id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.18267 | Incomplete Multi-view Multi-label Classification via a Dual-level
Contrastive Learning Framework | [
"cs.CV"
] | Recently, multi-view and multi-label classification have become significant domains for comprehensive data analysis and exploration. However, incompleteness both in views and labels is still a real-world scenario for multi-view multi-label classification. In this paper, we seek to focus on double missing multi-view mul... | {
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2411.18268 | Information geometry of bosonic Gaussian thermal states | [
"quant-ph",
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"hep-th",
"math-ph",
"math.IT",
"math.MP"
] | Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on characterizing the distance between two nearby states and the geometry induced by a parameterization in terms of their mean vectors and Hamiltonian... | {
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2411.18269 | Hidden Data Privacy Breaches in Federated Learning | [
"cs.CL",
"cs.CR"
] | Federated Learning (FL) emerged as a paradigm for conducting machine learning across broad and decentralized datasets, promising enhanced privacy by obviating the need for direct data sharing. However, recent studies show that attackers can steal private data through model manipulation or gradient analysis. Existing at... | {
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2411.18270 | Grid-augmented vision: A simple yet effective approach for enhanced
spatial understanding in multi-modal agents | [
"cs.CV"
] | Recent advances in multimodal models have demonstrated impressive capabilities in object recognition and scene understanding. However, these models often struggle with precise spatial localization - a critical capability for real-world applications. Inspired by how humans use grid-based references like chess boards and... | {
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2411.18272 | NeoHebbian Synapses to Accelerate Online Training of Neuromorphic
Hardware | [
"cs.ET",
"cs.NE"
] | Neuromorphic systems that employ advanced synaptic learning rules, such as the three-factor learning rule, require synaptic devices of increased complexity. Herein, a novel neoHebbian artificial synapse utilizing ReRAM devices has been proposed and experimentally validated to meet this demand. This synapse features two... | {
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2411.18275 | Visual Adversarial Attack on Vision-Language Models for Autonomous
Driving | [
"cs.CV"
] | Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain highly vulnerable to adversarial attacks. While existing research has primarily focused on general VLM attacks, the development of attacks tailored to the safety-critical A... | {
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2411.18276 | GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic
Articulated Object Manipulation | [
"cs.RO",
"cs.AI"
] | Effectively manipulating articulated objects in household scenarios is a crucial step toward achieving general embodied artificial intelligence. Mainstream research in 3D vision has primarily focused on manipulation through depth perception and pose detection. However, in real-world environments, these methods often fa... | {
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2411.18277 | Large Models Enabled Ubiquitous Wireless Sensing | [
"cs.LG"
] | In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We begin by outlining the significance of accurate CSI in enabling advanced functiona... | {
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2411.18279 | Large Language Model-Brained GUI Agents: A Survey | [
"cs.AI",
"cs.CL",
"cs.HC"
] | GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particularly multimodal models, has ushered in a new era of GUI automation. They have demonstrated exceptional capabilities in natural language under... | {
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2411.18280 | Neutralizing Backdoors through Information Conflicts for Large Language
Models | [
"cs.CL"
] | Large language models (LLMs) have seen significant advancements, achieving superior performance in various Natural Language Processing (NLP) tasks, from understanding to reasoning. However, they remain vulnerable to backdoor attacks, where models behave normally for standard queries but generate harmful responses or un... | {
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2411.18281 | MotionCharacter: Identity-Preserving and Motion Controllable Human Video
Generation | [
"cs.CV"
] | Recent advancements in personalized Text-to-Video (T2V) generation highlight the importance of integrating character-specific identities and actions. However, previous T2V models struggle with identity consistency and controllable motion dynamics, mainly due to limited fine-grained facial and action-based textual promp... | {
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2411.18286 | DualCast: Disentangling Aperiodic Events from Traffic Series with a
Dual-Branch Model | [
"cs.LG",
"cs.AI"
] | Traffic forecasting is an important problem in the operation and optimisation of transportation systems. State-of-the-art solutions train machine learning models by minimising the mean forecasting errors on the training data. The trained models often favour periodic events instead of aperiodic ones in their prediction ... | {
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2411.18288 | Optimizing Multispectral Object Detection: A Bag of Tricks and
Comprehensive Benchmarks | [
"cs.CV"
] | Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task. It requires not only the effective extraction of features from both modalities and robust fusion strategies, but also the ability to address issues such as spectral discrepancies, spatial mis... | {
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2411.18289 | Don't Let Your Robot be Harmful: Responsible Robotic Manipulation | [
"cs.RO",
"cs.CV"
] | Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present responsible robotic manipulation, which requires robots to consider potential hazards in the real-world environment while completing instructions ... | {
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2411.18290 | Leveraging Semantic Asymmetry for Precise Gross Tumor Volume
Segmentation of Nasopharyngeal Carcinoma in Planning CT | [
"eess.IV",
"cs.CV"
] | In the radiation therapy of nasopharyngeal carcinoma (NPC), clinicians typically delineate the gross tumor volume (GTV) using non-contrast planning computed tomography to ensure accurate radiation dose delivery. However, the low contrast between tumors and adjacent normal tissues necessitates that radiation oncologists... | {
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2411.18293 | HiFiVFS: High Fidelity Video Face Swapping | [
"cs.CV"
] | Face swapping aims to generate results that combine the identity from the source with attributes from the target. Existing methods primarily focus on image-based face swapping. When processing videos, each frame is handled independently, making it difficult to ensure temporal stability. From a model perspective, face s... | {
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2411.18294 | Aligning Pre-trained Models for Spoken Language Translation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This paper investigates a novel approach to end-to-end speech translation (ST) based on aligning frozen pre-trained automatic speech recognition (ASR) and machine translation (MT) models via a small connector module (Q-Former, our Subsampler-Transformer Encoder). This connector bridges the gap between the speech and te... | {
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2411.18295 | Optimizing energy consumption for legged robot by adapting equilibrium
position and stiffness of a parallel torsion spring | [
"cs.RO"
] | This paper is dedicated to the development of a novel adaptive torsion spring mechanism for optimizing energy consumption in legged robots. By adjusting the equilibrium position and stiffness of the spring, the system improves energy efficiency during cyclic movements, such as walking and jumping. The adaptive complian... | {
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2411.18296 | HUPE: Heuristic Underwater Perceptual Enhancement with Semantic
Collaborative Learning | [
"cs.CV"
] | Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement methods primarily focus on improving visual quality while overlooking practical implications. To strike a balance between visual quality and ap... | {
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2411.18298 | Capacity Maximization for MIMO Channels Assisted by Beyond-Diagonal RIS | [
"eess.SP",
"cs.IT",
"math.IT"
] | Reconfigurable intelligent surfaces (RISs) can improve the capacity of wireless communication links by passively beamforming the impinging signals in desired directions. This feature has been demonstrated both analytically and experimentally for conventional RISs, consisting of independently reflecting elements. To fur... | {
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2411.18301 | Enhancing MMDiT-Based Text-to-Image Models for Similar Subject
Generation | [
"cs.CV"
] | Representing the cutting-edge technique of text-to-image models, the latest Multimodal Diffusion Transformer (MMDiT) largely mitigates many generation issues existing in previous models. However, we discover that it still suffers from subject neglect or mixing when the input text prompt contains multiple subjects of si... | {
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2411.18302 | InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for
Autonomous Driving | [
"cs.RO"
] | The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present I... | {
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2411.18303 | InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment
Score Distillation | [
"cs.CV"
] | We present InfiniDreamer, a novel framework for arbitrarily long human motion generation. InfiniDreamer addresses the limitations of current motion generation methods, which are typically restricted to short sequences due to the lack of long motion training data. To achieve this, we first generate sub-motions correspon... | {
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2411.18305 | Application of Soft Actor-Critic Algorithms in Optimizing Wastewater
Treatment with Time Delays Integration | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY"
] | Wastewater treatment plants face unique challenges for process control due to their complex dynamics, slow time constants, and stochastic delays in observations and actions. These characteristics make conventional control methods, such as Proportional-Integral-Derivative controllers, suboptimal for achieving efficient ... | {
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2411.18306 | Delineating Feminist Studies through bibliometric analysis | [
"cs.DL",
"cs.IR"
] | The multidisciplinary and socially anchored nature of Feminist Studies presents unique challenges for bibliometric analysis, as this research area transcends traditional disciplinary boundaries and reflects discussions from feminist and LGBTQIA+ social movements. This paper proposes a novel approach for identifying gen... | {
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2411.18309 | MvKeTR: Chest CT Report Generation with Multi-View Perception and
Knowledge Enhancement | [
"cs.CV",
"cs.AI"
] | CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essent... | {
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2411.18311 | Neural Surface Priors for Editable Gaussian Splatting | [
"cs.CV"
] | In computer graphics and vision, recovering easily modifiable scene appearance from image data is crucial for applications such as content creation. We introduce a novel method that integrates 3D Gaussian Splatting with an implicit surface representation, enabling intuitive editing of recovered scenes through mesh mani... | {
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2411.18314 | Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural
Networks (CNN) | [
"cs.CV"
] | Thispaperaimstoresearchandimplementa real-timevideotargettrackingalgorithmbasedon ConvolutionalNeuralNetworks(CNN),enhancingthe accuracyandrobustnessoftargettrackingincomplex scenarios.Addressingthelimitationsoftraditionaltracking algorithmsinhandlingissuessuchastargetocclusion,morphologicalchanges,andbackgroundinterfe... | {
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2411.18316 | Decoding convolutional codes over finite rings. A linear dynamical
systems approach | [
"cs.IT",
"math.IT"
] | Observable convolutional codes defined over Zpr with the Predictable Degree Property admits minimal input state output representations that behaves well under restriction of scalars. We make use of this fact to present Rosenthal's decoding algorithm for these convolutional codes. When combined with the Greferath-Vellbi... | {
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2411.18317 | Benchmarking Agility and Reconfigurability in Satellite Systems for
Tropical Cyclone Monitoring | [
"eess.SY",
"cs.SY",
"math.OC"
] | Tropical cyclones (TCs) are highly dynamic natural disasters that travel vast distances and occupy a large spatial scale, leading to loss of life, economic strife, and destruction of infrastructure. The severe impact of TCs makes them crucial to monitor such that the collected data contributes to forecasting their traj... | {
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2411.18318 | SRG Analysis of Lur'e Systems and the Generalized Circle Criterion | [
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"cs.SY",
"math.OC"
] | Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from some pitfalls that limit its applicability in analysing practical nonlinear systems. We overcome these pitfalls by modifying the SRG of a lin... | {
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2411.18320 | Continual Learning in Machine Speech Chain Using Gradient Episodic
Memory | [
"cs.CL",
"cs.AI",
"eess.AS"
] | Continual learning for automatic speech recognition (ASR) systems poses a challenge, especially with the need to avoid catastrophic forgetting while maintaining performance on previously learned tasks. This paper introduces a novel approach leveraging the machine speech chain framework to enable continual learning in A... | {
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2411.18321 | Learning optimal objective values for MILP | [
"math.OC",
"cs.AI",
"cs.LG",
"cs.MS"
] | Modern Mixed Integer Linear Programming (MILP) solvers use the Branch-and-Bound algorithm together with a plethora of auxiliary components that speed up the search. In recent years, there has been an explosive development in the use of machine learning for enhancing and supporting these algorithmic components. Within t... | {
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2411.18322 | Mixture of Experts in Image Classification: What's the Sweet Spot? | [
"cs.CV",
"cs.LG"
] | Mixture-of-Experts (MoE) models have shown promising potential for parameter-efficient scaling across various domains. However, the implementation in computer vision remains limited, and often requires large-scale datasets comprising billions of samples. In this study, we investigate the integration of MoE within compu... | {
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2411.18324 | RITA: Automatic Framework for Designing of Resilient IoT Applications | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Designing resilient Internet of Things (IoT) systems requires i) identification of IoT Critical Objects (ICOs) such as services, devices, and resources, ii) threat analysis, and iii) mitigation strategy selection. However, the traditional process for designing resilient IoT systems is still manual, leading to inefficie... | {
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2411.18328 | EventCrab: Harnessing Frame and Point Synergy for Event-based Action
Recognition and Beyond | [
"cs.CV"
] | Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current leading EAR solutions typically follow two regimes: project unconstructed event streams into dense constructed event frames and adopt powerfu... | {
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2411.18329 | Two-Timescale Digital Twin Assisted Model Interference and Retraining
over Wireless Network | [
"eess.SP",
"cs.IT",
"math.IT"
] | In this paper, we investigate a resource allocation and model retraining problem for dynamic wireless networks by utilizing incremental learning, in which the digital twin (DT) scheme is employed for decision making. A two-timescale framework is proposed for computation resource allocation, mobile user association, and... | {
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2411.18335 | Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth
Estimation | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Despite considerable progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, consisting of 40K frames from video sequences across diverse environments, includi... | {
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2411.18337 | Can LLMs assist with Ambiguity? A Quantitative Evaluation of various
Large Language Models on Word Sense Disambiguation | [
"cs.CL"
] | Ambiguous words are often found in modern digital communications. Lexical ambiguity challenges traditional Word Sense Disambiguation (WSD) methods, due to limited data. Consequently, the efficiency of translation, information retrieval, and question-answering systems is hindered by these limitations. This study investi... | {
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2411.18343 | FreqX: What neural networks learn is what network designers say | [
"cs.LG",
"cs.AI"
] | Personalized Federal learning(PFL) allows clients to cooperatively train a personalized model without disclosing their private dataset. However, PFL suffers from Non-IID, heterogeneous devices, lack of fairness, and unclear contribution which urgently need the interpretability of deep learning model to overcome these c... | {
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2411.18350 | TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction
using Diffusion Models | [
"cs.CV",
"cs.AI"
] | This paper introduces Virtual Try-Off (VTOFF), a novel task focused on generating standardized garment images from single photos of clothed individuals. Unlike traditional Virtual Try-On (VTON), which digitally dresses models, VTOFF aims to extract a canonical garment image, posing unique challenges in capturing garmen... | {
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2411.18363 | ChatRex: Taming Multimodal LLM for Joint Perception and Understanding | [
"cs.CV"
] | Perception and understanding are two pillars of computer vision. While multimodal large language models (MLLM) have demonstrated remarkable visual understanding capabilities, they arguably lack accurate perception abilities, e.g. the stage-of-the-art model Qwen2-VL only achieves a 43.9 recall rate on the COCO dataset, ... | {
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2411.18365 | GPT as ghostwriter at the White House | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Recently several large language models (LLMs) have demonstrated their capability to generate a message in response to a user request. Such scientific breakthroughs promote new perspectives but also some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT 3.5 by comparing its ge... | {
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2411.18368 | AMPS: ASR with Multimodal Paraphrase Supervision | [
"cs.CL",
"cs.AI",
"cs.LG",
"eess.AS"
] | Spontaneous or conversational multilingual speech presents many challenges for state-of-the-art automatic speech recognition (ASR) systems. In this work, we present a new technique AMPS that augments a multilingual multimodal ASR system with paraphrase-based supervision for improved conversational ASR in multiple langu... | {
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2411.18369 | G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable
Object Manipulation | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.SY",
"eess.SY"
] | Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic ... | {
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2411.18375 | Individual Content and Motion Dynamics Preserved Pruning for Video
Diffusion Models | [
"cs.CV",
"eess.IV"
] | The high computational cost and slow inference time are major obstacles to deploying the video diffusion model (VDM) in practical applications. To overcome this, we introduce a new Video Diffusion Model Compression approach using individual content and motion dynamics preserved pruning and consistency loss. First, we e... | {
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2411.18376 | Preserving Deep Representations In One-Shot Pruning: A Hessian-Free
Second-Order Optimization Framework | [
"cs.LG"
] | We present SNOWS, a one-shot post-training pruning framework aimed at reducing the cost of vision network inference without retraining. Current leading one-shot pruning methods minimize layer-wise least squares reconstruction error which does not take into account deeper network representations. We propose to optimize ... | {
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2411.18377 | XR-MBT: Multi-modal Full Body Tracking for XR through Self-Supervision
with Learned Depth Point Cloud Registration | [
"cs.CV",
"cs.LG"
] | Tracking the full body motions of users in XR (AR/VR) devices is a fundamental challenge to bring a sense of authentic social presence. Due to the absence of dedicated leg sensors, currently available body tracking methods adopt a synthesis approach to generate plausible motions given a 3-point signal from the head and... | {
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2411.18382 | ChatGPT as speechwriter for the French presidents | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Generative AI proposes several large language models (LLMs) to automatically generate a message in response to users' requests. Such scientific breakthroughs promote new writing assistants but with some fears. The main focus of this study is to analyze the written style of one LLM called ChatGPT by comparing its genera... | {
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2411.18383 | Topic Modeling and Sentiment Analysis on Japanese Online Media's
Coverage of Nuclear Energy | [
"cs.CL",
"cs.SI"
] | Thirteen years after the Fukushima Daiichi nuclear power plant accident, Japan's nuclear energy accounts for only approximately 6% of electricity production, as most nuclear plants remain shut down. To revitalize the nuclear industry and achieve sustainable development goals, effective communication with Japanese citiz... | {
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2411.18384 | Optimal In-Network Distribution of Learning Functions for a
Secure-by-Design Programmable Data Plane of Next-Generation Networks | [
"cs.NI",
"cs.AI",
"math.OC"
] | The rise of programmable data plane (PDP) and in-network computing (INC) paradigms paves the way for the development of network devices (switches, network interface cards, etc.) capable of performing advanced computing tasks. This allows to execute algorithms of various nature, including machine learning ones, within t... | {
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2411.18385 | Federated Learning with Uncertainty and Personalization via Efficient
Second-order Optimization | [
"cs.LG",
"cs.CV",
"stat.ML"
] | Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement of data ever leaving the clients. Recent works on FL have advocated taking a Bayesian approach to FL as it offers a principled way to accoun... | {
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2411.18387 | A Novel Kinesthetic Haptic Feedback Device Driven by Soft
Electrohydraulic Actuators | [
"cs.RO"
] | Developing kinesthetic haptic devices with advanced haptic rendering capabilities is challenging due to the limitations on driving mechanisms. In this study, we introduce a novel soft electrohydraulic actuator and develop a kinesthetic haptic device utilizing it as the driving unit. We established a mathematical model ... | {
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2411.18388 | Convolutional Neural Networks Do Work with Pre-Defined Filters | [
"cs.CV"
] | We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all nxn convolution kernels with n>1 are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In ... | {
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2411.18391 | GeneQuery: A General QA-based Framework for Spatial Gene Expression
Predictions from Histology Images | [
"cs.CV"
] | Gene expression profiling provides profound insights into molecular mechanisms, but its time-consuming and costly nature often presents significant challenges. In contrast, whole-slide hematoxylin and eosin (H&E) stained histological images are readily accessible and allow for detailed examinations of tissue structure ... | {
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2411.18403 | Politicians vs ChatGPT. A study of presuppositions in French and Italian
political communication | [
"cs.CL",
"cs.CY"
] | This paper aims to provide a comparison between texts produced by French and Italian politicians on polarizing issues, such as immigration and the European Union, and their chatbot counterparts created with ChatGPT 3.5. In this study, we focus on implicit communication, in particular on presuppositions and their functi... | {
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2411.18406 | When does a bridge become an aeroplane? | [
"cs.LG"
] | Despite recent advances in population-based structural health monitoring (PBSHM), knowledge transfer between highly-disparate structures (i.e., heterogeneous populations) remains a challenge. It has been proposed that heterogeneous transfer may be accomplished via intermediate structures that bridge the gap in informat... | {
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2411.18409 | Deep Fourier-embedded Network for RGB and Thermal Salient Object
Detection | [
"cs.CV"
] | The rapid development of deep learning has significantly improved salient object detection (SOD) combining both RGB and thermal (RGB-T) images. However, existing deep learning-based RGB-T SOD models suffer from two major limitations. First, Transformer-based models with quadratic complexity are computationally expensiv... | {
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2411.18410 | Preserving Information: How does Topological Data Analysis improve
Neural Network performance? | [
"cs.NE"
] | Artificial Neural Networks (ANNs) require significant amounts of data and computational resources to achieve high effectiveness in performing the tasks for which they are trained. To reduce resource demands, various techniques, such as Neuron Pruning, are applied. Due to the complex structure of ANNs, interpreting the ... | {
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2411.18412 | Adaptive Blind All-in-One Image Restoration | [
"cs.CV"
] | Blind all-in-one image restoration models aim to recover a high-quality image from an input degraded with unknown distortions. However, these models require all the possible degradation types to be defined during the training stage while showing limited generalization to unseen degradations, which limits their practica... | {
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2411.18413 | Robust Dynamic Gesture Recognition at Ultra-Long Distances | [
"cs.RO"
] | Dynamic hand gestures play a crucial role in conveying nonverbal information for Human-Robot Interaction (HRI), eliminating the need for complex interfaces. Current models for dynamic gesture recognition suffer from limitations in effective recognition range, restricting their application to close proximity scenarios. ... | {
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2411.18415 | Neural Image Unfolding: Flattening Sparse Anatomical Structures using
Neural Fields | [
"cs.CV"
] | Tomographic imaging reveals internal structures of 3D objects and is crucial for medical diagnoses. Visualizing the morphology and appearance of non-planar sparse anatomical structures that extend over multiple 2D slices in tomographic volumes is inherently difficult but valuable for decision-making and reporting. Henc... | {
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2411.18423 | Efficient and Diverse Generative Robot Designs using Evolution and
Intrinsic Motivation | [
"cs.RO"
] | Methods for generative design of robot physical configurations can automatically find optimal and innovative solutions for challenging tasks in complex environments. The vast search-space includes the physical design-space and the controller parameter-space, making it a challenging problem in machine learning and optim... | {
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2411.18424 | FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware
Large Language Model Serving | [
"cs.LG",
"cs.DC"
] | Serving numerous users and requests concurrently requires good fairness in Large Language Models (LLMs) serving system. This ensures that, at the same cost, the system can meet the Service Level Objectives (SLOs) of more users , such as time to first token (TTFT) and time between tokens (TBT), rather than allowing a fe... | {
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2411.18425 | Streamlining Prediction in Bayesian Deep Learning | [
"cs.LG"
] | The rising interest in Bayesian deep learning (BDL) has led to a plethora of methods for estimating the posterior distribution. However, efficient computation of inferences, such as predictions, has been largely overlooked with Monte Carlo integration remaining the standard. In this work we examine streamlining predict... | {
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2411.18428 | MM-Path: Multi-modal, Multi-granularity Path Representation Learning --
Extended Version | [
"cs.LG",
"cs.AI"
] | Developing effective path representations has become increasingly essential across various fields within intelligent transportation. Although pre-trained path representation learning models have shown improved performance, they predominantly focus on the topological structures from single modality data, i.e., road netw... | {
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2411.18432 | An End-to-End Smart Predict-then-Optimize Framework for Vehicle
Relocation Problems in Large-Scale Vehicle Crowd Sensing | [
"cs.LG",
"math.OC"
] | Ubiquitous mobile devices have catalyzed the development of vehicle crowd sensing (VCS). In particular, vehicle sensing systems show great potential in the flexible acquisition of spatio-temporal urban data through built-in sensors under diverse sensing scenarios. However, vehicle systems often exhibit biased coverage ... | {
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2411.18435 | 6G Takes Shape | [
"cs.IT",
"math.IT"
] | The contours of 6G -- its key technical components and driving requirements -- are finally coming into focus. Through twenty questions and answers, this article defines the important aspects of 6G across four categories. First, we identify the key themes and forces driving the development of 6G, and what will make 6G u... | {
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2411.18440 | Learning the Evolution of Physical Structure of Galaxies via Diffusion
Models | [
"astro-ph.GA",
"cs.CV"
] | In astrophysics, understanding the evolution of galaxies in primarily through imaging data is fundamental to comprehending the formation of the Universe. This paper introduces a novel approach to conditioning Denoising Diffusion Probabilistic Models (DDPM) on redshifts for generating galaxy images. We explore whether t... | {
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2411.18442 | Metric-DST: Mitigating Selection Bias Through Diversity-Guided
Semi-Supervised Metric Learning | [
"cs.LG",
"cs.AI"
] | Selection bias poses a critical challenge for fairness in machine learning, as models trained on data that is less representative of the population might exhibit undesirable behavior for underrepresented profiles. Semi-supervised learning strategies like self-training can mitigate selection bias by incorporating unlabe... | {
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2411.18443 | Efficient Dynamic LiDAR Odometry for Mobile Robots with Structured Point
Clouds | [
"cs.RO"
] | We propose a real-time dynamic LiDAR odometry pipeline for mobile robots in Urban Search and Rescue (USAR) scenarios. Existing approaches to dynamic object detection often rely on pretrained learned networks or computationally expensive volumetric maps. To enhance efficiency on computationally limited robots, we reuse ... | {
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2411.18444 | Is my Meeting Summary Good? Estimating Quality with a Multi-LLM
Evaluator | [
"cs.CL",
"cs.AI"
] | The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established metrics such as ROUGE and BERTScore have a relatively low correlation with human judgments and fail to capture nuanced errors. Recent studies suggest using large language models (LLMs), ... | {
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2411.18447 | Continuous Autoregressive Models with Noise Augmentation Avoid Error
Accumulation | [
"cs.LG",
"cs.AI",
"cs.SD",
"eess.AS"
] | Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive manner is also feasible. However, such Continuous Autoregressive Models (CAMs) can suffer from a decline in generation quality over extended ... | {
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2411.18448 | A Game-theoretic model of forex trading with stochastic strategies and
information asymmetry | [
"cs.CE"
] | Interaction strategies for reward in competitive environments are significantly influenced by the nature and extent of available information. In financial markets, particularly foreign exchange (forex), traders operate independently with limited information, often yielding highly unpredictable outcomes. This study intr... | {
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2411.18451 | Advancements in Myocardial Infarction Detection and Classification Using
Wearable Devices: A Comprehensive Review | [
"cs.LG"
] | Myocardial infarction (MI), commonly known as a heart attack, is a critical health condition caused by restricted blood flow to the heart. Early-stage detection through continuous ECG monitoring is essential to minimize irreversible damage. This review explores advancements in MI classification methodologies for wearab... | {
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2411.18454 | Optimizing Coverage in Convex Quadrilateral Regions with a Single UAV | [
"eess.SY",
"cs.SY"
] | This letter investigates the optimal hovering altitude of a single UAV to provide coverage over any convex quadrilateral region on the ground. The UAV employs a directional antenna with a tiltable beam, producing an elliptical coverage pattern. Two scenarios are considered: (1) inscribing the largest ellipse within the... | {
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2411.18456 | Synthetic ECG Generation for Data Augmentation and Transfer Learning in
Arrhythmia Classification | [
"cs.LG",
"cs.AI"
] | Deep learning models need a sufficient amount of data in order to be able to find the hidden patterns in it. It is the purpose of generative modeling to learn the data distribution, thus allowing us to sample more data and augment the original dataset. In the context of physiological data, and more specifically electro... | {
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2411.18459 | What do physics-informed DeepONets learn? Understanding and improving
training for scientific computing applications | [
"cs.LG",
"cs.NA",
"math.NA"
] | Physics-informed deep operator networks (DeepONets) have emerged as a promising approach toward numerically approximating the solution of partial differential equations (PDEs). In this work, we aim to develop further understanding of what is being learned by physics-informed DeepONets by assessing the universality of t... | {
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2411.18462 | Draft Model Knows When to Stop: A Self-Verification Length Policy for
Speculative Decoding | [
"cs.CL",
"cs.AI"
] | Speculative Decoding (SD) has become an important technique in accelerating the inference speed of large language models. Conventional SD methods employ a fixed draft length, which ignores the token generation difficulty across tasks. Consequently, in this paper, we address such an issue and introduce SVIP - a difficul... | {
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2411.18463 | Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive
Extension | [
"q-bio.BM",
"cs.AI",
"cs.LG"
] | Peptides, short chains of amino acids, interact with target proteins, making them a unique class of protein-based therapeutics for treating human diseases. Recently, deep generative models have shown great promise in peptide generation. However, several challenges remain in designing effective peptide binders. First, n... | {
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2411.18466 | Complexity Experts are Task-Discriminative Learners for Any Image
Restoration | [
"cs.CV"
] | Recent advancements in all-in-one image restoration models have revolutionized the ability to address diverse degradations through a unified framework. However, parameters tied to specific tasks often remain inactive for other tasks, making mixture-of-experts (MoE) architectures a natural extension. Despite this, MoEs ... | {
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2411.18468 | Parole de pr\'esidents (1958-2022) | [
"cs.CL"
] | En plus de soixante ans, huit pr\'esidents se sont succ\'ed\'e \`a la t\^ete de la Ve R\'epublique fran\c{c}aise (de Gaulle, Pompidou, Giscard d'Estaing, Mitterrand, Chirac, Sarkozy, Hollande, Macron). Apr\`es avoir pr\'esent\'e le corpus de leurs discours -- soit 9202 textes et plus de 20 millions de mots \'etiquet\'e... | {
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2411.18472 | Isolating authorship from content with semantic embeddings and
contrastive learning | [
"cs.CL",
"cs.LG"
] | Authorship has entangled style and content inside. Authors frequently write about the same topics in the same style, so when different authors write about the exact same topic the easiest way out to distinguish them is by understanding the nuances of their style. Modern neural models for authorship can pick up these fe... | {
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2411.18473 | HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression | [
"cs.CV"
] | Fast progress in 3D Gaussian Splatting (3DGS) has made 3D Gaussians popular for 3D modeling and image rendering, but this creates big challenges in data storage and transmission. To obtain a highly compact 3DGS representation, we propose a hybrid entropy model for Gaussian Splatting (HEMGS) data compression, which comp... | {
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2411.18475 | Weakly Supervised Framework Considering Multi-temporal Information for
Large-scale Cropland Mapping with Satellite Imagery | [
"cs.CV",
"cs.AI"
] | Accurately mapping large-scale cropland is crucial for agricultural production management and planning. Currently, the combination of remote sensing data and deep learning techniques has shown outstanding performance in cropland mapping. However, those approaches require massive precise labels, which are labor-intensiv... | {
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2411.18476 | A comparison of extended object tracking with multi-modal sensors in
indoor environment | [
"cs.RO",
"cs.CV"
] | This paper presents a preliminary study of an efficient object tracking approach, comparing the performance of two different 3D point cloud sensory sources: LiDAR and stereo cameras, which have significant price differences. In this preliminary work, we focus on single object tracking. We first developed a fast heurist... | {
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2411.18478 | Beyond Examples: High-level Automated Reasoning Paradigm in In-Context
Learning via MCTS | [
"cs.CL"
] | In-context Learning (ICL) enables large language models (LLMs) to tackle downstream tasks through sophisticated prompting and high-quality demonstrations. However, this traditional ICL paradigm shows limitations when facing complex mathematical reasoning tasks, primarily due to its heavy dependence on example quality a... | {
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2411.18479 | SoK: Watermarking for AI-Generated Content | [
"cs.CR",
"cs.AI",
"cs.LG"
] | As the outputs of generative AI (GenAI) techniques improve in quality, it becomes increasingly challenging to distinguish them from human-created content. Watermarking schemes are a promising approach to address the problem of distinguishing between AI and human-generated content. These schemes embed hidden signals wit... | {
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2411.18480 | A Novel Q-stem Connected Architecture for Beyond-Diagonal Reconfigurable
Intelligent Surfaces | [
"cs.IT",
"eess.SP",
"math.IT"
] | Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has garnered significant research interest recently due to its ability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide enhanced performance through flexible inter-connection among RIS elements. However, current BD-RIS ... | {
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2411.18484 | SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time
Estimation | [
"cs.LG"
] | Accurate travel time estimation is essential for navigation and itinerary planning. While existing research employs probabilistic modeling to assess travel time uncertainty and account for correlations between multiple trips, modeling the temporal variability of multi-trip travel time distributions remains a significan... | {
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2411.18497 | Multiple Choice Learning for Efficient Speech Separation with Many
Speakers | [
"cs.SD",
"cs.LG",
"eess.AS",
"stat.ML"
] | Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated signals. This inherently ambiguous task is customarily solved using Permutation Invariant Training (PIT). In this article, we instead consid... | {
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} |
2411.18498 | Collective decision making by embodied neural agents | [
"cs.MA",
"q-bio.NC"
] | Collective decision making using simple social interactions has been studied in many types of multi-agent systems, including robot swarms and human social networks. However, existing multi-agent studies have rarely modeled the neural dynamics that underlie sensorimotor coordination in embodied biological agents. In thi... | {
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} |
2411.18499 | GATE OpenING: A Comprehensive Benchmark for Judging Open-ended
Interleaved Image-Text Generation | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a challenge, which requires integrated multimodal understanding and generation abilities. While the progress in unified models offers new soluti... | {
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} |
2411.18502 | Isometry pursuit | [
"stat.ML",
"cs.AI",
"cs.IR",
"cs.LG",
"stat.ME"
] | Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization method followed by multitask basis pursuit. Applied to Jacobians of putative coordinate functions, it helps identity isometric embeddings from within interpretable dictionaries. W... | {
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} |
2411.18503 | Graph-Based Orchestration of Service-Oriented Model-Based Control
Systems | [
"eess.SY",
"cs.SY"
] | This paper presents a novel graph-based method for adapting control system architectures at runtime. We use a service-oriented architecture as a basis for its formulation. In our method, adaptation is achieved by selecting the most suitable elements, such as filters and controllers, for a control system architecture to... | {
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} |
2411.18506 | LLM-ABBA: Understanding time series via symbolic approximation | [
"cs.LG",
"cs.AI"
] | The success of large language models (LLMs) for time series has been demonstrated in previous work. Utilizing a symbolic time series representation, one can efficiently bridge the gap between LLMs and time series. However, the remaining challenge is to exploit the semantic information hidden in time series by using sym... | {
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} |
2411.18507 | At First Contact: Stiffness Estimation Using Vibrational Information for
Prosthetic Grasp Modulation | [
"cs.RO"
] | Stiffness estimation is crucial for delicate object manipulation in robotic and prosthetic hands but remains challenging due to dependence on force and displacement measurement and real-time sensory integration. This study presents a piezoelectric sensing framework for stiffness estimation at first contact during pinch... | {
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} |
2411.18513 | Enhancing weed detection performance by means of GenAI-based image
augmentation | [
"cs.CV"
] | Precise weed management is essential for sustaining crop productivity and ecological balance. Traditional herbicide applications face economic and environmental challenges, emphasizing the need for intelligent weed control systems powered by deep learning. These systems require vast amounts of high-quality training dat... | {
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} |
2411.18516 | Living off the Analyst: Harvesting Features from Yara Rules for Malware
Detection | [
"cs.CR",
"cs.LG"
] | A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent. In this work, we ask if there is a way for anti-virus developers to similarly re-purpose existing work to improve their malware ... | {
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} |
2411.18519 | A Talent-infused Policy-gradient Approach to Efficient Co-Design of
Morphology and Task Allocation Behavior of Multi-Robot Systems | [
"cs.RO",
"cs.MA"
] | Interesting and efficient collective behavior observed in multi-robot or swarm systems emerges from the individual behavior of the robots. The functional space of individual robot behaviors is in turn shaped or constrained by the robot's morphology or physical design. Thus the full potential of multi-robot systems can ... | {
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} |
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