id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.08649 | Principles for Open Data Curation: A Case Study with the New York City
311 Service Request Data | [
"cs.DB",
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] | In the early 21st century, the open data movement began to transform societies and governments by promoting transparency, innovation, and public engagement. The City of New York (NYC) has been at the forefront of this movement since the enactment of the Open Data Law in 2012, creating the NYC Open Data portal. The port... | {
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2502.08651 | Democratizing AI Governance: Balancing Expertise and Public
Participation | [
"cs.CY",
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] | The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance. Historically, technological innovations have been governed by concentrated expertise with limited public input. However, AI's pervasive influence across domains such as... | {
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2502.08652 | LegalScore: Development of a Benchmark for Evaluating AI Models in Legal
Career Exams in Brazil | [
"cs.CY",
"cs.AI"
] | This research introduces LegalScore, a specialized index for assessing how generative artificial intelligence models perform in a selected range of career exams that require a legal background in Brazil. The index evaluates fourteen different types of artificial intelligence models' performance, from proprietary to ope... | {
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2502.08655 | Personalizing Education through an Adaptive LMS with Integrated LLMs | [
"cs.AI"
] | The widespread adoption of large language models (LLMs) marks a transformative era in technology, especially within the educational sector. This paper explores the integration of LLMs within learning management systems (LMSs) to develop an adaptive learning management system (ALMS) personalized for individual learners ... | {
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2502.08657 | Refining Positive and Toxic Samples for Dual Safety Self-Alignment of
LLMs with Minimal Human Interventions | [
"cs.CL",
"cs.AI"
] | Recent AI agents, such as ChatGPT and LLaMA, primarily rely on instruction tuning and reinforcement learning to calibrate the output of large language models (LLMs) with human intentions, ensuring the outputs are harmless and helpful. Existing methods heavily depend on the manual annotation of high-quality positive sam... | {
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2502.08658 | Analyzable Parameters Dominated Vehicle Platoon Dynamics Modeling and
Analysis: A Physics-Encoded Deep Learning Approach | [
"cs.RO",
"cs.AI"
] | Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon dynamics modeling plays a crucial role in predicting and optimizing the interactions between vehicles. Existing efforts lack the extraction and capture of vehicle behavior interaction features at the platoon scale. More importantly, maintaining hi... | {
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2502.08659 | Deployment-friendly Lane-changing Intention Prediction Powered by
Brain-inspired Spiking Neural Networks | [
"cs.RO"
] | Accurate and real-time prediction of surrounding vehicles' lane-changing intentions is a critical challenge in deploying safe and efficient autonomous driving systems in open-world scenarios. Existing high-performing methods remain hard to deploy due to their high computational cost, long training times, and excessive ... | {
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2502.08660 | Semantic Role Labeling: A Systematical Survey | [
"cs.CL"
] | Semantic role labeling (SRL) is a central natural language processing (NLP) task aiming to understand the semantic roles within texts, facilitating a wide range of downstream applications. While SRL has garnered extensive and enduring research, there is currently a lack of a comprehensive survey that thoroughly organiz... | {
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2502.08661 | Few-shot LLM Synthetic Data with Distribution Matching | [
"cs.CL",
"cs.AI"
] | As large language models (LLMs) advance, their ability to perform in-context learning and few-shot language generation has improved significantly. This has spurred using LLMs to produce high-quality synthetic data to enhance the performance of smaller models like online retrievers or weak LLMs. However, LLM-generated s... | {
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2502.08662 | RoToR: Towards More Reliable Responses for Order-Invariant Inputs | [
"cs.CL",
"cs.AI"
] | Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have been proposed to solve this issue, their success on practical listwise problems has been limited. In this work, as a first contribution, we i... | {
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2502.08663 | Hallucination Detection: A Probabilistic Framework Using Embeddings
Distance Analysis | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Hallucinations are one of the major issues affecting LLMs, hindering their wide adoption in production systems. While current research solutions for detecting hallucinations are mainly based on heuristics, in this paper we introduce a mathematically sound methodology to reason about hallucination, and leverage it to bu... | {
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2502.08664 | Motion Forecasting for Autonomous Vehicles: A Survey | [
"cs.RO",
"cs.AI"
] | In recent years, the field of autonomous driving has attracted increasingly significant public interest. Accurately forecasting the future behavior of various traffic participants is essential for the decision-making of Autonomous Vehicles (AVs). In this paper, we focus on both scenario-based and perception-based motio... | {
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2502.08666 | Hallucination, Monofacts, and Miscalibration: An Empirical Investigation | [
"cs.CL",
"cs.AI"
] | Recent theoretical work by [Kalai and Vempala 2024] proves that a particular notion of hallucination rate in LLMs must be lower bounded by the training data monofact rate (related to the classical Good-Turing missing mass estimator) minus model miscalibration. Through systematic experiments with n-gram models and in-co... | {
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2502.08667 | Unpaired Image-to-Image Translation with Content Preserving Perspective:
A Review | [
"eess.IV",
"cs.CV"
] | Image-to-image translation (I2I) transforms an image from a source domain to a target domain while preserving source content. Most computer vision applications are in the field of image-to-image translation, such as style transfer, image segmentation, and photo enhancement. The degree of preservation of the content of ... | {
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2502.08668 | Style Extraction on Text Embeddings Using VAE and Parallel Dataset | [
"cs.CL"
] | This study investigates the stylistic differences among various Bible translations using a Variational Autoencoder (VAE) model. By embedding textual data into high-dimensional vectors, the study aims to detect and analyze stylistic variations between translations, with a specific focus on distinguishing the American St... | {
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2502.08669 | Assessing the Impact of the Quality of Textual Data on Feature
Representation and Machine Learning Models | [
"cs.CL"
] | Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well established that the quality of a dataset significantly impacts the performance of machine learning models. Methods: A rudi... | {
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2502.08671 | Color Universal Design Neural Network for the Color Vision Deficiencies | [
"eess.IV",
"cs.CV"
] | Information regarding images should be visually understood by anyone, including those with color deficiency. However, such information is not recognizable if the color that seems to be distorted to the color deficiencies meets an adjacent object. The aim of this paper is to propose a color universal design network, cal... | {
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2502.08673 | High-Throughput SAT Sampling | [
"cs.AI",
"cs.LG"
] | In this work, we present a novel technique for GPU-accelerated Boolean satisfiability (SAT) sampling. Unlike conventional sampling algorithms that directly operate on conjunctive normal form (CNF), our method transforms the logical constraints of SAT problems by factoring their CNF representations into simplified multi... | {
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2502.08674 | COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by
Silhouette Masks and Fashion Styles | [
"cs.CV",
"cs.GR",
"cs.MM"
] | How to recommend outfits has gained considerable attention in both academia and industry in recent years. Many studies have been carried out regarding fashion compatibility learning, to determine whether the fashion items in an outfit are compatible or not. These methods mainly focus on evaluating the compatibility of ... | {
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2502.08676 | LIR-LIVO: A Lightweight,Robust LiDAR/Vision/Inertial Odometry with
Illumination-Resilient Deep Features | [
"cs.RO",
"cs.CV",
"cs.SY",
"eess.SP",
"eess.SY"
] | In this paper, we propose LIR-LIVO, a lightweight and robust LiDAR-inertial-visual odometry system designed for challenging illumination and degraded environments. The proposed method leverages deep learning-based illumination-resilient features and LiDAR-Inertial-Visual Odometry (LIVO). By incorporating advanced techn... | {
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2502.08678 | Multispectral Remote Sensing for Weed Detection in West Australian
Agricultural Lands | [
"cs.CV",
"eess.IV"
] | The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts. This study constructs a tailored multispectral remote sensing dataset and an end-to-end framework for weed detection to advance precision agriculture pra... | {
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2502.08679 | Deep Learning-Driven Malware Classification with API Call Sequence
Analysis and Concept Drift Handling | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Malware classification in dynamic environments presents a significant challenge due to concept drift, where the statistical properties of malware data evolve over time, complicating detection efforts. To address this issue, we propose a deep learning framework enhanced with a genetic algorithm to improve malware classi... | {
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2502.08680 | Mathematical Reasoning in Large Language Models: Assessing Logical and
Arithmetic Errors across Wide Numerical Ranges | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Mathematical reasoning in Large Language Models (LLMs) is often evaluated using benchmarks with limited numerical ranges, failing to reflect real-world problem-solving across diverse scales. Furthermore, most existing evaluation methods only compare model outputs to ground-truth answers, obscuring insights into reasoni... | {
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2502.08681 | Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid
Topology Control | [
"cs.MA",
"cs.AI",
"cs.LG"
] | Power grid operation is becoming more complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. However, the combinatorial nature of the actio... | {
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2502.08682 | On the Role of Pre-trained Embeddings in Binary Code Analysis | [
"cs.LG",
"cs.AI"
] | Deep learning has enabled remarkable progress in binary code analysis. In particular, pre-trained embeddings of assembly code have become a gold standard for solving analysis tasks, such as measuring code similarity or recognizing functions. These embeddings are capable of learning a vector representation from unlabele... | {
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2502.08683 | A Deep Learning approach for parametrized and time dependent Partial
Differential Equations using Dimensionality Reduction and Neural ODEs | [
"cs.LG"
] | Partial Differential Equations (PDEs) are central to science and engineering. Since solving them is computationally expensive, a lot of effort has been put into approximating their solution operator via both traditional and recently increasingly Deep Learning (DL) techniques. A conclusive methodology capable of account... | {
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2502.08684 | Self-Evaluation for Job-Shop Scheduling | [
"cs.LG",
"cs.AI"
] | Combinatorial optimization problems, such as scheduling and route planning, are crucial in various industries but are computationally intractable due to their NP-hard nature. Neural Combinatorial Optimization methods leverage machine learning to address these challenges but often depend on sequential decision-making, w... | {
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2502.08685 | Beyond Models! Explainable Data Valuation and Metric Adaption for
Recommendation | [
"cs.LG",
"cs.AI"
] | User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-quality data from low-quality data. However, they tend to employ black-box design, lacking transparenc... | {
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2502.08686 | EEG Artifact Detection and Correction with Deep Autoencoders | [
"cs.LG",
"cs.AI"
] | EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert interventi... | {
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2502.08687 | Data Augmentation to Improve Large Language Models in Food Hazard and
Product Detection | [
"cs.CL"
] | The primary objective of this study is to demonstrate the impact of data augmentation using ChatGPT-4o-mini on food hazard and product analysis. The augmented data is generated using ChatGPT-4o-mini and subsequently used to train two large language models: RoBERTa-base and Flan-T5-base. The models are evaluated on test... | {
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2502.08688 | FAST: A Future Aircraft Sizing Tool for Advanced Aircraft and Propulsion
System Design | [
"cs.CE",
"physics.comp-ph"
] | Without radical technological advancements, the global aviation industry will continue to be a major carbon emitter. To reduce aviation's carbon emissions, innovative aircraft technology, including electrified aircraft propulsion, is under development. However, current aircraft sizing tools require detailed design info... | {
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2502.08689 | Advancing machine fault diagnosis: A detailed examination of
convolutional neural networks | [
"cs.LG",
"cs.AI"
] | The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capab... | {
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2502.08690 | Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient
Text-to-Image Generation | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Large-scale text encoders in text-to-image (T2I) diffusion models have demonstrated exceptional performance in generating high-quality images from textual prompts. Unlike denoising modules that rely on multiple iterative steps, text encoders require only a single forward pass to produce text embeddings. However, despit... | {
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2502.08691 | AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents
Advances Understanding of Human Behaviors and Society | [
"cs.SI",
"cs.AI"
] | Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic comp... | {
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2502.08692 | Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices | [
"cs.LG",
"cs.DC"
] | Split Learning (SL) recently emerged as an efficient paradigm for distributed Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems. However, deploying SL on resource-constrained edge IoT platforms poses a significant challenge in terms of balancing the model performance against the processing, ... | {
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2502.08695 | A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of
Distribution Detection | [
"stat.ML",
"cs.LG"
] | Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian non... | {
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2502.08696 | Scalable Discrete Diffusion Samplers: Combinatorial Optimization and
Statistical Physics | [
"cs.LG",
"cond-mat.stat-mech",
"cs.AI",
"physics.comp-ph",
"stat.ML"
] | Learning to sample from complex unnormalized distributions over discrete domains emerged as a promising research direction with applications in statistical physics, variational inference, and combinatorial optimization. Recent work has demonstrated the potential of diffusion models in this domain. However, existing met... | {
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2502.08697 | Bilevel Learning for Bilevel Planning | [
"cs.RO"
] | A robot that learns from demonstrations should not just imitate what it sees -- it should understand the high-level concepts that are being demonstrated and generalize them to new tasks. Bilevel planning is a hierarchical model-based approach where predicates (relational state abstractions) can be leveraged to achieve ... | {
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2502.08728 | A Comparative Study of Machine Learning Algorithms for Stock Price
Prediction Using Insider Trading Data | [
"cs.LG"
] | The research paper empirically investigates several machine learning algorithms to forecast stock prices depending on insider trading information. Insider trading offers special insights into market sentiment, pointing to upcoming changes in stock prices. This study examines the effectiveness of algorithms like decisio... | {
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2502.08729 | Policy Selection and Schedules for Exclusive Bus Lane and High Occupancy
Vehicle Lane in a Bi-modal Transportation Corridor | [
"math.OC",
"cs.SY",
"eess.SY"
] | Efficient management of transportation corridors is critical for sustaining urban mobility, directly influencing transportation efficiency. Two prominent strategies for enhancing public transit services and alleviating congestion, Exclusive Bus Lane (EBL) and High Occupancy Vehicle Lane (HOVL), are gaining increasing a... | {
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2502.08730 | New Bounds for Sparse Variational Gaussian Processes | [
"cs.LG",
"stat.ME",
"stat.ML"
] | Sparse variational Gaussian processes (GPs) construct tractable posterior approximations to GP models. At the core of these methods is the assumption that the true posterior distribution over training function values ${\bf f}$ and inducing variables ${\bf u}$ is approximated by a variational distribution that incorpora... | {
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2502.08731 | Equity-aware Design and Timing of Fare-free Transit Zoning under Demand
Uncertainty | [
"math.OC",
"cs.SY",
"eess.SY"
] | We propose the first analytical stochastic model for optimizing the configuration and implementation policies of fare-free transit. The model focuses on a transportation corridor with two transportation modes: automobiles and buses. The corridor is divided into two sections, an inner one with fare-free transit service ... | {
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2502.08736 | Recurrent Memory for Online Interdomain Gaussian Processes | [
"cs.LG",
"stat.ML"
] | We propose a novel online Gaussian process (GP) model that is capable of capturing long-term memory in sequential data in an online regression setting. Our model, Online HiPPO Sparse Variational Gaussian Process Regression (OHSGPR), leverages the HiPPO (High-order Polynomial Projection Operators) framework, which is po... | {
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2502.08742 | "Active Neighbour": A Novel Monitoring Model for Cyber-Physical Systems | [
"eess.SY",
"cs.SY"
] | Over the past decade, advancements in technology have enabled Cyber-Physical Systems (CPS) to monitor sensor networks through various methodologies. However, these developments have concurrently introduced significant security challenges, necessitating robust protective measures. As a result, securing CPS has become a ... | {
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2502.08744 | Are Expressions for Music Emotions the Same Across Cultures? | [
"cs.CL",
"cs.HC",
"cs.SD",
"eess.AS"
] | Music evokes profound emotions, yet the universality of emotional descriptors across languages remains debated. A key challenge in cross-cultural research on music emotion is biased stimulus selection and manual curation of taxonomies, predominantly relying on Western music and languages. To address this, we propose a ... | {
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2502.08745 | IHEval: Evaluating Language Models on Following the Instruction
Hierarchy | [
"cs.CL"
] | The instruction hierarchy, which establishes a priority order from system messages to user messages, conversation history, and tool outputs, is essential for ensuring consistent and safe behavior in language models (LMs). Despite its importance, this topic receives limited attention, and there is a lack of comprehensiv... | {
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2502.08754 | HistoSmith: Single-Stage Histology Image-Label Generation via
Conditional Latent Diffusion for Enhanced Cell Segmentation and
Classification | [
"cs.CV",
"cs.AI"
] | Precise segmentation and classification of cell instances are vital for analyzing the tissue microenvironment in histology images, supporting medical diagnosis, prognosis, treatment planning, and studies of brain cytoarchitecture. However, the creation of high-quality annotated datasets for training remains a major cha... | {
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2502.08756 | From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven
Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs,
Context-Aware Visual Prompting, and the React Framework | [
"cs.AI",
"cs.SE"
] | Developing web-based GIS applications, commonly known as CyberGIS dashboards, for querying and visualizing GIS data in environmental research often demands repetitive and resource-intensive efforts. While Generative AI offers automation potential for code generation, it struggles with complex scientific applications du... | {
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2502.08757 | A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO
Precoding Across Sites | [
"eess.SP",
"cs.LG"
] | Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches, such as weighted minimum mean square error (W... | {
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2502.08758 | Compression of Site-Specific Deep Neural Networks for Massive MIMO
Precoding | [
"eess.SP",
"cs.LG"
] | The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy consumption. In this paper, we investigate the compute energy efficiency of mMIMO precoders using DL-based approaches, comparing them to conve... | {
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2502.08759 | Contextual bandits with entropy-based human feedback | [
"cs.AI"
] | In recent years, preference-based human feedback mechanisms have become essential for enhancing model performance across diverse applications, including conversational AI systems such as ChatGPT. However, existing approaches often neglect critical aspects, such as model uncertainty and the variability in feedback quali... | {
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2502.08764 | Demand Response Optimization MILP Framework for Microgrids with DERs | [
"eess.SY",
"cs.LG",
"cs.SY"
] | The integration of renewable energy sources in microgrids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response (DR) strategies are crucial for maintaining system stability and economic efficiency, particularly i... | {
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2502.08766 | Unlocking Mental Health: Exploring College Students' Well-being through
Smartphone Behaviors | [
"cs.CY",
"cs.HC",
"cs.LG",
"cs.SE"
] | The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been linked to negative outcomes such as addiction and regret, significantly impacting ... | {
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2502.08767 | SelfElicit: Your Language Model Secretly Knows Where is the Relevant
Evidence | [
"cs.CL",
"cs.AI"
] | Providing Language Models (LMs) with relevant evidence in the context (either via retrieval or user-provided) can significantly improve their ability to provide factually correct grounded responses. However, recent studies have found that LMs often struggle to fully comprehend and utilize key evidence from the context,... | {
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2502.08769 | Cluster and Predict Latent Patches for Improved Masked Image Modeling | [
"cs.CV",
"cs.AI"
] | Masked Image Modeling (MIM) offers a promising approach to self-supervised representation learning, however existing MIM models still lag behind the state-of-the-art. In this paper, we systematically analyze target representations, loss functions, and architectures, to introduce CAPI - a novel pure-MIM framework that r... | {
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2502.08773 | Universal Model Routing for Efficient LLM Inference | [
"cs.CL",
"cs.LG"
] | Large language models' significant advances in capabilities are accompanied by significant increases in inference costs. Model routing is a simple technique for reducing inference cost, wherein one maintains a pool of candidate LLMs, and learns to route each prompt to the smallest feasible LLM. Existing works focus on ... | {
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2502.08774 | Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal
Brain in 3D Ultrasound | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions is a challenging task, but recent work has shown that it can be automated using deep learning. However, applying pretrained models to unseen... | {
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2502.08776 | Treatment response as a latent variable | [
"stat.ME",
"cs.LG",
"stat.ML"
] | Scientists often need to analyze the samples in a study that responded to treatment in order to refine their hypotheses and find potential causal drivers of response. Natural variation in outcomes makes teasing apart responders from non-responders a statistical inference problem. To handle latent responses, we introduc... | {
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2502.08777 | Zero-Shot Belief: A Hard Problem for LLMs | [
"cs.CL"
] | We present two LLM-based approaches to zero-shot source-and-target belief prediction on FactBank: a unified system that identifies events, sources, and belief labels in a single pass, and a hybrid approach that uses a fine-tuned DeBERTa tagger for event detection. We show that multiple open-sourced, closed-source, and ... | {
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2502.08779 | SB-Bench: Stereotype Bias Benchmark for Large Multimodal Models | [
"cs.CV"
] | Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. As LMMs grow increasingly influential, addressing and mitigating inherent biases related to stereotypes, harmful generations, and ambiguous assumptions in real-world scenar... | {
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2502.08782 | A comparative study of different TSO-DSO coordination in the reserve
market | [
"eess.SY",
"cs.SY"
] | The increasing penetration of Distributed Energy Resources (DERs) in the distribution system has led to the emergence of a new market actor - the aggregator. The aggregator serves as a facilitator, enabling flexibility asset owners to get access to different markets. In which, EVs aggregators are gaining more attention... | {
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2502.08783 | Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small
Linear Convolutional Neural Networks | [
"cs.LG",
"cs.NA",
"math.NA"
] | In recent years, there has been an increasing interest in using deep learning and neural networks to tackle scientific problems, particularly in solving partial differential equations (PDEs). However, many neural network-based methods, such as physics-informed neural networks, depend on automatic differentiation and th... | {
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2502.08784 | Acoustic Wave Manipulation Through Sparse Robotic Actuation | [
"cs.RO",
"cs.AI"
] | Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, the use of deep neural networks to represent dynamics that are partially observed by robot sensors, as well as effective control using sparse... | {
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2502.08785 | Decision Tree Based Wrappers for Hearing Loss | [
"cs.LG",
"cs.SD"
] | Audiology entities are using Machine Learning (ML) models to guide their screening towards people at risk. Feature Engineering (FE) focuses on optimizing data for ML models, with evolutionary methods being effective in feature selection and construction tasks. This work aims to benchmark an evolutionary FE wrapper, usi... | {
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2502.08786 | MRUCT: Mixed Reality Assistance for Acupuncture Guided by Ultrasonic
Computed Tomography | [
"cs.HC",
"cs.CV",
"cs.GR"
] | Chinese acupuncture practitioners primarily depend on muscle memory and tactile feedback to insert needles and accurately target acupuncture points, as the current workflow lacks imaging modalities and visual aids. Consequently, new practitioners often learn through trial and error, requiring years of experience to bec... | {
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2502.08788 | If Multi-Agent Debate is the Answer, What is the Question? | [
"cs.CL",
"cs.LG"
] | Multi-agent debate (MAD) has emerged as a promising approach to enhance the factual accuracy and reasoning quality of large language models (LLMs) by engaging multiple agents in iterative discussions during inference. Despite its potential, we argue that current MAD research suffers from critical shortcomings in evalua... | {
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2502.08789 | Delay Analysis of 5G HARQ in the Presence of Decoding and Feedback
Latencies | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | The growing demand for stringent quality of service (QoS) guarantees in 5G networks requires accurate characterisation of delay performance, often measured using Delay Violation Probability (DVP) for a given target delay. Widely used retransmission schemes like Automatic Repeat reQuest (ARQ) and Hybrid ARQ (HARQ) impro... | {
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2502.08791 | ClipRover: Zero-shot Vision-Language Exploration and Target Discovery by
Mobile Robots | [
"cs.RO"
] | Vision-language navigation (VLN) has emerged as a promising paradigm, enabling mobile robots to perform zero-shot inference and execute tasks without specific pre-programming. However, current systems often separate map exploration and path planning, with exploration relying on inefficient algorithms due to limited (pa... | {
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2502.08792 | Auction Design using Value Prediction with Hallucinations | [
"cs.GT",
"cs.AI"
] | We investigate a Bayesian mechanism design problem where a seller seeks to maximize revenue by selling an indivisible good to one of n buyers, incorporating potentially unreliable predictions (signals) of buyers' private values derived from a machine learning model. We propose a framework where these signals are someti... | {
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2502.08794 | Spectral Journey: How Transformers Predict the Shortest Path | [
"cs.LG"
] | Decoder-only transformers lead to a step-change in capability of large language models. However, opinions are mixed as to whether they are really planning or reasoning. A path to making progress in this direction is to study the model's behavior in a setting with carefully controlled data. Then interpret the learned re... | {
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2502.08795 | Low-Resolution Neural Networks | [
"cs.LG"
] | The expanding scale of large neural network models introduces significant challenges, driving efforts to reduce memory usage and enhance computational efficiency. Such measures are crucial to ensure the practical implementation and effective application of these sophisticated models across a wide array of use cases. Th... | {
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2502.08796 | A Systematic Review on the Evaluation of Large Language Models in Theory
of Mind Tasks | [
"cs.CL",
"cs.CY",
"cs.HC"
] | In recent years, evaluating the Theory of Mind (ToM) capabilities of large language models (LLMs) has received significant attention within the research community. As the field rapidly evolves, navigating the diverse approaches and methodologies has become increasingly complex. This systematic review synthesizes curren... | {
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2502.08803 | Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with
Generative Adversarial Networks | [
"cs.LG"
] | Electroencephalography (EEG) activity contains a wealth of information about what is happening within the human brain. Recording more of this data has the potential to unlock endless future applications. However, the cost of EEG hardware is increasingly expensive based upon the number of EEG channels being recorded sim... | {
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2502.08806 | CLOVER: A Test Case Generation Benchmark with Coverage, Long-Context,
and Verification | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Software testing is a critical aspect of software development, yet generating test cases remains a routine task for engineers. This paper presents a benchmark, CLOVER, to evaluate models' capabilities in generating and completing test cases under specific conditions. Spanning from simple assertion completions to writin... | {
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2502.08807 | InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data
Volume Variation in DNNs | [
"cs.AR",
"cs.LG"
] | The rise of deep neural networks (DNNs) has driven a boom in AI services, which results in an increased demand for computing power and memory. In modern DNNs, the data sizes produced and consumed are highly varied across operations (high data volume variation, HDV). Because existing design paradigms use fixed execution... | {
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2502.08808 | A First-order Generative Bilevel Optimization Framework for Diffusion
Models | [
"cs.LG",
"math.OC",
"stat.ML"
] | Diffusion models, which iteratively denoise data samples to synthesize high-quality outputs, have achieved empirical success across domains. However, optimizing these models for downstream tasks often involves nested bilevel structures, such as tuning hyperparameters for fine-tuning tasks or noise schedules in training... | {
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2502.08813 | Measuring Anxiety Levels with Head Motion Patterns in Severe Depression
Population | [
"cs.CV"
] | Depression and anxiety are prevalent mental health disorders that frequently cooccur, with anxiety significantly influencing both the manifestation and treatment of depression. An accurate assessment of anxiety levels in individuals with depression is crucial to develop effective and personalized treatment plans. This ... | {
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2502.08818 | Lexical Manifold Reconfiguration in Large Language Models: A Novel
Architectural Approach for Contextual Modulation | [
"cs.CL"
] | Contextual adaptation in token embeddings plays a central role in determining how well language models maintain coherence and retain semantic relationships over extended text sequences. Static embeddings often impose constraints on lexical flexibility, leading to suboptimal performance when faced with complex sentence ... | {
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2502.08820 | Can a Single Model Master Both Multi-turn Conversations and Tool Use?
CoALM: A Unified Conversational Agentic Language Model | [
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) with API-calling capabilities enabled building effective Language Agents (LA), while also revolutionizing the conventional task-oriented dialogue (TOD) paradigm. However, current approaches face a critical dilemma: TOD systems are often trained on a limited set of target APIs, requiring new... | {
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2502.08821 | DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with
Saliency Maps | [
"cs.CV",
"cs.AI",
"cs.LG"
] | The recent surge in advanced generative models, such as diffusion models and generative adversarial networks (GANs), has led to an alarming rise in AI-generated images across various domains on the web. While such technologies offer benefits such as democratizing artistic creation, they also pose challenges in misinfor... | {
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2502.08822 | $\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based
Pre-training | [
"cs.CV"
] | Automated analysis of surgical videos is crucial for improving surgical training, workflow optimization, and postoperative assessment. We introduce a CSMAE, Masked Autoencoder (MAE)-based pretraining approach, specifically developed for Cataract Surgery video analysis, where instead of randomly selecting tokens for mas... | {
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2502.08825 | Examining and Adapting Time for Multilingual Classification via Mixture
of Temporal Experts | [
"cs.CL"
] | Time is implicitly embedded in classification process: classifiers are usually built on existing data while to be applied on future data whose distributions (e.g., label and token) may change. However, existing state-of-the-art classification models merely consider the temporal variations and primarily focus on English... | {
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2502.08826 | Ask in Any Modality: A Comprehensive Survey on Multimodal
Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Large Language Models (LLMs) struggle with hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating external dynamic information enhancing factual and updated grounding. Recent advances in multimodal learning have led... | {
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2502.08828 | A Survey on Data-Centric AI: Tabular Learning from Reinforcement
Learning and Generative AI Perspective | [
"cs.LG",
"cs.AI"
] | Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey foc... | {
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2502.08829 | PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo
Federated Learning | [
"cs.LG"
] | Non-identically distributed data is a major challenge in Federated Learning (FL). Personalized FL tackles this by balancing local model adaptation with global model consistency. One variant, partial FL, leverages the observation that early layers learn more transferable features by federating only early layers. However... | {
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2502.08832 | LSM Trees in Adversarial Environments | [
"cs.DB",
"cs.CR"
] | The Log Structured Merge (LSM) Tree is a popular choice for key-value stores that focus on optimized write throughput while maintaining performant, production-ready read latencies. To optimize read performance, LSM stores rely on a probabilistic data structure called the Bloom Filter (BF). In this paper, we focus on ad... | {
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} |
2502.08834 | A Reversible Solver for Diffusion SDEs | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Diffusion models have quickly become the state-of-the-art for generation tasks across many different data modalities. An important ability of diffusion models is the ability to encode samples from the data distribution back into the sampling prior distribution. This is useful for performing alterations to real data sam... | {
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2502.08835 | A Bundle-based Augmented Lagrangian Framework: Algorithm, Convergence,
and Primal-dual Principles | [
"math.OC",
"cs.SY",
"eess.SY"
] | We propose a new bundle-based augmented Lagrangian framework for solving constrained convex problems. Unlike the classical (inexact) augmented Lagrangian method (ALM) that has a nested double-loop structure, our framework features a $\textit{single-loop}$ process. Motivated by the proximal bundle method (PBM), we use a... | {
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2502.08836 | Survey on Single-Image Reflection Removal using Deep Learning Techniques | [
"cs.CV"
] | The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing. Traditional methods for reflection removal often struggle to achieve clean results while maintaining high fidelity and robustness, particular... | {
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2502.08840 | Thresholds for Reconstruction of Random Hypergraphs From Graph
Projections | [
"math.ST",
"cs.IT",
"math.IT",
"math.PR",
"stat.TH"
] | The graph projection of a hypergraph is a simple graph with the same vertex set and with an edge between each pair of vertices that appear in a hyperedge. We consider the problem of reconstructing a random $d$-uniform hypergraph from its projection. Feasibility of this task depends on $d$ and the density of hyperedges ... | {
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} |
2502.08841 | Delayed takedown of illegal content on social media makes moderation
ineffective | [
"cs.SI",
"cs.CY"
] | Social media platforms face legal and regulatory demands to swiftly remove illegal content, sometimes under strict takedown deadlines. However, the effects of moderation speed and the impact of takedown deadlines remain underexplored. This study models the relationship between the timeliness of illegal content removal ... | {
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} |
2502.08844 | MuJoCo Playground | [
"cs.RO"
] | We introduce MuJoCo Playground, a fully open-source framework for robot learning built with MJX, with the express goal of streamlining simulation, training, and sim-to-real transfer onto robots. With a simple "pip install playground", researchers can train policies in minutes on a single GPU. Playground supports divers... | {
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} |
2502.08845 | Optimal Dataset Size for Recommender Systems: Evaluating Algorithms'
Performance via Downsampling | [
"cs.IR"
] | This thesis investigates dataset downsampling as a strategy to optimize energy efficiency in recommender systems while maintaining competitive performance. With increasing dataset sizes posing computational and environmental challenges, this study explores the trade-offs between energy efficiency and recommendation qua... | {
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} |
2502.08856 | A Systematic Evaluation of Generative Models on Tabular Transportation
Data | [
"cs.LG"
] | The sharing of large-scale transportation data is beneficial for transportation planning and policymaking. However, it also raises significant security and privacy concerns, as the data may include identifiable personal information, such as individuals' home locations. To address these concerns, synthetic data generati... | {
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} |
2502.08858 | Estimating Probabilities of Causation with Machine Learning Models | [
"cs.AI"
] | Probabilities of causation play a crucial role in modern decision-making. This paper addresses the challenge of predicting probabilities of causation for subpopulations with insufficient data using machine learning models. Tian and Pearl first defined and derived tight bounds for three fundamental probabilities of caus... | {
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} |
2502.08859 | EnigmaEval: A Benchmark of Long Multimodal Reasoning Challenges | [
"cs.AI",
"cs.CL"
] | As language models master existing reasoning benchmarks, we need new challenges to evaluate their cognitive frontiers. Puzzle-solving events are rich repositories of challenging multimodal problems that test a wide range of advanced reasoning and knowledge capabilities, making them a unique testbed for evaluating front... | {
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} |
2502.08860 | Brain in the Dark: Design Principles for Neuromimetic Inference under
the Free Energy Principle | [
"cs.NE"
] | Deep learning has revolutionised artificial intelligence (AI) by enabling automatic feature extraction and function approximation from raw data. However, it faces challenges such as a lack of out-of-distribution generalisation, catastrophic forgetting and poor interpretability. In contrast, biological neural networks, ... | {
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} |
2502.08864 | Off-Switching Not Guaranteed | [
"cs.AI"
] | Hadfield-Menell et al. (2017) propose the Off-Switch Game, a model of Human-AI cooperation in which AI agents always defer to humans because they are uncertain about our preferences. I explain two reasons why AI agents might not defer. First, AI agents might not value learning. Second, even if AI agents value learning,... | {
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} |
2502.08866 | BrainWavLM: Fine-tuning Speech Representations with Brain Responses to
Language | [
"cs.CL"
] | Speech encoding models use auditory representations to predict how the human brain responds to spoken language stimuli. Most performant encoding models linearly map the hidden states of artificial neural networks to brain data, but this linear restriction may limit their effectiveness. In this work, we use low-rank ada... | {
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} |
2502.08869 | Harnessing Vision Models for Time Series Analysis: A Survey | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs). Efforts in leveraging vision models for time series analysis have also been made along the way but are less visible to the community due to ... | {
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} |
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