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train · 541k rows

id stringlengths 9 16	title stringlengths 4 278	categories listlengths 1 13	abstract stringlengths 3 4.08k	filtered_category_membership dict
2501.02469	LoRaConnect: Unlocking HTTP Potential on LoRa Backbones for Remote Areas and Ad-Hoc Networks	[ "cs.NI", "cs.CY", "cs.SY", "eess.SY" ]	The minimal infrastructure requirements of LoRa make it suitable for deployments in remote and disaster-stricken areas. Concomitantly, the modern era is witnessing the proliferation of web applications in all aspects of human life, including IoT and other network services. Contemporary IoT and network solutions heavily...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02471	Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine	[ "cs.CL", "cs.AI" ]	Large language models (LLMs) primarily trained on English texts, often face biases and inaccuracies in Chinese contexts. Their limitations are pronounced in fields like Traditional Chinese Medicine (TCM), where cultural and clinical subtleties are vital, further hindered by a lack of domain-specific data, such as rheum...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02473	IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors	[ "astro-ph.IM", "cs.LG", "eess.IV" ]	Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we introduce Imaging for Radio Interferometry with Score-based models (IRIS). We use score-based models trained on optical images of galaxies as...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02474	Generalization-Enhanced Few-Shot Object Detection in Remote Sensing	[ "cs.CV" ]	Remote sensing object detection is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning me...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02476	Noise-Tolerant Hybrid Prototypical Learning with Noisy Web Data	[ "cs.CV", "cs.LG" ]	We focus on the challenging problem of learning an unbiased classifier from a large number of potentially relevant but noisily labeled web images given only a few clean labeled images. This problem is particularly practical because it reduces the expensive annotation costs by utilizing freely accessible web images with...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02477	A Deep Positive-Negative Prototype Approach to Integrated Prototypical Discriminative Learning	[ "cs.LG", "cs.CV" ]	This paper proposes a novel Deep Positive-Negative Prototype (DPNP) model that combines prototype-based learning (PbL) with discriminative methods to improve class compactness and separability in deep neural networks. While PbL traditionally emphasizes interpretability by classifying samples based on their similarity t...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02481	The Meta-Representation Hypothesis	[ "cs.LG", "cs.AI" ]	Humans rely on high-level understandings of things, i.e., meta-representations, to engage in abstract reasoning. In complex cognitive tasks, these meta-representations help individuals abstract general rules from experience. However, constructing such meta-representations from high-dimensional observations remains a lo...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02482	Decoding News Bias: Multi Bias Detection in News Articles	[ "cs.CL" ]	News Articles provides crucial information about various events happening in the society but they unfortunately come with different kind of biases. These biases can significantly distort public opinion and trust in the media, making it essential to develop techniques to detect and address them. Previous works have majo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02486	LLMPC: Large Language Model Predictive Control	[ "cs.AI", "cs.CL" ]	Recent advancements in prompting techniques for Large Language Models (LLMs) have improved their reasoning, planning, and action abilities. This paper examines these prompting techniques through the lens of model predictive control (MPC). We show that LLMs act as implicit planning cost function minimizers when planning...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02487	ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling	[ "cs.CV" ]	We report ACE++, an instruction-based diffusion framework that tackles various image generation and editing tasks. Inspired by the input format for the inpainting task proposed by FLUX.1-Fill-dev, we improve the Long-context Condition Unit (LCU) introduced in ACE and extend this input paradigm to any editing and genera...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02491	Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective	[ "cs.SE", "cs.AI" ]	As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like mismatched user preferences. We propose Hyper-Dimensional (HD) vector spaces to model Human-Computer Interaction, focusing on user actions, s...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02493	Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines	[ "cs.CR", "cs.LG" ]	In an era of escalating cyber threats, malware poses significant risks to individuals and organizations, potentially leading to data breaches, system failures, and substantial financial losses. This study addresses the urgent need for effective malware detection strategies by leveraging Machine Learning (ML) techniques...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02497	Test-time Computing: from System-1 Thinking to System-2 Thinking	[ "cs.AI", "cs.CL", "cs.LG" ]	The remarkable performance of the o1 model in complex reasoning demonstrates that test-time computing scaling can further unlock the model's potential, enabling powerful System-2 thinking. However, there is still a lack of comprehensive surveys for test-time computing scaling. We trace the concept of test-time computin...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02504	Watch Video, Catch Keyword: Context-aware Keyword Attention for Moment Retrieval and Highlight Detection	[ "cs.CV", "cs.AI" ]	The goal of video moment retrieval and highlight detection is to identify specific segments and highlights based on a given text query. With the rapid growth of video content and the overlap between these tasks, recent works have addressed both simultaneously. However, they still struggle to fully capture the overall v...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02505	Learning when to rank: Estimation of partial rankings from sparse, noisy comparisons	[ "physics.soc-ph", "cs.SI", "stat.ML" ]	A common task arising in various domains is that of ranking items based on the outcomes of pairwise comparisons, from ranking players and teams in sports to ranking products or brands in marketing studies and recommendation systems. Statistical inference-based methods such as the Bradley-Terry model, which extract rank...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 1, "cs.SY": 0 }
2501.02506	ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use	[ "cs.CL" ]	Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models (LLMs). However, progress has been hindered by a lack of reliable evaluation datasets. To address this, we present ToolHop, a dataset comprising 995 user queries ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02508	PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization	[ "cs.LG", "cs.AI", "cs.CV" ]	For many practical applications, a high computational cost of inference over deep network architectures might be unacceptable. A small degradation in the overall inference accuracy might be a reasonable price to pay for a significant reduction in the required computational resources. In this work, we describe a method ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02509	Facial Attractiveness Prediction in Live Streaming: A New Benchmark and Multi-modal Method	[ "cs.CV" ]	Facial attractiveness prediction (FAP) has long been an important computer vision task, which could be widely applied in live streaming for facial retouching, content recommendation, etc. However, previous FAP datasets are either small, closed-source, or lack diversity. Moreover, the corresponding FAP models exhibit li...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02511	Can Impressions of Music be Extracted from Thumbnail Images?	[ "cs.CL", "cs.CV", "cs.IR", "cs.SD", "eess.AS" ]	In recent years, there has been a notable increase in research on machine learning models for music retrieval and generation systems that are capable of taking natural language sentences as inputs. However, there is a scarcity of large-scale publicly available datasets, consisting of music data and their corresponding ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 1, "cs.SI": 0, "cs.SY": 0 }
2501.02518	CHAIR -- Classifier of Hallucination as Improver	[ "cs.CL" ]	In this work, we introduce CHAIR (Classifier of Hallucination As ImproveR), a supervised framework for detecting hallucinations by analyzing internal logits from each layer of every token. Our method extracts a compact set of features such as maximum, minimum, mean, standard deviation, and slope-from the token logits a...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02519	Layout2Scene: 3D Semantic Layout Guided Scene Generation via Geometry and Appearance Diffusion Priors	[ "cs.CV" ]	3D scene generation conditioned on text prompts has significantly progressed due to the development of 2D diffusion generation models. However, the textual description of 3D scenes is inherently inaccurate and lacks fine-grained control during training, leading to implausible scene generation. As an intuitive and feasi...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02521	Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization	[ "eess.SP", "cs.AI" ]	A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried out over rate-limited channels, necessitating data compression for reducing latency. While deep learn...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02523	Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation	[ "cs.CV", "cs.AI" ]	Facial images have extensive practical applications. Although the current large-scale text-image diffusion models exhibit strong generation capabilities, it is challenging to generate the desired facial images using only text prompt. Image prompts are a logical choice. However, current methods of this type generally fo...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02526	Unified Guidance for Geometry-Conditioned Molecular Generation	[ "q-bio.BM", "cs.LG" ]	Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain, which has experienced great attention through the success of generative models and, in particular, diffusion models. However, current molecular diffusion models are tailored towards a specific downstream task and ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02527	Vision-Driven Prompt Optimization for Large Language Models in Multimodal Generative Tasks	[ "cs.CV" ]	Vision generation remains a challenging frontier in artificial intelligence, requiring seamless integration of visual understanding and generative capabilities. In this paper, we propose a novel framework, Vision-Driven Prompt Optimization (VDPO), that leverages Large Language Models (LLMs) to dynamically generate text...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02530	UDMC: Unified Decision-Making and Control Framework for Urban Autonomous Driving with Motion Prediction of Traffic Participants	[ "cs.RO", "cs.DC", "cs.SY", "eess.SY" ]	Current autonomous driving systems often struggle to balance decision-making and motion control while ensuring safety and traffic rule compliance, especially in complex urban environments. Existing methods may fall short due to separate handling of these functionalities, leading to inefficiencies and safety compromises...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02531	Towards New Benchmark for AI Alignment & Sentiment Analysis in Socially Important Issues: A Comparative Study of Human and LLMs in the Context of AGI	[ "cs.CY", "cs.CL" ]	With the expansion of neural networks, such as large language models, humanity is exponentially heading towards superintelligence. As various AI systems are increasingly integrated into the fabric of societies-through recommending values, devising creative solutions, and making decisions-it becomes critical to assess h...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02532	Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm	[ "cs.CL", "cs.AI", "cs.CY" ]	In the era of rapid digital communication, vast amounts of textual data are generated daily, demanding efficient methods for latent content analysis to extract meaningful insights. Large Language Models (LLMs) offer potential for automating this process, yet comprehensive assessments comparing their performance to huma...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02534	Pixel-Wise Feature Selection for Perceptual Edge Detection without post-processing	[ "cs.CV" ]	Although deep convolutional neutral networks (CNNs) have significantly enhanced performance in image edge detection (ED), current models remain highly dependent on post-processing techniques such as non-maximum suppression (NMS), and often fail to deliver satisfactory perceptual results, while the performance will dete...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02535	A completely uniform transformer for parity	[ "cs.LG", "cs.AI" ]	We construct a 3-layer constant-dimension transformer, recognizing the parity language, where neither parameter matrices nor the positional encoding depend on the input length. This improves upon a construction of Chiang and Cholak who use a positional encoding, depending on the input length (but their construction has...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02536	Low RCS High-Gain Broadband Substrate Integrated Waveguide Antenna Based on Elliptical Polarization Conversion Metasurface	[ "eess.SY", "cs.SY" ]	Designed an elliptical polarization conversion metasurface (PCM) for Ka-band applications, alongside a high-gain substrate integrated waveguide (SIW) antenna. The PCM elements are integrated into the antenna design in a chessboard array configuration, with the goal of achieving effective reduction in the antenna's rada...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02539	AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition	[ "cs.CV" ]	Micro-expression recognition (MER) presents a significant challenge due to the transient and subtle nature of the motion changes involved. In recent years, deep learning methods based on attention mechanisms have made some breakthroughs in MER. However, these methods still suffer from the limitations of insufficient fe...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02546	TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain	[ "cs.CL", "cs.AI" ]	Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All-words Task) that has been adapted for use in SemEval 2010 Task 17 (All-words Word Sense Disambiguation on a Specific ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02547	Transformers Simulate MLE for Sequence Generation in Bayesian Networks	[ "stat.ML", "cs.LG" ]	Transformers have achieved significant success in various fields, notably excelling in tasks involving sequential data like natural language processing. Despite these achievements, the theoretical understanding of transformers' capabilities remains limited. In this paper, we investigate the theoretical capabilities of ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02548	AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control	[ "cs.LG", "cs.AI" ]	Traffic signal control (TSC) is an important and widely studied direction. Recently, reinforcement learning (RL) methods have been used to solve TSC problems and achieve superior performance over conventional TSC methods. However, applying RL methods to the real world is challenging due to the huge cost of experiments ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02549	From Language To Vision: A Case Study of Text Animation	[ "cs.CL" ]	Information can be expressed in multiple formats including natural language, images, and motions. Human intelligence usually faces little difficulty to convert from one format to another format, which often shows a true understanding of encoded information. Moreover, such conversions have broad application in many real...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02552	Multi-LLM Collaborative Caption Generation in Scientific Documents	[ "cs.CL", "cs.CV" ]	Scientific figure captioning is a complex task that requires generating contextually appropriate descriptions of visual content. However, existing methods often fall short by utilizing incomplete information, treating the task solely as either an image-to-text or text summarization problem. This limitation hinders the ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02556	Spatial Network Calculus: Toward Deterministic Wireless Networking	[ "cs.NI", "cs.IT", "math.IT" ]	This paper extends the classical network calculus to spatial scenarios, focusing on wireless networks with heterogeneous traffic and varying transmit power levels. Building on spatial network calculus, a prior extension of network calculus to spatial settings, we propose a generalized framework by introducing spatial r...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02558	Neural Error Covariance Estimation for Precise LiDAR Localization	[ "cs.RO", "cs.CV" ]	Autonomous vehicles have gained significant attention due to technological advancements and their potential to transform transportation. A critical challenge in this domain is precise localization, particularly in LiDAR-based map matching, which is prone to errors due to degeneracy in the data. Most sensor fusion techn...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02559	KM-UNet KAN Mamba UNet for medical image segmentation	[ "eess.IV", "cs.AI", "cs.CV" ]	Medical image segmentation is a critical task in medical imaging analysis. Traditional CNN-based methods struggle with modeling long-range dependencies, while Transformer-based models, despite their success, suffer from quadratic computational complexity. To address these limitations, we propose KM-UNet, a novel U-shap...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02564	Balanced Multi-view Clustering	[ "cs.CV", "cs.AI", "cs.LG" ]	Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-sp...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02565	Efficient Graph Condensation via Gaussian Process	[ "cs.LG" ]	Graph condensation reduces the size of large graphs while preserving performance, addressing the scalability challenges of Graph Neural Networks caused by computational inefficiencies on large datasets. Existing methods often rely on bi-level optimization, requiring extensive GNN training and limiting their scalability...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02569	A review on reinforcement learning methods for mobility on demand systems	[ "cs.MA" ]	Mobility on Demand (MoD) refers to mobility systems that operate on the basis of immediate travel demand. Typically, such a system consists of a fleet of vehicles that can be booked by customers when needed. The operation of these services consists of two main tasks: deciding how vehicles are assigned to requests (vehi...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 1, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02570	Decoding fMRI Data into Captions using Prefix Language Modeling	[ "cs.CV", "cs.AI", "cs.CL" ]	With the advancements in Large Language and Latent Diffusion models, brain decoding has achieved remarkable results in recent years. The works on the NSD dataset, with stimuli images from the COCO dataset, leverage the embeddings from the CLIP model for image reconstruction and GIT for captioning. However, the current ...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02572	Energy Optimization of Multi-task DNN Inference in MEC-assisted XR Devices: A Lyapunov-Guided Reinforcement Learning Approach	[ "cs.NI", "cs.AI", "cs.SY", "eess.SY" ]	Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this paper, we developed a distributed queue model for multi-task DNN inference,...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02573	LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations	[ "cs.LG", "cs.CL", "cs.MS" ]	The machine learning and data science community has made significant while dispersive progress in accelerating transformer-based large language models (LLMs), and one promising approach is to replace the original causal attention in a generative pre-trained transformer (GPT) with \emph{exponentially decaying causal lin...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02576	DepthMaster: Taming Diffusion Models for Monocular Depth Estimation	[ "cs.CV" ]	Monocular depth estimation within the diffusion-denoising paradigm demonstrates impressive generalization ability but suffers from low inference speed. Recent methods adopt a single-step deterministic paradigm to improve inference efficiency while maintaining comparable performance. However, they overlook the gap betwe...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02580	LP-ICP: General Localizability-Aware Point Cloud Registration for Robust Localization in Extreme Unstructured Environments	[ "cs.RO" ]	The Iterative Closest Point (ICP) algorithm is a crucial component of LiDAR-based SLAM algorithms. However, its performance can be negatively affected in unstructured environments that lack features and geometric structures, leading to low accuracy and poor robustness in localization and mapping. It is known that degen...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02583	Gaze Behavior During a Long-Term, In-Home, Social Robot Intervention for Children with ASD	[ "cs.RO", "cs.CV" ]	Atypical gaze behavior is a diagnostic hallmark of Autism Spectrum Disorder (ASD), playing a substantial role in the social and communicative challenges that individuals with ASD face. This study explores the impacts of a month-long, in-home intervention designed to promote triadic interactions between a social robot, ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02584	Efficient Architectures for High Resolution Vision-Language Models	[ "cs.CV", "cs.AI", "cs.CL", "cs.LG" ]	Vision-Language Models (VLMs) have recently experienced significant advancements. However, challenges persist in the accurate recognition of fine details within high resolution images, which limits performance in multiple tasks. This work introduces Pheye, a novel architecture that efficiently processes high-resolution...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02593	Evolving Skeletons: Motion Dynamics in Action Recognition	[ "cs.CV", "cs.AI", "cs.LG" ]	Skeleton-based action recognition has gained significant attention for its ability to efficiently represent spatiotemporal information in a lightweight format. Most existing approaches use graph-based models to process skeleton sequences, where each pose is represented as a skeletal graph structured around human physic...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02595	Rotatable Antenna Enabled Wireless Communication: Modeling and Optimization	[ "cs.IT", "eess.SP", "math.IT" ]	Fluid antenna system (FAS) and movable antenna (MA) have recently emerged as promising technologies to exploit new spatial degrees of freedom (DoFs), which have attracted growing attention in wireless communication. In this paper, we propose a new rotatable antenna (RA) model to improve the performance of wireless comm...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02598	GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation	[ "cs.CL", "cs.CV", "cs.LG" ]	Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals attest their findings, but their writing is time consuming and requires specialized clinical expertise. The automated generation of r...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02599	Empowering Bengali Education with AI: Solving Bengali Math Word Problems through Transformer Models	[ "cs.CL", "cs.AI", "cs.CY", "cs.LG" ]	Mathematical word problems (MWPs) involve the task of converting textual descriptions into mathematical equations. This poses a significant challenge in natural language processing, particularly for low-resource languages such as Bengali. This paper addresses this challenge by developing an innovative approach to solvi...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02600	TAPAS: Thermal- and Power-Aware Scheduling for LLM Inference in Cloud Platforms	[ "cs.DC", "cs.AI" ]	The rising demand for generative large language models (LLMs) poses challenges for thermal and power management in cloud datacenters. Traditional techniques often are inadequate for LLM inference due to the fine-grained, millisecond-scale execution phases, each with distinct performance, thermal, and power profiles. Ad...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02604	Collision-resistant hash-shuffles on the reals	[ "math.LO", "cs.CC", "cs.IT", "math.IT" ]	Oneway real functions are effective maps on positive-measure sets of reals that preserve randomness and have no effective probabilistic inversions. We construct a oneway real function which is collision-resistant: the probability of effectively producing distinct reals with the same image is zero, and each real has unc...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02612	Chameleon2++: An Efficient Chameleon2 Clustering with Approximate Nearest Neighbors	[ "cs.LG", "cs.DS" ]	Clustering algorithms are fundamental tools in data analysis, with hierarchical methods being particularly valuable for their flexibility. Chameleon is a widely used hierarchical clustering algorithm that excels at identifying high-quality clusters of arbitrary shapes, sizes, and densities. Chameleon2 is the most recen...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02613	LWFNet: Coherent Doppler Wind Lidar-Based Network for Wind Field Retrieval	[ "physics.ao-ph", "cs.LG" ]	Accurate detection of wind fields within the troposphere is essential for atmospheric dynamics research and plays a crucial role in extreme weather forecasting. Coherent Doppler wind lidar (CDWL) is widely regarded as the most suitable technique for high spatial and temporal resolution wind field detection. However, si...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02615	Parsings of Stationary Processes, Stopping Times and the Fundamental Pointwise Convergence Theorems of Ergodic Theory	[ "math.DS", "cs.IT", "math.CO", "math.IT", "math.PR" ]	The idea of a parsing of a stationary process according to a collection of words is introduced, and the basic framework required for the asymptotic analysis of these parsings is presented. We demonstrate how the pointwise ergodic theorem and the Shannon-McMillan-Breiman theorem can be deduced from their respective weak...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02616	Multi-layer Radial Basis Function Networks for Out-of-distribution Detection	[ "cs.LG", "cs.CV" ]	Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insight is that OOD detection can be simplified by using a neural network architecture which can effectively merge classification and OOD detecti...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02618	Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network	[ "cs.CV" ]	Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architectur...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02620	Back to Base: Towards Hands-Off Learning via Safe Resets with Reach-Avoid Safety Filters	[ "eess.SY", "cs.RO", "cs.SY" ]	Designing controllers that accomplish tasks while guaranteeing safety constraints remains a significant challenge. We often want an agent to perform well in a nominal task, such as environment exploration, while ensuring it can avoid unsafe states and return to a desired target by a specific time. In particular we are ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02621	LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment	[ "cs.NE", "cs.AI" ]	Decoding human activity from EEG signals has long been a popular research topic. While recent studies have increasingly shifted focus from single-subject to cross-subject analysis, few have explored the model's ability to perform zero-shot predictions on EEG signals from previously unseen subjects. This research aims t...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02625	HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs	[ "cs.LG" ]	Quantized training of Large Language Models (LLMs) remains an open challenge, as maintaining accuracy while performing all matrix multiplications in low precision has proven difficult. This is particularly the case when fine-tuning pre-trained models, which can have large weight and activation outlier values that make ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02626	On the Independence Assumption in Quasi-Cyclic Code-Based Cryptography	[ "cs.IT", "cs.CR", "math.IT" ]	Cryptography based on the presumed hardness of decoding codes -- i.e., code-based cryptography -- has recently seen increased interest due to its plausible security against quantum attackers. Notably, of the four proposals for the NIST post-quantum standardization process that were advanced to their fourth round for fu...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 1, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02628	Cracks in The Stack: Hidden Vulnerabilities and Licensing Risks in LLM Pre-Training Datasets	[ "cs.SE", "cs.AI" ]	A critical part of creating code suggestion systems is the pre-training of Large Language Models on vast amounts of source code and natural language text, often of questionable origin or quality. This may contribute to the presence of bugs and vulnerabilities in code generated by LLMs. While efforts to identify bugs at...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02629	Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense	[ "cs.CR", "cs.AI", "cs.CL" ]	As large language models (LLMs) are increasingly deployed in diverse applications, including chatbot assistants and code generation, aligning their behavior with safety and ethical standards has become paramount. However, jailbreak attacks, which exploit vulnerabilities to elicit unintended or harmful outputs, threaten...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 1, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02630	Soft and Compliant Contact-Rich Hair Manipulation and Care	[ "cs.RO" ]	Hair care robots can help address labor shortages in elderly care while enabling those with limited mobility to maintain their hair-related identity. We present MOE-Hair, a soft robot system that performs three hair-care tasks: head patting, finger combing, and hair grasping. The system features a tendon-driven soft ro...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02631	Prune or Retrain: Optimizing the Vocabulary of Multilingual Models for Estonian	[ "cs.CL" ]	Adapting multilingual language models to specific languages can enhance both their efficiency and performance. In this study, we explore how modifying the vocabulary of a multilingual encoder model to better suit the Estonian language affects its downstream performance on the Named Entity Recognition (NER) task. The mo...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02635	Interactive Information Need Prediction with Intent and Context	[ "cs.IR" ]	The ability to predict a user's information need would have wide-ranging implications, from saving time and effort to mitigating vocabulary gaps. We study how to interactively predict a user's information need by letting them select a pre-search context (e.g., a paragraph, sentence, or singe word) and specify an option...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02640	Multispectral Pedestrian Detection with Sparsely Annotated Label	[ "cs.CV" ]	Although existing Sparsely Annotated Object Detection (SAOD) approches have made progress in handling sparsely annotated environments in multispectral domain, where only some pedestrians are annotated, they still have the following limitations: (i) they lack considerations for improving the quality of pseudo-labels for...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02647	Trust and Dependability in Blockchain & AI Based MedIoT Applications: Research Challenges and Future Directions	[ "cs.CR", "cs.AI", "cs.CY" ]	This paper critically reviews the integration of Artificial Intelligence (AI) and blockchain technologies in the context of Medical Internet of Things (MedIoT) applications, where they collectively promise to revolutionize healthcare delivery. By examining current research, we underscore AI's potential in advancing dia...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 1, "cs.CV": 0, "cs.CY": 1, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02648	Representation Learning of Lab Values via Masked AutoEncoder	[ "cs.LG", "cs.AI" ]	Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare. Existing methods, such as variational autoencoders (VAEs) and decision tree-based approaches such as XGBoost, struggle to model the comple...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02649	Tighnari: Multi-modal Plant Species Prediction Based on Hierarchical Cross-Attention Using Graph-Based and Vision Backbone-Extracted Features	[ "cs.CV", "cs.AI" ]	Predicting plant species composition in specific spatiotemporal contexts plays an important role in biodiversity management and conservation, as well as in improving species identification tools. Our work utilizes 88,987 plant survey records conducted in specific spatiotemporal contexts across Europe. We also use the c...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02652	A New Interpretation of the Certainty-Equivalence Approach for PAC Reinforcement Learning with a Generative Model	[ "cs.LG", "stat.ML" ]	Reinforcement learning (RL) enables an agent interacting with an unknown MDP $M$ to optimise its behaviour by observing transitions sampled from $M$. A natural entity that emerges in the agent's reasoning is $\widehat{M}$, the maximum likelihood estimate of $M$ based on the observed transitions. The well-known \textit{...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02654	Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks	[ "cs.CL", "cs.AI" ]	Recent advancements in natural language processing have highlighted the vulnerability of deep learning models to adversarial attacks. While various defence mechanisms have been proposed, there is a lack of comprehensive benchmarks that evaluate these defences across diverse datasets, models, and tasks. In this work, we...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02662	Incentive-Compatible Federated Learning with Stackelberg Game Modeling	[ "cs.LG", "cs.DC" ]	Federated Learning (FL) has gained prominence as a decentralized machine learning paradigm, allowing clients to collaboratively train a global model while preserving data privacy. Despite its potential, FL faces significant challenges in heterogeneous environments, where varying client resources and capabilities can un...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02666	Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation	[ "cs.IR", "cs.AI" ]	Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the cha...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02667	Markov Decision Processes for Satellite Maneuver Planning and Collision Avoidance	[ "cs.RO", "cs.SY", "eess.SY" ]	This paper presents a decentralized, online planning approach for scalable maneuver planning for large constellations. While decentralized, rule-based strategies have facilitated efficient scaling, optimal decision-making algorithms for satellite maneuvers remain underexplored. As commercial satellite constellations gr...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 1, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02669	Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?	[ "cs.CV", "cs.CL", "cs.LG" ]	While Vision Language Models (VLMs) are impressive in tasks such as visual question answering (VQA) and image captioning, their ability to apply multi-step reasoning to images has lagged, giving rise to perceptions of modality imbalance or brittleness. Towards systematic study of such issues, we introduce a synthetic f...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02670	Neural networks meet hyperelasticity: A monotonic approach	[ "cs.CE" ]	We apply physics-augmented neural network (PANN) constitutive models to experimental uniaxial tensile data of rubber-like materials whose behavior depends on manufacturing parameters. For this, we conduct experimental investigations on a 3D printed digital material at different mix ratios and consider several datasets ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 1, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02671	Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural Networks	[ "cs.IR" ]	Current recommendation systems recommend goods by considering users' historical behaviors, social relations, ratings, and other multi-modals. Although outdated user information presents the trends of a user's interests, no recommendation system can know the users' real-time thoughts indeed. With the development of brai...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 1, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02672	Re-examining Granger Causality from Causal Bayesian Networks Perspective	[ "stat.ML", "cs.LG", "econ.EM", "stat.ME" ]	Characterizing cause-effect relationships in complex systems could be critical to understanding these systems. For many, Granger causality (GC) remains a computational tool of choice to identify causal relations in time series data. Like other causal discovery tools, GC has limitations and has been criticized as a non-...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02673	Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency	[ "cs.LG" ]	Having a sufficient quantity of quality data is a critical enabler of training effective machine learning models. Being able to effectively determine the adequacy of a dataset prior to training and evaluating a model's performance would be an essential tool for anyone engaged in experimental design or data collection. ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02675	A Novel First-Principles Model of Injection-Locked Oscillator Phase Noise	[ "eess.SY", "cs.SY" ]	The paper documents the development of a novel time-domain model of injection-locked oscillator phase-noise response. The methodology follows a first-principle approach and applies to all circuit topologies, coupling configurations, parameter dependencies etc. The corresponding numerical algorithm is readily integrated...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }
2501.02680	From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering	[ "q-bio.QM", "cs.AI", "cs.LG" ]	Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generati...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02683	From Superficial Patterns to Semantic Understanding: Fine-Tuning Language Models on Contrast Sets	[ "cs.CL", "cs.AI" ]	Large-scale pre-trained language models have demonstrated high performance on standard datasets for natural language inference (NLI) tasks. Unfortunately, these evaluations can be misleading, as although the models can perform well on in-distribution data, they perform poorly on out-of-distribution test sets, such as c...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02687	Improving Quantum Machine Learning via Heat-Bath Algorithmic Cooling	[ "quant-ph", "cs.LG" ]	This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this concept, we develop a quantum refrigerator protocol that enhances sample efficie...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02688	Decoding specialised feature neurons in LLMs with the final projection layer	[ "cs.CL" ]	Large Language Models (LLMs) typically have billions of parameters and are thus often difficult to interpret in their operation. Such black-box models can pose a significant risk to safety when trusted to make important decisions. The lack of interpretability of LLMs is more related to their sheer size, rather than the...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02690	GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking	[ "cs.CV" ]	4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsupported by existing methods. Training a video Diffusion Transformer (DiT) directly to control 4D content requires expensive multi-view videos. I...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02699	EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models	[ "cs.CV", "cs.AI" ]	Large language models and vision transformers have demonstrated impressive zero-shot capabilities, enabling significant transferability in downstream tasks. The fusion of these models has resulted in multi-modal architectures with enhanced instructional capabilities. Despite incorporating vast image and language pre-tr...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02701	Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis	[ "cs.CV" ]	Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity. In response to these challenges, we propose a novel Color Balance Prior \textbf{Guided} \textbf{Hyb}rid \textbf{Sens}e \textbf{U}nderwater \textbf{I}mage \textbf{R}estoration framework (\textbf{G...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02702	QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance	[ "cs.CL", "cs.AI", "cs.LG" ]	This work presents a novel architecture for building Retrieval-Augmented Generation (RAG) systems to improve Question Answering (QA) tasks from a target corpus. Large Language Models (LLMs) have revolutionized the analyzing and generation of human-like text. These models rely on pre-trained data and lack real-time upda...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02704	Persistence of Backdoor-based Watermarks for Neural Networks: A Comprehensive Evaluation	[ "cs.LG", "cs.MM" ]	Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated models is resource intensive, resulting in many to consider DNNs to be intellectual property (IP) to model owners. In this era of cloud comput...	{ "Other": 1, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02705	Knowledge Distillation with Adapted Weight	[ "cs.LG", "stat.AP" ]	Although large models have shown a strong capacity to solve large-scale problems in many areas including natural language and computer vision, their voluminous parameters are hard to deploy in a real-time system due to computational and energy constraints. Addressing this, knowledge distillation through Teacher-Student...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02706	Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment	[ "cs.CV" ]	The rapid development of diffusion models has greatly advanced AI-generated videos in terms of length and consistency recently, yet assessing AI-generated videos still remains challenging. Previous approaches have often focused on User-Generated Content(UGC), but few have targeted AI-Generated Video Quality Assessment ...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 1, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02709	Horizon Generalization in Reinforcement Learning	[ "cs.LG", "cs.AI" ]	We study goal-conditioned RL through the lens of generalization, but not in the traditional sense of random augmentations and domain randomization. Rather, we aim to learn goal-directed policies that generalize with respect to the horizon: after training to reach nearby goals (which are easy to learn), these policies s...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02711	KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models	[ "cs.AI", "cs.CL" ]	Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing triplets, rather than ranking-based tasks, where the model ranks candidate entities based on plausibil...	{ "Other": 0, "cs.AI": 1, "cs.CE": 0, "cs.CL": 1, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02715	Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing	[ "cs.ET", "cs.AI", "cs.LG", "cs.NE" ]	Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages ...	{ "Other": 1, "cs.AI": 1, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 1, "cs.MA": 0, "cs.NE": 1, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 0 }
2501.02718	Multi-Transmission Node DER Aggregation: Chance-Constrained Unit Commitment with Bounded Hetero-Dimensional Mixture Model for Uncertain Distribution Factors	[ "eess.SY", "cs.SY" ]	To facilitate the integration of distributed energy resources (DERs) into the wholesale market while maintaining the tractability of associated market operation tools such as unit commitment (UC), existing DER aggregation (DERA) studies usually consider that each DERA is presented on a single node of the transmission n...	{ "Other": 0, "cs.AI": 0, "cs.CE": 0, "cs.CL": 0, "cs.CR": 0, "cs.CV": 0, "cs.CY": 0, "cs.DB": 0, "cs.HC": 0, "cs.IR": 0, "cs.IT": 0, "cs.LG": 0, "cs.MA": 0, "cs.NE": 0, "cs.RO": 0, "cs.SD": 0, "cs.SI": 0, "cs.SY": 1 }

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