id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.09870 | An LLM-Guided Tutoring System for Social Skills Training | [
"cs.LG"
] | Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication -- one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduce... | {
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2501.09876 | Geometry-Preserving Encoder/Decoder in Latent Generative Models | [
"math.NA",
"cs.LG",
"cs.NA"
] | Generative modeling aims to generate new data samples that resemble a given dataset, with diffusion models recently becoming the most popular generative model. One of the main challenges of diffusion models is solving the problem in the input space, which tends to be very high-dimensional. Recently, solving diffusion m... | {
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2501.09877 | CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic
Recognition | [
"eess.AS",
"cs.LG"
] | Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acousti... | {
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2501.09878 | ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction | [
"cs.CV",
"cs.AI"
] | We present ASTRA (A} Scene-aware TRAnsformer-based model for trajectory prediction), a light-weight pedestrian trajectory forecasting model that integrates the scene context, spatial dynamics, social inter-agent interactions and temporal progressions for precise forecasting. We utilised a U-Net-based feature extractor,... | {
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2501.09884 | Semi-Supervised Image-Based Narrative Extraction: A Case Study with
Historical Photographic Records | [
"cs.CV",
"cs.IR"
] | This paper presents a semi-supervised approach to extracting narratives from historical photographic records using an adaptation of the narrative maps algorithm. We extend the original unsupervised text-based method to work with image data, leveraging deep learning techniques for visual feature extraction and similarit... | {
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2501.09887 | FLORA: Formal Language Model Enables Robust Training-free Zero-shot
Object Referring Analysis | [
"cs.CV"
] | Object Referring Analysis (ORA), commonly known as referring expression comprehension, requires the identification and localization of specific objects in an image based on natural descriptions. Unlike generic object detection, ORA requires both accurate language understanding and precise visual localization, making it... | {
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2501.09889 | Learning port maneuvers from data for automatic guidance of Unmanned
Surface Vehicles | [
"eess.SY",
"cs.SY"
] | At shipping ports, some repetitive maneuvering tasks such as entering/leaving port, transporting goods inside it or just making surveillance activities, can be efficiently and quickly carried out by a domestic pilot according to his experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in order to ... | {
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2501.09890 | Exploring the Implementation of AI in Early Onset Interviews to Help
Mitigate Bias | [
"cs.AI"
] | This paper investigates the application of artificial intelligence (AI) in early-stage recruitment interviews in order to reduce inherent bias, specifically sentiment bias. Traditional interviewers are often subject to several biases, including interviewer bias, social desirability effects, and even confirmation bias. ... | {
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2501.09891 | Evolving Deeper LLM Thinking | [
"cs.AI"
] | We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine candidate responses. The proposed approach avoids the need to formalize the underlying inference problem whenever a solut... | {
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2501.09893 | Sparse Binary Representation Learning for Knowledge Tracing | [
"cs.LG"
] | Knowledge tracing (KT) models aim to predict students' future performance based on their historical interactions. Most existing KT models rely exclusively on human-defined knowledge concepts (KCs) associated with exercises. As a result, the effectiveness of these models is highly dependent on the quality and completene... | {
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2501.09898 | FoundationStereo: Zero-Shot Stereo Matching | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Tremendous progress has been made in deep stereo matching to excel on benchmark datasets through per-domain fine-tuning. However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching. We introduce FoundationStereo, a foundat... | {
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2501.09900 | SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft
Semi-multivariate Split Rules | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Bayesian Additive Regression Trees [BART, Chipman et al., 2010] have gained significant popularity due to their remarkable predictive performance and ability to quantify uncertainty. However, standard decision tree models rely on recursive data splits at each decision node, using deterministic decision rules based on a... | {
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2501.09905 | SLIM: Sim-to-Real Legged Instructive Manipulation via Long-Horizon
Visuomotor Learning | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | We present a low-cost legged mobile manipulation system that solves long-horizon real-world tasks, trained by reinforcement learning purely in simulation. This system is made possible by 1) a hierarchical design of a high-level policy for visual-mobile manipulation following task instructions, and a low-level quadruped... | {
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2501.09909 | Demo: Interactive Visualization of Semantic Relationships in a
Biomedical Project's Talent Knowledge Graph | [
"cs.SI"
] | We present an interactive visualization of the Cell Map for AI Talent Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately 28,000 experts and 1,000 datasets focused on the biomedical field. Our tool leverages transformer-based embeddings, WebGL visualization techniques, and generative AI, spe... | {
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2501.09913 | Towards A Litmus Test for Common Sense | [
"cs.AI"
] | This paper is the second in a planned series aimed at envisioning a path to safe and beneficial artificial intelligence. Building on the conceptual insights of "Common Sense Is All You Need," we propose a more formal litmus test for common sense, adopting an axiomatic approach that combines minimal prior knowledge (MPK... | {
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2501.09918 | GenSC-6G: A Prototype Testbed for Integrated Generative AI, Quantum, and
Semantic Communication | [
"cs.AI",
"eess.SP",
"quant-ph"
] | We introduce a prototyping testbed, GenSC-6G, developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth-generation (6G) applications. The GenSC-6G dataset is designed with noise-augmented synth... | {
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2501.09921 | TalkingEyes: Pluralistic Speech-Driven 3D Eye Gaze Animation | [
"cs.CV"
] | Although significant progress has been made in the field of speech-driven 3D facial animation recently, the speech-driven animation of an indispensable facial component, eye gaze, has been overlooked by recent research. This is primarily due to the weak correlation between speech and eye gaze, as well as the scarcity o... | {
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2501.09923 | Study on a Fast Solver for Combined Field Integral Equations of 3D
Conducting Bodies Based on Graph Neural Networks | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA"
] | In this paper, we present a graph neural networks (GNNs)-based fast solver (GraphSolver) for solving combined field integral equations (CFIEs) of 3D conducting bodies. Rao-Wilton-Glisson (RWG) basis functions are employed to discretely and accurately represent the geometry of 3D conducting bodies. A concise and informa... | {
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2501.09926 | ForestProtector: An IoT Architecture Integrating Machine Vision and Deep
Reinforcement Learning for Efficient Wildfire Monitoring | [
"cs.AI",
"cs.CV"
] | Early detection of forest fires is crucial to minimizing the environmental and socioeconomic damage they cause. Indeed, a fire's duration directly correlates with the difficulty and cost of extinguishing it. For instance, a fire burning for 1 minute might require 1 liter of water to extinguish, while a 2-minute fire co... | {
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2501.09927 | IE-Bench: Advancing the Measurement of Text-Driven Image Editing for
Human Perception Alignment | [
"cs.CV",
"cs.AI"
] | Recent advances in text-driven image editing have been significant, yet the task of accurately evaluating these edited images continues to pose a considerable challenge. Different from the assessment of text-driven image generation, text-driven image editing is characterized by simultaneously conditioning on both text ... | {
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2501.09928 | Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective
Retrieval-Augmented LLMs | [
"cs.CL",
"cs.AI"
] | Dialogue benchmarks are crucial in training and evaluating chatbots engaging in domain-specific conversations. Knowledge graphs (KGs) represent semantically rich and well-organized data spanning various domains, such as DBLP, DBpedia, and YAGO. Traditionally, dialogue benchmarks have been manually created from document... | {
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2501.09929 | Steering Large Language Models with Feature Guided Activation Additions | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Effective and reliable control over large language model (LLM) behavior is a significant challenge. While activation steering methods, which add steering vectors to a model's hidden states, are a promising approach, existing techniques often lack precision and interpretability in how they influence model outputs. We in... | {
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2501.09933 | Statistical Inference for Sequential Feature Selection after Domain
Adaptation | [
"stat.ML",
"cs.LG"
] | In high-dimensional regression, feature selection methods, such as sequential feature selection (SeqFS), are commonly used to identify relevant features. When data is limited, domain adaptation (DA) becomes crucial for transferring knowledge from a related source domain to a target domain, improving generalization perf... | {
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2501.09934 | HEART: Achieving Timely Multi-Model Training for
Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning | [
"cs.LG",
"cs.AI"
] | The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect whic... | {
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2501.09935 | Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked
Diffusion | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Diffusion model shows remarkable potential on sparse-view computed tomography (SVCT) reconstruction. However, when a network is trained on a limited sample space, its generalization capability may be constrained, which degrades performance on unfamiliar data. For image generation tasks, this can lead to issues such as ... | {
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2501.09937 | Adaptive Twisting Sliding Control for Integrated Attack UAV's Autopilot
and Guidance | [
"cs.RO"
] | This paper investigates an adaptive sliding-mode control for an integrated UAV autopilot and guidance system. First, a two-dimensional mathematical model of the system is derived by considering the incorporated lateral dynamics and relative kinematics of the UAV and its potential target of attack. Then, a sliding surfa... | {
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2501.09938 | A Multi-Scale Feature Extraction and Fusion Deep Learning Method for
Classification of Wheat Diseases | [
"cs.CV",
"cs.LG"
] | Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this... | {
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2501.09940 | Passage Segmentation of Documents for Extractive Question Answering | [
"cs.CL",
"cs.IR"
] | Retrieval-Augmented Generation (RAG) has proven effective in open-domain question answering. However, the chunking process, which is essential to this pipeline, often receives insufficient attention relative to retrieval and synthesis components. This study emphasizes the critical role of chunking in improving the perf... | {
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2501.09943 | Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech
Resources | [
"cs.CL"
] | Argentina has a large yet little-known Indigenous linguistic diversity, encompassing at least 40 different languages. The majority of these languages are at risk of disappearing, resulting in a significant loss of world heritage and cultural knowledge. Currently, unified information on speakers and computational tools ... | {
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2501.09944 | Minimum-Time Sequential Traversal by a Team of Small Unmanned Aerial
Vehicles in an Unknown Environment with Winds | [
"eess.SY",
"cs.SY"
] | We consider the problem of transporting multiple packages from an initial location to a destination location in a windy urban environment using a team of SUAVs. Each SUAV carries one package. We assume that the wind field is unknown, but wind speed can be measured by SUAVs during flight. The SUAVs fly sequentially one ... | {
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2501.09946 | Client-Centric Federated Adaptive Optimization | [
"cs.LG",
"cs.AI",
"math.OC"
] | Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. With an increasing scale of clients and models, FL encounters two key challenges, client drift due to a high degree of statistical/system heterogeneity, and lack of adaptivity. Ho... | {
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2501.09947 | Surface-SOS: Self-Supervised Object Segmentation via Neural Surface
Representation | [
"cs.CV"
] | Self-supervised Object Segmentation (SOS) aims to segment objects without any annotations. Under conditions of multi-camera inputs, the structural, textural and geometrical consistency among each view can be leveraged to achieve fine-grained object segmentation. To make better use of the above information, we propose S... | {
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2501.09948 | AI Explainability for Power Electronics: From a Lipschitz Continuity
Perspective | [
"eess.SY",
"cs.AI",
"cs.SY"
] | Lifecycle management of power converters continues to thrive with emerging artificial intelligence (AI) solutions, yet AI mathematical explainability remains unexplored in power electronics (PE) community. The lack of theoretical rigor challenges adoption in mission-critical applications. Therefore, this letter propose... | {
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2501.09949 | MultiPruner: Balanced Structure Removal in Foundation Models | [
"cs.LG",
"cs.AI"
] | Recently, state-of-the-art approaches for pruning large pre-trained models (LPMs) have demonstrated that the training-free removal of non-critical residual blocks in Transformers is viable for reducing model size, achieving results that outperform previous training-free pruning approaches. Motivated by these findings, ... | {
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2501.09950 | Sympathy over Polarization: A Computational Discourse Analysis of Social
Media Posts about the July 2024 Trump Assassination Attempt | [
"cs.SI",
"cs.CL"
] | On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to assassinate Republican Presidential Candidate Donald Trump. This attempt sparked a large-scale discussion on social media. We collected posts from X (formerly known as Twitter) one week before and after the assassination attempt and aimed to mod... | {
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2501.09954 | AIRCHITECT v2: Learning the Hardware Accelerator Design Space through
Unified Representations | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Design space exploration (DSE) plays a crucial role in enabling custom hardware architectures, particularly for emerging applications like AI, where optimized and specialized designs are essential. With the growing complexity of deep neural networks (DNNs) and the introduction of advanced foundational models (FMs), the... | {
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2501.09957 | FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation
based on Knowledge Graphs | [
"cs.CL"
] | To mitigate the hallucination and knowledge deficiency in large language models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potential by utilizing KGs as external resource to enhance LLMs reasoning. However, existing KG-RAG approaches struggle with a trade-off between fle... | {
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2501.09958 | Evaluation and Efficiency Comparison of Evolutionary Algorithms for
Service Placement Optimization in Fog Architectures | [
"cs.NE",
"cs.DC"
] | This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog app... | {
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2501.09959 | A Survey on Multi-Turn Interaction Capabilities of Large Language Models | [
"cs.CL"
] | Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and contextually relevant responses. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interactio... | {
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2501.09960 | Discrete Prior-based Temporal-coherent Content Prediction for Blind Face
Video Restoration | [
"cs.CV"
] | Blind face video restoration aims to restore high-fidelity details from videos subjected to complex and unknown degradations. This task poses a significant challenge of managing temporal heterogeneity while at the same time maintaining stable face attributes. In this paper, we introduce a Discrete Prior-based Temporal-... | {
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2501.09961 | A High-Resolution Analysis of Receiver Quantization in Communication | [
"cs.IT",
"math.IT"
] | We investigate performance limits and design of communication in the presence of uniform output quantization with moderate to high resolution. Under independent and identically distributed (i.i.d.) complex Gaussian codebook and nearest neighbor decoding rule, an achievable rate is derived in an analytical form by the g... | {
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2501.09967 | Explainable artificial intelligence (XAI): from inherent explainability
to large language models | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Artificial Intelligence (AI) has continued to achieve tremendous success in recent times. However, the decision logic of these frameworks is often not transparent, making it difficult for stakeholders to understand, interpret or explain their behavior. This limitation hinders trust in machine learning systems and cause... | {
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2501.09972 | GVMGen: A General Video-to-Music Generation Model with Hierarchical
Attentions | [
"cs.SD",
"cs.AI",
"cs.MM",
"eess.AS"
] | Composing music for video is essential yet challenging, leading to a growing interest in automating music generation for video applications. Existing approaches often struggle to achieve robust music-video correspondence and generative diversity, primarily due to inadequate feature alignment methods and insufficient da... | {
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2501.09976 | Dendritic Localized Learning: Toward Biologically Plausible Algorithm | [
"cs.NE"
] | Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due to three primary limitations: weight symmetry, reliance on global error signals, and the dual-phase nature of training, as highlighted by t... | {
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2501.09978 | GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
Editor | [
"cs.CV"
] | We introduce GaussianAvatar-Editor, an innovative framework for text-driven editing of animatable Gaussian head avatars that can be fully controlled in expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing animatable 4D Gaussian avatars presents challenges related to motion occlusion and spatial-t... | {
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2501.09980 | Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm
Hemodynamics | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Intracranial aneurysm (IA) is a common cerebrovascular disease that is usually asymptomatic but may cause severe subarachnoid hemorrhage (SAH) if ruptured. Although clinical practice is usually based on individual factors and morphological features of the aneurysm, its pathophysiology and hemodynamic mechanisms remain ... | {
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2501.09982 | RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding
Interpolation | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Text-to-video generation models have made impressive progress, but they still struggle with generating videos with complex features. This limitation often arises from the inability of the text encoder to produce accurate embeddings, which hinders the video generation model. In this work, we propose a novel approach to ... | {
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2501.09993 | Agent-as-Judge for Factual Summarization of Long Narratives | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore. However, these metrics do not adequately capture critical aspects of summarization quality, such as factual accuracy, particularly for long narratives (>100K tokens). Re... | {
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2501.09994 | Multi-Modal Attention Networks for Enhanced Segmentation and Depth
Estimation of Subsurface Defects in Pulse Thermography | [
"cs.CV",
"cs.AI",
"eess.IV"
] | AI-driven pulse thermography (PT) has become a crucial tool in non-destructive testing (NDT), enabling automatic detection of hidden anomalies in various industrial components. Current state-of-the-art techniques feed segmentation and depth estimation networks compressed PT sequences using either Principal Component An... | {
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2501.09996 | Fast energy-aware OLSR routing in VANETs by means of a parallel
evolutionary algorithm | [
"cs.NE",
"cs.AI",
"cs.NI"
] | This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search fo... | {
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2501.09997 | Attention-guided Self-reflection for Zero-shot Hallucination Detection
in Large Language Models | [
"cs.CL",
"cs.AI"
] | Hallucination has emerged as a significant barrier to the effective application of Large Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf-Reflection (AGSER) approach for zero-shot hallucination detection in LLMs. The AGSER method utilizes attention contributions to categorize the input q... | {
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2501.09999 | Deep Learning for Early Alzheimer Disease Detection with MRI Scans | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of ... | {
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2501.10007 | A swarm algorithm for collaborative traffic in vehicular networks | [
"cs.NE",
"cs.NI"
] | Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning messages with each other. These specific kinds of networks help reduce hazardous traffic situations and improve safety, which are two of the main objectives in developing Intelligent Transportation Systems (ITS). For this, the performance of VANETs s... | {
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2501.10010 | Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph
Learning | [
"cs.LG",
"cs.AI"
] | Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in edges caused by random noise, overemphasizing recent edges while neglecting earlier ones may lead to t... | {
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2501.10011 | Mitigating Hallucinations on Object Attributes using Multiview Images
and Negative Instructions | [
"cs.CV",
"cs.AI"
] | Current popular Large Vision-Language Models (LVLMs) are suffering from Hallucinations on Object Attributes (HoOA), leading to incorrect determination of fine-grained attributes in the input images. Leveraging significant advancements in 3D generation from a single image, this paper proposes a novel method to mitigate ... | {
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2501.10016 | Infrastructure Deployment in Vehicular Communication Networks Using a
Parallel Multiobjective Evolutionary Algorithm | [
"cs.NE",
"cs.NI"
] | This article describes the application of a multiobjective evolutionary algorithm for locating roadside infrastructure for vehicular communication networks over realistic urban areas. A multiobjective formulation of the problem is introduced, considering quality-of-service and cost objectives. The experimental analysis... | {
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2501.10017 | Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by
Hybrid VAE-Diffusion-Based Generative Neural Networks | [
"cs.AI",
"cs.DB"
] | Crash frequency modelling analyzes the impact of factors like traffic volume, road geometry, and environmental conditions on crash occurrences. Inaccurate predictions can distort our understanding of these factors, leading to misguided policies and wasted resources, which jeopardize traffic safety. A key challenge in c... | {
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2501.10018 | DiffuEraser: A Diffusion Model for Video Inpainting | [
"cs.CV"
] | Recent video inpainting algorithms integrate flow-based pixel propagation with transformer-based generation to leverage optical flow for restoring textures and objects using information from neighboring frames, while completing masked regions through visual Transformers. However, these approaches often encounter blurri... | {
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2501.10020 | Textoon: Generating Vivid 2D Cartoon Characters from Text Descriptions | [
"cs.CV"
] | The 2D cartoon style is a prominent art form in digital character creation, particularly popular among younger audiences. While advancements in digital human technology have spurred extensive research into photorealistic digital humans and 3D characters, interactive 2D cartoon characters have received comparatively les... | {
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2501.10021 | X-Dyna: Expressive Dynamic Human Image Animation | [
"cs.CV"
] | We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on hu... | {
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2501.10024 | Automatic Speech Recognition for Sanskrit with Transfer Learning | [
"cs.CL",
"cs.AI"
] | Sanskrit, one of humanity's most ancient languages, has a vast collection of books and manuscripts on diverse topics that have been accumulated over millennia. However, its digital content (audio and text), which is vital for the training of AI systems, is profoundly limited. Furthermore, its intricate linguistics make... | {
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2501.10030 | Informativity Conditions for Multiple Signals: Properties, Experimental
Design, and Applications | [
"eess.SY",
"cs.IT",
"cs.SY",
"math.IT"
] | Recent studies highlight the importance of persistently exciting condition in single signal sequence for model identification and data-driven control methodologies. However, maintaining prolonged excitation in control signals introduces significant challenges, as continuous excitation can reduce the lifetime of mechani... | {
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2501.10040 | LWGANet: A Lightweight Group Attention Backbone for Remote Sensing
Visual Tasks | [
"cs.CV"
] | Remote sensing (RS) visual tasks have gained significant academic and practical importance. However, they encounter numerous challenges that hinder effective feature extraction, including the detection and recognition of multiple objects exhibiting substantial variations in scale within a single image. While prior dual... | {
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2501.10041 | Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic
and Static Data with Generative Adversarial Networks | [
"cs.AI"
] | Data imbalance is a common issue in analyzing and predicting sudden traffic events. Secondary crashes constitute only a small proportion of all crashes. These secondary crashes, triggered by primary crashes, significantly exacerbate traffic congestion and increase the severity of incidents. However, the severe imbalanc... | {
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2501.10048 | Virtual Nodes Improve Long-term Traffic Prediction | [
"cs.LG",
"cs.AI"
] | Effective traffic prediction is a cornerstone of intelligent transportation systems, enabling precise forecasts of traffic flow, speed, and congestion. While traditional spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in short-term traffic forecasting, their performance in long-term predic... | {
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2501.10049 | PandaSkill - Player Performance and Skill Rating in Esports: Application
to League of Legends | [
"cs.LG"
] | To take the esports scene to the next level, we introduce PandaSkill, a framework for assessing player performance and skill rating. Traditional rating systems like Elo and TrueSkill often overlook individual contributions and face challenges in professional esports due to limited game data and fragmented competitive s... | {
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2501.10050 | Tracking student skills real-time through a continuous-variable dynamic
Bayesian network | [
"stat.ML",
"cs.LG"
] | The field of Knowledge Tracing is focused on predicting the success rate of a student for a given skill. Modern methods like Deep Knowledge Tracing provide accurate estimates given enough data, but being based on neural networks they struggle to explain how these estimates are formed. More classical methods like Dynami... | {
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2501.10053 | AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented
Generation using Tree-based Search | [
"cs.AI"
] | Leveraging the autonomous decision-making capabilities of large language models (LLMs) has demonstrated superior performance in reasoning tasks. However, despite the success of iterative or recursive retrieval-augmented generation (RAG) techniques, these methods are often constrained to a single solution space when con... | {
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2501.10054 | Accelerating Large Language Models through Partially Linear Feed-Forward
Network | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) demonstrate remarkable capabilities but face deployment challenges due to their massive parameter counts. While existing compression techniques like pruning can reduce model size, it leads to significant accuracy degradation under high compression ratios. We present a novel perspective insp... | {
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2501.10057 | MSTS: A Multimodal Safety Test Suite for Vision-Language Models | [
"cs.CL"
] | Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards, however, VLMs may give harmful advice (e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs). Despite these clear ha... | {
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2501.10062 | OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal
Finetuning | [
"cs.LG",
"cs.CL"
] | Building mixture-of-experts (MoE) architecture for Low-rank adaptation (LoRA) is emerging as a potential direction in parameter-efficient fine-tuning (PEFT) for its modular design and remarkable performance. However, simply stacking the number of experts cannot guarantee significant improvement. In this work, we first ... | {
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2501.10063 | Hybrid Parallel Collaborative Simulation Framework Integrating Device
Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment | [
"eess.SY",
"cs.SY"
] | Optimizing high-performance power electronic equipment, such as power converters, requires multiscale simulations that incorporate the physics of power semiconductor devices and the dynamics of other circuit components, especially in conducting Design of Experiments (DoEs), defining the safe operating area of devices, ... | {
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2501.10064 | One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression | [
"cs.CV",
"cs.LG"
] | Current image tokenization methods require a large number of tokens to capture the information contained within images. Although the amount of information varies across images, most image tokenizers only support fixed-length tokenization, leading to inefficiency in token allocation. In this study, we introduce One-D-Pi... | {
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2501.10066 | A Comprehensive Insights into Drones: History, Classification,
Architecture, Navigation, Applications, Challenges, and Future Trends | [
"cs.RO",
"cs.IT",
"math.IT"
] | Unmanned Aerial Vehicles (UAVs), commonly known as Drones, are one of 21st century most transformative technologies. Emerging first for military use, advancements in materials, electronics, and software have catapulted drones into multipurpose tools for a wide range of industries. In this paper, we have covered the his... | {
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2501.10067 | FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained
Descriptions and Deformable Localization | [
"cs.CV"
] | Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, such as cold start. Zero-shot and few-shot anomaly detection do not require labeled samples from the target class in advance, making them a pr... | {
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2501.10069 | A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling,
Search Algorithms, and Relevant Frameworks | [
"cs.AI"
] | LLM test-time compute (or LLM inference) via search has emerged as a promising research area with rapid developments. However, current frameworks often adopt distinct perspectives on three key aspects (task definition, LLM profiling, and search procedures), making direct comparisons challenging. Moreover, the search al... | {
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2501.10071 | CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for
Point Cloud Quality Assessment | [
"cs.CV",
"cs.MM"
] | In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA) research has achieved significant progress. However, existing methods mostly seek a direct mapping function from visual data to the Mean Opinion Score (MOS), which is contradictory to the mechanism of practical subjective evaluation. To address this... | {
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2501.10072 | Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on
Word Class Distributions | [
"cs.CL"
] | Deep learning methods have been increasingly applied to computational linguistics to uncover patterns in text data. This study investigates author-specific word class distributions using part-of-speech (POS) tagging and bigram analysis. By leveraging deep neural networks, we classify literary authors based on POS tag v... | {
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2501.10074 | SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and
Chain-of-Thought for Embodied Task Planning | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Spatial reasoning is an essential problem in embodied AI research. Efforts to enhance spatial reasoning abilities through supplementary spatial data and fine-tuning have proven limited and ineffective when addressing complex embodied tasks, largely due to their dependence on language-based outputs. While some approache... | {
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2501.10075 | Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and
MModalCC Framework | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | Remote sensing change captioning (RSICC) aims to describe changes between bitemporal images in natural language. Existing methods often fail under challenges like illumination differences, viewpoint changes, blur effects, leading to inaccuracies, especially in no-change regions. Moreover, the images acquired at differe... | {
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2501.10077 | Double descent in quantum machine learning | [
"quant-ph",
"cs.LG",
"stat.ML"
] | The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behavior has been observed in a variety of classical machine learning models, particularly modern neural ... | {
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2501.10080 | Few-shot Structure-Informed Machinery Part Segmentation with Foundation
Models and Graph Neural Networks | [
"cs.CV"
] | This paper proposes a novel approach to few-shot semantic segmentation for machinery with multiple parts that exhibit spatial and hierarchical relationships. Our method integrates the foundation models CLIPSeg and Segment Anything Model (SAM) with the interest point detector SuperPoint and a graph convolutional network... | {
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2501.10081 | Leveraging Confident Image Regions for Source-Free Domain-Adaptive
Object Detection | [
"cs.CV"
] | Source-free domain-adaptive object detection is an interesting but scarcely addressed topic. It aims at adapting a source-pretrained detector to a distinct target domain without resorting to source data during adaptation. So far, there is no data augmentation scheme tailored to source-free domain-adaptive object detect... | {
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2501.10084 | Two-level Solar Irradiance Clustering with Season Identification: A
Comparative Analysis | [
"cs.LG"
] | Solar irradiance clustering can enhance solar power capacity planning and help improve forecasting models by identifying similar irradiance patterns influenced by seasonal and weather changes. In this study, we adopt an efficient two-level clustering approach to automatically identify seasons using the clear sky irradi... | {
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2501.10088 | A recursive Bayesian neural network for constitutive modeling of sands
under monotonic loading | [
"cs.LG"
] | In geotechnical engineering, constitutive models play a crucial role in describing soil behavior under varying loading conditions. Data-driven deep learning (DL) models offer a promising alternative for developing predictive constitutive models. When prediction is the primary focus, quantifying the predictive uncertain... | {
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2501.10089 | Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural
Networks for Image Classification | [
"cs.CV"
] | This paper investigates novel classifier ensemble techniques for uncertainty calibration applied to various deep neural networks for image classification. We evaluate both accuracy and calibration metrics, focusing on Expected Calibration Error (ECE) and Maximum Calibration Error (MCE). Our work compares different meth... | {
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2501.10091 | How Do Programming Students Use Generative AI? | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Programming students have a widespread access to powerful Generative AI tools like ChatGPT. While this can help understand the learning material and assist with exercises, educators are voicing more and more concerns about an over-reliance on generated outputs and lack of critical thinking skills. It is thus important ... | {
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2501.10093 | An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the
SUPERIOT Framework | [
"cs.ET",
"cs.AR",
"cs.NI",
"cs.PF",
"cs.SY",
"eess.SY"
] | This paper presents a comprehensive analysis of the energy consumption characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node developed in the initial phase of the SUPERIOT project, focusing on key operating states, including Bluetooth Low Energy (BLE) communication, Narrow-Band Visible Light Communicat... | {
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2501.10097 | Decomposition and Quantification of SOTIF Requirements for Perception
Systems of Autonomous Vehicles | [
"eess.SY",
"cs.SY"
] | Ensuring the safety of autonomous vehicles (AVs) is paramount before they can be introduced to the market. More specifically, securing the Safety of the Intended Functionality (SOTIF) poses a notable challenge; while ISO 21448 outlines numerous activities to refine the performance of AVs, it offers minimal quantitati... | {
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2501.10098 | landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D
Images | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Anatomical landmark localization in 2D/3D images is a critical task in medical imaging. Although many general-purpose tools exist for landmark localization in classical computer vision tasks, such as pose estimation, they lack the specialized features and modularity necessary for anatomical landmark localization applic... | {
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} |
2501.10099 | Several Representations of $\alpha$-Mutual Information and
Interpretations as Privacy Leakage Measures | [
"cs.IT",
"math.IT"
] | In this paper, we present several novel representations of $\alpha$-mutual information ($\alpha$-MI) in terms of R{\' e}nyi divergence and conditional R{\' e}nyi entropy. The representations are based on the variational characterizations of $\alpha$-MI using a reverse channel. Based on these representations, we provide... | {
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} |
2501.10100 | Robotic World Model: A Neural Network Simulator for Robust Policy
Optimization in Robotics | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a d... | {
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} |
2501.10103 | Lossless data compression at pragmatic rates | [
"cs.IT",
"math.IT"
] | The problem of variable-rate lossless data compression is considered, for codes with and without prefix constraints. Sharp bounds are derived for the best achievable compression rate of memoryless sources, when the excess-rate probability is required to be exponentially small in the blocklength. Accurate nonasymptotic ... | {
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2501.10105 | Universal Actions for Enhanced Embodied Foundation Models | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticeable difficulties. Despite the availability of many crowd-sourced embodied datasets, their action spaces often exhibit significant heterogene... | {
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2501.10106 | LLM Reasoner and Automated Planner: A new NPC approach | [
"cs.AI"
] | In domains requiring intelligent agents to emulate plausible human-like behaviour, such as formative simulations, traditional techniques like behaviour trees encounter significant challenges. Large Language Models (LLMs), despite not always yielding optimal solutions, usually offer plausible and human-like responses to... | {
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} |
2501.10107 | BBPOS: BERT-based Part-of-Speech Tagging for Uzbek | [
"cs.CL",
"cs.AI"
] | This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task and introducing the first publicly available UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91% average accuracy, outp... | {
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} |
2501.10110 | DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion
Models for Advanced Visual Quality and Temporal Consistency | [
"cs.CV"
] | Diffusion models have demonstrated exceptional capabilities in image generation and restoration, yet their application to video super-resolution faces significant challenges in maintaining both high fidelity and temporal consistency. We present DiffVSR, a diffusion-based framework for real-world video super-resolution ... | {
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} |
2501.10114 | Infrastructure for AI Agents | [
"cs.AI"
] | Increasingly many AI systems can plan and execute interactions in open-ended environments, such as making phone calls or buying online goods. As developers grow the space of tasks that such AI agents can accomplish, we will need tools both to unlock their benefits and manage their risks. Current tools are largely insuf... | {
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} |
2501.10116 | GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning | [
"cs.MA"
] | In recent years, Model-based Multi-Agent Reinforcement Learning (MARL) has demonstrated significant advantages over model-free methods in terms of sample efficiency by using independent environment dynamics world models for data sample augmentation. However, without considering the limited sample size, these methods st... | {
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} |
2501.10120 | PaSa: An LLM Agent for Comprehensive Academic Paper Search | [
"cs.IR",
"cs.LG"
] | We introduce PaSa, an advanced Paper Search agent powered by large language models. PaSa can autonomously make a series of decisions, including invoking search tools, reading papers, and selecting relevant references, to ultimately obtain comprehensive and accurate results for complex scholarly queries. We optimize PaS... | {
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} |
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