OpenRaiser/Intern-Atlas huggingface dataset:
Data-induced multiscale losses and efficient multirate gradient descent schemes
Venue: arXiv cs.LG 2024
Year: 2024

This paper investigates the impact of multiscale data on machine learning algorithms, particularly in the context of deep learning. A dataset is multiscale if its distribution shows large variations in scale across different directions. This paper reveals multiscale structures in the loss landscape, including its gradients and Hessians inherited from the data. Correspondingly, it introduces a novel gradient descent approach, drawing inspiration from multiscale algorithms used in scientific computing. This approach seeks to transcend empirical learning rate selection, offering a more systematic, data-informed strategy to enhance training efficiency, especially in the later stages.
SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach
Venue: arXiv cs.CL 2024
Year: 2024

Explainable AI (XAI) aids in deciphering 'black-box' models. While several methods have been proposed and evaluated primarily in the image domain, the exploration of explainability in the text domain remains a growing research area. In this paper, we delve into the applicability of XAI methods for the text domain. In this context, the 'Similarity Difference and Uniqueness' (SIDU) XAI method, recognized for its superior capability in localizing entire salient regions in image-based classification is extended to textual data. The extended method, SIDU-TXT, utilizes feature activation maps from 'black-box' models to generate heatmaps at a granular, word-based level, thereby providing explanations that highlight contextually significant textual elements crucial for model predictions. Given the absence of a unified standard for assessing XAI methods, this study applies a holistic three-tiered comprehensive evaluation framework: Functionally-Grounded, Human-Grounded and Application-Grounded, to assess the effectiveness of the proposed SIDU-TXT across various experiments. We find that, in sentiment analysis task of a movie review dataset, SIDU-TXT excels in both functionally and human-grounded evaluations, demonstrating superior performance through quantitative and qualitative analyses compared to benchmarks like Grad-CAM and LIME. In the application-grounded evaluation within the sensitive and complex legal domain of asylum decision-making, SIDU-TXT and Grad-CAM demonstrate comparable performances, each with its own set of strengths and weaknesses. However, both methods fall short of entirely fulfilling the sophisticated criteria of expert expectations, highlighting the imperative need for additional research in XAI methods suitable for such domains.
Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian
Venue: arXiv cs.CL 2024
Year: 2024

This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance on partially preprocessed short text in Serbian. We also compared it to LDA and NMF on fully preprocessed text. The experiments were conducted on a dataset of tweets expressing hesitancy toward COVID-19 vaccination. Our results show that with adequate parameter setting, BERTopic can yield informative topics even when applied to partially pre-processed short text. When the same parameters are applied in both prepro-cessing scenarios, the performance drop on partially preprocessed text is minimal. Compared to LDA and NMF, judging by the keywords, BERTopic offers more informative topics and gives novel insights when the number of topics is not limited. The findings of this paper can be significant for re-searchers working with other morphologically rich low-resource languages and short text.
DARTS: Diffusion Approximated Residual Time Sampling for Time-of-flight Rendering in Homogeneous Scattering Media
Venue: arXiv cs.GR 2024
Year: 2024

Time-of-flight (ToF) devices have greatly propelled the advancement of various multi-modal perception applications. However, achieving accurate rendering of time-resolved information remains a challenge, particularly in scenes involving complex geometries, diverse materials and participating media. Existing ToF rendering works have demonstrated notable results, yet they struggle with scenes involving scattering media and camera-warped settings. Other steady-state volumetric rendering methods exhibit significant bias or variance when directly applied to ToF rendering tasks. To address these challenges, we integrate transient diffusion theory into path construction and propose novel sampling methods for free-path distance and scattering direction, via resampled importance sampling and offline tabulation. An elliptical sampling method is further adapted to provide controllable vertex connection satisfying any required photon traversal time. In contrast to the existing temporal uniform sampling strategy, our method is the first to consider the contribution of transient radiance to importance-sample the full path, and thus enables improved temporal path construction under multiple scattering settings. The proposed method can be integrated into both path tracing and photon-based frameworks, delivering significant improvements in quality and efficiency with at least a 5x MSE reduction versus SOTA methods in equal rendering time.
Augmenting Security and Privacy in the Virtual Realm: An Analysis of Extended Reality Devices
Venue: arXiv cs.CR 2024
Year: 2024

In this work, we present a device-centric analysis of security and privacy attacks and defenses on Extended Reality (XR) devices, highlighting the need for robust and privacy-aware security mechanisms. Based on our analysis, we present future research directions and propose design considerations to help ensure the security and privacy of XR devices.
Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers
Venue: arXiv cs.RO 2024
Year: 2024

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation.
DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation
Venue: arXiv cs.RO 2024
Year: 2024

This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain.
Decidable Reasoning About Time in Finite-Domain Situation Calculus Theories
Venue: arXiv cs.AI 2024
Year: 2024

Representing time is crucial for cyber-physical systems and has been studied extensively in the Situation Calculus. The most commonly used approach represents time by adding a real-valued fluent $\mathit{time}(a)$ that attaches a time point to each action and consequently to each situation. We show that in this approach, checking whether there is a reachable situation that satisfies a given formula is undecidable, even if the domain of discourse is restricted to a finite set of objects. We present an alternative approach based on well-established results from timed automata theory by introducing clocks as real-valued fluents with restricted successor state axioms and comparison operators. %that only allow comparisons against fixed rationals. With this restriction, we can show that the reachability problem for finite-domain basic action theories is decidable. Finally, we apply our results on Golog program realization by presenting a decidable procedure for determining an action sequence that is a successful execution of a given program.
Is Mamba Capable of In-Context Learning?
Venue: arXiv cs.LG 2024
Year: 2024

State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model. This useful ability emerges as a side product of the foundation model's massive pretraining. While transformer models are currently the state of the art in ICL, this work provides empirical evidence that Mamba, a newly proposed state space model which scales better than transformers w.r.t. the input sequence length, has similar ICL capabilities. We evaluated Mamba on tasks involving simple function approximation as well as more complex natural language processing problems. Our results demonstrate that, across both categories of tasks, Mamba closely matches the performance of transformer models for ICL. Further analysis reveals that, like transformers, Mamba appears to solve ICL problems by incrementally optimizing its internal representations. Overall, our work suggests that Mamba can be an efficient alternative to transformers for ICL tasks involving long input sequences. This is an exciting finding in meta-learning and may enable generalizations of in-context learned AutoML algorithms (like TabPFN or Optformer) to long input sequences.
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings
Venue: arXiv cs.CL 2024
Year: 2024

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches. This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work. We specifically employ a strong Transformer-based model as a text encoder and, to handle lengthy clinical narratives, we explored either (a) adapting the base encoder model into a Longformer, or (b) dividing the text into chunks and processing each chunk independently. The representations produced by the encoder are combined with a label embedding mechanism that explores diverse ICD code synonyms. Experiments with different splits of the MIMIC-III dataset show that the proposed approach outperforms the current state-of-the-art models in ICD coding, with the label embeddings significantly contributing to the good performance. Our approach also leads to properly calibrated classification results, which can effectively inform downstream tasks such as quantification.
Charting The Evolution of Solidity Error Handling
Venue: arXiv cs.SE 2024
Year: 2024

The usage of error handling in Solidity smart contracts is vital because smart contracts perform transactions that should be verified. Transactions that are not carefully handled, may lead to program crashes and vulnerabilities, implying financial loss and legal consequences. While Solidity designers attempt to constantly update the language with new features, including error-handling (EH) features, it is necessary for developers to promptly absorb how to use them. We conduct a large-scale empirical study on 283K unique open-source smart contracts to identify patterns regarding the usage of Solidity EH features over time. Overall, the usage of most EH features is limited. However, we observe an upward trend (> 60%) in the usage of a Solidity-tailored EH feature, i.e., require. This indicates that designers of modern programming languages may consider making error handling more tailored to the purposes of each language. Our analysis on 102 versions of the Solidity documentation indicates the volatile nature of Solidity, as the language changes frequently, i.e., there are changes on EH features once or twice a year. Such frequent releases may confuse smart contract developers, discouraging them to carefully read the Solidity documentation, and correctly adopt EH features. Furthermore, our findings reveal that nearly 70% of the examined smart contracts are exposed to potential failures due to missing error handing, e.g., unchecked external calls. Therefore, the use of EH features should be further supported via a more informative documentation containing (1) representative and meaningful examples and (2) details about the impact of potential EH misuses.
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems
Venue: arXiv cs.IT 2024
Year: 2024

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively.
Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
Venue: arXiv cs.CV 2024
Year: 2024

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.
Meeting Bridges: Designing Information Artifacts that Bridge from Synchronous Meetings to Asynchronous Collaboration
Venue: arXiv cs.HC 2024
Year: 2024

A recent surge in remote meetings has led to complaints of ``Zoom fatigue'' and ``collaboration overload,'' negatively impacting worker productivity and well-being. One way to alleviate the burden of meetings is to de-emphasize their synchronous participation by shifting work to and enabling sensemaking during post-meeting asynchronous activities. Towards this goal, we propose the design concept of meeting bridges, or information artifacts that can encapsulate meeting information towards bridging to and facilitating post-meeting activities. Through 13 interviews and a survey of 198 information workers, we learn how people use online meeting information after meetings are over, finding five main uses: as an archive, as task reminders, to onboard or support inclusion, for group sensemaking, and as a launching point for follow-on collaboration. However, we also find that current common meeting artifacts, such as notes and recordings, present challenges in serving as meeting bridges. After conducting co-design sessions with 16 participants, we distill key principles for the design of meeting bridges to optimally support asynchronous collaboration goals. Overall, our findings point to the opportunity of designing information artifacts that not only support users to access but also continue to transform and engage in meeting information post-meeting.
Bounding the Weisfeiler-Leman Dimension via a Depth Analysis of I/R-Trees
Venue: arXiv cs.DM 2024
Year: 2024

The Weisfeiler-Leman (WL) dimension is an established measure for the inherent descriptive complexity of graphs and relational structures. It corresponds to the number of variables that are needed and sufficient to define the object of interest in a counting version of first-order logic (FO). These bounded-variable counting logics were even candidates to capture graph isomorphism, until a celebrated construction due to Cai, Fürer, and Immerman [Combinatorica 1992] showed that $Ω(n)$ variables are required to distinguish all non-isomorphic $n$-vertex graphs. Still, very little is known about the precise number of variables required and sufficient to define every $n$-vertex graph. For the bounded-variable (non-counting) FO fragments, Pikhurko, Veith, and Verbitsky [Discret. Appl. Math. 2006] provided an upper bound of $\frac{n+3}{2}$ and showed that it is essentially tight. Our main result yields that, in the presence of counting quantifiers, $\frac{n}{4} + o(n)$ variables suffice. This shows that counting does allow us to save variables when defining graphs. As an application of our techniques, we also show new bounds in terms of the vertex cover number of the graph. To obtain the results, we introduce a new concept called the WL depth of a graph. We use it to analyze branching trees within the Individualization/Refinement (I/R) paradigm from the domain of isomorphism algorithms. We extend the recursive procedure from the I/R paradigm by the possibility of splitting the graphs into independent parts. Then we bound the depth of the obtained branching trees, which translates into bounds on the WL dimension and thereby on the number of variables that suffice to define the graphs.
Towards a Flexible Scale-out Framework for Efficient Visual Data Query Processing
Venue: arXiv cs.DB 2024
Year: 2024

There is growing interest in visual data management systems that support queries with specialized operations ranging from resizing an image to running complex machine learning models. With a plethora of such operations, the basic need to receive query responses in minimal time takes a hit, especially when the client desires to run multiple such operations in a single query. Existing systems provide an ad-hoc approach where different solutions are clubbed together to provide an end-to-end visual data management system. Unlike such solutions, the Visual Data Management System (VDMS) natively executes queries with multiple operations, thus providing an end-to-end solution. However, a fixed subset of native operations and a synchronous threading architecture limit its generality and scalability. In this paper, we develop VDMS-Async that adds the capability to run user-defined operations with VDMS and execute operations within a query on a remote server. VDMS-Async utilizes an event-driven architecture to create an efficient pipeline for executing operations within a query. Our experiments have shown that VDMS-Async reduces the query execution time by 2-3X compared to existing state-of-the-art systems. Further, remote operations coupled with an event-driven architecture enables VDMS-Async to scale query execution time linearly with the addition of every new remote server. We demonstrate a 64X reduction in query execution time when adding 64 remote servers.
Knowledge Acquisition and Integration with Expert-in-the-loop
Venue: arXiv cs.HC 2024
Year: 2024

Constructing and serving knowledge graphs (KGs) is an iterative and human-centered process involving on-demand programming and analysis. In this paper, we present Kyurem, a programmable and interactive widget library that facilitates human-in-the-loop knowledge acquisition and integration to enable continuous curation a knowledge graph (KG). Kyurem provides a seamless environment within computational notebooks where data scientists explore a KG to identify opportunities for acquiring new knowledge and verify recommendations provided by AI agents for integrating the acquired knowledge in the KG. We refined Kyurem through participatory design and conducted case studies in a real-world setting for evaluation. The case-studies show that introduction of Kyurem within an existing HR knowledge graph construction and serving platform improved the user experience of the experts and helped eradicate inefficiencies related to knowledge acquisition and integration tasks
Ginger: An Efficient Curvature Approximation with Linear Complexity for General Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

Second-order optimization approaches like the generalized Gauss-Newton method are considered more powerful as they utilize the curvature information of the objective function with preconditioning matrices. Albeit offering tempting theoretical benefits, they are not easily applicable to modern deep learning. The major reason is due to the quadratic memory and cubic time complexity to compute the inverse of the matrix. These requirements are infeasible even with state-of-the-art hardware. In this work, we propose Ginger, an eigendecomposition for the inverse of the generalized Gauss-Newton matrix. Our method enjoys efficient linear memory and time complexity for each iteration. Instead of approximating the conditioning matrix, we directly maintain its inverse to make the approximation more accurate. We provide the convergence result of Ginger for non-convex objectives. Our experiments on different tasks with different model architectures verify the effectiveness of our method. Our code is publicly available.