclidean ball of the same volume as $K$, and define its \\emph{surface diameter} $\\Delta_{d-1}$ analogously for surface area. It follows from generalizations of the isoperimetric inequality that $\\Delta \\geq \\Delta_{d-1} \\geq \\Delta_d$. Arya, da Fonseca, and Mount (SoCG 2012) demonstrated that the diameter-based bound could be made surface-area sensitive, improving the above bound to $O((\\Delta_{d-1}/\\varepsilon)^{(d-1)/2})$. In this paper, we strengthen this by proving the existence of an approximation with $O((\\Delta_d/\\varepsilon)^{(d-1)/2})$ facets.",
+    "neighbors": [
+      1244,
+      1438
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1616,
+    "label": 16,
+    "text": "Title: Boundary-Denoising for Video Activity Localization\nAbstract: Video activity localization aims at understanding the semantic content in long untrimmed videos and retrieving actions of interest. The retrieved action with its start and end locations can be used for highlight generation, temporal action detection, etc. Unfortunately, learning the exact boundary location of activities is highly challenging because temporal activities are continuous in time, and there are often no clear-cut transitions between actions. Moreover, the definition of the start and end of events is subjective, which may confuse the model. To alleviate the boundary ambiguity, we propose to study the video activity localization problem from a denoising perspective. Specifically, we propose an encoder-decoder model named DenoiseLoc. During training, a set of action spans is randomly generated from the ground truth with a controlled noise scale. Then we attempt to reverse this process by boundary denoising, allowing the localizer to predict activities with precise boundaries and resulting in faster convergence speed. Experiments show that DenoiseLoc advances %in several video activity understanding tasks. For example, we observe a gain of +12.36% average mAP on QV-Highlights dataset and +1.64% mAP@0.5 on THUMOS'14 dataset over the baseline. Moreover, DenoiseLoc achieves state-of-the-art performance on TACoS and MAD datasets, but with much fewer predictions compared to other current methods.",
+    "neighbors": [
+      1457
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1617,
+    "label": 10,
+    "text": "Title: The Flan Collection: Designing Data and Methods for Effective Instruction Tuning\nAbstract: We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022). Through careful ablation studies on the Flan Collection of tasks and methods, we tease apart the effect of design decisions which enable Flan-T5 to outperform prior work by 3-17%+ across evaluation settings. We find task balancing and enrichment techniques are overlooked but critical to effective instruction tuning, and in particular, training with mixed prompt settings (zero-shot, few-shot, and chain-of-thought) actually yields stronger (2%+) performance in all settings. In further experiments, we show Flan-T5 requires less finetuning to converge higher and faster than T5 on single downstream tasks, motivating instruction-tuned models as more computationally-efficient starting checkpoints for new tasks. Finally, to accelerate research on instruction tuning, we make the Flan 2022 collection of datasets, templates, and methods publicly available at https://github.com/google-research/FLAN/tree/main/flan/v2.",
+    "neighbors": [
+      126,
+      342,
+      529,
+      570,
+      761,
+      792,
+      811,
+      1002,
+      1112,
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+      1516,
+      1548,
+      1735,
+      1765,
+      1907,
+      1940,
+      2081,
+      2113,
+      2122,
+      2208,
+      2235
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1618,
+    "label": 16,
+    "text": "Title: Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore\nAbstract: In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential role in memory bank M-based methods. However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has a significant impact in industrial-based FSAD. Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as anomaly measurement feature. As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in M by a large amount. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and can improve the performance of anomaly detection. A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed fewshot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively.",
+    "neighbors": [
+      1255,
+      2119
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1619,
+    "label": 27,
+    "text": "Title: Towards Human-Robot Collaboration with Parallel Robots by Kinetostatic Analysis, Impedance Control and Contact Detection\nAbstract: Parallel robots provide the potential to be lever-aged for human-robot collaboration (HRC) due to low collision energies even at high speeds resulting from their reduced moving masses. However, the risk of unintended contact with the leg chains increases compared to the structure of serial robots. As a first step towards HRC, contact cases on the whole parallel robot structure are investigated and a disturbance observer based on generalized momenta and measurements of motor current is applied. In addition, a Kalman filter and a second-order sliding-mode observer based on generalized momenta are compared in terms of error and detection time. Gearless direct drives with low friction improve external force estimation and enable low impedance. The experimental validation is performed with two force-torque sensors and a kinetostatic model. This allows a new identification method of the motor torque constant of an assembled parallel robot to estimate external forces from the motor current and via a dynamics model. A Cartesian impedance control scheme for compliant robot-environmental dynamics with stiffness from 0.1-2N/mm and the force observation for low forces over the entire structure are validated. The observers are used for collisions and clamping at velocities of 0.4-0.9 m/s for detection within 9\u201358 ms and a reaction in the form of a zero-g mode.",
+    "neighbors": [
+      2261
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1620,
+    "label": 16,
+    "text": "Title: CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning\nAbstract: We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robustness, etc. These methods have been included in the toolbox with PyTorch implementations under NVIDIA computing system. It not only includes training and inference codes, but also provides model weights. We believe this toolbox is by far the most complete visual-linguitic causal reasoning toolbox. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new causal reasoning methods. Code and models are available at https://github.com/HCPLab-SYSU/Causal-VLReasoning. The project is under active development by HCP-Lab's contributors and we will keep this document updated.",
+    "neighbors": [
+      375
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1621,
+    "label": 15,
+    "text": "Title: Low-Power Data Streaming in Systolic Arrays with Bus-Invert Coding and Zero-Value Clock Gating\nAbstract: Systolic Array (SA) architectures are well suited for accelerating matrix multiplications through the use of a pipelined array of Processing Elements (PEs) communicating with local connections and pre-orchestrated data movements. Even though most of the dynamic power consumption in SAs is due to multiplications and additions, pipelined data movement within the SA constitutes an additional important contributor. The goal of this work is to reduce the dynamic power consumption associated with the feeding of data to the SA, by synergistically applying bus-invert coding and zero-value clock gating. By exploiting salient attributes of state-of-the-art CNNs, such as the value distribution of the weights, the proposed SA applies appropriate encoding only to the data that exhibits high switching activity. Similarly, when one of the inputs is zero, unnecessary operations are entirely skipped. This selectively targeted, application-aware encoding approach is demonstrated to reduce the dynamic power consumption of data streaming in CNN applications using Bfloat16 arithmetic by 1%\u201319%. This translates to an overall dynamic power reduction of 6.2%\u20139.4%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1622,
+    "label": 4,
+    "text": "Title: Do you still need a manual smart contract audit?\nAbstract: We investigate the feasibility of employing large language models (LLMs) for conducting the security audit of smart contracts, a traditionally time-consuming and costly process. Our research focuses on the optimization of prompt engineering for enhanced security analysis, and we evaluate the performance and accuracy of LLMs using a benchmark dataset comprising 52 Decentralized Finance (DeFi) smart contracts that have previously been compromised. Our findings reveal that, when applied to vulnerable contracts, both GPT-4 and Claude models correctly identify the vulnerability type in 40% of the cases. However, these models also demonstrate a high false positive rate, necessitating continued involvement from manual auditors. The LLMs tested outperform a random model by 20% in terms of F1-score. To ensure the integrity of our study, we conduct mutation testing on five newly developed and ostensibly secure smart contracts, into which we manually insert two and 15 vulnerabilities each. This testing yielded a remarkable best-case 78.7% true positive rate for the GPT-4-32k model. We tested both, asking the models to perform a binary classification on whether a contract is vulnerable, and a non-binary prompt. We also examined the influence of model temperature variations and context length on the LLM's performance. Despite the potential for many further enhancements, this work lays the groundwork for a more efficient and economical approach to smart contract security audits.",
+    "neighbors": [
+      62
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1623,
+    "label": 3,
+    "text": "Title: Experiential AI: A transdisciplinary framework for legibility and agency in AI\nAbstract: Experiential AI is presented as a research agenda in which scientists and artists come together to investigate the entanglements between humans and machines, and an approach to human-machine learning and development where knowledge is created through the transformation of experience. The paper discusses advances and limitations in the field of explainable AI; the contribution the arts can offer to address those limitations; and methods to bring creative practice together with emerging technology to create rich experiences that shed light on novel socio-technical systems, changing the way that publics, scientists and practitioners think about AI.",
+    "neighbors": [
+      1441
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1624,
+    "label": 3,
+    "text": "Title: Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1625,
+    "label": 24,
+    "text": "Title: Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning\nAbstract: In this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher network when dealing with out-of-distribution data. This causes large errors in the KD loss component, leading to performance degradation in CIL models. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main models during incremental training. Our method seamlessly integrates with KD-based CIL approaches and allows for consistent enhancement of their performance across multiple exemplar-free CIL benchmarks.",
+    "neighbors": [
+      1216
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1626,
+    "label": 16,
+    "text": "Title: ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System\nAbstract: Existing deep video models are limited by specific tasks, fixed input-output spaces, and poor generalization capabilities, making it difficult to deploy them in real-world scenarios. In this paper, we present our vision for multimodal and versatile video understanding and propose a prototype system, \\system. Our system is built upon a tracklet-centric paradigm, which treats tracklets as the basic video unit and employs various Video Foundation Models (ViFMs) to annotate their properties e.g., appearance, motion, \\etc. All the detected tracklets are stored in a database and interact with the user through a database manager. We have conducted extensive case studies on different types of in-the-wild videos, which demonstrates the effectiveness of our method in answering various video-related problems. Our project is available at https://www.wangjunke.info/ChatVideo/",
+    "neighbors": [
+      319,
+      719,
+      836,
+      887,
+      902,
+      1052,
+      1353,
+      1540
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1627,
+    "label": 23,
+    "text": "Title: A Meta-Summary of Challenges in Building Products with ML Components \u2013 Collecting Experiences from 4758+ Practitioners\nAbstract: Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing ones. Many researchers have invested significant effort in understanding the challenges of industry practitioners working on building products with ML components, through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews. We then collected, grouped, and organized the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and hope this meta-summary will be a useful resource for the research community to prioritize research and education in this field.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1628,
+    "label": 16,
+    "text": "Title: Bi3D: Bi-Domain Active Learning for Cross-Domain 3D Object Detection\nAbstract: Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently. Though preliminary progress has been made, the performance gap between the UDA-based 3D model and the supervised one trained with fully annotated target domain is still large. This motivates us to consider selecting partial-yet-important target data and labeling them at a minimum cost, to achieve a good trade-off between high performance and low annotation cost. To this end, we propose a Bi-domain active learning approach, namely Bi3D, to solve the cross-domain 3D object detection task. The Bi3D first develops a domainness-aware source sampling strategy, which identifies target-domain-like samples from the source domain to avoid the model being interfered by irrelevant source data. Then a diversity-based target sampling strategy is developed, which selects the most informative subset of target domain to improve the model adaptability to the target domain using as little annotation budget as possible. Experiments are conducted on typical cross-domain adaptation scenarios including cross-LiDAR-beam, cross-country, and cross-sensor, where Bi3D achieves a promising target-domain detection accuracy (89.63% on KITTI) compared with UDA-based work (84.29%), even surpassing the detector trained on the full set of the labeled target domain (88.98%). Our code is available at: https://github.com/PJLab-ADG/3DTrans.",
+    "neighbors": [
+      1080,
+      1358,
+      2150
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1629,
+    "label": 16,
+    "text": "Title: Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review\nAbstract: The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1630,
+    "label": 3,
+    "text": "Title: Beyond Fish and Bicycles: Exploring the Varieties of Online Women\u2019s Ideological Spaces\nAbstract: The Internet has been instrumental in connecting under-represented and vulnerable groups of people. Platforms built to foster social interaction and engagement have enabled historically disenfranchised groups to have a voice. One such vulnerable group is women. In this paper, we explore the diversity in online women\u2019s ideological spaces using a multi-dimensional approach. We perform a large-scale, data-driven analysis of over 6M Reddit comments and submissions from 14 subreddits. We elicit a diverse taxonomy of online women\u2019s ideological spaces, ranging from counterparts to the so-called Manosphere to Gender-Critical Feminism. We then perform content analysis, finding meaningful differences across topics and communities. Finally, we explore two platforms, namely, ovarit.com and thepinkpill.co, where two toxic communities of online women\u2019s ideological spaces (Gender-Critical Feminism and Femcels) migrated after their ban on Reddit.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1631,
+    "label": 3,
+    "text": "Title: Piloting Virtual Reality Photo-Based Tours among Students of a Filipino Language Class: A Case of Emergency Remote Teaching in Japan\nAbstract: The State of Emergency declaration in Japan due to the COVID-19 pandemic affected many aspects of society in the country, much like the rest of the world. One sector that felt its disruptive impact was education. As educational institutions raced to implement emergency remote teaching (ERT) to continue providing the learning needs of students, some have opened to innovative interventions. This paper describes a case of ERT where Filipino vocabulary was taught to a class of Japanese students taking Philippine Studies in a Japanese university using a cognitive innovation based on virtual reality, an immersive technology often researched for immersion and presence. Students were divided into three groups to experience six lessons designed around virtual reality photo-based tours at different immersion levels. While the effect of immersion on satisfaction was not found to be statistically significant, presence and satisfaction were found to be correlated. Despite challenges that were encountered, benefits like enjoyment, increased engagement, and perceived learning were reported by the students. Our findings exemplify how emerging multisensory technologies can be used to enhance affective and cognitive dimensions of human experience while responding to gaps created by the spatial limitations of remote learning.",
+    "neighbors": [
+      826
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1632,
+    "label": 36,
+    "text": "Title: Participatory Budgeting Design for the Real World\nAbstract: Participatory budgeting engages the public in the process of allocating public money to different types of projects. PB designs differ in how voters are asked to express their preferences over candidate projects and how these preferences are aggregated to determine which projects to fund. This paper studies two fundamental questions in PB design. Which voting format and aggregation method to use, and how to evaluate the outcomes of these design decisions? We conduct an extensive empirical study in which 1 800 participants vote in four participatory budgeting elections in a controlled setting to evaluate the practical effects of the choice of voting format and aggregation rule. We find that k-approval leads to the best user experience. With respect to the aggregation rule, greedy aggregation leads to outcomes that are highly sensitive to the input format used and the fraction of the population that participates. The method of equal shares, in contrast, leads to outcomes that are not sensitive to the type of voting format used, and these outcomes are remarkably stable even when the majority of the population does not participate in the election. These results carry valuable insights for PB practitioners and social choice researchers.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1633,
+    "label": 24,
+    "text": "Title: On Learning Latent Models with Multi-Instance Weak Supervision\nAbstract: We consider a weakly supervised learning scenario where the supervision signal is generated by a transition function $\\sigma$ of labels associated with multiple input instances. We formulate this problem as \\emph{multi-instance Partial Label Learning (multi-instance PLL)}, which is an extension to the standard PLL problem. Our problem is met in different fields, including latent structural learning and neuro-symbolic integration. Despite the existence of many learning techniques, limited theoretical analysis has been dedicated to this problem. In this paper, we provide the first theoretical study of multi-instance PLL with possibly an unknown transition $\\sigma$. Our main contributions are as follows. Firstly, we propose a necessary and sufficient condition for the learnability of the problem. This condition non-trivially generalizes and relaxes the existing small ambiguity degree in the PLL literature, since we allow the transition to be deterministic. Secondly, we derive Rademacher-style error bounds based on a top-$k$ surrogate loss that is widely used in the neuro-symbolic literature. Furthermore, we conclude with empirical experiments for learning under unknown transitions. The empirical results align with our theoretical findings; however, they also expose the issue of scalability in the weak supervision literature.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1634,
+    "label": 31,
+    "text": "Title: Robust Preference-Guided Denoising for Graph based Social Recommendation\nAbstract: Graph Neural Network (GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in social relations. However, in terms of both effectiveness and efficiency of recommendation, a large portion of social relations can be redundant or even noisy, e.g., it is quite normal that friends share no preference in a certain domain. Existing models do not fully solve this problem of relation redundancy and noise, as they directly characterize social influence over the full social network. In this paper, we instead propose to improve graph based social recommendation by only retaining the informative social relations to ensure an efficient and effective influence diffusion, i.e., graph denoising. Our designed denoising method is preference-guided to model social relation confidence and benefits user preference learning in return by providing a denoised but more informative social graph for recommendation models. Moreover, to avoid interference of noisy social relations, it designs a self-correcting curriculum learning module and an adaptive denoising strategy, both favoring highly-confident samples. Experimental results on three public datasets demonstrate its consistent capability of improving three state-of-the-art social recommendation models by robustly removing 10-40% of original relations. We release the source code at https://github.com/tsinghua-fib-lab/Graph-Denoising-SocialRec.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1635,
+    "label": 27,
+    "text": "Title: DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstacles\nAbstract: This article proposes a novel learning-based control policy with strong generalizability to new environments that enables a mobile robot to navigate autonomously through spaces filled with both static obstacles and dense crowds of pedestrians. The policy uses a unique combination of input data to generate the desired steering angle and forward velocity: a short history of lidar data, kinematic data about nearby pedestrians, and a subgoal point. The policy is trained in a reinforcement learning setting using a reward function that contains a novel term based on velocity obstacles to guide the robot to actively avoid pedestrians and move toward the goal. Through a series of 3-D simulated experiments with up to 55 pedestrians, this control policy is able to achieve a better balance between collision avoidance and speed (i.e., higher success rate and faster average speed) than state-of-the-art model-based and learning-based policies, and it also generalizes better to different crowd sizes and unseen environments. An extensive series of hardware experiments demonstrate the ability of this policy to directly work in different real-world environments with different crowd sizes with zero retraining. Furthermore, a series of simulated and hardware experiments show that the control policy also works in highly constrained static environments on a different robot platform without any additional training. Lastly, several important lessons that can be applied to other robot learning systems are summarized.",
+    "neighbors": [
+      764
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1636,
+    "label": 30,
+    "text": "Title: A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets\nAbstract: The development of large language models (LLMs) such as ChatGPT has brought a lot of attention recently. However, their evaluation in the benchmark academic datasets remains under-explored due to the difficulty of evaluating the generative outputs produced by this model against the ground truth. In this paper, we aim to present a thorough evaluation of ChatGPT's performance on diverse academic datasets, covering tasks like question-answering, text summarization, code generation, commonsense reasoning, mathematical problem-solving, machine translation, bias detection, and ethical considerations. Specifically, we evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in these datasets. This makes our work the largest evaluation of ChatGPT in NLP benchmarks. In short, our study aims to validate the strengths and weaknesses of ChatGPT in various tasks and provide insights for future research using LLMs. We also report a new emergent ability to follow multi-query instructions that we mostly found in ChatGPT and other instruction-tuned models. Our extensive evaluation shows that even though ChatGPT is capable of performing a wide variety of tasks, and may obtain impressive performance in several benchmark datasets, it is still far from achieving the ability to reliably solve many challenging tasks. By providing a thorough assessment of ChatGPT's performance across diverse NLP tasks, this paper sets the stage for a targeted deployment of ChatGPT-like LLMs in real-world applications.",
+    "neighbors": [
+      3,
+      121,
+      519,
+      613,
+      748,
+      975,
+      1001,
+      1052,
+      1114,
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+      1210,
+      1322,
+      1384,
+      1520,
+      1727,
+      1735,
+      1949,
+      1952,
+      2062,
+      2158,
+      2249,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1637,
+    "label": 16,
+    "text": "Title: DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions\nAbstract: As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enhances the location awareness of ViTs is becoming evident. To address this, we present DropPos, a novel pretext task designed to reconstruct Dropped Positions. The formulation of DropPos is simple: we first drop a large random subset of positional embeddings and then the model classifies the actual position for each non-overlapping patch among all possible positions solely based on their visual appearance. To avoid trivial solutions, we increase the difficulty of this task by keeping only a subset of patches visible. Additionally, considering there may be different patches with similar visual appearances, we propose position smoothing and attentive reconstruction strategies to relax this classification problem, since it is not necessary to reconstruct their exact positions in these cases. Empirical evaluations of DropPos show strong capabilities. DropPos outperforms supervised pre-training and achieves competitive results compared with state-of-the-art self-supervised alternatives on a wide range of downstream benchmarks. This suggests that explicitly encouraging spatial reasoning abilities, as DropPos does, indeed contributes to the improved location awareness of ViTs. The code is publicly available at https://github.com/Haochen-Wang409/DropPos.",
+    "neighbors": [
+      726,
+      1211,
+      1234,
+      1489,
+      2048
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1638,
+    "label": 17,
+    "text": "Title: Alternately denoising and reconstructing unoriented point sets\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1639,
+    "label": 30,
+    "text": "Title: Exploring Large Language Models for Classical Philology\nAbstract: Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. While prior work on Classical languages unanimously uses BERT, in this work we create four language models for Ancient Greek that vary along two dimensions to study their versatility for tasks of interest for Classical languages: we explore (i) encoder-only and encoder-decoder architectures using RoBERTa and T5 as strong model types, and create for each of them (ii) a monolingual Ancient Greek and a multilingual instance that includes Latin and English. We evaluate all models on morphological and syntactic tasks, including lemmatization, which demonstrates the added value of T5\u2019s decoding abilities. We further define two probing tasks to investigate the knowledge acquired by models pre-trained on Classical texts. Our experiments provide the first benchmarking analysis of existing models of Ancient Greek. Results show that our models provide significant improvements over the SoTA. The systematic analysis of model types can inform future research in designing language models for Classical languages, including the development of novel generative tasks. We make all our models available as community resources, along with a large curated pre-training corpus for Ancient Greek, to support the creation of a larger, comparable model zoo for Classical Philology.",
+    "neighbors": [
+      1959
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1640,
+    "label": 24,
+    "text": "Title: Adversarial robustness of amortized Bayesian inference\nAbstract: Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is to initially invest computational cost in training an inference network on simulated data, which can subsequently be used to rapidly perform inference (i.e., to return estimates of posterior distributions) for new observations. This approach has been applied to many real-world models in the sciences and engineering, but it is unclear how robust the approach is to adversarial perturbations in the observed data. Here, we study the adversarial robustness of amortized Bayesian inference, focusing on simulation-based estimation of multi-dimensional posterior distributions. We show that almost unrecognizable, targeted perturbations of the observations can lead to drastic changes in the predicted posterior and highly unrealistic posterior predictive samples, across several benchmark tasks and a real-world example from neuroscience. We propose a computationally efficient regularization scheme based on penalizing the Fisher information of the conditional density estimator, and show how it improves the adversarial robustness of amortized Bayesian inference.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1641,
+    "label": 16,
+    "text": "Title: Deep Residual Axial Networks\nAbstract: While convolutional neural networks (CNNs) demonstrate outstanding performance on computer vision tasks, their computational costs remain high. Several techniques are used to reduce these costs, like reducing channel count, and using separable and depthwise separable convolutions. This paper reduces computational costs by introducing a novel architecture, axial CNNs, which replaces spatial 2D convolution operations with two consecutive depthwise separable 1D operations. The axial CNNs are predicated on the assumption that the dataset supports approximately separable convolution operations with little or no loss of training accuracy. Deep axial separable CNNs still suffer from gradient problems when training deep networks. We modify the construction of axial separable CNNs with residual connections to improve the performance of deep axial architectures and introduce our final novel architecture namely residual axial networks (RANs). Extensive benchmark evaluation shows that RANs achieve at least 1% higher performance with about 77%, 86%, 75%, and 34% fewer parameters and about 75%, 80%, 67%, and 26% fewer flops than ResNets, wide ResNets, MobileNets, and SqueezeNexts on CIFAR benchmarks, SVHN, and Tiny ImageNet image classification datasets. Moreover, our proposed RANs improve deep recursive residual networks performance with 94% fewer parameters on the image super-resolution dataset.",
+    "neighbors": [
+      1686,
+      2234
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1642,
+    "label": 16,
+    "text": "Title: MixFormer: End-to-End Tracking with Iterative Mixed Attention\nAbstract: Tracking often uses a multistage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer tracking framework simply by stacking multiple MAMs with progressive patch embedding and placing a localization head on top. In addition, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer sets a new state-of-the-art performance on five tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on VOT2020. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration. Code and trained models are publicly available at https://github.com/MCG-NJU/MixFormer.",
+    "neighbors": [
+      1540
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1643,
+    "label": 10,
+    "text": "Title: Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment\nAbstract: Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attributes (including literals), but ignore images that are common in modern multi-modal KGs. In this study we first constructed Multi-OpenEA -- eight large-scale, image-equipped EA benchmarks, and then evaluated some existing embedding-based methods for utilizing images. In view of the complementary nature of visual modal information and logical deduction, we further developed a new multi-modal EA method named LODEME using logical deduction and multi-modal KG embedding, with state-of-the-art performance achieved on Multi-OpenEA and other existing multi-modal EA benchmarks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1644,
+    "label": 24,
+    "text": "Title: Agile Modeling: From Concept to Classifier in Minutes\nAbstract: The application of computer vision to nuanced subjective use cases is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a\"zebra\"), it now falters on tasks where there is substantial subjectivity in the concept (such as identifying\"gourmet tuna\"). However, empowering any user to develop a classifier for their concept is technically difficult: users are neither machine learning experts, nor have the patience to label thousands of examples. In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through a real-time user-in-the-loop interactions. We instantiate an Agile Modeling prototype for image classification and show through a user study (N=14) that users can create classifiers with minimal effort under 30 minutes. We compare this user driven process with the traditional crowdsourcing paradigm and find that the crowd's notion often differs from that of the user's, especially as the concepts become more subjective. Finally, we scale our experiments with simulations of users training classifiers for ImageNet21k categories to further demonstrate the efficacy.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1645,
+    "label": 3,
+    "text": "Title: Benchmark Dataset Dynamics, Bias and Privacy Challenges in Voice Biometrics Research\nAbstract: Speaker recognition is a widely used voice-based biometric technology with applications in various industries, including banking, education, recruitment, immigration, law enforcement, healthcare, and well-being. However, while dataset evaluations and audits have improved data practices in face recognition and other computer vision tasks, the data practices in speaker recognition have gone largely unquestioned. Our research aims to address this gap by exploring how dataset usage has evolved over time and what implications this has on bias, fairness and privacy in speaker recognition systems. Previous studies have demonstrated the presence of historical, representation, and measurement biases in popular speaker recognition benchmarks. In this paper, we present a longitudinal study of speaker recognition datasets used for training and evaluation from 2012 to 2021. We survey close to 700 papers to investigate community adoption of datasets and changes in usage over a crucial time period where speaker recognition approaches transitioned to the widespread adoption of deep neural networks. Our study identifies the most commonly used datasets in the field, examines their usage patterns, and assesses their attributes that affect bias, fairness, and other ethical concerns. Our findings suggest areas for further research on the ethics and fairness of speaker recognition technology.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1646,
+    "label": 16,
+    "text": "Title: Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models\nAbstract: Deep neural networks (DNNs) have achieved tremendous success in many remote sensing (RS) applications, in which DNNs are vulnerable to adversarial perturbations. Unfortunately, current adversarial defense approaches in RS studies usually suffer from performance fluctuation and unnecessary re-training costs due to the need for prior knowledge of the adversarial perturbations among RS data. To circumvent these challenges, we propose a universal adversarial defense approach in RS imagery (UAD-RS) using pre-trained diffusion models to defend the common DNNs against multiple unknown adversarial attacks. Specifically, the generative diffusion models are first pre-trained on different RS datasets to learn generalized representations in various data domains. After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples. Furthermore, an adaptive noise level selection (ANLS) mechanism is built to capture the optimal noise level of the diffusion model that can achieve the best purification results closest to the clean samples according to their Frechet Inception Distance (FID) in deep feature space. As a result, only a single pre-trained diffusion model is needed for the universal purification of adversarial samples on each dataset, which significantly alleviates the re-training efforts and maintains high performance without prior knowledge of the adversarial perturbations. Experiments on four heterogeneous RS datasets regarding scene classification and semantic segmentation verify that UAD-RS outperforms state-of-the-art adversarial purification approaches with a universal defense against seven commonly existing adversarial perturbations. Codes and the pre-trained models are available online (https://github.com/EricYu97/UAD-RS).",
+    "neighbors": [
+      1287
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1647,
+    "label": 24,
+    "text": "Title: Chain of Hindsight Aligns Language Models with Feedback\nAbstract: Learning from human preferences is important for language models to be helpful and useful for humans, and to align with human and social values. Prior work have achieved remarkable successes by learning from human feedback to understand and follow instructions. Nonetheless, these methods are either founded on hand-picked model generations that are favored by human annotators, rendering them ineffective in terms of data utilization and challenging to apply in general, or they depend on reward functions and reinforcement learning, which are prone to imperfect reward function and extremely challenging to optimize. In this work, we propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity. Our idea is inspired by how humans learn from extensive feedback presented in the form of languages. We convert all types of feedback into sentences, which are then used to fine-tune the model, allowing us to take advantage of the language comprehension capabilities of language models. We condition the model on a sequence of model generations paired with feedback. By doing so, models are trained to generate outputs based on feedback, and models can learn to identify and correct negative attributes or errors. Applying our method to large language models, we observed that Chain of Hindsight significantly surpasses previous methods in aligning language models with human preferences. We observed significant improvements on summarization and dialogue tasks and our approach is markedly preferred in human evaluations.",
+    "neighbors": [
+      126,
+      430,
+      1430,
+      1490,
+      1878,
+      1969,
+      2007,
+      2016,
+      2038,
+      2184
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1648,
+    "label": 4,
+    "text": "Title: Decentralized Inverse Transparency With Blockchain\nAbstract: \n Employee data can be used to facilitate work, but their misusage may pose risks for individuals.\n Inverse transparency\n therefore aims to track all usages of personal data, allowing individuals to monitor them to ensure accountability for potential misusage. This necessitates a trusted log to establish an agreed-upon and non-repudiable timeline of events. The unique properties of blockchain facilitate this by providing immutability and availability. For power asymmetric environments such as the workplace, permissionless blockchain is especially beneficial as no trusted third party is required. Yet, two issues remain: (1) In a decentralized environment, no arbiter can facilitate and attest to data exchanges. Simple peer-to-peer sharing of data, conversely, lacks the required non-repudiation. (2) With data governed by privacy legislation such as the GDPR, the core advantage of immutability becomes a liability. After a rightful request, an individual\u2019s personal data need to be rectified or deleted, which is impossible in an immutable blockchain.\n \n \n To solve these issues, we present\n Kovacs\n , a decentralized data exchange and usage logging system for inverse transparency built on blockchain. Its new-usage protocol ensures non-repudiation, and therefore accountability, for inverse transparency. Its one-time pseudonym generation algorithm guarantees unlinkability and enables proof of ownership, which allows data subjects to exercise their legal rights regarding their personal data. With our implementation, we show the viability of our solution. The decentralized communication impacts performance and scalability, but exchange duration and storage size are still reasonable. More importantly, the provided information security meets high requirements.\n \n \n We conclude that\n Kovacs\n realizes decentralized inverse transparency through secure and GDPR-compliant use of permissionless blockchain.\n",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1649,
+    "label": 16,
+    "text": "Title: Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning\nAbstract: 3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated\"detect-then-describe\"pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional\"detect-then-describe\"methods by a large margin. Codes will be made available at https://github.com/ch3cook-fdu/Vote2Cap-DETR.",
+    "neighbors": [
+      887
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1650,
+    "label": 6,
+    "text": "Title: Inform the Uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbot\nAbstract: Informed consent is a core cornerstone of ethics in human subject research. Through the informed consent process, participants learn about the study procedure, benefits, risks, and more to make an informed decision. However, recent studies showed that current practices might lead to uninformed decisions and expose participants to unknown risks, especially in online studies. Without the researcher\u2019s presence and guidance, online participants must read a lengthy form on their own with no answers to their questions. In this paper, we examined the role of an AI-powered chatbot in improving informed consent online. By comparing the chatbot with form-based interaction, we found the chatbot improved consent form reading, promoted participants\u2019 feelings of agency, and closed the power gap between the participant and the researcher. Our exploratory analysis further revealed the altered power dynamic might eventually benefit study response quality. We discussed design implications for creating AI-powered chatbots to offer effective informed consent in broader settings.",
+    "neighbors": [
+      1806
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1651,
+    "label": 24,
+    "text": "Title: A survey of Generative AI Applications\nAbstract: Generative AI has experienced remarkable growth in recent years, leading to a wide array of applications across diverse domains. In this paper, we present a comprehensive survey of more than 350 generative AI applications, providing a structured taxonomy and concise descriptions of various unimodal and even multimodal generative AIs. The survey is organized into sections, covering a wide range of unimodal generative AI applications such as text, images, video, gaming and brain information. Our survey aims to serve as a valuable resource for researchers and practitioners to navigate the rapidly expanding landscape of generative AI, facilitating a better understanding of the current state-of-the-art and fostering further innovation in the field.",
+    "neighbors": [
+      1027,
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+      1773,
+      2113,
+      2161
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1652,
+    "label": 16,
+    "text": "Title: Minimalist and High-Quality Panoramic Imaging with PSF-aware Transformers\nAbstract: High-quality panoramic images with a Field of View (FoV) of 360-degree are essential for contemporary panoramic computer vision tasks. However, conventional imaging systems come with sophisticated lens designs and heavy optical components. This disqualifies their usage in many mobile and wearable applications where thin and portable, minimalist imaging systems are desired. In this paper, we propose a Panoramic Computational Imaging Engine (PCIE) to address minimalist and high-quality panoramic imaging. With less than three spherical lenses, a Minimalist Panoramic Imaging Prototype (MPIP) is constructed based on the design of the Panoramic Annular Lens (PAL), but with low-quality imaging results due to aberrations and small image plane size. We propose two pipelines, i.e. Aberration Correction (AC) and Super-Resolution and Aberration Correction (SR&AC), to solve the image quality problems of MPIP, with imaging sensors of small and large pixel size, respectively. To provide a universal network for the two pipelines, we leverage the information from the Point Spread Function (PSF) of the optical system and design a PSF-aware Aberration-image Recovery Transformer (PART), in which the self-attention calculation and feature extraction are guided via PSF-aware mechanisms. We train PART on synthetic image pairs from simulation and put forward the PALHQ dataset to fill the gap of real-world high-quality PAL images for low-level vision. A comprehensive variety of experiments on synthetic and real-world benchmarks demonstrates the impressive imaging results of PCIE and the effectiveness of plug-and-play PSF-aware mechanisms. We further deliver heuristic experimental findings for minimalist and high-quality panoramic imaging. Our dataset and code will be available at https://github.com/zju-jiangqi/PCIE-PART.",
+    "neighbors": [
+      1375,
+      1886
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1653,
+    "label": 30,
+    "text": "Title: Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets\nAbstract: Dialogue acts (DAs) can represent conversational actions of tutors or students that take place during tutoring dialogues. Automating the identification of DAs in tutoring dialogues is significant to the design of dialogue-based intelligent tutoring systems. Many prior studies employ machine learning models to classify DAs in tutoring dialogues and invest much effort to optimize the classification accuracy by using limited amounts of training data (i.e., low-resource data scenario). However, beyond the classification accuracy, the robustness of the classifier is also important, which can reflect the capability of the classifier on learning the patterns from different class distributions. We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution. The DA classifiers in these studies tend to prioritize accuracy on the majority class at the expense of the minority class which might not be robust to the data with imbalanced ratios of different DA classes. To optimize the robustness of classifiers on imbalanced class distributions, we propose to optimize the performance of the DA classifier by maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization). Through extensive experiments, our study provides evidence that (i) by maximizing AUC in the training process, the DA classifier achieves significant performance improvement compared to the CE approach under low-resource data, and (ii) AUC maximization approaches can improve the robustness of the DA classifier under different class imbalance ratios.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1654,
+    "label": 4,
+    "text": "Title: TxT: Real-Time Transaction Encapsulation for Ethereum Smart Contracts\nAbstract: Ethereum is a permissionless blockchain ecosystem that supports execution of smart contracts, the key enablers of decentralized finance (DeFi) and non-fungible tokens (NFT). However, the expressiveness of Ethereum smart contracts is a double-edged sword: while it enables blockchain programmability, it also introduces security vulnerabilities, i.e., the exploitable discrepancies between expected and actual behaviors of the contract code. To address these discrepancies and increase the vulnerability coverage, we propose a new smart contract security testing approach called transaction encapsulation. The core idea lies in the local execution of transactions on a fully-synchronized yet isolated Ethereum node, which creates a preview of outcomes of transaction sequences on the current state of blockchain. This approach poses a critical technical challenge \u2014 the well-known time-of-check/time-of-use (TOCTOU) problem, i.e., the assurance that the final transactions will exhibit the same execution paths as the encapsulated test transactions. In this work, we determine the exact conditions for guaranteed execution path replicability of the tested transactions. To demonstrate the transaction encapsulation, we implement a transaction testing tool, TxT, which reveals the actual outcomes (either benign or malicious) of Ethereum transactions. To ensure the correctness of testing, TxT deterministically verifies whether a given sequence of transactions ensues an identical execution path on the current state of blockchain. We analyze over 1.3 billion Ethereum transactions and determine that 96.5% of them can be verified by TxT. We further show that TxT successfully reveals the suspicious behaviors associated with 31 out of 37 vulnerabilities (83.8% coverage) in the smart contract weakness classification (SWC) registry. In comparison, the vulnerability coverage of all the existing defense approaches combined only reaches 40.5%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1655,
+    "label": 30,
+    "text": "Title: SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning\nAbstract: We present SeaEval, a benchmark for multilingual foundation models. In addition to characterizing how these models understand and reason with natural language, we also investigate how well they comprehend cultural practices, nuances, and values. Alongside standard accuracy metrics, we investigate the brittleness of foundation models in the dimensions of semantics and multilinguality. Our analyses span both open-sourced and closed models, leading to empirical results across classic NLP tasks, reasoning, and cultural comprehension. Key findings indicate (1) Most models exhibit varied behavior when given paraphrased instructions. (2) Many models still suffer from exposure bias (e.g., positional bias, majority label bias). (3) For questions rooted in factual, scientific, and commonsense knowledge, consistent responses are expected across multilingual queries that are semantically equivalent. Yet, most models surprisingly demonstrate inconsistent performance on these queries. (4) Multilingually-trained models have not attained\"balanced multilingual\"capabilities. Our endeavors underscore the need for more generalizable semantic representations and enhanced multilingual contextualization. SeaEval can serve as a launchpad for more thorough investigations and evaluations for multilingual and multicultural scenarios.",
+    "neighbors": [
+      143,
+      685,
+      811,
+      949,
+      1001,
+      1034,
+      1146,
+      1322,
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+      2029,
+      2062,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1656,
+    "label": 10,
+    "text": "Title: Maximal Ordinal Two-Factorizations\nAbstract: Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1657,
+    "label": 9,
+    "text": "Title: On the Existence of Anomalies, The Reals Case\nAbstract: The Independence Postulate (IP) is a finitary Church-Turing Thesis, saying mathematical sequences are independent from physical ones. Modelling observations as infinite sequences of real numbers, IP implies the existence of anomalies.",
+    "neighbors": [
+      1529
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1658,
+    "label": 6,
+    "text": "Title: A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective\nAbstract: User trust in Artificial Intelligence (AI) enabled systems has been increasingly recognized and proven as a key element to fostering adoption. It has been suggested that AI-enabled systems must go beyond technical-centric approaches and towards embracing a more human centric approach, a core principle of the human-computer interaction (HCI) field. This review aims to provide an overview of the user trust definitions, influencing factors, and measurement methods from 23 empirical studies to gather insight for future technical and design strategies, research, and initiatives to calibrate the user AI relationship. The findings confirm that there is more than one way to define trust. Selecting the most appropriate trust definition to depict user trust in a specific context should be the focus instead of comparing definitions. User trust in AI-enabled systems is found to be influenced by three main themes, namely socio-ethical considerations, technical and design features, and user characteristics. User characteristics dominate the findings, reinforcing the importance of user involvement from development through to monitoring of AI enabled systems. In conclusion, user trust needs to be addressed directly in every context where AI-enabled systems are being used or discussed. In addition, calibrating the user-AI relationship requires finding the optimal balance that works for not only the user but also the system.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1659,
+    "label": 30,
+    "text": "Title: Tool Learning with Foundation Models\nAbstract: Humans possess an extraordinary ability to create and utilize tools, allowing them to overcome physical limitations and explore new frontiers. With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. This paradigm, i.e., tool learning with foundation models, combines the strengths of specialized tools and foundation models to achieve enhanced accuracy, efficiency, and automation in problem-solving. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field. To this end, we present a systematic investigation of tool learning in this paper. We first introduce the background of tool learning, including its cognitive origins, the paradigm shift of foundation models, and the complementary roles of tools and models. Then we recapitulate existing tool learning research into tool-augmented and tool-oriented learning. We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools. We also discuss how to train models for improved tool-use capabilities and facilitate the generalization in tool learning. Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools. Finally, we discuss several open problems that require further investigation for tool learning. Overall, we hope this paper could inspire future research in integrating tools with foundation models.",
+    "neighbors": [
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+      2249,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1660,
+    "label": 4,
+    "text": "Title: SoK: Evaluating Privacy and Security Concerns of Using Web Services for the Disabled Population\nAbstract: The online privacy and security of the disabled community is a complex field that has implications for every user who navigates web services. While many disciplines have separately researched the disabled population and their online privacy and security concerns, the overlap between the two is very high but under-researched. Moreover, a complex relationship exists between the disabled population and web services where the interaction depends on several web service developmental factors, including usability and accessibility. To this aid, we explored this intersection of privacy and security of web services as perceived by the disabled community through previous studies by conducting a detailed systematic literature review and analysis of 63 articles. Our findings encompassed several topics, including how the disabled population navigates around authentication interfaces, online privacy concerns, universal design practices, and how security methods such as CAPTCHAs can be improved to become more accessible and usable for people of all needs and abilities. We further discuss the gap in the current research, including solutions such as the universal implementation of inclusive privacy and security tools and protocols.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1661,
+    "label": 16,
+    "text": "Title: Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning\nAbstract: Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. Finetuning the backbone or adjusting the classifier prototypes trained in the prior sessions would inevitably cause a misalignment between the feature and classifier of old classes, which explains the well-known catastrophic forgetting problem. In this paper, we deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse, which reveals that the last-layer features of the same class will collapse into a vertex, and the vertices of all classes are aligned with the classifier prototypes, which are formed as a simplex equiangular tight frame (ETF). It corresponds to an optimal geometric structure for classification due to the maximized Fisher Discriminant Ratio. We propose a neural collapse inspired framework for FSCIL. A group of classifier prototypes are pre-assigned as a simplex ETF for the whole label space, including the base session and all the incremental sessions. During training, the classifier prototypes are not learnable, and we adopt a novel loss function that drives the features into their corresponding prototypes. Theoretical analysis shows that our method holds the neural collapse optimality and does not break the feature-classifier alignment in an incremental fashion. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances. Code address: https://github.com/NeuralCollapseApplications/FSCIL",
+    "neighbors": [
+      1115
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1662,
+    "label": 24,
+    "text": "Title: Recursive Algorithmic Reasoning\nAbstract: Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently unable to execute recursive algorithms because they do not have arbitrarily large memory to store and recall state. To address this, we (1) propose a way to augment graph neural networks (GNNs) with a stack, and (2) develop an approach for capturing intermediate algorithm trajectories that improves algorithmic alignment with recursive algorithms over previous methods. The stack allows the network to learn to store and recall a portion of the state of the network at a particular time, analogous to the action of a call stack in a recursive algorithm. This augmentation permits the network to reason recursively. We empirically demonstrate that our proposals significantly improve generalization to larger input graphs over prior work on depth-first search (DFS).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1663,
+    "label": 10,
+    "text": "Title: A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?\nAbstract: As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to creation, it is worth noting that ChatGPT, with its most recent language model GPT-4, is just a tool out of numerous AIGC tasks. Impressed by the capability of the ChatGPT, many people are wondering about its limits: can GPT-5 (or other future GPT variants) help ChatGPT unify all AIGC tasks for diversified content creation? Toward answering this question, a comprehensive review of existing AIGC tasks is needed. As such, our work comes to fill this gap promptly by offering a first look at AIGC, ranging from its techniques to applications. Modern generative AI relies on various technical foundations, ranging from model architecture and self-supervised pretraining to generative modeling methods (like GAN and diffusion models). After introducing the fundamental techniques, this work focuses on the technological development of various AIGC tasks based on their output type, including text, images, videos, 3D content, etc., which depicts the full potential of ChatGPT's future. Moreover, we summarize their significant applications in some mainstream industries, such as education and creativity content. Finally, we discuss the challenges currently faced and present an outlook on how generative AI might evolve in the near future.",
+    "neighbors": [
+      341,
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+      1436,
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+      2085
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1664,
+    "label": 24,
+    "text": "Title: Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment\nAbstract: Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.",
+    "neighbors": [
+      673
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1665,
+    "label": 24,
+    "text": "Title: Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science\nAbstract: Explainable artificial intelligence (XAI) methods shed light on the predictions of deep neural networks (DNNs). Several different approaches exist and have partly already been successfully applied in climate science. However, the often missing ground truth explanations complicate their evaluation and validation, subsequently compounding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the context of climate research and assess different desired explanation properties, namely, robustness, faithfulness, randomization, complexity, and localization. To this end we build upon previous work and train a multi-layer perceptron (MLP) and a convolutional neural network (CNN) to predict the decade based on annual-mean temperature maps. Next, multiple local XAI methods are applied and their performance is quantified for each evaluation property and compared against a baseline test. Independent of the network type, we find that the XAI methods Integrated Gradients, Layer-wise relevance propagation, and InputGradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization. The opposite is true for Gradient, SmoothGrad, NoiseGrad, and FusionGrad. Notably, explanations using input perturbations, such as SmoothGrad and Integrated Gradients, do not improve robustness and faithfulness, contrary to previous claims. Overall, our experiments offer a comprehensive overview of different properties of explanation methods in the climate science context and supports users in the selection of a suitable XAI method.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1666,
+    "label": 16,
+    "text": "Title: PathLDM: Text conditioned Latent Diffusion Model for Histopathology\nAbstract: To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathology. Conditioning on labeled data is known to help in data-efficient model training. Therefore, histopathology reports, which are rich in valuable clinical information, are an ideal choice as guidance for a histopathology generative model. In this paper, we introduce PathLDM, the first text-conditioned Latent Diffusion Model tailored for generating high-quality histopathology images. Leveraging the rich contextual information provided by pathology text reports, our approach fuses image and textual data to enhance the generation process. By utilizing GPT's capabilities to distill and summarize complex text reports, we establish an effective conditioning mechanism. Through strategic conditioning and necessary architectural enhancements, we achieved a SoTA FID score of 7.64 for text-to-image generation on the TCGA-BRCA dataset, significantly outperforming the closest text-conditioned competitor with FID 30.1.",
+    "neighbors": [
+      533,
+      1670,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1667,
+    "label": 31,
+    "text": "Title: Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System\nAbstract: Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.",
+    "neighbors": [
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+      1915
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1668,
+    "label": 16,
+    "text": "Title: LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention\nAbstract: We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the word tokens at higher transformer layers. Then, a zero-initialized attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With our efficient training, LLaMA-Adapter can generate high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Besides language commands, our approach can be simply extended to multi-modal instructions for learning image-conditioned LLaMA model, which achieves superior reasoning performance on ScienceQA and COCO Caption benchmarks. Furthermore, we also evaluate the zero-initialized attention mechanism for fine-tuning other pre-trained models (ViT, RoBERTa) on traditional vision and language tasks, demonstrating the superior generalization capacity of our approach. Code is released at https://github.com/OpenGVLab/LLaMA-Adapter.",
+    "neighbors": [
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+      2216,
+      2286
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1669,
+    "label": 16,
+    "text": "Title: Unified Concept Editing in Diffusion Models\nAbstract: Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyright, and offensive content in text-to-image models. However, in the real world, all of these issues appear simultaneously in the same model. We present a method that tackles all issues with a single approach. Our method, Unified Concept Editing (UCE), edits the model without training using a closed-form solution, and scales seamlessly to concurrent edits on text-conditional diffusion models. We demonstrate scalable simultaneous debiasing, style erasure, and content moderation by editing text-to-image projections, and we present extensive experiments demonstrating improved efficacy and scalability over prior work. Our code is available at https://unified.baulab.info",
+    "neighbors": [
+      1273,
+      2251,
+      2267,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1670,
+    "label": 16,
+    "text": "Title: Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images\nAbstract: Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of imagetext pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 \u00d7 100,000 pixels. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pretrain our text encoder. By effectively leveraging strong pretrained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. Our code is available at: https://github.com/mahmoodlab/MI-Zero.",
+    "neighbors": [
+      533,
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+      2219
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1671,
+    "label": 30,
+    "text": "Title: Prompting Large Language Model for Machine Translation: A Case Study\nAbstract: Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explored in the literature. We fill this gap by offering a systematic study on prompting strategies for translation, examining various factors for prompt template and demonstration example selection. We further explore the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning in prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the testbed show that 1) the number and the quality of prompt examples matter, where using suboptimal examples degenerates translation; 2) several features of prompt examples, such as semantic similarity, show significant Spearman correlation with their prompting performance; yet, none of the correlations are strong enough; 3) using pseudo parallel prompt examples constructed from monolingual data via zero-shot prompting could improve translation; and 4) improved performance is achievable by transferring knowledge from prompt examples selected in other settings. We finally provide an analysis on the model outputs and discuss several problems that prompting still suffers from.",
+    "neighbors": [
+      1098,
+      1146
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1672,
+    "label": 24,
+    "text": "Title: Black-Box Variational Inference Converges\nAbstract: We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked on simplified versions of BBVI (e.g., bounded domain, bounded support, only optimizing for the scale, and such), our setup does not need any such algorithmic modifications. Our results hold for log-smooth posterior densities with and without strong log-concavity and the location-scale variational family. Also, our analysis reveals that certain algorithm design choices commonly employed in practice, particularly, nonlinear parameterizations of the scale of the variational approximation, can result in suboptimal convergence rates. Fortunately, running BBVI with proximal stochastic gradient descent fixes these limitations, and thus achieves the strongest known convergence rate guarantees. We evaluate this theoretical insight by comparing proximal SGD against other standard implementations of BBVI on large-scale Bayesian inference problems.",
+    "neighbors": [
+      2145
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1673,
+    "label": 1,
+    "text": "Title: Improving Adaptive Real-Time Video Communication Via Cross-layer Optimization\nAbstract: Effective Adaptive BitRate (ABR) algorithm or policy is of paramount importance for Real-Time Video Communication (RTVC) amid this pandemic to pursue uncompromised quality of experience (QoE). Existing ABR methods mainly separate the network bandwidth estimation and video encoder control, and fine-tune video bitrate towards estimated bandwidth, assuming the maximization of bandwidth utilization yields the optimal QoE. However, the QoE of a RTVC system is jointly determined by the quality of compressed video, fluency of video playback, and interaction delay. Solely maximizing the bandwidth utilization without comprehensively considering compound impacts incurred by both network and video application layers, does not assure the satisfactory QoE. And the decoupling of network and video layer further exacerbates the user experience due to network-codec incoordination. This work therefore proposes the Palette, a reinforcement learning based ABR scheme that unifies the processing of network and video application layers to directly maximize the QoE formulated as the weighted function of video quality, stalling rate and delay. To this aim, a cross-layer optimization is proposed to derive fine-grained compression factor of upcoming frame(s) using cross-layer observations like network conditions, video encoding parameters, and video content complexity. As a result, Palette manages to resolve the network-codec incoordination and to best catch up with the network fluctuation. Compared with state-of-the-art schemes in real-world tests, Palette not only reduces 3.1%-46.3% of the stalling rate, 20.2%-50.8% of the delay, but also improves 0.2%-7.2% of the video quality with comparable bandwidth consumption, under a variety of application scenarios.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1674,
+    "label": 28,
+    "text": "Title: Asymptotically Optimal Cascaded Coded Distributed Computing via Combinatorial Designs\nAbstract: Coded distributed computing (CDC) introduced by Li \\emph{et al.} can greatly reduce the communication load for MapReduce computing systems. In the general cascaded CDC with $K$ workers, $N$ input files and $Q$ Reduce functions, each input file will be mapped by $r$ workers and each Reduce function will be computed by $s$ workers such that coding techniques can be applied to achieve the maximum multicast gain. The main drawback of most existing CDC schemes is that they require the original data to be split into a large number of input files that grows exponentially with $K$, which can significantly increase the coding complexity and degrade system performance. In this paper, we first use a classic combinatorial structure $t$-design, for any integer $t\\geq 2$, to develop a low-complexity and asymptotically optimal CDC with $r=s$. The main advantages of our scheme via $t$-design are two-fold: 1) having much smaller $N$ and $Q$ than the existing schemes under the same parameters $K$, $r$ and $s$; and 2) achieving smaller communication loads compared with the state-of-the-art schemes. Remarkably, unlike the previous schemes that realize on large operation fields, our scheme operates on the minimum binary field $\\mathbb{F}_2$. Furthermore, we show that our construction method can incorporate the other combinatorial structures that have a similar property to $t$-design. For instance, we use $t$-GDD to obtain another asymptotically optimal CDC scheme over $\\mathbb{F}_2$ that has different parameters from $t$-design. Finally, we show that our construction method can also be used to construct CDC schemes with $r\\neq s$ that have small file number and Reduce function number.",
+    "neighbors": [
+      467,
+      605,
+      1998
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1675,
+    "label": 16,
+    "text": "Title: Monocular Depth Estimation using Diffusion Models\nAbstract: We formulate monocular depth estimation using denoising diffusion models, inspired by their recent successes in high fidelity image generation. To that end, we introduce innovations to address problems arising due to noisy, incomplete depth maps in training data, including step-unrolled denoising diffusion, an $L_1$ loss, and depth infilling during training. To cope with the limited availability of data for supervised training, we leverage pre-training on self-supervised image-to-image translation tasks. Despite the simplicity of the approach, with a generic loss and architecture, our DepthGen model achieves SOTA performance on the indoor NYU dataset, and near SOTA results on the outdoor KITTI dataset. Further, with a multimodal posterior, DepthGen naturally represents depth ambiguity (e.g., from transparent surfaces), and its zero-shot performance combined with depth imputation, enable a simple but effective text-to-3D pipeline. Project page: https://depth-gen.github.io",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1676,
+    "label": 17,
+    "text": "Title: Creative NFT-Copyrighted AR Face Mask Authoring Using Unity3D Editor\nAbstract: In this paper, we extend well-designed 3D face masks into AR face masks and demonstrate the possibility of transforming this into an NFT-copyrighted AR face mask that helps authenticate the ownership of the AR mask user so as to improve creative control, brand identification, and ID protection. The output of this project will not only potentially validate the value of the NFT technology but also explore how to combine the NFT technology with the AR technology so as to be applied to the e-commerce and e-business aspects of the multimedia industry.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1677,
+    "label": 16,
+    "text": "Title: Direct learning-based deep spiking neural networks: a review\nAbstract: The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.",
+    "neighbors": [
+      1602,
+      2014
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1678,
+    "label": 30,
+    "text": "Title: REPLUG: Retrieval-Augmented Black-Box Language Models\nAbstract: We introduce REPLUG, a retrieval-augmented language modeling framework that treats the language model (LM) as a black box and augments it with a tuneable retrieval model. Unlike prior retrieval-augmented LMs that train language models with special cross attention mechanisms to encode the retrieved text, REPLUG simply prepends retrieved documents to the input for the frozen black-box LM. This simple design can be easily applied to any existing retrieval and language models. Furthermore, we show that the LM can be used to supervise the retrieval model, which can then find documents that help the LM make better predictions. Our experiments demonstrate that REPLUG with the tuned retriever significantly improves the performance of GPT-3 (175B) on language modeling by 6.3%, as well as the performance of Codex on five-shot MMLU by 5.1%.",
+    "neighbors": [
+      3,
+      36,
+      704,
+      818,
+      1834,
+      1877,
+      1878,
+      2038,
+      2104
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1679,
+    "label": 16,
+    "text": "Title: Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective\nAbstract: For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical regions and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing such challenging safety-critical tasks.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1680,
+    "label": 16,
+    "text": "Title: Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal Contrast\nAbstract: Video-based remote physiological measurement utilizes facial videos to measure the blood volume change signal, which is also called remote photoplethysmography (rPPG). Supervised methods for rPPG measurements have been shown to achieve good performance. However, the drawback of these methods is that they require facial videos with ground truth (GT) physiological signals, which are often costly and difficult to obtain. In this paper, we propose Contrast-Phys+, a method that can be trained in both unsupervised and weakly-supervised settings. We employ a 3DCNN model to generate multiple spatiotemporal rPPG signals and incorporate prior knowledge of rPPG into a contrastive loss function. We further incorporate the GT signals into contrastive learning to adapt to partial or misaligned labels. The contrastive loss encourages rPPG/GT signals from the same video to be grouped together, while pushing those from different videos apart. We evaluate our methods on five publicly available datasets that include both RGB and Near-infrared videos. Contrast-Phys+ outperforms the state-of-the-art supervised methods, even when using partially available or misaligned GT signals, or no labels at all. Additionally, we highlight the advantages of our methods in terms of computational efficiency, noise robustness, and generalization.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1681,
+    "label": 16,
+    "text": "Title: Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction\nAbstract: Monitoring sustainable development goals requires accurate and timely socioeconomic statistics, while ubiquitous and frequently-updated urban imagery in web like satellite/street view images has emerged as an important source for socioeconomic prediction. Especially, recent studies turn to self-supervised contrastive learning with manually designed similarity metrics for urban imagery representation learning and further socioeconomic prediction, which however suffers from effectiveness and robustness issues. To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction. Specifically, we firstly introduce knowledge graph (KG) to effectively model the urban knowledge in spatiality, mobility, etc., and then build neural network based encoders to learn representations of an urban image in associated semantic and visual spaces, respectively. Finally, we design a cross-modality based contrastive learning framework with a novel image-KG contrastive loss, which maximizes the mutual information between semantic and visual representations for knowledge infusion. Extensive experiments of applying the learnt visual representations for socioeconomic prediction on three datasets demonstrate the superior performance of KnowCL with over 30% improvements on R2 compared with baselines. Especially, our proposed KnowCL model can apply to both satellite and street imagery with both effectiveness and transferability achieved, which provides insights into urban imagery-based socioeconomic prediction.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1682,
+    "label": 16,
+    "text": "Title: Boosting Detection in Crowd Analysis via Underutilized Output Features\nAbstract: Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence score of output proposals and bounding boxes provide insight into the scale and density of the crowd. To leverage these underutilized features, we propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models. This module uses a mixed 2D-1D compression technique to refine the output features and obtain the spatial and numerical distribution of crowd-specific information. Based on these features, we further propose region-adaptive NMS thresholds and a decouple-then-align paradigm that address the major limitations of detection-based methods. Our extensive evaluations on various crowd analysis tasks, including crowd counting, localization, and detection, demonstrate the effectiveness of utilizing output features and the potential of detection-based methods in crowd analysis. Our code is available at https://github.com/wskingdom/Crowd-Hat.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1683,
+    "label": 30,
+    "text": "Title: SSS at SemEval-2023 Task 10: Explainable Detection of Online Sexism using Majority Voted Fine-Tuned Transformers\nAbstract: This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more important than ever to make social media safer and more accessible for women. Our approach consists of experimenting and finetuning BERT-based models and using a Majority Voting ensemble model that outperforms individual baseline model scores. Our system achieves a macro F1 score of 0.8392 for Task A, 0.6092 for Task B, and 0.4319 for Task C.",
+    "neighbors": [
+      216,
+      1219
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1684,
+    "label": 16,
+    "text": "Title: Diffusion-based Generation, Optimization, and Planning in 3D Scenes\nAbstract: We introduce the SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior work, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative sampling strategy, SceneDiffuser jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion. Such a design alleviates the discrepancies among different modules and the posterior collapse of previous scene-conditioned generative models. We evaluate the SceneDiffuser on various 3D scene understanding tasks, including human pose and motion generation, dexterous grasp generation, path planning for 3D navigation, and motion planning for robot arms. The results show significant improvements compared with previous models, demonstrating the tremendous potential of the SceneDiffuser for the broad community of 3D scene understanding.",
+    "neighbors": [
+      146,
+      787,
+      2040
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1685,
+    "label": 16,
+    "text": "Title: Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation\nAbstract: Vision-Language Pretraining (VLP) has demonstrated remarkable capabilities in learning visual representations from textual descriptions of images without annotations. Yet, effective VLP demands large-scale image-text pairs, a resource that suffers scarcity in the medical domain. Moreover, conventional VLP is limited to 2D images while medical images encompass diverse modalities, often in 3D, making the learning process more challenging. To address these challenges, we present Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation (GTGM), a framework that extends of VLP to 3D medical images without relying on paired textual descriptions. Specifically, GTGM utilizes large language models (LLM) to generate medical-style text from 3D medical images. This synthetic text is then used to supervise 3D visual representation learning. Furthermore, a negative-free contrastive learning objective strategy is introduced to cultivate consistent visual representations between augmented 3D medical image patches, which effectively mitigates the biases associated with strict positive-negative sample pairings. We evaluate GTGM on three imaging modalities - Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and electron microscopy (EM) over 13 datasets. GTGM's superior performance across various medical image segmentation tasks underscores its effectiveness and versatility, by enabling VLP extension into 3D medical imagery while bypassing the need for paired text.",
+    "neighbors": [
+      1392,
+      1525,
+      1698,
+      2229
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1686,
+    "label": 16,
+    "text": "Title: Deep Axial Hypercomplex Networks\nAbstract: Over the past decade, deep hypercomplex-inspired networks have enhanced feature extraction for image classification by enabling weight sharing across input channels. Recent works make it possible to improve representational capabilities by using hypercomplex-inspired networks which consume high computational costs. This paper reduces this cost by factorizing a quaternion 2D convolutional module into two consecutive vectormap 1D convolutional modules. Also, we use 5D parameterized hypercomplex multiplication based fully connected layers. Incorporating both yields our proposed hypercomplex network, a novel architecture that can be assembled to construct deep axial-hypercomplex networks (DANs) for image classifications. We conduct experiments on CIFAR benchmarks, SVHN, and Tiny ImageNet datasets and achieve better performance with fewer trainable parameters and FLOPS. Our proposed model achieves almost 2% higher performance for CIFAR and SVHN datasets, and more than 3% for the ImageNet-Tiny dataset and takes six times fewer parameters than the real-valued ResNets. Also, it shows state-of-the-art performance on CIFAR benchmarks in hypercomplex space.",
+    "neighbors": [
+      1641,
+      2234
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1687,
+    "label": 24,
+    "text": "Title: REFORMS: Reporting Standards for Machine Learning Based Science\nAbstract: Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\\textbf{Re}$porting Standards $\\textbf{For}$ $\\textbf{M}$achine Learning Based $\\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.",
+    "neighbors": [
+      1760
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1688,
+    "label": 16,
+    "text": "Title: FACET: Fairness in Computer Vision Evaluation Benchmark\nAbstract: Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the people in the image. These disparities have been shown to exist, but until now there has not been a unified approach to measure these differences for common use-cases of computer vision models. We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation. For every image in FACET, we hired expert reviewers to manually annotate person-related attributes such as perceived skin tone and hair type, manually draw bounding boxes and label fine-grained person-related classes such as disk jockey or guitarist. In addition, we use FACET to benchmark state-of-the-art vision models and present a deeper understanding of potential performance disparities and challenges across sensitive demographic attributes. With the exhaustive annotations collected, we probe models using single demographics attributes as well as multiple attributes using an intersectional approach (e.g. hair color and perceived skin tone). Our results show that classification, detection, segmentation, and visual grounding models exhibit performance disparities across demographic attributes and intersections of attributes. These harms suggest that not all people represented in datasets receive fair and equitable treatment in these vision tasks. We hope current and future results using our benchmark will contribute to fairer, more robust vision models. FACET is available publicly at https://facet.metademolab.com/",
+    "neighbors": [
+      750
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1689,
+    "label": 24,
+    "text": "Title: Generative Sliced MMD Flows with Riesz Kernels\nAbstract: Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \\Vert x-y\\Vert^r$, $r \\in (0,2)$ have exceptional properties which allow their efficient computation. We prove that the MMD of Riesz kernels coincides with the MMD of their sliced version. As a consequence, the computation of gradients of MMDs can be performed in the one-dimensional setting. Here, for $r=1$, a simple sorting algorithm can be applied to reduce the complexity from $O(MN+N^2)$ to $O((M+N)\\log(M+N))$ for two measures with $M$ and $N$ support points. As another interesting follow-up result, the MMD of compactly supported measures can be estimated from above and below by the Wasserstein-1 distance. For the implementations we approximate the gradient of the sliced MMD by using only a finite number $P$ of slices. We show that the resulting error has complexity $O(\\sqrt{d/P})$, where $d$ is the data dimension. These results enable us to train generative models by approximating MMD gradient flows by neural networks even for image applications. We demonstrate the efficiency of our model by image generation on MNIST, FashionMNIST and CIFAR10.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1690,
+    "label": 16,
+    "text": "Title: Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection\nAbstract: SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However, its ability to generalize to specific scenes such as camouflaged scenes is still unknown. Camouflaged object detection (COD) involves identifying objects that are seamlessly integrated into their surroundings and has numerous practical applications in fields such as medicine, art, and agriculture. In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation. We also compare SAM's performance with 22 state-of-the-art COD methods. Our results indicate that while SAM shows promise in generic object segmentation, its performance on the COD task is limited. This presents an opportunity for further research to explore how to build a stronger SAM that may address the COD task. The results of this paper are provided in \\url{https://github.com/luckybird1994/SAMCOD}.",
+    "neighbors": [
+      341,
+      1050,
+      1207,
+      1932,
+      2272
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1691,
+    "label": 16,
+    "text": "Title: Self-Supervised Learning of Gait-Based Biomarkers\nAbstract: Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meaningful information from gait data. In multiple fields ranging from image processing to natural language processing, self-supervised learning (SSL) from large amounts of unannotated data produces very effective representations for downstream tasks. However, there has only been limited use of SSL to learn effective representations of gait and movement, and it has not been applied to gait analysis with MMC. One SSL objective that has not been applied to gait is contrastive learning, which finds representations that place similar samples closer together in the learned space. If the learned similarity metric captures clinically meaningful differences, this could produce a useful representation for many downstream clinical tasks. Contrastive learning can also be combined with causal masking to predict future timesteps, which is an appealing SSL objective given the dynamical nature of gait. We applied these techniques to gait analyses performed with MMC in a rehabilitation hospital from a diverse clinical population. We find that contrastive learning on unannotated gait data learns a representation that captures clinically meaningful information. We probe this learned representation using the framework of biomarkers and show it holds promise as both a diagnostic and response biomarker, by showing it can accurately classify diagnosis from gait and is responsive to inpatient therapy, respectively. We ultimately hope these learned representations will enable predictive and prognostic gait-based biomarkers that can facilitate precision rehabilitation through greater use of MMC to quantify movement in rehabilitation.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1692,
+    "label": 28,
+    "text": "Title: New Euclidean and Hermitian Self-Dual Cyclic Codes with Square-Root-Like Minimum Distances\nAbstract: Binary self-dual codes with large minimum distances, such as the extended Hamming code and the Golay code, are fascinating objects in the coding theory. They are closely related to sporadic simple groups, lattices and invariant theory. A family of binary self-dual repeated-root cyclic codes with lengths $n_i$ and minimum distances $d_i \\geq \\frac{1}{2} \\sqrt{n_i+2}$, $n_i$ goes to the infinity for $i=1,2, \\ldots$, was constructed in a paper of IEEE Trans. Inf. Theory, 2009. In this paper, we construct families of Euclidean self-dual repeated-root cyclic codes over the field ${\\bf F}_{2^s}$, $s \\geq 2$, with lengths $n_i$ and minimum distances at least $\\sqrt{2^{s-1}n}-2^s$, where lengths $n_i$ go to the infinity. We also construct families of Hermitian self-dual repeated-root cyclic codes over the field ${\\bf F}_{2^{2s}}$, $s \\geq 1$, with lengths $n_i$ and minimum distances at least $\\sqrt{n_i/2}$, where lengths $n_i$ go to the infinity. Our results show that Euclidean and Hermitian self-dual codes with large automorphism groups and large minimum distances can always be constructed.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1693,
+    "label": 16,
+    "text": "Title: Part Aware Contrastive Learning for Self-Supervised Action Recognition\nAbstract: In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local body parts, such as legs or hands, which are advantageous for skeleton-based action recognition. This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations. To achieve this, a multi-head attention mask module is employed to learn the soft attention mask features from the skeletons, suppressing non-salient local features while accentuating local salient features, thereby bringing similar local features closer in the feature space. Additionally, ample contrastive pairs are generated by expanding contrastive pairs based on salient and non-salient features with global features, which guide the network to learn the semantic representations of the entire skeleton. Therefore, with the attention mask mechanism, SkeAttnCLR learns local features under different data augmentation views. The experiment results demonstrate that the inclusion of local feature similarity significantly enhances skeleton-based action representation. Our proposed SkeAttnCLR outperforms state-of-the-art methods on NTURGB+D, NTU120-RGB+D, and PKU-MMD datasets. The code and settings are available at this repository: https://github.com/GitHubOfHyl97/SkeAttnCLR.",
+    "neighbors": [
+      483
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1694,
+    "label": 30,
+    "text": "Title: Benchmarking Large Language Models for News Summarization\nAbstract: Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LMM summaries are judged to be on par with human written summaries.",
+    "neighbors": [
+      735,
+      971,
+      1713,
+      1940,
+      2087,
+      2140,
+      2215
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1695,
+    "label": 8,
+    "text": "Title: Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks\nAbstract: This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a two-level hierarchical architecture, which dynamically reconfigures the network while considering Integrated Access-Backhaul (IAB) constraints. The high-layer decision process determines the number of MAPs through consensus, and we develop a joint optimization process to account for co-dependence in network self-management. In the low-layer, MAPs manage their placement using a double-attention based Deep Reinforcement Learning (DRL) model that encourages cooperation without retraining. To improve generalization and reduce complexity, we propose a federated mechanism for training and sharing one placement model for every MAP in the low-layer. Additionally, we jointly optimize the placement and backhaul connectivity of MAPs using a multi-objective reward function, considering the impact of varying MAP placement on wireless backhaul connectivity.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1696,
+    "label": 25,
+    "text": "Title: SoundSieve: Seconds-Long Audio Event Recognition on Intermittently-Powered Systems\nAbstract: A fundamental problem of every intermittently-powered sensing system is that signals acquired by these systems over a longer period in time are also intermittent. As a consequence, these systems fail to capture parts of a longer-duration event that spans over multiple charge-discharge cycles of the capacitor that stores the harvested energy. From an application's perspective, this is viewed as sporadic bursts of missing values in the input data - which may not be recoverable using statistical interpolation or imputation methods. In this paper, we study this problem in the light of an intermittent audio classification system and design an end-to-end system - SoundSieve - that is capable of accurately classifying audio events that span multiple on-off cycles of the intermittent system. SoundSieve employs an offline audio analyzer that learns to identify and predict important segments of an audio clip that must be sampled to ensure accurate classification of the audio. At runtime, SoundSieve employs a lightweight, energy- and content-aware audio sampler that decides when the system should wake up to capture the next chunk of audio; and a lightweight, intermittence-aware audio classifier that performs imputation and on-device inference. Through extensive evaluations using popular audio datasets as well as real systems, we demonstrate that SoundSieve yields 5%--30% more accurate inference results than the state-of-the-art.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1697,
+    "label": 30,
+    "text": "Title: Detect Depression from Social Networks with Sentiment Knowledge Sharing\nAbstract: Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1698,
+    "label": 16,
+    "text": "Title: Advancing Radiograph Representation Learning with Masked Record Modeling\nAbstract: Modern studies in radiograph representation learning rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins.",
+    "neighbors": [
+      1488,
+      1525,
+      1685,
+      2102
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1699,
+    "label": 3,
+    "text": "Title: Bridging adaptive management and reinforcement learning for more robust decisions\nAbstract: From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue \u2018Detecting and attributing the causes of biodiversity change: needs, gaps and solutions\u2019.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1700,
+    "label": 13,
+    "text": "Title: Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms\nAbstract: Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update is a key component in multi-objective EAs (MOEAs), and it is performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the first population-size ranked solutions (based on some selection criteria, e.g., non-dominated sorting, crowdedness and indicators) from the collections of the current population and newly-generated solutions. In this paper, we question this practice. We analytically present that introducing randomness into the population update procedure in MOEAs can be beneficial for the search. More specifically, we prove that the expected running time of a well-established MOEA (SMS-EMOA) for solving a commonly studied bi-objective problem, OneJumpZeroJump, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed stochastic population update method. This work is an attempt to challenge a common practice for the population update in MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.",
+    "neighbors": [
+      614,
+      1253,
+      1610,
+      2074
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1701,
+    "label": 39,
+    "text": "Title: Set-alternating schemes: A new class of large Condorcet domains\nAbstract: In this paper, we introduce set-alternating schemes to construct Condorcet domains. These schemes lead to domains which are copious, connected, and peak-pit. The resulting family of domains includes some of Arrow's single-peaked domains of size $2^{n-1}$, which we prove to be the smallest possible domains. Still, schemes of this type lead to domains larger than the domains of Fishburn's alternating scheme. Thanks to the concise form of our schemes, we can analyze the growth of our fastest-growing domains. We show that the domain size for sufficiently high $n$ exceeds $2.1973^n$, improving the previous record $2.1890^n$ from \\cite{karpov2023constructing}. To perform this analysis, a bijection between suborders and Dyck words, counted by the Catalan numbers, is developed.",
+    "neighbors": [
+      1597,
+      2053
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1702,
+    "label": 13,
+    "text": "Title: Reservoir Computing with Noise\nAbstract: This paper investigates in detail the effects of measurement noise on the performance of reservoir computing. We focus on an application in which reservoir computers are used to learn the relationship between different state variables of a chaotic system. We recognize that noise can affect the training and testing phases differently. We find that the best performance of the reservoir is achieved when the strength of the noise that affects the input signal in the training phase equals the strength of the noise that affects the input signal in the testing phase. For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1703,
+    "label": 24,
+    "text": "Title: Minimizing Dynamic Regret on Geodesic Metric Spaces\nAbstract: In this paper, we consider the sequential decision problem where the goal is to minimize the general dynamic regret on a complete Riemannian manifold. The task of offline optimization on such a domain, also known as a geodesic metric space, has recently received significant attention. The online setting has received significantly less attention, and it has remained an open question whether the body of results that hold in the Euclidean setting can be transplanted into the land of Riemannian manifolds where new challenges (e.g., curvature) come into play. In this paper, we show how to get optimistic regret bound on manifolds with non-positive curvature whenever improper learning is allowed and propose an array of adaptive no-regret algorithms. To the best of our knowledge, this is the first work that considers general dynamic regret and develops\"optimistic\"online learning algorithms which can be employed on geodesic metric spaces.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1704,
+    "label": 10,
+    "text": "Title: On the Computation of Meaning, Language Models and Incomprehensible Horrors\nAbstract: We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.",
+    "neighbors": [
+      701,
+      1864
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1705,
+    "label": 30,
+    "text": "Title: On Bias and Fairness in NLP: How to have a fairer text classification?\nAbstract: In this paper, we provide a holistic analysis of the different sources of bias, Upstream, Sample and Overampflication biases, in NLP models. We investigate how they impact the fairness of the task of text classification. We also investigate the impact of removing these biases using different debiasing techniques on the fairness of text classification. We found that overamplification bias is the most impactful bias on the fairness of text classification. And that removing overamplification bias by fine-tuning the LM models on a dataset with balanced representations of the different identity groups leads to fairer text classification models. Finally, we build on our findings and introduce practical guidelines on how to have a fairer text classification model.",
+    "neighbors": [
+      225,
+      767
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1706,
+    "label": 30,
+    "text": "Title: Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues\nAbstract: User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the user\u2019s task goals were fulfilled by the system. Task-oriented dialogue systems use task schema, which is a set of task attributes, to encode the user\u2019s task goals. Existing studies on USM neglect explicitly modeling the user\u2019s task goals fulfillment using the task schema. In this paper, we propose SG-USM, a novel schema-guided user satisfaction modeling framework. It explicitly models the degree to which the user\u2019s preferences regarding the task attributes are fulfilled by the system for predicting the user\u2019s satisfaction level. SG-USM employs a pre-trained language model for encoding dialogue context and task attributes. Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes. Finally, it predicts the user satisfaction based on task attribute fulfillment and task attribute importance. Experimental results on benchmark datasets (i.e. MWOZ, SGD, ReDial, and JDDC) show that SG-USM consistently outperforms competitive existing methods. Our extensive analysis demonstrates that SG-USM can improve the interpretability of user satisfaction modeling, has good scalability as it can effectively deal with unseen tasks and can also effectively work in low-resource settings by leveraging unlabeled data.Code is available at https://github.com/amzn/user-satisfaction-modeling.",
+    "neighbors": [
+      1462
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1707,
+    "label": 16,
+    "text": "Title: Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising\nAbstract: Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.",
+    "neighbors": [
+      364,
+      1020,
+      1173,
+      1251,
+      1420,
+      1902,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1708,
+    "label": 31,
+    "text": "Title: The Evolution of Web Search User Interfaces - An Archaeological Analysis of Google Search Engine Result Pages\nAbstract: Web search engines have marked everyone\u2019s life by transforming how one searches and accesses information. Search engines give special attention to the user interface, especially search engine result pages (SERP). The well-known \u201c10 blue links\u201d list has evolved into richer interfaces, often personalized to the search query, the user, and other aspects. More than 20 years later, the literature has not adequately portrayed this development. We present a study on the evolution of SERP interfaces during the last two decades using Google Search as a case study. We used the most searched queries by year to extract a sample of SERP from the Internet Archive. Using this dataset, we analyzed how SERP evolved in content, layout, design (e.g., color scheme, text styling, graphics), navigation, and file size. We have also analyzed the user interface design patterns associated with SERP elements. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers. This systematic analysis portrays evolution trends in search engine user interfaces and, more generally, web design. We expect this work will trigger other, more specific studies that can take advantage of our dataset.",
+    "neighbors": [
+      1971
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1709,
+    "label": 36,
+    "text": "Title: Anonymous Refugee Housing with Upper-Bounds\nAbstract: Knop and Schierreich [AAMAS '23] recently introduced a novel model of refugee housing and specifically asked for the computational complexity picture of the following variant. Given a topology modelled as an undirected graph, a set of inhabitants, a number of refugees $R$, an assignment of inhabitants to houses of the topology, and an upper-bound for every inhabitant, find a set $\\pi$ of unoccupied houses of size $R$ intended such that the number of refugees in the neighbourhood of every inhabitant is at most its upper-bound. If such a set $\\pi$ exists, we say that the instance admits an inhabitant-respecting housing. In this paper, we show that the existence of inhabitant-respecting housing is not guaranteed even under several further restrictions of the upper-bounds. Then, we focus on the computational complexity of deciding whether inhabitant-respecting housing exists. To this end, we provide tractable algorithms for several restrictions of the topology. We complement these results with appropriate hardness results and running-time lower-bounds. Furthermore, we introduce a relaxed (or approximate) version of the inhabitant-respecting housing, where we allow at most $t$ upper-bounds to be exceeded.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1710,
+    "label": 16,
+    "text": "Title: Muse: Text-To-Image Generation via Masked Generative Transformers\nAbstract: We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io",
+    "neighbors": [
+      170,
+      706,
+      848,
+      955,
+      1025,
+      1303,
+      1436,
+      1582,
+      1651,
+      1731,
+      1758,
+      1768,
+      1789,
+      1898,
+      1978,
+      1994,
+      2161,
+      2251,
+      2306
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1711,
+    "label": 6,
+    "text": "Title: Determining Which Sine Wave Frequencies Correspond to Signal and Which Correspond to Noise in Eye-Tracking Time-Series\nAbstract: The Fourier theorem proposes that any time-series can be decomposed into a set of sinusoidal frequencies, each with its own phase and amplitude. The literature suggests that some of these frequencies are important to reproduce key qualities of eye-movements (``signal'') and some of these frequencies are not important (``noise''). To understand what is signal and what is noise, we analyzed our dataset in three ways: (1) visual inspection of plots of saccade, microsaccade and smooth pursuit exemplars; (2) an analysis of the percentage of variance accounted for (PVAF) in each of 1,033 unfiltered saccade trajectories by each frequency cutoff; (3) an analysis of saccade peak velocity in the unfiltered and various filtered conditions. Visual inspection suggested that frequencies up to 75 Hz are required to represent microsaccades. Our PVAF analysis indicated that data in the 0-25 Hz band are sufficient to account for nearly 100% of the variance in unfiltered saccade trajectories. Our analysis also indicated that frequencies below 100 Hz are sufficient to maintain peak velocities. Therefore, our overall conclusion is that to maintain eye-movement signal and reduce noise, a cutoff frequency of 100 Hz is appropriate. Our results have implications for the proposed sampling rate of eye-tracking recordings. If one is working in the frequency domain and 100 Hz needs to be preserved, the minimum required sampling rate would be 200 Hz. However, in a time domain analysis, a minimum 1000 Hz sampling rate is recommended.",
+    "neighbors": [
+      1460
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1712,
+    "label": 27,
+    "text": "Title: FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback\nAbstract: In this paper, we address the problem of using visuo-tactile feedback for 6-DoF localization and 3D reconstruction of unknown in-hand objects. We propose FingerSLAM, a closed-loop factor graph-based pose estimator that combines local tactile sensing at finger-tip and global vision sensing from a wrist-mount camera. FingerSLAM is constructed with two constituent pose estimators: a multi-pass refined tactile-based pose estimator that captures movements from detailed local textures, and a single-pass vision-based pose estimator that predicts from a global view of the object. We also design a loop closure mechanism that actively matches current vision and tactile images to previously stored key-frames to reduce accumulated error. FingerSLAM incorporates the two sensing modalities of tactile and vision, as well as the loop closure mechanism with a factor graph-based optimization framework. Such a framework produces an optimized pose estimation solution that is more accurate than the standalone estimators. The estimated poses are then used to reconstruct the shape of the unknown object incrementally by stitching the local point clouds recovered from tactile images. We train our system on real-world data collected with 20 objects. We demonstrate reliable visuo-tactile pose estimation and shape reconstruction through quantitative and qualitative real-world evaluations on 6 objects that are unseen during training.",
+    "neighbors": [
+      1883
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1713,
+    "label": 10,
+    "text": "Title: A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation\nAbstract: Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many knowledge domains. In response to their fast adoption in many industrial applications, this survey concerns their safety and trustworthiness. First, we review known vulnerabilities and limitations of the LLMs, categorising them into inherent issues, attacks, and unintended bugs. Then, we consider if and how the Verification and Validation (V&V) techniques, which have been widely developed for traditional software and deep learning models such as convolutional neural networks as independent processes to check the alignment of their implementations against the specifications, can be integrated and further extended throughout the lifecycle of the LLMs to provide rigorous analysis to the safety and trustworthiness of LLMs and their applications. Specifically, we consider four complementary techniques: falsification and evaluation, verification, runtime monitoring, and regulations and ethical use. In total, 370+ references are considered to support the quick understanding of the safety and trustworthiness issues from the perspective of V&V. While intensive research has been conducted to identify the safety and trustworthiness issues, rigorous yet practical methods are called for to ensure the alignment of LLMs with safety and trustworthiness requirements.",
+    "neighbors": [
+      69,
+      315,
+      377,
+      519,
+      896,
+      1035,
+      1039,
+      1133,
+      1194,
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+      1259,
+      1531,
+      1560,
+      1596,
+      1694,
+      1799,
+      1940,
+      1952,
+      1969,
+      2007,
+      2081,
+      2100,
+      2113,
+      2158,
+      2244,
+      2249,
+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1714,
+    "label": 16,
+    "text": "Title: GREC: Generalized Referring Expression Comprehension\nAbstract: The objective of Classic Referring Expression Comprehension (REC) is to produce a bounding box corresponding to the object mentioned in a given textual description. Commonly, existing datasets and techniques in classic REC are tailored for expressions that pertain to a single target, meaning a sole expression is linked to one specific object. Expressions that refer to multiple targets or involve no specific target have not been taken into account. This constraint hinders the practical applicability of REC. This study introduces a new benchmark termed as Generalized Referring Expression Comprehension (GREC). This benchmark extends the classic REC by permitting expressions to describe any number of target objects. To achieve this goal, we have built the first large-scale GREC dataset named gRefCOCO. This dataset encompasses a range of expressions: those referring to multiple targets, expressions with no specific target, and the single-target expressions. The design of GREC and gRefCOCO ensures smooth compatibility with classic REC. The proposed gRefCOCO dataset, a GREC method implementation code, and GREC evaluation code are available at https://github.com/henghuiding/gRefCOCO.",
+    "neighbors": [
+      959,
+      1540,
+      2156
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1715,
+    "label": 34,
+    "text": "Title: Tight Bounds for Budgeted Maximum Weight Independent Set in Bipartite and Perfect Graphs\nAbstract: We consider the classic budgeted maximum weight independent set (BMWIS) problem. The input is a graph $G = (V,E)$, a weight function $w:V \\rightarrow \\mathbb{R}_{\\geq 0}$, a cost function $c:V \\rightarrow \\mathbb{R}_{\\geq 0}$, and a budget $B \\in \\mathbb{R}_{\\geq 0}$. The goal is to find an independent set $S \\subseteq V$ in $G$ such that $\\sum_{v \\in S} c(v) \\leq B$, which maximizes the total weight $\\sum_{v \\in S} w(v)$. Since the problem on general graphs cannot be approximated within ratio $|V|^{1-\\varepsilon}$ for any $\\varepsilon>0$, BMWIS has attracted significant attention on graph families for which a maximum weight independent set can be computed in polynomial time. Two notable such graph families are bipartite and perfect graphs. BMWIS is known to be NP-hard on both of these graph families; however, the best possible approximation guarantees for these graphs are wide open. In this paper, we give a tight $2$-approximation for BMWIS on perfect graphs and bipartite graphs. In particular, we give We a $(2-\\varepsilon)$ lower bound for BMWIS on bipartite graphs, already for the special case where the budget is replaced by a cardinality constraint, based on the Small Set Expansion Hypothesis (SSEH). For the upper bound, we design a $2$-approximation for BMWIS on perfect graphs using a Lagrangian relaxation based technique. Finally, we obtain a tight lower bound for the capacitated maximum weight independent set (CMWIS) problem, the special case of BMWIS where $w(v) = c(v)~\\forall v \\in V$. We show that CMWIS on bipartite and perfect graphs is unlikely to admit an efficient polynomial-time approximation scheme (EPTAS). Thus, the existing PTAS for CMWIS is essentially the best we can expect.",
+    "neighbors": [
+      1753
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1716,
+    "label": 30,
+    "text": "Title: Transferring Procedural Knowledge across Commonsense Tasks\nAbstract: Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and procedural understanding, current AI has no mechanisms to automatically track and explain procedures in unseen stories. To bridge this gap, we study the ability of AI models to transfer procedural knowledge to novel narrative tasks in a transparent manner. We design LEAP: a comprehensive framework that integrates state-of-the-art modeling architectures, training regimes, and augmentation strategies based on both natural and synthetic stories. To address the lack of densely annotated training data, we devise a robust automatic labeler based on few-shot prompting to enhance the augmented data. Our experiments with in- and out-of-domain tasks reveal insights into the interplay of different architectures, training regimes, and augmentation strategies. LEAP's labeler has a clear positive impact on out-of-domain datasets, while the resulting dense annotation provides native explainability.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1717,
+    "label": 7,
+    "text": "Title: An efficient mass lumping scheme for isogeometric analysis based on approximate dual basis functions\nAbstract: In this contribution, we provide a new mass lumping scheme for explicit dynamics in isogeometric analysis (IGA). To this end, an element formulation based on the idea of dual functionals is developed. Non-Uniform Rational B-splines (NURBS) are applied as shape functions and their corresponding dual basis functions are applied as test functions in the variational form, where two kinds of dual basis functions are compared. The first type are approximate dual basis functions (AD) with varying degree of reproduction, resulting in banded mass matrices. Dual basis functions derived from the inversion of the Gram matrix (IG) are the second type and already yield diagonal mass matrices. We will show that it is possible to apply the dual scheme as a transformation of the resulting system of equations based on NURBS as shape and test functions. Hence, it can be easily implemented into existing IGA routines. Treating the application of dual test functions as preconditioner reduces the additional computational effort, but it cannot entirely erase it and the density of the stiffness matrix still remains higher than in standard Bubnov-Galerkin formulations. In return applying additional row-sum lumping to the mass matrices is either not necessary for IG or the caused loss of accuracy is lowered to a reasonable magnitude in the case of AD. Numerical examples show a significantly better approximation of the dynamic behavior for the dual lumping scheme compared to standard NURBS approaches making use of row-sum lumping. Applying IG yields accurate numerical results without additional lumping. But as result of the global support of the IG dual basis functions, fully populated stiffness matrices occur, which are entirely unsuitable for explicit dynamic simulations. Combining AD and row-sum lumping leads to an efficient computation regarding effort and accuracy.",
+    "neighbors": [
+      1749
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1718,
+    "label": 3,
+    "text": "Title: A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics\nAbstract: In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1719,
+    "label": 4,
+    "text": "Title: Reinforcing Security and Usability of Crypto-Wallet with Post-Quantum Cryptography and Zero-Knowledge Proof\nAbstract: Crypto-wallets or digital asset wallets are a crucial aspect of managing cryptocurrencies and other digital assets such as NFTs. However, these wallets are not immune to security threats, particularly from the growing risk of quantum computing. The use of traditional public-key cryptography systems in digital asset wallets makes them vulnerable to attacks from quantum computers, which may increase in the future. Moreover, current digital wallets require users to keep track of seed-phrases, which can be challenging and lead to additional security risks. To overcome these challenges, a new algorithm is proposed that uses post-quantum cryptography (PQC) and zero-knowledge proof (ZKP) to enhance the security of digital asset wallets. The research focuses on the use of the Lattice-based Threshold Secret Sharing Scheme (LTSSS), Kyber Algorithm for key generation and ZKP for wallet unlocking, providing a more secure and user-friendly alternative to seed-phrase, brain and multi-sig protocol wallets. This algorithm also includes several innovative security features such as recovery of wallets in case of downtime of the server, and the ability to rekey the private key associated with a specific username-password combination, offering improved security and usability. The incorporation of PQC and ZKP provides a robust and comprehensive framework for securing digital assets in the present and future. This research aims to address the security challenges faced by digital asset wallets and proposes practical solutions to ensure their safety in the era of quantum computing.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1720,
+    "label": 24,
+    "text": "Title: Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark\nAbstract: Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (LMs) may incentivize toxicity. So do agents naturally learn to be Machiavellian? And how do we measure these behaviors in general-purpose models such as GPT-4? Towards answering these questions, we introduce MACHIAVELLI, a benchmark of 134 Choose-Your-Own-Adventure games containing over half a million rich, diverse scenarios that center on social decision-making. Scenario labeling is automated with LMs, which are more performant than human annotators. We mathematize dozens of harmful behaviors and use our annotations to evaluate agents' tendencies to be power-seeking, cause disutility, and commit ethical violations. We observe some tension between maximizing reward and behaving ethically. To improve this trade-off, we investigate LM-based methods to steer agents' towards less harmful behaviors. Our results show that agents can both act competently and morally, so concrete progress can currently be made in machine ethics--designing agents that are Pareto improvements in both safety and capabilities.",
+    "neighbors": [
+      57,
+      106,
+      1044,
+      1353,
+      2081,
+      2258
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1721,
+    "label": 2,
+    "text": "Title: Termination of Graph Transformation Systems using Weighted Subgraph Counting\nAbstract: We introduce a termination method for the algebraic graph transformation framework PBPO+, in which we weigh objects by summing a class of weighted morphisms targeting them. The method is well-defined in rm-adhesive quasitoposes (which include toposes and therefore many graph categories of interest), and is applicable to non-linear rules. The method is also defined for other frameworks, including DPO and SqPO, because we have previously shown that they are naturally encodable into PBPO+ in the quasitopos setting.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1722,
+    "label": 27,
+    "text": "Title: Safety-Assured Speculative Planning with Adaptive Prediction\nAbstract: Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to plan its trajectory in complex scenarios when it is difficult to accurately predict its surrounding vehicles' behaviors and trajectories. In this work, to maximize performance while ensuring safety, we propose a novel speculative planning framework based on a prediction-planning interface that quantifies both the behavior-level and trajectory-level uncertainties of surrounding vehicles. Our framework leverages recent prediction algorithms that can provide one or more possible behaviors and trajectories of the surrounding vehicles with probability estimation. It adapts those predictions based on the latest system states and traffic environment, and conducts planning to maximize the expected reward of the ego vehicle by considering the probabilistic predictions of all scenarios and ensure system safety by ruling out actions that may be unsafe in worst case. We demonstrate the effectiveness of our approach in improving system performance and ensuring system safety over other baseline methods, via extensive simulations in SUMO on a challenging multi-lane highway lane-changing case study.",
+    "neighbors": [
+      1483,
+      1909
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1723,
+    "label": 24,
+    "text": "Title: Large-scale statistical forecasting models reassess the unpredictability of chaotic systems\nAbstract: Chaos and unpredictability are often considered synonymous, yet recent advances in statistical forecasting suggest that large machine learning models gain unexpected insight from extended observation of complex systems. We perform a large-scale comparison of 24 state-of-the-art multivariate forecasting methods on a crowdsourced database of 135 distinct low-dimensional chaotic systems. Large, domain-agnostic time series forecasting methods consistently exhibit the strongest performance, producing accurate predictions lasting up to two dozen Lyapunov times. The best-performing models contain no inductive biases for dynamical systems, and include hierarchical neural basis functions, transformers, and recurrent neural networks. However, physics-based hybrid methods like neural ordinary differential equations and reservoir computers perform more strongly in data-limited settings. Diverse forecasting methods correlate despite their widely-varying architectures, yet the Lyapunov exponent fails to fully explain variation in the predictability of different chaotic systems over long time horizons. Our results show that a key advantage of modern forecasting methods stems not from their architectural details, but rather from their capacity to learn the large-scale structure of chaotic attractors.",
+    "neighbors": [
+      1558,
+      1580
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1724,
+    "label": 7,
+    "text": "Title: Solving the Discretised Neutron Diffusion Equations using Neural Networks\nAbstract: This paper presents a new approach which uses the tools within Artificial Intelligence (AI) software libraries as an alternative way of solving partial differential equations (PDEs) that have been discretised using standard numerical methods. In particular, we describe how to represent numerical discretisations arising from the finite volume and finite element methods by pre-determining the weights of convolutional layers within a neural network. As the weights are defined by the discretisation scheme, no training of the network is required and the solutions obtained are identical (accounting for solver tolerances) to those obtained with standard codes often written in Fortran or C++. We also explain how to implement the Jacobi method and a multigrid solver using the functions available in AI libraries. For the latter, we use a U-Net architecture which is able to represent a sawtooth multigrid method. A benefit of using AI libraries in this way is that one can exploit their power and their built-in technologies. For example, their executions are already optimised for different computer architectures, whether it be CPUs, GPUs or new-generation AI processors. In this article, we apply the proposed approach to eigenvalue problems in reactor physics where neutron transport is described by diffusion theory. For a fuel assembly benchmark, we demonstrate that the solution obtained from our new approach is the same (accounting for solver tolerances) as that obtained from the same discretisation coded in a standard way using Fortran. We then proceed to solve a reactor core benchmark using the new approach.",
+    "neighbors": [
+      1270
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1725,
+    "label": 10,
+    "text": "Title: Generalization through Diversity: Improving Unsupervised Environment Design\nAbstract: Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board). Due to this dependence, small changes in the environment (e.g., positions of obstacles in the maze, size of the board) can severely affect the effectiveness of the policy learned by the agent. To that end, existing work has proposed training RL agents on an adaptive curriculum of environments (generated automatically) to improve performance on out-of-distribution (OOD) test scenarios. Specifically, existing research has employed the potential for the agent to learn in an environment (captured using Generalized Advantage Estimation, GAE) as the key factor to select the next environment(s) to train the agent. However, such a mechanism can select similar environments (with a high potential to learn) thereby making agent training redundant on all but one of those environments. To that end, we provide a principled approach to adaptively identify diverse environments based on a novel distance measure relevant to environment design. We empirically demonstrate the versatility and effectiveness of our method in comparison to multiple leading approaches for unsupervised environment design on three distinct benchmark problems used in literature.",
+    "neighbors": [
+      2058
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1726,
+    "label": 27,
+    "text": "Title: A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding\nAbstract: We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the folding plans for different classes of garments, allowing for replicating the folding on unseen items of the same class. We evaluate the framework in the context of automating garment folding tasks. A quantitative analysis is performed on 3 classes of garments, demonstrating that the framework reduces the need for intervention by the user. We also compare skeleton representations with RGB and binary images in a classification task on a large dataset of clothing items, motivating the use of the framework for other classes of garments.",
+    "neighbors": [
+      252
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1727,
+    "label": 30,
+    "text": "Title: Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech\nAbstract: Recent studies have alarmed that many online hate speeches are implicit. With its subtle nature, the explainability of the detection of such hateful speech has been a challenging problem. In this work, we examine whether ChatGPT can be used for providing natural language explanations (NLEs) for implicit hateful speech detection. We design our prompt to elicit concise ChatGPT-generated NLEs and conduct user studies to evaluate their qualities by comparison with human-written NLEs. We discuss the potential and limitations of ChatGPT in the context of implicit hateful speech research.",
+    "neighbors": [
+      551,
+      811,
+      929,
+      1036,
+      1227,
+      1436,
+      1520,
+      1596,
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+      1949,
+      2035,
+      2094,
+      2235,
+      2244
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1728,
+    "label": 30,
+    "text": "Title: SLTUNET: A Simple Unified Model for Sign Language Translation\nAbstract: Despite recent successes with neural models for sign language translation (SLT), translation quality still lags behind spoken languages because of the data scarcity and modality gap between sign video and text. To address both problems, we investigate strategies for cross-modality representation sharing for SLT. We propose SLTUNET, a simple unified neural model designed to support multiple SLTrelated tasks jointly, such as sign-to-gloss, gloss-to-text and sign-to-text translation. Jointly modeling different tasks endows SLTUNET with the capability to explore the cross-task relatedness that could help narrow the modality gap. In addition, this allows us to leverage the knowledge from external resources, such as abundant parallel data used for spoken-language machine translation (MT). We show in experiments that SLTUNET achieves competitive and even state-of-the-art performance on PHOENIX-2014T and CSL-Daily when augmented with MT data and equipped with a set of optimization techniques. We further use the DGS Corpus for end-to-end SLT for the first time. It covers broader domains with a significantly larger vocabulary, which is more challenging and which we consider to allow for a more realistic assessment of the current state of SLT than the former two. Still, SLTUNET obtains improved results on the DGS Corpus. Code is available at https://github.com/bzhangGo/sltunet.",
+    "neighbors": [
+      956
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1729,
+    "label": 24,
+    "text": "Title: Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization\nAbstract: Differentiable architecture search (DARTS) is a promising end to end NAS method which directly optimizes the architecture parameters through general gradient descent. However, DARTS is brittle to the catastrophic failure incurred by the skip connection in the search space. Recent studies also cast doubt on the basic underlying hypotheses of DARTS which are argued to be inherently prone to the performance discrepancy between the continuous-relaxed supernet in the training phase and the discretized finalnet in the evaluation phase. We Figure out that the robustness problem and the skepticism can both be explained by the information bypass leakage during the training of the supernet. This naturally highlights the vital role of the sparsity of architecture parameters in the training phase which has not been well developed in the past. We thus propose a novel sparse-regularized approximation and an efficient mixed-sparsity training scheme to robustify DARTS by eliminating the information bypass leakage. We subsequently conduct extensive experiments on multiple search spaces to demonstrate the effectiveness of our method.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1730,
+    "label": 16,
+    "text": "Title: Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation\nAbstract: Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can also raise security and privacy concerns, such as copyright infringement, and sensitive data leakage. To tackle these issues, we propose a method, Unlearnable Diffusion Perturbation, to safeguard images from unauthorized exploitation. Our approach involves designing an algorithm to generate sample-wise perturbation noise for each image to be protected. This imperceptible protective noise makes the data almost unlearnable for diffusion models, i.e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data. Theoretically, we frame this as a max-min optimization problem and introduce EUDP, a noise scheduler-based method to enhance the effectiveness of the protective noise. We evaluate our methods on both Denoising Diffusion Probabilistic Model and Latent Diffusion Models, demonstrating that training diffusion models on the protected data lead to a significant reduction in the quality of the generated images. Especially, the experimental results on Stable Diffusion demonstrate that our method effectively safeguards images from being used to train Diffusion Models in various tasks, such as training specific objects and styles. This achievement holds significant importance in real-world scenarios, as it contributes to the protection of privacy and copyright against AI-generated content.",
+    "neighbors": [
+      428,
+      736,
+      1900,
+      2021,
+      2154,
+      2267,
+      2303
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1731,
+    "label": 16,
+    "text": "Title: Anti-DreamBooth: Protecting users from personalized text-to-image synthesis\nAbstract: Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \\href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.",
+    "neighbors": [
+      428,
+      1710,
+      1902
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1732,
+    "label": 16,
+    "text": "Title: Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos\nAbstract: Temporal sentence localization in videos (TSLV) aims to retrieve the most interested segment in an untrimmed video according to a given sentence query. However, almost of existing TSLV approaches suffer from the same limitations: (1) They only focus on either frame-level or object-level visual representation learning and corresponding correlation reasoning, but fail to integrate them both; (2) They neglect to leverage the rich semantic contexts to further benefit the query reasoning. To address these issues, in this paper, we propose a novel Hierarchical Visual- and Semantic-Aware Reasoning Network (HVSARN), which enables both visual- and semantic-aware query reasoning from object-level to frame-level. Specifically, we present a new graph memory mechanism to perform visual-semantic query reasoning: For visual reasoning, we design a visual graph memory to leverage visual information of video; For semantic reasoning, a semantic graph memory is also introduced to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform correlation reasoning in the semantic space. Experiments on three datasets demonstrate that our HVSARN achieves a new state-of-the-art performance.",
+    "neighbors": [
+      40
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1733,
+    "label": 30,
+    "text": "Title: Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling\nAbstract: How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \\textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \\textit{Pythia} to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at \\url{https://github.com/EleutherAI/pythia}.",
+    "neighbors": [
+      11,
+      529,
+      896,
+      1002,
+      1237,
+      1548,
+      1556,
+      1566,
+      1606,
+      1906,
+      1907,
+      1972,
+      2113,
+      2208,
+      2300
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1734,
+    "label": 34,
+    "text": "Title: Solving Knapsack with Small Items via L0-Proximity\nAbstract: We study pseudo-polynomial time algorithms for the fundamental \\emph{0-1 Knapsack} problem. In terms of $n$ and $w_{\\max}$, previous algorithms for 0-1 Knapsack have cubic time complexities: $O(n^2w_{\\max})$ (Bellman 1957), $O(nw_{\\max}^2)$ (Kellerer and Pferschy 2004), and $O(n + w_{\\max}^3)$ (Polak, Rohwedder, and W\\k{e}grzycki 2021). On the other hand, fine-grained complexity only rules out $O((n+w_{\\max})^{2-\\delta})$ running time, and it is an important question in this area whether $\\tilde O(n+w_{\\max}^2)$ time is achievable. Our main result makes significant progress towards solving this question: - The 0-1 Knapsack problem has a deterministic algorithm in $\\tilde O(n + w_{\\max}^{2.5})$ time. Our techniques also apply to the easier \\emph{Subset Sum} problem: - The Subset Sum problem has a randomized algorithm in $\\tilde O(n + w_{\\max}^{1.5})$ time. This improves (and simplifies) the previous $\\tilde O(n + w_{\\max}^{5/3})$-time algorithm by Polak, Rohwedder, and W\\k{e}grzycki (2021) (based on Galil and Margalit (1991), and Bringmann and Wellnitz (2021)). Similar to recent works on Knapsack (and integer programs in general), our algorithms also utilize the \\emph{proximity} between optimal integral solutions and fractional solutions. Our new ideas are as follows: - Previous works used an $O(w_{\\max})$ proximity bound in the $\\ell_1$-norm. As our main conceptual contribution, we use an additive-combinatorial theorem by Erd\\H{o}s and S\\'{a}rk\\\"{o}zy (1990) to derive an $\\ell_0$-proximity bound of $\\tilde O(\\sqrt{w_{\\max}})$. - Then, the main technical component of our Knapsack result is a dynamic programming algorithm that exploits both $\\ell_0$- and $\\ell_1$-proximity. It is based on a vast extension of the ``witness propagation'' method, originally designed by Deng, Mao, and Zhong (2023) for the easier \\emph{unbounded} setting only.",
+    "neighbors": [
+      875,
+      1511,
+      1518
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1735,
+    "label": 30,
+    "text": "Title: PaLM 2 Technical Report\nAbstract: We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report.",
+    "neighbors": [
+      268,
+      430,
+      607,
+      644,
+      652,
+      719,
+      761,
+      811,
+      855,
+      896,
+      1034,
+      1238,
+      1249,
+      1306,
+      1307,
+      1322,
+      1566,
+      1617,
+      1636,
+      1990,
+      2064,
+      2215
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1736,
+    "label": 10,
+    "text": "Title: Farspredict: A benchmark dataset for link prediction\nAbstract: Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a knowledge graph's entities and relations. This paper proposes\"Farspredict\"a Persian knowledge graph based on Farsbase (the most comprehensive knowledge graph in Persian). It also explains how the knowledge graph structure affects link prediction accuracy in KGE. To evaluate Farspredict, we implemented the popular models of KGE on it and compared the results with Freebase. Given the analysis results, some optimizations on the knowledge graph are carried out to improve its functionality in the KGE. As a result, a new Persian knowledge graph is achieved. Implementation results in the KGE models on Farspredict outperforming Freebases in many cases. At last, we discuss what improvements could be effective in enhancing the quality of Farspredict and how much it improves.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1737,
+    "label": 16,
+    "text": "Title: SAAM: Stealthy Adversarial Attack on Monoculor Depth Estimation\nAbstract: In this paper, we investigate the vulnerability of MDE to adversarial patches. We propose a novel \\underline{S}tealthy \\underline{A}dversarial \\underline{A}ttacks on \\underline{M}DE (SAAM) that compromises MDE by either corrupting the estimated distance or causing an object to seamlessly blend into its surroundings. Our experiments, demonstrate that the designed stealthy patch successfully causes a DNN-based MDE to misestimate the depth of objects. In fact, our proposed adversarial patch achieves a significant 60\\% depth error with 99\\% ratio of the affected region. Importantly, despite its adversarial nature, the patch maintains a naturalistic appearance, making it inconspicuous to human observers. We believe that this work sheds light on the threat of adversarial attacks in the context of MDE on edge devices. We hope it raises awareness within the community about the potential real-life harm of such attacks and encourages further research into developing more robust and adaptive defense mechanisms.",
+    "neighbors": [
+      331
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1738,
+    "label": 36,
+    "text": "Title: Multi-Agent Contract Design: How to Commission Multiple Agents with Individual Outcomes\nAbstract: We study hidden-action principal-agent problems with multiple agents. These are problems in which a principal commits to an outcome-dependent payment scheme (called contract) in order to incentivize some agents to take costly, unobservable actions that lead to favorable outcomes. Previous works study models where the principal observes a single outcome determined by the actions of all the agents. This considerably limits the contracting power of the principal, since payments can only depend on the joint result achieved by the agents. In this paper, we consider a model in which each agent determines their own individual outcome as an effect of their action only, the principal observes all the individual outcomes separately, and they perceive a reward that jointly depends on all these outcomes. This considerably enhances the principal's contracting capabilities, by allowing them to pay each agent on the basis of their individual result. We analyze the computational complexity of finding principal-optimal contracts, revolving around two properties of principal's rewards, namely IR-supermodularity and DR-submodularity. The former captures settings with increasing returns, where the rewards grow faster as the agents' effort increases, while the latter models the case of diminishing returns, in which rewards grow slower instead. These naturally model diseconomies and economies of scale. We first address basic instances in which the principal knows everything about the agents, and, then, more general Bayesian instances where each agent has their own private type determining their features, such as action costs and how actions stochastically determine individual outcomes. As a preliminary result, we show that finding an optimal contract in a non-Bayesian instance can be reduced in polynomial time to a maximization problem over a matroid having a particular structure. This is needed to prove our main positive results in the rest of the paper. We start by analyzing non-Bayesian instances, where we first prove that the problem of computing a principal-optimal contract is inapproximable with either IR-supermodular or DR-submodular rewards. Nevertheless, we show that in the former case the problem becomes polynomial-time solvable under some mild regularity assumptions, while in the latter case it admits a polynomial-time (1 \u2212 1/e)-approximation algorithm. In conclusion, we extend our positive results to Bayesian instances. First, we show that the principal's optimization problem can be approximately solved by means of a linear formulation. This is non-trivial since in general the problem may not admit a maximum, but only a supremum. Then, by working on such a linear formulation, we provide algorithms based on the ellipsoid method that (almost) match the guarantees obtained for non-Bayesian instances.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1739,
+    "label": 18,
+    "text": "Title: Transfer Functions of Proteinoid Microspheres\nAbstract: Proteinoids, or thermal proteins, are inorganic entities formed by heating amino acids to their melting point and commencing polymerisation to form polymeric chains. Typically, their diameters range from 1\u03bcm\u00a0to\u00a010\u03bcm. Some amino acids incorporated into proteinoid chains are more hydrophobic than others, leading proteinoids to cluster together when they are present in aqueous solutions at specific concentrations, allowing them to grow into microspheres. The peculiar structure of proteinoids composed of linked amino acids endows them with unique properties, including action-potential like spiking of electrical potential. These unique properties make ensembles of proteinoid microspheres a promising substrate for designing future artificial brains and unconventional computing devices. To evaluate a potential of proteinoid microspheres for unconventional electronic devices we measure and analyse the data-transfer capacities of proteinoid microspheres. In experimental laboratory conditions we demonstrate that the transfer function of proteinoids microspheres is a nontrivial phenomenon, which might be due to the wide range of proteinoid shapes, sizes, and structures.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1740,
+    "label": 4,
+    "text": "Title: MIRA: a Digital Signature Scheme based on the MinRank problem and the MPC-in-the-Head paradigm\nAbstract: We exploit the idea of [Fen22] which proposes to build an efficient signature scheme based on a zero-knowledge proof of knowledge of a solution of a MinRank instance. The scheme uses the MPCitH paradigm, which is an efficient way to build ZK proofs. We combine this idea with another idea, the hypercube technique introduced in [AMGH+22], which leads to more efficient MPCitH-based scheme. This new approach is more efficient than classical MPCitH, as it allows to reduce the number of party computation. This gives us a first scheme called MIRA-Additive. We then present an other scheme, based on low-threshold secret sharings, called MIRA-Threshold, which is a faster scheme, at the price of larger signatures. The construction of MPCitH using threshold secret sharing is detailed in [FR22]. These two constructions allows us to be faster than classical MPCitH, with a size of signature around 5.6kB with MIRA-Additive, and 8.3kB with MIRA-Threshold. We detail here the constructions and optimizations of the schemes, as well as their security proofs.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1741,
+    "label": 24,
+    "text": "Title: Intelligent sampling for surrogate modeling, hyperparameter optimization, and data analysis\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1742,
+    "label": 27,
+    "text": "Title: Autonomous Strawberry Picking Robotic System (Robofruit)\nAbstract: Challenges in strawberry picking made selective harvesting robotic technology very demand-ing. However, the selective harvesting of strawberries is a complicated robotic task forming a few scienti\ufb01c research questions. Most available solutions only deal with a speci\ufb01c picking scenario, e.g., picking only a single variety of fruit in isolation. Nonetheless, most econom-ically viable (e.g. high-yielding and/or disease-resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is ine\ufb03cient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, e.g. dense clusters. Our concept of a modular system makes our system recon\ufb01gurable to adapt to di\ufb00erent picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5 degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit \ufb02esh to avoid damage and bruising. In addition, we developed a novel perception system to localise strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new datasets. Finally, we tested the system in a commercial strawberry growing \ufb01eld and our research farm with three di\ufb00erent strawberry varieties. The results show the e\ufb00ectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries e\ufb00ectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.",
+    "neighbors": [
+      1811
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1743,
+    "label": 10,
+    "text": "Title: Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfinding\nAbstract: This study extends the recently-developed LaCAM algorithm for multi-agent pathfinding (MAPF). LaCAM is a sub-optimal search-based algorithm that uses lazy successor generation to dramatically reduce the planning effort. We present two enhancements. First, we propose its anytime version, called LaCAM*, which eventually converges to optima, provided that solution costs are accumulated transition costs. Second, we improve the successor generation to quickly obtain initial solutions. Exhaustive experiments demonstrate their utility. For instance, LaCAM* sub-optimally solved 99% of the instances retrieved from the MAPF benchmark, where the number of agents varied up to a thousand, within ten seconds on a standard desktop PC, while ensuring eventual convergence to optima; developing a new horizon of MAPF algorithms.",
+    "neighbors": [
+      125
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1744,
+    "label": 16,
+    "text": "Title: MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders\nAbstract: Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on the image-level or on the representation-level. The representation-level morphing has been performed so far based on generative adversarial networks (GAN) where the encoded images are interpolated in the latent space to produce a morphed image based on the interpolated vector. Such a process was constrained by the limited reconstruction fidelity of GAN architectures. Recent advances in the diffusion autoencoder models have overcome the GAN limitations, leading to high reconstruction fidelity. This theoretically makes them a perfect candidate to perform representation-level face morphing. This work investigates using diffusion autoencoders to create face morphing attacks by comparing them to a wide range of image-level and representation-level morphs. Our vulnerability analyses on four state-of-the-art face recognition models have shown that such models are highly vulnerable to the created attacks, the MorDIFF, especially when compared to existing representation-level morphs. Detailed detectability analyses are also performed on the MorDIFF, showing that they are as challenging to detect as other morphing attacks created on the image- or representation-level. Data and morphing script are made public1.",
+    "neighbors": [
+      1503
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1745,
+    "label": 27,
+    "text": "Title: RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments\nAbstract: Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: https://github.com/HITSZ-NRSL/Dynamic-VINS.git",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1746,
+    "label": 24,
+    "text": "Title: Trajectory Alignment: Understanding the Edge of Stability Phenomenon via Bifurcation Theory\nAbstract: Cohen et al. (2021) empirically study the evolution of the largest eigenvalue of the loss Hessian, also known as sharpness, along the gradient descent (GD) trajectory and observe a phenomenon called the Edge of Stability (EoS). The sharpness increases at the early phase of training (referred to as progressive sharpening), and eventually saturates close to the threshold of $2 / \\text{(step size)}$. In this paper, we start by demonstrating through empirical studies that when the EoS phenomenon occurs, different GD trajectories (after a proper reparameterization) align on a specific bifurcation diagram independent of initialization. We then rigorously prove this trajectory alignment phenomenon for a two-layer fully-connected linear network and a single-neuron nonlinear network trained with a single data point. Our trajectory alignment analysis establishes both progressive sharpening and EoS phenomena, encompassing and extending recent findings in the literature.",
+    "neighbors": [
+      320
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1747,
+    "label": 26,
+    "text": "Title: A Fast Algorithm for Moderating Critical Nodes via Edge Removal\nAbstract: Critical nodes in networks are extremely vulnerable to malicious attacks to trigger negative cascading events such as the spread of misinformation and diseases. Therefore, effective moderation of critical nodes is very vital for mitigating the potential damages caused by such malicious diffusions. The current moderation methods are computationally expensive. Furthermore, they disregard the fundamental metric of information centrality, which measures the dissemination power of nodes. We investigate the problem of removing $k$ edges from a network to minimize the information centrality of a target node $\\lea$ while preserving the network's connectivity. We prove that this problem is computationally challenging: it is NP-complete and its objective function is not supermodular. However, we propose three approximation greedy algorithms using novel techniques such as random walk-based Schur complement approximation and fast sum estimation. One of our algorithms runs in nearly linear time in the number of edges. To complement our theoretical analysis, we conduct a comprehensive set of experiments on synthetic and real networks with over one million nodes. Across various settings, the experimental results illustrate the effectiveness and efficiency of our proposed algorithms.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1748,
+    "label": 34,
+    "text": "Title: k-Center Clustering with Outliers in the MPC and Streaming Model\nAbstract: Given a point set P \u2286 X of size n in a metric space (X, dist) of doubling dimension d and two parameters k \u2208 \u2115 and z \u2208 \u2115, the k-center problem with z outliers asks to return a set ${{\\mathcal{C}}^ * } = \\{ c_1^ * , \\cdots ,c_k^ * \\} \\subseteq X$ of k centers such that the maximum distance of all but z points of P to their nearest center in C* is minimized. An (\u03b5, k, z)-coreset for this problem is a weighted point set P* such that an optimal solution for the k-center problem with z outliers on P* gives a (1 \u00b1 \u03b5)-approximation for the k-center problem with z outliers on P. We study the construction of such coresets in the Massively Parallel Computing (MPC) model, and in the insertion-only as well as the fully dynamic streaming model. We obtain the following results, for any given 0 < \u03b5 \u2a7d 1: In all cases, the size of the computed coreset is O(k/\u03b5d + z).\u2022 In the MPC model the data are distributed over m machines. One is the coordinator machine, which will contain the final answer, the others are worker machines.We present a deterministic 2-round algorithm using $O(\\sqrt n )$ machines, where the worker machines have $O(\\sqrt {nk/{\\varepsilon ^d}} + \\sqrt n \\cdot \\log (z + 1))$ local memory, and the coordinator has $O(\\sqrt {nk/{\\varepsilon ^d}} + \\sqrt n \\cdot \\log (z + 1) + z)$ local memory. The algorithm can handle point sets P that are distributed arbitrarily (possibly adversarially) over the machines. We also present a randomized algorithm that uses only a single round, under the assumption that the input set P is initially distributed randomly over the machines. Then we present a deterministic algorithm that obtains a trade-off between the number of rounds, R, and the storage per machine.In the streaming model we have a single machine with limited storage, and P is revealed in a streaming fashion.\u25cb We present the first lower bound for the insertion-only streaming model, where the points arrive one by one and no points are deleted. We show that any deterministic algorithm that maintains an (\u03b5, k, z)-coreset must use \u03a9(k/\u03b5d + z) space. We complement this by a deterministic streaming algorithm using O(k/\u03b5d + z) space, which is thus optimal. \u25cb For the fully dynamic data streams, where points can be inserted as well as deleted we give a randomized algorithm for point sets from a d-dimensional discrete Euclidean space [\u0394]d, where \u0394 \u2208 \u2115 indicates the size of the universe from which the coordinates are taken. Our algorithm uses only O((k/\u03b5d + z)log4(k\u0394/\u03b5\u03b4)) space, and it is the first algorithm for this setting. We also present an \u03a9((k/\u03b5d)log\u0394 + z) lower bound for deterministic fully dynamic streaming algorithms. \u25cb For the sliding-window model, we show that any deterministic streaming algorithm that guarantees a (1 + \u03b5)-approximation for the k-center problem with outliers in \u211dd must use \u03a9((kz/\u03b5d) log\u03c3) space, where \u03c3 is the ratio of the largest and smallest distance between any two points in the stream. This (negatively) answers a question posed by De Berg, Monemizadeh, and Zhong [1].",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1749,
+    "label": 7,
+    "text": "Title: Towards higher-order accurate mass lumping in explicit isogeometric analysis for structural dynamics\nAbstract: nan",
+    "neighbors": [
+      1717
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1750,
+    "label": 16,
+    "text": "Title: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders\nAbstract: Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt [33], have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE) [14]. However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.",
+    "neighbors": [
+      880
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1751,
+    "label": 27,
+    "text": "Title: Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination\nAbstract: This paper investigates the problem of on-orbit spacecraft inspection using a single free-flying deputy spacecraft, equipped with an optical sensor, whose controller is a neural network control system trained with Reinforcement Learning (RL). This work considers the illumination of the inspected spacecraft (chief) by the Sun in order to incentivize acquisition of well-illuminated optical data. The agent's performance is evaluated through statistically efficient metrics. Results demonstrate that the RL agent is able to inspect all points on the chief successfully, while maximizing illumination on inspected points in a simulated environment, using only low-level actions. Due to the stochastic nature of RL, 10 policies were trained using 10 random seeds to obtain a more holistic measure of agent performance. Over these 10 seeds, the interquartile mean (IQM) percentage of inspected points for the finalized model was 98.82%.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1752,
+    "label": 16,
+    "text": "Title: PyramidFlow: High-Resolution Defect Contrastive Localization Using Pyramid Normalizing Flow\nAbstract: During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in lowresolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.",
+    "neighbors": [
+      2119
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1753,
+    "label": 34,
+    "text": "Title: An FPTAS for Budgeted Laminar Matroid Independent Set\nAbstract: We study the budgeted laminar matroid independent set problem. The input is a ground set, where each element has a cost and a non-negative profit, along with a laminar matroid over the elements and a budget. The goal is to select a maximum profit independent set of the matroid whose total cost is bounded by the budget. Several well known special cases, where we have, e.g., no matroid constraint (the classic knapsack problem) or a uniform matroid constraint (knapsack with a cardinality constraint), admit a fully polynomial-time approximation scheme (FPTAS). In contrast, the budgeted matroid independent set (BMI) problem with a general matroid has an efficient polynomial-time approximation scheme (EPTAS) but does not admit an FPTAS. This implies an EPTAS for our problem, which is the best known result prior to this work. We present an FPTAS for budgeted laminar matroid independent set, improving the previous EPTAS for this matroid family and generalizing the FPTAS known for knapsack with a cardinality constraint and multiple-choice knapsack. Our scheme is based on a simple dynamic program which utilizes the tree-like structure of laminar matroids.",
+    "neighbors": [
+      1518,
+      1715
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1754,
+    "label": 24,
+    "text": "Title: SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization\nAbstract: Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs. Existing graph structure learning (GSL) frameworks still lack robustness and interpretability. This paper proposes a general GSL framework, SE-GSL, through structural entropy and the graph hierarchy abstracted in the encoding tree. Particularly, we exploit the one-dimensional structural entropy to maximize embedded information content when auxiliary neighbourhood attributes is fused to enhance the original graph. A new scheme of constructing optimal encoding trees are proposed to minimize the uncertainty and noises in the graph whilst assuring proper community partition in hierarchical abstraction. We present a novel sample-based mechanism for restoring the graph structure via node structural entropy distribution. It increases the connectivity among nodes with larger uncertainty in lower-level communities. SE-GSL is compatible with various GNN models and enhances the robustness towards noisy and heterophily structures. Extensive experiments show significant improvements in the effectiveness and robustness of structure learning and node representation learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1755,
+    "label": 16,
+    "text": "Title: Visual Instruction Tuning with Polite Flamingo\nAbstract: Recent research has demonstrated that the multi-task fine-tuning of multi-modal Large Language Models (LLMs) using an assortment of annotated downstream vision-language datasets significantly enhances their performance. Yet, during this process, a side effect, which we termed as the\"multi-modal alignment tax\", surfaces. This side effect negatively impacts the model's ability to format responses appropriately -- for instance, its\"politeness\"-- due to the overly succinct and unformatted nature of raw annotations, resulting in reduced human preference. In this paper, we introduce Polite Flamingo, a multi-modal response rewriter that transforms raw annotations into a more appealing,\"polite\"format. Polite Flamingo is trained to reconstruct high-quality responses from their automatically distorted counterparts and is subsequently applied to a vast array of vision-language datasets for response rewriting. After rigorous filtering, we generate the PF-1M dataset and further validate its value by fine-tuning a multi-modal LLM with it. Combined with novel methodologies including U-shaped multi-stage tuning and multi-turn augmentation, the resulting model, Clever Flamingo, demonstrates its advantages in both multi-modal understanding and response politeness according to automated and human evaluations.",
+    "neighbors": [
+      173,
+      522,
+      529,
+      792,
+      811,
+      907,
+      1039,
+      1052,
+      1148,
+      1344,
+      1485,
+      1668,
+      1765,
+      1972,
+      2036,
+      2113,
+      2122,
+      2155,
+      2208,
+      2216
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1756,
+    "label": 28,
+    "text": "Title: Intelligent Reflecting Vehicle Surface: A Novel IRS Paradigm for Moving Vehicular Networks\nAbstract: Intelligent reflecting surface (IRS) has recently received much attention from the research community due to its potential to achieve high spectral and power efficiency cost-effectively. In addition to traditional cellular networks, the use of IRS in vehicular networks is also considered. Prior works on IRS-aided vehicle-to-everything communications focus on deploying reflection surfaces on the facades of buildings along the road for sidelink performance enhancement. This paper goes beyond the state of the art by presenting a novel paradigm coined Intelligent Reflecting Vehicle Surface (IRVS). It embeds a massive number of reflection elements on vehicles' surfaces to aid moving vehicular networks in military and emergency communications. We propose an alternative optimization method to optimize jointly active beamforming at the base station and passive reflection across multiple randomly-distributed vehicle surfaces. Performance evaluation in terms of sum spectral efficiency under continuous, discrete, and random phase shifts is conducted. Numerical results reveal that IRVS can substantially improve the capacity of a moving vehicular network.",
+    "neighbors": [
+      1844
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1757,
+    "label": 13,
+    "text": "Title: Structured mutation inspired by evolutionary theory enriches population performance and diversity\nAbstract: Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we apply principles from the theory of Facilitated Variation and knowledge about heterogeneous mutation rates and mutation effects to improve the variation operators. We term this new method of variation Facilitated Mutation (FM). We test FM performance on the evolution of neural network optimizers for image classification, a relevant task in evolutionary computation, with important implications for the field of machine learning. We compare FM and FM combined with crossover (FMX) against a typical mutation regime to assess the benefits of the approach. We find that FMX in particular provides statistical improvements in key metrics, creating a superior optimizer overall (+0.48\\% average test accuracy), improving the average quality of solutions (+50\\% average population fitness), and discovering more diverse high-quality behaviors (+400 high-quality solutions discovered per run on average). Additionally, FM and FMX can reduce the number of fitness evaluations in an evolutionary run, reducing computational costs in some scenarios.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1758,
+    "label": 24,
+    "text": "Title: ChatGPT is not all you need. A State of the Art Review of large Generative AI models\nAbstract: During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several sectors. Consequently, the implications that these generative models have in the industry and society are enormous, as several job positions may be transformed. For example, Generative AI is capable of transforming effectively and creatively texts to images, like the DALLE-2 model; text to 3D images, like the Dreamfusion model; images to text, like the Flamingo model; texts to video, like the Phenaki model; texts to audio, like the AudioLM model; texts to other texts, like ChatGPT; texts to code, like the Codex model; texts to scientific texts, like the Galactica model or even create algorithms like AlphaTensor. This work consists on an attempt to describe in a concise way the main models are sectors that are affected by generative AI and to provide a taxonomy of the main generative models published recently.",
+    "neighbors": [
+      170,
+      924,
+      1035,
+      1436,
+      1651,
+      1710,
+      1788,
+      2085,
+      2184
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1759,
+    "label": 21,
+    "text": "Title: Manuscript of a method for improving wear in intermittently computing file systems\nAbstract: For the first time, the repeated wear phenomenon of high-frequency power failure on the data block area in intermittent computing file system is found. A method to improve NVM wear in ICFS under high-frequency power failure scenarios is proposed.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1760,
+    "label": 24,
+    "text": "Title: Is My Prediction Arbitrary? The Confounding Effects of Variance in Fair Classification Benchmarks\nAbstract: Variance in predictions across different trained models is a significant, under-explored source of error in fair classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions: We 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fairness classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply common fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should fundamentally reconsider how we choose to measure fairness in machine learning.",
+    "neighbors": [
+      1687
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1761,
+    "label": 16,
+    "text": "Title: MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection\nAbstract: Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a significant 70-90% drop in detection rate due to variations in lidar, geography, or weather from their training dataset. This domain gap leads to missing detections for densely observed objects, misaligned confidence scores, and increased high-confidence false positives, rendering the detector highly unreliable. To address this, we introduce MS3D++, a self-training framework for multi-source unsupervised domain adaptation in 3D object detection. MS3D++ generates high-quality pseudo-labels, allowing 3D detectors to achieve high performance on a range of lidar types, regardless of their density. Our approach effectively fuses predictions of an ensemble of multi-frame pre-trained detectors from different source domains to improve domain generalization. We subsequently refine predictions temporally to ensure temporal consistency in box localization and object classification. Furthermore, we present an in-depth study into the performance and idiosyncrasies of various 3D detector components in a cross-domain context, providing valuable insights for improved cross-domain detector ensembling. Experimental results on Waymo, nuScenes and Lyft demonstrate that detectors trained with MS3D++ pseudo-labels achieve state-of-the-art performance, comparable to training with human-annotated labels in Bird's Eye View (BEV) evaluation for both low and high density lidar. Code is available at https://github.com/darrenjkt/MS3D",
+    "neighbors": [
+      2278
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1762,
+    "label": 31,
+    "text": "Title: Is ChatGPT a Good Recommender? A Preliminary Study\nAbstract: Recommendation systems have witnessed significant advancements and have been widely used over the past decades. However, most traditional recommendation methods are task-specific and therefore lack efficient generalization ability. Recently, the emergence of ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. Nonetheless, the application of ChatGPT in the recommendation domain has not been thoroughly investigated. In this paper, we employ ChatGPT as a general-purpose recommendation model to explore its potential for transferring extensive linguistic and world knowledge acquired from large-scale corpora to recommendation scenarios. Specifically, we design a set of prompts and evaluate ChatGPT's performance on five recommendation scenarios. Unlike traditional recommendation methods, we do not fine-tune ChatGPT during the entire evaluation process, relying only on the prompts themselves to convert recommendation tasks into natural language tasks. Further, we explore the use of few-shot prompting to inject interaction information that contains user potential interest to help ChatGPT better understand user needs and interests. Comprehensive experimental results on Amazon Beauty dataset show that ChatGPT has achieved promising results in certain tasks and is capable of reaching the baseline level in others. We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models. And the human evaluations show ChatGPT can truly understand the provided information and generate clearer and more reasonable results. We hope that our study can inspire researchers to further explore the potential of language models like ChatGPT to improve recommendation performance and contribute to the advancement of the recommendation systems field.",
+    "neighbors": [
+      424,
+      840,
+      1092,
+      1238,
+      1327,
+      1667,
+      1799,
+      1952
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1763,
+    "label": 16,
+    "text": "Title: Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing\nAbstract: In the modern world, one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy, which will result in retinal damage, and, thus, lead to blindness. Diabetic retinopathy can be well treated with early diagnosis. Retinal fundus images of humans are used to screen for lesions in the retina. However, detecting DR in the early stages is challenging due to the minimal symptoms. Furthermore, the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition. Nevertheless, the resources required for manually identifying the lesions are high. Similarly, training for Convolutional Neural Networks is more time-consuming. This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model for timely DR identification that is potentially more accurate than existing CNN-based models. The proposed model will detect various lesions from retinal images in the early stages. First, characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification. For dimensionality reduction, EDLM is used. Additionally, the classification and feature extraction processes are optimized using the stochastic gradient descent optimizer. The EDLM effectiveness is assessed on the KAG GLE dataset with 3459 retinal images, and results are compared over VGG16, VGG19, RESNET18, RESNET34, and RESNET50.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1764,
+    "label": 34,
+    "text": "Title: An Improved Approximation for Maximum Weighted k-Set Packing\nAbstract: We consider the weighted $k$-set packing problem, in which we are given a collection of weighted sets, each with at most $k$ elements and must return a collection of pairwise disjoint sets with maximum total weight. For $k = 3$, this problem generalizes the classical 3-dimensional matching problem listed as one of the Karp's original 21 NP-complete problems. We give an algorithm attaining an approximation factor of $1.786$ for weighted 3-set packing, improving on the recent best result of $2-\\frac{1}{63,700,992}$ due to Neuwohner. Our algorithm is based on the local search procedure of Berman that attempts to improve the sum of squared weights rather than the problem's objective. When using exchanges of size at most $k$, this algorithm attains an approximation factor of $\\frac{k+1}{2}$. Using exchanges of size $k^2(k-1) + k$, we provide a relatively simple analysis to obtain an approximation factor of 1.811 when $k = 3$. We then show that the tools we develop can be adapted to larger exchanges of size $2k^2(k-1) + k$ to give an approximation factor of 1.786. Although our primary focus is on the case $k = 3$, our approach in fact gives slightly stronger improvements on the factor $\\frac{k+1}{2}$ for all $k>3$. As in previous works, our guarantees hold also for the more general problem of finding a maximum weight independent set in a $(k+1)$-claw free graph.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1765,
+    "label": 30,
+    "text": "Title: Language Is Not All You Need: Aligning Perception with Language Models\nAbstract: A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce Kosmos-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, we train Kosmos-1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. We evaluate various settings, including zero-shot, few-shot, and multimodal chain-of-thought prompting, on a wide range of tasks without any gradient updates or finetuning. Experimental results show that Kosmos-1 achieves impressive performance on (i) language understanding, generation, and even OCR-free NLP (directly fed with document images), (ii) perception-language tasks, including multimodal dialogue, image captioning, visual question answering, and (iii) vision tasks, such as image recognition with descriptions (specifying classification via text instructions). We also show that MLLMs can benefit from cross-modal transfer, i.e., transfer knowledge from language to multimodal, and from multimodal to language. In addition, we introduce a dataset of Raven IQ test, which diagnoses the nonverbal reasoning capability of MLLMs.",
+    "neighbors": [
+      0,
+      173,
+      176,
+      392,
+      602,
+      719,
+      887,
+      902,
+      945,
+      1001,
+      1050,
+      1071,
+      1129,
+      1315,
+      1344,
+      1436,
+      1450,
+      1574,
+      1617,
+      1651,
+      1755,
+      1893,
+      2030,
+      2036,
+      2064,
+      2113,
+      2155,
+      2226
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1766,
+    "label": 24,
+    "text": "Title: Defending Label Inference Attacks in Split Learning under Regression Setting\nAbstract: As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched. However, numerous studies have indicated that the privacy-preserving capability of Split Learning is insufficient. In this paper, we primarily focus on label inference attacks in Split Learning under regression setting, which are mainly implemented through the gradient inversion method. To defend against label inference attacks, we propose Random Label Extension (RLE), where labels are extended to obfuscate the label information contained in the gradients, thereby preventing the attacker from utilizing gradients to train an attack model that can infer the original labels. To further minimize the impact on the original task, we propose Model-based adaptive Label Extension (MLE), where original labels are preserved in the extended labels and dominate the training process. The experimental results show that compared to the basic defense methods, our proposed defense methods can significantly reduce the attack model's performance while preserving the original task's performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1767,
+    "label": 4,
+    "text": "Title: No Easy Way Out: The Effectiveness of Deplatforming an Extremist Forum to Suppress Hate and Harassment\nAbstract: Legislators and policymakers worldwide are debating options for suppressing illegal, harmful and undesirable material online. Drawing on several quantitative data sources, we show that deplatforming an active community to suppress online hate and harassment, even with a substantial concerted effort involving several tech firms, can be hard. Our case study is the disruption of the largest and longest-running harassment forum Kiwi Farms in late 2022, which is probably the most extensive industry effort to date. Despite the active participation of a number of tech companies over several consecutive months, this campaign failed to shut down the forum and remove its objectionable content. While briefly raising public awareness, it led to rapid platform displacement and traffic fragmentation. Part of the activity decamped to Telegram, while traffic shifted from the primary domain to previously abandoned alternatives. The forum experienced intermittent outages for several weeks, after which the community leading the campaign lost interest, traffic was directed back to the main domain, users quickly returned, and the forum was back online and became even more connected. The forum members themselves stopped discussing the incident shortly thereafter, and the net effect was that forum activity, active users, threads, posts and traffic were all cut by about half. Deplatforming a community without a court order raises philosophical issues about censorship versus free speech; ethical and legal issues about the role of industry in online content moderation; and practical issues on the efficacy of private-sector versus government action. Deplatforming a dispersed community using a series of court orders against individual service providers appears unlikely to be very effective if the censor cannot incapacitate the key maintainers, whether by arresting them, enjoining them or otherwise deterring them.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1768,
+    "label": 16,
+    "text": "Title: Scaling up GANs for Text-to-Image Synthesis\nAbstract: The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL.E 2, autoregressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that na\u00efvely increasing the capacity of the StyleGan architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel images in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.",
+    "neighbors": [
+      170,
+      706,
+      1303,
+      1710,
+      1994,
+      2251,
+      2306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1769,
+    "label": 24,
+    "text": "Title: Time Fairness in Online Knapsack Problems\nAbstract: The online knapsack problem is a classic problem in the field of online algorithms. Its canonical version asks how to pack items of different values and weights arriving online into a capacity-limited knapsack so as to maximize the total value of the admitted items. Although optimal competitive algorithms are known for this problem, they may be fundamentally unfair, i.e., individual items may be treated inequitably in different ways. Inspired by recent attention to fairness in online settings, we develop a natural and practically-relevant notion of time fairness for the online knapsack problem, and show that the existing optimal algorithms perform poorly under this metric. We propose a parameterized deterministic algorithm where the parameter precisely captures the Pareto-optimal trade-off between fairness and competitiveness. We show that randomization is theoretically powerful enough to be simultaneously competitive and fair; however, it does not work well in practice, using trace-driven experiments. To further improve the trade-off between fairness and competitiveness, we develop a fair, robust (competitive), and consistent learning-augmented algorithm with substantial performance improvement in trace-driven experiments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1770,
+    "label": 2,
+    "text": "Title: What Do We Know When We Know That a Theory Is Consistent?\nAbstract: nan",
+    "neighbors": [
+      2282
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1771,
+    "label": 16,
+    "text": "Title: GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields\nAbstract: Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes. Our goal is to mitigate these imperfections from various sources with a joint solution: we take advantage of the ability of generative adversarial networks (GANs) to produce realistic images and use them to enhance realism in 3D scene reconstruction with NeRFs. To this end, we learn the patch distribution of a scene using an adversarial discriminator, which provides feedback to the radiance field reconstruction, thus improving realism in a 3D-consistent fashion. Thereby, rendering artifacts are repaired directly in the underlying 3D representation by imposing multi-view path rendering constraints. In addition, we condition a generator with multi-resolution NeRF renderings which is adversarially trained to further improve rendering quality. We demonstrate that our approach significantly improves rendering quality, e.g., nearly halving LPIPS scores compared to Nerfacto while at the same time improving PSNR by 1.4dB on the advanced indoor scenes of Tanks and Temples.",
+    "neighbors": [
+      1355,
+      1902
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1772,
+    "label": 23,
+    "text": "Title: An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications\nAbstract: Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Question / problem: We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. Principal ideas/results: Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. Contribution: The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1773,
+    "label": 16,
+    "text": "Title: RealFusion 360\u00b0 Reconstruction of Any Object from a Single Image\nAbstract: We consider the problem of reconstructing a full 360\u00b0 photographic model of an object from a single image of it. We do so by fitting a neural radiance field to the image, but find this problem to be severely ill-posed. We thus take an off-the-self conditional image generator based on diffusion and engineer a prompt that encourages it to \u201cdream up\u201d novel views of the object. Using the recent DreamFusion method, we fuse the given input view, the conditional prior, and other regularizers into a final, consistent reconstruction. We demonstrate state-of-the-art reconstruction results on benchmark images when compared to prior methods for monocular 3D reconstruction of objects. Qualitatively, our reconstructions provide a faithful match of the input view and a plausible extrapolation of its appearance and 3D shape, including to the side of the object not visible in the image.",
+    "neighbors": [
+      48,
+      1125,
+      1651,
+      2117
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1774,
+    "label": 2,
+    "text": "Title: Hyper Hoare Logic: (Dis-)Proving Program Hyperproperties (extended version)\nAbstract: Hoare logics are proof systems that allow one to formally establish properties of computer programs. Traditional Hoare logics prove properties of individual program executions (so-called trace properties, such as functional correctness). Hoare logic has been generalized to prove also properties of multiple executions of a program (so-called hyperproperties, such as determinism or non-interference). These program logics prove the absence of (bad combinations of) executions. On the other hand, program logics similar to Hoare logic have been proposed to disprove program properties (e.g., Incorrectness Logic), by proving the existence of (bad combinations of) executions. All of these logics have in common that they specify program properties using assertions over a fixed number of states, for instance, a single pre- and post-state for functional properties or pairs of pre- and post-states for non-interference. In this paper, we present Hyper Hoare Logic, a generalization of Hoare logic that lifts assertions to properties of arbitrary sets of states. The resulting logic is simple yet expressive: its judgments can express arbitrary trace- and hyperproperties over the terminating executions of a program. By allowing assertions to reason about sets of states, Hyper Hoare Logic can reason about both the absence and the existence of (combinations of) executions, and, thereby, supports both proving and disproving program (hyper-)properties within the same logic. In fact, we prove that Hyper Hoare Logic subsumes the properties handled by numerous existing correctness and incorrectness logics, and can express hyperproperties that no existing Hoare logic can. We also prove that Hyper Hoare Logic is sound and complete, and admits powerful compositionality rules. All our technical results have been proved in Isabelle/HOL.",
+    "neighbors": [
+      2054
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1775,
+    "label": 16,
+    "text": "Title: DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing\nAbstract: Precise and controllable image editing is a challenging task that has attracted significant attention. Recently, DragGAN enables an interactive point-based image editing framework and achieves impressive editing results with pixel-level precision. However, since this method is based on generative adversarial networks (GAN), its generality is upper-bounded by the capacity of the pre-trained GAN models. In this work, we extend such an editing framework to diffusion models and propose DragDiffusion. By leveraging large-scale pretrained diffusion models, we greatly improve the applicability of interactive point-based editing in real world scenarios. While most existing diffusion-based image editing methods work on text embeddings, DragDiffusion optimizes the diffusion latent to achieve precise spatial control. Although diffusion models generate images in an iterative manner, we empirically show that optimizing diffusion latent at one single step suffices to generate coherent results, enabling DragDiffusion to complete high-quality editing efficiently. Extensive experiments across a wide range of challenging cases (e.g., multi-objects, diverse object categories, various styles, etc.) demonstrate the versatility and generality of DragDiffusion. Code: https://github.com/Yujun-Shi/DragDiffusion.",
+    "neighbors": [
+      1079,
+      1179,
+      1970,
+      2085
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1776,
+    "label": 16,
+    "text": "Title: How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models\nAbstract: Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized usage of data during the training process. One example is when a model trainer collects a set of images created by a particular artist and attempts to train a model capable of generating similar images without obtaining permission from the artist. To address this issue, it becomes crucial to detect unauthorized data usage. In this paper, we propose a method for detecting such unauthorized data usage by planting injected memorization into the text-to-image diffusion models trained on the protected dataset. Specifically, we modify the protected image dataset by adding unique contents on the images such as stealthy image wrapping functions that are imperceptible to human vision but can be captured and memorized by diffusion models. By analyzing whether the model has memorization for the injected content (i.e., whether the generated images are processed by the chosen post-processing function), we can detect models that had illegally utilized the unauthorized data. Our experiments conducted on Stable Diffusion and LoRA model demonstrate the effectiveness of the proposed method in detecting unauthorized data usages.",
+    "neighbors": [
+      1426,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1777,
+    "label": 16,
+    "text": "Title: Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution\nAbstract: Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in high-resolution (HR) RGB images from the same scene. The critical step of this task is to effectively extract domain-shared and domain-private RGB/depth features. In addition, three detailed issues, namely blurry edges, noisy surfaces, and over-transferred RGB texture, need to be addressed. In this paper, we propose the Spherical Space feature Decomposition Network (SSDNet) to solve the above issues. To better model cross-modality features, Restormer block-based RGB/depth encoders are employed for extracting local-global features. Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features. Shared features of RGB are fused with the depth features to complete the GDSR task. Subsequently, a spherical contrast refinement (SCR) module is proposed to further address the detail issues. Patches that are classified according to imperfect categories are input into the SCR module, where the patch features are pulled closer to the ground truth and pushed away from the corresponding imperfect samples in the spherical feature space via contrastive learning. Extensive experiments demonstrate that our method can achieve state-of-the-art results on four test datasets, as well as successfully generalize to real-world scenes. The code is available at \\url{https://github.com/Zhaozixiang1228/GDSR-SSDNet}.",
+    "neighbors": [
+      380
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1778,
+    "label": 16,
+    "text": "Title: Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensator\nAbstract: Weakly-supervised image segmentation (WSIS) is a critical task in computer vision that relies on image-level class labels. Multi-stage training procedures have been widely used in existing WSIS approaches to obtain high-quality pseudo-masks as ground-truth, resulting in significant progress. However, single-stage WSIS methods have recently gained attention due to their potential for simplifying training procedures, despite often suffering from low-quality pseudo-masks that limit their practical applications. To address this issue, we propose a novel single-stage WSIS method that utilizes a siamese network with contrastive learning to improve the quality of class activation maps (CAMs) and achieve a self-refinement process. Our approach employs a cross-representation refinement method that expands reliable object regions by utilizing different feature representations from the backbone. Additionally, we introduce a cross-transform regularization module that learns robust class prototypes for contrastive learning and captures global context information to feed back rough CAMs, thereby improving the quality of CAMs. Our final high-quality CAMs are used as pseudo-masks to supervise the segmentation result. Experimental results on the PASCAL VOC 2012 dataset demonstrate that our method significantly outperforms other state-of-the-art methods, achieving 67.2% and 68.76% mIoU on PASCAL VOC 2012 val set and test set, respectively. Furthermore, our method has been extended to weakly supervised object localization task, and experimental results demonstrate that our method continues to achieve very competitive results.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1779,
+    "label": 27,
+    "text": "Title: UAV Tracking with Lidar as a Camera Sensor in GNSS-Denied Environments\nAbstract: Light detection and ranging (LiDAR) sensor has become one of the primary sensors in robotics and autonomous system for high-accuracy situational awareness. In recent years, multi-modal LiDAR systems emerged, and among them, LiDAR-as-a-camera sensors provide not only 3D point clouds but also fixed-resolution 360\u00b0panoramic images by encoding either depth, reflectivity, or near-infrared light in the image pixels. This potentially brings computer vision capabilities on top of the potential of LiDAR itself. In this paper, we are specifically interested in utilizing LiDARs and LiDAR-generated images for tracking Unmanned Aerial Vehicles (UAVs) in real-time which can benefit applications including docking, remote identification, or counter-UAV systems, among others. This is, to the best of our knowledge, the first work that explores the possibility of fusing the images and point cloud generated by a single LiDAR sensor to track a UAV without a priori known initialized position. We trained a custom YOLOv5 model for detecting UAVs based on the panoramic images collected in an indoor experiment arena with a motion capture (MOCAP) system. By integrating with the point cloud, we are able to continuously provide the position of the UAV. Our experiment demonstrated the effectiveness of the proposed UAV tracking approach compared with methods based only on point clouds or images. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1780,
+    "label": 24,
+    "text": "Title: Robustness Implies Fairness in Causal Algorithmic Recourse\nAbstract: Algorithmic recourse discloses the internal procedures of a black-box decision process where decisions have significant consequences by providing recommendations to empower beneficiaries to achieve a more favorable outcome. To ensure an effective remedy, suggested interventions must not only be cost-effective but also robust and fair. To that end, it is essential to provide similar explanations to similar individuals. This study explores the concept of individual fairness and adversarial robustness in causal algorithmic recourse and addresses the challenge of achieving both. To resolve the challenges, we propose a new framework for defining adversarially robust recourse. That setting observes the protected feature as a pseudometric and demonstrates that individual fairness is a special case of adversarial robustness. Finally, we introduce the fair robust recourse problem and establish solutions to achieve both desirable properties both theoretically and empirically.",
+    "neighbors": [
+      995
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1781,
+    "label": 16,
+    "text": "Title: AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems\nAbstract: Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Current studies have demonstrated that\"printed adversarial attacks\", known as physical adversarial attacks, can successfully mislead perception models such as object detectors and image classifiers. However, most of these physical attacks are based on noticeable and eye-catching patterns for generated perturbations making them identifiable/detectable by human eye or in test drives. In this paper, we propose a camera-based inconspicuous adversarial attack (\\textbf{AdvRain}) capable of fooling camera-based perception systems over all objects of the same class. Unlike mask based fake-weather attacks that require access to the underlying computing hardware or image memory, our attack is based on emulating the effects of a natural weather condition (i.e., Raindrops) that can be printed on a translucent sticker, which is externally placed over the lens of a camera. To accomplish this, we provide an iterative process based on performing a random search aiming to identify critical positions to make sure that the performed transformation is adversarial for a target classifier. Our transformation is based on blurring predefined parts of the captured image corresponding to the areas covered by the raindrop. We achieve a drop in average model accuracy of more than $45\\%$ and $40\\%$ on VGG19 for ImageNet and Resnet34 for Caltech-101, respectively, using only $20$ raindrops.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1782,
+    "label": 16,
+    "text": "Title: Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation\nAbstract: Standard unsupervised domain adaptation methods adapt models from a source to a target domain using labeled source data and unlabeled target data jointly. In model adaptation, on the other hand, access to the labeled source data is prohibited, i.e., only the source-trained model and unlabeled target data are available. We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain. The target set consists of unlabeled pairs of adverse- and normal-condition street images taken at GPS-matched locations. Our method -- CMA -- leverages such image pairs to learn condition-invariant features via contrastive learning. In particular, CMA encourages features in the embedding space to be grouped according to their condition-invariant semantic content and not according to the condition under which respective inputs are captured. To obtain accurate cross-domain semantic correspondences, we warp the normal image to the viewpoint of the adverse image and leverage warp-confidence scores to create robust, aggregated features. With this approach, we achieve state-of-the-art semantic segmentation performance for model adaptation on several normal-to-adverse adaptation benchmarks, such as ACDC and Dark Zurich. We also evaluate CMA on a newly procured adverse-condition generalization benchmark and report favorable results compared to standard unsupervised domain adaptation methods, despite the comparative handicap of CMA due to source data inaccessibility. Code is available at https://github.com/brdav/cma.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1783,
+    "label": 24,
+    "text": "Title: Minimizing the Outage Probability in a Markov Decision Process\nAbstract: Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain. We propose an algorithm which enables to optimize an alternative objective: the probability that the gain is greater than a given value. The algorithm can be seen as an extension of the value iteration algorithm. We also show how the proposed algorithm could be generalized to use neural networks, similarly to the deep Q learning extension of Q learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1784,
+    "label": 16,
+    "text": "Title: Data-Efficient Large Scale Place Recognition with Graded Similarity Supervision\nAbstract: Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1785,
+    "label": 6,
+    "text": "Title: How to Communicate Robot Motion Intent: A Scoping Review\nAbstract: Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot\u2019s motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.",
+    "neighbors": [
+      198,
+      2227
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1786,
+    "label": 30,
+    "text": "Title: CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation\nAbstract: Commonsense knowledge is crucial to many natural language processing tasks. Existing works usually incorporate graph knowledge with conventional graph neural networks (GNNs), leading to the text and graph knowledge encoding processes being separated in a serial pipeline. We argue that these separate representation learning stages may be suboptimal for neural networks to learn the overall context contained in both types of input knowledge. In this paper, we propose a novel context-aware graph-attention model (Context-aware GAT), which can effectively incorporate global features of relevant knowledge graphs based on a context-enhanced knowledge aggregation process. Specifically, our framework leverages a novel representation learning approach to process heterogeneous features - combining flattened graph knowledge with text. To the best of our knowledge, this is the first attempt at hierarchically applying graph knowledge aggregation on a connected subgraph in addition to contextual information to support commonsense dialogue generation. This framework shows superior performance compared to conventional GNN-based language frameworks. Both automatic and human evaluation demonstrates that our proposed model has significant performance uplifts over state-of-the-art baselines.",
+    "neighbors": [
+      2283
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1787,
+    "label": 16,
+    "text": "Title: UATVR: Uncertainty-Adaptive Text-Video Retrieval\nAbstract: With the explosive growth of web videos and emerging large-scale vision-language pre-training models, e.g., CLIP, retrieving videos of interest with text instructions has attracted increasing attention. A common practice is to transfer text-video pairs to the same embedding space and craft cross-modal interactions with certain entities in specific granularities for semantic correspondence. Unfortunately, the intrinsic uncertainties of optimal entity combinations in appropriate granularities for cross-modal queries are understudied, which is especially critical for modalities with hierarchical semantics, e.g., video, text, etc. In this paper, we propose an Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models each look-up as a distribution matching procedure. Concretely, we add additional learnable tokens in the encoders to adaptively aggregate multi-grained semantics for flexible high-level reasoning. In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation. Comprehensive experiments on four benchmarks justify the superiority of our UATVR, which achieves new state-of-the-art results on MSR-VTT (50.8%), VATEX (64.5%), MSVD (49.7%), and DiDeMo (45.8%). The code is available at https://github.com/bofang98/UATVR.",
+    "neighbors": [
+      758,
+      979,
+      1151
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1788,
+    "label": 8,
+    "text": "Title: Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services\nAbstract: Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, finetuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGCdriven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.",
+    "neighbors": [
+      319,
+      1758,
+      2059,
+      2085
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1789,
+    "label": 16,
+    "text": "Title: A Unified Prompt-Guided In-Context Inpainting Framework for Reference-based Image Manipulations\nAbstract: Recent advancements in Text-to-Image (T2I) generative models have yielded impressive results in generating high-fidelity images based on consistent text prompts. However, there is a growing interest in exploring the potential of these models for more diverse reference-based image manipulation tasks that require spatial understanding and visual context. Previous approaches have achieved this by incorporating additional control modules or fine-tuning the generative models specifically for each task until convergence. In this paper, we propose a different perspective. We conjecture that current large-scale T2I generative models already possess the capability to perform these tasks but are not fully activated within the standard generation process. To unlock these capabilities, we introduce a unified Prompt-Guided In-Context inpainting (PGIC) framework, which leverages large-scale T2I models to re-formulate and solve reference-guided image manipulations. In the PGIC framework, the reference and masked target are stitched together as a new input for the generative models, enabling the filling of masked regions as producing final results. Furthermore, we demonstrate that the self-attention modules in T2I models are well-suited for establishing spatial correlations and efficiently addressing challenging reference-guided manipulations. These large T2I models can be effectively driven by task-specific prompts with minimal training cost or even with frozen backbones. We synthetically evaluate the effectiveness of the proposed PGIC framework across various tasks, including reference-guided image inpainting, faithful inpainting, outpainting, local super-resolution, and novel view synthesis. Our results show that PGIC achieves significantly better performance while requiring less computation compared to other fine-tuning based approaches.",
+    "neighbors": [
+      1125,
+      1173,
+      1418,
+      1601,
+      1710,
+      1902,
+      2161
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1790,
+    "label": 16,
+    "text": "Title: TSSR: A Truncated and Signed Square Root Activation Function for Neural Networks\nAbstract: Activation functions are essential components of neural networks. In this paper, we introduce a new activation function called the Truncated and Signed Square Root (TSSR) function. This function is distinctive because it is odd, nonlinear, monotone and differentiable. Its gradient is continuous and always positive. Thanks to these properties, it has the potential to improve the numerical stability of neural networks. Several experiments confirm that the proposed TSSR has better performance than other stat-of-the-art activation functions. The proposed function has significant implications for the development of neural network models and can be applied to a wide range of applications in fields such as computer vision, natural language processing, and speech recognition.",
+    "neighbors": [
+      831,
+      1668,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1791,
+    "label": 30,
+    "text": "Title: Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)\nAbstract: ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings. MUG includes five tracks, including topic segmentation, topic-level and session-level extractive summarization, topic title generation, keyphrase extraction, and action item detection. To facilitate MUG, we construct and release a large-scale meeting dataset, the AliMeeting4MUG Corpus.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1792,
+    "label": 16,
+    "text": "Title: Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling\nAbstract: Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information exploiting. First, we represent the fine-grained semantic structures of the input image and text with the visual and textual scene graphs, which are further fused into a unified cross-modal graph (CMG). Based on CMG, we perform structure refinement with the guidance of the graph information bottleneck principle, actively denoising the less-informative features. Next, we perform topic modeling over the input image and text, incorporating latent multimodal topic features to enrich the contexts. On the benchmark MRE dataset, our system outperforms the current best model significantly. With further in-depth analyses, we reveal the great potential of our method for the MRE task.",
+    "neighbors": [
+      466
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1793,
+    "label": 24,
+    "text": "Title: Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding\nAbstract: A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effective offline RL. In the present paper, we tackle the problem of hidden confounding in the nonidentifiable setting. We propose a definition of uncertainty due to hidden confounding bias, termed delphic uncertainty, which uses variation over world models compatible with the observations, and differentiate it from the well-known epistemic and aleatoric uncertainties. We derive a practical method for estimating the three types of uncertainties, and construct a pessimistic offline RL algorithm to account for them. Our method does not assume identifiability of the unobserved confounders, and attempts to reduce the amount of confounding bias. We demonstrate through extensive experiments and ablations the efficacy of our approach on a sepsis management benchmark, as well as on electronic health records. Our results suggest that nonidentifiable hidden confounding bias can be mitigated to improve offline RL solutions in practice.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1794,
+    "label": 10,
+    "text": "Title: Temporalising Unique Characterisability and Learnability of Ontology-Mediated Queries\nAbstract: Recently, the study of the unique characterisability and learnability of database queries by means of examples has been extended to ontology-mediated queries. Here, we study in how far the obtained results can be lifted to temporalised ontology-mediated queries. We provide a systematic introduction to the relevant approaches in the non-temporal case and then show general transfer results pinpointing under which conditions existing results can be lifted to temporalised queries.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1795,
+    "label": 31,
+    "text": "Title: Popularity Debiasing from Exposure to Interaction in Collaborative Filtering\nAbstract: Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations (exposure) of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1796,
+    "label": 10,
+    "text": "Title: A Strategy-Oriented Bayesian Soft Actor-Critic Model\nAbstract: Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -- soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1797,
+    "label": 30,
+    "text": "Title: Zero-Shot Information Extraction via Chatting with ChatGPT\nAbstract: Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show promising performance on zero-shot settings, thus inspiring us to explore prompt-based methods. In this work, we ask whether strong IE models can be constructed by directly prompting LLMs. Specifically, we transform the zero-shot IE task into a multi-turn question-answering problem with a two-stage framework (ChatIE). With the power of ChatGPT, we extensively evaluate our framework on three IE tasks: entity-relation triple extract, named entity recognition, and event extraction. Empirical results on six datasets across two languages show that ChatIE achieves impressive performance and even surpasses some full-shot models on several datasets (e.g., NYT11-HRL). We believe that our work could shed light on building IE models with limited resources.",
+    "neighbors": [
+      121,
+      735,
+      834,
+      929,
+      1199,
+      1520,
+      1556,
+      1949,
+      1950,
+      2062,
+      2244,
+      2265,
+      2289
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1798,
+    "label": 1,
+    "text": "Title: Green video complexity analysis for efficient encoding in Adaptive Video Streaming\nAbstract: For adaptive streaming applications, low-complexity and accurate video complexity features are necessary to analyze the video content in real time, which ensures fast and compression-efficient video streaming without disruptions. State-of-the-art video complexity features are Spatial Information (SI) and Temporal Information (TI) features which do not correlate well with the encoding parameters in adaptive streaming applications. To this light, Video Complexity Analyzer (VCA) was introduced, determining the features based on Discrete Cosine Transform (DCT)-energy. This paper presents optimizations on VCA for faster and energy-efficient video complexity analysis. Experimental results show that VCA v2.0, using eight CPU threads, Single Instruction Multiple Data (SIMD), and low-pass DCT optimization, determines seven complexity features of Ultra High Definition 8-bit videos with better accuracy at a speed of up to 292.68 fps and an energy consumption of 97.06% lower than the reference SITI implementation.",
+    "neighbors": [
+      2212
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1799,
+    "label": 30,
+    "text": "Title: Unifying Large Language Models and Knowledge Graphs: A Roadmap\nAbstract: Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the field of natural language processing and artificial intelligence, due to their emergent ability and generalizability. However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolving by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to unify LLMs and KGs together and simultaneously leverage their advantages. In this article, we present a forward-looking roadmap for the unification of LLMs and KGs. Our roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs, which incorporate KGs during the pre-training and inference phases of LLMs, or for the purpose of enhancing understanding of the knowledge learned by LLMs; 2) LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding, completion, construction, graph-to-text generation, and question answering; and 3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a mutually beneficial way to enhance both LLMs and KGs for bidirectional reasoning driven by both data and knowledge. We review and summarize existing efforts within these three frameworks in our roadmap and pinpoint their future research directions.",
+    "neighbors": [
+      834,
+      1035,
+      1052,
+      1327,
+      1537,
+      1544,
+      1713,
+      1762,
+      1851,
+      1940,
+      1952,
+      2113,
+      2155,
+      2281
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1800,
+    "label": 30,
+    "text": "Title: HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge\nAbstract: Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks. Nevertheless, LLMs have not yet performed optimally in biomedical domain tasks due to the need for medical expertise in the responses. In response to this challenge, we propose HuaTuo, a LLaMA-based model that has been supervised-fine-tuned with generated QA (Question-Answer) instances. The experimental results demonstrate that HuaTuo generates responses that possess more reliable medical knowledge. Our proposed HuaTuo model is accessible at https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese.",
+    "neighbors": [
+      855,
+      1052,
+      1556
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1801,
+    "label": 30,
+    "text": "Title: Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench\nAbstract: Recently, the community has witnessed the advancement of Large Language Models (LLMs), which have shown remarkable performance on various downstream tasks. Led by powerful models like ChatGPT and Claude, LLMs are revolutionizing how users engage with software, assuming more than mere tools but intelligent assistants. Consequently, evaluating LLMs' anthropomorphic capabilities becomes increasingly important in contemporary discourse. Utilizing the emotion appraisal theory from psychology, we propose to evaluate the empathy ability of LLMs, i.e., how their feelings change when presented with specific situations. After a careful and comprehensive survey, we collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. Categorizing the situations into 36 factors, we conduct a human evaluation involving more than 1,200 subjects worldwide. With the human evaluation results as references, our evaluation includes five LLMs, covering both commercial and open-source models, including variations in model sizes, featuring the latest iterations, such as GPT-4 and LLaMA 2. A conclusion can be drawn from the results that, despite several misalignments, LLMs can generally respond appropriately to certain situations. Nevertheless, they fall short in alignment with the emotional behaviors of human beings and cannot establish connections between similar situations. Our collected dataset of situations, the human evaluation results, and the code of our testing framework, dubbed EmotionBench, is made publicly in https://github.com/CUHK-ARISE/EmotionBench. We aspire to contribute to the advancement of LLMs regarding better alignment with the emotional behaviors of human beings, thereby enhancing their utility and applicability as intelligent assistants.",
+    "neighbors": [
+      69,
+      519,
+      682,
+      924,
+      1001,
+      1044,
+      1863,
+      1878,
+      2235
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1802,
+    "label": 24,
+    "text": "Title: Comprehensive Assessment of the Performance of Deep Learning Classifiers Reveals a Surprising Lack of Robustness\nAbstract: Reliable and robust evaluation methods are a necessary first step towards developing machine learning models that are themselves robust and reliable. Unfortunately, current evaluation protocols typically used to assess classifiers fail to comprehensively evaluate performance as they tend to rely on limited types of test data, and ignore others. For example, using the standard test data fails to evaluate the predictions made by the classifier to samples from classes it was not trained on. On the other hand, testing with data containing samples from unknown classes fails to evaluate how well the classifier can predict the labels for known classes. This article advocates bench-marking performance using a wide range of different types of data and using a single metric that can be applied to all such data types to produce a consistent evaluation of performance. Using such a benchmark it is found that current deep neural networks, including those trained with methods that are believed to produce state-of-the-art robustness, are extremely vulnerable to making mistakes on certain types of data. This means that such models will be unreliable in real-world scenarios where they may encounter data from many different domains, and that they are insecure as they can easily be fooled into making the wrong decisions. It is hoped that these results will motivate the wider adoption of more comprehensive testing methods that will, in turn, lead to the development of more robust machine learning methods in the future. Code is available at: \\url{https://codeberg.org/mwspratling/RobustnessEvaluation}",
+    "neighbors": [
+      1873
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1803,
+    "label": 30,
+    "text": "Title: ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews\nAbstract: Revising scientific papers based on peer feedback is a challenging task that requires not only deep scientific knowledge and reasoning, but also the ability to recognize the implicit requests in high-level feedback and to choose the best of many possible ways to update the manuscript in response. We introduce this task for large language models and release ARIES, a dataset of review comments and their corresponding paper edits, to enable training and evaluating models. We study two versions of the task: comment-edit alignment and edit generation, and evaluate several baselines, including GPT-4. We find that models struggle even to identify the edits that correspond to a comment, especially in cases where the comment is phrased in an indirect way or where the edit addresses the spirit of a comment but not the precise request. When tasked with generating edits, GPT-4 often succeeds in addressing comments on a surface level, but it rigidly follows the wording of the feedback rather than the underlying intent, and includes fewer technical details than human-written edits. We hope that our formalization, dataset, and analysis will form a foundation for future work in this area.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1804,
+    "label": 16,
+    "text": "Title: Reliability in Semantic Segmentation: Are we on the Right Track?\nAbstract: Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance follows a constant, upward trend, properties like robustness or uncertainty estimation are less explored-leaving doubts about advances in model reliability. Studies along these axes exist, but they are mainly limited to classification models. In contrast, we carry out a study on semantic segmentation, a relevant task for many real-world applications where model reliability is paramount. We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection. We find that while recent models are significantly more robust, they are not overall more reliable in terms of uncertainty estimation. We further explore methods that can come to the rescue and show that improving calibration can also help with other uncertainty metrics such as misclassification or OOD detection. This is the first study on modern segmentation models focused on both robustness and uncertainty estimation and we hope it will help practitioners and researchers interested in this fundamental vision task11Code available at https://github.com/naver/relis.",
+    "neighbors": [
+      1103
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1805,
+    "label": 30,
+    "text": "Title: DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text\nAbstract: Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT.",
+    "neighbors": [
+      27,
+      42,
+      1052,
+      1487,
+      1600,
+      2044
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1806,
+    "label": 6,
+    "text": "Title: Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?\nAbstract: Sensing technologies deployed in the workplace can unobtrusively collect detailed data about individual activities and group interactions that are otherwise difficult to capture. A hopeful application of these technologies is that they can help businesses and workers optimize productivity and wellbeing. However, given the inherent and structural power dynamics in the workplace, the prevalent approach of accepting tacit compliance to monitor work activities rather than seeking workers\u2019 meaningful consent raises privacy and ethical concerns. This paper unpacks challenges workers face when consenting to workplace wellbeing technologies. Using a hypothetical case to prompt reflection among six multi-stakeholder focus groups involving 15 participants, we explored participants\u2019 expectations and capacity to consent to these technologies. We sketched possible interventions that could better support meaningful consent to workplace wellbeing technologies, by drawing on critical computing and feminist scholarship\u2014which reframes consent from a purely individual choice to a structural condition experienced at the individual level that needs to be freely given, reversible, informed, enthusiastic, and specific (FRIES). The focus groups revealed how workers are vulnerable to \u201cmeaningless\u201d consent\u2014as they may be subject to power dynamics that minimize their ability to withhold consent and may thus experience an erosion of autonomy in their workplace, also undermining the value of data gathered in the name of \u201cwellbeing.\u201d To meaningfully consent, participants wanted changes to how the technology works and is being used, as well as to the policies and practices surrounding the technology. Our mapping of what prevents workers from meaningfully consenting to workplace wellbeing technologies (challenges) and what they require to do so (interventions) illustrates how the lack of meaningful consent is a structural problem requiring socio-technical solutions.",
+    "neighbors": [
+      1650
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1807,
+    "label": 24,
+    "text": "Title: Multimodal Subtask Graph Generation from Instructional Videos\nAbstract: Real-world tasks consist of multiple inter-dependent subtasks (e.g., a dirty pan needs to be washed before it can be used for cooking). In this work, we aim to model the causal dependencies between such subtasks from instructional videos describing the task. This is a challenging problem since complete information about the world is often inaccessible from videos, which demands robust learning mechanisms to understand the causal structure of events. We present Multimodal Subtask Graph Generation (MSG2), an approach that constructs a Subtask Graph defining the dependency between a task's subtasks relevant to a task from noisy web videos. Graphs generated by our multimodal approach are closer to human-annotated graphs compared to prior approaches. MSG2 further performs the downstream task of next subtask prediction 85% and 30% more accurately than recent video transformer models in the ProceL and CrossTask datasets, respectively.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1808,
+    "label": 24,
+    "text": "Title: TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization\nAbstract: Despite their success with unstructured data, deep neural networks are not yet a panacea for structured tabular data. In the tabular domain, their efficiency crucially relies on various forms of regularization to prevent overfitting and provide strong generalization performance. Existing regularization techniques include broad modelling decisions such as choice of architecture, loss functions, and optimization methods. In this work, we introduce Tabular Neural Gradient Orthogonalization and Specialization (TANGOS), a novel framework for regularization in the tabular setting built on latent unit attributions. The gradient attribution of an activation with respect to a given input feature suggests how the neuron attends to that feature, and is often employed to interpret the predictions of deep networks. In TANGOS, we take a different approach and incorporate neuron attributions directly into training to encourage orthogonalization and specialization of latent attributions in a fully-connected network. Our regularizer encourages neurons to focus on sparse, non-overlapping input features and results in a set of diverse and specialized latent units. In the tabular domain, we demonstrate that our approach can lead to improved out-of-sample generalization performance, outperforming other popular regularization methods. We provide insight into why our regularizer is effective and demonstrate that TANGOS can be applied jointly with existing methods to achieve even greater generalization performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1809,
+    "label": 30,
+    "text": "Title: Active Prompting with Chain-of-Thought for Large Language Models\nAbstract: The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is known that the effective design of task-specific prompts is critical for LLMs' ability to produce high-quality answers. In particular, an effective approach for complex question-and-answer tasks is example-based prompting with chain-of-thought (CoT) reasoning, which significantly improves the performance of LLMs. However, current CoT methods rely on a fixed set of human-annotated exemplars, which are not necessarily the most effective examples for different tasks. This paper proposes a new method, Active-Prompt, to adapt LLMs to different tasks with task-specific example prompts (annotated with human-designed CoT reasoning). For this purpose, we propose a solution to the key problem of determining which questions are the most important and helpful ones to annotate from a pool of task-specific queries. By borrowing ideas from the related problem of uncertainty-based active learning, we introduce several metrics to characterize the uncertainty so as to select the most uncertain questions for annotation. Experimental results demonstrate the superiority of our proposed method, achieving state-of-the-art on eight complex reasoning tasks. Further analyses of different uncertainty metrics, pool sizes, zero-shot learning, and accuracy-uncertainty relationship demonstrate the effectiveness of our method. Our code will be available at https://github.com/shizhediao/active-prompt.",
+    "neighbors": [
+      2184
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1810,
+    "label": 30,
+    "text": "Title: Low-code LLM: Visual Programming over LLMs\nAbstract: Effectively utilizing LLMs for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions, all supported by clicking, dragging, or text editing, to achieve more controllable and stable responses. Through visual interaction with a graphical user interface, users can incorporate their ideas into the workflow without writing trivial prompts. The proposed Low-code LLM framework consists of a Planning LLM that designs a structured planning workflow for complex tasks, which can be correspondingly edited and confirmed by users through low-code visual programming operations, and an Executing LLM that generates responses following the user-confirmed workflow. We highlight three advantages of the low-code LLM: controllable generation results, user-friendly human-LLM interaction, and broadly applicable scenarios. We demonstrate its benefits using four typical applications. By introducing this approach, we aim to bridge the gap between humans and LLMs, enabling more effective and efficient utilization of LLMs for complex tasks. Our system will be soon publicly available at LowCodeLLM.",
+    "neighbors": [
+      0,
+      127,
+      319,
+      924,
+      1044,
+      1142,
+      1353,
+      1952,
+      2166,
+      2230,
+      2249
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1811,
+    "label": 27,
+    "text": "Title: Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control\nAbstract: This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.",
+    "neighbors": [
+      1742
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1812,
+    "label": 30,
+    "text": "Title: Hallucinations in Large Multilingual Translation Models\nAbstract: Large-scale multilingual machine translation systems have demonstrated remarkable ability to translate directly between numerous languages, making them increasingly appealing for real-world applications. However, when deployed in the wild, these models may generate hallucinated translations which have the potential to severely undermine user trust and raise safety concerns. Existing research on hallucinations has primarily focused on small bilingual models trained on high-resource languages, leaving a gap in our understanding of hallucinations in massively multilingual models across diverse translation scenarios. In this work, we fill this gap by conducting a comprehensive analysis on both the M2M family of conventional neural machine translation models and ChatGPT, a general-purpose large language model~(LLM) that can be prompted for translation. Our investigation covers a broad spectrum of conditions, spanning over 100 translation directions across various resource levels and going beyond English-centric language pairs. We provide key insights regarding the prevalence, properties, and mitigation of hallucinations, paving the way towards more responsible and reliable machine translation systems.",
+    "neighbors": [
+      1146,
+      2013,
+      2038,
+      2042
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1813,
+    "label": 25,
+    "text": "Title: Extending Audio Masked Autoencoders toward Audio Restoration\nAbstract: Audio classification and restoration are among major downstream tasks in audio signal processing. However, restoration derives less of a benefit from pretrained models compared to the overwhelming success of pretrained models in classification tasks. Due to such unbalanced benefits, there has been rising interest in how to improve the performance of pretrained models for restoration tasks, e.g., speech enhancement (SE). Previous works have shown that the features extracted by pretrained audio encoders are effective for SE tasks, but these speech-specialized encoder-only models usually require extra decoders to become compatible with SE, and involve complicated pretraining procedures or complex data augmentation. Therefore, in pursuit of a universal audio model, the audio masked autoencoder (MAE) whose backbone is the autoencoder of Vision Transformers (ViT-AE), is extended from audio classification to SE, a representative restoration task with well-established evaluation standards. ViT-AE learns to restore masked audio signal via a mel-to-mel mapping during pretraining, which is similar to restoration tasks like SE. We propose variations of ViT-AE for a better SE performance, where the mel-to-mel variations yield high scores in non-intrusive metrics and the STFT-oriented variation is effective at intrusive metrics such as PESQ. Different variations can be used in accordance with the scenarios. Comprehensive evaluations reveal that MAE pretraining is beneficial to SE tasks and help the ViT-AE to better generalize to out-of-domain distortions. We further found that large-scale noisy data of general audio sources, rather than clean speech, is sufficiently effective for pretraining.",
+    "neighbors": [
+      98
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1814,
+    "label": 24,
+    "text": "Title: ManyDG: Many-domain Generalization for Healthcare Applications\nAbstract: The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients data and evaluated on new patients. Many of them might suffer from poor generalizability. One key reason can be overfitting due to the unique information related to patient identities and their data collection environments, referred to as patient covariates in the paper. These patient covariates usually do not contribute to predicting the targets but are often difficult to remove. As a result, they can bias the model training process and impede generalization. In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG, that can scale to such many-domain problems. Our method identifies the patient domain covariates by mutual reconstruction and removes them via an orthogonal projection step. Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1815,
+    "label": 8,
+    "text": "Title: Age of Processing-Based Data Offloading for Autonomous Vehicles in MultiRATs Open RAN\nAbstract: Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle\u2019s cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34% improvement over similar method.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1816,
+    "label": 24,
+    "text": "Title: Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization\nAbstract: In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization. REASON consists of Topological Causal Discovery and Individual Causal Discovery. The Topological Causal Discovery component aims to model the fault propagation in order to trace back to the root causes. To achieve this, we propose novel hierarchical graph neural networks to construct interdependent causal networks by modeling both intra-level and inter-level non-linear causal relations. Based on the learned interdependent causal networks, we then leverage random walks with restarts to model the network propagation of a system fault. The Individual Causal Discovery component focuses on capturing abrupt change patterns of a single system entity. This component examines the temporal patterns of each entity's metric data (i.e., time series), and estimates its likelihood of being a root cause based on the Extreme Value theory. Combining the topological and individual causal scores, the top K system entities are identified as root causes. Extensive experiments on three real-world datasets with case studies demonstrate the effectiveness and superiority of the proposed framework.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1817,
+    "label": 16,
+    "text": "Title: CLUSTSEG: Clustering for Universal Segmentation\nAbstract: We present CLUSTSEG, a general, transformer-based framework that tackles different image segmentation tasks (i.e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme. Regarding queries as cluster centers, CLUSTSEG is innovative in two aspects:1) cluster centers are initialized in heterogeneous ways so as to pointedly address task-specific demands (e.g., instance- or category-level distinctiveness), yet without modifying the architecture; and 2) pixel-cluster assignment, formalized in a cross-attention fashion, is alternated with cluster center update, yet without learning additional parameters. These innovations closely link CLUSTSEG to EM clustering and make it a transparent and powerful framework that yields superior results across the above segmentation tasks.",
+    "neighbors": [
+      2156
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1818,
+    "label": 24,
+    "text": "Title: Optimistic Meta-Gradients\nAbstract: We study the connection between gradient-based meta-learning and convex optimisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta-learning in the single task setting. While a meta-learned update rule can yield faster convergence up to constant factor, it is not suf\ufb01cient for acceleration. Instead, some form of optimism is required. We show that optimism in meta-learning can be captured through Bootstrapped Meta-Gradients [Flennerhag et al., 2022], providing deeper insight into its underlying mechanics",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1819,
+    "label": 14,
+    "text": "Title: Drinfeld Modules in SageMath\nAbstract: We present the first implementation of Drinfeld modules fully integrated in the SageMath ecosystem. First features were released with SageMath 10.0.",
+    "neighbors": [
+      2132
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1820,
+    "label": 24,
+    "text": "Title: A Data-Driven State Aggregation Approach for Dynamic Discrete Choice Models\nAbstract: We study dynamic discrete choice models, where a commonly studied problem involves estimating parameters of agent reward functions (also known as\"structural\"parameters), using agent behavioral data. Maximum likelihood estimation for such models requires dynamic programming, which is limited by the curse of dimensionality. In this work, we present a novel algorithm that provides a data-driven method for selecting and aggregating states, which lowers the computational and sample complexity of estimation. Our method works in two stages. In the first stage, we use a flexible inverse reinforcement learning approach to estimate agent Q-functions. We use these estimated Q-functions, along with a clustering algorithm, to select a subset of states that are the most pivotal for driving changes in Q-functions. In the second stage, with these selected\"aggregated\"states, we conduct maximum likelihood estimation using a commonly used nested fixed-point algorithm. The proposed two-stage approach mitigates the curse of dimensionality by reducing the problem dimension. Theoretically, we derive finite-sample bounds on the associated estimation error, which also characterize the trade-off of computational complexity, estimation error, and sample complexity. We demonstrate the empirical performance of the algorithm in two classic dynamic discrete choice estimation applications.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1821,
+    "label": 31,
+    "text": "Title: Group Membership Bias\nAbstract: When learning to rank from user interactions, search and recommendation systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recently been studied in the ranking literature is when sensitive attributes, such as gender, have an impact on a user's judgment about an item's utility. For example, in a search for an expertise area, some users may be biased towards clicking on male candidates over female candidates. We call this type of bias group membership bias or group bias for short. Increasingly, we seek rankings that not only have high utility but are also fair to individuals and sensitive groups. Merit-based fairness measures rely on the estimated merit or utility of the items. With group bias, the utility of the sensitive groups is under-estimated, hence, without correcting for this bias, a supposedly fair ranking is not truly fair. In this paper, first, we analyze the impact of group bias on ranking quality as well as two well-known merit-based fairness metrics and show that group bias can hurt both ranking and fairness. Then, we provide a correction method for group bias that is based on the assumption that the utility score of items in different groups comes from the same distribution. This assumption has two potential issues of sparsity and equality-instead-of-equity, which we use an amortized approach to solve. We show that our correction method can consistently compensate for the negative impact of group bias on ranking quality and fairness metrics.",
+    "neighbors": [
+      1981
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1822,
+    "label": 27,
+    "text": "Title: Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT\nAbstract: Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the current robot system\u2019s sequential understanding ability, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrates its feasibility and effectiveness through experimental evaluation including two case studies and 80 trials involving real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics.",
+    "neighbors": [
+      25,
+      719,
+      984,
+      1036
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1823,
+    "label": 28,
+    "text": "Title: Synergizing Beyond Diagonal Reconfigurable Intelligent Surface and Rate-Splitting Multiple Access\nAbstract: This work focuses on the synergy of rate-splitting multiple access (RSMA) and beyond diagonal reconfigurable intelligent surface (BD-RIS) to enlarge the coverage, improve the performance, and save on antennas. Specifically, we employ a multi-sector BD-RIS modeled as a prism, which can achieve highly directional full-space coverage, in a multiuser multiple input single output communication system. With the multi-sector BD-RIS aided RSMA model, we jointly design the transmit precoder and BD-RIS matrix under the imperfect channel state information (CSI) conditions. The robust design is performed by solving a stochastic average sum-rate maximization problem. With sample average approximation and weighted minimum mean square error-rate relationship, the stochastic problem is transformed into a deterministic one with multiple blocks, each of which is iteratively designed. Simulation results show that multi-sector BD-RIS aided RSMA outperforms space division multiple access schemes. More importantly, synergizing multi-sector BD-RIS with RSMA is an efficient strategy to reduce the number of active antennas at the transmitter and the number of passive antennas in BD-RIS.",
+    "neighbors": [
+      1143
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1824,
+    "label": 24,
+    "text": "Title: Semi-Supervised Classification with Graph Convolutional Kernel Machines\nAbstract: We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs. First, we introduce an unsupervised kernel machine propagating the node features in a one-hop neighbourhood. Then, we specify a semi-supervised classification kernel machine through the lens of the Fenchel-Young inequality. The deep graph convolutional kernel machine is obtained by stacking multiple shallow kernel machines. After showing that unsupervised and semi-supervised layer corresponds to an eigenvalue problem and a linear system on the aggregated node features, respectively, we derive an efficient end-to-end training algorithm in the dual variables. Numerical experiments demonstrate that our approach is competitive with state-of-the-art graph neural networks for homophilious and heterophilious benchmark datasets. Notably, GCKM achieves superior performance when very few labels are available.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1825,
+    "label": 15,
+    "text": "Title: Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning\nAbstract: Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a general-purpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an $R^2$ score of 0.77 and a mean error of 24% using regressors, and with an F1 score above 0.6 for classifiers applying a six-months resolution.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1826,
+    "label": 27,
+    "text": "Title: Fast Path Planning Through Large Collections of Safe Boxes\nAbstract: We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the number of these safe boxes is large, and basic preprocessing of them (such as finding their intersections) can be done offline. At runtime we quickly generate a smooth path between given initial and terminal positions. Our algorithm designs trajectories that are guaranteed to be safe at all times, and it detects infeasibility whenever such a trajectory does not exist. Our algorithm is based on two subproblems that we can solve very efficiently: finding a shortest path in a weighted graph, and solving (multiple) convex optimal control problems. We demonstrate the proposed path planner on large-scale numerical examples, and we provide an efficient open-source software implementation, fastpathplanning.",
+    "neighbors": [
+      904
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1827,
+    "label": 27,
+    "text": "Title: Passive Shape Locking for Multi-Bend Growing Inflated Beam Robots\nAbstract: Shape change enables new capabilities for robots. One class of robots capable of dramatic shape change is soft growing \u201cvine\u201d robots. These robots usually feature global actuation methods for bending that limit them to simple, constant-curvature shapes. Achieving more complex \u201cmulti-bend\u201d configurations has also been explored but requires choosing the desired configuration ahead of time, exploiting contact with the environment to maintain previous bends, or using pneumatic actuation for shape locking. In this paper, we present a novel design that enables passive, on-demand shape locking. Our design leverages a passive tip mount to apply hook-and-loop fasteners that hold bends without any pneumatic or electrical input. We characterize the robot's kinematics and ability to hold locked bends. We also experimentally evaluate the effect of hook-and-loop fasteners on beam and joint stiffness. Finally, we demonstrate our proof-of-concept prototype in 2D. Our passive shape locking design is a step towards easily reconfigurable robots that are lightweight, low-cost, and low-power.",
+    "neighbors": [
+      747,
+      1914
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1828,
+    "label": 27,
+    "text": "Title: ERRA: An Embodied Representation and Reasoning Architecture for Long-Horizon Language-Conditioned Manipulation Tasks\nAbstract: This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions.",
+    "neighbors": [
+      719,
+      1369
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1829,
+    "label": 27,
+    "text": "Title: Direct LiDAR-Inertial Odometry and Mapping: Perceptive and Connective SLAM\nAbstract: This paper presents Direct LiDAR-Inertial Odometry and Mapping (DLIOM), a robust SLAM algorithm with an explicit focus on computational efficiency, operational reliability, and real-world efficacy. DLIOM contains several key algorithmic innovations in both the front-end and back-end subsystems to design a resilient LiDAR-inertial architecture that is perceptive to the environment and produces accurate localization and high-fidelity 3D mapping for autonomous robotic platforms. Our ideas spawned after a deep investigation into modern LiDAR SLAM systems and their inabilities to generalize across different operating environments, in which we address several common algorithmic failure points by means of proactive safe-guards to provide long-term operational reliability in the unstructured real world. We detail several important innovations to localization accuracy and mapping resiliency distributed throughout a typical LiDAR SLAM pipeline to comprehensively increase algorithmic speed, accuracy, and robustness. In addition, we discuss insights gained from our ground-up approach while implementing such a complex system for real-time state estimation on resource-constrained systems, and we experimentally show the increased performance of our method as compared to the current state-of-the-art on both public benchmark and self-collected datasets.",
+    "neighbors": [
+      906
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1830,
+    "label": 31,
+    "text": "Title: HAMUR: Hyper Adapter for Multi-Domain Recommendation\nAbstract: Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, current MDR models are confronted with two limitations. Firstly, the majority of these models adopt an approach that explicitly shares parameters between domains, leading to mutual interference among them. Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains. To address these challenges, we propose a novel model Hyper Adapter for Multi-Domain Recommendation (HAMUR). Specifically, HAMUR consists of two components: (1). Domain-specific adapter, designed as a pluggable module that can be seamlessly integrated into various existing multi-domain backbone models, and (2). Domain-shared hyper-network, which implicitly captures shared information among domains and dynamically generates the parameters for the adapter. We conduct extensive experiments on two public datasets using various backbone networks. The experimental results validate the effectiveness and scalability of the proposed model.",
+    "neighbors": [
+      209,
+      2143
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1831,
+    "label": 16,
+    "text": "Title: A Semi-Paired Approach For Label-to-Image Translation\nAbstract: Data efficiency, or the ability to generalize from a few labeled data, remains a major challenge in deep learning. Semi-supervised learning has thrived in traditional recognition tasks alleviating the need for large amounts of labeled data, yet it remains understudied in image-to-image translation (I2I) tasks. In this work, we introduce the first semi-supervised (semi-paired) framework for label-to-image translation, a challenging subtask of I2I which generates photorealistic images from semantic label maps. In the semi-paired setting, the model has access to a small set of paired data and a larger set of unpaired images and labels. Instead of using geometrical transformations as a pretext task like previous works, we leverage an input reconstruction task by exploiting the conditional discriminator on the paired data as a reverse generator. We propose a training algorithm for this shared network, and we present a rare classes sampling algorithm to focus on under-represented classes. Experiments on 3 standard benchmarks show that the proposed model outperforms state-of-the-art unsupervised and semi-supervised approaches, as well as some fully supervised approaches while using a much smaller number of paired samples.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1832,
+    "label": 16,
+    "text": "Title: Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition\nAbstract: Facial Expression Recognition (FER) is an important task in computer vision and has wide applications in many fields. In this paper, we introduce our approach to the fifth Affective Behavior Analysis in-the-wild (ABAW) Competition which will be held in CVPR20223. For facial expression recognition task, there is an urgent need to solve the problem that the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffective performance. To address this problem, we propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module (DTM) that can adaptively adjust the confidence threshold to fully utilize the face recognition (FR) data to generate pseudo-labels, thus improving the model\u2019s ability to model facial expressions. In the 5th ABAW Expression Classification Challenge, our method achieves good results on the Aff-Wild2 validation and test sets, demonstrating that large scale unlabeled faces can indeed improve the performance of face expression recognition.",
+    "neighbors": [
+      1541,
+      2101
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1833,
+    "label": 24,
+    "text": "Title: Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks Using a Malware Dataset: A Comparative Analysis\nAbstract: The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.",
+    "neighbors": [
+      692
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1834,
+    "label": 30,
+    "text": "Title: In-Context Retrieval-Augmented Language Models\nAbstract: Retrieval-Augmented Language Modeling (RALM) methods, which condition a language model (LM) on relevant documents from a grounding corpus during generation, were shown to significantly improve language modeling performance. In addition, they can mitigate the problem of factually inaccurate text generation and provide natural source attribution mechanism. Existing RALM approaches focus on modifying the LM architecture in order to facilitate the incorporation of external information, significantly complicating deployment. This paper considers a simple alternative, which we dub In-Context RALM: leaving the LM architecture unchanged and prepending grounding documents to the input, without any further training of the LM. We show that In-Context RALM that builds on off-the-shelf general purpose retrievers provides surprisingly large LM gains across model sizes and diverse corpora. We also demonstrate that the document retrieval and ranking mechanism can be specialized to the RALM setting to further boost performance. We conclude that In-Context RALM has considerable potential to increase the prevalence of LM grounding, particularly in settings where a pretrained LM must be used without modification or even via API access.",
+    "neighbors": [
+      36,
+      945,
+      1052,
+      1678,
+      1915,
+      2104,
+      2226
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1835,
+    "label": 16,
+    "text": "Title: Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion\nAbstract: In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our bag of tricks. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models. All code is available at https://github.com/Jordan-HS/Diversity_is_Definitely_Needed",
+    "neighbors": [
+      616,
+      1049
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1836,
+    "label": 24,
+    "text": "Title: The Importance of Robust Features in Mitigating Catastrophic Forgetting\nAbstract: Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or data distributions. The adversarial robustness has decomposed features into robust and non-robust types and demonstrated that models trained on robust features significantly enhance adversarial robustness. However, no study has been conducted on the efficacy of robust features from the lens of the CL model in mitigating catastrophic forgetting in CL. In this paper, we introduce the CL robust dataset and train four baseline models on both the standard and CL robust datasets. Our results demonstrate that the CL models trained on the CL robust dataset experienced less catastrophic forgetting of the previously learned tasks than when trained on the standard dataset. Our observations highlight the significance of the features provided to the underlying CL models, showing that CL robust features can alleviate catastrophic forgetting.",
+    "neighbors": [
+      1948
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1837,
+    "label": 5,
+    "text": "Title: Ethereum's Proposer-Builder Separation: Promises and Realities\nAbstract: With Ethereum's transition from Proof-of-Work to Proof-of-Stake in September 2022 came another paradigm shift, the Proposer-Builder Separation (PBS) scheme. PBS was introduced to decouple the roles of selecting and ordering transactions in a block (i.e., the builder), from those validating its contents and proposing the block to the network as the new head of the blockchain (i.e., the proposer). In this landscape, proposers are the validators in the Proof-of-Stake consensus protocol who validate and secure the network, while now relying on specialized block builders for creating blocks with the most value (e.g., transaction fees) for the proposer. Additionally, relays play a crucial new role in this ecosystem, acting as mediators between builders and proposers, being entrusted with the responsibility of transmitting the most lucrative blocks from the builders to the proposers. PBS is currently an opt-in protocol (i.e., a proposer can still opt-out and build their own blocks). In this work, we study it's adoption and show that the current PBS landscape exhibits significant centralization amongst the builders and relays. We further explore whether PBS effectively achieves its intended objectives of enabling hobbyist validators to maximize block profitability and preventing censorship. Our findings reveal that although PBS grants all validators the same opportunity to access optimized and competitive blocks, it tends to stimulate censorship rather than reduce it. Additionally, our analysis demonstrates that relays do not consistently uphold their commitments and may prove unreliable. Specifically, there are instances where proposers do not receive the complete value as initially promised, and the censorship or filtering capabilities pledged by the relay exhibit significant gaps.",
+    "neighbors": [
+      1496
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1838,
+    "label": 27,
+    "text": "Title: Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing\nAbstract: In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate's configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.",
+    "neighbors": [
+      686,
+      1924
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1839,
+    "label": 16,
+    "text": "Title: NormAUG: Normalization-guided Augmentation for Domain Generalization\nAbstract: Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test sets, resulting in a significant drop in performance during testing. To address this issue, several domain generalization methods have been developed to learn robust and domain-invariant features from multiple training domains that can generalize well to unseen test domains. Data augmentation plays a crucial role in achieving this goal by enhancing the diversity of the training data. In this paper, inspired by the observation that normalizing an image with different statistics generated by different batches with various domains can perturb its feature, we propose a simple yet effective method called NormAUG (Normalization-guided Augmentation). Our method includes two paths: the main path and the auxiliary (augmented) path. During training, the auxiliary path includes multiple sub-paths, each corresponding to batch normalization for a single domain or a random combination of multiple domains. This introduces diverse information at the feature level and improves the generalization of the main path. Moreover, our NormAUG method effectively reduces the existing upper boundary for generalization based on theoretical perspectives. During the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance. Extensive experiments are conducted on multiple benchmark datasets to validate the effectiveness of our proposed method.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1840,
+    "label": 30,
+    "text": "Title: StarCoder: may the source be with you!\nAbstract: The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python, can be prompted to achieve 40\\% pass@1 on HumanEval, and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.",
+    "neighbors": [
+      240,
+      268,
+      855,
+      1052,
+      1114,
+      1128,
+      1171,
+      1249,
+      1490,
+      1556,
+      1606,
+      1879,
+      1907,
+      1950
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1841,
+    "label": 30,
+    "text": "Title: Enhancing Language Representation with Constructional Information for Natural Language Understanding\nAbstract: Natural language understanding (NLU) is an essential branch of natural language processing, which relies on representations generated by pre-trained language models (PLMs). However, PLMs primarily focus on acquiring lexico-semantic information, while they may be unable to adequately handle the meaning of constructions. To address this issue, we introduce construction grammar (CxG), which highlights the pairings of form and meaning, to enrich language representation. We adopt usage-based construction grammar as the basis of our work, which is highly compatible with statistical models such as PLMs. Then a HyCxG framework is proposed to enhance language representation through a three-stage solution. First, all constructions are extracted from sentences via a slot-constraints approach. As constructions can overlap with each other, bringing redundancy and imbalance, we formulate the conditional max coverage problem for selecting the discriminative constructions. Finally, we propose a relational hypergraph attention network to acquire representation from constructional information by capturing high-order word interactions among constructions. Extensive experiments demonstrate the superiority of the proposed model on a variety of NLU tasks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1842,
+    "label": 6,
+    "text": "Title: Multitasking While Driving: How Drivers Self-Regulate Their Interaction with In-Vehicle Touchscreens in Automated Driving\nAbstract: Abstract Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers\u2019 interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers\u2019 self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems.",
+    "neighbors": [
+      1433
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1843,
+    "label": 7,
+    "text": "Title: NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations\nAbstract: We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP) constitutive modeling framework for predicting the flow response in metals as a function of underlying grain size. The developed NN-EVP algorithm is based on input convex neural networks as a means to strictly enforce thermodynamic consistency, while allowing high expressivity towards model discovery from limited data. It utilizes state-of-the-art machine learning tools within PyTorch's high-performance library providing a flexible tool for data-driven, automated constitutive modeling. To test the performance of the framework, we generate synthetic stress-strain curves using a power law-based model with phenomenological hardening at small strains and test the trained model for strain amplitudes beyond the training data. Next, experimentally measured flow responses obtained from uniaxial deformations are used to train the framework under large plastic deformations. Ultimately, the Hall-Petch relationship corresponding to grain size strengthening is discovered by training flow response as a function of grain size, also leading to efficient extrapolation. The present work demonstrates a successful integration of neural networks into elasto-viscoplastic constitutive laws, providing a robust automated framework for constitutive model discovery that can efficiently generalize, while also providing insights into predictions of flow response and grain size-property relationships in metals and metallic alloys under large plastic deformations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1844,
+    "label": 28,
+    "text": "Title: A Simple Multiple-Access Design for Reconfigurable Intelligent Surface-Aided Systems\nAbstract: This paper focuses on the design of transmission methods and reflection optimization for a wireless system assisted by a single or multiple reconfigurable intelligent surfaces (RISs). The existing techniques are either too complex to implement in practical systems or too inefficient to achieve high performance. To overcome the shortcomings of the existing schemes, we propose a simple but efficient approach based on \\textit{opportunistic reflection} and \\textit{non-orthogonal transmission}. The key idea is opportunistically selecting the best user that can reap the maximal gain from the optimally reflected signals via RIS. That is to say, only the channel state information of the best user is used for RIS reflection optimization, which can in turn lower complexity substantially. In addition, the second user is selected to superpose its signal on that of the primary user, where the benefits of non-orthogonal transmission, i.e., high system capacity and improved user fairness, are obtained. Additionally, a simplified variant exploiting random phase shifts is proposed to avoid the high overhead of RIS channel estimation.",
+    "neighbors": [
+      765,
+      1756
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1845,
+    "label": 10,
+    "text": "Title: Explainability is NOT a Game\nAbstract: Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of relative feature importance, which are theoretically justified through the use of Shapley values. This paper builds on recent work and offers a simple argument for why Shapley values can provide misleading measures of relative feature importance, by assigning more importance to features that are irrelevant for a prediction, and assigning less importance to features that are relevant for a prediction. The significance of these results is that they effectively challenge the many proposed uses of measures of relative feature importance in a fast-growing range of high-stakes application domains.",
+    "neighbors": [
+      1403
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1846,
+    "label": 24,
+    "text": "Title: Towards Interpretable Solar Flare Prediction with Attention-based Deep Neural Networks\nAbstract: Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecasting. In this work, we developed an attention-based deep learning model as an improvement over the standard convolutional neural network (CNN) pipeline to perform full-disk binary flare predictions for the occurrence of $\\geq$M1.0-class flares within the next 24 hours. For this task, we collected compressed images created from full-disk line-of-sight (LoS) magnetograms. We used data-augmented oversampling to address the class imbalance issue and used true skill statistic (TSS) and Heidke skill score (HSS) as the evaluation metrics. Furthermore, we interpreted our model by overlaying attention maps on input magnetograms and visualized the important regions focused on by the model that led to the eventual decision. The significant findings of this study are: (i) We successfully implemented an attention-based full-disk flare predictor ready for operational forecasting where the candidate model achieves an average TSS=0.54$\\pm$0.03 and HSS=0.37$\\pm$0.07. (ii) we demonstrated that our full-disk model can learn conspicuous features corresponding to active regions from full-disk magnetogram images, and (iii) our experimental evaluation suggests that our model can predict near-limb flares with adept skill and the predictions are based on relevant active regions (ARs) or AR characteristics from full-disk magnetograms.",
+    "neighbors": [
+      1329
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1847,
+    "label": 28,
+    "text": "Title: RIS-Enabled Smart Wireless Environments: Deployment Scenarios, Network Architecture, Bandwidth and Area of Influence\nAbstract: Reconfigurable Intelligent Surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments, and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localization, and sustainability requirements of next generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardized interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signaling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflectarray measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth and area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future networks.",
+    "neighbors": [
+      1230
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1848,
+    "label": 27,
+    "text": "Title: Optimal Initialization Strategies for Range-Only Trajectory Estimation\nAbstract: Range-only (RO) pose estimation involves determining a robot's pose over time by measuring the distance between multiple devices on the robot, known as tags, and devices installed in the environment, known as anchors. The nonconvex nature of the range measurement model results in a cost function with possible local minima. In the absence of a good initialization, commonly used iterative solvers can get stuck in these local minima resulting in poor trajectory estimation accuracy. In this work, we propose convex relaxations to the original nonconvex problem based on semidefinite programs (SDPs). Specifically, we formulate computationally tractable SDP relaxations to obtain accurate initial pose and trajectory estimates for RO trajectory estimation under static and dynamic (i.e., constant-velocity motion) conditions. Through simulation and real experiments, we demonstrate that our proposed initialization strategies estimate the initial state accurately compared to iterative local solvers. Additionally, the proposed relaxations recover global minima under moderate range measurement noise levels.",
+    "neighbors": [
+      1224,
+      1443
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1849,
+    "label": 30,
+    "text": "Title: End-to-End Simultaneous Speech Translation with Differentiable Segmentation\nAbstract: End-to-end simultaneous speech translation (SimulST) outputs translation while receiving the streaming speech inputs (a.k.a. streaming speech translation), and hence needs to segment the speech inputs and then translate based on the current received speech. However, segmenting the speech inputs at unfavorable moments can disrupt the acoustic integrity and adversely affect the performance of the translation model. Therefore, learning to segment the speech inputs at those moments that are beneficial for the translation model to produce high-quality translation is the key to SimulST. Existing SimulST methods, either using the fixed-length segmentation or external segmentation model, always separate segmentation from the underlying translation model, where the gap results in segmentation outcomes that are not necessarily beneficial for the translation process. In this paper, we propose Differentiable Segmentation (DiSeg) for SimulST to directly learn segmentation from the underlying translation model. DiSeg turns hard segmentation into differentiable through the proposed expectation training, enabling it to be jointly trained with the translation model and thereby learn translation-beneficial segmentation. Experimental results demonstrate that DiSeg achieves state-of-the-art performance and exhibits superior segmentation capability.",
+    "neighbors": [
+      798
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1850,
+    "label": 13,
+    "text": "Title: Informed Down-Sampled Lexicase Selection: Identifying productive training cases for efficient problem solving\nAbstract: Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases allowing for more individuals to be explored with the same amount of program executions. However, creating a down-sample randomly might exclude important cases from the current down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused despite their redundancy. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while also benefiting from reduced per-evaluation costs.",
+    "neighbors": [
+      157,
+      1017
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1851,
+    "label": 24,
+    "text": "Title: A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection\nAbstract: Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.",
+    "neighbors": [
+      1544,
+      1799,
+      2057,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1852,
+    "label": 34,
+    "text": "Title: Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries\nAbstract: In fully dynamic clustering problems, a clustering of a given data set in a metric space must be maintained while it is modified through insertions and deletions of individual points. In this paper, we resolve the complexity of fully dynamic $k$-center clustering against both adaptive and oblivious adversaries. Against oblivious adversaries, we present the first algorithm for fully dynamic $k$-center in an arbitrary metric space that maintains an optimal $(2+\\epsilon)$-approximation in $O(k \\cdot \\mathrm{polylog}(n,\\Delta))$ amortized update time. Here, $n$ is an upper bound on the number of active points at any time, and $\\Delta$ is the aspect ratio of the metric space. Previously, the best known amortized update time was $O(k^2\\cdot \\mathrm{polylog}(n,\\Delta))$, and is due to Chan, Gourqin, and Sozio (2018). Moreover, we demonstrate that our runtime is optimal up to $\\mathrm{polylog}(n,\\Delta)$ factors. In fact, we prove that even offline algorithms for $k$-clustering tasks in arbitrary metric spaces, including $k$-medians, $k$-means, and $k$-center, must make at least $\\Omega(n k)$ distance queries to achieve any non-trivial approximation factor. This implies a lower bound of $\\Omega(k)$ which holds even for the insertions-only setting. We also show deterministic lower and upper bounds for adaptive adversaries, demonstrate that an update time sublinear in $k$ is possible against oblivious adversaries for metric spaces which admit locally sensitive hash functions (LSH) and give the first fully dynamic $O(1)$-approximation algorithms for the closely related $k$-sum-of-radii and $k$-sum-of-diameter problems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1853,
+    "label": 27,
+    "text": "Title: Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications\nAbstract: This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections. This same approach of HIL testing followed by testing in real intersections using our experimental vehicle is later extended to the GLOSA application. The GLOSA application that is tested in this paper has both an optimal speed advisory for passing at the green light and also includes a red light violation warning system. The paper presents the HIL and experimental vehicle evaluation systems, information about RLVW and GLOSA and HIL simulation and road testing results and their interpretations.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1854,
+    "label": 27,
+    "text": "Title: HISSbot: Sidewinding with a Soft Snake Robot\nAbstract: Snake robots are characterized by their ability to navigate through small spaces and loose terrain by utilizing efficient cyclic forms of locomotion. Soft snake robots are a subset of these robots which utilize soft, compliant actuators to produce movement. Prior work on soft snake robots has primarily focused on planar gaits, such as undulation. More efficient spatial gaits, such as sidewinding, are unexplored gaits for soft snake robots. We propose a novel means of constructing a soft snake robot capable of sidewinding, and introduce the Helical Inflating Soft Snake Robot (HISSbot). We validate this actuation through the physical HISSbot, and demonstrate its ability to sidewind across various surfaces. Our tests show robustness in locomotion through low-friction and granular media.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1855,
+    "label": 4,
+    "text": "Title: Smart Contract and DeFi Security: Insights from Tool Evaluations and Practitioner Surveys\nAbstract: The growth of the decentralized finance (DeFi) ecosystem built on blockchain technology and smart contracts has led to an increased demand for secure and reliable smart contract development. However, attacks targeting smart contracts are increasing, causing an estimated \\$6.45 billion in financial losses. Researchers have proposed various automated security tools to detect vulnerabilities, but their real-world impact remains uncertain. In this paper, we aim to shed light on the effectiveness of automated security tools in identifying vulnerabilities that can lead to high-profile attacks, and their overall usage within the industry. Our comprehensive study encompasses an evaluation of five SoTA automated security tools, an analysis of 127 high-impact real-world attacks resulting in \\$2.3 billion in losses, and a survey of 49 developers and auditors working in leading DeFi protocols. Our findings reveal a stark reality: the tools could have prevented a mere 8% of the attacks in our dataset, amounting to \\$149 million out of the \\$2.3 billion in losses. Notably, all preventable attacks were related to reentrancy vulnerabilities. Furthermore, practitioners distinguish logic-related bugs and protocol layer vulnerabilities as significant threats that are not adequately addressed by existing security tools. Our results emphasize the need to develop specialized tools catering to the distinct demands and expectations of developers and auditors. Further, our study highlights the necessity for continuous advancements in security tools to effectively tackle the ever-evolving challenges confronting the DeFi ecosystem.",
+    "neighbors": [
+      2163,
+      2299
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1856,
+    "label": 16,
+    "text": "Title: Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection\nAbstract: Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major challenge in achieving satisfactory performance for these tasks in the real world is the notable imbalance in data distribution. For instance, in fashion attribute detection, there may be only six 'puff sleeve' clothes among 1000 products in most E-commerce fashion catalogs. To address this issue, we explore more data-efficient model training techniques rather than acquiring a huge amount of annotations to collect sufficient samples, which is neither economic nor scalable. In this paper, we propose a state-of-the-art weighted objective function to boost the performance of deep neural networks (DNNs) for multi-label classification with long-tailed data distribution. Our experiments involve image-based attribute classification of fashion apparels, and the results demonstrate favorable performance for the new weighting method compared to non-weighted and inverse-frequency-based weighting mechanisms. We further evaluate the robustness of the new weighting mechanism using two popular fashion attribute types in today's fashion industry: sleevetype and archetype.",
+    "neighbors": [
+      1096,
+      2241
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1857,
+    "label": 16,
+    "text": "Title: Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1858,
+    "label": 16,
+    "text": "Title: Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach\nAbstract: The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79% to 100% for Region of Interest (ROI) without location classifications, 73.98% to 100% for ROI with location classifications, and 78.10% to 100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1859,
+    "label": 3,
+    "text": "Title: Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems through Natural Interaction and Text Mining\nAbstract: Amid Education 4.0 and the Fourth Industrial Revolution, we explore the integration of self-directed learning within the metaverse. This study envisions empowered learners, merging the metaverse\u2019s immersive potential with self-directed learning. Using text mining and co-occurrence analysis for student responses prompts questions about their preference over traditional methods. Text mining extracts nuanced insights from open-ended responses, surpassing structured data. Co-occurrence analysis reveals hidden concept relationships, enhancing student engagement and understanding. Beyond XR, the metaverse encompasses avatars, virtual experiences, and value systems. Educators navigate this landscape with text mining, shaping value exchange through engaging content. Integrating real-world experiences in the metaverse merges immersion and personalized learning. Challenges include data precision and semantic intricacies in co-occurrence graphs. Future solutions involve real- time adaptability and sentiment analysis for holistic insights into learner emotions. This study envisions a synergy of self- directed learning and the metaverse, bridging digital and physical realms. Learners navigate interconnected experiences, fostering autonomy. Uncovering the metaverse\u2019s potential contributes to education for digitally adept learners.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1860,
+    "label": 24,
+    "text": "Title: Federated Domain Generalization: A Survey\nAbstract: Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future.",
+    "neighbors": [
+      1584
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1861,
+    "label": 13,
+    "text": "Title: NeuSort: an automatic adaptive spike sorting approach with neuromorphic models\nAbstract: Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort\u2019s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1862,
+    "label": 8,
+    "text": "Title: Cell-Free mMIMO Support in the O-RAN Architecture: A PHY Layer Perspective for 5G and Beyond Networks\nAbstract: To keep supporting next-generation requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) network architectures are emerging, combining dense deployments with extreme flexibility in allocating resources to users. In parallel, the Open Radio Access Networks (O-RAN) paradigm is transforming RAN toward an open, intelligent, virtualized, and fully interoperable architecture. This paradigm brings the needed flexibility and intelligent control opportunities for CF networking. In this article, we document the current O-RAN terminology and contrast it with some common CF processing approaches. We then discuss the main O-RAN innovations and research challenges that remain to be solved.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1863,
+    "label": 30,
+    "text": "Title: GPT-4 Technical Report\nAbstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1864,
+    "label": 10,
+    "text": "Title: The Optimal Choice of Hypothesis Is the Weakest, Not the Shortest\nAbstract: If $A$ and $B$ are sets such that $A \\subset B$, generalisation may be understood as the inference from $A$ of a hypothesis sufficient to construct $B$. One might infer any number of hypotheses from $A$, yet only some of those may generalise to $B$. How can one know which are likely to generalise? One strategy is to choose the shortest, equating the ability to compress information with the ability to generalise (a proxy for intelligence). We examine this in the context of a mathematical formalism of enactive cognition. We show that compression is neither necessary nor sufficient to maximise performance (measured in terms of the probability of a hypothesis generalising). We formulate a proxy unrelated to length or simplicity, called weakness. We show that if tasks are uniformly distributed, then there is no choice of proxy that performs at least as well as weakness maximisation in all tasks while performing strictly better in at least one. In experiments comparing maximum weakness and minimum description length in the context of binary arithmetic, the former generalised at between $1.1$ and $5$ times the rate of the latter. We argue this demonstrates that weakness is a far better proxy, and explains why Deepmind's Apperception Engine is able to generalise effectively.",
+    "neighbors": [
+      701,
+      1704
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1865,
+    "label": 28,
+    "text": "Title: Performance Analysis of RIS-Aided Space Shift Keying With Channel Estimation Errors\nAbstract: In this paper, we investigate the reconfigurable intelligent surface (RIS) assisted space shift keying (SSK) downlink communication systems under the imperfect channel state information (CSI), where the channel between the base station to RIS follows the Rayleigh fading, while the channel between the RIS to user equipment obeys the Rician fading. Based on the maximum likelihood detector, the conditional pairwise error probability of the composite channel is derived. Then, the probability density function for a non-central chi-square distribution with one degree of freedom is derived. Based on this, the closed-form analytical expression of the RIS-SSK scheme with imperfect CSI is derived. To gain more valuable insights, the asymptotic ABEP expression is also given. Finally, we validate the derived closed-form and asymptotic expressions by Monte Carlo simulations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1866,
+    "label": 16,
+    "text": "Title: NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM\nAbstract: Neural implicit representations have recently become popular in simultaneous localization and mapping (SLAM), especially in dense visual SLAM. However, previous works in this direction either rely on RGB-D sensors, or require a separate monocular SLAM approach for camera tracking and do not produce high-fidelity dense 3D scene reconstruction. In this paper, we present NICER-SLAM, a dense RGB SLAM system that simultaneously optimizes for camera poses and a hierarchical neural implicit map representation, which also allows for high-quality novel view synthesis. To facilitate the optimization process for mapping, we integrate additional supervision signals including easy-to-obtain monocular geometric cues and optical flow, and also introduce a simple warping loss to further enforce geometry consistency. Moreover, to further boost performance in complicated indoor scenes, we also propose a local adaptive transformation from signed distance functions (SDFs) to density in the volume rendering equation. On both synthetic and real-world datasets we demonstrate strong performance in dense mapping, tracking, and novel view synthesis, even competitive with recent RGB-D SLAM systems.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1867,
+    "label": 24,
+    "text": "Title: Dissecting Chain-of-Thought: A Study on Compositional In-Context Learning of MLPs\nAbstract: Chain-of-thought (CoT) is a method that enables language models to handle complex reasoning tasks by decomposing them into simpler steps. Despite its success, the underlying mechanics of CoT are not yet fully understood. In an attempt to shed light on this, our study investigates the impact of CoT on the ability of transformers to in-context learn a simple to study, yet general family of compositional functions: multi-layer perceptrons (MLPs). In this setting, we reveal that the success of CoT can be attributed to breaking down in-context learning of a compositional function into two distinct phases: focusing on data related to each step of the composition and in-context learning the single-step composition function. Through both experimental and theoretical evidence, we demonstrate how CoT significantly reduces the sample complexity of in-context learning (ICL) and facilitates the learning of complex functions that non-CoT methods struggle with. Furthermore, we illustrate how transformers can transition from vanilla in-context learning to mastering a compositional function with CoT by simply incorporating an additional layer that performs the necessary filtering for CoT via the attention mechanism. In addition to these test-time benefits, we highlight how CoT accelerates pretraining by learning shortcuts to represent complex functions and how filtering plays an important role in pretraining. These findings collectively provide insights into the mechanics of CoT, inviting further investigation of its role in complex reasoning tasks.",
+    "neighbors": [
+      342,
+      1946
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1868,
+    "label": 24,
+    "text": "Title: Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach\nAbstract: Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single predictor can be highly variable due to shifts in the underlying data distribution. This paper proposes a new methodology for building robust ensembles of time series forecasting models. Our approach utilizes Adaptive Robust Optimization (ARO) to construct a linear regression ensemble in which the models' weights can adapt over time. We demonstrate the effectiveness of our method through a series of synthetic experiments and real-world applications, including air pollution management, energy consumption forecasting, and tropical cyclone intensity forecasting. Our results show that our adaptive ensembles outperform the best ensemble member in hindsight by 16-26% in root mean square error and 14-28% in conditional value at risk and improve over competitive ensemble techniques.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1869,
+    "label": 25,
+    "text": "Title: Intelligible Lip-to-Speech Synthesis with Speech Units\nAbstract: In this paper, we propose a novel Lip-to-Speech synthesis (L2S) framework, for synthesizing intelligible speech from a silent lip movement video. Specifically, to complement the insufficient supervisory signal of the previous L2S model, we propose to use quantized self-supervised speech representations, named speech units, as an additional prediction target for the L2S model. Therefore, the proposed L2S model is trained to generate multiple targets, mel-spectrogram and speech units. As the speech units are discrete while mel-spectrogram is continuous, the proposed multi-target L2S model can be trained with strong content supervision, without using text-labeled data. Moreover, to accurately convert the synthesized mel-spectrogram into a waveform, we introduce a multi-input vocoder that can generate a clear waveform even from blurry and noisy mel-spectrogram by referring to the speech units. Extensive experimental results confirm the effectiveness of the proposed method in L2S.",
+    "neighbors": [
+      12
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1870,
+    "label": 24,
+    "text": "Title: Finding Regions of Counterfactual Explanations via Robust Optimization\nAbstract: Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not be achievable for the user. In this work we derive an iterative method to calculate robust CEs, i.e. CEs that remain valid even after the features are slightly perturbed. To this end, our method provides a whole region of CEs allowing the user to choose a suitable recourse to obtain a desired outcome. We use algorithmic ideas from robust optimization and prove convergence results for the most common machine learning methods including logistic regression, decision trees, random forests, and neural networks. Our experiments show that our method can efficiently generate globally optimal robust CEs for a variety of common data sets and classification models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1871,
+    "label": 27,
+    "text": "Title: Learning Off-Road Terrain Traversability With Self-Supervisions Only\nAbstract: Estimating the traversability of terrain should be reliable and accurate in diverse conditions for autonomous driving in off-road environments. However, learning-based approaches often yield unreliable results when confronted with unfamiliar contexts, and it is challenging to obtain manual annotations frequently for new circumstances. In this letter, we introduce a method for learning traversability from images that utilizes only self-supervision and no manual labels, enabling it to easily learn traversability in new circumstances. To this end, we first generate self-supervised traversability labels from past driving trajectories by labeling regions traversed by the vehicle as highly traversable. Using the self-supervised labels, we then train a neural network that identifies terrains that are safe to traverse from an image using a one-class classification algorithm. Additionally, we supplement the limitations of self-supervised labels by incorporating methods of self-supervised learning of visual representations. To conduct a comprehensive evaluation, we collect data in a variety of driving environments and perceptual conditions and show that our method produces reliable estimations in various environments. In addition, the experimental results validate that our method outperforms other self-supervised traversability estimation methods and achieves comparable performances with supervised learning methods trained on manually labeled data.",
+    "neighbors": [
+      2188
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1872,
+    "label": 16,
+    "text": "Title: ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning\nAbstract: In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-attention ViT to squeeze the best of the data. We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions. To ensure correct label classification and improve the models' discriminative power, we introduce anchor loss, which encourages large margins between anchor points. Additionally, the multi-head self-attention mechanism, which is also trainable, plays an integral role in identifying accurate labels. This approach provides critical elements for improving the reliability of predictions and has a substantial positive effect on final prediction capabilities. Our adaptive model can be integrated with any deep neural network to forestall challenges in various recognition tasks. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.",
+    "neighbors": [
+      1265,
+      1541
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1873,
+    "label": 16,
+    "text": "Title: Better Diffusion Models Further Improve Adversarial Training\nAbstract: It has been recognized that the data generated by the denoising diffusion probabilistic model (DDPM) improves adversarial training. After two years of rapid development in diffusion models, a question naturally arises: can better diffusion models further improve adversarial training? This paper gives an affirmative answer by employing the most recent diffusion model which has higher efficiency ($\\sim 20$ sampling steps) and image quality (lower FID score) compared with DDPM. Our adversarially trained models achieve state-of-the-art performance on RobustBench using only generated data (no external datasets). Under the $\\ell_\\infty$-norm threat model with $\\epsilon=8/255$, our models achieve $70.69\\%$ and $42.67\\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i.e. improving upon previous state-of-the-art models by $+4.58\\%$ and $+8.03\\%$. Under the $\\ell_2$-norm threat model with $\\epsilon=128/255$, our models achieve $84.86\\%$ on CIFAR-10 ($+4.44\\%$). These results also beat previous works that use external data. We also provide compelling results on the SVHN and TinyImageNet datasets. Our code is available at https://github.com/wzekai99/DM-Improves-AT.",
+    "neighbors": [
+      1802
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1874,
+    "label": 24,
+    "text": "Title: Privacy-Preserving Taxi-Demand Prediction Using Federated Learning\nAbstract: Taxi-demand prediction is an important application of machine learning that enables taxi-providing facilities to optimize their operations and city planners to improve transportation infrastructure and services. However, the use of sensitive data in these systems raises concerns about privacy and security. In this paper, we propose the use of federated learning for taxi-demand prediction that allows multiple parties to train a machine learning model on their own data while keeping the data private and secure. This can enable organizations to build models on data they otherwise would not be able to access. Evaluation with real-world data collected from 16 taxi service providers in Japan over a period of six months showed that the proposed system can predict the demand level accurately within 1% error compared to a single model trained with integrated data.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1875,
+    "label": 23,
+    "text": "Title: A model-driven approach for continuous performance engineering in microservice-based systems\nAbstract: nan",
+    "neighbors": [
+      403,
+      1328
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1876,
+    "label": 16,
+    "text": "Title: S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification\nAbstract: Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian point coordinates can be employed to learn features. When input point clouds are not aligned, the classification performance drops significantly. In this work, we focus on a mathematically transparent point cloud classification method called PointHop, analyze its reason for failure due to pose variations, and solve the problem by replacing its pose dependent modules with rotation invariant counterparts. The proposed method is named SO(3)-Invariant PointHop (or S3I-PointHop in short). We also significantly simplify the PointHop pipeline using only one single hop along with multiple spatial aggregation techniques. The idea of exploiting more spatial information is novel. Experiments on the ModelNet40 dataset demonstrate the superiority of S3I-PointHop over traditional PointHop-like methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1877,
+    "label": 10,
+    "text": "Title: LLM+P: Empowering Large Language Models with Optimal Planning Proficiency\nAbstract: Large language models (LLMs) have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life. However, so far, LLMs cannot reliably solve long-horizon planning problems. By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal, plans. In an effort to get the best of both worlds, this paper introduces LLM+P, the first framework that incorporates the strengths of classical planners into LLMs. LLM+P takes in a natural language description of a planning problem, then returns a correct (or optimal) plan for solving that problem in natural language. LLM+P does so by first converting the language description into a file written in the planning domain definition language (PDDL), then leveraging classical planners to quickly find a solution, and then translating the found solution back into natural language. Along with LLM+P, we define a diverse set of different benchmark problems taken from common planning scenarios. Via a comprehensive set of experiments on these benchmark problems, we find that LLM+P is able to provide optimal solutions for most problems, while LLMs fail to provide even feasible plans for most problems.\\footnote{The code and results are publicly available at https://github.com/Cranial-XIX/llm-pddl.git.",
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1878,
+    "label": 10,
+    "text": "Title: A Survey on Large Language Model based Autonomous Agents\nAbstract: Autonomous agents have long been a prominent research focus in both academic and industry communities. Previous research in this field often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and thus makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of web knowledge, large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This has sparked an upsurge in studies investigating LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective. More specifically, we first discuss the construction of LLM-based autonomous agents, for which we propose a unified framework that encompasses a majority of the previous work. Then, we present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. To keep track of this field and continuously update our survey, we maintain a repository of relevant references at https://github.com/Paitesanshi/LLM-Agent-Survey.",
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1879,
+    "label": 30,
+    "text": "Title: CodeT5+: Open Code Large Language Models for Code Understanding and Generation\nAbstract: Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downstream tasks. The former paradigm is limited by inflexibility in applications while in the latter, the model is treated as a single system for all tasks, leading to suboptimal performance on a subset of tasks. Secondly, they often employ a limited set of pretraining objectives which might not be relevant to some downstream tasks and hence result in substantial performance degrade. To address these limitations, we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which component modules can be flexibly combined to suit a wide range of downstream code tasks. Such flexibility is enabled by our proposed mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy. These objectives cover span denoising, contrastive learning, text-code matching, and causal LM pretraining tasks, on both unimodal and bimodal multilingual code corpora. Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs without training from scratch to efficiently scale up our models, and explore instruction-tuning to align with natural language instructions. We extensively evaluate CodeT5+ on over 20 code-related benchmarks in different settings, including zero-shot, finetuning, and instruction-tuning. We observe state-of-the-art (SoTA) model performance on various code-related tasks, such as code generation and completion, math programming, and text-to-code retrieval tasks. Particularly, our instruction-tuned CodeT5+ 16B achieves new SoTA results on HumanEval code generation task against other open code LLMs.",
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+      1907
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1880,
+    "label": 18,
+    "text": "Title: Optimizations for a Current-Controlled Memristor-based Neuromorphic Synapse Design\nAbstract: The synapse is a key element of neuromorphic computing in terms of efficiency and accuracy. In this paper, an optimized current-controlled memristive synapse circuit is proposed. Our proposed synapse demonstrates reliability in the face of process variation and the inherent stochastic behavior of memristors. Up to an 82% energy optimization can be seen during the SET operation over prior work. In addition, the READ process shows up to 54% energy savings. Our current-controlled approach also provides more reliable programming over traditional programming methods. This design is demonstrated with a 4-bit memory precision configuration. Using a spiking neural network (SNN), a neuromorphic application analysis was performed with this precision configuration. Our optimized design showed up to 82% improvement in control applications and a 2.7x improvement in classification applications compared with other design cases.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1881,
+    "label": 24,
+    "text": "Title: Competing for Shareable Arms in Multi-Player Multi-Armed Bandits\nAbstract: Competitions for shareable and limited resources have long been studied with strategic agents. In reality, agents often have to learn and maximize the rewards of the resources at the same time. To design an individualized competing policy, we model the competition between agents in a novel multi-player multi-armed bandit (MPMAB) setting where players are selfish and aim to maximize their own rewards. In addition, when several players pull the same arm, we assume that these players averagely share the arms' rewards by expectation. Under this setting, we first analyze the Nash equilibrium when arms' rewards are known. Subsequently, we propose a novel Selfish MPMAB with Averaging Allocation (SMAA) approach based on the equilibrium. We theoretically demonstrate that SMAA could achieve a good regret guarantee for each player when all players follow the algorithm. Additionally, we establish that no single selfish player can significantly increase their rewards through deviation, nor can they detrimentally affect other players' rewards without incurring substantial losses for themselves. We finally validate the effectiveness of the method in extensive synthetic experiments.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1882,
+    "label": 24,
+    "text": "Title: Spatial Attention Kinetic Networks with E(n)-Equivariance\nAbstract: Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling for tasks such as accurately but inexpensively modeling complex potential energy surfaces to guiding the sampling of complex dynamical systems or forecasting their time evolution. Current state-of-the-art methods employ spherical harmonics to encode higher-order interactions among particles, which are computationally expensive. In this paper, we propose a simple alternative functional form that uses neurally parametrized linear combinations of edge vectors to achieve equivariance while still universally approximating node environments. Incorporating this insight, we design spatial attention kinetic networks with E(n)-equivariance, or SAKE, which are competitive in many-body system modeling tasks while being significantly faster.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1883,
+    "label": 27,
+    "text": "Title: Embedded Object Detection and Mapping in Soft Materials Using Optical Tactile Sensing\nAbstract: In this paper, we present a methodology that uses an optical tactile sensor for efficient tactile exploration of embedded objects within soft materials. The methodology consists of an exploration phase, where a probabilistic estimate of the location of the embedded objects is built using a Bayesian approach. The exploration phase is then followed by a mapping phase which exploits the probabilistic map to reconstruct the underlying topography of the workspace by sampling in more detail regions where there is expected to be embedded objects. To demonstrate the effectiveness of the method, we tested our approach on an experimental setup that consists of a series of quartz beads located underneath a polyethylene foam that prevents direct observation of the configuration and requires the use of tactile exploration to recover the location of the beads. We show the performance of our methodology using ten different configurations of the beads where the proposed approach is able to approximate the underlying configuration. We benchmark our results against a random sampling policy.",
+    "neighbors": [
+      1712
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1884,
+    "label": 27,
+    "text": "Title: Deployment of Reliable Visual Inertial Odometry Approaches for Unmanned Aerial Vehicles in Real-world Environment\nAbstract: Integration of Visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1885,
+    "label": 5,
+    "text": "Title: Is RISC-V ready for HPC prime-time: Evaluating the 64-core Sophon SG2042 RISC-V CPU\nAbstract: The Sophon SG2042 is the world's first commodity 64-core RISC-V CPU for high performance workloads and an important question is whether the SG2042 has the potential to encourage the HPC community to embrace RISC-V. In this paper we undertaking a performance exploration of the SG2042 against existing RISC-V hardware and high performance x86 CPUs in use by modern supercomputers. Leveraging the RAJAPerf benchmarking suite, we discover that on average, the SG2042 delivers, per core, between five and ten times the performance compared to the nearest widely available RISC-V hardware. We found that, on average, the x86 high performance CPUs under test outperform the SG2042 by between four and eight times for multi-threaded workloads, although some individual kernels do perform faster on the SG2042. The result of this work is a performance study that not only contrasts this new RISC-V CPU against existing technologies, but furthermore shares performance best practice.",
+    "neighbors": [
+      910,
+      1134
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1886,
+    "label": 16,
+    "text": "Title: Efficient and Explicit Modelling of Image Hierarchies for Image Restoration\nAbstract: The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://github.com/ofsoundof/GRL-Image-Restoration.git.",
+    "neighbors": [
+      1652
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1887,
+    "label": 24,
+    "text": "Title: When No-Rejection Learning is Optimal for Regression with Rejection\nAbstract: Learning with rejection is a prototypical model for studying the interaction between humans and AI on prediction tasks. The model has two components, a predictor and a rejector. Upon the arrival of a sample, the rejector first decides whether to accept it; if accepted, the predictor fulfills the prediction task, and if rejected, the prediction will be deferred to humans. The learning problem requires learning a predictor and a rejector simultaneously. This changes the structure of the conventional loss function and often results in non-convexity and inconsistency issues. For the classification with rejection problem, several works develop surrogate losses for the jointly learning with provable consistency guarantees; in parallel, there has been less work for the regression counterpart. We study the regression with rejection (RwR) problem and investigate the no-rejection learning strategy which treats the RwR problem as a standard regression task to learn the predictor. We establish that the suboptimality of the no-rejection learning strategy observed in the literature can be mitigated by enlarging the function class of the predictor. Then we introduce the truncated loss to single out the learning for the predictor and we show that a consistent surrogate property can be established for the predictor individually in an easier way than for the predictor and the rejector jointly. Our findings advocate for a two-step learning procedure that first uses all the data to learn the predictor and then calibrates the prediction loss for the rejector. It is better aligned with the common intuition that more data samples will lead to a better predictor and it calls for more efforts on a better design of calibration algorithms for learning the rejector. While our discussions mainly focus on the regression problem, the theoretical results and insights generalize to the classification problem as well.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1888,
+    "label": 24,
+    "text": "Title: Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics\nAbstract: We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses. The system uses Deep Reinforcement Learning (DRL) to generate multi-objective policies for four different psychiatric conditions: anxiety, depression, schizophrenia, and suicidal cases. We present our experimental results on the accuracy of recommended topics using three different scales of working alliance ratings: task, bond, and goal. We show that the system is able to capture the real data (historical topics discussed by the therapists) relatively well, and that the best performing models vary by disorder and rating scale. To gain interpretable insights into the learned policies, we visualize policy trajectories in a 2D principal component analysis space and transition matrices. These visualizations reveal distinct patterns in the policies trained with different reward signals and trained on different clinical diagnoses. Our system\u2019s success in generating DIsorder-Specific Multi-Objective Policies (DISMOP) and interpretable policy dynamics demonstrates the potential of DRL in providing personalized and efficient therapeutic recommendations.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1889,
+    "label": 16,
+    "text": "Title: Effective pseudo-labeling based on heatmap for unsupervised domain adaptation in cell detection\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1890,
+    "label": 23,
+    "text": "Title: How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?\nAbstract: Although the open source model bears many advantages in software development, open source projects are always hard to sustain. Previous research on open source sustainability mainly focuses on projects that have already reached a certain level of maturity (e.g., with communities, releases, and downstream projects). However, limited attention is paid to the development of (sustainable) open source projects in their infancy, and we believe an understanding of early sustainability determinants is crucial for project initiators, incubators, newcomers, and users. In this paper, we aim to explore the relationship between early participation factors and long-term project sustainability. We leverage a novel methodology that measures the early participation of 290,255 GitHub projects during the first three months with reference to the Blumberg model, trains an XGBoost model to predict project's two-year sustained activity, and interprets the trained model using LIME. We quantitatively show that early participants have a positive effect on project's future sustained activity if they have prior experience in OSS project incubation and demonstrate concentrated focus and steady commitment. Participation from non-code contributors and detailed contribution documentation also promote project's sustained activity. Compared with individual projects, building a community that consists of more experienced core developers and more active peripheral developers is important for organizational projects. This study provides unique insights into the incubation and recognition of sustainable open source projects, and our interpretable prediction approach can also offer guidance to open source project initiators and newcomers.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1891,
+    "label": 4,
+    "text": "Title: Towards a Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD) technologies and communications\nAbstract: Aviation, maritime, and aerospace traffic control, radar, communication, and software technologies received increasing attention in the research literature over the past decade, as software-defined radios have enabled practical wireless attacks on communication links previously thought to be unreachable by unskilled or low-budget attackers. Moreover, recently it became apparent that both offensive and defensive cybersecurity has become a strategically differentiating factor for such technologies on the war fields (e.g., Ukraine), affecting both civilian and military missions regardless of their involvement. However, attacks and countermeasures are usually studied in simulated settings, thus introducing the lack of realism or non-systematic and highly customized practical setups, thus introducing high costs, overheads, and less reproducibility. Our\"Unified Cybersecurity Testing Lab\"seeks to close this gap by building a laboratory that can provide a systematic, affordable, highly-flexible, and extensible setup. In this paper, we introduce and motivate our\"Unified Cybersecurity Testing Lab for Satellite, Aerospace, Avionics, Maritime, Drone (SAAMD)\"technologies and communications, as well as some peer-reviewed results and evaluation of the targeted threat vectors. We show via referenced peer-reviewed works that the current modules of the lab were successfully used to realistically attack and analyze air-traffic control, radar, communication, and software technologies such as ADS-B, AIS, ACARS, EFB, EPIRB and COSPAS-SARSAT. We are currently developing and integrating support for additional technologies (e.g., CCSDS, FLARM), and we plan future extensions on our own as well as in collaboration with research and industry. Our\"Unified Cybersecurity Testing Lab\"is open for use, experimentation, and collaboration with other researchers, contributors and interested parties.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1892,
+    "label": 30,
+    "text": "Title: USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset\nAbstract: Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(POS) sentiment polarity determination. Such analysis challenges models to understand text holistically while also extracting nuanced information. With the rise of Large Language Models(LLMs), new avenues for sentiment analysis have opened. This paper proposes enhancing performance by leveraging the Mutual Reinforcement Effect(MRE) between individual words and the overall text. It delves into how word polarity influences the overarching sentiment of a passage. To support our research, we annotated four novel Sentiment Text Classification and Part of Speech(SCPOS) datasets, building upon existing sentiment classification datasets. Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a 7-billion parameter size. Experimental results revealed that our model surpassed the performance of gpt-3.5-turbo across all four datasets, underscoring the significance of MRE in sentiment analysis.",
+    "neighbors": [
+      566,
+      911,
+      1378
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1893,
+    "label": 16,
+    "text": "Title: ViperGPT: Visual Inference via Python Execution for Reasoning\nAbstract: Answering visual queries is a complex task that requires both visual processing and reasoning. End-to-end models, the dominant approach for this task, do not explicitly differentiate between the two, limiting interpretability and generalization. Learning modular programs presents a promising alternative, but has proven challenging due to the difficulty of learning both the programs and modules simultaneously. We introduce ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query. ViperGPT utilizes a provided API to access the available modules, and composes them by generating Python code that is later executed. This simple approach requires no further training, and achieves state-of-the-art results across various complex visual tasks.",
+    "neighbors": [
+      57,
+      173,
+      176,
+      618,
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+      1315,
+      1451,
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+      1878,
+      1990,
+      2155,
+      2166
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1894,
+    "label": 10,
+    "text": "Title: Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks\nAbstract: In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily\u2019s Garden1. We extract two condition-vectors from the real levels in an effort to control the details of the GAN\u2019s outputs. While the GANs performs well in approximating the first condition (map-shape), they struggle to approximate the second condition (piece distribution). We hypothesize that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1895,
+    "label": 6,
+    "text": "Title: INDCOR White Paper 2: Interactive Narrative Design for Representing Complexity\nAbstract: This white paper was written by the members of the Work Group focusing on design practices of the COST Action 18230 - Interactive Narrative Design for Complexity Representation (INDCOR, WG1). It presents an overview of Interactive Digital Narratives (IDNs) design for complexity representations through IDN workflows and methodologies, IDN authoring tools and applications. It provides definitions of the central elements of the IDN alongside its best practices, designs and methods. Finally, it describes complexity as a feature of IDN, with related examples. In summary, this white paper serves as an orienting map for the field of IDN design, understanding where we are in the contemporary panorama while charting the grounds of their promising futures.",
+    "neighbors": [
+      1979
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1896,
+    "label": 16,
+    "text": "Title: MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method\nAbstract: Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been proposed, Mixed Sample Data Augmentation (MSDA) has shown great potential for enhancing model performance and generalization. We introduce a novel mixup method called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMix integrates image augmentation into the mixup framework, utilizes multiple diversified mixing methods concurrently, and improves the mixing method by randomly selecting mixing mask augmentation methods. Recent methods utilize saliency information and the MiAMix is designed for computational efficiency as well, reducing additional overhead and offering easy integration into existing training pipelines. We comprehensively evaluate MiaMix using four image benchmarks and pitting it against current state-of-the-art mixed sample data augmentation techniques to demonstrate that MIAMix improves performance without heavy computational overhead.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1897,
+    "label": 6,
+    "text": "Title: A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI\nAbstract: As Artificial Intelligence (AI) continues to advance rapidly, it becomes increasingly important to consider AI's ethical and societal implications. In this paper, we present a bottom-up mapping of the current state of research at the intersection of Human-Centered AI, Ethical, and Responsible AI (HCER-AI) by thematically reviewing and analyzing 164 research papers from leading conferences in ethical, social, and human factors of AI: AIES, CHI, CSCW, and FAccT. The ongoing research in HCER-AI places emphasis on governance, fairness, and explainability. These conferences, however, concentrate on specific themes rather than encompassing all aspects. While AIES has fewer papers on HCER-AI, it emphasizes governance and rarely publishes papers about privacy, security, and human flourishing. FAccT publishes more on governance and lacks papers on privacy, security, and human flourishing. CHI and CSCW, as more established conferences, have a broader research portfolio. We find that the current emphasis on governance and fairness in AI research may not adequately address the potential unforeseen and unknown implications of AI. Therefore, we recommend that future research should expand its scope and diversify resources to prepare for these potential consequences. This could involve exploring additional areas such as privacy, security, human flourishing, and explainability.",
+    "neighbors": [
+      1389
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1898,
+    "label": 16,
+    "text": "Title: VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation\nAbstract: Conditional 3D generation is undergoing a significant advancement, enabling the free creation of 3D content from inputs such as text or 2D images. However, previous approaches have suffered from low inference efficiency, limited generation categories, and restricted downstream applications. In this work, we revisit the impact of different 3D representations on generation quality and efficiency. We propose a progressive generation method through Voxel-Point Progressive Representation (VPP). VPP leverages structured voxel representation in the proposed Voxel Semantic Generator and the sparsity of unstructured point representation in the Point Upsampler, enabling efficient generation of multi-category objects. VPP can generate high-quality 8K point clouds within 0.2 seconds. Additionally, the masked generation Transformer allows for various 3D downstream tasks, such as generation, editing, completion, and pre-training. Extensive experiments demonstrate that VPP efficiently generates high-fidelity and diverse 3D shapes across different categories, while also exhibiting excellent representation transfer performance. Codes will be released on https://github.com/qizekun/VPP.",
+    "neighbors": [
+      1125,
+      1226,
+      1710,
+      2205
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1899,
+    "label": 16,
+    "text": "Title: StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data\nAbstract: The remarkable multimodal capabilities demonstrated by OpenAI's GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructions. Current methodologies often rely on annotations derived from benchmark datasets to construct image-dialogue datasets for training purposes, akin to instruction tuning in LLMs. However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models. In an effort to mitigate these limitations, we propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models to yield a diverse and controllable dataset with varied image content. This not only provides greater flexibility compared to existing methodologies but also significantly enhances several model capabilities. Our research includes comprehensive experiments conducted on various datasets using the open-source LLAVA model as a testbed for our proposed pipeline. Our results underscore marked enhancements across more than ten commonly assessed capabilities,",
+    "neighbors": [
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+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1900,
+    "label": 16,
+    "text": "Title: Hierarchical Fine-Grained Image Forgery Detection and Localization\nAbstract: Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. Our proposed IFDL framework contains three components: multi-branch feature extractor, localization and classification modules. Each branch of the feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on 7 different benchmarks, for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found: github.com/CHELSEA234/HiFi-IFDL.",
+    "neighbors": [
+      154,
+      1730
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1901,
+    "label": 16,
+    "text": "Title: UniSim: A Neural Closed-Loop Sensor Simulator\nAbstract: Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be collected safely in the world, as many scenarios happen rarely on our roads. To accurately evaluate performance, we need to test the SDV on these scenarios in closed-loop, where the SDV and other actors interact with each other at each timestep. Previously recorded driving logs provide a rich resource to build these new scenarios from, but for closed loop evaluation, we need to modify the sensor data based on the new scene configuration and the SDV's decisions, as actors might be added or removed and the trajectories of existing actors and the SDV will differ from the original log. In this paper, we present UniSim, a neural sensor simulator that takes a single recorded log captured by a sensor-equipped vehicle and converts it into a realistic closed-loop multi-sensor simulation. UniSim builds neural feature grids to reconstruct both the static background and dynamic actors in the scene, and composites them together to simulate LiDAR and camera data at new viewpoints, with actors added or removed and at new placements. To better handle extrapolated views, we incorporate learnable priors for dynamic objects, and leverage a convolutional network to complete unseen regions. Our experiments show UniSim can simulate realistic sensor data with small domain gap on downstream tasks. With UniSim, we demonstrate, for the first time, closed-loop evaluation of an autonomy system on safety-critical scenarios as if it were in the real world.",
+    "neighbors": [
+      513,
+      1080
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1902,
+    "label": 16,
+    "text": "Title: Adding Conditional Control to Text-to-Image Diffusion Models\nAbstract: We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with\"zero convolutions\"(zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.",
+    "neighbors": [
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+      1906,
+      1935,
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+      2161,
+      2233,
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+      2303,
+      2306
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1903,
+    "label": 16,
+    "text": "Title: Component-aware anomaly detection framework for adjustable and logical industrial visual inspection\nAbstract: Industrial visual inspection aims at detecting surface defects in products during the manufacturing process. Although existing anomaly detection models have shown great performance on many public benchmarks, their limited adjustability and ability to detect logical anomalies hinder their broader use in real-world settings. To this end, in this paper, we propose a novel component-aware anomaly detection framework (ComAD) which can simultaneously achieve adjustable and logical anomaly detection for industrial scenarios. Specifically, we propose to segment images into multiple components based on a lightweight and nearly training-free unsupervised semantic segmentation model. Then, we design an interpretable logical anomaly detection model through modeling the metrological features of each component and their relationships. Despite its simplicity, our framework achieves state-of-the-art performance on image-level logical anomaly detection. Meanwhile, segmenting a product image into multiple components provides a novel perspective for industrial visual inspection, demonstrating great potential in model customization, noise resistance, and anomaly classification. The code will be available at https://github.com/liutongkun/ComAD.",
+    "neighbors": [
+      1255,
+      2098,
+      2106
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1904,
+    "label": 3,
+    "text": "Title: Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods\nAbstract: The United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm implemented for the 1990, 2000, and 2010 Censuses. Our evaluation leverages the recent release of the Noisy Measure File (NMF) as well as the availability of two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful alone, especially for Hispanic and multiracial populations. TopDown's post-processing dramatically reduces the NMF noise and produces similarly accurate data to swapping in terms of bias and noise. These patterns hold across census geographies with varying population sizes and racial diversity. While the estimated errors for both TopDown and swapping are generally no larger than other sources of Census error, they can be relatively substantial for geographies with small total populations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1905,
+    "label": 16,
+    "text": "Title: TEXTure: Text-Guided Texturing of 3D Shapes\nAbstract: In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D model from different viewpoints. Yet, while depth-to-image models can create plausible textures from a single viewpoint, the stochastic nature of the generation process can cause many inconsistencies when texturing an entire 3D object. To tackle these problems, we dynamically define a trimap partitioning of the rendered image into three progression states, and present a novel elaborated diffusion sampling process that uses this trimap representation to generate seamless textures from different views. We then show that one can transfer the generated texture maps to new 3D geometries without requiring explicit surface-to-surface mapping, as well as extract semantic textures from a set of images without requiring any explicit reconstruction. Finally, we show that TEXTure can be used to not only generate new textures but also edit and refine existing textures using either a text prompt or user-provided scribbles. We demonstrate that our TEXTuring method excels at generating, transferring, and editing textures through extensive evaluation, and further close the gap between 2D image generation and 3D texturing. Code is available via our project page: https://texturepaper.github.io/TEXTurePaper/.",
+    "neighbors": [
+      63,
+      1205,
+      1573,
+      2085,
+      2117
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1906,
+    "label": 30,
+    "text": "Title: Mini-Giants: \"Small\" Language Models and Open Source Win-Win\nAbstract: ChatGPT is phenomenal. However, it is prohibitively expensive to train and refine such giant models. Fortunately, small language models are flourishing and becoming more and more competent. We call them\"mini-giants\". We argue that open source community like Kaggle and mini-giants will win-win in many ways, technically, ethically and socially. In this article, we present a brief yet rich background, discuss how to attain small language models, present a comparative study of small language models and a brief discussion of evaluation methods, discuss the application scenarios where small language models are most needed in the real world, and conclude with discussion and outlook.",
+    "neighbors": [
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+      1972,
+      2136
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+    "mask": "Test"
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+  {
+    "node_id": 1907,
+    "label": 30,
+    "text": "Title: OctoPack: Instruction Tuning Code Large Language Models\nAbstract: Finetuning large language models (LLMs) on instructions leads to vast performance improvements on natural language tasks. We apply instruction tuning using code, leveraging the natural structure of Git commits, which pair code changes with human instructions. We compile CommitPack: 4 terabytes of Git commits across 350 programming languages. We benchmark CommitPack against other natural and synthetic code instructions (xP3x, Self-Instruct, OASST) on the 16B parameter StarCoder model, and achieve state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark (46.2% pass@1). We further introduce HumanEvalPack, expanding the HumanEval benchmark to a total of 3 coding tasks (Code Repair, Code Explanation, Code Synthesis) across 6 languages (Python, JavaScript, Java, Go, C++, Rust). Our models, OctoCoder and OctoGeeX, achieve the best performance across HumanEvalPack among all permissive models, demonstrating CommitPack's benefits in generalizing to a wider set of languages and natural coding tasks. Code, models and data are freely available at https://github.com/bigcode-project/octopack.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1908,
+    "label": 10,
+    "text": "Title: Imitating Human Behaviour with Diffusion Models\nAbstract: Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Human behaviour is stochastic and multimodal, with structured correlations between action dimensions. Meanwhile, standard modelling choices in behaviour cloning are limited in their expressiveness and may introduce bias into the cloned policy. We begin by pointing out the limitations of these choices. We then propose that diffusion models are an excellent fit for imitating human behaviour, since they learn an expressive distribution over the joint action space. We introduce several innovations to make diffusion models suitable for sequential environments; designing suitable architectures, investigating the role of guidance, and developing reliable sampling strategies. Experimentally, diffusion models closely match human demonstrations in a simulated robotic control task and a modern 3D gaming environment.",
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+      2018,
+      2245
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1909,
+    "label": 27,
+    "text": "Title: Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic\nAbstract: Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior and quantifying uncertainty for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged to further improve efficiency. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles in dynamic environment, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.",
+    "neighbors": [
+      1483,
+      1722
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1910,
+    "label": 27,
+    "text": "Title: Embodied Self-Supervised Learning (EMSSL) with Sampling and Training Coordination for Robot Arm Inverse Kinematics Model Learning\nAbstract: Forward and inverse kinematics models are fundamental to robot arms, serving as the basis for the robot arm's operational tasks. However, in model learning of robot arms, especially in the presence of redundant degrees of freedom, inverse model learning is more challenging than forward model learning due to the non-convex problem caused by multiple solutions. In this paper, we propose a framework for autonomous learning of the robot arm inverse model based on embodied self-supervised learning (EMSSL) with sampling and training coordination. We investigate batch inference and parallel computation strategies for data sampling in order to accelerate model learning and propose two approaches for fast adaptation of the robot arm model. A series of experiments demonstrate the effectiveness of the method we proposed. The related code will be available soon.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1911,
+    "label": 31,
+    "text": "Title: FARA: Future-aware Ranking Algorithm for Fairness Optimization\nAbstract: Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose \\textbf{FARA}, a novel \\textbf{F}uture-\\textbf{A}ware \\textbf{R}anking \\textbf{A}lgorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Specifically, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods. We make our implementation public at \\href{https://github.com/Taosheng-ty/QP_fairness/}{https://github.com/Taosheng-ty/QP\\_fairness/}.",
+    "neighbors": [
+      1981
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1912,
+    "label": 5,
+    "text": "Title: CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency\nAbstract: Cloud platforms are increasingly emphasizing sustainable operations in order to reduce their operational carbon footprint. One approach for reducing emissions is to exploit the temporal flexibility inherent in many cloud workloads by executing them in time periods with the greenest electricity supply and suspending them at other times. Since such suspend-resume approaches can incur long delays in job completion times, we present a new approach that exploits the workload elasticity of batch workloads in the cloud to optimize their carbon emissions. Our approach is based on the notion of carbon scaling, similar to cloud autoscaling, where a job's server allocations are varied dynamically based on fluctuations in the carbon cost of the grid's electricity supply. We present an optimal greedy algorithm for minimizing a job's emissions through carbon scaling and implement a prototype of our \\systemName system in Kubernetes using its autoscaling capabilities, along with an analytic tool to guide the carbon-efficient deployment of batch applications in the cloud. We evaluate CarbonScaler using real-world machine learning training and MPI jobs on a commercial cloud platform and show that \\systemName can yield up to 50\\% carbon savings over a carbon agnostic execution and up to 35% over the state-of-the-art suspend resume policies.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1913,
+    "label": 16,
+    "text": "Title: LISA: Reasoning Segmentation via Large Language Model\nAbstract: Although perception systems have made remarkable advancements in recent years, they still rely on explicit human instruction to identify the target objects or categories before executing visual recognition tasks. Such systems lack the ability to actively reason and comprehend implicit user intentions. In this work, we propose a new segmentation task -- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text. Furthermore, we establish a benchmark comprising over one thousand image-instruction pairs, incorporating intricate reasoning and world knowledge for evaluation purposes. Finally, we present LISA: large Language Instructed Segmentation Assistant, which inherits the language generation capabilities of the multi-modal Large Language Model (LLM) while also possessing the ability to produce segmentation masks. We expand the original vocabulary with atoken and propose the embedding-as-mask paradigm to unlock the segmentation capability. Remarkably, LISA can handle cases involving: 1) complex reasoning; 2) world knowledge; 3) explanatory answers; 4) multi-turn conversation. Also, it demonstrates robust zero-shot capability when trained exclusively on reasoning-free datasets. In addition, fine-tuning the model with merely 239 reasoning segmentation image-instruction pairs results in further performance enhancement. Experiments show our method not only unlocks new reasoning segmentation capabilities but also proves effective in both complex reasoning segmentation and standard referring segmentation tasks. Code, models, and demo are at https://github.com/dvlab-research/LISA.",
+    "neighbors": [
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+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1914,
+    "label": 27,
+    "text": "Title: Finite Element Modeling of Pneumatic Bending Actuators for Inflated-Beam Robots\nAbstract: Inflated-beam soft robots, such as tip-everting vine robots, can control their curvature by contracting one side of the beam using pneumatic actuation. In this work, a general finite element modeling approach is developed and applied to characterize bending of inflated-beam soft robots. The model is tested on four types of pneumatic actuators used in these robots (series, compression, embedded, and fabric pneumatic artificial muscles) and can be extended to other designs. Actuators rely on two types of bending mechanisms: geometry-based contraction and material-based contraction. Geometry-based contraction implies shape-change of the muscles from a flat to an inflated shortened configuration that causes buckling of the inflated beam. Material-based contraction relies on material anisotropy to produce a contraction effect. The model depicts both mechanisms and accommodates for the complex and highly nonlinear effects of buckling and anisotropy. Simulation results are verified experimentally for each actuator type at three working pressures (10, 20, and 30 kPa). Geometry-based contraction achieves the largest deformation at accuracy values of 92.1% and higher once the buckling pattern is established, and 80.7% and higher for lower pressures due to the stress singularities occurring with buckling formation. Material-based contraction achieves smaller bending angles but is at least 96.7% accurate. The models are freely available online, and can thus be used by others to design inflated-beam robots, such as tip-everting vine robots. Labor and material waste can be reduced with this tool by optimizing designs that use knowledge of material properties and stress to distributions to enable bending and manage stress peaks.",
+    "neighbors": [
+      747,
+      1827
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1915,
+    "label": 31,
+    "text": "Title: Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community\nAbstract: The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop's outcomes, including the rethinking of IR's core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.",
+    "neighbors": [
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+      1834
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1916,
+    "label": 16,
+    "text": "Title: Facial Affective Behavior Analysis Method for 5th ABAW Competition\nAbstract: Facial affective behavior analysis is important for human-computer interaction. 5th ABAW competition includes three challenges from Aff-Wild2 database. Three common facial affective analysis tasks are involved, i.e. valence-arousal estimation, expression classification, action unit recognition. For the three challenges, we construct three different models to solve the corresponding problems to improve the results, such as data unbalance and data noise. For the experiments of three challenges, we train the models on the provided training data and validate the models on the validation data.",
+    "neighbors": [
+      1533,
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1917,
+    "label": 16,
+    "text": "Title: Single-Stage Heavy-Tailed Food Classification\nAbstract: Deep learning based food image classification has enabled more accurate nutrition content analysis for image-based dietary assessment by predicting the types of food in eating occasion images. However, there are two major obstacles to apply food classification in real life applications. First, real life food images are usually heavy-tailed distributed, resulting in severe class-imbalance issue. Second, it is challenging to train a single-stage (i.e. end-to-end) framework under heavy-tailed data distribution, which cause the over-predictions towards head classes with rich instances and under-predictions towards tail classes with rare instance. In this work, we address both issues by introducing a novel single-stage heavy-tailed food classification framework. Our method is evaluated on two heavy-tailed food benchmark datasets, Food101-LT and VFN-LT, and achieves the best performance compared to existing work with over 5% improvements for top-1 accuracy.",
+    "neighbors": [
+      778
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1918,
+    "label": 24,
+    "text": "Title: Leveraging Deep Learning Approaches for Deepfake Detection: A Review\nAbstract: Abstract\u2014 Conspicuous progression in the field of machine learning (ML) and deep learning (DL) have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks (CNN). This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset.\u202f",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1919,
+    "label": 36,
+    "text": "Title: Towards a Switching-Algebraic Theory of Weighted Voting Systems: Exploring Restrictions on Coalition Formation\nAbstract: We explore the switching-algebraic computation of the Banzhaf indices for general and monotone or unrestricted systems. This computation is achieved via (a) two Boolean-quotient formulas that are valid when the voting system is not necessarily monotone (e.g., when coalition formation is restricted), (b) four Boolean differencing formulas and six Boolean-quotient formulas that are applicable when the decision switching function is a positively polarized unate one. We also provide switching-algebraic formulas for certain Banzhaf-related indices, including the power-to-initiate index (PII), and the power-to-prevent index (PPI), as well as satisfaction indices. Moreover, we briefly address other Banzhaf-related indices, including the Strict Power Index (SPI) and the Public Good Index (PGI). We illustrate the various indices formulas by way of four examples of voting systems, each considered first as an unrestricted monotone system and then subjected to a restriction on the formation of a coalition between two particular voters.",
+    "neighbors": [
+      915
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1920,
+    "label": 2,
+    "text": "Title: Automated Termination Proofs for C Programs with Lists (Short WST Version)\nAbstract: There are many techniques and tools for termination of C programs, but up to now they were not very powerful for termination proofs of programs whose termination depends on recursive data structures like lists. We present the first approach that extends powerful techniques for termination analysis of C programs (with memory allocation and explicit pointer arithmetic) to lists.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1921,
+    "label": 34,
+    "text": "Title: Multiplicative Auction Algorithm for Approximate Maximum Weight Bipartite Matching\nAbstract: $\\newcommand{\\eps}{\\varepsilon}$We present an auction algorithm using multiplicative instead of constant weight updates to compute a $(1-\\eps)$-approximate maximum weight matching (MWM) in a bipartite graph with $n$ vertices and $m$ edges in time $O(m\\eps^{-1}\\log(\\eps^{-1}))$, matching the running time of the linear-time approximation algorithm of Duan and Pettie [JACM '14]. Our algorithm is very simple and it can be extended to give a dynamic data structure that maintains a $(1-\\eps)$-approximate maximum weight matching under (1) one-sided vertex deletions (with incident edges) and (2) one-sided vertex insertions (with incident edges sorted by weight) to the other side. The total time time used is $O(m\\eps^{-1}\\log(\\eps^{-1}))$, where $m$ is the sum of the number of initially existing and inserted edges.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1922,
+    "label": 24,
+    "text": "Title: MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation\nAbstract: This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi offers an end-to-end differentiable approach that streamlines the molecule generation process. Our experimental results demonstrate the effectiveness of this approach. On the challenging GEOM-DRUGS dataset, MiDi generates 92% of stable molecules, against 6% for the previous EDM model that uses interatomic distances for bond prediction, and 40% using EDM followed by an algorithm that directly optimize bond orders for validity. Our code is available at github.com/cvignac/MiDi.",
+    "neighbors": [
+      64
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1923,
+    "label": 17,
+    "text": "Title: Project Elements: A computational entity-component-system in a scene-graph pythonic framework, for a neural, geometric computer graphics curriculum\nAbstract: We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first time, the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines (MGEs), that are based on the same approach but often present these notions in a black-box approach. It is designed to actively utilize software design patterns, under an extensible open-source approach. Although Elements provides a modern (i.e., shader-based as opposed to fixed-function OpenGL), simple to program approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1924,
+    "label": 27,
+    "text": "Title: Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight\nAbstract: Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.",
+    "neighbors": [
+      593,
+      1838
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1925,
+    "label": 12,
+    "text": "Title: The History of Quantum Games\nAbstract: In this paper, we explore the historical development of playable quantum physics related games (\\textit{\\textbf{quantum games}}). For the purpose of this examination, we have collected over 260 quantum games ranging from commercial games, applied and serious games, and games that have been developed at quantum themed game jams and educational courses. We provide an overview of the journey of quantum games across three dimensions: \\textit{the perceivable dimension of quantum physics, the dimension of scientific purposes, and the dimension of quantum technologies}. We then further reflect on the definition of quantum games and its implications. While motivations behind developing quantum games have typically been educational or academic, themes related to quantum physics have begun to be more broadly utilised across a range of commercial games. In addition, as the availability of quantum computer hardware has grown, entirely new variants of quantum games have emerged to take advantage of these machines' inherent capabilities, \\textit{quantum computer games}",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1926,
+    "label": 16,
+    "text": "Title: YOLOv8 for Defect Inspection of Hexagonal Directed Self-Assembly Patterns: A Data-Centric Approach\nAbstract: Shrinking pattern dimensions leads to an increased variety of defect types in semiconductor devices. This has spurred innovation in patterning approaches such as Directed self-assembly (DSA) for which no traditional, automatic defect inspection software exists. Machine Learning-based SEM image analysis has become an increasingly popular research topic for defect inspection with supervised ML models often showing the best performance. However, little research has been done on obtaining a dataset with high-quality labels for these supervised models. In this work, we propose a method for obtaining coherent and complete labels for a dataset of hexagonal contact hole DSA patterns while requiring minimal quality control effort from a DSA expert. We show that YOLOv8, a state-of-the-art neural network, achieves defect detection precisions of more than 0.9 mAP on our final dataset which best reflects DSA expert defect labeling expectations. We discuss the strengths and limitations of our proposed labeling approach and suggest directions for future work in data-centric ML-based defect inspection.",
+    "neighbors": [
+      427
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1927,
+    "label": 15,
+    "text": "Title: Evolutionary design of the memory subsystem\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1928,
+    "label": 16,
+    "text": "Title: BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset\nAbstract: While strides have been made in deep learning based Bengali Optical Character Recognition (OCR) in the past decade, the absence of large Document Layout Analysis (DLA) datasets has hindered the application of OCR in document transcription, e.g., transcribing historical documents and newspapers. Moreover, rule-based DLA systems that are currently being employed in practice are not robust to domain variations and out-of-distribution layouts. To this end, we present the first multidomain large Bengali Document Layout Analysis Dataset: BaDLAD. This dataset contains 33,695 human annotated document samples from six domains - i) books and magazines, ii) public domain govt. documents, iii) liberation war documents, iv) newspapers, v) historical newspapers, and vi) property deeds, with 710K polygon annotations for four unit types: text-box, paragraph, image, and table. Through preliminary experiments benchmarking the performance of existing state-of-the-art deep learning architectures for English DLA, we demonstrate the efficacy of our dataset in training deep learning based Bengali document digitization models.",
+    "neighbors": [
+      1498
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1929,
+    "label": 16,
+    "text": "Title: Beyond Document Page Classification: Design, Datasets, and Challenges\nAbstract: This paper highlights the need to bring document classification benchmarking closer to real-world applications, both in the nature of data tested ($X$: multi-channel, multi-paged, multi-industry; $Y$: class distributions and label set variety) and in classification tasks considered ($f$: multi-page document, page stream, and document bundle classification, ...). We identify the lack of public multi-page document classification datasets, formalize different classification tasks arising in application scenarios, and motivate the value of targeting efficient multi-page document representations. An experimental study on proposed multi-page document classification datasets demonstrates that current benchmarks have become irrelevant and need to be updated to evaluate complete documents, as they naturally occur in practice. This reality check also calls for more mature evaluation methodologies, covering calibration evaluation, inference complexity (time-memory), and a range of realistic distribution shifts (e.g., born-digital vs. scanning noise, shifting page order). Our study ends on a hopeful note by recommending concrete avenues for future improvements.}",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1930,
+    "label": 24,
+    "text": "Title: Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies\nAbstract: Recently, the impressive empirical success of policy gradient (PG) methods has catalyzed the development of their theoretical foundations. Despite the huge efforts directed at the design of efficient stochastic PG-type algorithms, the understanding of their convergence to a globally optimal policy is still limited. In this work, we develop improved global convergence guarantees for a general class of Fisher-non-degenerate parameterized policies which allows to address the case of continuous state action spaces. First, we propose a Normalized Policy Gradient method with Implicit Gradient Transport (N-PG-IGT) and derive a $\\tilde{\\mathcal{O}}(\\varepsilon^{-2.5})$ sample complexity of this method for finding a global $\\varepsilon$-optimal policy. Improving over the previously known $\\tilde{\\mathcal{O}}(\\varepsilon^{-3})$ complexity, this algorithm does not require the use of importance sampling or second-order information and samples only one trajectory per iteration. Second, we further improve this complexity to $\\tilde{ \\mathcal{\\mathcal{O}} }(\\varepsilon^{-2})$ by considering a Hessian-Aided Recursive Policy Gradient ((N)-HARPG) algorithm enhanced with a correction based on a Hessian-vector product. Interestingly, both algorithms are $(i)$ simple and easy to implement: single-loop, do not require large batches of trajectories and sample at most two trajectories per iteration; $(ii)$ computationally and memory efficient: they do not require expensive subroutines at each iteration and can be implemented with memory linear in the dimension of parameters.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1931,
+    "label": 10,
+    "text": "Title: Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning\nAbstract: Driver support systems that include human states in the support process is an active research field. Many recent approaches allow, for example, to sense the driver's drowsiness or awareness of the driving situation. However, so far, this rich information has not been utilized much for improving the effectiveness of support systems. In this paper, we therefore propose a warning system that uses human states in the form of driver errors and can warn users in some cases of upcoming risks several seconds earlier than the state of the art systems not considering human factors. The system consists of a behavior planner Risk Maps which directly changes its prediction of the surrounding driving situation based on the sensed driver errors. By checking if this driver's behavior plan is objectively safe, a more robust and foresighted driver warning is achieved. In different simulations of a dynamic lane change and intersection scenarios, we show how the driver's behavior plan can become unsafe, given the estimate of driver errors, and experimentally validate the advantages of considering human factors.",
+    "neighbors": [
+      1395
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1932,
+    "label": 16,
+    "text": "Title: A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering\nAbstract: Segment anything model (SAM) developed by Meta AI Research has recently attracted significant attention. Trained on a large segmentation dataset of over 1 billion masks, SAM is capable of segmenting any object on a certain image. In the original SAM work, the authors turned to zero-short transfer tasks (like edge detection) for evaluating the performance of SAM. Recently, numerous works have attempted to investigate the performance of SAM in various scenarios to recognize and segment objects. Moreover, numerous projects have emerged to show the versatility of SAM as a foundation model by combining it with other models, like Grounding DINO, Stable Diffusion, ChatGPT, etc. With the relevant papers and projects increasing exponentially, it is challenging for the readers to catch up with the development of SAM. To this end, this work conducts the first yet comprehensive survey on SAM. This is an ongoing project and we intend to update the manuscript on a regular basis. Therefore, readers are welcome to contact us if they complete new works related to SAM so that we can include them in our next version.",
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+      2272,
+      2278
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1933,
+    "label": 24,
+    "text": "Title: Guiding Pretraining in Reinforcement Learning with Large Language Models\nAbstract: Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by rewarding agents for visiting novel states or transitions, but these methods offer limited benefits in large environments where most discovered novelty is irrelevant for downstream tasks. We describe a method that uses background knowledge from text corpora to shape exploration. This method, called ELLM (Exploring with LLMs) rewards an agent for achieving goals suggested by a language model prompted with a description of the agent's current state. By leveraging large-scale language model pretraining, ELLM guides agents toward human-meaningful and plausibly useful behaviors without requiring a human in the loop. We evaluate ELLM in the Crafter game environment and the Housekeep robotic simulator, showing that ELLM-trained agents have better coverage of common-sense behaviors during pretraining and usually match or improve performance on a range of downstream tasks. Code available at https://github.com/yuqingd/ellm.",
+    "neighbors": [
+      1233,
+      1451
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1934,
+    "label": 24,
+    "text": "Title: Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study\nAbstract: The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge cases. This has led to an interest in models incorporating cognitive theory, but as such models are commonly developed for explanatory purposes, this approach's effectiveness in behavior prediction has remained largely untested so far. In this article, we investigate the usefulness of the \\emph{Commotions} model -- a novel cognitively plausible model incorporating the latest theories of human perception, decision-making, and motor control -- for predicting human behavior in gap acceptance scenarios, which entail many important traffic interactions such as lane changes and intersections. We show that this model can compete with or even outperform well-established data-driven prediction models across several naturalistic datasets. These results demonstrate the promise of incorporating cognitive theory in behavior prediction models for automated vehicles.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1935,
+    "label": 16,
+    "text": "Title: Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation\nAbstract: Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translation framework to adapt image models to videos. The framework includes two parts: key frame translation and full video translation. The first part uses an adapted diffusion model to generate key frames, with hierarchical cross-frame constraints applied to enforce coherence in shapes, textures and colors. The second part propagates the key frames to other frames with temporal-aware patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as customizing a specific subject with LoRA, and introducing extra spatial guidance with ControlNet. Extensive experimental results demonstrate the effectiveness of our proposed framework over existing methods in rendering high-quality and temporally-coherent videos.",
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+      1902
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1936,
+    "label": 24,
+    "text": "Title: Preserving Linear Separability in Continual Learning by Backward Feature Projection\nAbstract: Catastrophic forgetting has been a major challenge in continual learning, where the model needs to learn new tasks with limited or no access to data from previously seen tasks. To tackle this challenge, methods based on knowledge distillation in feature space have been proposed and shown to reduce forgetting [16, 17, 25 ]. However, most feature distillation methods directly constrain the new features to match the old ones, overlooking the need for plasticity. To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features. BFP preserves the linear separability of the old classes while allowing the emergence of new feature directions to accommodate new classes. BFP can be integrated with existing experience replay methods and boost performance by a significant margin. We also demonstrate that BFP helps learn a better representation space, in which linear separability is well preserved during continual learning and linear probing achieves high classification accuracy.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1937,
+    "label": 27,
+    "text": "Title: RobotKube: Orchestrating Large-Scale Cooperative Multi-Robot Systems with Kubernetes and ROS\nAbstract: Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport Systems (C-ITS) are increasingly defined by the software which operates these systems. In practice, microservice architectures can be employed, which may consist of containerized microservices running in a cluster comprised of robots and supporting infrastructure. These microservices need to be orchestrated dynamically according to ever changing requirements posed at the system. Additionally, these systems are embedded in DevOps processes aiming at continually updating and upgrading both the capabilities of CPS components and of the system as a whole. In this paper, we present RobotKube, an approach to orchestrating containerized microservices for large-scale cooperative multi-robot CPS based on Kubernetes. We describe how to automate the orchestration of software across a CPS, and include the possibility to monitor and selectively store relevant accruing data. In this context, we present two main components of such a system: an event detector capable of, e.g., requesting the deployment of additional applications, and an application manager capable of automatically configuring the required changes in the Kubernetes cluster. By combining the widely adopted Kubernetes platform with the Robot Operating System (ROS), we enable the use of standard tools and practices for developing, deploying, scaling, and monitoring microservices in C-ITS. We demonstrate and evaluate RobotKube in an exemplary and reproducible use case that we make publicly available at https://github.com/ika-rwth-aachen/robotkube .",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1938,
+    "label": 24,
+    "text": "Title: The Regular Expression Inference Challenge\nAbstract: We propose \\emph{regular expression inference (REI)} as a challenge for code/language modelling, and the wider machine learning community. REI is a supervised machine learning (ML) and program synthesis task, and poses the problem of finding minimal regular expressions from examples: Given two finite sets of strings $P$ and $N$ and a cost function $\\text{cost}(\\cdot)$, the task is to generate an expression $r$ that accepts all strings in $P$ and rejects all strings in $N$, while no other such expression $r'$ exists with $\\text{cost}(r')<\\text{cost}(r)$. REI has advantages as a challenge problem: (i) regular expressions are well-known, widely used, and a natural idealisation of code; (ii) REI's asymptotic worst-case complexity is well understood; (iii) REI has a small number of easy to understand parameters (e.g.~$P$ or $N$ cardinality, string lengths of examples, or the cost function); this lets us easily finetune REI-hardness; (iv) REI is an unsolved problem for deep learning based ML. Recently, an REI solver was implemented on GPUs, using program synthesis techniques. This enabled, for the first time, fast generation of minimal expressions for complex REI instances. Building on this advance, we generate and publish the first large-scale datasets for REI, and devise and evaluate several initial heuristic and machine learning baselines. We invite the community to participate and explore ML methods that learn to solve REI problems. We believe that progress in REI directly translates to code/language modelling.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1939,
+    "label": 16,
+    "text": "Title: TriDet: Temporal Action Detection with Relative Boundary Modeling\nAbstract: In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer to mitigate the rank loss problem of self-attention that takes place in the video features and aggregate information across different temporal granularities. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of 69.3% on THUMOS14, outperforming the previous best by 2.5%, but with only 74.6% of its latency. The code is released to https://github.com/dingfengshi/TriDet.",
+    "neighbors": [
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+    "mask": "Test"
+  },
+  {
+    "node_id": 1940,
+    "label": 30,
+    "text": "Title: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond\nAbstract: This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. Firstly, we offer an introduction and brief summary of current GPT- and BERT-style LLMs. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, natural language generation tasks, emergent abilities, and considerations for specific tasks.We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at \\url{https://github.com/Mooler0410/LLMsPracticalGuide}.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1941,
+    "label": 8,
+    "text": "Title: Characterizing the VPN Ecosystem in the Wild\nAbstract: With the increase of remote working during and after the COVID-19 pandemic, the use of Virtual Private Networks (VPNs) around the world has nearly doubled. Therefore, measuring the traffic and security aspects of the VPN ecosystem is more important now than ever. VPN users rely on the security of VPN solutions, to protect private and corporate communication. Thus a good understanding of the security state of VPN servers is crucial. Moreover, properly detecting and characterizing VPN traffic remains challenging, since some VPN protocols use the same port number as web traffic and port-based traffic classification will not help. In this paper, we aim at detecting and characterizing VPN servers in the wild, which facilitates detecting the VPN traffic. To this end, we perform Internet-wide active measurements to find VPN servers in the wild, and analyze their cryptographic certificates, vulnerabilities, locations, and fingerprints. We find 9.8M VPN servers distributed around the world using OpenVPN, SSTP, PPTP, and IPsec, and analyze their vulnerability. We find SSTP to be the most vulnerable protocol with more than 90% of detected servers being vulnerable to TLS downgrade attacks. Out of all the servers that respond to our VPN probes, 2% also respond to HTTP probes and therefore are classified as Web servers. Finally, we use our list of VPN servers to identify VPN traffic in a large European ISP and observe that 2.6% of all traffic is related to these VPN servers. \u00a9 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1942,
+    "label": 5,
+    "text": "Title: Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities: A Systematic Review\nAbstract: Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1943,
+    "label": 30,
+    "text": "Title: ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models\nAbstract: Large language models (LLMs) such as ChatGPT and GPT-4 have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point for LLMs. It remains unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are GPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlying commonsense knowledge for answering a specific question? (4) Can GPTs effectively leverage commonsense for answering questions? To evaluate the above commonsense problems, we conduct a series of experiments to evaluate ChatGPT's commonsense abilities, and the experimental results show that: (1) GPTs can achieve good QA accuracy in commonsense tasks, while they still struggle with certain types of knowledge. (2) ChatGPT is knowledgeable, and can accurately generate most of the commonsense knowledge using knowledge prompts. (3) Despite its knowledge, ChatGPT is an inexperienced commonsense problem solver, which cannot precisely identify the needed commonsense knowledge for answering a specific question, i.e., ChatGPT does not precisely know what commonsense knowledge is required to answer a question. The above findings raise the need to investigate better mechanisms for utilizing commonsense knowledge in LLMs, such as instruction following, better commonsense guidance, etc.",
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+    "mask": "Test"
+  },
+  {
+    "node_id": 1944,
+    "label": 31,
+    "text": "Title: Unsupervised\u00a0Entity\u00a0Alignment\u00a0for\u00a0Temporal\u00a0Knowledge\u00a0Graphs\nAbstract: Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1945,
+    "label": 25,
+    "text": "Title: Streaming Audio Transformers for Online Audio Tagging\nAbstract: Transformers have emerged as a prominent model framework for audio tagging (AT), boasting state-of-the-art (SOTA) performance on the widely-used Audioset dataset. However, their impressive performance often comes at the cost of high memory usage, slow inference speed, and considerable model delay, rendering them impractical for real-world AT applications. In this study, we introduce streaming audio transformers (SAT) that combine the vision transformer (ViT) architecture with Transformer-Xl-like chunk processing, enabling efficient processing of long-range audio signals. Our proposed SAT is benchmarked against other transformer-based SOTA methods, achieving significant improvements in terms of mean average precision (mAP) at a delay of 2s and 1s, while also exhibiting significantly lower memory usage and computational overhead. Checkpoints are publicly available https://github.com/RicherMans/SAT.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1946,
+    "label": 24,
+    "text": "Title: Max-Margin Token Selection in Attention Mechanism\nAbstract: Attention mechanism is a central component of the transformer architecture which led to the phenomenal success of large language models. However, the theoretical principles underlying the attention mechanism are poorly understood, especially its nonconvex optimization dynamics. In this work, we explore the seminal softmax-attention model $f(\\boldsymbol{X})=\\langle \\boldsymbol{Xv}, \\texttt{softmax}(\\boldsymbol{XWp})\\rangle$, where $\\boldsymbol{X}$ is the token sequence and $(\\boldsymbol{v},\\boldsymbol{W},\\boldsymbol{p})$ are trainable parameters. We prove that running gradient descent on $\\boldsymbol{p}$, or equivalently $\\boldsymbol{W}$, converges in direction to a max-margin solution that separates $\\textit{locally-optimal}$ tokens from non-optimal ones. This clearly formalizes attention as an optimal token selection mechanism. Remarkably, our results are applicable to general data and precisely characterize $\\textit{optimality}$ of tokens in terms of the value embeddings $\\boldsymbol{Xv}$ and problem geometry. We also provide a broader regularization path analysis that establishes the margin maximizing nature of attention even for nonlinear prediction heads. When optimizing $\\boldsymbol{v}$ and $\\boldsymbol{p}$ simultaneously with logistic loss, we identify conditions under which the regularization paths directionally converge to their respective hard-margin SVM solutions where $\\boldsymbol{v}$ separates the input features based on their labels. Interestingly, the SVM formulation of $\\boldsymbol{p}$ is influenced by the support vector geometry of $\\boldsymbol{v}$. Finally, we verify our theoretical findings via numerical experiments and provide insights.",
+    "neighbors": [
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+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1947,
+    "label": 31,
+    "text": "Title: Unbiased Learning to Rank with Biased Continuous Feedback\nAbstract: It is a well-known challenge to learn an unbiased ranker with biased feedback. Unbiased learning-to-rank(LTR) algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, the existing unbiased LTR methods cannot properly handle continuous feedback, which are essential for many industrial applications, such as content recommender systems. To provide personalized high-quality recommendation results, recommender systems need model both categorical and continuous biased feedback, such as click and dwell time. As unbiased LTR methods could not handle these continuous feedback and pair-wise learning without debiasing often performs worse than point-wise on biased feedback, which is also verified in our experiments, training multiple point-wise rankers to predict the absolute value of multiple objectives and leveraging a distinct shallow tower to estimate and alleviate the impact of position bias has been the mainstream approach in major industrial recommendation applications. However, with such a training paradigm, the optimization target differs a lot from the ranking metrics valuing the relative order of top-ranked items rather than the prediction precision of each item. Moreover, as the existing system tends to recommend more relevant items at higher positions, it is difficult for the shallow tower based methods to precisely attribute the user feedback to the impact of position or relevance. Therefore, there exists an exciting opportunity for us to get enhanced performance if we manage to solve the aforementioned issues. Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion and introduces the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly and can work for both continuous and categorical feedback. Experiment results on public benchmark datasets and internal live traffic of a large-scale recommender system at Tencent News show superior results for continuous labels and also competitive performance for categorical labels of the proposed method.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1948,
+    "label": 24,
+    "text": "Title: Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review\nAbstract: Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records. Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. There can be disease-related information that is too subtle for humans or existing technological tools to discern visually. Traditional methods typically focus on partial or unimodal information about biological systems at individual scales and fail to encapsulate the complete spectrum of the heterogeneous nature of data. Deep neural networks have facilitated the development of sophisticated multimodal data fusion approaches that can extract and integrate relevant information from multiple sources. Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning. This review article provides an in-depth analysis of the state-of-the-art in GNNs and Transformers for multimodal data fusion in oncology settings, highlighting notable research studies and their findings. We also discuss the foundations of multimodal learning, inherent challenges, and opportunities for integrative learning in oncology. By examining the current state and potential future developments of multimodal data integration in oncology, we aim to demonstrate the promising role that multimodal neural networks can play in cancer prevention, early detection, and treatment through informed oncology practices in personalized settings.",
+    "neighbors": [
+      1836
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1949,
+    "label": 30,
+    "text": "Title: Is ChatGPT a Good NLG Evaluator? A Preliminary Study\nAbstract: Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable performance on various NLP tasks in terms of automatic evaluation metrics. However, the ability of ChatGPT to serve as an evaluation metric is still underexplored. Considering assessing the quality of natural language generation (NLG) models is an arduous task and NLG metrics notoriously show their poor correlation with human judgments, we wonder whether ChatGPT is a good NLG evaluation metric. In this report, we provide a preliminary meta-evaluation on ChatGPT to show its reliability as an NLG metric. In detail, we regard ChatGPT as a human evaluator and give task-specific (e.g., summarization) and aspect-specific (e.g., relevance) instruction to prompt ChatGPT to evaluate the generated results of NLG models. We conduct experiments on five NLG meta-evaluation datasets (including summarization, story generation and data-to-text tasks). Experimental results show that compared with previous automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation with human judgments in most cases. In addition, we find that the effectiveness of the ChatGPT evaluator might be influenced by the creation method of the meta-evaluation datasets. For the meta-evaluation datasets which are created greatly depending on the reference and thus are biased, the ChatGPT evaluator might lose its effectiveness. We hope our preliminary study could prompt the emergence of a general-purposed reliable NLG metric.",
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+    "mask": "Train"
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+  {
+    "node_id": 1950,
+    "label": 30,
+    "text": "Title: Instruction Tuning for Large Language Models: A Survey\nAbstract: This paper surveys research works in the quickly advancing field of instruction tuning (IT), a crucial technique to enhance the capabilities and controllability of large language models (LLMs). Instruction tuning refers to the process of further training LLMs on a dataset consisting of \\textsc{(instruction, output)} pairs in a supervised fashion, which bridges the gap between the next-word prediction objective of LLMs and the users' objective of having LLMs adhere to human instructions. In this work, we make a systematic review of the literature, including the general methodology of IT, the construction of IT datasets, the training of IT models, and applications to different modalities, domains and applications, along with an analysis on aspects that influence the outcome of IT (e.g., generation of instruction outputs, size of the instruction dataset, etc). We also review the potential pitfalls of IT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies and suggest some avenues for fruitful research.",
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+    "mask": "Validation"
+  },
+  {
+    "node_id": 1951,
+    "label": 16,
+    "text": "Title: Query-based Video Summarization with Pseudo Label Supervision\nAbstract: Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels to pre-train a supervised deep model. In this work, we introduce segment-level pseudo labels from input videos to properly model both the relationship between a pretext task and a target task, and the implicit relationship between the pseudo label and the human-defined label. The pseudo labels are generated based on existing human-defined frame-level labels. To create more accurate query-dependent video summaries, a semantics booster is proposed to generate context-aware query representations. Furthermore, we propose mutual attention to help capture the interactive information between visual and textual modalities. Three commonly-used video summarization benchmarks are used to thoroughly validate the proposed approach. Experimental results show that the proposed video summarization algorithm achieves state-of-the-art performance.",
+    "neighbors": [
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1952,
+    "label": 30,
+    "text": "Title: A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity\nAbstract: This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 23 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 63.41% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn\"prompt engineering\"fashion. We also release codebase for evaluation set extraction.",
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+    "mask": "Test"
+  },
+  {
+    "node_id": 1953,
+    "label": 24,
+    "text": "Title: An Algorithm for Computing with Brauer's Group Equivariant Neural Network Layers\nAbstract: The learnable, linear neural network layers between tensor power spaces of $\\mathbb{R}^{n}$ that are equivariant to the orthogonal group, $O(n)$, the special orthogonal group, $SO(n)$, and the symplectic group, $Sp(n)$, were characterised in arXiv:2212.08630. We present an algorithm for multiplying a vector by any weight matrix for each of these groups, using category theoretic constructions to implement the procedure. We achieve a significant reduction in computational cost compared with a naive implementation by making use of Kronecker product matrices to perform the multiplication. We show that our approach extends to the symmetric group, $S_n$, recovering the algorithm of arXiv:2303.06208 in the process.",
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+    "mask": "Train"
+  },
+  {
+    "node_id": 1954,
+    "label": 5,
+    "text": "Title: FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement\nAbstract: With the increasing data volume, there is a trend of using large-scale pre-trained models to store the knowledge into an enormous number of model parameters. The training of these models is composed of lots of dense algebras, requiring a huge amount of hardware resources. Recently, sparsely-gated Mixture-of-Experts (MoEs) are becoming more popular and have demonstrated impressive pretraining scalability in various downstream tasks. However, such a sparse conditional computation may not be effective as expected in practical systems due to the routing imbalance and fluctuation problems. Generally, MoEs are becoming a new data analytics paradigm in the data life cycle and suffering from unique challenges at scales, complexities, and granularities never before possible. In this paper, we propose a novel DNN training framework, FlexMoE, which systematically and transparently address the inefficiency caused by dynamic dataflow. We first present an empirical analysis on the problems and opportunities of training MoE models, which motivates us to overcome the routing imbalance and fluctuation problems by a dynamic expert management and device placement mechanism. Then we introduce a novel scheduling module over the existing DNN runtime to monitor the data flow, make the scheduling plans, and dynamically adjust the model-to-hardware mapping guided by the real-time data traffic. A simple but efficient heuristic algorithm is exploited to dynamically optimize the device placement during training. We have conducted experiments on both NLP models (e.g., BERT and GPT) and vision models (e.g., Swin). And results show FlexMoE can achieve superior performance compared with existing systems on real-world workloads --- FlexMoE outperforms DeepSpeed by 1.70x on average and up to 2.10x, and outperforms FasterMoE by 1.30x on average and up to 1.45x.",
+    "neighbors": [
+      2184
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1955,
+    "label": 24,
+    "text": "Title: Federated Analytics: A survey\nAbstract: Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.",
+    "neighbors": [
+      647,
+      1285
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1956,
+    "label": 5,
+    "text": "Title: Quantifying OpenMP: Statistical Insights into Usage and Adoption\nAbstract: In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong\"common core\"of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques.",
+    "neighbors": [
+      1093,
+      1562
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1957,
+    "label": 4,
+    "text": "Title: A Feature Set of Small Size for the PDF Malware Detection\nAbstract: Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated. PDF files are often used as vectors for phishing attacks because they are widely regarded as trustworthy data resources, and are accessible across different platforms. Therefore, researchers have developed many different PDF malware detection methods. Performance in detecting PDF malware is greatly influenced by feature selection. In this research, we propose a small features set that don't require too much domain knowledge of the PDF file. We evaluate proposed features with six different machine learning models. We report the best accuracy of 99.75% when using Random Forest model. Our proposed feature set, which consists of just 12 features, is one of the most conciseness in the field of PDF malware detection. Despite its modest size, we obtain comparable results to state-of-the-art that employ a much larger set of features.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1958,
+    "label": 25,
+    "text": "Title: Simple and Controllable Music Generation\nAbstract: We tackle the task of conditional music generation. We introduce MusicGen, a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns, which eliminates the need for cascading several models, e.g., hierarchically or upsampling. Following this approach, we demonstrate how MusicGen can generate high-quality samples, while being conditioned on textual description or melodic features, allowing better controls over the generated output. We conduct extensive empirical evaluation, considering both automatic and human studies, showing the proposed approach is superior to the evaluated baselines on a standard text-to-music benchmark. Through ablation studies, we shed light over the importance of each of the components comprising MusicGen. Music samples, code, and models are available at https://github.com/facebookresearch/audiocraft.",
+    "neighbors": [
+      414,
+      644,
+      1033,
+      1052,
+      1156,
+      1307,
+      1450,
+      1536
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1959,
+    "label": 30,
+    "text": "Title: Graecia capta ferum victorem cepit. Detecting Latin Allusions to Ancient Greek Literature\nAbstract: Intertextual allusions hold a pivotal role in Classical Philology, with Latin authors frequently referencing Ancient Greek texts. Until now, the automatic identification of these intertextual references has been constrained to monolingual approaches, seeking parallels solely within Latin or Greek texts. In this study, we introduce SPhilBERTa, a trilingual Sentence-RoBERTa model tailored for Classical Philology, which excels at cross-lingual semantic comprehension and identification of identical sentences across Ancient Greek, Latin, and English. We generate new training data by automatically translating English texts into Ancient Greek. Further, we present a case study, demonstrating SPhilBERTa's capability to facilitate automated detection of intertextual parallels. Our models and resources are available at https://github.com/Heidelberg-NLP/ancient-language-models.",
+    "neighbors": [
+      1639
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1960,
+    "label": 24,
+    "text": "Title: Online Learning with Set-Valued Feedback\nAbstract: We study a variant of online multiclass classification where the learner predicts a single label but receives a \\textit{set of labels} as feedback. In this model, the learner is penalized for not outputting a label contained in the revealed set. We show that unlike online multiclass learning with single-label feedback, deterministic and randomized online learnability are \\textit{not equivalent} even in the realizable setting with set-valued feedback. Accordingly, we give two new combinatorial dimensions, named the Set Littlestone and Measure Shattering dimension, that tightly characterize deterministic and randomized online learnability respectively in the realizable setting. In addition, we show that the Measure Shattering dimension tightly characterizes online learnability in the agnostic setting. Finally, we show that practical learning settings like online multilabel ranking, online multilabel classification, and online interval learning are specific instances of our general framework.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1961,
+    "label": 27,
+    "text": "Title: Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation\nAbstract: To assist robots in teleoperation tasks, haptic rendering which allows human operators access a virtual touch feeling has been developed in recent years. Most previous haptic rendering methods strongly rely on data collected by tactile sensors. However, tactile data is not widely available for robots due to their limited reachable space and the restrictions of tactile sensors. To eliminate the need for tactile data, in this paper we propose a novel method named as Vis2Hap to generate haptic rendering from visual inputs that can be obtained from a distance without physical interaction. We take the surface texture of objects as key cues to be conveyed to the human operator. To this end, a generative model is designed to simulate the roughness and slipperiness of the object's surface. To embed haptic cues in Vis2Hap, we use height maps from tactile sensors and spectrograms from friction coefficients as the intermediate outputs of the generative model. Once Vis2Hap is trained, it can be used to generate height maps and spectrograms of new surface textures, from which a friction image can be obtained and displayed on a haptic display. The user study demonstrates that our proposed Vis2Hap method enables users to access a realistic haptic feeling similar to that of physical objects. The proposed vision-based haptic rendering has the potential to enhance human operators' perception of the remote environment and facilitate robotic manipulation.",
+    "neighbors": [
+      715
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1962,
+    "label": 24,
+    "text": "Title: A Machine Learning Approach for Predicting Deterioration in Alzheimer\u2019s Disease\nAbstract: This paper explores deterioration in Alzheimer\u2019s Disease using Machine Learning. Subjects were split into two datasets based on baseline diagnosis (Cognitively Normal, Mild Cognitive Impairment), with outcome of deterioration at final visit (a binomial essentially yes/no categorisation) using data from the Alzheimer\u2019s Disease Neuroimaging Initiative (demographics, genetics, CSF, imaging, and neuropsychological testing etc). Six machine learning models, including gradient boosting, were built, and evaluated on these datasets using a nested cross-validation procedure, with the best performing models being put through repeated nested cross-validation at 100 iterations. We were able to demonstrate good predictive ability using CART predicting which of those in the cognitively normal group deteriorated and received a worse diagnosis (AUC = 0.88). For the mild cognitive impairment group, we were able to achieve good predictive ability for deterioration with Elastic Net (AUC = 0.76).",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1963,
+    "label": 16,
+    "text": "Title: Eigenpatches - Adversarial Patches from Principal Components\nAbstract: Adversarial patches are still a simple yet powerful white box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computational expensiveness by analyzing 375 generated patches, calculating the principal components of these and show, that linear combinations of the resulting\"eigenpatches\"can be used to fool object detections successfully.",
+    "neighbors": [
+      1191
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1964,
+    "label": 16,
+    "text": "Title: Semi-Supervised learning for Face Anti-Spoofing using Apex frame\nAbstract: Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance. However, identifying the optimal frames that provide the most valuable input for the face anti-spoofing remains a challenging task. In this paper, we address this challenge by employing Gaussian weighting to create apex frames for videos. Specifically, an apex frame is derived from a video by computing a weighted sum of its frames, where the weights are determined using a Gaussian distribution centered around the video's central frame. Furthermore, we explore various temporal lengths to produce multiple unlabeled apex frames using a Gaussian function, without the need for convolution. By doing so, we leverage the benefits of semi-supervised learning, which considers both labeled and unlabeled apex frames to effectively discriminate between live and spoof classes. Our key contribution emphasizes the apex frame's capacity to represent the most significant moments in the video, while unlabeled apex frames facilitate efficient semi-supervised learning, as they enable the model to learn from videos of varying temporal lengths. Experimental results using four face anti-spoofing databases: CASIA, REPLAY-ATTACK, OULU-NPU, and MSU-MFSD demonstrate the apex frame's efficacy in advancing face anti-spoofing techniques.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1965,
+    "label": 30,
+    "text": "Title: AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning\nAbstract: The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40% of teams for each of the tracks.",
+    "neighbors": [
+      216,
+      1219
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1966,
+    "label": 24,
+    "text": "Title: Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace\nAbstract: To explain\"black-box\"properties of AI models, many approaches, such as post hoc and intrinsically interpretable models, have been proposed to provide plausible explanations that identify human-understandable features/concepts that a trained model uses to make predictions, and attention mechanisms have been widely used to aid in model interpretability by visualizing that information. However, the problem of configuring an interpretable model that effectively communicates and coordinates among computational modules has received less attention. A recently proposed shared global workspace theory demonstrated that networks of distributed modules can benefit from sharing information with a bandwidth-limited working memory because the communication constraints encourage specialization, compositionality, and synchronization among the modules. Inspired by this, we consider how such shared working memories can be realized to build intrinsically interpretable models with better interpretability and performance. Toward this end, we propose Concept-Centric Transformers, a simple yet effective configuration of the shared global workspace for interpretability consisting of: i) an object-centric-based architecture for extracting semantic concepts from input features, ii) a cross-attention mechanism between the learned concept and input embeddings, and iii) standard classification and additional explanation losses to allow human analysts to directly assess an explanation for the model's classification reasoning. We test our approach against other existing concept-based methods on classification tasks for various datasets, including CIFAR100 (super-classes), CUB-200-2011 (bird species), and ImageNet, and we show that our model achieves better classification accuracy than all selected methods across all problems but also generates more consistent concept-based explanations of classification output.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1967,
+    "label": 4,
+    "text": "Title: A Deep Dive into NFT Rug Pulls\nAbstract: NFT rug pull is one of the most prominent type of scam that the developers of a project abandon it and then run away with investors' funds. Although they have drawn attention from our community, to the best of our knowledge, the NFT rug pulls have not been systematically explored. To fill the void, this paper presents the first in-depth study of NFT rug pulls. Specifically, we first compile a list of 253 known NFT rug pulls as our initial ground truth, based on which we perform a pilot study, highlighting the key symptoms of NFT rug pulls. Then, we enforce a strict rule-based method to flag more rug pulled NFT projects in the wild, and have labelled 7,487 NFT rug pulls as our extended ground truth. Atop it, we have investigated the art of NFT rug pulls, with kinds of tricks including explicit ones that are embedded with backdoors, and implicit ones that manipulate the market. To release the expansion of the scam, we further design a prediction model to proactively identify the potential rug pull projects in an early stage ahead of the scam happens. We have implemented a prototype system deployed in the real-world setting for over 5 months. Our system has raised alarms for 7,821 NFT projects, by the time of this writing, which can work as a whistle blower that pinpoints rug pull scams timely, thus mitigating the impacts.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1968,
+    "label": 8,
+    "text": "Title: Toward Standardized Performance Evaluation of Flow-guided Nanoscale Localization\nAbstract: Nanoscale devices featuring Terahertz (THz)-based wireless communication capabilities are envisioned to be deployed within human bloodstreams. Such devices are envisaged to enable fine-grained sensing-based applications for detecting events for early indications of various health conditions, as well as actuation-based ones such as the targeted drug delivery. Intuitively, associating the locations of such events with the events themselves would provide an additional utility for precision diagnostics and treatment. This vision recently yielded a new class of in-body localization coined under the term\"flow-guided nanoscale localization\". Such localization can be piggybacked on THz-based communication for detecting body regions in which events were observed based on the duration of one circulation of a nanodevice in the bloodstream. From a decades-long research on objective benchmarking of\"traditional\"indoor localization, as well as its eventual standardization (e.g., ISO/IEC 18305:2016), we know that in early stages the reported performance results were often incomplete (e.g., targeting a subset of relevant metrics), carrying out benchmarking experiments in different evaluation environments and scenarios, and utilizing inconsistent performance indicators. To avoid such a\"lock-in\"in flow-guided localization, in this paper we discuss a workflow for standardized evaluation of such localization. The workflow is implemented in the form of an open-source framework that is able to jointly account for the mobility of the nanodevices in the bloodstream, in-body THz communication between the nanodevices and on-body anchors, and energy-related and other technological constraints at the nanodevice level. Accounting for these constraints, the framework is able to generate the raw data that can be streamlined into different flow-guided solutions for generating standardized performance benchmarks.",
+    "neighbors": [
+      2126
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1969,
+    "label": 24,
+    "text": "Title: Aligning Text-to-Image Models using Human Feedback\nAbstract: Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text prompts. We propose a fine-tuning method for aligning such models using human feedback, comprising three stages. First, we collect human feedback assessing model output alignment from a set of diverse text prompts. We then use the human-labeled image-text dataset to train a reward function that predicts human feedback. Lastly, the text-to-image model is fine-tuned by maximizing reward-weighted likelihood to improve image-text alignment. Our method generates objects with specified colors, counts and backgrounds more accurately than the pre-trained model. We also analyze several design choices and find that careful investigations on such design choices are important in balancing the alignment-fidelity tradeoffs. Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.",
+    "neighbors": [
+      126,
+      313,
+      1079,
+      1165,
+      1475,
+      1588,
+      1647,
+      1713,
+      1994,
+      2007,
+      2238,
+      2258,
+      2306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1970,
+    "label": 16,
+    "text": "Title: Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks\nAbstract: Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy. Recently, synthetic images generated by text-to-image diffusion models, have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. To address this, we start by uncovering that diffusion models' cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent unsupervised learning techniques ( i.e., contrastive learning, masked modeling, and vision-language pretraining) and introduce customized solutions by fully exploiting the aforementioned free attention masks. Our approach is validated through extensive experiments that show consistent improvements in baseline models across various downstream tasks, including image classification, detection, segmentation, and image-text retrieval. By utilizing our method, it is possible to close the performance gap between unsupervised pretraining on synthetic data and real-world scenarios.",
+    "neighbors": [
+      1775
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1971,
+    "label": 31,
+    "text": "Title: From 10 Blue Links Pages to Feature-Full Search Engine Results Pages - Analysis of the Temporal Evolution of SERP Features\nAbstract: Web Search Engine Results Pages (SERP) are one of the most well-known and used web pages. These pages have started as simple \u201c10 blue links\u201d pages, but the information in SERP currently goes way beyond these links. Several features have been included in these pages to complement organic and sponsored results and attempt to provide answers to the query instead of just pointing to websites that might deliver that information. In this work, we analyze the appearance and evolution of SERP features in the two leading web search engines, Google Search and Microsoft Bing. Using a sample of SERP from the Internet Archive, we analyzed the appearance and evolution of these features. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers.",
+    "neighbors": [
+      1708
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1972,
+    "label": 30,
+    "text": "Title: OpenAssistant Conversations - Democratizing Large Language Model Alignment\nAbstract: Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) greatly reduce the required skill and domain knowledge to effectively harness the capabilities of LLMs, increasing their accessibility and utility across various domains. However, state-of-the-art alignment techniques like RLHF rely on high-quality human feedback data, which is expensive to create and often remains proprietary. In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages distributed across 66,497 conversation trees, in 35 different languages, annotated with 461,292 quality ratings. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers. To demonstrate the OpenAssistant Conversations dataset's effectiveness, we present OpenAssistant, the first fully open-source large-scale instruction-tuned model to be trained on human data. A preference study revealed that OpenAssistant replies are comparably preferred to GPT-3.5-turbo (ChatGPT) with a relative winrate of 48.3% vs. 51.7% respectively. We release our code and data under fully permissive licenses.",
+    "neighbors": [
+      81,
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+      566,
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+      1112,
+      1114,
+      1461,
+      1556,
+      1733,
+      1755,
+      1906,
+      1907,
+      1950,
+      2122,
+      2208,
+      2235,
+      2258
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1973,
+    "label": 27,
+    "text": "Title: On Semidefinite Relaxations for Matrix-Weighted State-Estimation Problems in Robotics\nAbstract: In recent years, there has been remarkable progress in the development of so-called certifiable perception methods, which leverage semidefinite, convex relaxations to find global optima of perception problems in robotics. However, many of these relaxations rely on simplifying assumptions that facilitate the problem formulation, such as an isotropic measurement noise distribution. In this paper, we explore the tightness of the semidefinite relaxations of matrix-weighted (anisotropic) state-estimation problems and reveal the limitations lurking therein: matrix-weighted factors can cause convex relaxations to lose tightness. In particular, we show that the semidefinite relaxations of localization problems with matrix weights may be tight only for low noise levels. We empirically explore the factors that contribute to this loss of tightness and demonstrate that redundant constraints can be used to regain tightness, albeit at the expense of real-time performance. As a second technical contribution of this paper, we show that the state-of-the-art relaxation of scalar-weighted SLAM cannot be used when matrix weights are considered. We provide an alternate formulation and show that its SDP relaxation is not tight (even for very low noise levels) unless specific redundant constraints are used. We demonstrate the tightness of our formulations on both simulated and real-world data.",
+    "neighbors": [
+      1224,
+      1443
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1974,
+    "label": 4,
+    "text": "Title: The Doctrine of Cyber Effect: An Ethics Framework for Defensive Cyber Deception\nAbstract: The lack of established rules and regulations in cyberspace is attributed to the absence of agreed-upon ethical principles, making it difficult to establish accountability, regulations, and laws. Addressing this challenge requires examining cyberspace from fundamental philosophical principles. This work focuses on the ethics of using defensive deception in cyberspace, proposing a doctrine of cyber effect that incorporates five ethical principles: goodwill, deontology, no-harm, transparency, and fairness. To guide the design of defensive cyber deception, we develop a reasoning framework, the game of ethical duplicity, which is consistent with the doctrine. While originally intended for cyber deception, this doctrine has broader applicability, including for ethical issues such as AI accountability and controversies related to YouTube recommendations. By establishing ethical principles, we can promote greater accountability, regulation, and protection in the digital realm.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1975,
+    "label": 31,
+    "text": "Title: Continuous Input Embedding Size Search For Recommender Systems\nAbstract: Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1976,
+    "label": 16,
+    "text": "Title: EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation\nAbstract: Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, so that 2D-3D point correspondences can be partly learned by backpropagating the gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D points from scratch fails to converge with existing approaches, since the deterministic pose is inherently non-differentiable. In this paper, we propose the EPro-PnP a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose on the SE(3) manifold, essentially bringing categorical Softmax to the continuous domain. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle unifies the existing approaches and resembles the attention mechanism. EPro-PnP significantly outperforms competitive baselines, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation and nuScenes 3D object detection benchmarks.3",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1977,
+    "label": 4,
+    "text": "Title: Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach\nAbstract: Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilize the data-rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this article, a geometric graph alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer. Specifically, each intrusion domain is formulated as a graph where vertices and edges represent intrusion categories and category-wise inter-relationships, respectively. The overall shape is preserved via a confused discriminator incapable to identify adjacency matrices between different intrusion domain graphs. A rotation avoidance mechanism and a center point matching mechanism are used to avoid graph misalignment due to rotation and symmetry, respectively. Besides, category-wise semantic knowledge is transferred to act as vertex-level alignment. To exploit the target data, a pseudo-label (PL) election mechanism that jointly considers network prediction, geometric property, and neighborhood information is used to produce fine-grained PL assignment. Upon aligning the intrusion graphs geometrically from different granularities, the transferred intrusion knowledge can boost IID performance. Comprehensive experiments on several intrusion data sets demonstrate state-of-the-art performance of the GGA approach and validate the usefulness of GGA-constituting components.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1978,
+    "label": 16,
+    "text": "Title: Generative Image Dynamics\nAbstract: We present an approach to modeling an image-space prior on scene dynamics. Our prior is learned from a collection of motion trajectories extracted from real video sequences containing natural, oscillating motion such as trees, flowers, candles, and clothes blowing in the wind. Given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a per-pixel long-term motion representation in the Fourier domain, which we call a neural stochastic motion texture. This representation can be converted into dense motion trajectories that span an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping dynamic videos, or allowing users to realistically interact with objects in real pictures.",
+    "neighbors": [
+      490,
+      908,
+      1303,
+      1539,
+      1710
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1979,
+    "label": 6,
+    "text": "Title: INDCOR white paper 3: Interactive Digital Narratives and Interaction\nAbstract: The nature of interaction within Interactive Digital Narrative (IDN) is inherently complex. This is due, in part, to the wide range of potential interaction modes through which IDNs can be conceptualised, produced and deployed and the complex dynamics this might entail. The purpose of this whitepaper is to provide IDN practitioners with the essential knowledge on the nature of interaction in IDNs and allow them to make informed design decisions that lead to the incorporation of complexity thinking throughout the design pipeline, the implementation of the work, and the ways its audience perceives it. This white paper is concerned with the complexities of authoring, delivering and processing dynamic interactive contents from the perspectives of both creators and audiences. This white paper is part of a series of publications by the INDCOR COST Action 18230 (Interactive Narrative Design for Complexity Representations), which all clarify how IDNs representing complexity can be understood and applied (INDCOR WP 0 - 5, 2023).",
+    "neighbors": [
+      1895
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1980,
+    "label": 27,
+    "text": "Title: RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks\nAbstract: Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1981,
+    "label": 31,
+    "text": "Title: Overview of the TREC 2022 Fair Ranking Track\nAbstract: The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents topical content or authors. The 2022 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.",
+    "neighbors": [
+      1821,
+      1911
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1982,
+    "label": 24,
+    "text": "Title: Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks\nAbstract: Multi-armed bandits are extensively used to model sequential decision-making, making them ubiquitous in many real-life applications such as online recommender systems and wireless networking. We consider a multi-agent setting where each agent solves their own bandit instance endowed with a different set of arms. Their goal is to minimize their group regret while collaborating via some communication protocol over a given network. Previous literature on this problem only considered arm heterogeneity and networked agents separately. In this work, we introduce a setting that encompasses both features. For this novel setting, we first provide a rigorous regret analysis for a standard flooding protocol combined with the classic UCB policy. Then, to mitigate the issue of high communication costs incurred by flooding in complex networks, we propose a new protocol called Flooding with Absorption (FwA). We provide a theoretical analysis of the resulting regret bound and discuss the advantages of using FwA over flooding. Lastly, we experimentally verify on various scenarios, including dynamic networks, that FwA leads to significantly lower communication costs despite minimal regret performance loss compared to other network protocols.",
+    "neighbors": [
+      276
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1983,
+    "label": 30,
+    "text": "Title: Theory of Mind Might Have Spontaneously Emerged in Large Language Models\nAbstract: We explore the intriguing possibility that theory of mind (ToM), or the uniquely human ability to impute unobservable mental states to others, might have spontaneously emerged in large language models (LLMs). We designed 40 false-belief tasks, considered a gold standard in testing ToM in humans, and administered them to several LLMs. Each task included a false-belief scenario, three closely matched true-belief controls, and the reversed versions of all four. Smaller and older models solved no tasks; GPT-3-davinci-001 (from May 2020) and GPT-3-davinci-002 (from January 2022) solved 10%; and GPT-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 35% of the tasks, mirroring the performance of three-year-old children. ChatGPT-4 (from June 2023) solved 90% of the tasks, matching the performance of seven-year-old children. These findings suggest the intriguing possibility that ToM, previously considered exclusive to humans, may have spontaneously emerged as a byproduct of LLMs' improving language skills.",
+    "neighbors": [
+      505,
+      1069,
+      1384,
+      1531,
+      1863,
+      2113,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1984,
+    "label": 16,
+    "text": "Title: Explaining Deep Models Through Forgettable Learning Dynamics\nAbstract: Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we address this issue by employing a simple yet effective method by analysing the learning dynamics of deep neural networks in semantic segmentation tasks. Specifically, we visualize the learning behaviour during training by tracking how often samples are learned and forgotten in subsequent training epochs. This further allows us to derive important information about the proximity to the class decision boundary and identify regions that pose a particular challenge to the model. Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions. Finally, we show that our method consistently reduces the amount of regions that are forgotten frequently. We further evaluate our method in light of the segmentation performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1985,
+    "label": 23,
+    "text": "Title: On Using Information Retrieval to Recommend Machine Learning Good Practices for Software Engineers\nAbstract: Machine learning (ML) is nowadays widely used for different purposes and in several disciplines. From self-driving cars to automated medical diagnosis, machine learning models extensively support users' daily activities, and software engineering tasks are no exception. Not embracing good ML practices may lead to pitfalls that hinder the performance of an ML system and potentially lead to unexpected results. Despite the existence of documentation and literature about ML best practices, many non-ML experts turn towards gray literature like blogs and Q&A systems when looking for help and guidance when implementing ML systems. To better aid users in distilling relevant knowledge from such sources, we propose a recommender system that recommends ML practices based on the user's context. As a first step in creating a recommender system for machine learning practices, we implemented Idaka. A tool that provides two different approaches for retrieving/generating ML best practices: i) an information retrieval (IR) engine and ii) a large language model. The IR-engine uses BM25 as the algorithm for retrieving the practices, and a large language model, in our case Alpaca. The platform has been designed to allow comparative studies of best practices retrieval tools. Idaka is publicly available at GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM.",
+    "neighbors": [
+      1052,
+      1481
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1986,
+    "label": 24,
+    "text": "Title: Connector 0.5: A unified framework for graph representation learning\nAbstract: Graph representation learning models aim to represent the graph structure and its features into low-dimensional vectors in a latent space, which can benefit various downstream tasks, such as node classification and link prediction. Due to its powerful graph data modelling capabilities, various graph embedding models and libraries have been proposed to learn embeddings and help researchers ease conducting experiments. In this paper, we introduce a novel graph representation framework covering various graph embedding models, ranging from shallow to state-of-the-art models, namely Connector. First, we consider graph generation by constructing various types of graphs with different structural relations, including homogeneous, signed, heterogeneous, and knowledge graphs. Second, we introduce various graph representation learning models, ranging from shallow to deep graph embedding models. Finally, we plan to build an efficient open-source framework that can provide deep graph embedding models to represent structural relations in graphs. The framework is available at https://github.com/NSLab-CUK/Connector.",
+    "neighbors": [
+      727
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1987,
+    "label": 18,
+    "text": "Title: Training Deep Boltzmann Networks with Sparse Ising Machines\nAbstract: The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization problems. In this paper, we show a new application domain for probabilistic bit (p-bit) based Ising machines by training deep generative AI models with them. Using sparse, asynchronous, and massively parallel Ising machines we train deep Boltzmann networks in a hybrid probabilistic-classical computing setup. We use the full MNIST dataset without any downsampling or reduction in hardware-aware network topologies implemented in moderately sized Field Programmable Gate Arrays (FPGA). Our machine, which uses only 4,264 nodes (p-bits) and about 30,000 parameters, achieves the same classification accuracy (90%) as an optimized software-based restricted Boltzmann Machine (RBM) with approximately 3.25 million parameters. Additionally, the sparse deep Boltzmann network can generate new handwritten digits, a task the 3.25 million parameter RBM fails at despite achieving the same accuracy. Our hybrid computer takes a measured 50 to 64 billion probabilistic flips per second, which is at least an order of magnitude faster than superficially similar Graphics and Tensor Processing Unit (GPU/TPU) based implementations. The massively parallel architecture can comfortably perform the contrastive divergence algorithm (CD-n) with up to n = 10 million sweeps per update, beyond the capabilities of existing software implementations. These results demonstrate the potential of using Ising machines for traditionally hard-to-train deep generative Boltzmann networks, with further possible improvement in nanodevice-based realizations.",
+    "neighbors": [
+      1508,
+      2200
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1988,
+    "label": 6,
+    "text": "Title: Continuous Scatterplot Operators for Bivariate Analysis and Study of Electronic Transitions\nAbstract: Electronic transitions in molecules due to the absorption or emission of light is a complex quantum mechanical process. Their study plays an important role in the design of novel materials. A common yet challenging task in the study is to determine the nature of electronic transitions, namely which subgroups of the molecule are involved in the transition by donating or accepting electrons, followed by an investigation of the variation in the donor-acceptor behavior for different transitions or conformations of the molecules. In this paper, we present a novel approach for the analysis of a bivariate field and show its applicability to the study of electronic transitions. This approach is based on two novel operators, the continuous scatterplot (CSP) lens operator and the CSP peel operator, that enable effective visual analysis of bivariate fields. Both operators can be applied independently or together to facilitate analysis. The operators motivate the design of control polygon inputs to extract fiber surfaces of interest in the spatial domain. The CSPs are annotated with a quantitative measure to further support the visual analysis. We study different molecular systems and demonstrate how the CSP peel and CSP lens operators help identify and study donor and acceptor characteristics in molecular systems.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1989,
+    "label": 16,
+    "text": "Title: Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation\nAbstract: Research in Image Generation has recently made significant progress, particularly boosted by the introduction of Vision-Language models which are able to produce high-quality visual content based on textual inputs. Despite ongoing advancements in terms of generation quality and realism, no methodical frameworks have been defined yet to quantitatively measure the quality of the generated content and the adherence with the prompted requests: so far, only human-based evaluations have been adopted for quality satisfaction and for comparing different generative methods. We introduce a novel automated method for Visual Concept Evaluation (ViCE), i.e. to assess consistency between a generated/edited image and the corresponding prompt/instructions, with a process inspired by the human cognitive behaviour. ViCE combines the strengths of Large Language Models (LLMs) and Visual Question Answering (VQA) into a unified pipeline, aiming to replicate the human cognitive process in quality assessment. This method outlines visual concepts, formulates image-specific verification questions, utilizes the Q&A system to investigate the image, and scores the combined outcome. Although this brave new hypothesis of mimicking humans in the image evaluation process is in its preliminary assessment stage, results are promising and open the door to a new form of automatic evaluation which could have significant impact as the image generation or the image target editing tasks become more and more sophisticated.",
+    "neighbors": [
+      887,
+      1165,
+      2136
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1990,
+    "label": 16,
+    "text": "Title: IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models\nAbstract: The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot reasoning tasks that require multi-step inferencing. To achieve this goal, previous works resort to a divide-and-conquer pipeline. In this paper, we argue that previous efforts have several inherent shortcomings: 1) They rely on domain-specific sub-question decomposing models. 2) They force models to predict the final answer even if the sub-questions or sub-answers provide insufficient information. We address these limitations via IdealGPT, a framework that iteratively decomposes VL reasoning using large language models (LLMs). Specifically, IdealGPT utilizes an LLM to generate sub-questions, a VLM to provide corresponding sub-answers, and another LLM to reason to achieve the final answer. These three modules perform the divide-and-conquer procedure iteratively until the model is confident about the final answer to the main question. We evaluate IdealGPT on multiple challenging VL reasoning tasks under a zero-shot setting. In particular, our IdealGPT outperforms the best existing GPT-4-like models by an absolute 10% on VCR and 15% on SNLI-VE. Code is available at https://github.com/Hxyou/IdealGPT",
+    "neighbors": [
+      173,
+      319,
+      618,
+      855,
+      887,
+      1344,
+      1735,
+      1863,
+      1893,
+      2036,
+      2155,
+      2216
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1991,
+    "label": 24,
+    "text": "Title: Manifold Learning by Mixture Models of VAEs for Inverse Problems\nAbstract: Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice. However, this requires that the data manifold admits a global parameterization. In order to represent manifolds of arbitrary topology, we propose to learn a mixture model of variational autoencoders. Here, every encoder-decoder pair represents one chart of a manifold. We propose a loss function for maximum likelihood estimation of the model weights and choose an architecture that provides us the analytical expression of the charts and of their inverses. Once the manifold is learned, we use it for solving inverse problems by minimizing a data fidelity term restricted to the learned manifold. To solve the arising minimization problem we propose a Riemannian gradient descent algorithm on the learned manifold. We demonstrate the performance of our method for low-dimensional toy examples as well as for deblurring and electrical impedance tomography on certain image manifolds.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 1992,
+    "label": 30,
+    "text": "Title: ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning\nAbstract: This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet. The LLM is compared to manual annotation by both expert classifiers and crowd workers, generally considered the gold standard for such tasks. We use Twitter messages from United States politicians during the 2020 election, providing a ground truth against which to measure accuracy. The paper finds that ChatGPT-4 has achieves higher accuracy, higher reliability, and equal or lower bias than the human classifiers. The LLM is able to correctly annotate messages that require reasoning on the basis of contextual knowledge, and inferences around the author's intentions - traditionally seen as uniquely human abilities. These findings suggest that LLM will have substantial impact on the use of textual data in the social sciences, by enabling interpretive research at a scale.",
+    "neighbors": [
+      352,
+      549,
+      1044,
+      1952,
+      2035,
+      2094
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1993,
+    "label": 24,
+    "text": "Title: On the Robustness of Randomized Ensembles to Adversarial Perturbations\nAbstract: Randomized ensemble classifiers (RECs), where one classifier is randomly selected during inference, have emerged as an attractive alternative to traditional ensembling methods for realizing adversarially robust classifiers with limited compute requirements. However, recent works have shown that existing methods for constructing RECs are more vulnerable than initially claimed, casting major doubts on their efficacy and prompting fundamental questions such as:\"When are RECs useful?\",\"What are their limits?\", and\"How do we train them?\". In this work, we first demystify RECs as we derive fundamental results regarding their theoretical limits, necessary and sufficient conditions for them to be useful, and more. Leveraging this new understanding, we propose a new boosting algorithm (BARRE) for training robust RECs, and empirically demonstrate its effectiveness at defending against strong $\\ell_\\infty$ norm-bounded adversaries across various network architectures and datasets. Our code can be found at https://github.com/hsndbk4/BARRE.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1994,
+    "label": 16,
+    "text": "Title: Boosting Text-to-Image Diffusion Models with Fine-Grained Semantic Rewards\nAbstract: Recent advances in text-to-image diffusion models have achieved remarkable success in generating high-quality, realistic images from given text prompts. However, previous methods fail to perform accurate modality alignment between text concepts and generated images due to the lack of fine-level semantic guidance that successfully diagnoses the modality discrepancy. In this paper, we propose FineRewards to improve the alignment between text and images in text-to-image diffusion models by introducing two new fine-grained semantic rewards: the caption reward and the Semantic Segment Anything (SAM) reward. From the global semantic view, the caption reward generates a corresponding detailed caption that depicts all important contents in the synthetic image via a BLIP-2 model and then calculates the reward score by measuring the similarity between the generated caption and the given prompt. From the local semantic view, the SAM reward segments the generated images into local parts with category labels, and scores the segmented parts by measuring the likelihood of each category appearing in the prompted scene via a large language model, i.e., Vicuna-7B. Additionally, we adopt an assemble reward-ranked learning strategy to enable the integration of multiple reward functions to jointly guide the model training. Adapting results of text-to-image models on the MS-COCO benchmark show that the proposed semantic reward outperforms other baseline reward functions with a considerable margin on both visual quality and semantic similarity with the input prompt. Moreover, by adopting the assemble reward-ranked learning strategy, we further demonstrate that model performance is further improved when adapting under the unifying of the proposed semantic reward with the current image rewards.",
+    "neighbors": [
+      887,
+      1475,
+      1710,
+      1768,
+      1932,
+      1969,
+      2007
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1995,
+    "label": 27,
+    "text": "Title: SphereMap: Dynamic Multi-Layer Graph Structure for Rapid Safety-Aware UAV Planning\nAbstract: A flexible topological representation consisting of a two-layer graph structure built on-board an Unmanned Aerial Vehicle (UAV) by continuously filling the free space of an occupancy map with intersecting spheres is proposed in this letter. Most state-of-the-art planning methods find the shortest paths while keeping the UAV at a pre-defined distance from obstacles. Planning over the proposed structure reaches this pre-defined distance only when necessary, maintaining a safer distance otherwise, while also being orders of magnitude faster than other state-of-the-art methods. Furthermore, we demonstrate how this graph representation can be converted into a lightweight shareable topological-volumetric map of the environment, which enables decentralized multi-robot cooperation. The proposed approach was successfully validated in several kilometers of real subterranean environments, such as caves, devastated industrial buildings, and in the harsh and complex setting of the final event of the DARPA SubT Challenge, which aims to mimic the conditions of real search and rescue missions as closely as possible, and where our approach achieved the 2$^\\mathrm{nd}$ place in the virtual track.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 1996,
+    "label": 24,
+    "text": "Title: Zero-shot Task Preference Addressing Enabled by Imprecise Bayesian Continual Learning\nAbstract: Like generic multi-task learning, continual learning has the nature of multi-objective optimization, and therefore faces a trade-off between the performance of different tasks. That is, to optimize for the current task distribution, it may need to compromise performance on some tasks to improve on others. This means there exist multiple models that are each optimal at different times, each addressing a distinct task-performance trade-off. Researchers have discussed how to train particular models to address specific preferences on these trade-offs. However, existing algorithms require additional sample overheads -- a large burden when there are multiple, possibly infinitely many, preferences. As a response, we propose Imprecise Bayesian Continual Learning (IBCL). Upon a new task, IBCL (1) updates a knowledge base in the form of a convex hull of model parameter distributions and (2) obtains particular models to address preferences with zero-shot. That is, IBCL does not require any additional training overhead to construct preference-addressing models from its knowledge base. We show that models obtained by IBCL have guarantees in identifying the preferred parameters. Moreover, experiments show that IBCL is able to locate the Pareto set of parameters given a preference, maintain similar to better performance than baseline methods, and significantly reduce training overhead via zero-shot preference addressing.",
+    "neighbors": [
+      1216,
+      2259
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1997,
+    "label": 6,
+    "text": "Title: \u201cThat\u2019s important, but...\u201d: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations\nAbstract: Computer science research has led to many breakthrough innovations but has also been scrutinized for enabling technology that has negative, unintended consequences for society. Given the increasing discussions of ethics in the news and among researchers, we interviewed 20 researchers in various CS sub-disciplines to identify whether and how they consider potential unintended consequences of their research innovations. We show that considering unintended consequences is generally seen as important but rarely practiced. Principal barriers are a lack of formal process and strategy as well as the academic practice that prioritizes fast progress and publications. Drawing on these findings, we discuss approaches to support researchers in routinely considering unintended consequences, from bringing diverse perspectives through community participation to increasing incentives to investigate potential consequences. We intend for our work to pave the way for routine explorations of the societal implications of technological innovations before, during, and after the research process.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1998,
+    "label": 28,
+    "text": "Title: Sharper Asymptotically Optimal CDC Schemes via Combinatorial Designs\nAbstract: Coded distributed computing (CDC) was introduced to greatly reduce the communication load for MapReduce computing systems. Such a system has $K$ nodes, $N$ input files, and $Q$ Reduce functions. Each input file is mapped by $r$ nodes and each Reduce function is computed by $s$ nodes. The architecture must allow for coding techniques that achieve the maximum multicast gain. Some CDC schemes that achieve optimal communication load have been proposed before. The parameters $N$ and $Q$ in those schemes, however, grow too fast with respect to $K$ to be of great practical value. To improve the situation, researchers have come up with some asymptotically optimal cascaded CDC schemes with $s+r=K$ from symmetric designs. In this paper, we propose new asymptotically optimal cascaded CDC schemes. Akin to known schemes, ours have $r+s=K$ and make use of symmetric designs as construction tools. Unlike previous schemes, ours have much smaller communication loads, given the same set of parameters $K$, $r$, $N$, and $Q$. We also expand the construction tools to include almost difference sets. Using them, we have managed to construct a new asymptotically optimal cascaded CDC scheme.",
+    "neighbors": [
+      467,
+      605,
+      1674
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 1999,
+    "label": 3,
+    "text": "Title: #RoeOverturned: Twitter Dataset on the Abortion Rights Controversy\nAbstract: On June 24, 2022, the United States Supreme Court overturned landmark rulings made in its 1973 verdict in Roe v. Wade. The justices by way of a majority vote in Dobbs v. Jackson Women's Health Organization, decided that abortion wasn't a constitutional right and returned the issue of abortion to the elected representatives. This decision triggered multiple protests and debates across the US, especially in the context of the midterm elections in November 2022. Given that many citizens use social media platforms to express their views and mobilize for collective action, and given that online debate provides tangible effects on public opinion, political participation, news media coverage, and the political decision-making, it is crucial to understand online discussions surrounding this topic. Toward this end, we present the first large-scale Twitter dataset collected on the abortion rights debate in the United States. We present a set of 74M tweets systematically collected over the course of one year from January 1, 2022 to January 6, 2023.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2000,
+    "label": 4,
+    "text": "Title: A Survey of Network Requirements for Enabling Effective Cyber Deception\nAbstract: In the evolving landscape of cybersecurity, the utilization of cyber deception has gained prominence as a proactive defense strategy against sophisticated attacks. This paper presents a comprehensive survey that investigates the crucial network requirements essential for the successful implementation of effective cyber deception techniques. With a focus on diverse network architectures and topologies, we delve into the intricate relationship between network characteristics and the deployment of deception mechanisms. This survey provides an in-depth analysis of prevailing cyber deception frameworks, highlighting their strengths and limitations in meeting the requirements for optimal efficacy. By synthesizing insights from both theoretical and practical perspectives, we contribute to a comprehensive understanding of the network prerequisites crucial for enabling robust and adaptable cyber deception strategies.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2001,
+    "label": 16,
+    "text": "Title: WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation\nAbstract: Visual anomaly classification and segmentation are vital for automating industrial quality inspection. The focus of prior research in the field has been on training custom models for each quality inspection task, which requires task-specific images and annotation. In this paper we move away from this regime, addressing zero-shot and few-normal-shot anomaly classification and segmentation. Recently CLIP, a vision-language model, has shown revolutionary generality with competitive zero-/few-shot performance in comparison to full-supervision. But CLIP falls short on anomaly classification and segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a compositional ensemble on state words and prompt templates and (2) efficient extraction and aggregation of window/patch/image-level features aligned with text. We also propose its few-normal-shot extension Win-CLIP+, which uses complementary information from normal images. In MVTec-AD (and VisA), without further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AU-ROC in zero-shot anomaly classification and segmentation while WinCLIP + does 93.1%/95.2% (83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.",
+    "neighbors": [
+      892
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2002,
+    "label": 16,
+    "text": "Title: Scaling Vision Transformers to 22 Billion Parameters\nAbstract: The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Vision Transformers (ViT) have introduced the same architecture to image and video modelling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters (Chen et al., 2022). We present a recipe for highly efficient and stable training of a 22B-parameter ViT (ViT-22B) and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features), ViT-22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between fairness and performance, state-of-the-art alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT-22B demonstrates the potential for\"LLM-like\"scaling in vision, and provides key steps towards getting there.",
+    "neighbors": [
+      173,
+      880,
+      1035,
+      1047,
+      1060
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2003,
+    "label": 16,
+    "text": "Title: Joint Adversarial and Collaborative Learning for Self-Supervised Action Recognition\nAbstract: Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning task to solve the self-supervised skeleton-based action recognition task. These methods usually use multiple data streams (i.e., joint, motion, and bone) for ensemble learning, meanwhile, how to construct a discriminative feature space within a single stream and effectively aggregate the information from multiple streams remains an open problem. To this end, we first apply a new contrastive learning method called BYOL to learn from skeleton data and formulate SkeletonBYOL as a simple yet effective baseline for self-supervised skeleton-based action recognition. Inspired by SkeletonBYOL, we further present a joint Adversarial and Collaborative Learning (ACL) framework, which combines Cross-Model Adversarial Learning (CMAL) and Cross-Stream Collaborative Learning (CSCL). Specifically, CMAL learns single-stream representation by cross-model adversarial loss to obtain more discriminative features. To aggregate and interact with multi-stream information, CSCL is designed by generating similarity pseudo label of ensemble learning as supervision and guiding feature generation for individual streams. Exhaustive experiments on three datasets verify the complementary properties between CMAL and CSCL and also verify that our method can perform favorably against state-of-the-art methods using various evaluation protocols. Our code and models are publicly available at \\url{https://github.com/Levigty/ACL}.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2004,
+    "label": 24,
+    "text": "Title: On the Limitations of Physics-Informed Deep Learning: Illustrations Using First-Order Hyperbolic Conservation Law-Based Traffic Flow Models\nAbstract: Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence points to the limitations of PIDL for learning certain types of PDEs. In this paper, we (a) present the challenges in training PIDL architecture, (b) contrast the performance of PIDL architecture in learning a first order scalar hyperbolic conservation law and its parabolic counterpart, (c) investigate the effect of training data sampling, which corresponds to various sensing scenarios in traffic networks, (d) comment on the implications of PIDL limitations for traffic flow estimation and prediction in practice. Case studies present the contrast in PIDL results between learning the traffic flow model (LWR PDE) and its diffusive variation. The outcome indicates that PIDL experiences significant challenges in learning the hyperbolic LWR equation due to the non-smoothness of its solution. Conversely, the architecture with parabolic PDE, augmented with the diffusion term, leads to the successful reassembly of the density data even with the shockwaves present. The paper concludes by providing a discussion on recent assessments of reasons behind the challenge PIDL encounters with hyperbolic PDEs and the corresponding mitigation strategies.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2005,
+    "label": 16,
+    "text": "Title: Generalized Unbiased Scene Graph Generation\nAbstract: Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while overlooking the concept-level imbalance. Actually, even if predicates themselves are balanced, there is still a significant concept-imbalance within them due to the long-tailed distribution of contexts (i.e., subject-object combinations). This concept-level imbalance poses a more pervasive and challenging issue compared to the predicate-level imbalance since subject-object pairs are inherently complex in combinations. Hence, we introduce a novel research problem: Generalized Unbiased Scene Graph Generation (G-USGG), which takes into account both predicate-level and concept-level imbalance. To the end, we propose the Multi-Concept Learning (MCL) framework, which ensures a balanced learning process across rare/ uncommon/ common concepts. MCL first quantifies the concept-level imbalance across predicates in terms of different amounts of concepts, representing as multiple concept-prototypes within the same class. It then effectively learns concept-prototypes by applying the Concept Regularization (CR) technique. Furthermore, to achieve balanced learning over different concepts, we introduce the Balanced Prototypical Memory (BPM), which guides SGG models to generate balanced representations for concept-prototypes. Extensive experiments demonstrate the remarkable efficacy of our model-agnostic strategy in enhancing the performance of benchmark models on both VG-SGG and OI-SGG datasets, leading to new state-of-the-art achievements in two key aspects: predicate-level unbiased relation recognition and concept-level compositional generability.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2006,
+    "label": 27,
+    "text": "Title: We are all Individuals: The Role of Robot Personality and Human Traits in Trustworthy Interaction\nAbstract: As robots take on roles in our society, it is important that their appearance, behaviour and personality are appropriate for the job they are given and are perceived favourably by the people with whom they interact. Here, we provide an extensive quantitative and qualitative study exploring robot personality but, importantly, with respect to individual human traits. Firstly, we show that we can accurately portray personality in a social robot, in terms of extroversion-introversion using vocal cues and linguistic features. Secondly, through garnering preferences and trust ratings for these different robot personalities, we establish that, for a Robo-Barista, an extrovert robot is preferred and trusted more than an introvert robot, regardless of the subject\u2019s own personality. Thirdly, we find that individual attitudes and predispositions towards robots do impact trust in the Robo-Baristas, and are therefore important considerations in addition to robot personality, roles and interaction context when designing any human-robot interaction study.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2007,
+    "label": 16,
+    "text": "Title: ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation\nAbstract: We present a comprehensive solution to learn and improve text-to-image models from human preference feedback. To begin with, we build ImageReward -- the first general-purpose text-to-image human preference reward model -- to effectively encode human preferences. Its training is based on our systematic annotation pipeline including rating and ranking, which collects 137k expert comparisons to date. In human evaluation, ImageReward outperforms existing scoring models and metrics, making it a promising automatic metric for evaluating text-to-image synthesis. On top of it, we propose Reward Feedback Learning (ReFL), a direct tuning algorithm to optimize diffusion models against a scorer. Both automatic and human evaluation support ReFL's advantages over compared methods. All code and datasets are provided at \\url{https://github.com/THUDM/ImageReward}.",
+    "neighbors": [
+      313,
+      847,
+      1079,
+      1475,
+      1647,
+      1713,
+      1969,
+      1994,
+      2238,
+      2258
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2008,
+    "label": 24,
+    "text": "Title: Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces\nAbstract: Benchmarking and co-design are essential for driving optimizations and innovation around ML models, ML software, and next-generation hardware. Full workload benchmarks, e.g. MLPerf, play an essential role in enabling fair comparison across different software and hardware stacks especially once systems are fully designed and deployed. However, the pace of AI innovation demands a more agile methodology to benchmark creation and usage by simulators and emulators for future system co-design. We propose Chakra, an open graph schema for standardizing workload specification capturing key operations and dependencies, also known as Execution Trace (ET). In addition, we propose a complementary set of tools/capabilities to enable collection, generation, and adoption of Chakra ETs by a wide range of simulators, emulators, and benchmarks. For instance, we use generative AI models to learn latent statistical properties across thousands of Chakra ETs and use these models to synthesize Chakra ETs. These synthetic ETs can obfuscate key proprietary information and also target future what-if scenarios. As an example, we demonstrate an end-to-end proof-of-concept that converts PyTorch ETs to Chakra ETs and uses this to drive an open-source training system simulator (ASTRA-sim). Our end-goal is to build a vibrant industry-wide ecosystem of agile benchmarks and tools to drive future AI system co-design.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2009,
+    "label": 16,
+    "text": "Title: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models\nAbstract: We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://github.com/NVlabs/ODISE.",
+    "neighbors": [
+      158,
+      269,
+      719,
+      1049,
+      1262,
+      2156,
+      2277
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2010,
+    "label": 3,
+    "text": "Title: On the Mechanics of NFT Valuation: AI Ethics and Social Media\nAbstract: As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2011,
+    "label": 10,
+    "text": "Title: Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins\nAbstract: We consider the problem of correct motion planning for T-intersection merge-ins of arbitrary geometry and vehicle density. A merge-in support system has to estimate the chances that a gap between two consecutive vehicles can be taken successfully. In contrast to previous models based on heuristic gap size rules, we present an approach which optimizes the integral risk of the situation using parametrized velocity ramps. It accounts for the risks from curves and all involved vehicles (front and rear on all paths) with a so-called survival analysis. For comparison, we also introduce a specially designed extension of the Intelligent Driver Model (IDM) for entering intersections. We show in a quantitative statistical evaluation that the survival method provides advantages in terms of lower absolute risk (i.e., no crash happens) and better risk-utility tradeoff (i.e., making better use of appearing gaps). Furthermore, our approach generalizes to more complex situations with additional risk sources.",
+    "neighbors": [
+      1395
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2012,
+    "label": 30,
+    "text": "Title: Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task\nAbstract: The evolution of Generative Pre-trained Transformer (GPT) models has led to significant advancements in various natural language processing applications, particularly in legal textual entailment. We present an analysis of GPT-3.5 (ChatGPT) and GPT-4 performances on COLIEE Task 4 dataset, a prominent benchmark in this domain. The study encompasses data from Heisei 18 (2006) to Reiwa 3 (2021), exploring the models' abilities to discern entailment relationships within Japanese statute law across different periods. Our preliminary experimental results unveil intriguing insights into the models' strengths and weaknesses in handling legal textual entailment tasks, as well as the patterns observed in model performance. In the context of proprietary models with undisclosed architectures and weights, black-box analysis becomes crucial for evaluating their capabilities. We discuss the influence of training data distribution and the implications on the models' generalizability. This analysis serves as a foundation for future research, aiming to optimize GPT-based models and enable their successful adoption in legal information extraction and entailment applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2013,
+    "label": 30,
+    "text": "Title: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models\nAbstract: Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules. In this work, we propose\"SelfCheckGPT\", a simple sampling-based approach that can be used to fact-check black-box models in a zero-resource fashion, i.e. without an external database. SelfCheckGPT leverages the simple idea that if a LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: i) detect non-factual and factual sentences; and ii) rank passages in terms of factuality. We compare our approach to several baselines and show that in sentence hallucination detection, our approach has AUC-PR scores comparable to or better than grey-box methods, while SelfCheckGPT is best at passage factuality assessment.",
+    "neighbors": [
+      36,
+      424,
+      857,
+      945,
+      1001,
+      1052,
+      1112,
+      1194,
+      1307,
+      1812,
+      2016,
+      2038,
+      2042
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2014,
+    "label": 16,
+    "text": "Title: Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization\nAbstract: nan",
+    "neighbors": [
+      1602,
+      1677
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2015,
+    "label": 30,
+    "text": "Title: THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment\nAbstract: This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task. This task requires the participant to identify a specific paragraph from a given supporting case that entails the decision for the query case. We try traditional lexical matching methods and pre-trained language models with different sizes. Furthermore, learning-to-rank methods are employed to further improve performance. However, learning-to-rank is not very robust on this task. which suggests that answer passages cannot simply be determined with information retrieval techniques. Experimental results show that more parameters and legal knowledge contribute to the legal case entailment task. Finally, we get the third place in COLIEE 2023. The implementation of our method can be found at https://github.com/CSHaitao/THUIR-COLIEE2023.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2016,
+    "label": 30,
+    "text": "Title: Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies\nAbstract: Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behaviors, including hallucination, unfaithful reasoning, and toxic content. A promising approach to rectify these flaws is self-correction, where the LLM itself is prompted or guided to fix problems in its own output. Techniques leveraging automated feedback -- either produced by the LLM itself or some external system -- are of particular interest as they are a promising way to make LLM-based solutions more practical and deployable with minimal human feedback. This paper presents a comprehensive review of this emerging class of techniques. We analyze and taxonomize a wide array of recent work utilizing these strategies, including training-time, generation-time, and post-hoc correction. We also summarize the major applications of this strategy and conclude by discussing future directions and challenges.",
+    "neighbors": [
+      126,
+      127,
+      430,
+      485,
+      644,
+      818,
+      897,
+      975,
+      1267,
+      1490,
+      1647,
+      1863,
+      2013,
+      2038,
+      2100,
+      2136,
+      2244
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2017,
+    "label": 24,
+    "text": "Title: Review of Extreme Multilabel Classification\nAbstract: Extreme multilabel classification or XML, is an active area of interest in machine learning. Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification. Using classical one versus all classification wont scale in this case due to large number of labels, same is true for any other classifiers. Embedding of labels as well as features into smaller label space is an essential first step. Moreover, other issues include existence of head and tail labels, where tail labels are labels which exist in relatively smaller number of given samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2018,
+    "label": 24,
+    "text": "Title: Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning\nAbstract: Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are also powerful in modeling complex policies or trajectories in offline datasets. However, these works have been limited to single-task settings where a generalist agent capable of addressing multi-task predicaments is absent. In this paper, we aim to investigate the effectiveness of a single diffusion model in modeling large-scale multi-task offline data, which can be challenging due to diverse and multimodal data distribution. Specifically, we propose Multi-Task Diffusion Model (\\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings. \\textsc{MTDiff} leverages vast amounts of knowledge available in multi-task data and performs implicit knowledge sharing among tasks. For generative planning, we find \\textsc{MTDiff} outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D. For data synthesis, \\textsc{MTDiff} generates high-quality data for testing tasks given a single demonstration as a prompt, which enhances the low-quality datasets for even unseen tasks.",
+    "neighbors": [
+      319,
+      787,
+      1052,
+      1908
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2019,
+    "label": 9,
+    "text": "Title: Non-Clashing Teaching Maps for Balls in Graphs\nAbstract: Recently, Kirkpatrick et al. [ALT 2019] and Fallat et al. [JMLR 2023] introduced non-clashing teaching and showed it to be the most efficient machine teaching model satisfying the benchmark for collusion-avoidance set by Goldman and Mathias. A teaching map $T$ for a concept class $\\cal{C}$ assigns a (teaching) set $T(C)$ of examples to each concept $C \\in \\cal{C}$. A teaching map is non-clashing if no pair of concepts are consistent with the union of their teaching sets. The size of a non-clashing teaching map (NCTM) $T$ is the maximum size of a $T(C)$, $C \\in \\cal{C}$. The non-clashing teaching dimension NCTD$(\\cal{C})$ of $\\cal{C}$ is the minimum size of an NCTM for $\\cal{C}$. NCTM$^+$ and NCTD$^+(\\cal{C})$ are defined analogously, except the teacher may only use positive examples. We study NCTMs and NCTM$^+$s for the concept class $\\mathcal{B}(G)$ consisting of all balls of a graph $G$. We show that the associated decision problem {\\sc B-NCTD$^+$} for NCTD$^+$ is NP-complete in split, co-bipartite, and bipartite graphs. Surprisingly, we even prove that, unless the ETH fails, {\\sc B-NCTD$^+$} does not admit an algorithm running in time $2^{2^{o(vc)}}\\cdot n^{O(1)}$, nor a kernelization algorithm outputting a kernel with $2^{o(vc)}$ vertices, where vc is the vertex cover number of $G$. These are extremely rare results: it is only the second (fourth, resp.) problem in NP to admit a double-exponential lower bound parameterized by vc (treewidth, resp.), and only one of very few problems to admit an ETH-based conditional lower bound on the number of vertices in a kernel. We complement these lower bounds with matching upper bounds. For trees, interval graphs, cycles, and trees of cycles, we derive NCTM$^+$s or NCTMs for $\\mathcal{B}(G)$ of size proportional to its VC-dimension. For Gromov-hyperbolic graphs, we design an approximate NCTM$^+$ for $\\mathcal{B}(G)$ of size 2.",
+    "neighbors": [
+      129
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2020,
+    "label": 16,
+    "text": "Title: Unsupervised Learning of Robust Spectral Shape Matching\nAbstract: We propose a novel learning-based approach for robust 3D shape matching. Our method builds upon deep functional maps and can be trained in a fully unsupervised manner. Previous deep functional map methods mainly focus on predicting optimised functional maps alone, and then rely on off-the-shelf post-processing to obtain accurate point-wise maps during inference. However, this two-stage procedure for obtaining point-wise maps often yields sub-optimal performance. In contrast, building upon recent insights about the relation between functional maps and point-wise maps, we propose a novel unsupervised loss to couple the functional maps and point-wise maps, and thereby directly obtain point-wise maps without any post-processing. Our approach obtains accurate correspondences not only for near-isometric shapes, but also for more challenging non-isometric shapes and partial shapes, as well as shapes with different discretisation or topological noise. Using a total of nine diverse datasets, we extensively evaluate the performance and demonstrate that our method substantially outperforms previous state-of-the-art methods, even compared to recent supervised methods. Our code is available at https://github.com/dongliangcao/Unsupervised-Learning-of-Robust-Spectral-Shape-Matching.",
+    "neighbors": [
+      1434
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2021,
+    "label": 16,
+    "text": "Title: DIRE for Diffusion-Generated Image Detection\nAbstract: Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusion-generated images. We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data. To address this issue, we propose a novel image representation called DIffusion Reconstruction Error (DIRE), which measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. We observe that diffusion-generated images can be approximately reconstructed by a diffusion model while real images cannot. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images. DIRE provides an effective way to detect images generated by most diffusion models, and it is general for detecting generated images from unseen diffusion models and robust to various perturbations. Furthermore, we establish a comprehensive diffusion-generated benchmark including images generated by eight diffusion models to evaluate the performance of diffusion-generated image detectors. Extensive experiments on our collected benchmark demonstrate that DIRE exhibits superiority over previous generated-image detectors. The code and dataset are available at https://github.com/ZhendongWang6/DIRE.",
+    "neighbors": [
+      154,
+      1179,
+      1730,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2022,
+    "label": 28,
+    "text": "Title: Automorphism Ensemble Polar Code Decoders for 6G URLLC\nAbstract: The URLLC scenario in the upcoming 6G standard requires low latency and ultra reliable transmission, i.e., error correction towards ML performance. Achieving near-ML performance is very challenging especially for short block lengths. Polar codes are a promising candidate and already part of the 5G standard. The Successive Cancellation List (SCL) decoding algorithm provides very good error correction performance but at the cost of high computational decoding complexity resulting in large latency and low area and energy efficiency. Recently, Automorphism Ensemble Decoding (AED) gained a lot of attention to improve the error correction capability. In contrast to SCL, AED performs several low-complexity (e.g., SC) decoding in parallel. However, it is an open question whether AED can compete with sophisticated SCL decoders, especially from an implementation perspective in state of the art silicon technologies. In this paper we present an elaborated AED architecture that uses an advanced path metric based candidate selection to reduce the implementation complexity and compare it to state of the art SCL decoders in a 12nm FinFET technology. Our AED implementation outperform state of the art SCL decoders by up to 4.4x in latency, 8.9x in area efficiency and 4.6x in energy efficiency, while providing the same or even better error correction performance.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2023,
+    "label": 24,
+    "text": "Title: Modeling Edge Features with Deep Bayesian Graph Networks\nAbstract: We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an additional Bayesian network mapping edge features into discrete states to be used by the original model. In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks. Moreover, we successfully test our proposal in a graph regression scenario where edge features are of fundamental importance, and we show that the learned edge representation provides substantial performance improvements against the original model on three link prediction tasks. By keeping the computational complexity linear in the number of edges, the proposed model is amenable to large-scale graph processing.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2024,
+    "label": 24,
+    "text": "Title: Recursive Euclidean Distance-based Robust Aggregation Technique for Federated Learning\nAbstract: Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. This paper proposes a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the Euclidean distance from the most recent global to local models and assigns weights accordingly. Local models which are far away from the most recent global model are assigned smaller weights to minimize the data poisoning effect during aggregation. Our experiments indicate that the proposed algorithm surpasses the latest algorithms by at least 5% in accuracy while reducing time complexity by less than 55%. Our contribution is significant as it addresses the critical issue of malicious attacks in federated learning while reducing aggregation time and improving the accuracy of the global model.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2025,
+    "label": 16,
+    "text": "Title: TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision\nAbstract: End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified framework. Typical methods heavily rely on Region-of-Interest (RoI) operations to extract local features and complex post-processing steps to produce final predictions. To address these limitations, we propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. Specifically, using query embedding per text instance, TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing without sacrificing flexibility or simplicity. Additionally, we design an Adaptive Global aGgregation (AGG) module to transfer global features into sequential features for reading arbitrarily-shaped texts, which overcomes the sub-optimization problem of RoI operations. Furthermore, potential corpus information is utilized from weak annotations to full labels through mixed supervision, further improving text detection and end-to-end text spotting results. Extensive experiments on various bilingual (i.e., English and Chinese) benchmarks demonstrate the superiority of our method. Especially on TDA-ReCTS dataset, TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.",
+    "neighbors": [
+      370
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2026,
+    "label": 10,
+    "text": "Title: Disagreement amongst counterfactual explanations: How transparency can be deceptive\nAbstract: Counterfactual explanations are increasingly used as an Explainable Artificial Intelligence (XAI) technique to provide stakeholders of complex machine learning algorithms with explanations for data-driven decisions. The popularity of counterfactual explanations resulted in a boom in the algorithms generating them. However, not every algorithm creates uniform explanations for the same instance. Even though in some contexts multiple possible explanations are beneficial, there are circumstances where diversity amongst counterfactual explanations results in a potential disagreement problem among stakeholders. Ethical issues arise when for example, malicious agents use this diversity to fairwash an unfair machine learning model by hiding sensitive features. As legislators worldwide tend to start including the right to explanations for data-driven, high-stakes decisions in their policies, these ethical issues should be understood and addressed. Our literature review on the disagreement problem in XAI reveals that this problem has never been empirically assessed for counterfactual explanations. Therefore, in this work, we conduct a large-scale empirical analysis, on 40 datasets, using 12 explanation-generating methods, for two black-box models, yielding over 192.0000 explanations. Our study finds alarmingly high disagreement levels between the methods tested. A malicious user is able to both exclude and include desired features when multiple counterfactual explanations are available. This disagreement seems to be driven mainly by the dataset characteristics and the type of counterfactual algorithm. XAI centers on the transparency of algorithmic decision-making, but our analysis advocates for transparency about this self-proclaimed transparency",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2027,
+    "label": 16,
+    "text": "Title: WIRE: Wavelet Implicit Neural Representations\nAbstract: Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of activation function employed in its MLP network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this trade off. Our Wavelet Implicit neural REpresentation (WIRE) uses as its activation function the complex Gabor wavelet that is well-known to be optimally concentrated in space-frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image over fitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2028,
+    "label": 28,
+    "text": "Title: Spherical Wavefronts Improve MU-MIMO Spectral Efficiency When Using Electrically Large Arrays\nAbstract: Modern multiple-input multiple-output (MIMO) communication systems are almost exclusively designed assuming locally plane wavefronts over antenna arrays. This is known as the far-field approximation and is soundly justified at sub-6-GHz frequencies at most relevant transmission ranges. However, when higher frequencies and shorter transmission ranges are used, the wave curvature over the array is no longer negligible, and arrays operate in the so-called radiative near-field region. This letter aims to show that the classical far-field approximation may significantly underestimate the achievable spectral efficiency of multi-user MIMO communications operating in the 30-GHz bands and above, even at ranges beyond the Fraunhofer distance. For planar arrays with typical sizes, we show that computing combining schemes based on the far-field model significantly reduces channel gain and spatial multiplexing capability. When the radiative near-field model is used, interference rejection schemes, such as the optimal minimum mean-square-error combiner, appear to be very promising, when combined with electrically large arrays, to meet the requirements of next-generation networks.",
+    "neighbors": [
+      606
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2029,
+    "label": 10,
+    "text": "Title: Voyager: An Open-Ended Embodied Agent with Large Language Models\nAbstract: We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. Voyager consists of three key components: 1) an automatic curriculum that maximizes exploration, 2) an ever-growing skill library of executable code for storing and retrieving complex behaviors, and 3) a new iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses the need for model parameter fine-tuning. The skills developed by Voyager are temporally extended, interpretable, and compositional, which compounds the agent's abilities rapidly and alleviates catastrophic forgetting. Empirically, Voyager shows strong in-context lifelong learning capability and exhibits exceptional proficiency in playing Minecraft. It obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill library in a new Minecraft world to solve novel tasks from scratch, while other techniques struggle to generalize. We open-source our full codebase and prompts at https://voyager.minedojo.org/.",
+    "neighbors": [
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+      1502,
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+      2100,
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2030,
+    "label": 16,
+    "text": "Title: VideoChat: Chat-Centric Video Understanding\nAbstract: In this study, we initiate an exploration into video understanding by introducing VideoChat, an end-to-end chat-centric video understanding system. It integrates video foundation models and large language models via a learnable neural interface, excelling in spatiotemporal reasoning, event localization, and causal relationship inference. To instructively tune this system, we propose a video-centric instruction dataset, composed of thousands of videos matched with detailed descriptions and conversations. This dataset emphasizes spatiotemporal reasoning and causal relationships, providing a valuable asset for training chat-centric video understanding systems. Preliminary qualitative experiments reveal our system's potential across a broad spectrum of video applications and set the standard for future research. Access our code and data at https://github.com/OpenGVLab/Ask-Anything",
+    "neighbors": [
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+      2274
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2031,
+    "label": 16,
+    "text": "Title: Emergence of Segmentation with Minimalistic White-Box Transformers\nAbstract: Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown that segmentation properties emerge in vision transformers (ViTs) trained using self-supervised methods such as DINO, but not in those trained on supervised classification tasks. In this study, we probe whether segmentation emerges in transformer-based models solely as a result of intricate self-supervised learning mechanisms, or if the same emergence can be achieved under much broader conditions through proper design of the model architecture. Through extensive experimental results, we demonstrate that when employing a white-box transformer-like architecture known as CRATE, whose design explicitly models and pursues low-dimensional structures in the data distribution, segmentation properties, at both the whole and parts levels, already emerge with a minimalistic supervised training recipe. Layer-wise finer-grained analysis reveals that the emergent properties strongly corroborate the designed mathematical functions of the white-box network. Our results suggest a path to design white-box foundation models that are simultaneously highly performant and mathematically fully interpretable. Code is at \\url{https://github.com/Ma-Lab-Berkeley/CRATE}.",
+    "neighbors": [
+      427,
+      2093
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2032,
+    "label": 27,
+    "text": "Title: Graph-based Global Robot Localization Informing Situational Graphs with Architectural Graphs\nAbstract: In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts moving in an environment, we assume it has no knowledge about it, and it estimates an online situational graph representation (S-Graph) of its surroundings. We develop a novel graph-to-graph matching method, in order to relate the S-Graph estimated online from the robot sensors and the A-Graph extracted from the building plans. Note the challenge in this, as the S-Graph may show a partial view of the full A-Graph, their nodes are heterogeneous and their reference frames are different. After the matching, both graphs are aligned and merged, resulting in what we denote as an informed Situational Graph (iS-Graph), with which we achieve global robot localization and exploitation of prior knowledge from the building plans. Our experiments show that our pipeline shows a higher robustness and a significantly lower pose error than several LiDAR localization baselines.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2033,
+    "label": 16,
+    "text": "Title: ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing\nAbstract: Recent studies have shown that higher accuracy on ImageNet usually leads to better robustness against different corruptions. Therefore, in this paper, instead of following the traditional research paradigm that investigates new out-of-distribution corruptions or perturbations deep models may encounter, we conduct model debugging in in-distribution data to explore which object attributes a model may be sensitive to. To achieve this goal, we create a toolkit for object editing with controls of backgrounds, sizes, positions, and directions, and create a rigorous benchmark named ImageNet-E(diting) for evaluating the image classifier robustness in terms of object attributes. With our ImageNet-E, we evaluate the performance of current deep learning models, including both convolutional neural networks and vision transformers. We find that most models are quite sensitive to attribute changes. A small change in the background can lead to an average of 9.23% drop on top-1 accuracy. We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models. Based on these findings, we discover ways to enhance attribute robustness with preprocessing, architecture designs, and training strategies. We hope this work can provide some insights to the community and open up a new avenue for research in robust computer vision. The code and dataset are available at https://github.com/alibaba/easyrobust.",
+    "neighbors": [
+      1191
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2034,
+    "label": 24,
+    "text": "Title: Computation and Communication Efficient Federated Learning over Wireless Networks\nAbstract: Federated learning (FL) allows model training from local data by edge devices while preserving data privacy. However, the learning accuracy decreases due to the heterogeneity of devices data, and the computation and communication latency increase when updating large scale learning models on devices with limited computational capability and wireless resources. To overcome these challenges, we consider a novel FL framework with partial model pruning and personalization. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine tuned for a specific device, which adapts the model size during FL to reduce both computation and communication overhead and minimize the overall training time, and increases the learning accuracy for the device with non independent and identically distributed (non IID) data. Then, the computation and communication latency and the convergence analysis of the proposed FL framework are mathematically analyzed. Based on the convergence analysis, an optimization problem is formulated to maximize the convergence rate under a latency threshold by jointly optimizing the pruning ratio and wireless resource allocation. By decoupling the optimization problem and deploying Karush Kuhn Tucker (KKT) conditions, we derive the closed form solutions of pruning ratio and wireless resource allocation. Finally, experimental results demonstrate that the proposed FL framework achieves a remarkable reduction of approximately 50 percents computation and communication latency compared with the scheme only with model personalization.",
+    "neighbors": [
+      601
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2035,
+    "label": 30,
+    "text": "Title: Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?\nAbstract: We evaluated the capability of generative pre-trained transformers~(GPT-4) in analysis of textual data in tasks that require highly specialized domain expertise. Specifically, we focused on the task of analyzing court opinions to interpret legal concepts. We found that GPT-4, prompted with annotation guidelines, performs on par with well-trained law student annotators. We observed that, with a relatively minor decrease in performance, GPT-4 can perform batch predictions leading to significant cost reductions. However, employing chain-of-thought prompting did not lead to noticeably improved performance on this task. Further, we demonstrated how to analyze GPT-4's predictions to identify and mitigate deficiencies in annotation guidelines, and subsequently improve the performance of the model. Finally, we observed that the model is quite brittle, as small formatting related changes in the prompt had a high impact on the predictions. These findings can be leveraged by researchers and practitioners who engage in semantic/pragmatic annotations of texts in the context of the tasks requiring highly specialized domain expertise.",
+    "neighbors": [
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+      1992,
+      2094
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2036,
+    "label": 16,
+    "text": "Title: Visual Instruction Tuning\nAbstract: Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant, an end-to-end trained large multimodal model that connects a vision encoder and LLM for general-purpose visual and language understanding.Our early experiments show that LLaVA demonstrates impressive multimodel chat abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen images/instructions, and yields a 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%. We make GPT-4 generated visual instruction tuning data, our model and code base publicly available.",
+    "neighbors": [
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+      2286
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2037,
+    "label": 24,
+    "text": "Title: Fast computation of permutation equivariant layers with the partition algebra\nAbstract: Linear neural network layers that are either equivariant or invariant to permutations of their inputs form core building blocks of modern deep learning architectures. Examples include the layers of DeepSets, as well as linear layers occurring in attention blocks of transformers and some graph neural networks. The space of permutation equivariant linear layers can be identified as the invariant subspace of a certain symmetric group representation, and recent work parameterized this space by exhibiting a basis whose vectors are sums over orbits of standard basis elements with respect to the symmetric group action. A parameterization opens up the possibility of learning the weights of permutation equivariant linear layers via gradient descent. The space of permutation equivariant linear layers is a generalization of the partition algebra, an object first discovered in statistical physics with deep connections to the representation theory of the symmetric group, and the basis described above generalizes the so-called orbit basis of the partition algebra. We exhibit an alternative basis, generalizing the diagram basis of the partition algebra, with computational benefits stemming from the fact that the tensors making up the basis are low rank in the sense that they naturally factorize into Kronecker products. Just as multiplication by a rank one matrix is far less expensive than multiplication by an arbitrary matrix, multiplication with these low rank tensors is far less expensive than multiplication with elements of the orbit basis. Finally, we describe an algorithm implementing multiplication with these basis elements.",
+    "neighbors": [
+      1953
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2038,
+    "label": 30,
+    "text": "Title: Cognitive Mirage: A Review of Hallucinations in Large Language Models\nAbstract: As large language models continue to develop in the field of AI, text generation systems are susceptible to a worrisome phenomenon known as hallucination. In this study, we summarize recent compelling insights into hallucinations in LLMs. We present a novel taxonomy of hallucinations from various text generation tasks, thus provide theoretical insights, detection methods and improvement approaches. Based on this, future research directions are proposed. Our contribution are threefold: (1) We provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks; (2) We provide theoretical analyses of hallucinations in LLMs and provide existing detection and improvement methods; (3) We propose several research directions that can be developed in the future. As hallucinations garner significant attention from the community, we will maintain updates on relevant research progress.",
+    "neighbors": [
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+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2039,
+    "label": 24,
+    "text": "Title: Least Square Value Iteration is Robust Under Locally Bounded Misspecification Error\nAbstract: The success of reinforcement learning heavily relies on the function approximation of policy, value or models, where misspecification (a mismatch between the ground-truth and best function approximators) naturally occurs especially when the ground-truth is complex. As misspecification error does not vanish even with infinite number of samples, designing algorithms that are robust under misspecification is of paramount importance. Recently, it is shown that policy-based approaches can be robust even when the policy function approximation is under a large locally-bounded misspecification error, with which the function class may have $\\Omega(1)$ approximation error in certain states and actions but is only small on average under a policy-induced state-distribution; whereas it is only known that value-based approach can effectively learn under globally-bounded misspecification error, i.e., the approximation errors to value functions have a uniform upper bound on all state-actions. Yet it remains an open question whether similar robustness can be achieved with value-based approaches. In this paper, we answer this question affirmatively by showing that the algorithm, Least-Square-Value-Iteration [Jin et al, 2020], with carefully designed exploration bonus can achieve robustness under local misspecification error bound. In particular, we show that algorithm achieves a regret bound of $\\widetilde{O}\\left(\\sqrt{d^3KH^4} + dKH^2\\zeta \\right)$, where $d$ is the dimension of linear features, $H$ is the length of the episode, $K$ is the total number of episodes, and $\\zeta$ is the local bound of the misspecification error. Moreover, we show that the algorithm can achieve the same regret bound without knowing $\\zeta$ and can be used as robust policy evaluation oracle that can be applied to improve sample complexity in policy-based approaches.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2040,
+    "label": 16,
+    "text": "Title: InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion\nAbstract: This paper addresses a novel task of anticipating 3D human-object interactions (HOIs). Most existing research on HOI synthesis lacks comprehensive whole-body interactions with dynamic objects, e.g., often limited to manipulating small or static objects. Our task is significantly more challenging, as it requires modeling dynamic objects with various shapes, capturing whole-body motion, and ensuring physically valid interactions. To this end, we propose InterDiff, a framework comprising two key steps: (i) interaction diffusion, where we leverage a diffusion model to encode the distribution of future human-object interactions; (ii) interaction correction, where we introduce a physics-informed predictor to correct denoised HOIs in a diffusion step. Our key insight is to inject prior knowledge that the interactions under reference with respect to contact points follow a simple pattern and are easily predictable. Experiments on multiple human-object interaction datasets demonstrate the effectiveness of our method for this task, capable of producing realistic, vivid, and remarkably long-term 3D HOI predictions.",
+    "neighbors": [
+      1684
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2041,
+    "label": 4,
+    "text": "Title: Fuzzification-based Feature Selection for Enhanced Website Content Encryption\nAbstract: We propose a novel approach that utilizes fuzzification theory to perform feature selection on website content for encryption purposes. Our objective is to identify and select the most relevant features from the website by harnessing the principles of fuzzy logic. Fuzzification allows us to transform the crisp website content into fuzzy representations, enabling a more nuanced analysis of their characteristics. By considering the degree of membership of each feature in different fuzzy categories, we can evaluate their importance and relevance for encryption. This approach enables us to prioritize and focus on the features that exhibit higher membership degrees, indicating their significance in the encryption process. By employing fuzzification-based feature selection, we aim to enhance the effectiveness and efficiency of website content encryption, ultimately improving the overall internet security.",
+    "neighbors": [
+      1576,
+      2162,
+      2280
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2042,
+    "label": 30,
+    "text": "Title: GPTScore: Evaluate as You Desire\nAbstract: Generative Artificial Intelligence (AI) has enabled the development of sophisticated models that are capable of producing high-caliber text, images, and other outputs through the utilization of large pre-trained models. Nevertheless, assessing the quality of the generation is an even more arduous task than the generation itself, and this issue has not been given adequate consideration recently. This paper proposes a novel evaluation framework, GPTScore, which utilizes the emergent abilities (e.g., zero-shot instruction) of generative pre-trained models to score generated texts. There are 19 pre-trained models explored in this paper, ranging in size from 80M (e.g., FLAN-T5-small) to 175B (e.g., GPT3). Experimental results on four text generation tasks, 22 evaluation aspects, and corresponding 37 datasets demonstrate that this approach can effectively allow us to achieve what one desires to evaluate for texts simply by natural language instructions. This nature helps us overcome several long-standing challenges in text evaluation--how to achieve customized, multi-faceted evaluation without the need for annotated samples. We make our code publicly available at https://github.com/jinlanfu/GPTScore.",
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+      2087
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2043,
+    "label": 5,
+    "text": "Title: CXL Memory as Persistent Memory for Disaggregated HPC: A Practical Approach\nAbstract: In the landscape of High-Performance Computing (HPC), the quest for efficient and scalable memory solutions remains paramount. The advent of Compute Express Link (CXL) introduces a promising avenue with its potential to function as a Persistent Memory (PMem) solution in the context of disaggregated HPC systems. This paper presents a comprehensive exploration of CXL memory's viability as a candidate for PMem, supported by physical experiments conducted on cutting-edge multi-NUMA nodes equipped with CXL-attached memory prototypes. Our study not only benchmarks the performance of CXL memory but also illustrates the seamless transition from traditional PMem programming models to CXL, reinforcing its practicality. To substantiate our claims, we establish a tangible CXL prototype using an FPGA card embodying CXL 1.1/2.0 compliant endpoint designs (Intel FPGA CXL IP). Performance evaluations, executed through the STREAM and STREAM-PMem benchmarks, showcase CXL memory's ability to mirror PMem characteristics in App-Direct and Memory Mode while achieving impressive bandwidth metrics with Intel 4th generation Xeon (Sapphire Rapids) processors. The results elucidate the feasibility of CXL memory as a persistent memory solution, outperforming previously established benchmarks. In contrast to published DCPMM results, our CXL-DDR4 memory module offers comparable bandwidth to local DDR4 memory configurations, albeit with a moderate decrease in performance. The modified STREAM-PMem application underscores the ease of transitioning programming models from PMem to CXL, thus underscoring the practicality of adopting CXL memory.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2044,
+    "label": 30,
+    "text": "Title: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature\nAbstract: The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection. Specifically, we demonstrate that text sampled from an LLM tends to occupy negative curvature regions of the model's log probability function. Leveraging this observation, we then define a new curvature-based criterion for judging if a passage is generated from a given LLM. This approach, which we call DetectGPT, does not require training a separate classifier, collecting a dataset of real or generated passages, or explicitly watermarking generated text. It uses only log probabilities computed by the model of interest and random perturbations of the passage from another generic pre-trained language model (e.g., T5). We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection, notably improving detection of fake news articles generated by 20B parameter GPT-NeoX from 0.81 AUROC for the strongest zero-shot baseline to 0.95 AUROC for DetectGPT. See https://ericmitchell.ai/detectgpt for code, data, and other project information.",
+    "neighbors": [
+      27,
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+      1600,
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+      1805
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2045,
+    "label": 24,
+    "text": "Title: Unwrapping All ReLU Networks\nAbstract: Deep ReLU Networks can be decomposed into a collection of linear models, each defined in a region of a partition of the input space. This paper provides three results extending this theory. First, we extend this linear decompositions to Graph Neural networks and tensor convolutional networks, as well as networks with multiplicative interactions. Second, we provide proofs that neural networks can be understood as interpretable models such as Multivariate Decision trees and logical theories. Finally, we show how this model leads to computing cheap and exact SHAP values. We validate the theory through experiments with on Graph Neural Networks.",
+    "neighbors": [
+      2248
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2046,
+    "label": 16,
+    "text": "Title: Raw Image Reconstruction with Learned Compact Metadata\nAbstract: While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements. Very recent works propose to compress raw images by designing the sampling masks in the raw image pixel space, leading to suboptimal image representations and redundant metadata. In this paper, we propose a novel framework to learn a compact representation in the latent space serving as the metadata in an end-to-end manner. Furthermore, we propose a novel sRGB-guided context model with the improved entropy estimation strategies, which leads to better reconstruction quality, smaller size of metadata, and faster speed. We illustrate how the proposed raw image compression scheme can adaptively allocate more bits to image regions that are important from a global perspective. The experimental results show that the proposed method can achieve superior raw image reconstruction results using a smaller size of the metadata on both uncompressed sRGB images and JPEG images. The code will be released at https://github.com/wyf0912/R2LCM",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2047,
+    "label": 10,
+    "text": "Title: Are Emergent Abilities of Large Language Models a Mirage?\nAbstract: Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities; (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.",
+    "neighbors": [
+      619,
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2048,
+    "label": 16,
+    "text": "Title: Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation\nAbstract: The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability. However, we argue that every pixel matters to the model training, even those unreliable and ambiguous pixels. Intuitively, an unreliable prediction may get confused among the top classes, however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative key to those most unlikely categories. Therefore, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative keys, and manage to train the model with all candidate pixels. Considering the training evolution, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.",
+    "neighbors": [
+      1489,
+      1637
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2049,
+    "label": 16,
+    "text": "Title: Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision\nAbstract: Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals. However, their applicability is limited to scenarios where training samples are readily available, which is not always the case in real-world applications. For example, in inverse graphics, the goal is to generate samples from a distribution of 3D scenes that align with a given image, but ground-truth 3D scenes are unavailable and only 2D images are accessible. To address this limitation, we propose a novel class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never directly observed. Instead, these signals are measured indirectly through a known differentiable forward model, which produces partial observations of the unknown signal. Our approach involves integrating the forward model directly into the denoising process. This integration effectively connects the generative modeling of observations with the generative modeling of the underlying signals, allowing for end-to-end training of a conditional generative model over signals. During inference, our approach enables sampling from the distribution of underlying signals that are consistent with a given partial observation. We demonstrate the effectiveness of our method on three challenging computer vision tasks. For instance, in the context of inverse graphics, our model enables direct sampling from the distribution of 3D scenes that align with a single 2D input image.",
+    "neighbors": [
+      357,
+      1125,
+      1426
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2050,
+    "label": 27,
+    "text": "Title: Autonomous Needle Navigation in Retinal Microsurgery: Evaluation in ex vivo Porcine Eyes\nAbstract: Important challenges in retinal microsurgery in-clude prolonged operating time, inadequate force feedback, and poor depth perception due to a constrained top-down view of the surgery. The introduction of robot-assisted technology could potentially deal with such challenges and improve the surgeon's performance. Motivated by such challenges, this work develops a strategy for autonomous needle navigation in retinal microsurgery aiming to achieve precise manipulation, reduced end-to-end surgery time, and enhanced safety. This is accomplished through real-time geometry estimation and chance-constrained Model Predictive Control (MPC) resulting in high positional accuracy while keeping scleral forces within a safe level. The robotic system is validated using both open-sky and intact (with lens and partial vitreous removal) ex vivo porcine eyes. The experimental results demonstrate that the generation of safe control trajectories is robust to small motions associated with head drift. The mean navigation time and scleral force for MPC navigation experiments are 7.208 s and 11.97 mN, which can be considered efficient and well within acceptable safe limits. The resulting mean errors along lateral directions of the retina are below 0.06 mm, which is below the typical hand tremor amplitude in retinal microsurgery.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2051,
+    "label": 16,
+    "text": "Title: HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes\nAbstract: Hyperbolic manifolds for visual representation learning allow for effective learning of semantic class hierarchies by naturally embedding tree-like structures with low distortion within a low-dimensional representation space. The highly separable semantic class hierarchies produced by hyperbolic learning have shown to be powerful in low-shot tasks, however, their application in self-supervised learning is yet to be explored fully. In this work, we explore the use of hyperbolic representation space for self-supervised representation learning for prototype-based clustering approaches. First, we extend the Masked Siamese Networks to operate on the Poincar\\'e ball model of hyperbolic space, secondly, we place prototypes on the ideal boundary of the Poincar\\'e ball. Unlike previous methods we project to the hyperbolic space at the output of the encoder network and utilise a hyperbolic projection head to ensure that the representations used for downstream tasks remain hyperbolic. Empirically we demonstrate the ability of these methods to perform comparatively to Euclidean methods in lower dimensions for linear evaluation tasks, whilst showing improvements in extreme few-shot learning tasks.",
+    "neighbors": [
+      1211,
+      1609
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2052,
+    "label": 27,
+    "text": "Title: A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems\nAbstract: Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article provides an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and consistency checks. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2053,
+    "label": 39,
+    "text": "Title: Condorcet Domains of Degree at most Seven\nAbstract: In this paper we give the first explicit enumeration of all maximal Condorcet domains on $n\\leq 7$ alternatives. This has been accomplished by developing a new algorithm for constructing Condorcet domains, and an implementation of that algorithm which has been run on a supercomputer. We follow this up by the first survey of the properties of all maximal Condorcet domains up to degree 7, with respect to many properties studied in the social sciences and mathematical literature. We resolve several open questions posed by other authors, both by examples from our data and theorems. We give a new set of results on the symmetry properties of Condorcet domains which unify earlier works. Finally we discuss connections to other domain types such as non-dictatorial domains and generalisations of single-peaked domains. All our data is made freely available for other researches via a new website.",
+    "neighbors": [
+      1701
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2054,
+    "label": 2,
+    "text": "Title: Outcome Separation Logic: Local Reasoning for Correctness and Incorrectness with Computational Effects\nAbstract: Separation logic's compositionality and local reasoning properties have led to significant advances in scalable static analysis. But program analysis has new challenges--many programs display computational effects (e.g. randomization) and, orthogonally, static analysers must handle incorrectness too. We present Outcome Separation Logic (OSL), a program logic that is sound for both correctness and incorrectness reasoning with varying effects. OSL has a frame rule--just like separation logic--but uses different underlying assumptions that lift restrictions imposed by SL, which precluded reasoning about incorrectness and effects. Building on this foundational theory, we also define symbolic execution algorithms that use bi-abduction to derive specifications for programs with effects. This involves a new tri-abduction procedure to analyze programs whose execution branches due to effects such as nondeterministic or probabilistic choice. This work furthers the compositionality promised by separation logic by opening up the possibility for greater reuse of analysis tools across two dimensions: bug-finding vs verification in programs with varying effects.",
+    "neighbors": [
+      1774
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2055,
+    "label": 16,
+    "text": "Title: Exploiting Diverse Feature for Multimodal Sentiment Analysis\nAbstract: In this paper, we present our solution to the MuSe-Personalisation sub-challenge in the MuSe 2023 Multimodal Sentiment Analysis Challenge. The task of MuSe-Personalisation aims to predict the continuous arousal and valence values of a participant based on their audio-visual, language, and physiological signal modalities data. Considering different people have personal characteristics, the main challenge of this task is how to build robustness feature presentation for sentiment prediction. To address this issue, we propose exploiting diverse features. Specifically, we proposed a series of feature extraction methods to build a robust representation and model ensemble. We empirically evaluate the performance of the utilized method on the officially provided dataset. \\textbf{As a result, we achieved 3rd place in the MuSe-Personalisation sub-challenge.} Specifically, we achieve the results of 0.8492 and 0.8439 for MuSe-Personalisation in terms of arousal and valence CCC.",
+    "neighbors": [
+      142,
+      459
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2056,
+    "label": 27,
+    "text": "Title: Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models\nAbstract: Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multi-objective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning~(RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Videos: https://sites.google.com/view/naturalwalkingrl",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2057,
+    "label": 16,
+    "text": "Title: Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey\nAbstract: nan",
+    "neighbors": [
+      106,
+      1851
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2058,
+    "label": 10,
+    "text": "Title: Diversity Induced Environment Design via Self-Play\nAbstract: Recent work on designing an appropriate distribution of environments has shown promise for training effective generally capable agents. Its success is partly because of a form of adaptive curriculum learning that generates environment instances (or levels) at the frontier of the agent's capabilities. However, such an environment design framework often struggles to find effective levels in challenging design spaces and requires costly interactions with the environment. In this paper, we aim to introduce diversity in the Unsupervised Environment Design (UED) framework. Specifically, we propose a task-agnostic method to identify observed/hidden states that are representative of a given level. The outcome of this method is then utilized to characterize the diversity between two levels, which as we show can be crucial to effective performance. In addition, to improve sampling efficiency, we incorporate the self-play technique that allows the environment generator to automatically generate environments that are of great benefit to the training agent. Quantitatively, our approach, Diversity-induced Environment Design via Self-Play (DivSP), shows compelling performance over existing methods.",
+    "neighbors": [
+      1725
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2059,
+    "label": 5,
+    "text": "Title: Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks\nAbstract: Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the diffusion model-based AIGC technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices. Our code is available at https://github.com/HongyangDu/DistributedDiffusion.",
+    "neighbors": [
+      146,
+      1788
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2060,
+    "label": 24,
+    "text": "Title: Progress measures for grokking via mechanistic interpretability\nAbstract: Neural networks often exhibit emergent behavior, where qualitatively new capabilities arise from scaling up the amount of parameters, training data, or training steps. One approach to understanding emergence is to find continuous \\textit{progress measures} that underlie the seemingly discontinuous qualitative changes. We argue that progress measures can be found via mechanistic interpretability: reverse-engineering learned behaviors into their individual components. As a case study, we investigate the recently-discovered phenomenon of ``grokking'' exhibited by small transformers trained on modular addition tasks. We fully reverse engineer the algorithm learned by these networks, which uses discrete Fourier transforms and trigonometric identities to convert addition to rotation about a circle. We confirm the algorithm by analyzing the activations and weights and by performing ablations in Fourier space. Based on this understanding, we define progress measures that allow us to study the dynamics of training and split training into three continuous phases: memorization, circuit formation, and cleanup. Our results show that grokking, rather than being a sudden shift, arises from the gradual amplification of structured mechanisms encoded in the weights, followed by the later removal of memorizing components.",
+    "neighbors": [
+      687,
+      1074
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2061,
+    "label": 36,
+    "text": "Title: Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations\nAbstract: We consider the problem of guaranteeing a fraction of the maximin-share (MMS) when allocating a set of indivisible items to a set of agents with fractionally subadditive (XOS) valuations. For XOS valuations, it has been previously shown that for some instances no allocation can guarantee a fraction better than $1/2$ of the maximin-share to all the agents. Also, a deterministic allocation exists that guarantees $0.219225$ of the maximin-share to each agent. Our results pertain to deterministic and randomized allocations. On the deterministic side, we improve the best approximation guarantee for fractionally subadditive valuations to $3/13 = 0.230769$. We develop new ideas for allocating large items which might be of independent interest. Furthermore, we investigate randomized algorithms and best-of-both-worlds fairness guarantees. We propose a randomized allocation that is $1/4$-MMS ex-ante and $1/8$-MMS ex-post for XOS valuations. Moreover, we prove an upper bound of $3/4$ on the ex-ante guarantee for this class of valuations.",
+    "neighbors": [
+      1594
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2062,
+    "label": 30,
+    "text": "Title: ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning\nAbstract: Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for language generation and highly attract public attention. Among various exciting applications discovered for ChatGPT in English, the model can process and generate texts for multiple languages due to its multilingual training data. Given the broad adoption of ChatGPT for English in different problems and areas, a natural question is whether ChatGPT can also be applied effectively for other languages or it is necessary to develop more language-specific technologies. The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i.e., beyond reported anecdotes), which is still missing or limited in current research. Our work aims to fill this gap for the evaluation of ChatGPT and similar LLMs to provide more comprehensive information for multilingual NLP applications. While this work will be an ongoing effort to include additional experiments in the future, our current paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. We also focus on the zero-shot learning setting for ChatGPT to improve reproducibility and better simulate the interactions of general users. Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages, calling for further research to develop better models and understanding for multilingual learning.",
+    "neighbors": [
+      485,
+      748,
+      834,
+      971,
+      975,
+      1001,
+      1034,
+      1035,
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+      1133,
+      1349,
+      1636,
+      1655,
+      1797,
+      1949,
+      1952,
+      2244,
+      2252
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2063,
+    "label": 24,
+    "text": "Title: Deep-OSG: Deep Learning of Operators in Semigroup\nAbstract: This paper proposes a novel deep learning approach for learning operators in semigroup, with applications to modeling unknown autonomous dynamical systems using time series data collected at varied time lags. It is a sequel to the previous flow map learning (FML) works [T. Qin, K. Wu, and D. Xiu, J. Comput. Phys., 395:620--635, 2019], [K. Wu and D. Xiu, J. Comput. Phys., 408:109307, 2020], and [Z. Chen, V. Churchill, K. Wu, and D. Xiu, J. Comput. Phys., 449:110782, 2022], which focused on learning single evolution operator with a fixed time step. This paper aims to learn a family of evolution operators with variable time steps, which constitute a semigroup for an autonomous system. The semigroup property is very crucial and links the system's evolutionary behaviors across varying time scales, but it was not considered in the previous works. We propose for the first time a framework of embedding the semigroup property into the data-driven learning process, through a novel neural network architecture and new loss functions. The framework is very feasible, can be combined with any suitable neural networks, and is applicable to learning general autonomous ODEs and PDEs. We present the rigorous error estimates and variance analysis to understand the prediction accuracy and robustness of our approach, showing the remarkable advantages of semigroup awareness in our model. Moreover, our approach allows one to arbitrarily choose the time steps for prediction and ensures that the predicted results are well self-matched and consistent. Extensive numerical experiments demonstrate that embedding the semigroup property notably reduces the data dependency of deep learning models and greatly improves the accuracy, robustness, and stability for long-time prediction.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2064,
+    "label": 16,
+    "text": "Title: What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?\nAbstract: Recent advancements in Large Language Models (LLMs) such as GPT4 have displayed exceptional multi-modal capabilities in following open-ended instructions given images. However, the performance of these models heavily relies on design choices such as network structures, training data, and training strategies, and these choices have not been extensively discussed in the literature, making it difficult to quantify progress in this field. To address this issue, this paper presents a systematic and comprehensive study, quantitatively and qualitatively, on training such models. We implement over 20 variants with controlled settings. Concretely, for network structures, we compare different LLM backbones and model designs. For training data, we investigate the impact of data and sampling strategies. For instructions, we explore the influence of diversified prompts on the instruction-following ability of the trained models. For benchmarks, we contribute the first, to our best knowledge, comprehensive evaluation set including both image and video tasks through crowd-sourcing. Based on our findings, we present Lynx, which performs the most accurate multi-modal understanding while keeping the best multi-modal generation ability compared to existing open-sourced GPT4-style models.",
+    "neighbors": [
+      34,
+      144,
+      173,
+      319,
+      392,
+      887,
+      1039,
+      1047,
+      1052,
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+      1526,
+      1735,
+      1765,
+      1863,
+      1907,
+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2065,
+    "label": 24,
+    "text": "Title: Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery\nAbstract: Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especially in the low-data and high-noise limit. In this paper, we perform a theoretical study on ensemble sparse model discovery, which shows empirical success in terms of accuracy and robustness to noise. In particular, we analyse the bootstrapping-based sequential thresholding least-squares estimator. We show that this bootstrapping-based ensembling technique can perform a provably correct variable selection procedure with an exponential convergence rate of the error rate. In addition, we show that the ensemble sparse model discovery method can perform computationally efficient uncertainty estimation, compared to expensive Bayesian uncertainty quantification methods via MCMC. We demonstrate the convergence properties and connection to uncertainty quantification in various numerical studies on synthetic sparse linear regression and sparse model discovery. The experiments on sparse linear regression support that the bootstrapping-based sequential thresholding least-squares method has better performance for sparse variable selection compared to LASSO, thresholding least-squares, and bootstrapping-based LASSO. In the sparse model discovery experiment, we show that the bootstrapping-based sequential thresholding least-squares method can provide valid uncertainty quantification, converging to a delta measure centered around the true value with increased sample sizes. Finally, we highlight the improved robustness to hyperparameter selection under shifting noise and sparsity levels of the bootstrapping-based sequential thresholding least-squares method compared to other sparse regression methods.",
+    "neighbors": [
+      683,
+      1580
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2066,
+    "label": 16,
+    "text": "Title: Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation\nAbstract: Existing knowledge distillation works for semantic seg-mentation mainly focus on transfering high-level contextual knowledge from teacher to student. However, low-level texture knowledge is also of vital importance for characterizing the local structural pattern and global statistical prop-erty, such as boundary, smoothness, regularity and color contrast, which may not be well addressed by high-level deep features. In this paper, we are intended to take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for Semantic Segmentation. Specifically, for structural texture knowledge, we introduce a Contourlet Decomposition Module (CDM) that decomposes low-level features with iterative laplacian pyramid and directional filter bank to mine the structural texture knowledge. For statistical knowledge, we propose a Denoised Texture Intensity Equalization Module (DTIEM) to adaptively extract and enhance statistical texture knowledge through heuristics iterative quantization and denoised operation. Finally, each knowledge learning is supervised by an individual loss function, forcing the student network to mimic the teacher better from a broader perspective. Experiments show that the proposed method achieves state-of-the-art performance on Cityscapes, Pascal VOC 2012 and ADE20K datasets.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2067,
+    "label": 27,
+    "text": "Title: iPlanner: Imperative Path Planning\nAbstract: The problem of path planning has been studied for years. Classic planning pipelines, including perception, mapping, and path searching, can result in latency and compounding errors between modules. While recent studies have demonstrated the effectiveness of end-to-end learning methods in achieving high planning efficiency, these methods often struggle to match the generalization abilities of classic approaches in handling different environments. Moreover, end-to-end training of policies often requires a large number of labeled data or training iterations to reach convergence. In this paper, we present a novel Imperative Learning (IL) approach. This approach leverages a differentiable cost map to provide implicit supervision during policy training, eliminating the need for demonstrations or labeled trajectories. Furthermore, the policy training adopts a Bi-Level Optimization (BLO) process, which combines network update and metric-based trajectory optimization, to generate a smooth and collision-free path toward the goal based on a single depth measurement. The proposed method allows task-level costs of predicted trajectories to be backpropagated through all components to update the network through direct gradient descent. In our experiments, the method demonstrates around 4x faster planning than the classic approach and robustness against localization noise. Additionally, the IL approach enables the planner to generalize to various unseen environments, resulting in an overall 26-87% improvement in SPL performance compared to baseline learning methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2068,
+    "label": 16,
+    "text": "Title: TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation\nAbstract: This paper proposes a novel batch normalization strategy for test-time adaptation. Recent test-time adaptation methods heavily rely on the modified batch normalization, i.e., transductive batch normalization (TBN), which calculates the mean and the variance from the current test batch rather than using the running mean and variance obtained from the source data, i.e., conventional batch normalization (CBN). Adopting TBN that employs test batch statistics mitigates the performance degradation caused by the domain shift. However, re-estimating normalization statistics using test data depends on impractical assumptions that a test batch should be large enough and be drawn from i.i.d. stream, and we observed that the previous methods with TBN show critical performance drop without the assumptions. In this paper, we identify that CBN and TBN are in a trade-off relationship and present a new test-time normalization (TTN) method that interpolates the statistics by adjusting the importance between CBN and TBN according to the domain-shift sensitivity of each BN layer. Our proposed TTN improves model robustness to shifted domains across a wide range of batch sizes and in various realistic evaluation scenarios. TTN is widely applicable to other test-time adaptation methods that rely on updating model parameters via backpropagation. We demonstrate that adopting TTN further improves their performance and achieves state-of-the-art performance in various standard benchmarks.",
+    "neighbors": [
+      2232
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2069,
+    "label": 16,
+    "text": "Title: WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition\nAbstract: Though research has shown the complementarity of camera- and inertial-based data, datasets which offer both modalities remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 18 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 10 different outside locations. Unlike previous egocentric datasets, WEAR provides a challenging prediction scenario marked by purposely introduced activity variations as well as an overall small information overlap across modalities. Provided benchmark results reveal that single-modality architectures each have different strengths and weaknesses in their prediction performance. Further, in light of the recent success of transformer-based temporal action localization models, we demonstrate their versatility by applying them in a plain fashion using vision, inertial and combined (vision + inertial) features as input. Results demonstrate both the applicability of vision-based transformers for inertial data and fusing both modalities by means of simple concatenation, with the combined approach (vision + inertial features) being able to produce the highest mean average precision and close-to-best F1-score. The dataset and code to reproduce experiments is publicly available via: https://mariusbock.github.io/wear/",
+    "neighbors": [
+      1939
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2070,
+    "label": 7,
+    "text": "Title: An efficient phase-field model of shear fractures using deviatoric stress split\nAbstract: We propose a phase-field model of shear fractures using the deviatoric stress decomposition. This choice allows us to use general three-dimensional Mohr\u2013Coulomb\u2019s failure function for formulating the relations and evaluating peak and residual stresses. We apply the model to a few benchmark problems of shear fracture and strain localization and report remarkable performance. Our model is able to capture conjugate failure modes under biaxial compression test and for the slope stability problem, a challenging task for most models of geomechanics.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2071,
+    "label": 10,
+    "text": "Title: Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence\nAbstract: In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\\em explainable AI}, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2072,
+    "label": 27,
+    "text": "Title: Teleoperation of Humanoid Robots: A Survey\nAbstract: Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey article gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2073,
+    "label": 27,
+    "text": "Title: DeRi-IGP: Manipulating Rigid Objects Using Deformable Objects via Iterative Grasp-Pull\nAbstract: Heterogeneous systems manipulation, i.e., manipulating rigid objects via deformable (soft) objects, is an emerging field that remains in its early stages of research. Existing works in this field suffer from limited action and operational space, poor generalization ability, and expensive development. To address these challenges, we propose a universally applicable and effective moving primitive, Iterative Grasp-Pull (IGP), and a sample-based framework, DeRi-IGP, to solve the heterogeneous system manipulation task. The DeRi-IGP framework uses local onboard robots' RGBD sensors to observe the environment, comprising a soft-rigid body system. It then uses this information to iteratively grasp and pull a soft body (e.g., rope) to move the attached rigid body to a desired location. We evaluate the effectiveness of our framework in solving various heterogeneous manipulation tasks and compare its performance with several state-of-the-art baselines. The result shows that DeRi-IGP outperforms other methods by a significant margin. In addition, we also demonstrate the advantage of the large operational space of IGP in the long-distance object acquisition task within both simulated and real environments.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2074,
+    "label": 13,
+    "text": "Title: How Well Does the Metropolis Algorithm Cope With Local Optima?\nAbstract: The Metropolis algorithm (MA) is a classic stochastic local search heuristic. It avoids getting stuck in local optima by occasionally accepting inferior solutions. To better and in a rigorous manner understand this ability, we conduct a mathematical runtime analysis of the MA on the CLIFF benchmark. Apart from one local optimum, cliff functions are monotonically increasing towards the global optimum. Consequently, to optimize a cliff function, the MA only once needs to accept an inferior solution. Despite seemingly being an ideal benchmark for the MA to profit from its main working principle, our mathematical runtime analysis shows that this hope does not come true. Even with the optimal temperature (the only parameter of the MA), the MA optimizes most cliff functions less efficiently than simple elitist evolutionary algorithms (EAs), which can only leave the local optimum by generating a superior solution possibly far away. This result suggests that our understanding of why the MA is often very successful in practice is not yet complete. Our work also suggests to equip the MA with global mutation operators, an idea supported by our preliminary experiments.",
+    "neighbors": [
+      1610,
+      1700
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2075,
+    "label": 24,
+    "text": "Title: Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation\nAbstract: We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonparametric probabilistic model such as a Gaussian process (GP). Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods. The key idea is to use two probabilistic models: a coarse GP to identify the ROI, and a localized GP for optimization within the ROI. We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO without ROI filtering. We demonstrate empirically the effectiveness of BALLET on both synthetic and real-world optimization tasks.",
+    "neighbors": [
+      1336
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2076,
+    "label": 28,
+    "text": "Title: Age of Information in Deep Learning-Driven Task-Oriented Communications\nAbstract: This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter. The transmitter-receiver operations are modeled as an encoder-decoder pair that is jointly trained while considering channel effects. The encoder converts data samples into feature vectors of small dimension and transmits them with a small number of channel uses thereby reducing the number of transmissions and latency. Instead of reconstructing input samples, the decoder performs a task, e.g., classification, on the received signals. Applying different deep neural networks of encoder-decoder pairs on MNIST and CIFAR-10 image datasets, the classifier accuracy is shown to increase with the number of channel uses at the expense of longer service time. The peak age of task information (PAoTI) is introduced to analyze this accuracy-latency tradeoff when the age grows unless a received signal is classified correctly. By incorporating channel and traffic effects, design guidelines are obtained for task-oriented communications by characterizing how the PAoTI first decreases and then increases with the number of channel uses. A dynamic update mechanism is presented to adapt the number of channel uses to channel and traffic conditions, and reduce the PAoTI in task-oriented communications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2077,
+    "label": 16,
+    "text": "Title: HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions\nAbstract: Supervised learning of skeleton sequence encoders for action recognition has received significant attention in recent times. However, learning such encoders without labels continues to be a challenging problem. While prior works have shown promising results by applying contrastive learning to pose sequences, the quality of the learned representations is often observed to be closely tied to data augmentations that are used to craft the positives. However, augmenting pose sequences is a difficult task as the geometric constraints among the skeleton joints need to be enforced to make the augmentations realistic for that action. In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels. Our key contribution is a simple module, HaLP - to Hallucinate Latent Positives for contrastive learning. Specifically, HaLP explores the latent space of poses in suitable directions to generate new positives. To this end, we present a novel optimization formulation to solve for the synthetic positives with an explicit control on their hardness. We propose approximations to the objective, making them solvable in closed form with minimal overhead. We show via experiments that using these generated positives within a standard contrastive learning framework leads to consistent improvements across benchmarks such as NTU-60, NTU-120, and PKU-II on tasks like linear evaluation, transfer learning, and kNN evaluation. Our code can be found at https://github.com/anshulbshah/HaLP.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2078,
+    "label": 30,
+    "text": "Title: Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining\nAbstract: nan",
+    "neighbors": [
+      346
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2079,
+    "label": 16,
+    "text": "Title: pNNCLR: Stochastic Pseudo Neighborhoods for Contrastive Learning based Unsupervised Representation Learning Problems\nAbstract: Nearest neighbor (NN) sampling provides more semantic variations than pre-defined transformations for self-supervised learning (SSL) based image recognition problems. However, its performance is restricted by the quality of the support set, which holds positive samples for the contrastive loss. In this work, we show that the quality of the support set plays a crucial role in any nearest neighbor based method for SSL. We then provide a refined baseline (pNNCLR) to the nearest neighbor based SSL approach (NNCLR). To this end, we introduce pseudo nearest neighbors (pNN) to control the quality of the support set, wherein, rather than sampling the nearest neighbors, we sample in the vicinity of hard nearest neighbors by varying the magnitude of the resultant vector and employing a stochastic sampling strategy to improve the performance. Additionally, to stabilize the effects of uncertainty in NN-based learning, we employ a smooth-weight-update approach for training the proposed network. Evaluation of the proposed method on multiple public image recognition and medical image recognition datasets shows that it performs up to 8 percent better than the baseline nearest neighbor method, and is comparable to other previously proposed SSL methods.",
+    "neighbors": [
+      929
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2080,
+    "label": 16,
+    "text": "Title: ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images\nAbstract: Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from CT/MRI scans requires time-intensive preprocessing operations, including but not limited to anatomy segmentation annotations, registration, and texture denoising. Deep learning models have demonstrated exceptional capabilities in learning shape representations directly from volumetric images, giving rise to highly effective and efficient Image-to-SSM networks. Nevertheless, these models are data-hungry and due to the limited availability of medical data, deep learning models tend to overfit. Offline data augmentation techniques, that use kernel density estimation based (KDE) methods for generating shape-augmented samples, have successfully aided Image-to-SSM networks in achieving comparable accuracy to traditional SSM methods. However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models. This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. The proposed framework is trained as an adversary to the Image-to-SSM network, augmenting diverse and challenging noisy samples. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2081,
+    "label": 30,
+    "text": "Title: Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning\nAbstract: Task semantics can be expressed by a set of input-to-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of task-specific examples. Two issues arise: first, collecting task-specific labeled examples does not apply to scenarios where tasks may be too complicated or costly to annotate, or the system is required to handle a new task immediately; second, this is not user-friendly since end-users are probably more willing to provide task description rather than a set of examples before using the system. Therefore, the community is paying increasing interest in a new supervision-seeking paradigm for NLP: learning from task instructions. Despite its impressive progress, there are some common issues that the community struggles with. This survey paper tries to summarize and provide insights into the current research on instruction learning, particularly by answering the following questions: (i) What is task instruction, and what instruction types exist? (ii) How to model instructions? (iii) What factors influence and explain the instructions' performance? (iv) What challenges remain in instruction learning? To our knowledge, this is the first comprehensive survey about textual instructions.",
+    "neighbors": [
+      975,
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+      1039,
+      1617,
+      1713,
+      1720,
+      1863,
+      2113,
+      2244
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2082,
+    "label": 30,
+    "text": "Title: SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects\nAbstract: Despite the progress we have recorded in the last few years in multilingual natural language processing, evaluation is typically limited to a small set of languages with available datasets which excludes a large number of low-resource languages. In this paper, we created SIB-200 -- a large-scale open-sourced benchmark dataset for topic classification in 200 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). For many of the languages covered in SIB-200, this is the first publicly available evaluation dataset for NLU. The dataset is based on Flores-200 machine translation corpus. We annotated the English portion of the dataset and extended the sentence-level annotation to the remaining 203 languages covered in the corpus. Despite the simplicity of this task, our evaluation in full-supervised setting, cross-lingual transfer setting and prompting of large language model setting show that there is still a large gap between the performance of high-resource and low-resource languages when multilingual evaluation is scaled to numerous world languages. We found that languages unseen during the pre-training of multilingual language models, under-represented language families (like Nilotic and Altantic-Congo), and languages from the regions of Africa, Americas, Oceania and South East Asia, often have the lowest performance on our topic classification dataset. We hope our dataset will encourage a more inclusive evaluation of multilingual language models on a more diverse set of languages. https://github.com/dadelani/sib-200",
+    "neighbors": [
+      1940,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2083,
+    "label": 24,
+    "text": "Title: A Survey on Oversmoothing in Graph Neural Networks\nAbstract: Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and gives rise to new quantitative measures of over-smoothing. Moreover, we empirically demonstrate this behavior for several over-smoothing measures on different graphs (small-, medium-, and large-scale). We also review several approaches for mitigating over-smoothing and empirically test their effectiveness on real-world graph datasets. Through illustrative examples, we demonstrate that mitigating over-smoothing is a necessary but not sufficient condition for building deep GNNs that are expressive on a wide range of graph learning tasks. Finally, we extend our definition of over-smoothing to the rapidly emerging field of continuous-time GNNs.",
+    "neighbors": [
+      1544
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2084,
+    "label": 8,
+    "text": "Title: Space-Air-Ground Integrated Network (SAGIN): A Survey\nAbstract: Since existing mobile communication networks may not be able to meet the low latency and high-efficiency requirements of emerging technologies and applications, novel network architectures need to be investigated to support these new requirements. As a new network architecture that integrates satellite systems, air networks and ground communication, Space-Air-Ground Integrated Network (SAGIN) has attracted extensive attention in recent years. This paper summarizes the recent research work on SAGIN from several aspects, with the basic information of SAGIN first introduced, followed by the physical characteristics. Then the drive and prospects of the current SAGIN architecture in supporting new requirements are deeply analyzed. On this basis, the requirements and challenges are analyzed. Finally, it summarizes the existing solutions and prospects the future research directions.",
+    "neighbors": [
+      1004
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2085,
+    "label": 16,
+    "text": "Title: AIGC for Various Data Modalities: A Survey\nAbstract: AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the demonstrated potential of recent works, AIGC developments have been attracting lots of attention recently, and AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape (as voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human avatar (body and head), 3D motion, and audio -- each presenting different characteristics and challenges. Furthermore, there have also been many significant developments in cross-modality AIGC methods, where generative methods can receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D shape, 3D scene, 3D avatar (body and head), 3D motion (skeleton and avatar), and audio modalities. In this paper, we provide a comprehensive review of AIGC methods across different data modalities, including both single-modality and cross-modality methods, highlighting the various challenges, representative works, and recent technical directions in each setting. We also survey the representative datasets throughout the modalities, and present comparative results for various modalities. Moreover, we also discuss the challenges and potential future research directions.",
+    "neighbors": [
+      63,
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+      1047,
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+      1426,
+      1481,
+      1490,
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+      1663,
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+      1788,
+      1863,
+      1902,
+      1905,
+      1940,
+      2161,
+      2183,
+      2190,
+      2221,
+      2235,
+      2279
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2086,
+    "label": 30,
+    "text": "Title: Lexical Diversity in Kinship Across Languages and Dialects\nAbstract: Languages are known to describe the world in diverse ways. Across lexicons, diversity is pervasive, appearing through phenomena such as lexical gaps and untranslatability. However, in computational resources, such as multilingual lexical databases, diversity is hardly ever represented. In this paper, we introduce a method to enrich computational lexicons with content relating to linguistic diversity. The method is verified through two large-scale case studies on kinship terminology, a domain known to be diverse across languages and cultures: one case study deals with seven Arabic dialects, while the other one with three Indonesian languages. Our results, made available as browseable and downloadable computational resources, extend prior linguistics research on kinship terminology, and provide insight into the extent of diversity even within linguistically and culturally close communities.",
+    "neighbors": [
+      1554
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2087,
+    "label": 30,
+    "text": "Title: G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment\nAbstract: The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references. However, these LLM-based evaluators still have lower human correspondence than medium-size neural evaluators. In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs. We experiment with two generation tasks, text summarization and dialogue generation. We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of 0.514 with human on summarization task, outperforming all previous methods by a large margin. We also propose preliminary analysis on the behavior of LLM-based evaluators, and highlight the potential issue of LLM-based evaluators having a bias towards the LLM-generated texts. The code is at https://github.com/nlpyang/geval",
+    "neighbors": [
+      126,
+      247,
+      551,
+      652,
+      1007,
+      1114,
+      1461,
+      1516,
+      1556,
+      1694,
+      1907,
+      1940,
+      1949,
+      2042
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2088,
+    "label": 11,
+    "text": "Title: Measuring Causal Responsibility in Multi-Agent Spatial Interactions with Feasible Action-Space Reduction\nAbstract: Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In this work we propose a metric of causal responsibility which is tailored to multi-agent spatial interactions, for instance interactions in traffic. In such interactions, a given agent can, by reducing another agent's feasible action space, influence the latter. Therefore, we propose feasible action space reduction (FeAR) as a metric for causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur - a consistent set of prescribed actions for agents - to model the effect of norms on responsibility allocation. We apply the metric in a grid world simulation for spatial interactions and show how the actions, contexts, and norms affect the causal responsibility ascribed to agents. Finally, we demonstrate the application of this metric in complex multi-agent interactions. We argue that the FeAR metric is a step towards an interdisciplinary framework for quantifying responsibility that is needed to ensure safety and meaningful human control in human-AI systems.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2089,
+    "label": 28,
+    "text": "Title: Goal-oriented Tensor: Beyond AoI Towards Semantics-Empowered Goal-oriented Communications\nAbstract: The intricate interplay of source dynamics, unreliable channels, and staleness of information has long been recognized as a significant impediment for the receiver to achieve accurate, timely, and most importantly, goal-oriented decision making. Thus, a plethora of promising metrics, such as Age of Information, Value of Information, and Mean Square Error, have emerged to quantify these underlying adverse factors. Following this avenue, optimizing these metrics has indirectly improved the utility of goal-oriented decision making. Nevertheless, no metric has hitherto been expressly devised to evaluate the utility of a goal-oriented decision-making process. To this end, this paper investigates a novel performance metric, the Goal-oriented Tensor (GoT), to directly quantify the impact of semantic mismatches on the goal-oriented decision making. Based on the GoT, we consider a sampler-decision maker pair that work collaboratively and distributively to achieve a shared goal of communications. We formulate an infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. The simulation results reveal that the sampler-decision maker co-design surpasses the current literature on AoI and its variants in terms of both goal achievement utility and sparse sampling rate, signifying a notable accomplishment for a sparse sampler and goal-oriented decision maker co-design.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2090,
+    "label": 30,
+    "text": "Title: Sign Language Translation from Instructional Videos\nAbstract: The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset.We train a Transformer over I3D video features, using the reduced BLEU as a reference metric for validation, instead of the widely used BLEU score. We report a result of 8.03 on the BLEU score, and publish the first open-source implementation of its kind to promote further advances.",
+    "neighbors": [
+      956
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2091,
+    "label": 24,
+    "text": "Title: Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching\nAbstract: Current techniques and systems for distributed model training mostly assume that clusters are comprised of homogeneous servers with a constant resource availability. However, cluster heterogeneity is pervasive in computing infrastructure, and is a fundamental characteristic of low-cost transient resources (such as EC2 spot instances). In this paper, we develop a dynamic batching technique for distributed data-parallel training that adjusts the mini-batch sizes on each worker based on its resource availability and throughput. Our mini-batch controller seeks to equalize iteration times on all workers, and facilitates training on clusters comprised of servers with different amounts of CPU and GPU resources. This variable mini-batch technique uses proportional control and ideas from PID controllers to find stable mini-batch sizes. Our empirical evaluation shows that dynamic batching can reduce model training times by more than $ 4\\times$ on heterogeneous clusters.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2092,
+    "label": 30,
+    "text": "Title: Graph of Thoughts: Solving Elaborate Problems with Large Language Models\nAbstract: We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage of GoT is the ability to model the information generated by an LLM as an arbitrary graph, where units of information (\"LLM thoughts\") are vertices, and edges correspond to dependencies between these vertices. This approach enables combining arbitrary LLM thoughts into synergistic outcomes, distilling the essence of whole networks of thoughts, or enhancing thoughts using feedback loops. We illustrate that GoT offers advantages over state of the art on different tasks, for example increasing the quality of sorting by 62% over ToT, while simultaneously reducing costs by>31%. We ensure that GoT is extensible with new thought transformations and thus can be used to spearhead new prompting schemes. This work brings the LLM reasoning closer to human thinking or brain mechanisms such as recurrence, both of which form complex networks.",
+    "neighbors": [
+      127,
+      1044,
+      1052,
+      1267,
+      1490,
+      1544,
+      1878,
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+      2235
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2093,
+    "label": 24,
+    "text": "Title: Symbolic Discovery of Optimization Algorithms\nAbstract: We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\\textbf{Lion}$ ($\\textit{Evo$\\textbf{L}$ved S$\\textbf{i}$gn M$\\textbf{o}$me$\\textbf{n}$tum}$). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\\textit{zero-shot}$ and 91.1% $\\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. Lion is also successfully deployed in production systems such as Google search ads CTR model.",
+    "neighbors": [
+      427,
+      494,
+      880,
+      2031
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2094,
+    "label": 30,
+    "text": "Title: ChatGPT outperforms crowd workers for text-annotation tasks\nAbstract: Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (n = 6,183), we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by about 25 percentage points on average, while ChatGPT\u2019s intercoder agreement exceeds that of both crowd workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003\u2014about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.",
+    "neighbors": [
+      3,
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+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2095,
+    "label": 27,
+    "text": "Title: EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought\nAbstract: Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the\"Chain of Thoughts\"mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1.6 times increase in success rate on the Franka Kitchen benchmark and a 1.3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.",
+    "neighbors": [
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+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2096,
+    "label": 4,
+    "text": "Title: Formal and Fuzzing Amplification: Targeting Vulnerability Detection in 5G and Beyond\nAbstract: Softwarization and virtualization in 5G and beyond require rigorous testing against vulnerabilities and unintended emergent behaviors for critical infrastructure and network security assurance. Formal methods operates efficiently in protocol-level abstract specification models, and fuzz testing offers comprehensive experimental evaluation of system implementations. In this paper, we propose a novel framework that leverages the respective advantages and coverage of both formal and fuzzing methods to efficiently detect vulnerabilities from protocol logic to implementation stacks hierarchically. The detected attack traces from the formal verification results in critical protocols guide the case generation of fuzz testing, and the feedbacks from fuzz testing further broaden the scope of the formal verification. We examine the proposed framework with the 5G Non Standard-Alone (NSA) security processes, focusing on the Radio Resource Control (RRC) connection process. We first identify protocol-level vulnerabilities of user credentials via formal methods. Following this, we implement bit-level fuzzing to evaluate potential impacts and risks of integrity-vulnerable identifier variation. Concurrently, we conduct command-level mutation-based fuzzing by fixing the assumption identifier to assess the potential impacts and risks of confidentiality-vulnerable identifiers. During this approach, we established 1 attack model and detected 53 vulnerabilities. The vulnerabilities identified used to fortify protocol-level assumptions could further refine search space for the following detection cycles. Consequently, it addresses the prevalent scalability challenges in detecting vulnerabilities and unintended emergent behaviors in large-scale systems in 5G and beyond.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2097,
+    "label": 24,
+    "text": "Title: On Feature Diversity in Energy-based Models\nAbstract: Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches. An energy-based model (EBM) is typically formed of inner-model(s) that learn a combination of the different features to generate an energy mapping for each input configuration. In this paper, we focus on the diversity of the produced feature set. We extend the probably approximately correct (PAC) theory of EBMs and analyze the effect of redundancy reduction on the performance of EBMs. We derive generalization bounds for various learning contexts, i.e., regression, classification, and implicit regression, with different energy functions and we show that indeed reducing redundancy of the feature set can consistently decrease the gap between the true and empirical expectation of the energy and boosts the performance of the model.",
+    "neighbors": [
+      166
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2098,
+    "label": 16,
+    "text": "Title: Multimodal Industrial Anomaly Detection via Hybrid Fusion\nAbstract: 2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields. Existing multimodal industrial anomaly detection methods directly concatenate the multimodal features, which leads to a strong disturbance between features and harms the detection performance. In this paper, we propose Multi-3D-Memory (M3DM), a novel multimodal anomaly detection method with hybrid fusion scheme: firstly, we design an unsupervised feature fusion with patch-wise contrastive learning to encourage the interaction of different modal features; secondly, we use a decision layer fusion with multiple memory banks to avoid loss of information and additional novelty classifiers to make the final decision. We further propose a point feature alignment operation to better align the point cloud and RGB features. Extensive experiments show that our multi-modal industrial anomaly detection model outperforms the state-of-the-art (SOTA) methods on both detection and segmentation precision on MVTec-3D AD dataset. Code at github.com/nomewang/M3DM.",
+    "neighbors": [
+      353,
+      892,
+      1903,
+      2119
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2099,
+    "label": 30,
+    "text": "Title: IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images\nAbstract: Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis\"You shall know a word by the company it keeps\"(Harris, 1954), whereas modern prediction-based neural network embeddings rely on design choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects level from 1M image+text pairs. JE is a way to encode multimodal data into a vector space where the text modality serves as the ground-ing key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three individual representations: (i) object-object co-location, (ii) word-object co-location, and (iii) word-object correlation. These three ways capture complementary aspects of the two modalities which are further combined to obtain the final JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextuality and real-world analogies. We also evaluate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Image2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned down-stream tasks by outperforming the current SoTA on all the selected tasks. IMAGINATOR will be made publicly available. The codes are available at https://github.com/varunakk/IMAGINATOR",
+    "neighbors": [
+      1037,
+      1302
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2100,
+    "label": 24,
+    "text": "Title: LEVER: Learning to Verify Language-to-Code Generation with Execution\nAbstract: The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation. State-of-the-art approaches in this area combine LLM decoding with sample pruning and reranking using test cases or heuristics based on the execution results. However, it is challenging to obtain test cases for many real-world language-to-code applications, and heuristics cannot well capture the semantic features of the execution results, such as data type and value range, which often indicates the correctness of the program. In this work, we propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. The sampled programs are reranked by combining the verification score with the LLM generation probability, and marginalizing over programs with the same execution results. On four datasets across the domains of table QA, math QA and basic Python programming, LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci-002) and achieves new state-of-the-art results on all of them.",
+    "neighbors": [
+      240,
+      924,
+      1490,
+      1713,
+      1907,
+      2016,
+      2029
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2101,
+    "label": 16,
+    "text": "Title: A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild\nAbstract: In recent years, transformer architecture has been a dominating paradigm in many applications, including affective computing. In this report, we propose our transformer-based model to handle Emotion Classification Task in the 5th Affective Behavior Analysis In-the-wild Competition. By leveraging the attentive model and the synthetic dataset, we attain a score of 0.4775 on the validation set of Aff-Wild2, the dataset provided by the organizer.",
+    "neighbors": [
+      1533,
+      1541,
+      1832
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2102,
+    "label": 16,
+    "text": "Title: Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning\nAbstract: Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face a number of challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and capability of utilizing very limited or no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a novel multi-modal medical foundation model that explores masked contrastive learning to achieve granular alignment and zero-shot learning for a variety of medical imaging tasks. MaCo incorporates a correlation weighting mechanism to adjust the correlation between masked image patches and their corresponding reports, thereby enhancing the representation learning capabilities. We evaluate MaCo on six well-known open-source X-ray datasets, and the experimental results show it outperforms seven state-of-the-art approaches for classification, segmentation, and zero-shot phase grounding, demonstrating its great potential to promote a wide range of medical image analysis tasks.",
+    "neighbors": [
+      103,
+      1698
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2103,
+    "label": 25,
+    "text": "Title: Leveraging Pre-trained AudioLDM for Text to Sound Generation: A Benchmark Study\nAbstract: Deep neural networks have recently achieved breakthroughs in sound generation with text prompts. Despite their promising performance, current text-to-sound generation models face issues on small-scale datasets (e.g., overfitting), significantly limiting their performance. In this paper, we investigate the use of pre-trained AudioLDM, the state-of-the-art model for text-to-audio generation, as the backbone for sound generation. Our study demonstrates the advantages of using pre-trained models for text-to-sound generation, especially in data-scarcity scenarios. In addition, experiments show that different training strategies (e.g., training conditions) may affect the performance of AudioLDM on datasets of different scales. To facilitate future studies, we also evaluate various text-to-sound generation systems on several frequently used datasets under the same evaluation protocols, which allow fair comparisons and benchmarking of these methods on the common ground.",
+    "neighbors": [
+      414,
+      1156
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2104,
+    "label": 30,
+    "text": "Title: Lost in the Middle: How Language Models Use Long Contexts\nAbstract: While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze language model performance on two tasks that require identifying relevant information within their input contexts: multi-document question answering and key-value retrieval. We find that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly degrades when models must access relevant information in the middle of long contexts. Furthermore, performance substantially decreases as the input context grows longer, even for explicitly long-context models. Our analysis provides a better understanding of how language models use their input context and provides new evaluation protocols for future long-context models.",
+    "neighbors": [
+      57,
+      1052,
+      1678,
+      1834,
+      1907
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2105,
+    "label": 16,
+    "text": "Title: ALOFT: A Lightweight MLP-Like Architecture with Dynamic Low-Frequency Transform for Domain Generalization\nAbstract: Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focus too much on local representations (e.g., texture), which inherently causes the model more prone to overfit to the source domains and hampers its generalization ability. Recently, several MLP-based methods have achieved promising results in supervised learning tasks by learning global interactions among different patches of the image. Inspired by this, in this paper, we first analyze the difference between CNN and MLP methods in DG and find that MLP methods exhibit a better generalization ability because they can better capture the global representations (e.g., structure) than CNN methods. Then, based on a recent lightweight MLP method, we obtain a strong baseline that outperforms most state-of-the-art CNN-based methods. The baseline can learn global structure representations with a filter to suppress structureirrelevant information in the frequency space. Moreover, we propose a dynAmic LOw-Frequency spectrum Transform (ALOFT) that can perturb local texture features while preserving global structure features, thus enabling the filter to remove structure-irrelevant information sufficiently. Extensive experiments on four benchmarks have demonstrated that our method can achieve great performance improvement with a small number of parameters compared to SOTA CNN-based DG methods. Our code is available at https://github.com/lingeringlight/ALOFT/.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2106,
+    "label": 16,
+    "text": "Title: Set Features for Fine-grained Anomaly Detection\nAbstract: Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements. We compute the anomaly score of each sample using a simple density estimation method. Our simple-to-implement approach outperforms the state-of-the-art in image-level logical anomaly detection (+3.4%) and sequence-level time-series anomaly detection (+2.4%).",
+    "neighbors": [
+      1903
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2107,
+    "label": 26,
+    "text": "Title: Social Honeypot for Humans: Luring People through Self-managed Instagram Pages\nAbstract: Social Honeypots are tools deployed in Online Social Networks (OSN) to attract malevolent activities performed by spammers and bots. To this end, their content is designed to be of maximum interest to malicious users. However, by choosing an appropriate content topic, this attractive mechanism could be extended to any OSN users, rather than only luring malicious actors. As a result, honeypots can be used to attract individuals interested in a wide range of topics, from sports and hobbies to more sensitive subjects like political views and conspiracies. With all these individuals gathered in one place, honeypot owners can conduct many analyses, from social to marketing studies. In this work, we introduce a novel concept of social honeypot for attracting OSN users interested in a generic target topic. We propose a framework based on fully-automated content generation strategies and engagement plans to mimic legit Instagram pages. To validate our framework, we created 21 self-managed social honeypots (i.e., pages) on Instagram, covering three topics, four content generation strategies, and three engaging plans. In nine weeks, our honeypots gathered a total of 753 followers, 5387 comments, and 15739 likes. These results demonstrate the validity of our approach, and through statistical analysis, we examine the characteristics of effective social honeypots.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2108,
+    "label": 16,
+    "text": "Title: Surgical-VQLA:Transformer with Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic Surgery\nAbstract: Despite the availability of computer-aided simulators and recorded videos of surgical procedures, junior residents still heavily rely on experts to answer their queries. However, expert surgeons are often overloaded with clinical and academic workloads and limit their time in answering. For this purpose, we develop a surgical question-answering system to facilitate robot-assisted surgical scene and activity understanding from recorded videos. Most of the existing visual question answering (VQA) methods require an object detector and regions based feature extractor to extract visual features and fuse them with the embedded text of the question for answer generation. However, (i) surgical object detection model is scarce due to smaller datasets and lack of bounding box annotation; (ii) current fusion strategy of heterogeneous modalities like text and image is naive; (iii) the localized answering is missing, which is crucial in complex surgical scenarios. In this paper, we propose Visual Question Localized-Answering in Robotic Surgery (Surgical-VQLA) to localize the specific surgical area during the answer prediction. To deal with the fusion of the heterogeneous modalities, we design gated vision-language embedding (GVLE) to build input patches for the Language Vision Transformer (LViT) to predict the answer. To get localization, we add the detection head in parallel with the prediction head of the LViT. We also integrate generalized intersection over union (GIoU) loss to boost localization performance by preserving the accuracy of the question-answering model. We annotate two datasets of VQLA by utilizing publicly available surgical videos from EndoVis-17 and 18 of the MICCAI challenges. Our validation results suggest that Surgical-VQLA can better understand the surgical scene and localized the specific area related to the question-answering. GVLE presents an efficient language-vision embedding technique by showing superior performance over the existing benchmarks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2109,
+    "label": 24,
+    "text": "Title: Unleashing the Power of Graph Learning through LLM-based Autonomous Agents\nAbstract: Graph structured data are widely existed and applied in the real-world applications, while it is a challenge to handling these diverse data and learning tasks on graph in an efficient manner. When facing the complicated graph learning tasks, experts have designed diverse Graph Neural Networks (GNNs) in recent years. They have also implemented AutoML in Graph, also known as AutoGraph, to automatically generate data-specific solutions. Despite their success, they encounter limitations in (1) managing diverse learning tasks at various levels, (2) dealing with different procedures in graph learning beyond architecture design, and (3) the huge requirements on the prior knowledge when using AutoGraph. In this paper, we propose to use Large Language Models (LLMs) as autonomous agents to simplify the learning process on diverse real-world graphs. Specifically, in response to a user request which may contain varying data and learning targets at the node, edge, or graph levels, the complex graph learning task is decomposed into three components following the agent planning, namely, detecting the learning intent, configuring solutions based on AutoGraph, and generating a response. The AutoGraph agents manage crucial procedures in automated graph learning, including data-processing, AutoML configuration, searching architectures, and hyper-parameter fine-tuning. With these agents, those components are processed by decomposing and completing step by step, thereby generating a solution for the given data automatically, regardless of the learning task on node or graph. The proposed method is dubbed Auto$^2$Graph, and the comparable performance on different datasets and learning tasks. Its effectiveness is demonstrated by its comparable performance on different datasets and learning tasks, as well as the human-like decisions made by the agents.",
+    "neighbors": [
+      118,
+      344,
+      1238,
+      1878,
+      2252
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2110,
+    "label": 24,
+    "text": "Title: Applications of Federated Learning in Manufacturing: Identifying the Challenges and Exploring the Future Directions with Industry 4.0 and 5.0 Visions\nAbstract: In manufacturing settings, data collection and analysis are often a time-consuming, challenging, and costly process. It also hinders the use of advanced machine learning and data-driven methods which require a substantial amount of offline training data to generate good results. It is particularly challenging for small manufacturers who do not share the resources of a large enterprise. Recently, with the introduction of the Internet of Things (IoT), data can be collected in an integrated manner across the factory in real-time, sent to the cloud for advanced analysis, and used to update the machine learning model sequentially. Nevertheless, small manufacturers face two obstacles in reaping the benefits of IoT: they may be unable to afford or generate enough data to operate a private cloud, and they may be hesitant to share their raw data with a public cloud. Federated learning (FL) is an emerging concept of collaborative learning that can help small-scale industries address these issues and learn from each other without sacrificing their privacy. It can bring together diverse and geographically dispersed manufacturers under the same analytics umbrella to create a win-win situation. However, the widespread adoption of FL across multiple manufacturing organizations remains a significant challenge. This study aims to review the challenges and future directions of applying federated learning in the manufacturing industry, with a specific emphasis on the perspectives of Industry 4.0 and 5.0.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2111,
+    "label": 28,
+    "text": "Title: Timely Opportunistic Gossiping in Dense Networks\nAbstract: We consider gossiping in a fully-connected wireless network consisting of $n$ nodes. The network receives Poisson up-dates from a source, which generates new information. The nodes gossip their available information with the neighboring nodes to maintain network timeliness. In this work, we propose two gossiping schemes, one semi-distributed and the other one fully-distributed. In the semi-distributed scheme, the freshest nodes use pilot signals to interact with the network and gossip with the full available update rate $B$. In the fully-distributed scheme, each node gossips for a fixed amount of time duration with the full update rate $B$. Both schemes achieve $O(1^{\\backslash }$, age scaling, and the semi-distributed scheme has the best age performance for any symmetric randomized gossiping policy. We compare the results with the recently proposed ASUMAN scheme [1], which also gives $O(1)$ age performance, but the nodes need to be age-aware.",
+    "neighbors": [
+      1366
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2112,
+    "label": 8,
+    "text": "Title: Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications\nAbstract: Due to advantages in security and privacy, blockchain is considered a key enabling technology to support 6G communications. Practical Byzantine Fault Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms (CMs) in blockchain-enabled wireless networks. However, previous studies on PBFT and RAFT rarely consider the channel performance of the physical layer, such as path loss and channel fading, resulting in research results that are far from real networks. Additionally, 6G communications will widely deploy high-frequency signals such as terahertz (THz) and millimeter wave (mmWave), while performances of PBFT and RAFT are still unknown when these signals are transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study the performance of non-ideal wireless PBFT and RAFT networks with THz and mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this paper, we study and compare the performance of THz and mmWave signals in non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and close-in Free Space (FS) reference distance path loss. Performance is evaluated by five metrics: consensus success rate, latency, throughput, reliability gain, and energy consumption. Meanwhile, we find and derive that there is a maximum distance between two nodes that can make CMs inevitably successful, and it is named the active distance of CMs. The research results analyze the performance of non-ideal wireless PBFT and RAFT networks, and provide important references for the future transmission of THz and mmWave signals in PBFT and RAFT networks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2113,
+    "label": 30,
+    "text": "Title: A Survey of Large Language Models\nAbstract: Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.",
+    "neighbors": [
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+      2286,
+      2289
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2114,
+    "label": 24,
+    "text": "Title: Fine-Tuning Language Models with Just Forward Passes\nAbstract: Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory. Zeroth-order (ZO) methods can in principle estimate gradients using only two forward passes but are theorized to be catastrophically slow for optimizing large models. In this work, we propose a memory-efficient zerothorder optimizer (MeZO), adapting the classical ZO-SGD method to operate in-place, thereby fine-tuning LMs with the same memory footprint as inference. For example, with a single A100 80GB GPU, MeZO can train a 30-billion parameter model, whereas fine-tuning with backpropagation can train only a 2.7B LM with the same budget. We conduct comprehensive experiments across model types (masked and autoregressive LMs), model scales (up to 66B), and downstream tasks (classification, multiple-choice, and generation). Our results demonstrate that (1) MeZO significantly outperforms in-context learning and linear probing; (2) MeZO achieves comparable performance to fine-tuning with backpropagation across multiple tasks, with up to 12x memory reduction; (3) MeZO is compatible with both full-parameter and parameter-efficient tuning techniques such as LoRA and prefix tuning; (4) MeZO can effectively optimize non-differentiable objectives (e.g., maximizing accuracy or F1). We support our empirical findings with theoretical insights, highlighting how adequate pre-training and task prompts enable MeZO to fine-tune huge models, despite classical ZO analyses suggesting otherwise.",
+    "neighbors": [
+      748,
+      1436,
+      1863
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2115,
+    "label": 16,
+    "text": "Title: Turning a CLIP Model into a Scene Text Detector\nAbstract: The recent large-scale Contrastive Language-Image Pretraining (CLIP) model has shown great potential in various downstream tasks via leveraging the pretrained vision and language knowledge. Scene text, which contains rich textual and visual information, has an inherent connection with a model like CLIP. Recently, pretraining approaches based on vision language models have made effective progresses in the field of text detection. In contrast to these works, this paper proposes a new method, termed TCM, focusing on Turning the CLIP Model directly for text detection without pretraining process. We demonstrate the advantages of the proposed TCM as follows: (1) The underlying principle of our framework can be applied to improve existing scene text detector. (2) It facilitates the few-shot training capability of existing methods, e.g., by using 10% of labeled data, we significantly improve the performance of the baseline method with an average of 22% in terms of the F-measure on 4 benchmarks. (3) By turning the CLIP model into existing scene text detection methods, we further achieve promising domain adaptation ability. The code will be publicly released at https://github.com/wenwenyu/TCM.",
+    "neighbors": [
+      438
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2116,
+    "label": 16,
+    "text": "Title: Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification\nAbstract: Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE.",
+    "neighbors": [
+      1234
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2117,
+    "label": 16,
+    "text": "Title: Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors\nAbstract: We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images. Our code, models, and generated 3D assets are available at https://github.com/guochengqian/Magic123.",
+    "neighbors": [
+      63,
+      357,
+      1125,
+      1773,
+      1905,
+      2205
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2118,
+    "label": 16,
+    "text": "Title: Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation\nAbstract: In recent years, 3D representation learning has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment strategies, aligning 3D representations with single-view 2D images and coarse-grained parent category text. These approaches introduce information degradation and insufficient synergy issues, leading to performance loss. Information degradation arises from overlooking the fact that a 3D representation should be equivalent to a series of multi-view images and more fine-grained subcategory text. Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality. In this paper, we propose a multi-view joint modality modeling approach, termed JM3D, to obtain a unified representation for point cloud, text, and image. Specifically, a novel Structured Multimodal Organizer (SMO) is proposed to address the information degradation issue, which introduces contiguous multi-view images and hierarchical text to enrich the representation of vision and language modalities. A Joint Multi-modal Alignment (JMA) is designed to tackle the insufficient synergy problem, which models the joint modality by incorporating language knowledge into the visual modality. Extensive experiments on ModelNet40 and ScanObjectNN demonstrate the effectiveness of our proposed method, JM3D, which achieves state-of-the-art performance in zero-shot 3D classification. JM3D outperforms ULIP by approximately 4.3% on PointMLP and achieves an improvement of up to 6.5% accuracy on PointNet++ in top-1 accuracy for zero-shot 3D classification on ModelNet40. The source code and trained models for all our experiments are publicly available at https://github.com/Mr-Neko/JM3D.",
+    "neighbors": [
+      1573
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2119,
+    "label": 16,
+    "text": "Title: Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval\nAbstract: In this work, by re-examining the\"matching\"nature of Anomaly Detection (AD), we propose a new AD framework that simultaneously enjoys new records of AD accuracy and dramatically high running speed. In this framework, the anomaly detection problem is solved via a cascade patch retrieval procedure that retrieves the nearest neighbors for each test image patch in a coarse-to-fine fashion. Given a test sample, the top-K most similar training images are first selected based on a robust histogram matching process. Secondly, the nearest neighbor of each test patch is retrieved over the similar geometrical locations on those\"global nearest neighbors\", by using a carefully trained local metric. Finally, the anomaly score of each test image patch is calculated based on the distance to its\"local nearest neighbor\"and the\"non-background\"probability. The proposed method is termed\"Cascade Patch Retrieval\"(CPR) in this work. Different from the conventional patch-matching-based AD algorithms, CPR selects proper\"targets\"(reference images and locations) before\"shooting\"(patch-matching). On the well-acknowledged MVTec AD, BTAD and MVTec-3D AD datasets, the proposed algorithm consistently outperforms all the comparing SOTA methods by remarkable margins, measured by various AD metrics. Furthermore, CPR is extremely efficient. It runs at the speed of 113 FPS with the standard setting while its simplified version only requires less than 1 ms to process an image at the cost of a trivial accuracy drop. The code of CPR is available at https://github.com/flyinghu123/CPR.",
+    "neighbors": [
+      1618,
+      1752,
+      2098
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2120,
+    "label": 30,
+    "text": "Title: Sentence-Incremental Neural Coreference Resolution\nAbstract: We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method. The system is aimed at bridging two recent approaches at coreference resolution: (1) state-of-the-art non-incremental models that incur quadratic complexity in document length with high computational cost, and (2) memory network-based models which operate incrementally but do not generalize beyond pronouns. For comparison, we simulate an incremental setting by constraining non-incremental systems to form partial coreference chains before observing new sentences. In this setting, our system outperforms comparable state-of-the-art methods by 2 F1 on OntoNotes and 6.8 F1 on the CODI-CRAC 2021 corpus. In a conventional coreference setup, our system achieves 76.3 F1 on OntoNotes and 45.5 F1 on CODI-CRAC 2021, which is comparable to state-of-the-art baselines. We also analyze variations of our system and show that the degree of incrementality in the encoder has a surprisingly large effect on the resulting performance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2121,
+    "label": 24,
+    "text": "Title: Physics-Informed Polynomial Chaos Expansions\nAbstract: Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physics of the model. This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model. Physical constraints investigated in this paper are represented by a set of differential equations and specified boundary conditions. A computationally efficient means for construction of physically constrained PCE is proposed and compared to standard sparse PCE. It is shown that the proposed algorithms lead to superior accuracy of the approximation and does not add significant computational burden. Although the main purpose of the proposed method lies in combining data and physical constraints, we show that physically constrained PCEs can be constructed from differential equations and boundary conditions alone without requiring evaluations of the original model. We further show that the constrained PCEs can be easily applied for uncertainty quantification through analytical post-processing of a reduced PCE filtering out the influence of all deterministic space-time variables. Several deterministic examples of increasing complexity are provided and the proposed method is applied for uncertainty quantification.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2122,
+    "label": 10,
+    "text": "Title: From Instructions to Intrinsic Human Values - A Survey of Alignment Goals for Big Models\nAbstract: Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models. However, the growing intertwining of big models with everyday human lives poses potential risks and might cause serious social harm. Therefore, many efforts have been made to align LLMs with humans to make them better follow user instructions and satisfy human preferences. Nevertheless, `what to align with' has not been fully discussed, and inappropriate alignment goals might even backfire. In this paper, we conduct a comprehensive survey of different alignment goals in existing work and trace their evolution paths to help identify the most essential goal. Particularly, we investigate related works from two perspectives: the definition of alignment goals and alignment evaluation. Our analysis encompasses three distinct levels of alignment goals and reveals a goal transformation from fundamental abilities to value orientation, indicating the potential of intrinsic human values as the alignment goal for enhanced LLMs. Based on such results, we further discuss the challenges of achieving such intrinsic value alignment and provide a collection of available resources for future research on the alignment of big models.",
+    "neighbors": [
+      126,
+      143,
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+      685,
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+      1617,
+      1755,
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+      1972,
+      2140,
+      2235,
+      2238
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2123,
+    "label": 9,
+    "text": "Title: Efficiently-Verifiable Strong Uniquely Solvable Puzzles and Matrix Multiplication\nAbstract: We advance the Cohn-Umans framework for developing fast matrix multiplication algorithms. We introduce, analyze, and search for a new subclass of strong uniquely solvable puzzles (SUSP), which we call simplifiable SUSPs. We show that these puzzles are efficiently verifiable, which remains an open question for general SUSPs. We also show that individual simplifiable SUSPs can achieve the same strength of bounds on the matrix multiplication exponent $\\omega$ that infinite families of SUSPs can. We report on the construction, by computer search, of larger SUSPs than previously known for small width. This, combined with our tighter analysis, strengthens the upper bound on the matrix multiplication exponent from $2.66$ to $2.505$ obtainable via this computational approach, and nears the results of the handcrafted constructions of Cohn et al.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2124,
+    "label": 5,
+    "text": "Title: Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud\nAbstract: The precise estimation of resource usage is a complex and challenging issue due to the high variability and dimensionality of heterogeneous service types and dynamic workloads. Over the last few years, the prediction of resource usage and traffic has received ample attention from the research community. Many machine learning-based workload forecasting models have been developed by exploiting their computational power and learning capabilities. This paper presents the first systematic survey cum performance analysis-based comparative study of diversified machine learning-driven cloud workload prediction models. The discussion initiates with the significance of predictive resource management followed by a schematic description, operational design, motivation, and challenges concerning these workload prediction models. Classification and taxonomy of different prediction approaches into five distinct categories are presented focusing on the theoretical concepts and mathematical functioning of the existing state-of-the-art workload prediction methods. The most prominent prediction approaches belonging to a distinct class of machine learning models are thoroughly surveyed and compared. All five classified machine learning-based workload prediction models are implemented on a common platform for systematic investigation and comparison using three distinct benchmark cloud workload traces via experimental analysis. The essential key performance indicators of state-of-the-art approaches are evaluated for comparison and the paper is concluded by discussing the trade-offs and notable remarks.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2125,
+    "label": 8,
+    "text": "Title: Colosseum as a Digital Twin: Bridging Real-World Experimentation and Wireless Network Emulation\nAbstract: Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate or minimize the impact of errors of emulation models, in this work we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly-available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average accuracy of up to 92.5% in throughput and 80% in Signal to Interference plus Noise Ratio (SINR).",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2126,
+    "label": 8,
+    "text": "Title: Insights from the Design Space Exploration of Flow-Guided Nanoscale Localization\nAbstract: Nanodevices with Terahertz (THz)-based wireless communication capabilities are providing a primer for flow-guided localization within the human bloodstreams. Such localization is allowing for assigning the locations of sensed events with the events themselves, providing benefits in precision medicine along the lines of early and precise diagnostics, and reduced costs and invasiveness. Flow-guided localization is still in a rudimentary phase, with only a handful of works targeting the problem. Nonetheless, the performance assessments of the proposed solutions are already carried out in a non-standardized way, usually along a single performance metric, and ignoring various aspects that are relevant at such a scale (e.g., nanodevices' limited energy) and for such a challenging environment (e.g., extreme attenuation of in-body THz propagation). As such, these assessments feature low levels of realism and cannot be compared in an objective way. Toward addressing this issue, we account for the environmental and scale-related peculiarities of the scenario and assess the performance of two state-of-the-art flow-guided localization approaches along a set of heterogeneous performance metrics such as the accuracy and reliability of localization.",
+    "neighbors": [
+      1968
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2127,
+    "label": 4,
+    "text": "Title: Piecewise Linear and Stochastic Models for the Analysis of Cyber Resilience\nAbstract: We model a vehicle equipped with an autonomous cyber-defense system in addition to its inherent physical resilience features. When attacked, this ensemble of cyber-physical features (i.e., \u201cbonware\u201d) strives to resist and recover from the performance degradation caused by the malware's attack. We model the underlying differential equations governing such attacks for piecewise linear characterizations of malware and bonware, develop a discrete time stochastic model, and show that averages of instantiations of the stochastic model approximate solutions to the continuous differential equation. We develop a theory and methodology for approximating the parameters associated with these equations.",
+    "neighbors": [
+      868
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2128,
+    "label": 27,
+    "text": "Title: We, Vertiport 6, are temporarily closed: Interactional Ontological Methods for Changing the Destination\nAbstract: This paper presents a continuation of the previous research on the interaction between a human traffic manager and the UATMS. In particular, we focus on the automation of the process of handling a vertiport outage, which was partially covered in the previous work. Once the manager reports that a vertiport is out of service, which means landings for all corresponding agents are prohibited, the air traffic system automates what it has to handle for this event. The entire process is simulated through knowledge representation and reasoning. Moreover, two distinct perspectives are respected for the human supervisor and the management system, and the related ontologies and rules address their interactions. We believe that applying non-monotonic reasoning can verify each step of the process and explain how the system works. After a short introduction with related works, this paper continues with problem formulation, primary solution, discussion, and conclusions.",
+    "neighbors": [
+      80
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2129,
+    "label": 3,
+    "text": "Title: A \u2018Human-in-the-Loop\u2019 approach for Information Extraction from Privacy Policies under Data Scarcity\nAbstract: Machine-readable representations of privacy policies are door openers for a broad variety of novel privacy-enhancing and, in particular, transparency-enhancing technologies (TETs). In order to generate such representations, transparency information needs to be extracted from written privacy policies. However, respective manual annotation and extraction processes are laborious and require expert knowledge. Approaches for fully automated annotation, in turn, have so far not succeeded due to overly high error rates in the specific domain of privacy policies. In the end, a lack of properly annotated privacy policies and respective machine-readable representations persists and enduringly hinders the development and establishment of novel technical approaches fostering policy perception and data subject informedness.In this work, we present a prototype system for a \u2018 Human-in-the-Loop\u2019 approach to privacy policy annotation that integrates ML-generated suggestions and ultimately human annotation decisions. We propose an ML-based suggestion system specifically tailored to the constraint of data scarcity prevalent in the domain of privacy policy annotation. On this basis, we provide meaningful predictions to users thereby streamlining the annotation process. Additionally, we also evaluate our approach through a prototypical implementation to show that our ML-based extraction approach provides superior performance over other recently used extraction models for legal documents.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2130,
+    "label": 36,
+    "text": "Title: Recent Developments in Pandora's Box Problem: Variants and Applications\nAbstract: In 1979, Weitzman introduced Pandora's box problem as a framework for sequential search with costly inspections. Recently, there has been a surge of interest in Pandora's box problem, particularly among researchers working at the intersection of economics and computation. This survey provides an overview of the recent literature on Pandora's box problem, including its latest extensions and applications in areas such as market design, decision theory, and machine learning.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2131,
+    "label": 24,
+    "text": "Title: Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation\nAbstract: We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov games with independent linear function approximation, where each agent has its own function approximation for the state-action value functions that are marginalized by other players' policies. We design new algorithms for learning the Markov coarse correlated equilibria (CCE) and Markov correlated equilibria (CE) with sample complexity bounds that only scale polynomially with each agent's own function class complexity, thus breaking the curse of multiagents. In contrast, existing works for Markov games with function approximation have sample complexity bounds scale with the size of the \\emph{joint action space} when specialized to the canonical tabular Markov game setting, which is exponentially large in the number of agents. Our algorithms rely on two key technical innovations: (1) utilizing policy replay to tackle non-stationarity incurred by multiple agents and the use of function approximation; (2) separating learning Markov equilibria and exploration in the Markov games, which allows us to use the full-information no-regret learning oracle instead of the stronger bandit-feedback no-regret learning oracle used in the tabular setting. Furthermore, we propose an iterative-best-response type algorithm that can learn pure Markov Nash equilibria in independent linear Markov potential games. In the tabular case, by adapting the policy replay mechanism for independent linear Markov games, we propose an algorithm with $\\widetilde{O}(\\epsilon^{-2})$ sample complexity to learn Markov CCE, which improves the state-of-the-art result $\\widetilde{O}(\\epsilon^{-3})$ in Daskalakis et al. 2022, where $\\epsilon$ is the desired accuracy, and also significantly improves other problem parameters.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2132,
+    "label": 14,
+    "text": "Title: Computing the Characteristic Polynomial of Endomorphisms of a finite Drinfeld Module using Crystalline Cohomology\nAbstract: We present a new algorithm for computing the characteristic polynomial of an arbitrary endomorphism of a finite Drinfeld module using its associated crystalline cohomology. Our approach takes inspiration from Kedlaya\u2019s p-adic algorithm for computing the characteristic polynomial of the Frobenius endomorphism on a hyperelliptic curve using Monsky-Washnitzer cohomology. The method is specialized using a baby-step giant-step algorithm for the particular case of the Frobenius endomorphism, and in this case we include a complexity analysis that demonstrates asymptotic gains over previously existing approaches.",
+    "neighbors": [
+      1819
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2133,
+    "label": 16,
+    "text": "Title: DETRs Beat YOLOs on Real-time Object Detection\nAbstract: Recently, end-to-end transformer-based detectors~(DETRs) have achieved remarkable performance. However, the issue of the high computational cost of DETRs has not been effectively addressed, limiting their practical application and preventing them from fully exploiting the benefits of no post-processing, such as non-maximum suppression (NMS). In this paper, we first analyze the influence of NMS in modern real-time object detectors on inference speed, and establish an end-to-end speed benchmark. To avoid the inference delay caused by NMS, we propose a Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge. Specifically, we design an efficient hybrid encoder to efficiently process multi-scale features by decoupling the intra-scale interaction and cross-scale fusion, and propose IoU-aware query selection to improve the initialization of object queries. In addition, our proposed detector supports flexibly adjustment of the inference speed by using different decoder layers without the need for retraining, which facilitates the practical application of real-time object detectors. Our RT-DETR-L achieves 53.0% AP on COCO val2017 and 114 FPS on T4 GPU, while RT-DETR-X achieves 54.8% AP and 74 FPS, outperforming all YOLO detectors of the same scale in both speed and accuracy. Furthermore, our RT-DETR-R50 achieves 53.1% AP and 108 FPS, outperforming DINO-Deformable-DETR-R50 by 2.2% AP in accuracy and by about 21 times in FPS. ource code and pre-trained models are available at https://github.com/lyuwenyu/RT-DETR.",
+    "neighbors": [
+      1311
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2134,
+    "label": 28,
+    "text": "Title: Randomly Punctured Reed-Solomon Codes Achieve the List Decoding Capacity over Polynomial-Size Alphabets\nAbstract: This paper shows that, with high probability, randomly punctured Reed-Solomon codes over fields of polynomial size achieve the list decoding capacity. More specifically, we prove that for any $\\epsilon>0$ and $R\\in (0,1)$, with high probability, randomly punctured Reed-Solomon codes of block length $n$ and rate $R$ are $\\left(1-R-\\epsilon, O({1}/{\\epsilon})\\right)$ list decodable over alphabets of size at least $2^{\\mathrm{poly}(1/\\epsilon)}n^2$. This extends the recent breakthrough of Brakensiek, Gopi, and Makam (STOC 2023) that randomly punctured Reed-Solomon codes over fields of exponential size attain the generalized Singleton bound of Shangguan and Tamo (STOC 2020).",
+    "neighbors": [
+      231
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2135,
+    "label": 16,
+    "text": "Title: Towards Consistent Video Editing with Text-to-Image Diffusion Models\nAbstract: Existing works have advanced Text-to-Image (TTI) diffusion models for video editing in a one-shot learning manner. Despite their low requirements of data and computation, these methods might produce results of unsatisfied consistency with text prompt as well as temporal sequence, limiting their applications in the real world. In this paper, we propose to address the above issues with a novel EI$^2$ model towards \\textbf{E}nhancing v\\textbf{I}deo \\textbf{E}diting cons\\textbf{I}stency of TTI-based frameworks. Specifically, we analyze and find that the inconsistent problem is caused by newly added modules into TTI models for learning temporal information. These modules lead to covariate shift in the feature space, which harms the editing capability. Thus, we design EI$^2$ to tackle the above drawbacks with two classical modules: Shift-restricted Temporal Attention Module (STAM) and Fine-coarse Frame Attention Module (FFAM). First, through theoretical analysis, we demonstrate that covariate shift is highly related to Layer Normalization, thus STAM employs a \\textit{Instance Centering} layer replacing it to preserve the distribution of temporal features. In addition, {STAM} employs an attention layer with normalized mapping to transform temporal features while constraining the variance shift. As the second part, we incorporate {STAM} with a novel {FFAM}, which efficiently leverages fine-coarse spatial information of overall frames to further enhance temporal consistency. Extensive experiments demonstrate the superiority of the proposed EI$^2$ model for text-driven video editing.",
+    "neighbors": [
+      736,
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+      1020,
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+      1420,
+      2190
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2136,
+    "label": 30,
+    "text": "Title: Tree of Thoughts: Deliberate Problem Solving with Large Language Models\nAbstract: Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/ysymyth/tree-of-thought-llm.",
+    "neighbors": [
+      118,
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+      1206,
+      1267,
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+      1877,
+      1878,
+      1906,
+      1950,
+      1989,
+      2016,
+      2038,
+      2092
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2137,
+    "label": 24,
+    "text": "Title: Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-Based Recruitment\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2138,
+    "label": 16,
+    "text": "Title: FSD V2: Improving Fully Sparse 3D Object Detection with Virtual Voxels\nAbstract: LiDAR-based fully sparse architecture has garnered increasing attention. FSDv1 stands out as a representative work, achieving impressive efficacy and efficiency, albeit with intricate structures and handcrafted designs. In this paper, we present FSDv2, an evolution that aims to simplify the previous FSDv1 while eliminating the inductive bias introduced by its handcrafted instance-level representation, thus promoting better general applicability. To this end, we introduce the concept of \\textbf{virtual voxels}, which takes over the clustering-based instance segmentation in FSDv1. Virtual voxels not only address the notorious issue of the Center Feature Missing problem in fully sparse detectors but also endow the framework with a more elegant and streamlined approach. Consequently, we develop a suite of components to complement the virtual voxel concept, including a virtual voxel encoder, a virtual voxel mixer, and a virtual voxel assignment strategy. Through empirical validation, we demonstrate that the virtual voxel mechanism is functionally similar to the handcrafted clustering in FSDv1 while being more general. We conduct experiments on three large-scale datasets: Waymo Open Dataset, Argoverse 2 dataset, and nuScenes dataset. Our results showcase state-of-the-art performance on all three datasets, highlighting the superiority of FSDv2 in long-range scenarios and its general applicability to achieve competitive performance across diverse scenarios. Moreover, we provide comprehensive experimental analysis to elucidate the workings of FSDv2. To foster reproducibility and further research, we have open-sourced FSDv2 at https://github.com/tusen-ai/SST.",
+    "neighbors": [
+      1283,
+      2278
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2139,
+    "label": 23,
+    "text": "Title: Requirements Elicitation and Modelling of Artificial Intelligence Systems: An Empirical Study\nAbstract: Artificial Intelligence (AI) systems have gained significant traction in the recent past, creating new challenges in requirements engineering (RE) when building AI software systems. RE for AI practices have not been studied much and have scarce empirical studies. Additionally, many AI software solutions tend to focus on the technical aspects and ignore human-centered values. In this paper, we report on a case study for eliciting and modeling requirements using our framework and a supporting tool for human-centred RE for AI systems. Our case study is a mobile health application for encouraging type-2 diabetic people to reduce their sedentary behavior. We conducted our study with three experts from the app team -- a software engineer, a project manager and a data scientist. We found in our study that most human-centered aspects were not originally considered when developing the first version of the application. We also report on other insights and challenges faced in RE for the health application, e.g., frequently changing requirements.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2140,
+    "label": 30,
+    "text": "Title: Towards Measuring the Representation of Subjective Global Opinions in Language Models\nAbstract: Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more similar to. We first build a dataset, GlobalOpinionQA, comprised of questions and answers from cross-national surveys designed to capture diverse opinions on global issues across different countries. Next, we define a metric that quantifies the similarity between LLM-generated survey responses and human responses, conditioned on country. With our framework, we run three experiments on an LLM trained to be helpful, honest, and harmless with Constitutional AI. By default, LLM responses tend to be more similar to the opinions of certain populations, such as those from the USA, and some European and South American countries, highlighting the potential for biases. When we prompt the model to consider a particular country's perspective, responses shift to be more similar to the opinions of the prompted populations, but can reflect harmful cultural stereotypes. When we translate GlobalOpinionQA questions to a target language, the model's responses do not necessarily become the most similar to the opinions of speakers of those languages. We release our dataset for others to use and build on. Our data is at https://huggingface.co/datasets/Anthropic/llm_global_opinions. We also provide an interactive visualization at https://llmglobalvalues.anthropic.com.",
+    "neighbors": [
+      75,
+      1694,
+      2122
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2141,
+    "label": 27,
+    "text": "Title: ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts\nAbstract: In this paper, we argue that the next generation of robots can be commanded using only Language Models' prompts. Every prompt interrogates separately a specific Robotic Modality via its Modality Language Model (MLM). A central Task Modality mediates the whole communication to execute the robotic mission via a Large Language Model (LLM). This paper gives this new robotic design pattern the name of: Prompting Robotic Modalities (PRM). Moreover, this paper applies this PRM design pattern in building a new robotic framework named ROSGPT_Vision. ROSGPT_Vision allows the execution of a robotic task using only two prompts: a Visual and an LLM prompt. The Visual Prompt extracts, in natural language, the visual semantic features related to the task under consideration (Visual Robotic Modality). Meanwhile, the LLM Prompt regulates the robotic reaction to the visual description (Task Modality). The framework automates all the mechanisms behind these two prompts. The framework enables the robot to address complex real-world scenarios by processing visual data, making informed decisions, and carrying out actions automatically. The framework comprises one generic vision module and two independent ROS nodes. As a test application, we used ROSGPT_Vision to develop CarMate, which monitors the driver's distraction on the roads and makes real-time vocal notifications to the driver. We showed how ROSGPT_Vision significantly reduced the development cost compared to traditional methods. We demonstrated how to improve the quality of the application by optimizing the prompting strategies, without delving into technical details. ROSGPT_Vision is shared with the community (link: https://github.com/bilel-bj/ROSGPT_Vision) to advance robotic research in this direction and to build more robotic frameworks that implement the PRM design pattern and enables controlling robots using only prompts.",
+    "neighbors": [
+      25,
+      1863,
+      2036,
+      2113,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2142,
+    "label": 28,
+    "text": "Title: Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems\nAbstract: Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF systems stems from the fact their application often involves a non-convex, highly complex optimization of large dimensions. In this paper, we propose HBF schemes that leverage data to enable efficient designs for both the fully-connected HBF (FC-HBF) and dynamic sub-connected HBF (SC-HBF) architectures. We develop a deep unfolding framework based on factorizing the optimal fully digital beamformer into analog and digital terms and formulating two corresponding equivalent least squares (LS) problems. Then, the digital beamformer is obtained via a closed-form LS solution, while the analog beamformer is obtained via ManNet, a lightweight sparsely-connected deep neural network based on unfolding projected gradient descent. Incorporating ManNet into the developed deep unfolding framework leads to the ManNet-based FC-HBF scheme. We show that the proposed ManNet can also be applied to SC-HBF designs after determining the connections between the radio frequency chain and antennas. We further develop a simplified version of ManNet, referred to as subManNet, that directly produces the sparse analog precoder for SC-HBF architectures. Both networks are trained with an unsupervised training procedure. Numerical results verify that the proposed ManNet/subManNet-based HBF approaches outperform the conventional model-based and deep unfolded counterparts with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains a slightly higher spectral efficiency than the Riemannian manifold scheme, but over 1000 times faster and with a complexity reduction of more than by a factor of six (6).",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2143,
+    "label": 31,
+    "text": "Title: PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information\nAbstract: With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challenges in multi-domain and multi-task recommendations. The core of multi-task and multi-domain recommendation is to accurately capture user interests in multiple scenarios given multiple user behaviors. In this paper, we propose a plug-and-play Parameter and Embedding Personalized Network (PEPNet) for multi-domain and multi-task recommendation. PEPNet takes personalized prior information as input and dynamically scales the bottom-level Embedding and top-level DNN hidden units through gate mechanisms. Embedding Personalized Network (EPNet) performs personalized selection on Embedding to fuse features with different importance for different users in multiple domains. Parameter Personalized Network (PPNet) executes personalized modification on DNN parameters to balance targets with different sparsity for different users in multiple tasks. We have made a series of special engineering optimizations combining the Kuaishou training framework and the online deployment environment. By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1% in multiple task metrics across multiple domains. We have deployed PEPNet in Kuaishou apps, serving over 300 million users every day.",
+    "neighbors": [
+      1066,
+      1830
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2144,
+    "label": 17,
+    "text": "Title: Boundary Value Caching for Walk on Spheres\nAbstract: Grid-free Monte Carlo methods such as walk on spheres can be used to solve elliptic partial differential equations without mesh generation or global solves. However, such methods independently estimate the solution at every point, and hence do not take advantage of the high spatial regularity of solutions to elliptic problems. We propose a fast caching strategy which first estimates solution values and derivatives at randomly sampled points along the boundary of the domain (or a local region of interest). These cached values then provide cheap, output-sensitive evaluation of the solution (or its gradient) at interior points, via a boundary integral formulation. Unlike classic boundary integral methods, our caching scheme introduces zero statistical bias and does not require a dense global solve. Moreover we can handle imperfect geometry (e.g., with self-intersections) and detailed boundary/source terms without repairing or resampling the boundary representation. Overall, our scheme is similar in spirit to virtual point light methods from photorealistic rendering: it suppresses the typical salt-and-pepper noise characteristic of independent Monte Carlo estimates, while still retaining the many advantages of Monte Carlo solvers: progressive evaluation, trivial parallelization, geometric robustness, etc. We validate our approach using test problems from visual and geometric computing.",
+    "neighbors": [
+      1557
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2145,
+    "label": 24,
+    "text": "Title: Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box\nAbstract: Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty estimates of mean-field variational Bayes (MFVB). We introduce\"deterministic ADVI\"(DADVI) to address these issues. DADVI replaces the intractable MFVB objective with a fixed Monte Carlo approximation, a technique known in the stochastic optimization literature as the\"sample average approximation\"(SAA). By optimizing an approximate but deterministic objective, DADVI can use off-the-shelf second-order optimization, and, unlike standard mean-field ADVI, is amenable to more accurate posterior covariances via linear response (LR). In contrast to existing worst-case theory, we show that, on certain classes of common statistical problems, DADVI and the SAA can perform well with relatively few samples even in very high dimensions, though we also show that such favorable results cannot extend to variational approximations that are too expressive relative to mean-field ADVI. We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.",
+    "neighbors": [
+      1672
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2146,
+    "label": 24,
+    "text": "Title: Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching\nAbstract: Offline imitation learning (IL) promises the ability to learn performant policies from pre-collected demonstrations without interactions with the environment. However, imitating behaviors fully offline typically requires numerous expert data. To tackle this issue, we study the setting where we have limited expert data and supplementary suboptimal data. In this case, a well-known issue is the distribution shift between the learned policy and the behavior policy that collects the offline data. Prior works mitigate this issue by regularizing the KL divergence between the stationary state-action distributions of the learned policy and the behavior policy. We argue that such constraints based on exact distribution matching can be overly conservative and hamper policy learning, especially when the imperfect offline data is highly suboptimal. To resolve this issue, we present RelaxDICE, which employs an asymmetrically-relaxed f-divergence for explicit support regularization. Specifically, instead of driving the learned policy to exactly match the behavior policy, we impose little penalty whenever the density ratio between their stationary state-action distributions is upper bounded by a constant. Note that such formulation leads to a nested min-max optimization problem, which causes instability in practice. RelaxDICE addresses this challenge by supporting a closed-form solution for the inner maximization problem. Extensive empirical study shows that our method significantly outperforms the best prior offline IL method in six standard continuous control environments with over 30% performance gain on average, across 22 settings where the imperfect dataset is highly suboptimal.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2147,
+    "label": 16,
+    "text": "Title: Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completion\nAbstract: Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data. We consider the problem of learning a nonnegative low-rank tensor, and using duality theory, we propose a novel factorization of such tensors. The factorization decouples the nonnegative constraints from the low-rank constraints. The resulting problem is an optimization problem on manifolds, and we propose a variant of Riemannian conjugate gradients to solve it. We test the proposed algorithm across various tasks such as colour image inpainting, video completion, and hyperspectral image completion. Experimental results show that the proposed method outperforms many state-of-the-art tensor completion algorithms.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2148,
+    "label": 26,
+    "text": "Title: Explicit time embedding based cascade attention network for information popularity prediction\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2149,
+    "label": 11,
+    "text": "Title: Effect of Swarm Density on Collective Tracking Performance\nAbstract: How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different `phases' corresponding to markedly distinct group dynamics. We identify a `transition' phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration-exploitation balance can be adjusted to maximize the system's performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm's behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2150,
+    "label": 16,
+    "text": "Title: Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?\nAbstract: Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, and so on. Inspired by the recent prompt learning in natural language processing, this work proposes a novel Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud classification. MvNet investigates the possibility of leveraging the off-the-shelf 2D pre-trained models to achieve the few-shot classification, which can alleviate the over-dependence issue of the existing baseline models towards the large-scale annotated 3D point cloud data. Specifically, MvNet first encodes a 3D point cloud into multi-view image features for a number of different views. Then, a novel multi-view prompt fusion module is developed to effectively fuse information from different views to bridge the gap between 3D point cloud data and 2D pre-trained models. A set of 2D image prompts can then be derived to better describe the suitable prior knowledge for a large-scale pre-trained image model for few-shot 3D point cloud classification. Extensive experiments on ModelNet, ScanObjectNN, and ShapeNet datasets demonstrate that MvNet achieves new state-of-the-art performance for 3D few-shot point cloud image classification. The source code of this work will be available soon.",
+    "neighbors": [
+      1358,
+      1628
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2151,
+    "label": 16,
+    "text": "Title: SAM Struggles in Concealed Scenes - Empirical Study on \"Segment Anything\"\nAbstract: Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision. We could not be more excited to probe the performance traits of SAM. In particular, exploring situations in which SAM does not perform well is interesting. In this report, we choose three concealed scenes, i.e., camouflaged animals, industrial defects, and medical lesions, to evaluate SAM under unprompted settings. Our main observation is that SAM looks unskilled in concealed scenes.",
+    "neighbors": [
+      1365,
+      1932
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2152,
+    "label": 24,
+    "text": "Title: Federated Survival Forests\nAbstract: Survival analysis is a subfield of statistics concerned with modeling the occurrence time of a particular event of interest for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, real-world applications involve survival datasets that are distributed, incomplete, censored, and confidential. In this context, federated learning can tremendously improve the performance of survival analysis applications. Federated learning provides a set of privacy-preserving techniques to jointly train machine learning models on multiple datasets without compromising user privacy, leading to a better generalization performance. However, despite the widespread development of federated learning in recent AI research, few studies focus on federated survival analysis. In this work, we present a novel federated algorithm for survival analysis based on one of the most successful survival models, the random survival forest. We call the proposed method Federated Survival Forest (FedSurF). With a single communication round, FedSurF obtains a discriminative power comparable to deep-learning-based federated models trained over hundreds of federated iterations. Moreover, FedSurF retains all the advantages of random forests, namely low computational cost and natural handling of missing values and incomplete datasets. These advantages are especially desirable in real-world federated environments with multiple small datasets stored on devices with low computational capabilities. Numerical experiments compare FedSurF with state-of-the-art survival models in federated networks, showing how FedSurF outperforms deep-learning-based federated algorithms in realistic environments with non-identically distributed data.",
+    "neighbors": [
+      1359,
+      2290
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2153,
+    "label": 5,
+    "text": "Title: SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient\nAbstract: Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters. In this work, we consider alternative setups for training large models: using cheap\"preemptible\"instances or pooling existing resources from multiple regions. We analyze the performance of existing model-parallel algorithms in these conditions and find configurations where training larger models becomes less communication-intensive. Based on these findings, we propose SWARM parallelism, a model-parallel training algorithm designed for poorly connected, heterogeneous and unreliable devices. SWARM creates temporary randomized pipelines between nodes that are rebalanced in case of failure. We empirically validate our findings and compare SWARM parallelism with existing large-scale training approaches. Finally, we combine our insights with compression strategies to train a large Transformer language model with 1B shared parameters (approximately 13B before sharing) on preemptible T4 GPUs with less than 200Mb/s network.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2154,
+    "label": 16,
+    "text": "Title: Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples\nAbstract: Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style. To address these emerging copyright violations, in this paper, we are the first to explore and propose to utilize adversarial examples for DMs to protect human-created artworks. Specifically, we first build a theoretical framework to define and evaluate the adversarial examples for DMs. Then, based on this framework, we design a novel algorithm, named AdvDM, which exploits a Monte-Carlo estimation of adversarial examples for DMs by optimizing upon different latent variables sampled from the reverse process of DMs. Extensive experiments show that the generated adversarial examples can effectively hinder DMs from extracting their features. Therefore, our method can be a powerful tool for human artists to protect their copyright against infringers equipped with DM-based AI-for-Art applications. The code of our method is available on GitHub: https://github.com/mist-project/mist.git.",
+    "neighbors": [
+      428,
+      1730
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2155,
+    "label": 16,
+    "text": "Title: MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models\nAbstract: The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. We believe the primary reason for GPT-4's advanced multi-modal generation capabilities lies in the utilization of a more advanced large language model (LLM). To examine this phenomenon, we present MiniGPT-4, which aligns a frozen visual encoder with a frozen LLM, Vicuna, using just one projection layer. Our findings reveal that MiniGPT-4 possesses many capabilities similar to those exhibited by GPT-4 like detailed image description generation and website creation from hand-written drafts. Furthermore, we also observe other emerging capabilities in MiniGPT-4, including writing stories and poems inspired by given images, providing solutions to problems shown in images, teaching users how to cook based on food photos, etc. In our experiment, we found that only performing the pretraining on raw image-text pairs could produce unnatural language outputs that lack coherency including repetition and fragmented sentences. To address this problem, we curate a high-quality, well-aligned dataset in the second stage to finetune our model using a conversational template. This step proved crucial for augmenting the model's generation reliability and overall usability. Notably, our model is highly computationally efficient, as we only train a projection layer utilizing approximately 5 million aligned image-text pairs. Our code, pre-trained model, and collected dataset are available at https://minigpt-4.github.io/.",
+    "neighbors": [
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+      2095,
+      2141,
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+      2226,
+      2274
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2156,
+    "label": 16,
+    "text": "Title: Transformer-Based Visual Segmentation: A Survey\nAbstract: Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several closely related settings, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmentation-With-Transformer. We will also continually monitor developments in this rapidly evolving field.",
+    "neighbors": [
+      959,
+      1714,
+      1817,
+      2009
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2157,
+    "label": 34,
+    "text": "Title: (Almost) Ruling Out SETH Lower Bounds for All-Pairs Max-Flow\nAbstract: The All-Pairs Max-Flow problem has gained significant popularity in the last two decades, and many results are known regarding its fine-grained complexity. Despite this, wide gaps remain in our understanding of the time complexity for several basic variants of the problem. In this paper, we aim to bridge this gap by providing algorithms, conditional lower bounds, and non-reducibility results. Our main result is that for most problem settings, deterministic reductions based on the Strong Exponential Time Hypothesis (SETH) cannot rule out $n^{4-o(1)}$ time algorithms under a hypothesis called NSETH. As a step towards ruling out even $mn^{1+\\varepsilon-o(1)}$ SETH lower bounds for undirected graphs with unit node-capacities, we design a new randomized $O(m^{2+o(1)})$ time combinatorial algorithm. This is an improvement over the recent $O(m^{11/5+o(1)})$ time algorithm [Huang et al., STOC 2023] and matching their $m^{2-o(1)}$ lower bound (up to subpolynomial factors), thus essentially settling the time complexity for this setting of the problem. More generally, our main technical contribution is the insight that $st$-cuts can be verified quickly, and that in most settings, $st$-flows can be shipped succinctly (i.e., with respect to the flow support). This is a key idea in our non-reducibility results, and it may be of independent interest.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2158,
+    "label": 30,
+    "text": "Title: Multi-step Jailbreaking Privacy Attacks on ChatGPT\nAbstract: With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring existing text data from various domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether the private information is included in the training data and what privacy threats can these LLMs and their downstream applications bring. In this paper, we study the privacy threats from OpenAI's ChatGPT and the New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause new privacy threats. To this end, we conduct extensive experiments to support our claims and discuss LLMs' privacy implications.",
+    "neighbors": [
+      549,
+      896,
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+      1305,
+      1307,
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+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2159,
+    "label": 24,
+    "text": "Title: Fundamental Bounds on Online Strategic Classification\nAbstract: We study the problem of online binary classification where strategic agents can manipulate their observable features in predefined ways, modeled by a manipulation graph, in order to receive a positive classification. We show this setting differs in fundamental ways from classic (non-strategic) online classification. For instance, whereas in the non-strategic case, a mistake bound of ln |H| is achievable via the halving algorithm when the target function belongs to a known class H, we show that no deterministic algorithm can achieve a mistake bound o(\u0394) in the strategic setting, where \u0394 is the maximum degree of the manipulation graph (even when |H| = O(\u0394)). We complement this with a general algorithm achieving mistake bound O(\u0394 ln |H|). We also extend this to the agnostic setting, and show that this algorithm achieves a \u0394 multiplicative regret (mistake bound of O(\u0394 \u00b7 OPT + \u0394 \u00b7 ln |H|)), and that no deterministic algorithm can achieve o(\u0394) multiplicative regret. Next, we study two randomized models based on whether the random choices are made before or after agents respond, and show they exhibit fundamental differences. In the first, fractional model, at each round the learner deterministically chooses a probability distribution over classifiers inducing expected values on each vertex (probabilities of being classified as positive), which the strategic agents respond to. We show that any learner in this model has to suffer linear regret. On the other hand, in the second randomized algorithms model, while the adversary who selects the next agent must respond to the learner's probability distribution over classifiers, the agent then responds to the actual hypothesis classifier drawn from this distribution. Surprisingly, we show this model is more advantageous to the learner, and we design randomized algorithms that achieve sublinear regret bounds against both oblivious and adaptive adversaries.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2160,
+    "label": 24,
+    "text": "Title: RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning\nAbstract: The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identification are mainly on semi-templated retrosynthesis methods. Moreover, they are limited to single reaction center identification. However, many reaction centers are comprised of multiple bonds or atoms in reality. We refer to it as the multiple reaction center. This paper presents RCsearcher, a unified framework for single and multiple reaction center identification that combines the advantages of the graph neural network and deep reinforcement learning. The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph. At each step, it considers choosing one node in the molecular product graph and adding it to the explored node-induced subgraph as an action. Comprehensive experiments demonstrate that RCsearcher consistently outperforms other baselines and can extrapolate the reaction center patterns that have not appeared in the training set. Ablation experiments verify the effectiveness of individual components, including the beam search and one-hop constraint of action space.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2161,
+    "label": 16,
+    "text": "Title: GLIGEN: Open-Set Grounded Text-to-Image Generation\nAbstract: Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN's zero-shot performance on COCO and LVIS outperforms existing supervised layout-to-image baselines by a large margin.",
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+    "mask": "Train"
+  },
+  {
+    "node_id": 2162,
+    "label": 4,
+    "text": "Title: Data Protection for Data Privacy-A South African Problem?\nAbstract: This study proposes a comprehensive framework for enhancing data security and privacy within organizations through data protection awareness. It employs a quantitative method and survey research strategy to assess the level of data protection awareness among employees of a public organization.",
+    "neighbors": [
+      1576,
+      2041,
+      2280
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2163,
+    "label": 4,
+    "text": "Title: SmartBugs 2.0: An Execution Framework for Weakness Detection in Ethereum Smart Contracts\nAbstract: Smart contracts are blockchain programs that often handle valuable assets. Writing secure smart contracts is far from trivial, and any vulnerability may lead to significant financial losses. To support developers in identifying and eliminating vulnerabilities, methods and tools for the automated analysis have been proposed. However, the lack of commonly accepted benchmark suites and performance metrics makes it difficult to compare and evaluate such tools. Moreover, the tools are heterogeneous in their interfaces and reports as well as their runtime requirements, and installing several tools is time-consuming. In this paper, we present SmartBugs 2.0, a modular execution framework. It provides a uniform interface to 19 tools aimed at smart contract analysis and accepts both Solidity source code and EVM bytecode as input. After describing its architecture, we highlight the features of the framework. We evaluate the framework via its reception by the community and illustrate its scalability by describing its role in a study involving 3.25 million analyses.",
+    "neighbors": [
+      1855
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2164,
+    "label": 27,
+    "text": "Title: A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions\nAbstract: Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots.",
+    "neighbors": [
+      285,
+      539,
+      861
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2165,
+    "label": 24,
+    "text": "Title: Convolutional Learning on Simplicial Complexes\nAbstract: We propose a simplicial complex convolutional neural network (SCCNN) to learn data representations on simplicial complexes. It performs convolutions based on the multi-hop simplicial adjacencies via common faces and cofaces independently and captures the inter-simplicial couplings, generalizing state-of-the-art. Upon studying symmetries of the simplicial domain and the data space, it is shown to be permutation and orientation equivariant, thus, incorporating such inductive biases. Based on the Hodge theory, we perform a spectral analysis to understand how SCCNNs regulate data in different frequencies, showing that the convolutions via faces and cofaces operate in two orthogonal data spaces. Lastly, we study the stability of SCCNNs to domain deformations and examine the effects of various factors. Empirical results show the benefits of higher-order convolutions and inter-simplicial couplings in simplex prediction and trajectory prediction.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2166,
+    "label": 10,
+    "text": "Title: TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents\nAbstract: With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks which necessitate a combination of task planning and the usage of external tools. In this paper, we first propose a structured framework tailored for LLM-based AI Agents and discuss the crucial capabilities necessary for tackling intricate problems. Within this framework, we design two distinct types of agents (i.e., one-step agent and sequential agent) to execute the inference process. Subsequently, we instantiate the framework using various LLMs and evaluate their Task Planning and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings and challenges, our goal is to provide a helpful resource for researchers and practitioners to leverage the power of LLMs in their AI applications. Our study emphasizes the substantial potential of these models, while also identifying areas that need more investigation and improvement.",
+    "neighbors": [
+      25,
+      57,
+      127,
+      173,
+      319,
+      363,
+      505,
+      618,
+      704,
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+      1047,
+      1052,
+      1128,
+      1353,
+      1659,
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+      1878,
+      1893,
+      1940,
+      2029,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2167,
+    "label": 24,
+    "text": "Title: Towards Omni-generalizable Neural Methods for Vehicle Routing Problems\nAbstract: Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2168,
+    "label": 30,
+    "text": "Title: LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization\nAbstract: Text Summarization is a popular task and an active area of research for the Natural Language Processing community. By definition, it requires to account for long input texts, a characteristic which poses computational challenges for neural models. Moreover, real-world documents come in a variety of complex, visually-rich, layouts. This information is of great relevance, whether to highlight salient content or to encode long-range interactions between textual passages. Yet, all publicly available summarization datasets only provide plain text content. To facilitate research on how to exploit visual/layout information to better capture long-range dependencies in summarization models, we present LoRaLay, a collection of datasets for long-range summarization with accompanying visual/layout information. We extend existing and popular English datasets (arXiv and PubMed) with layout information and propose four novel datasets \u2013 consistently built from scholar resources \u2013 covering French, Spanish, Portuguese, and Korean languages. Further, we propose new baselines merging layout-aware and long-range models \u2013 two orthogonal approaches \u2013 and obtain state-of-the-art results, showing the importance of combining both lines of research.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2169,
+    "label": 16,
+    "text": "Title: Teaching CLIP to Count to Ten\nAbstract: Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classification and text-to-image generation. Nevertheless, existing VLMs exhibit a prominent well-documented limitation - they fail to encapsulate compositional concepts such as counting. We introduce a simple yet effective method to improve the quantitative understanding of VLMs, while maintaining their overall performance on common benchmarks. Specifically, we propose a new counting-contrastive loss used to finetune a pre-trained VLM in tandem with its original objective. Our counting loss is deployed over automatically-created counterfactual examples, each consisting of an image and a caption containing an incorrect object count. For example, an image depicting three dogs is paired with the caption\"Six dogs playing in the yard\". Our loss encourages discrimination between the correct caption and its counterfactual variant which serves as a hard negative example. To the best of our knowledge, this work is the first to extend CLIP's capabilities to object counting. Furthermore, we introduce\"CountBench\"- a new image-text counting benchmark for evaluating a model's understanding of object counting. We demonstrate a significant improvement over state-of-the-art baseline models on this task. Finally, we leverage our count-aware CLIP model for image retrieval and text-conditioned image generation, demonstrating that our model can produce specific counts of objects more reliably than existing ones.",
+    "neighbors": [
+      2306
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2170,
+    "label": 30,
+    "text": "Title: SMATCH++: Standardized and Extended Evaluation of Semantic Graphs\nAbstract: The Smatch metric is a popular method for evaluating graph distances, as is necessary, for instance, to assess the performance of semantic graph parsing systems. However, we observe some issues in the metric that jeopardize meaningful evaluation. E.g., opaque pre-processing choices can affect results, and current graph-alignment solvers do not provide us with upper-bounds. Without upper-bounds, however, fair evaluation is not guaranteed. Furthermore, adaptions of Smatch for extended tasks (e.g., fine-grained semantic similarity) are spread out, and lack a unifying framework. For better inspection, we divide the metric into three modules: pre-processing, alignment, and scoring. Examining each module, we specify its goals and diagnose potential issues, for which we discuss and test mitigation strategies. For pre-processing, we show how to fully conform to annotation guidelines that allow structurally deviating but valid graphs. For safer and enhanced alignment, we show the feasibility of optimal alignment in a standard evaluation setup, and develop a lossless graph compression method that shrinks the search space and significantly increases efficiency. For improved scoring, we propose standardized and extended metric calculation of fine-grained sub-graph meaning aspects. Our code is available at https://github.com/flipz357/smatchpp",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2171,
+    "label": 24,
+    "text": "Title: FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance\nAbstract: There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In particular, using LLMs on large collections of queries and text can be expensive. Motivated by this, we outline and discuss three types of strategies that users can exploit to reduce the inference cost associated with using LLMs: 1) prompt adaptation, 2) LLM approximation, and 3) LLM cascade. As an example, we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy. Our experiments show that FrugalGPT can match the performance of the best individual LLM (e.g. GPT-4) with up to 98% cost reduction or improve the accuracy over GPT-4 by 4% with the same cost. The ideas and findings presented here lay a foundation for using LLMs sustainably and efficiently.",
+    "neighbors": [
+      505,
+      704,
+      1052,
+      1863
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2172,
+    "label": 28,
+    "text": "Title: Transmission Design for Active RIS-Aided Simultaneous Wireless Information and Power Transfer\nAbstract: Reconfigurable intelligent surface (RIS) is a revolutionary technology to enhance both the spectral efficiency and energy efficiency of wireless communication systems. However, most of the existing contributions mainly focused on the study of passive RIS, which suffers from the \u201cdouble fading\u201d effect. On the other hand, active RIS, which is equipped with amplifiers, can effectively address this issue. In this letter, we propose an active RIS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, we maximize the weighted sum rate of the information receivers, subject to the minimum power received at all energy receivers, amplification power constraint at the active RIS, and the maximum transmit power constraint at the base station (BS). By adopting alternating optimization framework, suboptimal solutions are obtained. Simulation results show that the active RIS-aided SWIPT system has higher performance gain with the same power budget.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2173,
+    "label": 23,
+    "text": "Title: Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion\nAbstract: Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.",
+    "neighbors": [
+      14,
+      1131
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2174,
+    "label": 10,
+    "text": "Title: Construction of Decision Trees and Acyclic Decision Graphs from Decision Rule Systems\nAbstract: Decision trees and systems of decision rules are widely used as classifiers, as a means for knowledge representation, and as algorithms. They are among the most interpretable models for data analysis. The study of the relationships between these two models can be seen as an important task of computer science. Methods for transforming decision trees into systems of decision rules are simple and well-known. In this paper, we consider the inverse transformation problem, which is not trivial. We study the complexity of constructing decision trees and acyclic decision graphs representing decision trees from decision rule systems, and we discuss the possibility of not building the entire decision tree, but describing the computation path in this tree for the given input.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2175,
+    "label": 24,
+    "text": "Title: EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System\nAbstract: The Cancer Registry of Norway (CRN) collects information on cancer patients by receiving cancer messages from different medical entities (e.g., medical labs, and hospitals) in Norway. Such messages are validated by an automated cancer registry system: GURI. Its correct operation is crucial since it lays the foundation for cancer research and provides critical cancer-related statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT) for GURI can facilitate various experiments and advanced analyses of the operational state of GURI without requiring intensive interactions with the real system. However, GURI constantly evolves due to novel medical diagnostics and treatment, technological advances, etc. Accordingly, CCDT should evolve as well to synchronize with GURI. A key challenge of achieving such synchronization is that evolving CCDT needs abundant data labelled by the new GURI. To tackle this challenge, we propose EvoCLINICAL, which considers the CCDT developed for the previous version of GURI as the pretrained model and fine-tunes it with the dataset labelled by querying a new GURI version. EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer messages from a candidate dataset and query GURI with it. We evaluate EvoCLINICAL on three evolution processes. The precision, recall, and F1 score are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL. Furthermore, we replace the active learning part of EvoCLINICAL with random selection to study the contribution of transfer learning to the overall performance of EvoCLINICAL. Results show that employing active learning in EvoCLINICAL increases its performances consistently.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2176,
+    "label": 16,
+    "text": "Title: A Survey of Label-Efficient Deep Learning for 3D Point Clouds\nAbstract: In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of point cloud data in various tasks and applications. Label-efficient learning offers a promising solution by enabling effective deep network training with much-reduced annotation efforts. This paper presents the first comprehensive survey of label-efficient learning of point clouds. We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the progress achieved in this area. To achieve this, we propose a taxonomy that organizes label-efficient learning methods based on the data prerequisites provided by different types of labels. We categorize four typical label-efficient learning approaches that significantly reduce point cloud annotation efforts: data augmentation, domain transfer learning, weakly-supervised learning, and pretrained foundation models. For each approach, we outline the problem setup and provide an extensive literature review that showcases relevant progress and challenges. Finally, we share insights into current research challenges and potential future directions. A project associated with this survey has been built at \\url{https://github.com/xiaoaoran/3D_label_efficient_learning}.",
+    "neighbors": [
+      613
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2177,
+    "label": 27,
+    "text": "Title: Failure-Sentient Composition For Swarm-Based Drone Services\nAbstract: We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2178,
+    "label": 16,
+    "text": "Title: Few-shot $\\mathbf{1/a}$ Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance\nAbstract: Over the past decade, previous balanced datasets have been used to advance deep learning algorithms for industrial applications. In urban infrastructures and living environments, damage data mining cannot avoid imbalanced data issues because of rare unseen events and the high-quality status of improved operations. For visual inspection, the deteriorated class acquired from the surface of concrete and steel components are occasionally imbalanced. From numerous related surveys, we conclude that imbalanced data problems can be categorised into four types: 1) missing range of target and label valuables, 2) majority-minority class imbalance, 3) foreground background of spatial imbalance, and 4) long-tailed class of pixel-wise imbalance. Since 2015, many imbalanced studies have been conducted using deep-learning approaches, including regression, image classification, object detection, and semantic segmentation. However, anomaly detection for imbalanced data is not well known. In this study, we highlight a one-class anomaly detection application, whether anomalous class or not, and demonstrate clear examples of imbalanced vision datasets: medical disease, hazardous behaviour, material deterioration, plant disease, river sludge, and disaster damage. We provide key results on the advantage of damage-vision mining, hypothesising that the more effective the range of the positive ratio, the higher the accuracy gain of the anomalies feedback. In our imbalanced studies, compared with the balanced case with a positive ratio of $1/1$, we find that there is an applicable positive ratio $1/a$ where the accuracy is consistently high. However, the extremely imbalanced range is from one shot to $1/2a$, the accuracy of which is inferior to that of the applicable ratio. In contrast, with a positive ratio ranging over $2/a$, it shifts in the over-mining phase without an effective gain in accuracy.",
+    "neighbors": [
+      1326,
+      1537,
+      1863
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2179,
+    "label": 16,
+    "text": "Title: BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification\nAbstract: Text-based person re-identification (TBPReID) aims to retrieve person images represented by a given textual query. In this task, how to effectively align images and texts globally and locally is a crucial challenge. Recent works have obtained high performances by solving Masked Language Modeling (MLM) to align image/text parts. However, they only performed uni-directional (i.e., from image to text) local-matching, leaving room for improvement by introducing opposite-directional (i.e., from text to image) local-matching. In this work, we introduce Bidirectional Local-Matching (BiLMa) framework that jointly optimize MLM and Masked Image Modeling (MIM) in TBPReID model training. With this framework, our model is trained so as the labels of randomly masked both image and text tokens are predicted by unmasked tokens. In addition, to narrow the semantic gap between image and text in MIM, we propose Semantic MIM (SemMIM), in which the labels of masked image tokens are automatically given by a state-of-the-art human parser. Experimental results demonstrate that our BiLMa framework with SemMIM achieves state-of-the-art Rank@1 and mAP scores on three benchmarks.",
+    "neighbors": [
+      50,
+      726,
+      2206
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2180,
+    "label": 30,
+    "text": "Title: NeuroX Library for Neuron Analysis of Deep NLP Models\nAbstract: Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding of our models, neuron analysis enables various applications such as debiasing, domain adaptation and architectural search. We present NeuroX, a comprehensive open-source toolkit to conduct neuron analysis of natural language processing models. It implements various interpretation methods under a unified API, and provides a framework for data processing and evaluation, thus making it easier for researchers and practitioners to perform neuron analysis. The Python toolkit is available at https://www.github.com/fdalvi/NeuroX.Demo Video available at: https://youtu.be/mLhs2YMx4u8",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2181,
+    "label": 27,
+    "text": "Title: MARBLER: An Open Platform for Standarized Evaluation of Multi-Robot Reinforcement Learning Algorithms\nAbstract: Multi-agent reinforcement learning (MARL) has enjoyed significant recent progress, thanks to deep learning. This is naturally starting to benefit multi-robot systems (MRS) in the form of multi-robot RL (MRRL). However, existing infrastructure to train and evaluate policies predominantly focus on challenges in coordinating virtual agents, and ignore characteristics important to robotic systems. Few platforms support realistic robot dynamics, and fewer still can evaluate Sim2Real performance of learned behavior. To address these issues, we contribute MARBLER: Multi-Agent RL Benchmark and Learning Environment for the Robotarium. MARBLER offers a robust and comprehensive evaluation platform for MRRL by marrying Georgia Tech's Robotarium (which enables rapid prototyping on physical MRS) and OpenAI's Gym framework (which facilitates standardized use of modern learning algorithms). MARBLER offers a highly controllable environment with realistic dynamics, including barrier certificate-based obstacle avoidance. It allows anyone across the world to train and deploy MRRL algorithms on a physical testbed with reproducibility. Further, we introduce five novel scenarios inspired by common challenges in MRS and provide support for new custom scenarios. Finally, we use MARBLER to evaluate popular MARL algorithms and provide insights into their suitability for MRRL. In summary, MARBLER can be a valuable tool to the MRS research community by facilitating comprehensive and standardized evaluation of learning algorithms on realistic simulations and physical hardware. Links to our open-source framework and the videos of real-world experiments can be found at https://shubhlohiya.github.io/MARBLER/.",
+    "neighbors": [
+      2295
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2182,
+    "label": 24,
+    "text": "Title: Are Defenses for Graph Neural Networks Robust?\nAbstract: A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard methodology has a serious flaw - virtually all of the defenses are evaluated against non-adaptive attacks leading to overly optimistic robustness estimates. We perform a thorough robustness analysis of 7 of the most popular defenses spanning the entire spectrum of strategies, i.e., aimed at improving the graph, the architecture, or the training. The results are sobering - most defenses show no or only marginal improvement compared to an undefended baseline. We advocate using custom adaptive attacks as a gold standard and we outline the lessons we learned from successfully designing such attacks. Moreover, our diverse collection of perturbed graphs forms a (black-box) unit test offering a first glance at a model's robustness.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2183,
+    "label": 16,
+    "text": "Title: Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models\nAbstract: Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language prompts. However, current personalization approaches struggle with lengthy training times, high storage requirements or loss of identity. To overcome these limitations, we propose an encoder-based domain-tuning approach. Our key insight is that by underfitting on a large set of concepts from a given domain, we can improve generalization and create a model that is more amenable to quickly adding novel concepts from the same domain. Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e.g. a specific face, and learns to map it into a word-embedding representing the concept. Second, a set of regularized weight-offsets for the text-to-image model that learn how to effectively injest additional concepts. Together, these components are used to guide the learning of unseen concepts, allowing us to personalize a model using only a single image and as few as 5 training steps --- accelerating personalization from dozens of minutes to seconds, while preserving quality. Code and trained encoders will be available at our project page.",
+    "neighbors": [
+      1079,
+      2085,
+      2243
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2184,
+    "label": 30,
+    "text": "Title: Complex QA and language models hybrid architectures, Survey\nAbstract: This paper reviews the state-of-the-art of language models architectures and strategies for\"complex\"question-answering (QA, CQA, CPS) with a focus on hybridization. Large Language Models (LLM) are good at leveraging public data on standard problems but once you want to tackle more specific complex questions or problems (e.g. How does the concept of personal freedom vary between different cultures ? What is the best mix of power generation methods to reduce climate change ?) you may need specific architecture, knowledge, skills, methods, sensitive data protection, explainability, human approval and versatile feedback... Recent projects like ChatGPT and GALACTICA have allowed non-specialists to grasp the great potential as well as the equally strong limitations of LLM in complex QA. In this paper, we start by reviewing required skills and evaluation techniques. We integrate findings from the robust community edited research papers BIG, BLOOM and HELM which open source, benchmark and analyze limits and challenges of LLM in terms of tasks complexity and strict evaluation on accuracy (e.g. fairness, robustness, toxicity, ...) as a baseline. We discuss some challenges associated with complex QA, including domain adaptation, decomposition and efficient multi-step QA, long form and non-factoid QA, safety and multi-sensitivity data protection, multimodal search, hallucinations, explainability and truthfulness, temporal reasoning. We analyze current solutions and promising research trends, using elements such as: hybrid LLM architectural patterns, training and prompting strategies, active human reinforcement learning supervised with AI, neuro-symbolic and structured knowledge grounding, program synthesis, iterated decomposition and others.",
+    "neighbors": [
+      57,
+      704,
+      1021,
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+      1647,
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+      1809,
+      1863,
+      1954,
+      2249,
+      2286
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2185,
+    "label": 39,
+    "text": "Title: Sparse dynamic discretization discovery via arc-dependent time discretizations\nAbstract: While many time-dependent network design problems can be formulated as time-indexed formulations with strong relaxations, the size of these formulations depends on the discretization of the time horizon and can become prohibitively large. The recently-developed dynamic discretization discovery (DDD) method allows many time-dependent problems to become more tractable by iteratively solving instances of the problem on smaller networks where each node has its own discrete set of departure times. However, in the current implementation of DDD, all arcs departing a common node share the same set of departure times. This causes DDD to be ineffective for solving problems where all near-optimal solutions require many distinct departure times at the majority of the high-degree nodes in the network. Region-based networks are one such structure that often leads to many high-degree nodes, and their increasing popularity underscores the importance of tailoring solution methods for these networks. To improve methods for solving problems that require many departure times at nodes, we develop a DDD framework where the set of departure times is determined on the arc level rather than the node level. We apply this arc-based DDD method to instances of the service network design problem (SND). We show that an arc-based approach is particularly advantageous when instances arise from region-based networks, and when candidate paths are fixed in the base graph for each commodity. Moreover, our algorithm builds upon the existing DDD framework and achieves these improvements with only benign modifications to the original implementation.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2186,
+    "label": 16,
+    "text": "Title: DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models\nAbstract: Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In contrast, synthetic data can be freely available using a generative model (e.g., DALL-E, Stable Diffusion). In this paper, we show that it is possible to automatically obtain accurate semantic masks of synthetic images generated by the Off-the-shelf Stable Diffusion model, which uses only text-image pairs during training. Our approach, called DiffuMask, exploits the potential of the cross-attention map between text and image, which is natural and seamless to extend the text-driven image synthesis to semantic mask generation. DiffuMask uses text-guided cross-attention information to localize class/word-specific regions, which are combined with practical techniques to create a novel high-resolution and class-discriminative pixel-wise mask. The methods help to reduce data collection and annotation costs obviously. Experiments demonstrate that the existing segmentation methods trained on synthetic data of DiffuMask can achieve a competitive performance over the counterpart of real data (VOC 2012, Cityscapes). For some classes (e.g., bird), DiffuMask presents promising performance, close to the stateof-the-art result of real data (within 3% mIoU gap). Moreover, in the open-vocabulary segmentation (zero-shot) setting, DiffuMask achieves a new SOTA result on Unseen class of VOC 2012. The project website can be found at https://weijiawu.github.io/DiffusionMask/.",
+    "neighbors": [
+      269
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2187,
+    "label": 30,
+    "text": "Title: BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2188,
+    "label": 27,
+    "text": "Title: METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation\nAbstract: Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments. We train the traversability prediction network to generate a dense and continuous-valued cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain interaction feedback in a self-supervised manner. Meta-learning is utilized to train a global model with driving data collected from multiple environments, effectively minimizing estimation uncertainty. During deployment, online adaptation is performed to rapidly adapt the network to the local environment by exploiting recent interaction experiences. To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty. Moreover, by integrating our model with a model predictive controller, we demonstrate that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.",
+    "neighbors": [
+      1871
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2189,
+    "label": 16,
+    "text": "Title: Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification\nAbstract: We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, with verified licences. They stem from diverse domains, such as ecology (fauna and flora), manufacturing (textures, vehicles), human actions, and optical character recognition, featuring various image scales (microscopic, human scales, remote sensing). All datasets are preprocessed, annotated, and formatted uniformly, and come in 3 versions (Micro $\\subset$ Mini $\\subset$ Extended) to match users' computational resources. We showcase the utility of the first 30 datasets on few-shot learning problems. The other 10 will be released shortly after. Meta-Album is already more diverse and larger (in number of datasets) than similar efforts, and we are committed to keep enlarging it via a series of competitions. As competitions terminate, their test data are released, thus creating a rolling benchmark, available through OpenML.org. Our website https://meta-album.github.io/ contains the source code of challenge winning methods, baseline methods, data loaders, and instructions for contributing either new datasets or algorithms to our expandable meta-dataset.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2190,
+    "label": 16,
+    "text": "Title: Structure and Content-Guided Video Synthesis with Diffusion Models\nAbstract: Text-guided generative diffusion models unlock powerful image creation and editing tools. While these have been extended to video generation, current approaches that edit the content of existing footage while retaining structure require expensive re-training for every input or rely on error-prone propagation of image edits across frames. In this work, we present a structure and content-guided video diffusion model that edits videos based on visual or textual descriptions of the desired output. Conflicts between user-provided content edits and structure representations occur due to insufficient disentanglement between the two aspects. As a solution, we show that training on monocular depth estimates with varying levels of detail provides control over structure and content fidelity. Our model is trained jointly on images and videos which also exposes explicit control of temporal consistency through a novel guidance method. Our experiments demonstrate a wide variety of successes; fine-grained control over output characteristics, customization based on a few reference images, and a strong user preference towards results by our model.",
+    "neighbors": [
+      170,
+      330,
+      860,
+      957,
+      1042,
+      1125,
+      1251,
+      1303,
+      1420,
+      1539,
+      1590,
+      1707,
+      2085,
+      2135,
+      2243
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2191,
+    "label": 21,
+    "text": "Title: Towards Mobility Data Science (Vision Paper)\nAbstract: Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2192,
+    "label": 27,
+    "text": "Title: Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics\nAbstract: Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and high-performing solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains - mainly applied to locomotion, where the fitness and the behavior signal are dense. Grasping is a crucial task for manipulation in robotics. Despite the efforts of many research communities, this task is yet to be solved. Grasping cumulates unprecedented challenges in QD literature: it suffers from reward sparsity, behavioral sparsity, and behavior space misalignment. The present work studies how QD can address grasping. Experiments have been conducted on 15 different methods on 10 grasping domains, corresponding to 2 different robot-gripper setups and 5 standard objects. An evaluation framework that distinguishes the evaluation of an algorithm from its internal components has also been proposed for a fair comparison. The obtained results show that MAP-Elites variants that select successful solutions in priority outperform all the compared methods on the studied metrics by a large margin. We also found experimental evidence that sparse interaction can lead to deceptive novelty. To our knowledge, the ability to efficiently produce examples of grasping trajectories demonstrated in this work has no precedent in the literature.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2193,
+    "label": 24,
+    "text": "Title: DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data\nAbstract: With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers 16.34x faster training and 2.89x faster inference. More importantly, DOMINO performs notably better when learning from partially labeled and highly imbalanced data, providing 10.93x higher robustness against hardware noises than SOTA DNNs.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2194,
+    "label": 24,
+    "text": "Title: CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation\nAbstract: Molecular conformer generation (MCG) is an important task in cheminformatics and drug discovery. The ability to efficiently generate low-energy 3D structures can avoid expensive quantum mechanical simulations, leading to accelerated screenings and enhanced structural exploration. Several generative models have been developed for MCG, but many struggle to consistently produce high-quality conformers. To address these issues, we introduce CoarsenConf, which coarse-grains molecular graphs based on torsional angles and integrates them into an SE(3)-equivariant hierarchical variational autoencoder. Through equivariant coarse-graining, we aggregate the fine-grained atomic coordinates of subgraphs connected via rotatable bonds, creating a variable-length coarse-grained latent representation. Our model uses a novel aggregated attention mechanism to restore fine-grained coordinates from the coarse-grained latent representation, enabling efficient autoregressive generation of large molecules. Furthermore, our work expands current conformer generation benchmarks and introduces new metrics to better evaluate the quality and viability of generated conformers. We demonstrate that CoarsenConf generates more accurate conformer ensembles compared to prior generative models and traditional cheminformatics methods.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2195,
+    "label": 24,
+    "text": "Title: Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates\nAbstract: We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront three themes that have been largely overlooked in the machine learning literature: responsibility, performativity and tensions between aggregate and individual.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2196,
+    "label": 6,
+    "text": "Title: Data Embroidery with Black-and-White Textures\nAbstract: We investigated data embroidery with black-and-white textures, identifying challenges in the use of textures for machine embroidery based on our own experience. Data embroidery, as a method of physically representing data, offers a unique way to integrate personal data into one's everyday fabric-based objects. Owing to their monochromatic characteristics, black-and-white textures promise to be easy to employ in machine embroidery. We experimented with different textured visualizations designed by experts and, in this paper, we detail our workflow and evaluate the performance and suitability of different textures. We then conducted a survey on vegetable preferences within a family and created a canvas bag as a case study, featuring the embroidered family data to show how embroidered data can be used in practice.",
+    "neighbors": [
+      197
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2197,
+    "label": 24,
+    "text": "Title: Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators\nAbstract: Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is important in many scientific applications to identify the underlying mechanisms of a treatment effect. However, in many scientific applications the mediator is unobserved, but there may exist related measurements. For example, we may want to identify how changes in brain activity or structure mediate an antidepressant's effect on behavior, but we may only have access to electrophysiological or imaging brain measurements. To date, most CMA methods assume that the mediator is one-dimensional and observable, which oversimplifies such real-world scenarios. To overcome this limitation, we introduce a CMA framework that can handle complex and indirectly observed mediators based on the identifiable variational autoencoder (iVAE) architecture. We prove that the true joint distribution over observed and latent variables is identifiable with the proposed method. Additionally, our framework captures a disentangled representation of the indirectly observed mediator and yields accurate estimation of the direct and mediated effects in synthetic and semi-synthetic experiments, providing evidence of its potential utility in real-world applications.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2198,
+    "label": 16,
+    "text": "Title: Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction\nAbstract: Multi-camera 3D perception has emerged as a prominent research field in autonomous driving, offering a viable and cost-effective alternative to LiDAR-based solutions. However, existing multi-camera algorithms primarily rely on monocular image pre-training, which overlooks the spatial and temporal correlations among different camera views. To address this limitation, we propose the first multi-camera unified pre-training framework called Occ-BEV, which involves initially reconstructing the 3D scene as the foundational stage and subsequently fine-tuning the model on downstream tasks. Specifically, a 3D decoder is designed for leveraging Bird's Eye View (BEV) features from multi-view images to predict the 3D geometric occupancy to enable the model to capture a more comprehensive understanding of the 3D environment. A significant benefit of Occ-BEV is its capability of utilizing a considerable volume of unlabeled image-LiDAR pairs for pre-training purposes. The proposed multi-camera unified pre-training framework demonstrates promising results in key tasks such as multi-camera 3D object detection and surrounding semantic scene completion. When compared to monocular pre-training methods on the nuScenes dataset, Occ-BEV shows a significant improvement of about 2.0% in mAP and 2.0% in NDS for multi-camera 3D object detection, as well as a 3% increase in mIoU for surrounding semantic scene completion. Codes are publicly available at https://github.com/chaytonmin/Occ-BEV.",
+    "neighbors": [
+      47,
+      1260,
+      1491,
+      1571
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2199,
+    "label": 4,
+    "text": "Title: Communication-Efficient Laplace Mechanism for Differential Privacy via Random Quantization\nAbstract: We propose the first method that realizes the Laplace mechanism exactly (i.e., a Laplace noise is added to the data) that requires only a finite amount of communication (whereas the original Laplace mechanism requires the transmission of a real number) while guaranteeing privacy against the server and database. Our mechanism can serve as a drop-in replacement for local or centralized differential privacy applications where the Laplace mechanism is used. Our mechanism is constructed using a random quantization technique. Unlike the simple and prevalent Laplace-mechanism-then-quantize approach, the quantization in our mechanism does not result in any distortion or degradation of utility. Unlike existing dithered quantization and channel simulation schemes for simulating additive Laplacian noise, our mechanism guarantees privacy not only against the database and downstream, but also against the honest but curious server which attempts to decode the data using the dither signals.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2200,
+    "label": 18,
+    "text": "Title: Thermodynamic AI and the fluctuation frontier\nAbstract: Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations. We connect these physics-inspired AI algorithms by unifying them under a single mathematical framework that we call Thermodynamic AI. Seemingly disparate algorithmic classes can be described by this framework, for example, (1) Generative diffusion models, (2) Bayesian neural networks, (3) Monte Carlo sampling and (4) Simulated annealing. Such Thermodynamic AI algorithms are currently run on digital hardware, ultimately limiting their scalability and overall potential. Stochastic fluctuations naturally occur in physical thermodynamic systems, and such fluctuations can be viewed as a computational resource. Hence, we propose a novel computing paradigm, where software and hardware become inseparable. Our algorithmic unification allows us to identify a single full-stack paradigm, involving Thermodynamic AI hardware, that could accelerate such algorithms. We contrast Thermodynamic AI hardware with quantum computing where noise is a roadblock rather than a resource. Thermodynamic AI hardware can be viewed as a novel form of computing, since it uses a novel fundamental building block. We identify stochastic bits (s-bits) and stochastic modes (s-modes) as the respective building blocks for discrete and continuous Thermodynamic AI hardware. In addition to these stochastic units, Thermodynamic AI hardware employs a Maxwell's demon device that guides the system to produce non-trivial states. We provide a few simple physical architectures for building these devices and we develop a formalism for programming the hardware via gate sequences. We hope to stimulate discussion around this new computing paradigm. Beyond acceleration, we believe it will impact the design of both hardware and algorithms, while also deepening our understanding of the connection between physics and intelligence.",
+    "neighbors": [
+      1508,
+      1987
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2201,
+    "label": 4,
+    "text": "Title: Physical Zero-Knowledge Proofs for Five Cells\nAbstract: Five Cells is a logic puzzle consisting of a rectangular grid, with some cells containg a number. The player has to partition the grid into pentominoes such that the number in each cell must be equal to the number of edges of that cell that are borders of pentominoes. In this paper, we propose two physical zero-knowledge proof protocols for Five Cells using a deck of playing cards, which allow a prover to physically show that he/she knows a solution of the puzzle without revealing it. In the optimization of our first protocol, we also develop a technique to reduce the number of required cards from quadratic to linear in the number of cells, which can be used in other zero-knowledge proof protocols related to graph coloring as well.",
+    "neighbors": [
+      2213
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2202,
+    "label": 16,
+    "text": "Title: Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8\nAbstract: Traffic safety is a major global concern. Helmet usage is a key factor in preventing head injuries and fatalities caused by motorcycle accidents. However, helmet usage violations continue to be a significant problem. To identify such violations, automatic helmet detection systems have been proposed and implemented using computer vision techniques. Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time. This study proposes a robust real-time helmet violation detection system. The proposed system utilizes a unique data processing strategy, referred to as few-shot data sampling, to develop a robust model with fewer annotations, and a single-stage object detection model, YOLOv8 (You Only Look Once Version 8), for detecting helmet violations in real-time from video frames. Our proposed method won 7th place in the 2023 AI City Challenge, Track 5, with an mAP score of 0.5861 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system. The code for the few-shot data sampling technique is available at https://github.com/aboah1994/few-shot-Video-Data-Sampling.git.",
+    "neighbors": [
+      297
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2203,
+    "label": 16,
+    "text": "Title: SegGPT: Segmenting Everything In Context\nAbstract: We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively.",
+    "neighbors": [
+      34,
+      106,
+      719,
+      959,
+      1050,
+      1262,
+      1365,
+      1380,
+      2226
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2204,
+    "label": 16,
+    "text": "Title: PSSTRNet: Progressive Segmentation-Guided Scene Text Removal Network\nAbstract: Scene text removal (STR) is a challenging task due to the complex text fonts, colors, sizes, and background textures in scene images. However, most previous methods learn both text location and background inpainting implicitly within a single network, which weakens the text localization mecha-nism and makes a lossy background. To tackle these prob-lems, we propose a simple Progressive Segmentation-guided Scene Text Removal Network(PSSTRNet) to remove the text in the image iteratively. It contains two decoder branches, a text segmentation branch, and a text removal branch, with a shared encoder. The text segmentation branch generates text mask maps as the guidance for the regional removal branch. In each iteration, the original image, previous text removal result, and text mask are input to the network to extract the rest part of the text segments and cleaner text removal result. To get a more accurate text mask map, an update module is developed to merge the mask map in the current and previous stages. The final text removal result is obtained by adaptive fusion of results from all previous stages. A sufficient number of experiments and ablation studies conducted on the real and synthetic public datasets demonstrate our proposed method achieves state-of-the-art performance.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2205,
+    "label": 16,
+    "text": "Title: Shap-E: Generating Conditional 3D Implicit Functions\nAbstract: We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at https://github.com/openai/shap-e.",
+    "neighbors": [
+      260,
+      357,
+      1033,
+      1315,
+      1467,
+      1898,
+      2117
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2206,
+    "label": 16,
+    "text": "Title: RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search\nAbstract: Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Relation-Aware learning (RA) and Sensitivity-Aware learning (SA). For one thing, existing methods cluster representations of all positive pairs without distinction and overlook the noise problem caused by the weak positive pairs where the text and the paired image have noise correspondences, thus leading to overfitting learning. RA offsets the overfitting risk by introducing a novel positive relation detection task (i.e., learning to distinguish strong and weak positive pairs). For another thing, learning invariant representation under data augmentation (i.e., being insensitive to some transformations) is a general practice for improving representation's robustness in existing methods. Beyond that, we encourage the representation to perceive the sensitive transformation by SA (i.e., learning to detect the replaced words), thus promoting the representation's robustness. Experiments demonstrate that RaSa outperforms existing state-of-the-art methods by 6.94%, 4.45% and 15.35% in terms of Rank@1 on CUHK-PEDES, ICFG-PEDES and RSTPReid datasets, respectively. Code is available at: https://github.com/Flame-Chasers/RaSa.",
+    "neighbors": [
+      2179
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2207,
+    "label": 16,
+    "text": "Title: SIESTA: Efficient Online Continual Learning with Sleep\nAbstract: In supervised continual learning, a deep neural network (DNN) is updated with an ever-growing data stream. Unlike the offline setting where data is shuffled, we cannot make any distributional assumptions about the data stream. Ideally, only one pass through the dataset is needed for computational efficiency. However, existing methods are inadequate and make many assumptions that cannot be made for real-world applications, while simultaneously failing to improve computational efficiency. In this paper, we propose a novel online continual learning method, SIESTA based on wake/sleep framework for training, which is well aligned to the needs of on-device learning. The major goal of SIESTA is to advance compute efficient continual learning so that DNNs can be updated efficiently using far less time and energy. The principal innovations of SIESTA are: 1) rapid online updates using a rehearsal-free, backpropagation-free, and data-driven network update rule during its wake phase, and 2) expedited memory consolidation using a compute-restricted rehearsal policy during its sleep phase. For memory efficiency, SIESTA adapts latent rehearsal using memory indexing from REMIND. Compared to REMIND and prior arts, SIESTA is far more computationally efficient, enabling continual learning on ImageNet-1K in under 2.4 hours on a single GPU; moreover, in the augmentation-free setting it matches the performance of the offline learner, a milestone critical to driving adoption of continual learning in real-world applications.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2208,
+    "label": 30,
+    "text": "Title: Enhancing Chat Language Models by Scaling High-quality Instructional Conversations\nAbstract: Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT. Scaling the diversity and quality of such data, although straightforward, stands a great chance of leading to improved performance. This paper aims to improve the upper bound of open-source models further. We first provide a systematically designed, diverse, informative, large-scale dataset of instructional conversations, UltraChat, which does not involve human queries. Our objective is to capture the breadth of interactions that a human might have with an AI assistant and employs a comprehensive framework to generate multi-turn conversation iteratively. UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions. Our statistical analysis of UltraChat reveals its superiority in various key metrics, including scale, average length, diversity, coherence, etc., solidifying its position as a leading open-source dataset. Building upon UltraChat, we fine-tune a LLaMA model to create a powerful conversational model, UltraLLaMA. Our evaluations indicate that UltraLLaMA consistently outperforms other open-source models, including Vicuna, the previously recognized state-of-the-art open-source model. The dataset and the model will be publicly released\\footnote{\\url{https://github.com/thunlp/UltraChat}}.",
+    "neighbors": [
+      566,
+      761,
+      817,
+      1617,
+      1733,
+      1755,
+      1863,
+      1950,
+      1972,
+      2094
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2209,
+    "label": 24,
+    "text": "Title: Provably Efficient Learning in Partially Observable Contextual Bandit\nAbstract: In this paper, we investigate transfer learning in partially observable contextual bandits, where agents have limited knowledge from other agents and partial information about hidden confounders. We first convert the problem to identifying or partially identifying causal effects between actions and rewards through optimization problems. To solve these optimization problems, we discretize the original functional constraints of unknown distributions into linear constraints, and sample compatible causal models via sequentially solving linear programmings to obtain causal bounds with the consideration of estimation error. Our sampling algorithms provide desirable convergence results for suitable sampling distributions. We then show how causal bounds can be applied to improving classical bandit algorithms and affect the regrets with respect to the size of action sets and function spaces. Notably, in the task with function approximation which allows us to handle general context distributions, our method improves the order dependence on function space size compared with previous literatures. We formally prove that our causally enhanced algorithms outperform classical bandit algorithms and achieve orders of magnitude faster convergence rates. Finally, we perform simulations that demonstrate the efficiency of our strategy compared to the current state-of-the-art methods. This research has the potential to enhance the performance of contextual bandit agents in real-world applications where data is scarce and costly to obtain.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2210,
+    "label": 16,
+    "text": "Title: Tuning computer vision models with task rewards\nAbstract: Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models. The issue is exacerbated when the task involves complex structured outputs, as it becomes harder to design procedures which address this misalignment. In natural language processing, this is often addressed using reinforcement learning techniques that align models with a task reward. We adopt this approach and show its surprising effectiveness across multiple computer vision tasks, such as object detection, panoptic segmentation, colorization and image captioning. We believe this approach has the potential to be widely useful for better aligning models with a diverse range of computer vision tasks.",
+    "neighbors": [
+      1165,
+      2258
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2211,
+    "label": 4,
+    "text": "Title: Privacy-Preserving 3-Layer Neural Network Training using Mere Homomorphic Encryption Technique\nAbstract: In this manuscript, we consider the problem of privacy-preserving training of neural networks in the mere homomorphic encryption setting. We combine several exsiting techniques available, extend some of them, and finally enable the training of 3-layer neural networks for both the regression and classification problems using mere homomorphic encryption technique.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2212,
+    "label": 1,
+    "text": "Title: Transcoding Quality Prediction for Adaptive Video Streaming\nAbstract: In recent years, video streaming applications have proliferated the demand for Video Quality Assessment (VQA). Reduced reference video quality assessment (RR-VQA) is a category of VQA where certain features (e.g., texture, edges) of the original video are provided for quality assessment. It is a popular research area for various applications such as social media, online games, and video streaming. This paper introduces a reduced reference Transcoding Quality Prediction Model (TQPM) to determine the visual quality score of the video possibly transcoded in multiple stages. The quality is predicted using Discrete Cosine Transform (DCT)-energy-based features of the video (i.e., the video's brightness, spatial texture information, and temporal activity) and the target bitrate representation of each transcoding stage. To do that, the problem is formulated, and a Long Short-Term Memory (LSTM)-based quality prediction model is presented. Experimental results illustrate that, on average, TQPM yields PSNR, SSIM, and VMAF predictions with an R2 score of 0.83, 0.85, and 0.87, respectively, and Mean Absolute Error (MAE) of 1.31 dB, 1.19 dB, and 3.01, respectively, for single-stage transcoding. Furthermore, an R2 score of 0.84, 0.86, and 0.91, respectively, and MAE of 1.32 dB, 1.33 dB, and 3.25, respectively, are observed for a two-stage transcoding scenario. Moreover, the average processing time of TQPM for 4s segments is 0.328s, making it a practical VQA method in online streaming applications.",
+    "neighbors": [
+      1798
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2213,
+    "label": 4,
+    "text": "Title: Physical Zero-Knowledge Proof for Ball Sort Puzzle\nAbstract: nan",
+    "neighbors": [
+      2201
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2214,
+    "label": 2,
+    "text": "Title: The Undecidability of Typability in the Lambda-Pi-Calculus\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2215,
+    "label": 30,
+    "text": "Title: Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond\nAbstract: Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP), exhibiting impressive achievements across various classic NLP tasks. However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include three representative LLMs (i.e., text-davinci-003, ChatGPT and BARD) and evaluate them on all selected datasets under zero-shot, one-shot and three-shot settings. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations from objective and subjective manners, covering both answers and explanations. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Thirdly, to avoid the influences of knowledge bias and purely focus on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. It contains 3,000 samples and covers deductive, inductive and abductive settings. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions. It reflects the pros and cons of LLMs and gives guiding directions for future works.",
+    "neighbors": [
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+      377,
+      975,
+      1039,
+      1694,
+      1735,
+      1943,
+      1952,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2216,
+    "label": 16,
+    "text": "Title: A Survey on Multimodal Large Language Models\nAbstract: Multimodal Large Language Model (MLLM) recently has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks. The surprising emergent capabilities of MLLM, such as writing stories based on images and OCR-free math reasoning, are rare in traditional methods, suggesting a potential path to artificial general intelligence. In this paper, we aim to trace and summarize the recent progress of MLLM. First of all, we present the formulation of MLLM and delineate its related concepts. Then, we discuss the key techniques and applications, including Multimodal Instruction Tuning (M-IT), Multimodal In-Context Learning (M-ICL), Multimodal Chain of Thought (M-CoT), and LLM-Aided Visual Reasoning (LAVR). Finally, we discuss existing challenges and point out promising research directions. In light of the fact that the era of MLLM has only just begun, we will keep updating this survey and hope it can inspire more research. An associated GitHub link collecting the latest papers is available at https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models.",
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+      2030,
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+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2217,
+    "label": 6,
+    "text": "Title: Is embodied interaction beneficial? A study on navigating network visualizations\nAbstract: Network visualizations are commonly used to analyze relationships in various contexts, such as social, biological, and geographical interactions. To efficiently explore a network visualization, the user needs to quickly navigate to different parts of the network and analyze local details. Recent advancements in display and interaction technologies inspire new visions for improved visualization and interaction design. Past research into network design has identified some key benefits to visualizing networks in 3D versus 2D. However, little work has been done to study the impact of varying levels of embodied interaction on network analysis. We present a controlled user study that compared four network visualization environments featuring conditions and hardware that leveraged different amounts of embodiment and visual perception ranging from a 2D visualization desktop environment with a standard mouse to a 3D visualization virtual reality environment. We measured the accuracy, speed, perceived workload, and preferences of 20 participants as they completed three network analytic tasks, each of which required unique navigation and substantial effort to complete. For the task that required participants to iterate over the entire visualization rather than focus on a specific area, we found that participants were more accurate using a VR HMD and a trackball mouse than conventional desktop settings. From a workload perspective, VR was generally considered the least mentally demanding and least frustrating to use in two of our three tasks. It was also preferred and ranked as the most effective and visually appealing condition overall. However, using VR to compare two side-by-side networks was difficult, and it was similar to or slower than other conditions in two of the three tasks. Overall, the accuracy and workload advantages of conditions with greater embodiment in specific tasks suggest promising opportunities to create more effective environments in which to analyze network visualizations.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2218,
+    "label": 24,
+    "text": "Title: SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning\nAbstract: SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label leakage. To harness the full potential of SecureBoost, hyperparameters of SecureBoost should be carefully chosen to strike an optimal balance between utility, efficiency, and privacy. Existing methods either set hyperparameters empirically or heuristically, which are far from optimal. To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage. We design measurements of the three objectives. In particular, the privacy leakage is measured using our proposed instance clustering attack. Experimental results demonstrate that the CMOSB yields not only hyperparameters superior to the baseline but also optimal sets of hyperparameters that can support the flexible requirements of FL participants.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2219,
+    "label": 16,
+    "text": "Title: A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision\nAbstract: Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. However, the initial attempts to transfer this new paradigm to medical imaging have shown less impressive performances than those observed in other domains, due to the significant domain shift and the complex, expert domain knowledge inherent to medical-imaging tasks. Motivated by the need for domain-expert foundation models, we present FLAIR, a pre-trained vision-language model for universal retinal fundus image understanding. To this end, we compiled 37 open-access, mostly categorical fundus imaging datasets from various sources, with up to 97 different target conditions and 284,660 images. We integrate the expert's domain knowledge in the form of descriptive textual prompts, during both pre-training and zero-shot inference, enhancing the less-informative categorical supervision of the data. Such a textual expert's knowledge, which we compiled from the relevant clinical literature and community standards, describes the fine-grained features of the pathologies as well as the hierarchies and dependencies between them. We report comprehensive evaluations, which illustrate the benefit of integrating expert knowledge and the strong generalization capabilities of FLAIR under difficult scenarios with domain shifts or unseen categories. When adapted with a lightweight linear probe, FLAIR outperforms fully-trained, dataset-focused models, more so in the few-shot regimes. Interestingly, FLAIR outperforms by a large margin more generalist, larger-scale image-language models, which emphasizes the potential of embedding experts' domain knowledge and the limitations of generalist models in medical imaging.",
+    "neighbors": [
+      533,
+      771,
+      1670
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2220,
+    "label": 4,
+    "text": "Title: Privacy Considerations for Risk-Based Authentication Systems\nAbstract: Risk-based authentication (RBA) extends authentication mechanisms to make them more robust against account takeover attacks, such as those using stolen passwords. RBA is recommended by NIST and NCSC to strengthen password-based authentication, and is already used by major online services. Also, users consider RBA to be more usable than two-factor authentication and just as secure. However, users currently obtain RBA\u2019s high security and usability benefits at the cost of exposing potentially sensitive personal data (e.g., IP address or browser information). This conflicts with user privacy and requires to consider user rights regarding the processing of personal data. We outline potential privacy challenges regarding different attacker models and propose improvements to balance privacy in RBA systems. To estimate the properties of the privacy-preserving RBA enhancements in practical environments, we evaluated a subset of them with long-term data from 780 users of a real-world online service. Our results show the potential to increase privacy in RBA solutions. However, it is limited to certain parameters that should guide RBA design to protect privacy. We outline research directions that need to be considered to achieve a widespread adoption of privacy preserving RBA with high user acceptance.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2221,
+    "label": 16,
+    "text": "Title: A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot\nAbstract: In machine learning, generative modeling aims to learn to generate new data statistically similar to the training data distribution. In this paper, we survey learning generative models under limited data, few shots and zero shot, referred to as Generative Modeling under Data Constraint (GM-DC). This is an important topic when data acquisition is challenging, e.g. healthcare applications. We discuss background, challenges, and propose two taxonomies: one on GM-DC tasks and another on GM-DC approaches. Importantly, we study interactions between different GM-DC tasks and approaches. Furthermore, we highlight research gaps, research trends, and potential avenues for future exploration. Project website: https://gmdc-survey.github.io.",
+    "neighbors": [
+      679,
+      887,
+      1418,
+      2085
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2222,
+    "label": 16,
+    "text": "Title: Transformer-Based Sensor Fusion for Autonomous Driving: A Survey\nAbstract: Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics. Transformers-based detection head and CNN-based feature encoder to extract features from raw sensor-data has emerged as one of the best performing sensor-fusion 3D-detection-framework, according to the dataset leaderboards. In this work we provide an in-depth literature survey of transformer based 3D-object detection task in the recent past, primarily focusing on the sensor fusion. We also briefly go through the Vision transformers (ViT) basics, so that readers can easily follow through the paper. Moreover, we also briefly go through few of the non-transformer based less-dominant methods for sensor fusion for autonomous driving. In conclusion we summarize with sensor-fusion trends to follow and provoke future research. More updated summary can be found at: https://github.com/ApoorvRoboticist/Transformers-Sensor-Fusion",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2223,
+    "label": 16,
+    "text": "Title: Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR\nAbstract: This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by \u224811 %. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not enforce holistic latent space geometry, and (ii) there are never enough sketches to train a high accuracy model. For the former, we propose a simple modification to the standard triplet loss, that explicitly enforces separation amongst photos/sketch instances. For the latter, we put forward a novel knowledge distillation module can leverage photo data for model training. Both modules are then plugged into a novel plug-n-playable training paradigm that allows for more stable training. More specifically, for (i) we employ an intra-modal triplet loss amongst sketches to bring sketches of the same instance closer from others, and one more amongst photos to push away different photo instances while bringing closer a structurally augmented version of the same photo (offering a gain of \u22484-6%). To tackle (ii), we first pre-train a teacher on the large set of unlabelled photos over the aforementioned intra-modal photo triplet loss. Then we distill the contextual similarity present amongst the instances in the teacher's embedding space to that in the student's embedding space, by matching the distribution over inter-feature distances of respective samples in both embedding spaces (delivering a further gain of \u2248 4-5%). Apart from outperforming prior arts significantly, our model also yields satisfactory results on generalising to new classes. Project page: https://aneeshan95.github.io/Sketch_PVT/",
+    "neighbors": [
+      951
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2224,
+    "label": 24,
+    "text": "Title: Can we avoid Double Descent in Deep Neural Networks?\nAbstract: Finding the optimal size of deep learning models is very actual and of broad impact, especially in energy-saving schemes. Very recently, an unexpected phenomenon, the ``double descent'', has caught the attention of the deep learning community. As the model's size grows, the performance gets first worse, and then goes back to improving. It raises serious questions about the optimal model's size to maintain high generalization: the model needs to be sufficiently over-parametrized, but adding too many parameters wastes training resources. Is it possible to find, in an efficient way, the best trade-off? Our work shows that the double descent phenomenon is potentially avoidable with proper conditioning of the learning problem, but a final answer is yet to be found. We empirically observe that there is hope to dodge the double descent in complex scenarios with proper regularization, as a simple $\\ell_2$ regularization is already positively contributing to such a perspective.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2225,
+    "label": 30,
+    "text": "Title: On the Origins of Bias in NLP through the Lens of the Jim Code\nAbstract: In this paper, we trace the biases in current natural language processing (NLP) models back to their origins in racism, sexism, and homophobia over the last 500 years. We review literature from critical race theory, gender studies, data ethics, and digital humanities studies, and summarize the origins of bias in NLP models from these social science perspective. We show how the causes of the biases in the NLP pipeline are rooted in social issues. Finally, we argue that the only way to fix the bias and unfairness in NLP is by addressing the social problems that caused them in the first place and by incorporating social sciences and social scientists in efforts to mitigate bias in NLP models. We provide actionable recommendations for the NLP research community to do so.",
+    "neighbors": [
+      767,
+      1064
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2226,
+    "label": 30,
+    "text": "Title: A Survey on In-context Learning\nAbstract: With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples. It has been a new trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress and challenges of ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques, including training strategies, demonstration designing strategies, as well as related analysis. Finally, we discuss the challenges of ICL and provide potential directions for further research. We hope that our work can encourage more research on uncovering how ICL works and improving ICL.",
+    "neighbors": [
+      342,
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+      1146,
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+      1450,
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+      2036,
+      2155,
+      2203
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2227,
+    "label": 6,
+    "text": "Title: Exploring AI-enhanced Shared Control for an Assistive Robotic Arm\nAbstract: Assistive technologies and in particular assistive robotic arms have the potential to enable people with motor impairments to live a self-determined life. More and more of these systems have become available for end users in recent years, such as the Kinova Jaco robotic arm. However, they mostly require complex manual control, which can overwhelm users. As a result, researchers have explored ways to let such robots act autonomously. However, at least for this specific group of users, such an approach has shown to be futile. Here, users want to stay in control to achieve a higher level of personal autonomy, to which an autonomous robot runs counter. In our research, we explore how Artifical Intelligence (AI) can be integrated into a shared control paradigm. In particular, we focus on the consequential requirements for the interface between human and robot and how we can keep humans in the loop while still significantly reducing the mental load and required motor skills.",
+    "neighbors": [
+      1785
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2228,
+    "label": 16,
+    "text": "Title: Zorro: the masked multimodal transformer\nAbstract: Attention-based models are appealing for multimodal processing because inputs from multiple modalities can be concatenated and fed to a single backbone network - thus requiring very little fusion engineering. The resulting representations are however fully entangled throughout the network, which may not always be desirable: in learning, contrastive audio-visual self-supervised learning requires independent audio and visual features to operate, otherwise learning collapses; in inference, evaluation of audio-visual models should be possible on benchmarks having just audio or just video. In this paper, we introduce Zorro, a technique that uses masks to control how inputs from each modality are routed inside Transformers, keeping some parts of the representation modality-pure. We apply this technique to three popular transformer-based architectures (ViT, Swin and HiP) and show that with contrastive pre-training Zorro achieves state-of-the-art results on most relevant benchmarks for multimodal tasks (AudioSet and VGGSound). Furthermore, the resulting models are able to perform unimodal inference on both video and audio benchmarks such as Kinetics-400 or ESC-50.",
+    "neighbors": [
+      431
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2229,
+    "label": 16,
+    "text": "Title: M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization\nAbstract: Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent representation space can be complex. Here we propose a novel way for pre-training and regularising medical vision-language models. The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry. We demonstrate the potential of the pre-trained model on three downstream tasks: medical image classification, segmentation, and object detection. Extensive experiments across five public datasets demonstrate that M-FLAG significantly outperforms existing medical vision-language pre-training approaches and reduces the number of parameters by 78\\%. Notably, M-FLAG achieves outstanding performance on the segmentation task while using only 1\\% of the RSNA dataset, even outperforming ImageNet pre-trained models that have been fine-tuned using 100\\% of the data.",
+    "neighbors": [
+      1685
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2230,
+    "label": 23,
+    "text": "Title: A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT\nAbstract: Prompt engineering is an increasingly important skill set needed to converse effectively with large language models (LLMs), such as ChatGPT. Prompts are instructions given to an LLM to enforce rules, automate processes, and ensure specific qualities (and quantities) of generated output. Prompts are also a form of programming that can customize the outputs and interactions with an LLM. This paper describes a catalog of prompt engineering techniques presented in pattern form that have been applied to solve common problems when conversing with LLMs. Prompt patterns are a knowledge transfer method analogous to software patterns since they provide reusable solutions to common problems faced in a particular context, i.e., output generation and interaction when working with LLMs. This paper provides the following contributions to research on prompt engineering that apply LLMs to automate software development tasks. First, it provides a framework for documenting patterns for structuring prompts to solve a range of problems so that they can be adapted to different domains. Second, it presents a catalog of patterns that have been applied successfully to improve the outputs of LLM conversations. Third, it explains how prompts can be built from multiple patterns and illustrates prompt patterns that benefit from combination with other prompt patterns.",
+    "neighbors": [
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+      2254
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2231,
+    "label": 30,
+    "text": "Title: Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System\nAbstract: Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks by tracking dialogue states and generating appropriate responses to help users achieve defined goals. Recently, end-to-end dialogue models pre-trained based on large datasets have shown promising performance in the conversational system. However, they share the same parameters to train tasks of the dialogue system (NLU, DST, NLG), so debugging each task is challenging. Also, they require a lot of effort to fine-tune large parameters to create a task-oriented chatbot, making it difficult for non-experts to handle. Therefore, we intend to train relatively lightweight and fast models compared to PLM. In this paper, we propose an End-to-end TOD system with Task-Optimized Adapters which learn independently per task, adding only small number of parameters after fixed layers of pre-trained network. We also enhance the performance of the DST and NLG modules through reinforcement learning, overcoming the learning curve that has lacked at the adapter learning and enabling the natural and consistent response generation that is appropriate for the goal. Our method is a model-agnostic approach and does not require prompt-tuning as only input data without a prompt. As results of the experiment, our method shows competitive performance on the MultiWOZ benchmark compared to the existing end-to-end models. In particular, we attain state-of-the-art performance on the DST task of 2.2 dataset.",
+    "neighbors": [
+      1284,
+      1462
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2232,
+    "label": 24,
+    "text": "Title: A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts\nAbstract: Machine learning methods strive to acquire a robust model during training that can generalize well to test samples, even under distribution shifts. However, these methods often suffer from a performance drop due to unknown test distributions. Test-time adaptation (TTA), an emerging paradigm, has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. Recent progress in this paradigm highlights the significant benefits of utilizing unlabeled data for training self-adapted models prior to inference. In this survey, we divide TTA into several distinct categories, namely, test-time (source-free) domain adaptation, test-time batch adaptation, online test-time adaptation, and test-time prior adaptation. For each category, we provide a comprehensive taxonomy of advanced algorithms, followed by a discussion of different learning scenarios. Furthermore, we analyze relevant applications of TTA and discuss open challenges and promising areas for future research. A comprehensive list of TTA methods can be found at \\url{https://github.com/tim-learn/awesome-test-time-adaptation}.",
+    "neighbors": [
+      182,
+      1115,
+      2068
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2233,
+    "label": 16,
+    "text": "Title: Matte Anything: Interactive Natural Image Matting with Segment Anything Models\nAbstract: Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimaps often requires significant labor, which limits the widespread application of matting algorithms on a large scale. To address the issue, we propose Matte Anything model (MatAny), an interactive natural image matting model which could produce high-quality alpha-matte with various simple hints. The key insight of MatAny is to generate pseudo trimap automatically with contour and transparency prediction. We leverage task-specific vision models to enhance the performance of natural image matting. Specifically, we use the segment anything model (SAM) to predict high-quality contour with user interaction and an open-vocabulary (OV) detector to predict the transparency of any object. Subsequently, a pretrained image matting model generates alpha mattes with pseudo trimaps. MatAny is the interactive matting algorithm with the most supported interaction methods and the best performance to date. It consists of orthogonal vision models without any additional training. We evaluate the performance of MatAny against several current image matting algorithms, and the results demonstrate the significant potential of our approach.",
+    "neighbors": [
+      584,
+      1006,
+      1052,
+      1084,
+      1863,
+      1902,
+      1932
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2234,
+    "label": 16,
+    "text": "Title: Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication\nAbstract: Recently, many deep networks have introduced hypercomplex and related calculations into their architectures. In regard to convolutional networks for classification, these enhancements have been applied to the convolution operations in the frontend to enhance accuracy and/or reduce the parameter requirements while maintaining accuracy. Although these enhancements have been applied to the convolutional frontend, it has not been studied whether adding hypercomplex calculations improves performance when applied to the densely connected backend. This paper studies ResNet architectures and incorporates parameterized hypercomplex multiplication (PHM) into the backend of residual, quaternion, and vectormap convolutional neural networks to assess the effect. We show that PHM does improve classification accuracy performance on several image datasets, including small, low-resolution CIFAR 10/100 and large high-resolution ImageNet and ASL, and can achieve state-of-the-art accuracy for hypercomplex networks.",
+    "neighbors": [
+      1641,
+      1686
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2235,
+    "label": 30,
+    "text": "Title: Llama 2: Open Foundation and Fine-Tuned Chat Models\nAbstract: In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2236,
+    "label": 24,
+    "text": "Title: Better Training of GFlowNets with Local Credit and Incomplete Trajectories\nAbstract: Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object $x$ through a sequence of steps with probability proportional to some reward function $R(x)$ (or $\\exp(-\\mathcal{E}(x))$ with $\\mathcal{E}(x)$ denoting the energy function), given at the end of the generative trajectory. Like for other RL settings where the reward is only given at the end, the efficiency of training and credit assignment may suffer when those trajectories are longer. With previous GFlowNet work, no learning was possible from incomplete trajectories (lacking a terminal state and the computation of the associated reward). In this paper, we consider the case where the energy function can be applied not just to terminal states but also to intermediate states. This is for example achieved when the energy function is additive, with terms available along the trajectory. We show how to reparameterize the GFlowNet state flow function to take advantage of the partial reward already accrued at each state. This enables a training objective that can be applied to update parameters even with incomplete trajectories. Even when complete trajectories are available, being able to obtain more localized credit and gradients is found to speed up training convergence, as demonstrated across many simulations.",
+    "neighbors": [
+      1149
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2237,
+    "label": 28,
+    "text": "Title: On Euclidean, Hermitian and symplectic quasi-cyclic complementary dual codes\nAbstract: Linear complementary dual codes (LCD) intersect trivially with their dual. In this paper, we develop a new characterization for LCD codes, which allows us to judge the complementary duality of linear codes from the codeword level. Further, we determine the sufficient and necessary conditions for one-generator quasi-cyclic codes to be LCD codes involving Euclidean, Hermitian, and symplectic inner products. Finally, we constructed many Euclidean, Hermitian and symmetric LCD codes with excellent parameters, some improving the results in the literature. Remarkably, we construct a symplectic LCD $[28,6]_2$ code with symplectic distance $10$, which corresponds to an trace Hermitian additive complementary dual $(14,3,10)_4$ code that outperforms the optimal quaternary Hermitian LCD $[14,3,9]_4$ code.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2238,
+    "label": 16,
+    "text": "Title: Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis\nAbstract: Recent text-to-image generative models can generate high-fidelity images from text inputs, but the quality of these generated images cannot be accurately evaluated by existing evaluation metrics. To address this issue, we introduce Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human preferences on images from a wide range of sources. HPD v2 comprises 798,090 human preference choices on 430,060 pairs of images, making it the largest dataset of its kind. The text prompts and images are deliberately collected to eliminate potential bias, which is a common issue in previous datasets. By fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a scoring model that can more accurately predict text-generated images' human preferences. Our experiments demonstrate that HPS v2 generalizes better than previous metrics across various image distributions and is responsive to algorithmic improvements of text-to-image generative models, making it a preferable evaluation metric for these models. We also investigate the design of the evaluation prompts for text-to-image generative models, to make the evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for text-to-image generative models using HPS v2, which includes a set of recent text-to-image models from the academia, community and industry. The code and dataset is / will be available at https://github.com/tgxs002/HPSv2.",
+    "neighbors": [
+      1969,
+      2007,
+      2122
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2239,
+    "label": 16,
+    "text": "Title: ViTO: Vision Transformer-Operator\nAbstract: We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs). Our approach, named ViTO, combines a U-Net based architecture with a vision transformer. We apply ViTO to solve inverse PDE problems of increasing complexity, namely for the wave equation, the Navier-Stokes equations and the Darcy equation. We focus on the more challenging case of super-resolution, where the input dataset for the inverse problem is at a significantly coarser resolution than the output. The results we obtain are comparable or exceed the leading operator network benchmarks in terms of accuracy. Furthermore, ViTO`s architecture has a small number of trainable parameters (less than 10% of the leading competitor), resulting in a performance speed-up of over 5x when averaged over the various test cases.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2240,
+    "label": 27,
+    "text": "Title: Learning and Adapting Agile Locomotion Skills by Transferring Experience\nAbstract: Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running. However, designing robust controllers for highly agile dynamic motions remains a substantial challenge for roboticists. Reinforcement learning (RL) offers a promising data-driven approach for automatically training such controllers. However, exploration in these high-dimensional, underactuated systems remains a significant hurdle for enabling legged robots to learn performant, naturalistic, and versatile agility skills. We propose a framework for training complex robotic skills by transferring experience from existing controllers to jumpstart learning new tasks. To leverage controllers we can acquire in practice, we design this framework to be flexible in terms of their source -- that is, the controllers may have been optimized for a different objective under different dynamics, or may require different knowledge of the surroundings -- and thus may be highly suboptimal for the target task. We show that our method enables learning complex agile jumping behaviors, navigating to goal locations while walking on hind legs, and adapting to new environments. We also demonstrate that the agile behaviors learned in this way are graceful and safe enough to deploy in the real world.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2241,
+    "label": 16,
+    "text": "Title: Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance\nAbstract: Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfactory performance for those classification tasks in real world is the wild visual background signal that contains irrelevant pixels which confuses model to focus onto the region of interest and make prediction upon the specific region. In this paper, we introduce a generic semantic-embedding deep neural network to apply the spatial awareness semantic feature incorporating a channel-wise attention based model to leverage the localization guidance to boost model performance for multi-label prediction. We observed an Avg.relative improvement of 15.27% in terms of AUC score across all labels compared to the baseline approach. Core experiment and ablation studies involve multi-label fashion attribute classification performed on Instagram fashion apparels' image. We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features. Results show favorable performance for our approach.",
+    "neighbors": [
+      1096,
+      1856
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2242,
+    "label": 16,
+    "text": "Title: Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models\nAbstract: Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, current state-of-the-art diffusion models may still fail in generating images that fully convey the semantics in the given text prompt. We analyze the publicly available Stable Diffusion model and assess the existence of catastrophic neglect, where the model fails to generate one or more of the subjects from the input prompt. Moreover, we find that in some cases the model also fails to correctly bind attributes (e.g., colors) to their corresponding subjects. To help mitigate these failure cases, we introduce the concept of Generative Semantic Nursing (GSN), where we seek to intervene in the generative process on the fly during inference time to improve the faithfulness of the generated images. Using an attention-based formulation of GSN, dubbed Attend-and-Excite, we guide the model to refine the cross-attention units to attend to all subject tokens in the text prompt and strengthen --- or excite --- their activations, encouraging the model to generate all subjects described in the text prompt. We compare our approach to alternative approaches and demonstrate that it conveys the desired concepts more faithfully across a range of text prompts. Code is available at our project page: https://attendandexcite.github.io/Attend-and-Excite/.",
+    "neighbors": [
+      170,
+      706,
+      736,
+      1079,
+      1582,
+      1588,
+      2243,
+      2306
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2243,
+    "label": 16,
+    "text": "Title: ConceptLab: Creative Generation using Diffusion Prior Constraints\nAbstract: Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery. The surge of personalization techniques that has followed has also allowed us to imagine unique concepts in new scenes. However, an intriguing question remains: How can we generate a new, imaginary concept that has never been seen before? In this paper, we present the task of creative text-to-image generation, where we seek to generate new members of a broad category (e.g., generating a pet that differs from all existing pets). We leverage the under-studied Diffusion Prior models and show that the creative generation problem can be formulated as an optimization process over the output space of the diffusion prior, resulting in a set of\"prior constraints\". To keep our generated concept from converging into existing members, we incorporate a question-answering model that adaptively adds new constraints to the optimization problem, encouraging the model to discover increasingly more unique creations. Finally, we show that our prior constraints can also serve as a strong mixing mechanism allowing us to create hybrids between generated concepts, introducing even more flexibility into the creative process.",
+    "neighbors": [
+      887,
+      1179,
+      1279,
+      2183,
+      2190,
+      2242
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2244,
+    "label": 30,
+    "text": "Title: How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection\nAbstract: The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.",
+    "neighbors": [
+      644,
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+      1604,
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+      1713,
+      1727,
+      1797,
+      2016,
+      2062,
+      2081,
+      2113,
+      2249
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2245,
+    "label": 24,
+    "text": "Title: Goal-Conditioned Imitation Learning using Score-based Diffusion Policies\nAbstract: We propose a new policy representation based on score-based diffusion models (SDMs). We apply our new policy representation in the domain of Goal-Conditioned Imitation Learning (GCIL) to learn general-purpose goal-specified policies from large uncurated datasets without rewards. Our new goal-conditioned policy architecture\"$\\textbf{BE}$havior generation with $\\textbf{S}$c$\\textbf{O}$re-based Diffusion Policies\"(BESO) leverages a generative, score-based diffusion model as its policy. BESO decouples the learning of the score model from the inference sampling process, and, hence allows for fast sampling strategies to generate goal-specified behavior in just 3 denoising steps, compared to 30+ steps of other diffusion based policies. Furthermore, BESO is highly expressive and can effectively capture multi-modality present in the solution space of the play data. Unlike previous methods such as Latent Plans or C-Bet, BESO does not rely on complex hierarchical policies or additional clustering for effective goal-conditioned behavior learning. Finally, we show how BESO can even be used to learn a goal-independent policy from play-data using classifier-free guidance. To the best of our knowledge this is the first work that a) represents a behavior policy based on such a decoupled SDM b) learns an SDM based policy in the domain of GCIL and c) provides a way to simultaneously learn a goal-dependent and a goal-independent policy from play-data. We evaluate BESO through detailed simulation and show that it consistently outperforms several state-of-the-art goal-conditioned imitation learning methods on challenging benchmarks. We additionally provide extensive ablation studies and experiments to demonstrate the effectiveness of our method for goal-conditioned behavior generation. Demonstrations and Code are available at https://intuitive-robots.github.io/beso-website/",
+    "neighbors": [
+      146,
+      787,
+      1186,
+      1908
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2246,
+    "label": 8,
+    "text": "Title: Internet of Everything (IoE) - From Molecules to the Universe\nAbstract: The universe is a vast heterogeneous network of interconnected entities that continuously generate and exchange information through various forms of interactions, some of which are yet to be discovered. Internet of Everything (IoE) framework, inspired by the ubiquitous and adaptive connectivity and the seamless interoperability within this universal network, puts forward a new road map beyond the conventional Internet of Things (IoT) towards maximizing the resolution of our interface with the universe to enable unprecedented applications. The first pillar of this road map is to reveal novel and tangible interconnections between seemingly noninteracting branches of IoT, which we call IoXs with X referring to their application domains, e.g., Internet of Energy (IoEn), Internet of Vehicles (IoV). The second pillar is to develop new IoXs that can complement the existing ones to complete the overall IoE picture and match its networking traits to that of the universe for a seamless and all-embracing cyber-physical interface. The objective of this paper is to evaluate the potential of this holistic IoE approach to expand the limited application landscape of the current IoT practice on a scale ranging $\\textit{from molecules to the universe}$. To this end, we identify several potential interaction pathways among IoXs and introduce novel and emerging IoXs that are essential to the comprehensiveness of IoE. We also discuss the potential applications that can be enabled by such interconnections within the IoE framework and identify the associated challenges.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2247,
+    "label": 24,
+    "text": "Title: Deep Reinforcement Learning for Cost-Effective Medical Diagnosis\nAbstract: Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the $F_1$ score instead of the error rate. However, optimizing the non-concave $F_1$ score is not a classic RL problem, thus invalidates standard RL methods. To remedy this issue, we develop a reward shaping approach, leveraging properties of the $F_1$ score and duality of policy optimization, to provably find the set of all Pareto-optimal policies for budget-constrained $F_1$ score maximization. To handle the combinatorially complex state space, we propose a Semi-Model-based Deep Diagnosis Policy Optimization (SM-DDPO) framework that is compatible with end-to-end training and online learning. SM-DDPO is tested on diverse clinical tasks: ferritin abnormality detection, sepsis mortality prediction, and acute kidney injury diagnosis. Experiments with real-world data validate that SM-DDPO trains efficiently and identifies all Pareto-front solutions. Across all tasks, SM-DDPO is able to achieve state-of-the-art diagnosis accuracy (in some cases higher than conventional methods) with up to $85\\%$ reduction in testing cost. The code is available at [https://github.com/Zheng321/Deep-Reinforcement-Learning-for-Cost-Effective-Medical-Diagnosis].",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2248,
+    "label": 24,
+    "text": "Title: Any Deep ReLU Network is Shallow\nAbstract: We constructively prove that every deep ReLU network can be rewritten as a functionally identical three-layer network with weights valued in the extended reals. Based on this proof, we provide an algorithm that, given a deep ReLU network, finds the explicit weights of the corresponding shallow network. The resulting shallow network is transparent and used to generate explanations of the model s behaviour.",
+    "neighbors": [
+      2045
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2249,
+    "label": 30,
+    "text": "Title: A Categorical Archive of ChatGPT Failures\nAbstract: Large language models have been demonstrated to be valuable in different fields. ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation by comprehending context and generating appropriate responses. It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries, with fluent and comprehensive answers surpassing prior public chatbots in both security and usefulness. However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study. Eleven categories of failures, including reasoning, factual errors, math, coding, and bias, are presented and discussed. The risks, limitations, and societal implications of ChatGPT are also highlighted. The goal of this study is to assist researchers and developers in enhancing future language models and chatbots.",
+    "neighbors": [
+      42,
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+      857,
+      929,
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+      1349,
+      1384,
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+      1713,
+      1810,
+      1983,
+      2113,
+      2184,
+      2244,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2250,
+    "label": 10,
+    "text": "Title: Analogical Inference Enhanced Knowledge Graph Embedding\nAbstract: Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.",
+    "neighbors": [
+      445
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2251,
+    "label": 16,
+    "text": "Title: Ablating Concepts in Text-to-Image Diffusion Models\nAbstract: Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. However, these models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. Furthermore, they have been found to replicate the style of various living artists or memorize exact training samples. How can we remove such copyrighted concepts or images without retraining the model from scratch? To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i.e., preventing the generation of a target concept. Our algorithm learns to match the image distribution for a target style, instance, or text prompt we wish to ablate to the distribution corresponding to an anchor concept. This prevents the model from generating target concepts given its text condition. Extensive experiments show that our method can successfully prevent the generation of the ablated concept while preserving closely related concepts in the model.",
+    "neighbors": [
+      529,
+      1179,
+      1273,
+      1669,
+      1710,
+      1768,
+      1902,
+      2267,
+      2279
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2252,
+    "label": 30,
+    "text": "Title: Exploring the Feasibility of ChatGPT for Event Extraction\nAbstract: Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challenging task due to the lack of annotated data, which is expensive and time-consuming to obtain. The emergence of large language models (LLMs) such as ChatGPT provides an opportunity to solve language tasks with simple prompts without the need for task-specific datasets and fine-tuning. While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it presents challenges when used for complex tasks like event extraction. Unlike other tasks, event extraction requires the model to be provided with a complex set of instructions defining all event types and their schemas. To explore the feasibility of ChatGPT for event extraction and the challenges it poses, we conducted a series of experiments. Our results show that ChatGPT has, on average, only 51.04% of the performance of a task-specific model such as EEQA in long-tail and complex scenarios. Our usability testing experiments indicate that ChatGPT is not robust enough, and continuous refinement of the prompt does not lead to stable performance improvements, which can result in a poor user experience. Besides, ChatGPT is highly sensitive to different prompt styles.",
+    "neighbors": [
+      735,
+      929,
+      1520,
+      1596,
+      1950,
+      1952,
+      2062,
+      2109,
+      2265
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2253,
+    "label": 28,
+    "text": "Title: NOMA-based Improper Signaling for MIMO STAR-RIS-assisted Broadcast Channels with Hardware Impairments\nAbstract: This paper proposes schemes to improve the spectral efficiency of a multiple-input multiple-output (MIMO) broadcast channel (BC) with I/Q imbalance (IQI) at transceivers by employing a combination of improper Gaussian signaling (IGS), non-orthogonal multiple access (NOMA) and simultaneously transmit and reflect (STAR) reconfigurable intelligent surface (RIS). When there exists IQI, the output RF signal is a widely linear transformation of the input signal, which may make the output signal improper. To compensate for IQI, we employ IGS, thus generating a transmit improper signal. We show that IGS alongside with NOMA can highly increase the minimum rate of the users. Moreover, we propose schemes for different operational modes of STAR-RIS and show that STAR-RIS can significantly improve the system performance. Additionally, we show that IQI can highly degrade the performance especially if it is overlooked in the design.",
+    "neighbors": [
+      238,
+      1410,
+      1530
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2254,
+    "label": 24,
+    "text": "Title: Mathematical Capabilities of ChatGPT\nAbstract: We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets also test whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT can be used most successfully as a mathematical assistant for querying facts, acting as a mathematical search engine and knowledge base interface. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if your goal is to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!",
+    "neighbors": [
+      121,
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+      551,
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+      1210,
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+      1636,
+      1713,
+      1863,
+      1949,
+      2230
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2255,
+    "label": 28,
+    "text": "Title: Age of Incorrect Information in Random Access Channels without Feedback\nAbstract: We focus on a system in which a set of two-state Markov sources report status update to a common receiver over a shared wireless channel. Inspired by practical IoT networks, we consider three variations of ALOHA as medium access protocol: i) a random approach in which a source transmits regardless of its status, ii) a reactive scheme in which updates are sent only when a source changes state, and iii) a hybrid solution which blends the two possibilities. We consider different criteria to capture the ability of the receiver to maintain an accurate perception of the tracked processes: average age of incorrect information (AoII), probability of missed detection (i.e., of not detecting a source transition), and average duration of intervals over which the receiver lingers with erroneous knowledge. We provide closed form analytical expressions for all the metrics, highlighting non-trivial trade-offs and providing useful protocol design hints.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2256,
+    "label": 24,
+    "text": "Title: Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning\nAbstract: Recent works have shown that self-supervised learning can achieve remarkable robustness when integrated with adversarial training (AT). However, the robustness gap between supervised AT (sup-AT) and self-supervised AT (self-AT) remains significant. Motivated by this observation, we revisit existing self-AT methods and discover an inherent dilemma that affects self-AT robustness: either strong or weak data augmentations are harmful to self-AT, and a medium strength is insufficient to bridge the gap. To resolve this dilemma, we propose a simple remedy named DYNACL (Dynamic Adversarial Contrastive Learning). In particular, we propose an augmentation schedule that gradually anneals from a strong augmentation to a weak one to benefit from both extreme cases. Besides, we adopt a fast post-processing stage for adapting it to downstream tasks. Through extensive experiments, we show that DYNACL can improve state-of-the-art self-AT robustness by 8.84% under Auto-Attack on the CIFAR-10 dataset, and can even outperform vanilla supervised adversarial training for the first time. Our code is available at \\url{https://github.com/PKU-ML/DYNACL}.",
+    "neighbors": [
+      502,
+      1065
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2257,
+    "label": 24,
+    "text": "Title: Baseline Defenses for Adversarial Attacks Against Aligned Language Models\nAbstract: As Large Language Models quickly become ubiquitous, it becomes critical to understand their security vulnerabilities. Recent work shows that text optimizers can produce jailbreaking prompts that bypass moderation and alignment. Drawing from the rich body of work on adversarial machine learning, we approach these attacks with three questions: What threat models are practically useful in this domain? How do baseline defense techniques perform in this new domain? How does LLM security differ from computer vision? We evaluate several baseline defense strategies against leading adversarial attacks on LLMs, discussing the various settings in which each is feasible and effective. Particularly, we look at three types of defenses: detection (perplexity based), input preprocessing (paraphrase and retokenization), and adversarial training. We discuss white-box and gray-box settings and discuss the robustness-performance trade-off for each of the defenses considered. We find that the weakness of existing discrete optimizers for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs. Future research will be needed to uncover whether more powerful optimizers can be developed, or whether the strength of filtering and preprocessing defenses is greater in the LLMs domain than it has been in computer vision.",
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+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2258,
+    "label": 24,
+    "text": "Title: Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards\nAbstract: Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; the diversity of objectives in real-world tasks and human opinions exacerbate the issue. This paper proposes embracing the heterogeneity of diverse rewards by following a multi-policy strategy. Rather than focusing on a single a priori reward, we aim for Pareto-optimal generalization across the entire space of preferences. To this end, we propose rewarded soup, first specializing multiple networks independently (one for each proxy reward) and then interpolating their weights linearly. This succeeds empirically because we show that the weights remain linearly connected when fine-tuned on diverse rewards from a shared pre-trained initialization. We demonstrate the effectiveness of our approach for text-to-text (summarization, Q&A, helpful assistant, review), text-image (image captioning, text-to-image generation, visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the alignment of deep models, and how they interact with the world in all its diversity.",
+    "neighbors": [
+      75,
+      430,
+      1052,
+      1165,
+      1233,
+      1267,
+      1475,
+      1720,
+      1969,
+      1972,
+      2007,
+      2210,
+      2305
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2259,
+    "label": 24,
+    "text": "Title: Imprecise Bayesian Neural Networks\nAbstract: Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present Imprecise Bayesian Neural Networks (IBNNs); they generalize and overcome some of the drawbacks of standard BNNs. These latter are trained using a single prior and likelihood distributions, whereas IBNNs are trained using credal prior and likelihood sets. They allow to distinguish between aleatoric and epistemic uncertainties, and to quantify them. In addition, IBNNs are more robust than BNNs to prior and likelihood misspecification, and to distribution shift. They can also be used to compute sets of outcomes that enjoy probabilistic guarantees. We apply IBNNs to two case studies. One, for motion prediction in autonomous driving scenarios, and two, to model blood glucose and insulin dynamics for artificial pancreas control. We show that IBNNs performs better when compared to an ensemble of BNNs benchmark.",
+    "neighbors": [
+      1996
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2260,
+    "label": 24,
+    "text": "Title: Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning\nAbstract: A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also ensuring that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that a conservative offline RL algorithm that also learns a calibrated value function leads to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/projects/Cal-QL",
+    "neighbors": [
+      1137,
+      1186
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2261,
+    "label": 27,
+    "text": "Title: Quantifying Uncertainties of Contact Classifications in a Human-Robot Collaboration with Parallel Robots\nAbstract: In human-robot collaboration, unintentional physical contacts occur in the form of collisions and clamping, which must be detected and classified separately for a reaction. If certain collision or clamping situations are misclassified, reactions might occur that make the true contact case more dangerous. This work analyzes data-driven modeling based on physically modeled features like estimated external forces for clamping and collision classification with a real parallel robot. The prediction reliability of a feedforward neural network is investigated. Quantification of the classification uncertainty enables the distinction between safe versus unreliable classifications and optimal reactions like a retraction movement for collisions, structure opening for the clamping joint, and a fallback reaction in the form of a zero-g mode. This hypothesis is tested with experimental data of clamping and collision cases by analyzing dangerous misclassifications and then reducing them by the proposed uncertainty quantification. Finally, it is investigated how the approach of this work influences correctly classified clamping and collision scenarios.",
+    "neighbors": [
+      1619
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2262,
+    "label": 30,
+    "text": "Title: Bridging Emotion Role Labeling and Appraisal-based Emotion Analysis\nAbstract: The term emotion analysis in text subsumes various natural language processing tasks which have in common the goal to enable computers to understand emotions. Most popular is emotion classification in which one or multiple emotions are assigned to a predefined textual unit. While such setting is appropriate to identify the reader's or author's emotion, emotion role labeling adds the perspective of mentioned entities and extracts text spans that correspond to the emotion cause. The underlying emotion theories agree on one important point; that an emotion is caused by some internal or external event and comprises several subcomponents, including the subjective feeling and a cognitive evaluation. We therefore argue that emotions and events are related in two ways. (1) Emotions are events; and this perspective is the fundament in NLP for emotion role labeling. (2) Emotions are caused by events; a perspective that is made explicit with research how to incorporate psychological appraisal theories in NLP models to interpret events. These two research directions, role labeling and (event-focused) emotion classification, have by and large been tackled separately. We contributed to both directions with the projects SEAT (Structured Multi-Domain Emotion Analysis from Text) and CEAT (Computational Event Evaluation based on Appraisal Theories for Emotion Analysis), both funded by the German Research Foundation. In this paper, we consolidate the findings and point out open research questions.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2263,
+    "label": 23,
+    "text": "Title: Antithesis of Object Orientation: Occurrence-Only Modeling Applied in Engineering and Medicine\nAbstract: This paper has a dual character, combining a philosophical ontological exploration with a conceptual modeling approach in systems and software engineering. Such duality is already practiced in software engineering, in which the current dominant modeling thesis is object orientation. This work embraces an anti-thesis that centers solely on the process rather than emphasizing the object. The approach is called occurrence-only modeling, in which an occurrence means an event or process where a process is defined as an orchestrated net of events that form a semantical whole. In contrast to object orientation, in this occurrence-only modeling objects are nothing more than long events. We apply this paradigm to (1) a UML/BPMN inventory system in simulation engineering and (2) an event-based system that represents medical occurrences that occur on a timeline. The aim of such a venture is to enhance the field of conceptual modeling by adding yet a new alternative methodology and clarifying differences among approaches. Conceptual modeling s importance has been recognized in many research areas. An active research community in simulation engineering demonstrates the growing interest in conceptual modeling. In the clinical domains, temporal information elucidates the occurrence of medical events (e.g., visits, laboratory tests). These applications give an opportunity to propose a new approach that includes (a) a Stoic ontology that has two types of being, existence and subsistence; (b) Thinging machines that limit activities to five generic actions; and (c) Lupascian logic, which handles negative events. With such a study, we aim to substantiate the assertion that the occurrence only approach is a genuine philosophical base for conceptual modeling. The results in this paper seem to support such a claim.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2264,
+    "label": 27,
+    "text": "Title: Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain\nAbstract: Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this paper, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2265,
+    "label": 30,
+    "text": "Title: EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models\nAbstract: Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results.",
+    "neighbors": [
+      363,
+      1001,
+      1047,
+      1052,
+      1203,
+      1520,
+      1797,
+      1943,
+      1952,
+      2252
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2266,
+    "label": 16,
+    "text": "Title: Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions\nAbstract: Deep neural networks have been critical in the task of Visual Question Answering (VQA), with research traditionally focused on improving model accuracy. Recently, however, there has been a trend towards evaluating the robustness of these models against adversarial attacks. This involves assessing the accuracy of VQA models under increasing levels of noise in the input, which can target either the image or the proposed query question, dubbed the main question. However, there is currently a lack of proper analysis of this aspect of VQA. This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models. It is hypothesized that as the similarity of a basic question to the main question decreases, the level of noise increases. To generate a reasonable noise level for a given main question, a pool of basic questions is ranked based on their similarity to the main question, and this ranking problem is cast as a LASSO optimization problem. Additionally, this work proposes a novel robustness measure, R_score, and two basic question datasets to standardize the analysis of VQA model robustness. The experimental results demonstrate that the proposed evaluation method effectively analyzes the robustness of VQA models. Moreover, the experiments show that in-context learning with a chain of basic questions can enhance model accuracy.",
+    "neighbors": [
+      282,
+      1951,
+      2286
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2267,
+    "label": 16,
+    "text": "Title: Erasing Concepts from Diffusion Models\nAbstract: Motivated by recent advancements in text-to-image diffusion, we study erasure of specific concepts from the model's weights. While Stable Diffusion has shown promise in producing explicit or realistic artwork, it has raised concerns regarding its potential for misuse. We propose a fine-tuning method that can erase a visual concept from a pre-trained diffusion model, given only the name of the style and using negative guidance as a teacher. We benchmark our method against previous approaches that remove sexually explicit content and demonstrate its effectiveness, performing on par with Safe Latent Diffusion and censored training. To evaluate artistic style removal, we conduct experiments erasing five modern artists from the network and conduct a user study to assess the human perception of the removed styles. Unlike previous methods, our approach can remove concepts from a diffusion model permanently rather than modifying the output at the inference time, so it cannot be circumvented even if a user has access to model weights. Our code, data, and results are available at https://erasing.baulab.info/",
+    "neighbors": [
+      1273,
+      1669,
+      1730,
+      2251
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2268,
+    "label": 27,
+    "text": "Title: Learn to Grasp Via Intention Discovery and Its Application to Challenging Clutter\nAbstract: Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn. Inspired by the human learning process, we propose a method to extract and exploit latent intents from demonstrations, and then learn diverse and robust grasping policies through self-exploration. The resulting policy can grasp challenging objects in various environments with an off-the-shelf parallel gripper. The key component is a learned intention estimator, which maps gripper pose and visual sensory to a set of sub-intents covering important phases of the grasping movement. Sub-intents can be used to build an intrinsic reward to guide policy learning. The learned policy demonstrates remarkable zero-shot generalization from simulation to the real world while retaining its robustness against states that have never been encountered during training, novel objects such as protractors and user manuals, and environments such as the cluttered conveyor.",
+    "neighbors": [
+      1369
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2269,
+    "label": 28,
+    "text": "Title: Stacked Intelligent Metasurfaces for Multiuser Downlink Beamforming in the Wave Domain\nAbstract: Intelligent metasurface has recently emerged as a promising technology that enables the customization of wireless environments by harnessing large numbers of inexpensive configurable scattering elements. However, prior studies have predominantly focused on single-layer metasurfaces, which have limitations in terms of the number of beam patterns they can steer accurately due to practical hardware restrictions. In contrast, this paper introduces a novel stacked intelligent metasurface (SIM) design. Specifically, we investigate the integration of SIM into the downlink of a multiuser multiple-input single-output (MISO) communication system, where a SIM, consisting of a multilayer metasurface structure, is deployed at the base station (BS) to facilitate transmit beamforming in the electromagnetic wave domain. This eliminates the need for conventional digital beamforming and high-resolution digital-to-analog converters at the BS. To this end, we formulate an optimization problem that aims to maximize the sum rate of all user equipments by jointly optimizing the transmit power allocation at the BS and the wave-based beamforming at the SIM, subject to both the transmit power budget and discrete phase shift constraints. Furthermore, we propose a computationally efficient algorithm for solving this joint optimization problem and elaborate on the potential benefits of employing SIM in wireless networks. Finally, the numerical results corroborate the effectiveness of the proposed SIM-enabled wave-based beamforming design and evaluate the performance improvement achieved by the proposed algorithm compared to various benchmark schemes. It is demonstrated that considering the same number of transmit antennas, the proposed SIM-based system achieves about 200\\% improvement in terms of sum rate compared to conventional MISO systems.",
+    "neighbors": [
+      226,
+      865
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2270,
+    "label": 2,
+    "text": "Title: Syntactically and semantically regular languages of lambda-terms coincide through logical relations\nAbstract: A fundamental theme in automata theory is regular languages of words and trees, and their many equivalent definitions. Salvati has proposed a generalization to regular languages of simply typed lambda-terms, defined using denotational semantics in finite sets. We provide here some evidence for its robustness. First, we give an equivalent characterization that naturally extends the seminal work of Hillebrand and Kanellakis connecting regular languages of words and syntactic lambda-definability. Second, we exhibit a class of categorical models of the simply typed lambda-calculus, which we call finitely pointable, and we show that, when used in Salvati's definition, they all recognize exactly the same class of languages of lambda-terms as the category of finite sets does. The proofs of these two results rely on logical relations and can be seen as instances of a more general construction of a categorical nature, inspired by previous categorical accounts of logical relations using the glueing construction",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2271,
+    "label": 24,
+    "text": "Title: NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification\nAbstract: Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge incompleteness and particularly, the absence of graphs altogether. While a plausible solution is to learn new adaptive topology for message passing, issues concerning quadratic complexity hinder simultaneous guarantees for scalability and precision in large networks. In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \\textsc{NodeFormer}. Specifically, the efficient computation is enabled by a kernerlized Gumbel-Softmax operator that reduces the algorithmic complexity to linearity w.r.t. node numbers for learning latent graph structures from large, potentially fully-connected graphs in a differentiable manner. We also provide accompanying theory as justification for our design. Extensive experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs (with up to 2M nodes) and graph-enhanced applications (e.g., image classification) where input graphs are missing.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2272,
+    "label": 16,
+    "text": "Title: SAM on Medical Images: A Comprehensive Study on Three Prompt Modes\nAbstract: The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capability. As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations. This large-scale dataset and its promptable nature endow the model with strong zero-shot generalization. Although the SAM has shown competitive performance on several datasets, we still want to investigate its zero-shot generalization on medical images. As we know, the acquisition of medical image annotation usually requires a lot of effort from professional practitioners. Therefore, if there exists a foundation model that can give high-quality mask prediction simply based on a few point prompts, this model will undoubtedly become the game changer for medical image analysis. To evaluate whether SAM has the potential to become the foundation model for medical image segmentation tasks, we collected more than 12 public medical image datasets that cover various organs and modalities. We also explore what kind of prompt can lead to the best zero-shot performance with different modalities. Furthermore, we find that a pattern shows that the perturbation of the box size will significantly change the prediction accuracy. Finally, Extensive experiments show that the predicted mask quality varied a lot among different datasets. And providing proper prompts, such as bounding boxes, to the SAM will significantly increase its performance.",
+    "neighbors": [
+      535,
+      1006,
+      1262,
+      1690,
+      1932
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2273,
+    "label": 16,
+    "text": "Title: Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism\nAbstract: The loss function for bounding box regression (BBR) is essential to object detection. Its good definition will bring significant performance improvement to the model. Most existing works assume that the examples in the training data are high-quality and focus on strengthening the fitting ability of BBR loss. If we blindly strengthen BBR on low-quality examples, it will jeopardize localization performance. Focal-EIoU v1 was proposed to solve this problem, but due to its static focusing mechanism (FM), the potential of non-monotonic FM was not fully exploited. Based on this idea, we propose an IoU-based loss with a dynamic non-monotonic FM named Wise-IoU (WIoU). The dynamic non-monotonic FM uses the outlier degree instead of IoU to evaluate the quality of anchor boxes and provides a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while also reducing the harmful gradient generated by low-quality examples. This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance. When WIoU is applied to the state-of-the-art real-time detector YOLOv7, the AP-75 on the MS-COCO dataset is improved from 53.03% to 54.50%. Code is available at https://github.com/Instinct323/wiou.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2274,
+    "label": 16,
+    "text": "Title: FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings\nAbstract: Logo embedding plays a crucial role in various e-commerce applications by facilitating image retrieval or recognition, such as intellectual property protection and product search. However, current methods treat logo embedding as a purely visual problem, which may limit their performance in real-world scenarios. A notable issue is that the textual knowledge embedded in logo images has not been adequately explored. Therefore, we propose a novel approach that leverages textual knowledge as an auxiliary to improve the robustness of logo embedding. The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding and could become valuable visual assistants in understanding logo images. Inspired by this observation, our proposed method, FashionLOGO, aims to utilize MLLMs to enhance fashion logo embedding. We explore how MLLMs can improve logo embedding by prompting them to generate explicit textual knowledge through three types of prompts, including image OCR, brief captions, and detailed descriptions prompts, in a zero-shot setting. We adopt a cross-attention transformer to enable image embedding queries to learn supplementary knowledge from textual embeddings automatically. To reduce computational costs, we only use the image embedding model in the inference stage, similar to traditional inference pipelines. Our extensive experiments on three real-world datasets demonstrate that FashionLOGO learns generalized and robust logo embeddings, achieving state-of-the-art performance in all benchmark datasets. Furthermore, we conduct comprehensive ablation studies to demonstrate the performance improvements resulting from the introduction of MLLMs.",
+    "neighbors": [
+      173,
+      319,
+      887,
+      1052,
+      1148,
+      2030,
+      2036,
+      2155
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2275,
+    "label": 16,
+    "text": "Title: Generalized Universal Domain Adaptation with Generative Flow Networks\nAbstract: We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essentially categorizing them as a unified problem, guiding to a comprehensive framework for thoroughly solving all the variants. The key challenge of GUDA is developing and identifying novel target categories while estimating the target label distribution. To address this problem, we take advantage of the powerful exploration capability of generative flow networks and propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function. To enhance the exploration capability and effectively perceive the target label distribution, we tailor the states and rewards, and introduce an efficient solution for parent exploration and state transition. We also propose a training paradigm for GUDA called Generalized Universal Adversarial Network (GUAN), which involves collaborative optimization between GUAN and GFlowNet. Theoretical analysis highlights the importance of exploration, and extensive experiments on benchmark datasets demonstrate the superiority of GFlowDA.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2276,
+    "label": 16,
+    "text": "Title: Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification\nAbstract: We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them prone to overfitting when relying solely on training data. Recent developments in pre-trained vision-language models inspire us to leverage the rich ordinal priors in human language by converting the original task into a vision-language alignment task. Consequently, we propose L2RCLIP, which fully utilizes the language priors from two perspectives. First, we introduce a complementary prompt tuning technique called RankFormer, designed to enhance the ordering relation of original rank prompts. It employs token-level attention with residual-style prompt blending in the word embedding space. Second, to further incorporate language priors, we revisit the approximate bound optimization of vanilla cross-entropy loss and restructure it within the cross-modal embedding space. Consequently, we propose a cross-modal ordinal pairwise loss to refine the CLIP feature space, where texts and images maintain both semantic alignment and ordering alignment. Extensive experiments on three ordinal classification tasks, including facial age estimation, historical color image (HCI) classification, and aesthetic assessment demonstrate its promising performance.",
+    "neighbors": [
+      151
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2277,
+    "label": 16,
+    "text": "Title: Augmenting CLIP with Improved Visio-Linguistic Reasoning\nAbstract: Image-text contrastive models such as CLIP are useful for a variety of downstream applications including zero-shot classification, image-text retrieval and transfer learning. However, these contrastively trained vision-language models often fail on compositional visio-linguistic tasks such as Winoground with performance equivalent to random chance. In our paper, we address this issue and propose a sample-efficient light-weight method called SDS-CLIP to improve the compositional visio-linguistic reasoning capabilities of CLIP. The core idea of our method is to use differentiable image parameterizations to fine-tune CLIP with a distillation objective from large text-to-image generative models such as Stable-Diffusion which are relatively good at visio-linguistic reasoning tasks. On the challenging Winoground compositional reasoning benchmark, our method improves the absolute visio-linguistic performance of different CLIP models by up to 7%, while on the ARO dataset, our method improves the visio-linguistic performance by upto 3%. As a byproduct of inducing visio-linguistic reasoning into CLIP, we also find that the zero-shot performance improves marginally on a variety of downstream datasets. Our method reinforces that carefully designed distillation objectives from generative models can be leveraged to extend existing contrastive image-text models with improved visio-linguistic reasoning capabilities.",
+    "neighbors": [
+      273,
+      1601,
+      2009
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2278,
+    "label": 16,
+    "text": "Title: VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking\nAbstract: 3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network VoxelNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS post-processing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxel-based representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark. Code and models are available at github.com/dvlab-research/VoxelNeXt.",
+    "neighbors": [
+      1283,
+      1761,
+      1932,
+      2138,
+      2308
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2279,
+    "label": 4,
+    "text": "Title: Extracting Training Data from Diffusion Models\nAbstract: Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images. In this work, we show that diffusion models memorize individual images from their training data and emit them at generation time. With a generate-and-filter pipeline, we extract over a thousand training examples from state-of-the-art models, ranging from photographs of individual people to trademarked company logos. We also train hundreds of diffusion models in various settings to analyze how different modeling and data decisions affect privacy. Overall, our results show that diffusion models are much less private than prior generative models such as GANs, and that mitigating these vulnerabilities may require new advances in privacy-preserving training.",
+    "neighbors": [
+      154,
+      315,
+      579,
+      736,
+      896,
+      1481,
+      1659,
+      1669,
+      1776,
+      2021,
+      2085,
+      2249,
+      2251,
+      2300
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2280,
+    "label": 4,
+    "text": "Title: Zero-Day Threats Detection for Critical Infrastructures\nAbstract: Technological advancements in various industries, such as network intelligence, vehicle networks, e-commerce, the Internet of Things (IoT), ubiquitous computing, and cloud-based applications, have led to an exponential increase in the volume of information flowing through critical systems. As a result, protecting critical infrastructures from intrusions and security threats have become a paramount concern in the field of intrusion detection systems (IDS). To address this concern, this research paper focuses on the importance of defending critical infrastructures against intrusions and security threats. It proposes a computational framework that incorporates feature selection through fuzzification. The effectiveness and performance of the proposed framework is evaluated using the NSL-KDD and UGRansome datasets in combination with selected machine learning (ML) models. The findings of the study highlight the effectiveness of fuzzy logic and the use of ensemble learning to enhance the performance of ML models. The research identifies Random Forest (RF) and Extreme Gradient Boosting (XGB) as the top performing algorithms to detect zero-day attacks. The results obtained from the implemented computational framework outperform previous methods documented in the IDS literature, reaffirming the significance of safeguarding critical infrastructures from intrusions and security threats.",
+    "neighbors": [
+      1576,
+      2041,
+      2162
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2281,
+    "label": 10,
+    "text": "Title: Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis\nAbstract: Large Language Models (LLMs) have garnered considerable interest within both academic and industrial. Yet, the application of LLMs to graph data remains under-explored. In this study, we evaluate the capabilities of four LLMs in addressing several analytical problems with graph data. We employ four distinct evaluation metrics: Comprehension, Correctness, Fidelity, and Rectification. Our results show that: 1) LLMs effectively comprehend graph data in natural language and reason with graph topology. 2) GPT models can generate logical and coherent results, outperforming alternatives in correctness. 3) All examined LLMs face challenges in structural reasoning, with techniques like zero-shot chain-of-thought and few-shot prompting showing diminished efficacy. 4) GPT models often produce erroneous answers in multi-answer tasks, raising concerns in fidelity. 5) GPT models exhibit elevated confidence in their outputs, potentially hindering their rectification capacities. Notably, GPT-4 has demonstrated the capacity to rectify responses from GPT-3.5-turbo and its own previous iterations. The code is available at: https://github.com/Ayame1006/LLMtoGraph.",
+    "neighbors": [
+      118,
+      1001,
+      1035,
+      1044,
+      1052,
+      1520,
+      1544,
+      1799,
+      1863,
+      1952,
+      2113
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2282,
+    "label": 2,
+    "text": "Title: From proof theory to theories theory\nAbstract: In the last decades, several objects such as grammars, economical agents, laws of physics... have been defined as algorithms. In particular, after Brouwer, Heyting, and Kolomogorov, mathematical proofs have been defined as algorithms. In this paper, we show that mathematical theories can be also be defined as algorithms and that this definition has some advantages over the usual definition of theories as sets of axioms.",
+    "neighbors": [
+      796,
+      1770
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2283,
+    "label": 30,
+    "text": "Title: Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation\nAbstract: Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in the graph representation hidden space differing from that of the text. This training regime of existing models therefore leads to a semantic gap between graph knowledge and text. In this study, we propose a novel framework for knowledge graph enhanced dialogue generation. We dynamically construct a multi-hop knowledge graph with pseudo nodes to involve the language model in feature aggregation within the graph at all steps. To avoid the semantic biases caused by learning on vanilla subgraphs, the proposed framework applies hierarchical graph attention to aggregate graph features on pseudo nodes and then attains a global feature. Therefore, the framework can better utilise the heterogeneous features from both the post and external graph knowledge. Extensive experiments demonstrate that our framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge. Moreover, the language model also learns how to better select knowledge triples for a more informative response via exploiting subgraph patterns within our feature aggregation process. Our code and resources are available at https://github.com/tangg555/SaBART.",
+    "neighbors": [
+      1786
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2284,
+    "label": 23,
+    "text": "Title: Eunomia: Enabling User-Specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries\nAbstract: Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that supports fine-grained search with local domain knowledge. Eunomia uses Aes, a DSL that lets users specify local searching strategies for different parts of the program. Eunomia also isolates the context of variables for different local searching strategies, avoiding conflicts. We implement Eunomia for WebAssembly, which can analyze applications written in various languages. Eunomia is the first symbolic execution engine that supports the full features of WebAssembly. We evaluate Eunomia with a microbenchmark suite and six real-world applications. Our evaluation shows that Eunomia improves bug detection by up to three orders of magnitude. We also conduct a user study that shows the benefits of using Aes. Moreover, Eunomia verifies six known bugs and detects two new zero-day bugs in Collections-C.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2285,
+    "label": 36,
+    "text": "Title: Computing Adequately Permissive Assumptions for Synthesis\nAbstract: We solve the problem of automatically computing a new class of environment assumptions in two-player turn-based finite graph games which characterize an ``adequate cooperation'' needed from the environment to allow the system player to win. Given an $\\omega$-regular winning condition $\\Phi$ for the system player, we compute an $\\omega$-regular assumption $\\Psi$ for the environment player, such that (i) every environment strategy compliant with $\\Psi$ allows the system to fulfill $\\Phi$ (sufficiency), (ii) $\\Psi$ can be fulfilled by the environment for every strategy of the system (implementability), and (iii) $\\Psi$ does not prevent any cooperative strategy choice (permissiveness). For parity games, which are canonical representations of $\\omega$-regular games, we present a polynomial-time algorithm for the symbolic computation of adequately permissive assumptions and show that our algorithm runs faster and produces better assumptions than existing approaches -- both theoretically and empirically. To the best of our knowledge, for $\\omega$-regular games, we provide the first algorithm to compute sufficient and implementable environment assumptions that are also permissive.",
+    "neighbors": [
+      95
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2286,
+    "label": 30,
+    "text": "Title: Multimodal Chain-of-Thought Reasoning in Language Models\nAbstract: Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have focused on the language modality. We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework that separates rationale generation and answer inference. In this way, answer inference can leverage better generated rationales that are based on multimodal information. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16 percentage points (75.17%->91.68% accuracy) on the ScienceQA benchmark and even surpasses human performance. Code is publicly available available at https://github.com/amazon-science/mm-cot.",
+    "neighbors": [
+      173,
+      319,
+      618,
+      664,
+      704,
+      719,
+      855,
+      975,
+      1007,
+      1026,
+      1071,
+      1481,
+      1668,
+      2036,
+      2113,
+      2184,
+      2216,
+      2266
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2287,
+    "label": 16,
+    "text": "Title: Tracking Objects and Activities with Attention for Temporal Sentence Grounding\nAbstract: Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video.Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.",
+    "neighbors": [
+      40,
+      737
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2288,
+    "label": 23,
+    "text": "Title: Test Case Generation for Drivability Requirements of an Automotive Cruise Controller: An Experience with an Industrial Simulator\nAbstract: Automotive software development requires engineers to test their systems to detect violations of both functional and drivability requirements. Functional requirements define the functionality of the automotive software. Drivability requirements refer to the driver's perception of the interactions with the vehicle; for example, they typically require limiting the acceleration and jerk perceived by the driver within given thresholds. While functional requirements are extensively considered by the research literature, drivability requirements garner less attention. This industrial paper describes our experience assessing the usefulness of an automated search-based software testing (SBST) framework in generating failure-revealing test cases for functional and drivability requirements. Our experience concerns the VI-CarRealTime simulator, an industrial virtual modeling and simulation environment widely used in the automotive domain. We designed a Cruise Control system in Simulink for a four-wheel vehicle, in an iterative fashion, by producing 21 model versions. We used the SBST framework for each version of the model to search for failure-revealing test cases revealing requirement violations. Our results show that the SBST framework successfully identified a failure-revealing test case for 66.7% of our model versions, requiring, on average, 245.9s and 3.8 iterations. We present lessons learned, reflect on the generality of our results, and discuss how our results improve the state of practice.",
+    "neighbors": [
+      1404
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2289,
+    "label": 30,
+    "text": "Title: On the (In)Effectiveness of Large Language Models for Chinese Text Correction\nAbstract: Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. As an outstanding representative of LLMs and the foundation model that set off this wave of research on LLMs, ChatGPT has attracted more and more researchers to study its capabilities and performance on various downstream Natural Language Processing (NLP) tasks. While marveling at ChatGPT's incredible performance on kinds of tasks, we notice that ChatGPT also has excellent multilingual processing capabilities, such as Chinese. To explore the Chinese processing ability of ChatGPT, we focus on Chinese Text Correction, a fundamental and challenging Chinese NLP task. Specifically, we evaluate ChatGPT on the Chinese Grammatical Error Correction (CGEC) and Chinese Spelling Check (CSC) tasks, which are two main Chinese Text Correction scenarios. From extensive analyses and comparisons with previous state-of-the-art fine-tuned models, we empirically find that the ChatGPT currently has both amazing performance and unsatisfactory behavior for Chinese Text Correction. We believe our findings will promote the landing and application of LLMs in the Chinese NLP community.",
+    "neighbors": [
+      519,
+      891,
+      971,
+      1797,
+      2113
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2290,
+    "label": 24,
+    "text": "Title: Heterogeneous Datasets for Federated Survival Analysis Simulation\nAbstract: Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test federated survival models. This work provides a novel technique for constructing realistic heterogeneous datasets by starting from existing non-federated datasets in a reproducible way. Specifically, we propose two dataset-splitting algorithms based on the Dirichlet distribution to assign each data sample to a carefully chosen client: quantity-skewed splitting and label-skewed splitting. Furthermore, these algorithms allow for obtaining different levels of heterogeneity by changing a single hyperparameter. Finally, numerical experiments provide a quantitative evaluation of the heterogeneity level using log-rank tests and a qualitative analysis of the generated splits. The implementation of the proposed methods is publicly available in favor of reproducibility and to encourage common practices to simulate federated environments for survival analysis.",
+    "neighbors": [
+      1359,
+      2152
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2291,
+    "label": 16,
+    "text": "Title: Deep Class-Incremental Learning: A Survey\nAbstract: Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. For example, a robot needs to understand new instructions, and an opinion monitoring system should analyze emerging topics every day. Class-Incremental Learning (CIL) enables the learner to incorporate the knowledge of new classes incrementally and build a universal classifier among all seen classes. Correspondingly, when directly training the model with new class instances, a fatal problem occurs -- the model tends to catastrophically forget the characteristics of former ones, and its performance drastically degrades. There have been numerous efforts to tackle catastrophic forgetting in the machine learning community. In this paper, we survey comprehensively recent advances in deep class-incremental learning and summarize these methods from three aspects, i.e., data-centric, model-centric, and algorithm-centric. We also provide a rigorous and unified evaluation of 16 methods in benchmark image classification tasks to find out the characteristics of different algorithms empirically. Furthermore, we notice that the current comparison protocol ignores the influence of memory budget in model storage, which may result in unfair comparison and biased results. Hence, we advocate fair comparison by aligning the memory budget in evaluation, as well as several memory-agnostic performance measures. The source code to reproduce these evaluations is available at https://github.com/zhoudw-zdw/CIL_Survey/",
+    "neighbors": [
+      634,
+      1115
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2292,
+    "label": 3,
+    "text": "Title: Impacts of Differential Privacy on Fostering more Racially and Ethnically Diverse Elementary Schools\nAbstract: In the face of increasingly severe privacy threats in the era of data and AI, the US Census Bureau has recently adopted differential privacy, the de facto standard of privacy protection for the 2020 Census release. Enforcing differential privacy involves adding carefully calibrated random noise to sensitive demographic information prior to its release. This change has the potential to impact policy decisions like political redistricting and other high-stakes practices, partly because tremendous federal funds and resources are allocated according to datasets (like Census data) released by the US government. One under-explored yet important application of such data is the redrawing of school attendance boundaries to foster less demographically segregated schools. In this study, we ask: how differential privacy might impact diversity-promoting boundaries in terms of resulting levels of segregation, student travel times, and school switching requirements? Simulating alternative boundaries using differentially-private student counts across 67 Georgia districts, we find that increasing data privacy requirements decreases the extent to which alternative boundaries might reduce segregation and foster more diverse and integrated schools, largely by reducing the number of students who would switch schools under boundary changes. Impacts on travel times are minimal. These findings point to a privacy-diversity tradeoff local educational policymakers may face in forthcoming years, particularly as computational methods are increasingly poised to facilitate attendance boundary redrawings in the pursuit of less segregated schools.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2293,
+    "label": 27,
+    "text": "Title: Integrated Ray-Tracing and Coverage Planning Control using Reinforcement Learning\nAbstract: In this work we propose a coverage planning control approach which allows a mobile agent, equipped with a controllable sensor (i.e., a camera) with limited sensing domain (i.e., finite sensing range and angle of view), to cover the surface area of an object of interest. The proposed approach integrates ray-tracing into the coverage planning process, thus allowing the agent to identify which parts of the scene are visible at any point in time. The problem of integrated ray-tracing and coverage planning control is first formulated as a constrained optimal control problem (OCP), which aims at determining the agent\u2019s optimal control inputs over a finite planning horizon, that minimize the coverage time. Efficiently solving the resulting OCP is however very challenging due to non-convex and nonlinear visibility constraints. To overcome this limitation, the problem is converted into a Markov decision process (MDP) which is then solved using reinforcement learning. In particular, we show that a controller which follows an optimal control law can be learned using off-policy temporal-difference control (i.e., Q-learning). Extensive numerical experiments demonstrate the effectiveness of the proposed approach for various configurations of the agent and the object of interest.",
+    "neighbors": [
+      512,
+      569
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2294,
+    "label": 24,
+    "text": "Title: Topologically Regularized Data Embeddings\nAbstract: Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings. For example, this may help one to embed the data into a given number of clusters, or to accommodate for noise that prevents one from deriving the distribution of the data over the model directly, which can then be learned more effectively. However, a general tool for integrating different prior topological knowledge into embeddings is lacking. Although differentiable topology layers have been recently developed that can (re)shape embeddings into prespecified topological models, they have two important limitations for representation learning, which we address in this paper. First, the currently suggested topological losses fail to represent simple models such as clusters and flares in a natural manner. Second, these losses neglect all original structural (such as neighborhood) information in the data that is useful for learning. We overcome these limitations by introducing a new set of topological losses, and proposing their usage as a way for topologically regularizing data embeddings to naturally represent a prespecified model. We include thorough experiments on synthetic and real data that highlight the usefulness and versatility of this approach, with applications ranging from modeling high-dimensional single-cell data, to graph embedding.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2295,
+    "label": 27,
+    "text": "Title: Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration\nAbstract: We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i.e., every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.",
+    "neighbors": [
+      2181
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2296,
+    "label": 16,
+    "text": "Title: Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification\nAbstract: With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern. Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers. Nevertheless, these methods can only discover the patterns or rules that naturally exist in models. In this work, rather than relying on post-hoc schemes, we proactively instill knowledge to alter the representation of human-understandable concepts in hidden layers. Specifically, we use a hierarchical tree of semantic concepts to store the knowledge, which is leveraged to regularize the representations of image data instances while training deep models. The axes of the latent space are aligned with the semantic concepts, where the hierarchical relations between concepts are also preserved. Experiments on real-world image datasets show that our method improves model interpretability, showing better disentanglement of semantic concepts, without negatively affecting model classification performance.",
+    "neighbors": [
+      106,
+      929,
+      1199
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2297,
+    "label": 16,
+    "text": "Title: MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions\nAbstract: Automatic analysis of human behaviour is a fundamental prerequisite for the creation of machines that can effectively interact with- and support humans in social interactions. In MultiMediate'23, we address two key human social behaviour analysis tasks for the first time in a controlled challenge: engagement estimation and bodily behaviour recognition in social interactions. This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks. For engagement estimation we collected novel annotations on the NOvice eXpert Interaction (NOXI) database. For bodily behaviour recognition, we annotated test recordings of the MPIIGroupInteraction corpus with the BBSI annotation scheme. In addition, we present baseline results for both challenge tasks.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2298,
+    "label": 23,
+    "text": "Title: Fuzzing Automatic Differentiation in Deep-Learning Libraries\nAbstract: Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL libraries and frameworks. However, existing work largely overlooked one crucial component of any DL system, automatic differentiation (AD), which is the basis for the recent development of DL. To this end, we propose \u2207Fuzz, the first general and practical approach specifically targeting the critical AD component in DL libraries. Our key insight is that each DL library API can be abstracted into a function processing tensors/vectors, which can be differentially tested under various execution scenarios (for computing outputs/gradients with different implementations). We have implemented $\\nabla \\text{Fuzz}$ as a fully automated API-level fuzzer targeting AD in DL libraries, which utilizes differential testing on different execution scenarios to test both first-order and high-order gradients, and also includes automated filtering strategies to remove false positives caused by numerical instability. We have performed an extensive study on four of the most popular and actively-maintained DL libraries, PyTorch, TensorFlow, JAX, and OneFlow. The result shows that $\\nabla \\text{Fuzz}$ substantially outperforms state-of-the-art fuzzers in terms of both code coverage and bug detection. To date, $\\nabla \\text{Fuzz}$ has detected 173 bugs for the studied DL libraries, with 144 already confirmed by developers (117 of which are previously unknown bugs and 107 are related to AD). Remarkably, $\\nabla \\text{Fuzz}$ contributed 58.3% (7/12) of all high-priority AD bugs for PyTorch and JAX during a two-month period. None of the confirmed AD bugs were detected by existing fuzzers.",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2299,
+    "label": 4,
+    "text": "Title: Empirical Review of Smart Contract and DeFi Security: Vulnerability Detection and Automated Repair\nAbstract: Decentralized Finance (DeFi) is emerging as a peer-to-peer financial ecosystem, enabling participants to trade products on a permissionless blockchain. Built on blockchain and smart contracts, the DeFi ecosystem has experienced explosive growth in recent years. Unfortunately, smart contracts hold a massive amount of value, making them an attractive target for attacks. So far, attacks against smart contracts and DeFi protocols have resulted in billions of dollars in financial losses, severely threatening the security of the entire DeFi ecosystem. Researchers have proposed various security tools for smart contracts and DeFi protocols as countermeasures. However, a comprehensive investigation of these efforts is still lacking, leaving a crucial gap in our understanding of how to enhance the security posture of the smart contract and DeFi landscape. To fill the gap, this paper reviews the progress made in the field of smart contract and DeFi security from the perspective of both vulnerability detection and automated repair. First, we analyze the DeFi smart contract security issues and challenges. Specifically, we lucubrate various DeFi attack incidents and summarize the attacks into six categories. Then, we present an empirical study of 42 state-of-the-art techniques that can detect smart contract and DeFi vulnerabilities. In particular, we evaluate the effectiveness of traditional smart contract bug detection tools in analyzing complex DeFi protocols. Additionally, we investigate 8 existing automated repair tools for smart contracts and DeFi protocols, providing insight into their advantages and disadvantages. To make this work useful for as wide of an audience as possible, we also identify several open issues and challenges in the DeFi ecosystem that should be addressed in the future.",
+    "neighbors": [
+      1855
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2300,
+    "label": 30,
+    "text": "Title: Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4\nAbstract: In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks. We argue that this supports a case for open models whose training data is known.",
+    "neighbors": [
+      1052,
+      1733,
+      2279
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2301,
+    "label": 4,
+    "text": "Title: High Recovery with Fewer Injections: Practical Binary Volumetric Injection Attacks against Dynamic Searchable Encryption\nAbstract: Searchable symmetric encryption enables private queries over an encrypted database, but it also yields information leakages. Adversaries can exploit these leakages to launch injection attacks (Zhang et al., USENIX'16) to recover the underlying keywords from queries. The performance of the existing injection attacks is strongly dependent on the amount of leaked information or injection. In this work, we propose two new injection attacks, namely BVA and BVMA, by leveraging a binary volumetric approach. We enable adversaries to inject fewer files than the existing volumetric attacks by using the known keywords and reveal the queries by observing the volume of the query results. Our attacks can thwart well-studied defenses (e.g., threshold countermeasure, static padding) without exploiting the distribution of target queries and client databases. We evaluate the proposed attacks empirically in real-world datasets with practical queries. The results show that our attacks can obtain a high recovery rate (>80%) in the best case and a roughly 60% recovery even under a large-scale dataset with a small number of injections (<20 files).",
+    "neighbors": [
+      751,
+      1095
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2302,
+    "label": 24,
+    "text": "Title: Denoising diffusion algorithm for inverse design of microstructures with fine-tuned nonlinear material properties\nAbstract: nan",
+    "neighbors": [],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2303,
+    "label": 16,
+    "text": "Title: The Stable Signature: Rooting Watermarks in Latent Diffusion Models\nAbstract: Generative image modeling enables a wide range of applications but raises ethical concerns about responsible deployment. This paper introduces an active strategy combining image watermarking and Latent Diffusion Models. The goal is for all generated images to conceal an invisible watermark allowing for future detection and/or identification. The method quickly fine-tunes the latent decoder of the image generator, conditioned on a binary signature. A pre-trained watermark extractor recovers the hidden signature from any generated image and a statistical test then determines whether it comes from the generative model. We evaluate the invisibility and robustness of the watermarks on a variety of generation tasks, showing that Stable Signature works even after the images are modified. For instance, it detects the origin of an image generated from a text prompt, then cropped to keep $10\\%$ of the content, with $90$+$\\%$ accuracy at a false positive rate below 10$^{-6}$.",
+    "neighbors": [
+      1730,
+      1902
+    ],
+    "mask": "Train"
+  },
+  {
+    "node_id": 2304,
+    "label": 16,
+    "text": "Title: Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption\nAbstract: Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. They are motivated by the pattern recognition system in the human brain, where complex objects are decomposed into a hierarchy of simpler object parts. Such a hierarchy is referred to as a parse-tree. Conceptually, capsule neural networks have been defined to mimic this behavior. The capsule neural network (CapsNet), by Sabour, Frosst, and Hinton, is the first actual implementation of the conceptual idea of capsule neural networks. CapsNets achieved state-of-the-art performance on simple image recognition tasks with fewer parameters and greater robustness to affine transformations than comparable approaches. This sparked extensive follow-up research. However, despite major efforts, no work was able to scale the CapsNet architecture to more reasonable-sized datasets. Here, we provide a reason for this failure and argue that it is most likely not possible to scale CapsNets beyond toy examples. In particular, we show that the concept of a parse-tree, the main idea behind capsule neuronal networks, is not present in CapsNets. We also show theoretically and experimentally that CapsNets suffer from a vanishing gradient problem that results in the starvation of many capsules during training.",
+    "neighbors": [],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2305,
+    "label": 10,
+    "text": "Title: Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback\nAbstract: Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of-the-art large language models (LLMs). Despite this popularity, there has been relatively little public work systematizing its flaws. In this paper, we (1) survey open problems and fundamental limitations of RLHF and related methods; (2) overview techniques to understand, improve, and complement RLHF in practice; and (3) propose auditing and disclosure standards to improve societal oversight of RLHF systems. Our work emphasizes the limitations of RLHF and highlights the importance of a multi-faceted approach to the development of safer AI systems.",
+    "neighbors": [
+      75,
+      352,
+      430,
+      566,
+      896,
+      899,
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+      2235,
+      2258
+    ],
+    "mask": "Test"
+  },
+  {
+    "node_id": 2306,
+    "label": 16,
+    "text": "Title: Grounded Text-to-Image Synthesis with Attention Refocusing\nAbstract: Driven by scalable diffusion models trained on large-scale paired text-image datasets, text-to-image synthesis methods have shown compelling results. However, these models still fail to precisely follow the text prompt when multiple objects, attributes, and spatial compositions are involved in the prompt. In this paper, we identify the potential reasons in both the cross-attention and self-attention layers of the diffusion model. We propose two novel losses to refocus the attention maps according to a given layout during the sampling process. We perform comprehensive experiments on the DrawBench and HRS benchmarks using layouts synthesized by Large Language Models, showing that our proposed losses can be integrated easily and effectively into existing text-to-image methods and consistently improve their alignment between the generated images and the text prompts.",
+    "neighbors": [
+      273,
+      706,
+      1052,
+      1079,
+      1418,
+      1710,
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+      1902,
+      1969,
+      2161,
+      2169,
+      2242
+    ],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2307,
+    "label": 27,
+    "text": "Title: UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning\nAbstract: We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++. To address the challenge of learning the vision-based policy across thousands of object instances, we propose Geometry-aware Curriculum Learning (GeoCurriculum) and Geometry-aware iterative Generalist-Specialist Learning (GiGSL) which leverage the geometry feature of the task and significantly improve the generalizability. With our proposed techniques, our final policy shows universal dexterous grasping on thousands of object instances with 85.4% and 78.2% success rate on the train set and test set which outperforms the state-of-the-art baseline UniDexGrasp by 11.7% and 11.3%, respectively.",
+    "neighbors": [],
+    "mask": "Validation"
+  },
+  {
+    "node_id": 2308,
+    "label": 16,
+    "text": "Title: PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction\nAbstract: Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The dense nature of the prediction space has rendered existing efficient 2D-projection-based methods (e.g., bird's eye view, range view, etc.) ineffective, as they can only describe a subspace of the 3D scene. To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system for more fine-grained modeling of nearer areas. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane. Finally, we obtain the features of each point by aggregating its projected features on each of the processed TPV planes without the need for any post-processing. Extensive experiments on both 3D occupancy prediction and LiDAR segmentation benchmarks demonstrate that the proposed PointOcc achieves state-of-the-art performance with much faster speed. Specifically, despite only using LiDAR, PointOcc significantly outperforms all other methods, including multi-modal methods, with a large margin on the OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.",
+    "neighbors": [
+      47,
+      1571,
+      2278
+    ],
+    "mask": "Train"
+  }
+]
\ No newline at end of file