pwc-archive/papers-with-abstracts huggingface dataset:
WineGraph: A Graph Representation For Food-Wine Pairing
Authors: Zuzanna Gawrysiak, Agata Żywot, Agnieszka Ławrynowicz
Date: 2024-06-27 00:00:00

We present WineGraph, an extended version of FlavorGraph, a heterogeneous graph incorporating wine data into its structure. This integration enables food-wine pairing based on taste and sommelier-defined rules. Leveraging a food dataset comprising 500,000 reviews and a wine reviews dataset with over 130,000 entries, we computed taste descriptors for both food and wine. This information was then utilised to pair food items with wine and augment FlavorGraph with additional data. The results demonstrate the potential of heterogeneous graphs to acquire supplementary information, proving beneficial for wine pairing.
Development of Hydrogen Bonding Magnetic Reaction-based Gene Regulation through Cyclic Electromagnetic DNA Simulation in Double-Stranded DNA
Authors: Yeon Sook Kim, Dae Gwan Lee, Suk Keun Lee
Date: 2012-10-26 00:00:00

The proton-magnetic reaction is commonly used in MRI machines with a strong magnetic field of over 1 T, while this study hypothesized that the electron magnetic reaction of hydrogen could affect the hydrogen bonds of double-stranded DNA (dsDNA) at a low magnetic field below 0.01 T. The goal is to develop a hydrogen bonding magnetic reaction-based gene regulation (HBMR-GR) system. The polarities of DNA base pairs are derived from the relative electrostatic charge between purines and pyrimidines, which become positively and negatively charged, respectively. The Pyu dsDNAs with pyrimidine(s)-purine(s) sequences, ds3T3A, ds3C3G, and ds3C3A, showed stronger DNA hybridization potential, increased infrared absorption at 3400-3200 cm-1, and a unique DNA conformation in HPLC analysis compared to the corresponding Puy dsDNAs. To target the three-dimensional structure of dsDNA based on the DNA base pair polarities, one can use cyclic electromagnetic DNA simulation (CEDS) with approximately 25% efficiency for randomly oriented dsDNAs. CEDS was found to induce sequence-specific hybridization of target oligo-dsDNAs in 0.005M NaCl solution and sequence-specific conformation of oligo-dsDNAs in 0.1M NaCl solution. It was found that the Pyu oligo-dsDNAs were more responsible for the hybridization and conformational changes by CEDS than the Puy oligo-dsDNAs. CEDS decreased ethidium bromide (EtBr) DNA intercalation and spermidine DNA condensation depending on CEDS time in the binding assay. The results also included that the Pyu oligo-dsDNAs were more responsible for CEDS by forming stable and unique conformation of oligo-dsDNA than the Therefore, it is postulated that the low-level HBMR-based CEDS can enhance the hybridization potential of oligo-dsDNAs and subsequently lead to the unique DNA conformation required for the initiation of various DNA functions.
The morphology of cosmological reionization by means of Minkowski Functionals
Authors: Liron Gleser, Adi Nusser, Benedetta Ciardi, Vincent Desjacques
Date: 2006-02-28 00:00:00

The morphology of the total gas and the neutral hydrogen (H I) distributions during the cosmological epoch of reionization can be quantified with Minkowski Functionals (MFs) of isodensity surfaces. We compute the MFs from the output of a high-resolution numerical simulation which includes explicit treatment of transfer of UV ionizing radiation. "Galaxies" identified in the simulation using semi-analytic models of galaxy formation are assumed to be the sole sources of UV photons. The MFs of the total gas distribution are well described by the analytic expressions derived for lognormal random fields. The statistical properties of the diffuse H I depend on the gas distribution and on the way ionized regions propagate in the inter-galactic medium (IGM). The deviations of the MFs of the H I distribution from those of a lognormal random field are, therefore, caused by reionization. We use the MFs to discriminate between the various stages of reionization in the simulation. We suggest a simple model of reionization which reproduces the MFs derived from this simulation. Using random realizations of lognormal density fields, we also assess the ability of MFs to distinguish between different reionization scenarios. Our results are relevant to the analysis of future cosmological twenty-one centimeter maps.
Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression
Authors: Allan Raventós, Mansheej Paul, Feng Chen, Surya Ganguli
Date: 2023-06-26 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Diversity, In-Context Learning, regression

Pretrained transformers exhibit the remarkable ability of in-context learning (ICL): they can learn tasks from just a few examples provided in the prompt without updating any weights. This raises a foundational question: can ICL solve fundamentally $\textit{new}$ tasks that are very different from those seen during pretraining? To probe this question, we examine ICL's performance on linear regression while varying the diversity of tasks in the pretraining dataset. We empirically demonstrate a $\textit{task diversity threshold}$ for the emergence of ICL. Below this threshold, the pretrained transformer cannot solve unseen regression tasks, instead behaving like a Bayesian estimator with the $\textit{non-diverse pretraining task distribution}$ as the prior. Beyond this threshold, the transformer significantly outperforms this estimator; its behavior aligns with that of ridge regression, corresponding to a Gaussian prior over $\textit{all tasks}$, including those not seen during pretraining. Thus, when pretrained on data with task diversity greater than the threshold, transformers $\textit{can}$ optimally solve fundamentally new tasks in-context. Importantly, this capability hinges on it deviating from the Bayes optimal estimator with the pretraining distribution as the prior. This study also explores the effect of regularization, model capacity and task structure and underscores, in a concrete example, the critical role of task diversity, alongside data and model scale, in the emergence of ICL. Code is available at https://github.com/mansheej/icl-task-diversity.
Degradation Detection Method for Railway Point Machines
Authors: Chong Bian, Shunkun Yang, Tingting Huang, Qingyang Xu, Jie Liu, Enrico Zio
Date: 2018-09-07 00:00:00

Point machines (PMs) are used for switching and locking railway turnouts, and are considered one of the most critical elements of a railway signal system. The failure of the point mechanism directly affects the operation of the railway and may cause serious safety accidents. Hence, there is a need for early detection of the anomalies in PMs. From normal operation to complete failure, the machine usually undergoes a series of degradation states. If the degradation states are detected in time, maintenance can be organized in advance to prevent the malfunction. This paper presents a degradation detection method that can effectively mine and identify the degradation state of the PM. First, power data is processed to obtain the feature set that can describe the PM characteristics effectively. Then, a clustering analysis of the feature set is carried out by self-organizing feature-mapping network, and various degradation states are mined. Finally, the optimized support vector machine is used to build the state classifier to identify the degradation state of the PM. The experimental results obtained with the Siemens S700K PM show that the proposed method could not only mine the effective degradation states, but also obtain high identification accuracy.
SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection
Authors: Zongxiang Hu, Zhaosheng Zhang
Date: 2024-07-04 00:00:00
Tasks: Anomaly Detection

Visual anomaly detection is essential in industrial manufacturing, yet traditional methods often rely heavily on extensive normal datasets and task-specific models, limiting their scalability. Recent advancements in large-scale vision-language models have significantly enhanced zero- and few-shot anomaly detection. However, these approaches may not fully leverage hierarchical features, potentially overlooking nuanced details crucial for accurate detection. To address this, we introduce a novel window self-attention mechanism based on the CLIP model, augmented with learnable prompts to process multi-level features within a Soldier-Officer Window Self-Attention (SOWA) framework. Our method has been rigorously evaluated on five benchmark datasets, achieving superior performance by leading in 18 out of 20 metrics, setting a new standard against existing state-of-the-art techniques.
FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learning
Authors: Saandeep Aathreya, Shaun Canavan
Date: 2024-07-03 00:00:00
Tasks: Contrastive Learning, Density Estimation, Out-of-Distribution Detection, Representation Learning

Identifying Out-of-distribution (OOD) data is becoming increasingly critical as the real-world applications of deep learning methods expand. Post-hoc methods modify softmax scores fine-tuned on outlier data or leverage intermediate feature layers to identify distinctive patterns between In-Distribution (ID) and OOD samples. Other methods focus on employing diverse OOD samples to learn discrepancies between ID and OOD. These techniques, however, are typically dependent on the quality of the outlier samples assumed. Density-based methods explicitly model class-conditioned distributions but this requires long training time or retraining the classifier. To tackle these issues, we introduce \textit{FlowCon}, a new density-based OOD detection technique. Our main innovation lies in efficiently combining the properties of normalizing flow with supervised contrastive learning, ensuring robust representation learning with tractable density estimation. Empirical evaluation shows the enhanced performance of our method across common vision datasets such as CIFAR-10 and CIFAR-100 pretrained on ResNet18 and WideResNet classifiers. We also perform quantitative analysis using likelihood plots and qualitative visualization using UMAP embeddings and demonstrate the robustness of the proposed method under various OOD contexts. Code will be open-sourced post decision.
Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing
Authors: Tong Wang, Taotao Gu, Huan Deng, Hu Li, Xiaohui Kuang, Gang Zhao
Date: 2024-07-05 00:00:00
Tasks: Autonomous Driving, Graph Neural Network, Trajectory Clustering

As autonomous driving systems (ADS) advance towards higher levels of autonomy, orchestrating their safety verification becomes increasingly intricate. This paper unveils ScenarioFuzz, a pioneering scenario-based fuzz testing methodology. Designed like a choreographer who understands the past performances, it uncovers vulnerabilities in ADS without the crutch of predefined scenarios. Leveraging map road networks, such as OPENDRIVE, we extract essential data to form a foundational scenario seed corpus. This corpus, enriched with pertinent information, provides the necessary boundaries for fuzz testing in the absence of starting scenarios. Our approach integrates specialized mutators and mutation techniques, combined with a graph neural network model, to predict and filter out high-risk scenario seeds, optimizing the fuzzing process using historical test data. Compared to other methods, our approach reduces the time cost by an average of 60.3%, while the number of error scenarios discovered per unit of time increases by 103%. Furthermore, we propose a self-supervised collision trajectory clustering method, which aids in identifying and summarizing 54 high-risk scenario categories prone to inducing ADS faults. Our experiments have successfully uncovered 58 bugs across six tested systems, emphasizing the critical safety concerns of ADS.
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detection
Authors: Zhiqiang Yang, Qiu Guan, Keer Zhao, Jianmin Yang, Xinli Xu, Haixia Long, Ying Tang
Date: 2024-07-05 00:00:00
Tasks: Novel Object Detection, object-detection, Object Detection, Real-Time Object Detection

Due to the effective performance of multi-scale feature fusion, Path Aggregation FPN (PAFPN) is widely employed in YOLO detectors. However, it cannot efficiently and adaptively integrate high-level semantic information with low-level spatial information simultaneously. We propose a new model named MAF-YOLO in this paper, which is a novel object detection framework with a versatile neck named Multi-Branch Auxiliary FPN (MAFPN). Within MAFPN, the Superficial Assisted Fusion (SAF) module is designed to combine the output of the backbone with the neck, preserving an optimal level of shallow information to facilitate subsequent learning. Meanwhile, the Advanced Assisted Fusion (AAF) module deeply embedded within the neck conveys a more diverse range of gradient information to the output layer. Furthermore, our proposed Re-parameterized Heterogeneous Efficient Layer Aggregation Network (RepHELAN) module ensures that both the overall model architecture and convolutional design embrace the utilization of heterogeneous large convolution kernels. Therefore, this guarantees the preservation of information related to small targets while simultaneously achieving the multi-scale receptive field. Finally, taking the nano version of MAF-YOLO for example, it can achieve 42.4% AP on COCO with only 3.76M learnable parameters and 10.51G FLOPs, and approximately outperforms YOLOv8n by about 5.1%. The source code of this work is available at: https://github.com/yang-0201/MAF-YOLO.
Speed-accuracy relations for diffusion models: Wisdom from nonequilibrium thermodynamics and optimal transport
Authors: Kotaro Ikeda, Tomoya Uda, Daisuke Okanohara, Sosuke Ito
Date: 2024-07-05 00:00:00

We discuss a connection between a generative model, called the diffusion model, and nonequilibrium thermodynamics for the Fokker-Planck equation, called stochastic thermodynamics. Using techniques from stochastic thermodynamics, we derive the speed-accuracy relations for diffusion models, which are inequalities that relate the accuracy of data generation to the entropy production rate. This relation can be interpreted as the speed of the diffusion dynamics in the absence of the non-conservative force. From a stochastic thermodynamic perspective, our results provide quantitative insight into how best to generate data in diffusion models. The optimal learning protocol is introduced by the geodesic of space of the 2-Wasserstein distance in optimal transport theory. We numerically illustrate the validity of the speed-accuracy relations for diffusion models with different noise schedules and different data. We numerically discuss our results for optimal and suboptimal learning protocols. We also demonstrate the applicability of our results to data generation from the real-world image datasets.
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
Authors: Yuxuan Kuang, Junjie Ye, Haoran Geng, Jiageng Mao, Congyue Deng, Leonidas Guibas, He Wang, Yue Wang
Date: 2024-07-05 00:00:00
Tasks: Human-Object Interaction Detection, Retrieval

This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, dubbed RAM, featuring generalizability across various objects, environments, and embodiments. Unlike existing approaches that learn manipulation from expensive in-domain demonstrations, RAM capitalizes on a retrieval-based affordance transfer paradigm to acquire versatile manipulation capabilities from abundant out-of-domain data. First, RAM extracts unified affordance at scale from diverse sources of demonstrations including robotic data, human-object interaction (HOI) data, and custom data to construct a comprehensive affordance memory. Then given a language instruction, RAM hierarchically retrieves the most similar demonstration from the affordance memory and transfers such out-of-domain 2D affordance to in-domain 3D executable affordance in a zero-shot and embodiment-agnostic manner. Extensive simulation and real-world evaluations demonstrate that our RAM consistently outperforms existing works in diverse daily tasks. Additionally, RAM shows significant potential for downstream applications such as automatic and efficient data collection, one-shot visual imitation, and LLM/VLM-integrated long-horizon manipulation. For more details, please check our website at https://yxkryptonite.github.io/RAM/.
Spatio-temporal cooperative control Method of Highway Ramp Merge Based on Vehicle-road Coordination
Authors: Xiaoxue Xu, Maokai Lai, HaiTao Zhang, Xiang Dong, Tao Li, Jie Wu, Yuan Li, Ting Peng
Date: 2024-07-04 00:00:00
Tasks: Autonomous Vehicles, Management

The merging area of highway ramps faces multiple challenges, including traffic congestion, collision risks, speed mismatches, driver behavior uncertainties, limited visibility, and bottleneck effects. However, autonomous vehicles engaging in depth coordination between vehicle and road in merging zones, by pre-planning and uploading travel trajectories, can significantly enhance the safety and efficiency of merging zones.In this paper,we mainly introduce mainline priority cooperation method to achieve the time and space cooperative control of highway merge.Vehicle-mounted intelligent units share real-time vehicle status and driving intentions with Road Section Management Units, which pre-plan the spatiotemporal trajectories of vehicle travel. After receiving these trajectories, Vehicle Intelligent Units strictly adhere to them. Through this deep collaboration between vehicles and roads, conflicts in time and space during vehicle travel are eliminated in advance.
BiosERC: Integrating Biography Speakers Supported by LLMs for ERC Tasks
Authors: Jieying Xue, Minh Phuong Nguyen, Blake Matheny, Le Minh Nguyen
Date: 2024-07-05 00:00:00
Tasks: Emotion Recognition, Emotion Recognition in Conversation

In the Emotion Recognition in Conversation task, recent investigations have utilized attention mechanisms exploring relationships among utterances from intra- and inter-speakers for modeling emotional interaction between them. However, attributes such as speaker personality traits remain unexplored and present challenges in terms of their applicability to other tasks or compatibility with diverse model architectures. Therefore, this work introduces a novel framework named BiosERC, which investigates speaker characteristics in a conversation. By employing Large Language Models (LLMs), we extract the "biographical information" of the speaker within a conversation as supplementary knowledge injected into the model to classify emotional labels for each utterance. Our proposed method achieved state-of-the-art (SOTA) results on three famous benchmark datasets: IEMOCAP, MELD, and EmoryNLP, demonstrating the effectiveness and generalization of our model and showcasing its potential for adaptation to various conversation analysis tasks. Our source code is available at https://github.com/yingjie7/BiosERC.
Prediction-Free Coordinated Dispatch of Microgrid: A Data-Driven Online Optimization Approach
Authors: Kaidi Huang, Lin Cheng, Ning Qi, David Wenzhong Gao, Asad Mujeeb, Qinglai Guo
Date: 2024-07-04 00:00:00
Tasks: Computational Efficiency, Decision Making, Model Predictive Control

Traditional prediction-dependent dispatch methods can face challenges when renewables and prices predictions are unreliable in microgrid. Instead, this paper proposes a novel prediction-free two-stage coordinated dispatch approach in microgrid. Empirical learning is conducted during the offline stage, where we calculate the offline optimal state of charge (SOC) sequences for generic energy storage under different historical scenarios. During the online stage, we synthesize a dynamically updated reference for SOC and a dynamic opportunity price (DOP) based on empirical learning and real-time observations. They provide a global vision for online operation and effectively address the myopic tendencies inherent to online decision-making. The real-time control action, generated from online optimization algorithm, aims to minimize the operational costs while tracking the reference and considering DOP. Additionally, we develop an adaptive virtual-queue-based online optimization algorithm based on online convex optimization (OCO) framework. We provide theoretical proof that the proposed algorithm outperforms the existing OCO algorithms and achieves sublinear dynamic regret bound and sublinear strict constraint violation bound. Simulation-based studies demonstrate that, compared with model predictive control-based methods, it reduces operational costs and voltage violation rate by 5% and 9%, respectively.
On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards
Authors: Zhimin Zhao, Abdul Ali Bangash, Filipe Roseiro Côgo, Bram Adams, Ahmed E. Hassan
Date: 2024-07-04 00:00:00
Tasks: Code Completion

Foundation models (FM), such as large language models (LLMs), which are large-scale machine learning (ML) models, have demonstrated remarkable adaptability in various downstream software engineering (SE) tasks, such as code completion, code understanding, and software development. As a result, FM leaderboards have become essential tools for SE teams to compare and select the best third-party FMs for their specific products and purposes. However, the lack of standardized guidelines for FM evaluation and comparison threatens the transparency of FM leaderboards and limits stakeholders' ability to perform effective FM selection. As a first step towards addressing this challenge, our research focuses on understanding how these FM leaderboards operate in real-world scenarios ("leaderboard operations") and identifying potential pitfalls and areas for improvement ("leaderboard smells"). In this regard, we collect up to 1,045 FM leaderboards from five different sources: GitHub, Hugging Face Spaces, Papers With Code, spreadsheet and independent platform, to examine their documentation and engage in direct communication with leaderboard operators to understand their workflows. Through card sorting and negotiated agreement, we identify five distinct workflow patterns and develop a domain model that captures the key components and their interactions within these workflows. We then identify eight unique types of leaderboard smells in LBOps. By mitigating these smells, SE teams can improve transparency, accountability, and collaboration in current LBOps practices, fostering a more robust and responsible ecosystem for FM comparison and selection.
Graph-Guided Test-Time Adaptation for Glaucoma Diagnosis using Fundus Photography
Authors: Qian Zeng, Le Zhang, Yipeng Liu, Ce Zhu, Fan Zhang
Date: 2024-07-05 00:00:00
Tasks: Test-time Adaptation

Glaucoma is a leading cause of irreversible blindness worldwide. While deep learning approaches using fundus images have largely improved early diagnosis of glaucoma, variations in images from different devices and locations (known as domain shifts) challenge the use of pre-trained models in real-world settings. To address this, we propose a novel Graph-guided Test-Time Adaptation (GTTA) framework to generalize glaucoma diagnosis models to unseen test environments. GTTA integrates the topological information of fundus images into the model training, enhancing the model's transferability and reducing the risk of learning spurious correlation. During inference, GTTA introduces a novel test-time training objective to make the source-trained classifier progressively adapt to target patterns with reliable class conditional estimation and consistency regularization. Experiments on cross-domain glaucoma diagnosis benchmarks demonstrate the superiority of the overall framework and individual components under different backbone networks.