id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.04675 | Enhancing Financial VQA in Vision Language Models using Intermediate
Structured Representations | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Chart interpretation is crucial for visual data analysis, but accurately extracting information from charts poses significant challenges for automated models. This study investigates the fine-tuning of DEPLOT, a modality conversion module that translates the image of a plot or chart to a linearized table, on a custom d... | {
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2501.04678 | RadGPT: Constructing 3D Image-Text Tumor Datasets | [
"eess.IV",
"cs.CV"
] | With over 85 million CT scans performed annually in the United States, creating tumor-related reports is a challenging and time-consuming task for radiologists. To address this need, we present RadGPT, an Anatomy-Aware Vision-Language AI Agent for generating detailed reports from CT scans. RadGPT first segments tumors,... | {
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2501.04682 | Towards System 2 Reasoning in LLMs: Learning How to Think With Meta
Chain-of-Thought | [
"cs.AI",
"cs.CL"
] | We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive at a particular CoT. We present empirical evidence from state-of-the-art models exhibiting behaviors consistent with in-context search, and ... | {
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2501.04683 | Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A
Test for ABROCA | [
"stat.ML",
"cs.LG"
] | Algorithmic bias is a pressing concern in educational data mining (EDM), as it risks amplifying inequities in learning outcomes. The Area Between ROC Curves (ABROCA) metric is frequently used to measure discrepancies in model performance across demographic groups to quantify overall model fairness. However, its skewed ... | {
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2501.04686 | URSA: Understanding and Verifying Chain-of-thought Reasoning in
Multimodal Mathematics | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Chain-of-Thought (CoT) reasoning is widely used to enhance the mathematical reasoning capabilities of large language models (LLMs). The introduction of process supervision for CoT trajectories has sparked discussions on improving test-time scaling, thereby unlocking the System 2-style thinking capabilities of these mod... | {
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2501.04689 | SPAR3D: Stable Point-Aware Reconstruction of 3D Objects from Single
Images | [
"cs.CV",
"cs.GR"
] | We study the problem of single-image 3D object reconstruction. Recent works have diverged into two directions: regression-based modeling and generative modeling. Regression methods efficiently infer visible surfaces, but struggle with occluded regions. Generative methods handle uncertain regions better by modeling dist... | {
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2501.04690 | Comparative Analysis of Quantum and Classical Support Vector Classifiers
for Software Bug Prediction: An Exploratory Study | [
"cs.SE",
"cs.LG"
] | Purpose: Quantum computing promises to transform problem-solving across various domains with rapid and practical solutions. Within Software Evolution and Maintenance, Quantum Machine Learning (QML) remains mostly an underexplored domain, particularly in addressing challenges such as detecting buggy software commits fro... | {
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2501.04693 | Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous
Sensors via Language Grounding | [
"cs.RO",
"cs.AI"
] | Interacting with the world is a multi-sensory experience: achieving effective general-purpose interaction requires making use of all available modalities -- including vision, touch, and audio -- to fill in gaps from partial observation. For example, when vision is occluded reaching into a bag, a robot should rely on it... | {
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2501.04694 | EpiCoder: Encompassing Diversity and Complexity in Code Generation | [
"cs.CL",
"cs.AI"
] | Effective instruction tuning is indispensable for optimizing code LLMs, aligning model behavior with user expectations and enhancing model performance in real-world applications. However, most existing methods focus on code snippets, which are limited to specific functionalities and rigid structures, restricting the co... | {
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2501.04695 | Re-ranking the Context for Multimodal Retrieval Augmented Generation | [
"cs.LG",
"cs.CV",
"cs.IR",
"cs.IT",
"math.IT"
] | Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-modal RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to us... | {
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2501.04696 | Test-Time Optimization for Domain Adaptive Open Vocabulary Segmentation | [
"cs.CV"
] | We present Seg-TTO, a novel framework for zero-shot, open-vocabulary semantic segmentation (OVSS), designed to excel in specialized domain tasks. While current open vocabulary approaches show impressive performance on standard segmentation benchmarks under zero-shot settings, they fall short of supervised counterparts ... | {
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2501.04697 | Grokking at the Edge of Numerical Stability | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Grokking, the sudden generalization that occurs after prolonged overfitting, is a surprising phenomenon challenging our understanding of deep learning. Although significant progress has been made in understanding grokking, the reasons behind the delayed generalization and its dependence on regularization remain unclear... | {
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2501.04698 | ConceptMaster: Multi-Concept Video Customization on Diffusion
Transformer Models Without Test-Time Tuning | [
"cs.CV"
] | Text-to-video generation has made remarkable advancements through diffusion models. However, Multi-Concept Video Customization (MCVC) remains a significant challenge. We identify two key challenges in this task: 1) the identity decoupling problem, where directly adopting existing customization methods inevitably mix at... | {
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2501.04699 | EditAR: Unified Conditional Generation with Autoregressive Models | [
"cs.CV"
] | Recent progress in controllable image generation and editing is largely driven by diffusion-based methods. Although diffusion models perform exceptionally well in specific tasks with tailored designs, establishing a unified model is still challenging. In contrast, autoregressive models inherently feature a unified toke... | {
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2501.04700 | Planarian Neural Networks: Evolutionary Patterns from Basic Bilateria
Shaping Modern Artificial Neural Network Architectures | [
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] | This study examined the viability of enhancing the prediction accuracy of artificial neural networks (ANNs) in image classification tasks by developing ANNs with evolution patterns similar to those of biological neural networks. ResNet is a widely used family of neural networks with both deep and wide variants; therefo... | {
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2501.04712 | Pressing Intensity: An Intuitive Measure for Pressing in Soccer | [
"stat.AP",
"cs.LG"
] | Pressing is a fundamental defensive strategy in football, characterized by applying pressure on the ball owning team to regain possession. Despite its significance, existing metrics for measuring pressing often lack precision or comprehensive consideration of positional data, player movement and speed. This research in... | {
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2501.04718 | Knowledge-Guided Biomarker Identification for Label-Free Single-Cell
RNA-Seq Data: A Reinforcement Learning Perspective | [
"q-bio.GN",
"cs.AI"
] | Gene panel selection aims to identify the most informative genomic biomarkers in label-free genomic datasets. Traditional approaches, which rely on domain expertise, embedded machine learning models, or heuristic-based iterative optimization, often introduce biases and inefficiencies, potentially obscuring critical bio... | {
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2501.04719 | Calculating Customer Lifetime Value and Churn using Beta Geometric
Negative Binomial and Gamma-Gamma Distribution in a NFT based setting | [
"stat.AP",
"cs.AI"
] | Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and ... | {
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2501.04721 | A Shape-Based Functional Index for Objective Assessment of Pediatric
Motor Function | [
"stat.AP",
"cs.LG",
"physics.med-ph"
] | Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during dai... | {
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2501.04724 | Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy
and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast
Cancer Patients | [
"stat.AP",
"cs.LG"
] | This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships between demographic factors, treatments, conditions, and outcomes from observational patient data, enabling insights beyond correlation. Unlike traditional randomized controlled... | {
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2501.04727 | A New Underdetermined Framework for Sparse Estimation of Fault Location
for Transmission Lines Using Limited Current Measurements | [
"eess.SY",
"cs.SY"
] | This letter proposes an alternative underdetermined framework for fault location that utilizes current measurements along with the branch-bus matrix, providing another option besides the traditional voltage-based methods. To enhance fault location accuracy in the presence of multiple outliers, the robust YALL1 algorith... | {
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2501.04729 | Stability Exchange near Folds: Analysis of an end-loaded Elastica with a
Lever Arm | [
"math.OC",
"cond-mat.soft",
"cs.RO"
] | Numerous problems in physical sciences can be expressed as parameter-dependent variational problems. The associated family of equilibria may or may not exist realistically and can be determined after examining its stability. Hence, it is crucial to determine the stability and track its transitions. Generally, the stabi... | {
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2501.04730 | Relative Phase Equivariant Deep Neural Systems for Physical Layer
Communications | [
"cs.IT",
"cs.NI",
"math.IT"
] | In the era of telecommunications, the increasing demand for complex and specialized communication systems has led to a focus on improving physical layer communications. Artificial intelligence (AI) has emerged as a promising solution avenue for doing so. Deep neural receivers have already shown significant promise in i... | {
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2501.04732 | SNR-EQ-JSCC: Joint Source-Channel Coding with SNR-Based Embedding and
Query | [
"cs.IT",
"cs.AI",
"math.IT"
] | Coping with the impact of dynamic channels is a critical issue in joint source-channel coding (JSCC)-based semantic communication systems. In this paper, we propose a lightweight channel-adaptive semantic coding architecture called SNR-EQ-JSCC. It is built upon the generic Transformer model and achieves channel adaptat... | {
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2501.04733 | AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions
and Interpretation to Transform Earth System Modeling | [
"cs.AI",
"cs.ET",
"cs.LG",
"physics.ao-ph"
] | Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven... | {
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2501.04734 | Generative Style Transfer for MRI Image Segmentation: A Case of Glioma
Segmentation in Sub-Saharan Africa | [
"eess.IV",
"cs.AI",
"cs.LG",
"physics.med-ph"
] | In Sub-Saharan Africa (SSA), the utilization of lower-quality Magnetic Resonance Imaging (MRI) technology raises questions about the applicability of machine learning methods for clinical tasks. This study aims to provide a robust deep learning-based brain tumor segmentation (BraTS) method tailored for the SSA populati... | {
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2501.04735 | Topology-based deep-learning segmentation method for deep anterior
lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data | [
"eess.IV",
"cs.CV"
] | Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial step in this procedure is the precise separation of the deep stroma from Descemet's membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and... | {
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2501.04746 | Towards resilient cities: A hybrid simulation framework for risk
mitigation through data driven decision making | [
"cs.MA",
"cs.SY",
"eess.SY"
] | Providing a comprehensive view of the city operation and offering useful metrics for decision making is a well known challenge for urban risk analysis systems. Existing systems are, in many cases, generalizations of previous domain specific tools and or methodologies that may not cover all urban interdependencies and m... | {
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2501.04747 | Discovering new robust local search algorithms with neuro-evolution | [
"cs.NE",
"cs.AI"
] | This paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. Our aim is to improve the decision process that takes place within a local search algorithm so as to make the best possible transitions in the neighborhood at each iteration. To improve this process, we... | {
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2501.04750 | Efficient License Plate Recognition in Videos Using Visual Rhythm and
Accumulative Line Analysis | [
"cs.CV",
"cs.LG"
] | Video-based Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate text information from video captures. Traditional systems typically rely heavily on high-end computing resources and utilize multiple frames to recognize license plates, leading to increased computational overhead. In this ... | {
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2501.04752 | A mathematical model for the bullying dynamics in schools | [
"physics.soc-ph",
"cs.SI"
] | We analyze a mathematical model to understand the dynamics of bullying in schools. The model considers a population divided into four groups: susceptible individuals, bullies, individuals exposed to bullying, and violent individuals. Transitions between these states occur at rates designed to capture the complex intera... | {
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2501.04754 | Development of an Adaptive Sliding Mode Controller using Neural Networks
for Trajectory Tracking of a Cylindrical Manipulator | [
"eess.SY",
"cs.RO",
"cs.SY",
"physics.app-ph"
] | Cylindrical manipulators are extensively used in industrial automation, especially in emerging technologies like 3D printing, which represents a significant future trend. However, controlling the trajectory of nonlinear models with system uncertainties remains a critical challenge, often leading to reduced accuracy and... | {
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2501.04755 | Improving Human-Robot Teaching by Quantifying and Reducing Mental Model
Mismatch | [
"cs.RO",
"cs.HC"
] | The rapid development of artificial intelligence and robotics has had a significant impact on our lives, with intelligent systems increasingly performing tasks traditionally performed by humans. Efficient knowledge transfer requires matching the mental model of the human teacher with the capabilities of the robot learn... | {
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2501.04757 | DAREK -- Distance Aware Error for Kolmogorov Networks | [
"eess.SP",
"cs.LG"
] | In this paper, we provide distance-aware error bounds for Kolmogorov Arnold Networks (KANs). We call our new error bounds estimator DAREK -- Distance Aware Error for Kolmogorov networks. Z. Liu et al. provide error bounds, which may be loose, lack distance-awareness, and are defined only up to an unknown constant of pr... | {
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2501.04759 | Optimize the parameters of the PID Controller using Genetic Algorithm
for Robot Manipulators | [
"eess.SY",
"cs.RO",
"cs.SY",
"math.OC"
] | This paper presents the design a Proportional-Integral-Derivative (PID) controller with optimized parameters for a two-degree-of-freedom robotic arm. A genetic algorithm (GA) is proposed to optimize the controller parameters, addressing the challenges in determining PID controller parameters for highly nonlinear system... | {
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2501.04761 | Evolution of Spots and Stripes in Cellular Automata | [
"nlin.CG",
"cs.NE"
] | Cellular automata are computers, similar to Turing machines. The main difference is that Turing machines use a one-dimensional tape, whereas cellular automata use a two-dimensional grid. The best-known cellular automaton is the Game of Life, which is a universal computer. It belongs to a family of cellular automata wit... | {
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2501.04762 | Efficient and Responsible Adaptation of Large Language Models for Robust
and Equitable Top-k Recommendations | [
"cs.IR",
"cs.LG"
] | Conventional recommendation systems (RSs) are typically optimized to enhance performance metrics uniformly across all training samples, inadvertently overlooking the needs of diverse user populations. The performance disparity among various populations can harm the model's robustness to sub-populations due to the varyi... | {
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2501.04763 | Search engines in polarized media environment: Auditing political
information curation on Google and Bing prior to 2024 US elections | [
"cs.CY",
"cs.IR",
"cs.SI"
] | Search engines play an important role in the context of modern elections. By curating information in response to user queries, search engines influence how individuals are informed about election-related developments and perceive the media environment in which elections take place. It has particular implications for (p... | {
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2501.04764 | Video Summarisation with Incident and Context Information using
Generative AI | [
"cs.CV",
"cs.MM"
] | The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Int... | {
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2501.04765 | TREAD: Token Routing for Efficient Architecture-agnostic Diffusion
Training | [
"cs.CV",
"cs.AI"
] | Diffusion models have emerged as the mainstream approach for visual generation. However, these models usually suffer from sample inefficiency and high training costs. This issue is particularly pronounced in the standard diffusion transformer architecture due to its quadratic complexity relative to input length. Recent... | {
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2501.04766 | Decoding rank metric Reed-Muller codes | [
"cs.IT",
"math.CO",
"math.IT"
] | In this article, we investigate the decoding of the rank metric Reed--Muller codes introduced by Augot, Couvreur, Lavauzelle and Neri in 2021. We propose a polynomial time algorithm that rests on the structure of Dickson matrices, works on any such code and corrects up to half the minimum distance. | {
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2501.04782 | GaussianVideo: Efficient Video Representation via Hierarchical Gaussian
Splatting | [
"cs.CV"
] | Efficient neural representations for dynamic video scenes are critical for applications ranging from video compression to interactive simulations. Yet, existing methods often face challenges related to high memory usage, lengthy training times, and temporal consistency. To address these issues, we introduce a novel neu... | {
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2501.04783 | Traffic Simulations: Multi-City Calibration of Metropolitan Highway
Networks | [
"cs.ET",
"cs.SY",
"eess.SY"
] | This paper proposes an approach to perform travel demand calibration for high-resolution stochastic traffic simulators. It employs abundant travel times at the path-level, departing from the standard practice of resorting to scarce segment-level sensor counts. The proposed approach is shown to tackle high-dimensional i... | {
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2501.04784 | Leveraging Registers in Vision Transformers for Robust Adaptation | [
"cs.CV",
"cs.LG"
] | Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of "registers" which are additi... | {
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2501.04793 | A Novel Observer Design for LuGre Friction Estimation and Control | [
"eess.SY",
"cs.SY"
] | Dynamic components of the friction may directly impact the stability and performance of the motion control systems. The LuGre model is a prevalent friction model utilized to express this dynamic behavior. Since the LuGre model is very comprehensive, friction compensation based on it might be challenging. Inspired by th... | {
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2501.04794 | A Steerable Deep Network for Model-Free Diffusion MRI Registration | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Nonrigid registration is vital to medical image analysis but remains challenging for diffusion MRI (dMRI) due to its high-dimensional, orientation-dependent nature. While classical methods are accurate, they are computationally demanding, and deep neural networks, though efficient, have been underexplored for nonrigid ... | {
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2501.04796 | Democratic Resilience and Sociotechnical Shocks | [
"cs.SI",
"cs.SY",
"eess.SY",
"stat.AP"
] | We focus on the potential fragility of democratic elections given modern information-communication technologies (ICT) in the Web 2.0 era. Our work provides an explanation for the cascading attrition of public officials recently in the United States and offers potential policy interventions from a dynamic system's persp... | {
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2501.04799 | Cued Speech Generation Leveraging a Pre-trained Audiovisual
Text-to-Speech Model | [
"cs.CL"
] | This paper presents a novel approach for the automatic generation of Cued Speech (ACSG), a visual communication system used by people with hearing impairment to better elicit the spoken language. We explore transfer learning strategies by leveraging a pre-trained audiovisual autoregressive text-to-speech model (AVTacot... | {
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2501.04802 | Reproducing HotFlip for Corpus Poisoning Attacks in Dense Retrieval | [
"cs.IR",
"cs.CL"
] | HotFlip is a topical gradient-based word substitution method for attacking language models. Recently, this method has been further applied to attack retrieval systems by generating malicious passages that are injected into a corpus, i.e., corpus poisoning. However, HotFlip is known to be computationally inefficient, wi... | {
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2501.04811 | Fast, Fine-Grained Equivalence Checking for Neural Decompilers | [
"cs.LG",
"cs.CR",
"cs.SE"
] | Neural decompilers are machine learning models that reconstruct the source code from an executable program. Critical to the lifecycle of any machine learning model is an evaluation of its effectiveness. However, existing techniques for evaluating neural decompilation models have substantial weaknesses, especially when ... | {
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2501.04815 | Towards Generalizable Trajectory Prediction Using Dual-Level
Representation Learning And Adaptive Prompting | [
"cs.CV"
] | Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimodal handling. We propose Perceiver with Register queries (PerReg+)... | {
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2501.04816 | Probabilistic Skip Connections for Deterministic Uncertainty
Quantification in Deep Neural Networks | [
"cs.LG",
"stat.ML"
] | Deterministic uncertainty quantification (UQ) in deep learning aims to estimate uncertainty with a single pass through a network by leveraging outputs from the network's feature extractor. Existing methods require that the feature extractor be both sensitive and smooth, ensuring meaningful input changes produce meaning... | {
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2501.04817 | Decentralised Resource Sharing in TinyML: Wireless Bilayer Gossip
Parallel SGD for Collaborative Learning | [
"cs.LG",
"cs.AI"
] | With the growing computational capabilities of microcontroller units (MCUs), edge devices can now support machine learning models. However, deploying decentralised federated learning (DFL) on such devices presents key challenges, including intermittent connectivity, limited communication range, and dynamic network topo... | {
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2501.04819 | Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly
Detection in Industrial Wood Planers | [
"cs.SD",
"cs.AI",
"eess.AS"
] | In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experi... | {
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2501.04820 | Unifying the Extremes: Developing a Unified Model for Detecting and
Predicting Extremist Traits and Radicalization | [
"cs.SI",
"cs.CL",
"cs.CY"
] | The proliferation of ideological movements into extremist factions via social media has become a global concern. While radicalization has been studied extensively within the context of specific ideologies, our ability to accurately characterize extremism in more generalizable terms remains underdeveloped. In this paper... | {
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2501.04823 | Learning Robot Safety from Sparse Human Feedback using Conformal
Prediction | [
"cs.RO",
"math.OC",
"stat.AP"
] | Ensuring robot safety can be challenging; user-defined constraints can miss edge cases, policies can become unsafe even when trained from safe data, and safety can be subjective. Thus, we learn about robot safety by showing policy trajectories to a human who flags unsafe behavior. From this binary feedback, we use the ... | {
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2501.04826 | Intelligent Gradient Boosting Algorithms for Estimating Strength of
Modified Subgrade Soil | [
"cs.LG",
"cs.AI",
"cs.CE"
] | The performance of pavement under loading depends on the strength of the subgrade. However, experimental estimation of properties of pavement strengths such as California bearing ratio (CBR), unconfined compressive strength (UCS) and resistance value (R) are often tedious, time-consuming and costly, thereby inspiring a... | {
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2501.04828 | Building Foundations for Natural Language Processing of Historical
Turkish: Resources and Models | [
"cs.CL"
] | This paper introduces foundational resources and models for natural language processing (NLP) of historical Turkish, a domain that has remained underexplored in computational linguistics. We present the first named entity recognition (NER) dataset, HisTR and the first Universal Dependencies treebank, OTA-BOUN for a his... | {
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2501.04830 | A Deep Learning-Based Method for Power System Resilience Evaluation | [
"eess.SY",
"cs.SY"
] | Power systems are critical infrastructure in modern society, and power outages can cause significant disruptions to communities and individuals' daily lives. The resilience of a power system measures its ability to maintain power supply during highly disruptive events such as hurricanes, earthquakes, and thunderstorms.... | {
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2501.04831 | Quantum Hybrid Support Vector Machines for Stress Detection in Older
Adults | [
"quant-ph",
"cs.LG"
] | Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work introduces a unique technique to address stress detection as an anomaly detection problem that uses qu... | {
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2501.04832 | ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via
Accelerating Active Predictive Coding with Information Geometry & Diverse
Cognitive Mechanisms | [
"cs.AI",
"cs.LG",
"cs.NE"
] | This paper introduces ActPC-Geom, an approach to accelerate Active Predictive Coding (ActPC) in neural networks by integrating information geometry, specifically using Wasserstein-metric-based methods for measure-dependent gradient flows. We propose replacing KL-divergence in ActPC's predictive error assessment with th... | {
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2501.04835 | Do Code LLMs Understand Design Patterns? | [
"cs.SE",
"cs.AI"
] | Code Large Language Models (LLMs) demonstrate great versatility in adapting to various downstream tasks, including code generation and completion, as well as bug detection and fixing. However, Code LLMs often fail to capture existing coding standards, leading to the generation of code that conflicts with the required d... | {
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2501.04839 | DRL-Based Medium-Term Planning of Renewable-Integrated Self-Scheduling
Cascaded Hydropower to Guide Wholesale Market Participation | [
"eess.SY",
"cs.SY"
] | For self-scheduling cascaded hydropower (S-CHP) facilities, medium-term planning is a critical step that coordinates water availability over the medium-term horizon, providing water usage guidance for their short-term operations in wholesale market participation. Typically, medium-term planning strategies (e.g., reserv... | {
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2501.04844 | Enhancing Listened Speech Decoding from EEG via Parallel Phoneme
Sequence Prediction | [
"eess.AS",
"cs.AI",
"cs.CL",
"eess.SP"
] | Brain-computer interfaces (BCI) offer numerous human-centered application possibilities, particularly affecting people with neurological disorders. Text or speech decoding from brain activities is a relevant domain that could augment the quality of life for people with impaired speech perception. We propose a novel app... | {
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2501.04845 | Intelligent experiments through real-time AI: Fast Data Processing and
Autonomous Detector Control for sPHENIX and future EIC detectors | [
"physics.ins-det",
"cs.LG",
"hep-ex",
"nucl-ex"
] | This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX exp... | {
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2501.04846 | EDMB: Edge Detector with Mamba | [
"cs.CV"
] | Transformer-based models have made significant progress in edge detection, but their high computational cost is prohibitive. Recently, vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this, we propose a novel edge detector with Mamba, termed EDMB, to e... | {
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2501.04848 | Exploring Large Language Models for Semantic Analysis and Categorization
of Android Malware | [
"cs.CR",
"cs.AI"
] | Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering... | {
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2501.04852 | Classification of Self-Dual Constacyclic Codes of Prime Power Length
$p^s$ Over $\frac{\mathbb{F}_{p^m}[u]}{\left\langle u^3\right\rangle} $ | [
"cs.IT",
"math.IT",
"math.RA"
] | Let $\mathbb{F}_{p^m}$ be a finite field of cardinality $p^m$, where $p$ is a prime number and $m$ is a positive integer. Self-dual constacyclic codes of length \( p^s \) over \( \frac{\mathbb{F}_{p^m}[u]}{\langle u^3 \rangle} \) exist only when \( p = 2 \). In this work, we classify and enumerate all self-dual cyclic ... | {
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2501.04854 | Higher-order Delsarte Dual LPs: Lifting, Constructions and Completeness | [
"cs.IT",
"cs.DM",
"math.CO",
"math.IT"
] | A central and longstanding open problem in coding theory is the rate-versus-distance trade-off for binary error-correcting codes. In a seminal work, Delsarte introduced a family of linear programs establishing relaxations on the size of optimum codes. To date, the state-of-the-art upper bounds for binary codes come fro... | {
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2501.04855 | A new rotation-free isogeometric thin shell formulation and a
corresponding continuity constraint for patch boundaries | [
"cs.CE"
] | This paper presents a general non-linear computational formulation for rotation-free thin shells based on isogeometric finite elements. It is a displacement-based formulation that admits general material models. The formulation allows for a wide range of constitutive laws, including both shell models that are extracted... | {
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2501.04858 | Advancing Retrieval-Augmented Generation for Persian: Development of
Language Models, Comprehensive Benchmarks, and Best Practices for
Optimization | [
"cs.CL"
] | This paper examines the specific obstacles of constructing Retrieval-Augmented Generation(RAG) systems in low-resource languages, with a focus on Persian's complicated morphology and versatile syntax. The research aims to improve retrieval and generation accuracy by introducing Persian-specific models, namely MatinaRob... | {
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2501.04860 | Exploring the Use of Robots for Diary Studies | [
"cs.RO",
"cs.HC"
] | As interest in studying in-the-wild human-robot interaction grows, there is a need for methods to collect data over time and in naturalistic or potentially private environments. HRI researchers have increasingly used the diary method for these studies, asking study participants to self-administer a structured data coll... | {
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2501.04861 | LayerMix: Enhanced Data Augmentation through Fractal Integration for
Robust Deep Learning | [
"cs.CV"
] | Deep learning models have demonstrated remarkable performance across various computer vision tasks, yet their vulnerability to distribution shifts remains a critical challenge. Despite sophisticated neural network architectures, existing models often struggle to maintain consistent performance when confronted with Out-... | {
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2501.04864 | A hybrid pressure formulation of the face-centred finite volume method
for viscous laminar incompressible flows | [
"math.NA",
"cs.CE",
"cs.NA",
"physics.flu-dyn"
] | This work presents a hybrid pressure face-centred finite volume (FCFV) solver to simulate steady-state incompressible Navier-Stokes flows. The method leverages the robustness, in the incompressible limit, of the hybridisable discontinuous Galerkin paradigm for compressible and weakly compressible flows to derive the fo... | {
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2501.04870 | Deep Transfer $Q$-Learning for Offline Non-Stationary Reinforcement
Learning | [
"stat.ML",
"cs.LG"
] | In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations, especially when sample sizes are limited. While existing transfer learning methods primarily foc... | {
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2501.04871 | RieszBoost: Gradient Boosting for Riesz Regression | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified treatment policy. By the Riesz representation theorem, these functionals can be expressed as the expected product of the conditional expectat... | {
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2501.04873 | Back Home: A Machine Learning Approach to Seashell Classification and
Ecosystem Restoration | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In Costa Rica, an average of 5 tons of seashells are extracted from ecosystems annually. Confiscated seashells, cannot be returned to their ecosystems due to the lack of origin recognition. To address this issue, we developed a convolutional neural network (CNN) specifically for seashell identification. We built a data... | {
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2501.04877 | Real-Time Textless Dialogue Generation | [
"cs.CL",
"cs.AI",
"cs.SD",
"eess.AS"
] | Recent advancements in large language models (LLMs) have led to significant progress in text-based dialogue systems. These systems can now generate high-quality responses that are accurate and coherent across a wide range of topics and tasks. However, spoken dialogue systems still lag behind in terms of naturalness. Th... | {
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2501.04878 | Topological Classification of points in $Z^2$ by using Topological
Numbers for $2$D discrete binary images | [
"cs.CV",
"cs.CG"
] | In this paper, we propose a topological classification of points for 2D discrete binary images. This classification is based on the values of the calculus of topological numbers. Six classes of points are proposed: isolated point, interior point, simple point, curve point, point of intersection of 3 curves, point of in... | {
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2501.04879 | Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in
Reinforcement Learning | [
"cs.LG"
] | Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state, with the goal of maximizing a cumulative reward function. Predominantly, there are two families of algorithms to solve RL problems: value-based and policy-based methods, with the latter designed to learn a probabilistic parame... | {
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2501.04880 | Leveraging Log Probabilities in Language Models to Forecast Future
Events | [
"cs.CL",
"cs.LG"
] | In the constantly changing field of data-driven decision making, accurately predicting future events is crucial for strategic planning in various sectors. The emergence of Large Language Models (LLMs) marks a significant advancement in this area, offering advanced tools that utilise extensive text data for prediction. ... | {
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2501.04881 | Geophysical inverse problems with measurement-guided diffusion models | [
"physics.geo-ph",
"cs.LG"
] | Solving inverse problems with the reverse process of a diffusion model represents an appealing avenue to produce highly realistic, yet diverse solutions from incomplete and possibly noisy measurements, ultimately enabling uncertainty quantification at scale. However, because of the intractable nature of the score funct... | {
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2501.04882 | Reach Measurement, Optimization and Frequency Capping In Targeted Online
Advertising Under k-Anonymity | [
"cs.GT",
"cs.AI",
"cs.LG",
"stat.AP",
"stat.ML"
] | The growth in the use of online advertising to foster brand awareness over recent years is largely attributable to the ubiquity of social media. One pivotal technology contributing to the success of online brand advertising is frequency capping, a mechanism that enables marketers to control the number of times an ad is... | {
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2501.04894 | A Look into How Machine Learning is Reshaping Engineering Models: the
Rise of Analysis Paralysis, Optimal yet Infeasible Solutions, and the
Inevitable Rashomon Paradox | [
"cs.LG",
"stat.ME"
] | The widespread acceptance of empirically derived codal provisions and equations in civil engineering stands in stark contrast to the skepticism facing machine learning (ML) models, despite their shared statistical foundations. This paper examines this philosophical tension through the lens of structural engineering and... | {
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2501.04896 | Quantifying Itch and its Impact on Sleep Using Machine Learning and
Radio Signals | [
"cs.LG",
"cs.AI",
"cs.CY"
] | Chronic itch affects 13% of the US population, is highly debilitating, and underlies many medical conditions. A major challenge in clinical care and new therapeutics development is the lack of an objective measure for quantifying itch, leading to reliance on subjective measures like patients' self-assessment of itch se... | {
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2501.04897 | Online Continual Learning: A Systematic Literature Review of Approaches,
Challenges, and Benchmarks | [
"cs.LG"
] | Online Continual Learning (OCL) is a critical area in machine learning, focusing on enabling models to adapt to evolving data streams in real-time while addressing challenges such as catastrophic forgetting and the stability-plasticity trade-off. This study conducts the first comprehensive Systematic Literature Review ... | {
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2501.04898 | Optimality and Adaptivity of Deep Neural Features for Instrumental
Variable Regression | [
"stat.ML",
"cs.LG"
] | We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features learned by deep neural networks in two stages. We prove that the DFIV algorithm achieves the minimax optimal learning rate when the target s... | {
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2501.04899 | SUGAR: Leveraging Contextual Confidence for Smarter Retrieval | [
"cs.CL",
"cs.AI"
] | Bearing in mind the limited parametric knowledge of Large Language Models (LLMs), retrieval-augmented generation (RAG) which supplies them with the relevant external knowledge has served as an approach to mitigate the issue of hallucinations to a certain extent. However, uniformly retrieving supporting context makes re... | {
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2501.04901 | ThriftLLM: On Cost-Effective Selection of Large Language Models for
Classification Queries | [
"cs.DB"
] | In recent years, large language models (LLMs) have demonstrated remarkable capabilities in comprehending and generating natural language content. An increasing number of services offer LLMs for various tasks via APIs. Different LLMs demonstrate expertise in different domains of queries (e.g., text classification querie... | {
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2501.04903 | Towards understanding the bias in decision trees | [
"stat.ML",
"cs.LG"
] | There is a widespread and longstanding belief that machine learning models are biased towards the majority (or negative) class when learning from imbalanced data, leading them to neglect or ignore the minority (or positive) class. In this study, we show that this belief is not necessarily correct for decision trees, an... | {
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} |
2501.04904 | JELLY: Joint Emotion Recognition and Context Reasoning with LLMs for
Conversational Speech Synthesis | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Recently, there has been a growing demand for conversational speech synthesis (CSS) that generates more natural speech by considering the conversational context. To address this, we introduce JELLY, a novel CSS framework that integrates emotion recognition and context reasoning for generating appropriate speech in conv... | {
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} |
2501.04911 | A Machine Learning Model for Crowd Density Classification in Hajj Video
Frames | [
"cs.CV",
"cs.CY"
] | Managing the massive annual gatherings of Hajj and Umrah presents significant challenges, particularly as the Saudi government aims to increase the number of pilgrims. Currently, around two million pilgrims attend Hajj and 26 million attend Umrah making crowd control especially in critical areas like the Grand Mosque d... | {
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} |
2501.04914 | From Mesh Completion to AI Designed Crown | [
"cs.CV",
"cs.LG"
] | Designing a dental crown is a time-consuming and labor intensive process. Our goal is to simplify crown design and minimize the tediousness of making manual adjustments while still ensuring the highest level of accuracy and consistency. To this end, we present a new end- to-end deep learning approach, coined Dental Mes... | {
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} |
2501.04916 | SpecTf: Transformers Enable Data-Driven Imaging Spectroscopy Cloud
Detection | [
"cs.LG"
] | Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth System processes across the globe. However, reliable cloud screening remains a fundamental challenge for these instruments, where traditional spatial and temporal approaches are ... | {
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} |
2501.04926 | FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with
Single-Step Flow Matching | [
"eess.AS",
"cs.AI",
"cs.CL",
"cs.SD"
] | Audio super-resolution is challenging owing to its ill-posed nature. Recently, the application of diffusion models in audio super-resolution has shown promising results in alleviating this challenge. However, diffusion-based models have limitations, primarily the necessity for numerous sampling steps, which causes sign... | {
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} |
2501.04927 | Investigating Numerical Translation with Large Language Models | [
"cs.CL"
] | The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made significant advancements in machine translation, their capacity for translating numbers has not been thoroughly explored. This study focuse... | {
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} |
2501.04928 | Image2CADSeq: Computer-Aided Design Sequence and Knowledge Inference
from Product Images | [
"cs.CV",
"cs.AI"
] | Computer-aided design (CAD) tools empower designers to design and modify 3D models through a series of CAD operations, commonly referred to as a CAD sequence. In scenarios where digital CAD files are not accessible, reverse engineering (RE) has been used to reconstruct 3D CAD models. Recent advances have seen the rise ... | {
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} |
2501.04929 | What Drives You to Interact?: The Role of User Motivation for a Robot in
the Wild | [
"cs.HC",
"cs.RO"
] | In this paper, we aim to understand how user motivation shapes human-robot interaction (HRI) in the wild. To explore this, we conducted a field study by deploying a fully autonomous conversational robot in a shopping mall over two days. Through sequential video analysis, we identified five patterns of interaction fluen... | {
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} |
2501.04931 | Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency | [
"cs.CR",
"cs.AI",
"cs.CL"
] | Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but they still have potential safety mechanism vulnerabilities. Jailbreak attacks are red teaming methods that aim to bypass safety mechanisms and discover MLLMs' potential risk... | {
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} |
2501.04934 | Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel
Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images | [
"cs.CV"
] | Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense distribution of changed instances (i.e., changed objects). In these scenarios, unchanged pixels between changed instances are also mistakenly... | {
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} |
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