id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.01507 | Transfer Learning Analysis of Variational Quantum Circuits | [
"quant-ph",
"cs.AI",
"cs.LG"
] | This work analyzes transfer learning of the Variational Quantum Circuit (VQC). Our framework begins with a pretrained VQC configured in one domain and calculates the transition of 1-parameter unitary subgroups required for a new domain. A formalism is established to investigate the adaptability and capability of a VQC ... | {
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2501.01508 | Garbage in Garbage out: Impacts of data quality on criminal network
intervention | [
"physics.soc-ph",
"cs.SI"
] | Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data q... | {
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2501.01509 | AI-Enabled Operations at Fermi Complex: Multivariate Time Series
Prediction for Outage Prediction and Diagnosis | [
"cs.LG",
"cs.AI",
"cs.ET",
"eess.SP"
] | The Main Control Room of the Fermilab accelerator complex continuously gathers extensive time-series data from thousands of sensors monitoring the beam. However, unplanned events such as trips or voltage fluctuations often result in beam outages, causing operational downtime. This downtime not only consumes operator ef... | {
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2501.01510 | Explainable Brain Age Gap Prediction in Neurodegenerative Conditions
using coVariance Neural Networks | [
"cs.LG",
"eess.SP",
"q-bio.QM"
] | Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age ga... | {
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2501.01511 | TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference
Acceleration Using Gradient Boosted Decision Trees | [
"cs.LG",
"cs.AR"
] | Accelerating machine learning inference has been an active research area in recent years. In this context, field-programmable gate arrays (FPGAs) have demonstrated compelling performance by providing massive parallelism in deep neural networks (DNNs). Neural networks (NNs) are computationally intensive during inference... | {
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2501.01515 | DiagrammaticLearning: A Graphical Language for Compositional Training
Regimes | [
"cs.LG",
"cs.AI",
"cs.PL",
"math.CT"
] | Motivated by deep learning regimes with multiple interacting yet distinct model components, we introduce learning diagrams, graphical depictions of training setups that capture parameterized learning as data rather than code. A learning diagram compiles to a unique loss function on which component models are trained. T... | {
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2501.01516 | Improving Robustness Estimates in Natural Language Explainable AI though
Synonymity Weighted Similarity Measures | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Explainable AI (XAI) has seen a surge in recent interest with the proliferation of powerful but intractable black-box models. Moreover, XAI has come under fire for techniques that may not offer reliable explanations. As many of the methods in XAI are themselves models, adversarial examples have been prominent in the li... | {
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2501.01525 | Transfer Neyman-Pearson Algorithm for Outlier Detection | [
"cs.LG",
"stat.ML"
] | We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less at... | {
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2501.01529 | SAFER: Sharpness Aware layer-selective Finetuning for Enhanced
Robustness in vision transformers | [
"cs.CV"
] | Vision transformers (ViTs) have become essential backbones in advanced computer vision applications and multi-modal foundation models. Despite their strengths, ViTs remain vulnerable to adversarial perturbations, comparable to or even exceeding the vulnerability of convolutional neural networks (CNNs). Furthermore, the... | {
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2501.01531 | A Global Games-Inspired Approach to Multi-Robot Task Allocation for
Heterogeneous Teams | [
"cs.RO",
"cs.MA",
"cs.SY",
"eess.SY"
] | In this article we propose a game-theoretic approach to the multi-robot task allocation problem using the framework of global games. Each task is associated with a global signal, a real-valued number that captures the task execution progress and/or urgency. We propose a linear objective function for each robot in the s... | {
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2501.01535 | A Metasemantic-Metapragmatic Framework for Taxonomizing Multimodal
Communicative Alignment | [
"cs.HC",
"cs.AI",
"cs.CL",
"cs.CY"
] | Drawing on contemporary pragmatist philosophy and linguistic theories on cognition, meaning, and communication, this paper presents a dynamic, metasemantic-metapragmatic taxonomy for grounding and conceptualizing human-like multimodal communicative alignment. The framework is rooted in contemporary developments of the ... | {
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2501.01539 | In Search of a Lost Metric: Human Empowerment as a Pillar of Socially
Conscious Navigation | [
"cs.RO",
"cs.AI",
"cs.HC"
] | In social robot navigation, traditional metrics like proxemics and behavior naturalness emphasize human comfort and adherence to social norms but often fail to capture an agent's autonomy and adaptability in dynamic environments. This paper introduces human empowerment, an information-theoretic concept that measures a ... | {
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2501.01540 | BoxingGym: Benchmarking Progress in Automated Experimental Design and
Model Discovery | [
"cs.LG",
"cs.AI"
] | Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test them, and then revising them based on data are fundamental to scientific discovery. Despite the significant promise of LLM-based scientific ag... | {
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2501.01544 | Many of Your DPOs are Secretly One: Attempting Unification Through
Mutual Information | [
"cs.LG",
"cs.CL",
"stat.ML"
] | Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become one of the most widely used algorithms for achieving this alignment, given its ability to optimise models based on human feedback directly. ... | {
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2501.01548 | Task-Driven Fixation Network: An Efficient Architecture with Fixation
Selection | [
"cs.CV"
] | This paper presents a novel neural network architecture featuring automatic fixation point selection, designed to efficiently address complex tasks with reduced network size and computational overhead. The proposed model consists of: a low-resolution channel that captures low-resolution global features from input image... | {
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2501.01555 | Indoor Position and Attitude Tracking with SO(3) Manifold | [
"eess.SP",
"cs.RO"
] | Driven by technological breakthroughs, indoor tracking and localization have gained importance in various applications including the Internet of Things (IoT), robotics, and unmanned aerial vehicles (UAVs). To tackle some of the challenges associated with indoor tracking, this study explores the potential benefits of in... | {
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2501.01556 | Extended Information Geometry: Large Deviation Theory, Statistical
Thermodynamics, and Empirical Counting Frequencies | [
"cs.IT",
"math.IT"
] | Combinatorics, probabilities, and measurements are fundamental to understanding information. This work explores how the application of large deviation theory (LDT) in counting phenomena leads to the emergence of various entropy functions, including Shannon's entropy, mutual information, and relative and conditional ent... | {
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2501.01557 | Click-Calib: A Robust Extrinsic Calibration Method for Surround-View
Systems | [
"cs.CV"
] | Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations. However, conventional offline extrinsic calibration methods are cumbersome and time-consuming as they rely heavily on physical patterns. Additionally, these methods primarily focus on shor... | {
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2501.01558 | Predicting the Performance of Black-box LLMs through Self-Queries | [
"cs.LG",
"cs.CL"
] | As large language models (LLMs) are increasingly relied on in AI systems, predicting when they make mistakes is crucial. While a great deal of work in the field uses internal representations to interpret model behavior, these representations are inaccessible when given solely black-box access through an API. In this pa... | {
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2501.01559 | K-ARC: Adaptive Robot Coordination for Multi-Robot Kinodynamic Planning | [
"cs.RO",
"cs.MA"
] | This work presents Kinodynamic Adaptive Robot Coordination (K-ARC), a novel algorithm for multi-robot kinodynamic planning. Our experimental results show the capability of K-ARC to plan for up to 32 planar mobile robots, while achieving up to an order of magnitude of speed-up compared to previous methods in various sce... | {
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2501.01564 | Semialgebraic Neural Networks: From roots to representations | [
"cs.LG",
"cs.NA",
"cs.NE",
"math.NA"
] | Many numerical algorithms in scientific computing -- particularly in areas like numerical linear algebra, PDE simulation, and inverse problems -- produce outputs that can be represented by semialgebraic functions; that is, the graph of the computed function can be described by finitely many polynomial equalities and in... | {
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2501.01568 | Interruption Handling for Conversational Robots | [
"cs.HC",
"cs.RO"
] | Interruptions, a fundamental component of human communication, can enhance the dynamism and effectiveness of conversations, but only when effectively managed by all parties involved. Despite advancements in robotic systems, state-of-the-art systems still have limited capabilities in handling user-initiated interruption... | {
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2501.01576 | Constructing and explaining machine learning models for chemistry:
example of the exploration and design of boron-based Lewis acids | [
"physics.chem-ph",
"cs.AI"
] | The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules with targeted properties. However, the focus has often been on creating highly efficient predictive models, sometimes at the expense of interpretability. In this study, we leverage explainable AI technique... | {
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2501.01579 | Unsupervised learning for anticipating critical transitions | [
"nlin.CD",
"cs.LG"
] | For anticipating critical transitions in complex dynamical systems, the recent approach of parameter-driven reservoir computing requires explicit knowledge of the bifurcation parameter. We articulate a framework combining a variational autoencoder (VAE) and reservoir computing to address this challenge. In particular, ... | {
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2501.01584 | Stackelberg Game Based Performance Optimization in Digital Twin Assisted
Federated Learning over NOMA Networks | [
"cs.LG",
"cs.CR",
"cs.GT",
"cs.NI"
] | Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computing resources of distributed clients and the unreliable wireless communication environment. By effectively imitating the distributed resources, digital twin (DT) shows great potentia... | {
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2501.01586 | GRAMC: General-purpose and reconfigurable analog matrix computing
architecture | [
"cs.AR",
"cs.ET",
"cs.SY",
"eess.SY"
] | In-memory analog matrix computing (AMC) with resistive random-access memory (RRAM) represents a highly promising solution that solves matrix problems in one step. However, the existing AMC circuits each have a specific connection topology to implement a single computing function, lack of the universality as a matrix pr... | {
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2501.01588 | (WhyPHI) Fine-Tuning PHI-3 for Multiple-Choice Question Answering:
Methodology, Results, and Challenges | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have become essential tools across various domains due to their impressive capabilities in understanding and generating human-like text. The ability to accurately answer multiple-choice questions (MCQs) holds significant value in education, particularly in automated tutoring systems and ass... | {
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2501.01589 | D$^3$-Human: Dynamic Disentangled Digital Human from Monocular Video | [
"cs.CV",
"cs.GR"
] | We introduce D$^3$-Human, a method for reconstructing Dynamic Disentangled Digital Human geometry from monocular videos. Past monocular video human reconstruction primarily focuses on reconstructing undecoupled clothed human bodies or only reconstructing clothing, making it difficult to apply directly in applications s... | {
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2501.01591 | Multivariate Time Series Anomaly Detection using DiffGAN Model | [
"cs.LG",
"math.ST",
"stat.TH"
] | In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the orig... | {
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2501.01593 | BLAST: A Stealthy Backdoor Leverage Attack against Cooperative
Multi-Agent Deep Reinforcement Learning based Systems | [
"cs.AI",
"cs.CR",
"cs.LG"
] | Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will perform malicious actions leading to failures or malicious goals. However, existing backdoor attacks suffer from several issues, e.g., instan... | {
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2501.01594 | PSYCHE: A Multi-faceted Patient Simulation Framework for Evaluation of
Psychiatric Assessment Conversational Agents | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent advances in large language models (LLMs) have accelerated the development of conversational agents capable of generating human-like responses. Since psychiatric assessments typically involve complex conversational interactions between psychiatrists and patients, there is growing interest in developing LLM-based ... | {
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2501.01595 | Adaptive Homophily Clustering: Structure Homophily Graph Learning with
Adaptive Filter for Hyperspectral Image | [
"cs.CV"
] | Hyperspectral image (HSI) clustering has been a fundamental but challenging task with zero training labels. Currently, some deep graph clustering methods have been successfully explored for HSI due to their outstanding performance in effective spatial structural information encoding. Nevertheless, insufficient structur... | {
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2501.01598 | Prism: Mining Task-aware Domains in Non-i.i.d. IMU Data for Flexible
User Perception | [
"cs.AI",
"cs.HC"
] | A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting appli... | {
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2501.01601 | Few-shot Implicit Function Generation via Equivariance | [
"cs.CV",
"cs.AI"
] | Implicit Neural Representations (INRs) have emerged as a powerful framework for representing continuous signals. However, generating diverse INR weights remains challenging due to limited training data. We introduce Few-shot Implicit Function Generation, a new problem setup that aims to generate diverse yet functionall... | {
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2501.01608 | Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical
Layer Optimization | [
"cs.LG",
"eess.SP"
] | Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in ... | {
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2501.01611 | Google is all you need: Semi-Supervised Transfer Learning Strategy For
Light Multimodal Multi-Task Classification Model | [
"cs.CV",
"cs.AI"
] | As the volume of digital image data increases, the effectiveness of image classification intensifies. This study introduces a robust multi-label classification system designed to assign multiple labels to a single image, addressing the complexity of images that may be associated with multiple categories (ranging from 1... | {
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2501.01614 | Evaluation of Rail Decarbonization Alternatives: Framework and
Application | [
"eess.SY",
"cs.SY",
"math.OC"
] | The Northwestern University Freight Rail Infrastructure and Energy Network Decarbonization (NUFRIEND) framework is a comprehensive industry-oriented tool for simulating the deployment of new energy technologies including biofuels, e-fuels, battery-electric, and hydrogen locomotives. By classifying fuel types into two c... | {
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2501.01615 | Equity Impacts of Public Transit Network Redesign with Shared Autonomous
Mobility Services | [
"eess.SY",
"cs.SY",
"math.OC"
] | This study examines the equity impacts of integrating shared autonomous mobility services (SAMS) into transit system redesign. Using the Greater Chicago area as a case study, we compare two optimization objectives in multimodal transit network redesign: minimizing total generalized costs (equity-agnostic) versus priori... | {
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2501.01618 | Merging Context Clustering with Visual State Space Models for Medical
Image Segmentation | [
"cs.CV",
"cs.AI"
] | Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by exce... | {
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2501.01620 | Adaptive Meta-learning-based Adversarial Training for Robust Automatic
Modulation Classification | [
"cs.LG",
"cs.CR"
] | DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications. While adversarially training an AMC model based on an adversarial attack significantly increases its robustness against that attack, the AM... | {
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2501.01625 | ICPC: In-context Prompt Compression with Faster Inference | [
"cs.CL",
"cs.AI"
] | Despite the recent success of Large Language Models (LLMs), it remains challenging to feed LLMs with long prompts due to the fixed size of LLM inputs. As a remedy, prompt compression becomes a promising solution by removing redundant tokens in the prompt. However, using LLM in the existing works requires additional com... | {
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2501.01629 | Crossing Language Borders: A Pipeline for Indonesian Manhwa Translation | [
"cs.LG",
"cs.CL",
"cs.CV"
] | In this project, we develop a practical and efficient solution for automating the Manhwa translation from Indonesian to English. Our approach combines computer vision, text recognition, and natural language processing techniques to streamline the traditionally manual process of Manhwa(Korean comics) translation. The pi... | {
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2501.01630 | A Probabilistic Model for Node Classification in Directed Graphs | [
"cs.LG",
"cs.SI"
] | In this work, we present a probabilistic model for directed graphs where nodes have attributes and labels. This model serves as a generative classifier capable of predicting the labels of unseen nodes using either maximum likelihood or maximum a posteriori estimations. The predictions made by this model are highly inte... | {
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2501.01631 | Revisiting Data Analysis with Pre-trained Foundation Models | [
"cs.DB"
] | Data analysis focuses on harnessing advanced statistics, programming, and machine learning techniques to extract valuable insights from vast datasets. An increasing volume and variety of research emerged, addressing datasets of diverse modalities, formats, scales, and resolutions across various industries. However, exp... | {
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2501.01632 | Integrated Communication and Bayesian Estimation of Fixed Channel States | [
"cs.IT",
"math.IT"
] | This work studies an information-theoretic performance limit of an integrated sensing and communication (ISAC) system where the goal of sensing is to estimate a random continuous state. Considering the mean-squared error (MSE) for estimation performance metric, the Bayesian Cram\'{e}r-Rao lower bound (BCRB) is widely u... | {
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2501.01633 | ACE: Anti-Editing Concept Erasure in Text-to-Image Models | [
"cs.CV"
] | Recent advance in text-to-image diffusion models have significantly facilitated the generation of high-quality images, but also raising concerns about the illegal creation of harmful content, such as copyrighted images. Existing concept erasure methods achieve superior results in preventing the production of erased con... | {
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2501.01638 | A non-ergodic framework for understanding emergent capabilities in Large
Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large language models have emergent capabilities that come unexpectedly at scale, but we need a theoretical framework to explain why and how they emerge. We prove that language models are actually non-ergodic systems while providing a mathematical framework based on Stuart Kauffman's theory of the adjacent possible (TA... | {
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2501.01639 | Implications of Artificial Intelligence on Health Data Privacy and
Confidentiality | [
"cs.CY",
"cs.AI"
] | The rapid integration of artificial intelligence (AI) in healthcare is revolutionizing medical diagnostics, personalized medicine, and operational efficiency. However, alongside these advancements, significant challenges arise concerning patient data privacy, ethical considerations, and regulatory compliance. This pape... | {
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2501.01640 | Uncertainty and Energy based Loss Guided Semi-Supervised Semantic
Segmentation | [
"cs.CV"
] | Semi-supervised (SS) semantic segmentation exploits both labeled and unlabeled images to overcome tedious and costly pixel-level annotation problems. Pseudolabel supervision is one of the core approaches of training networks with both pseudo labels and ground-truth labels. This work uses aleatoric or data uncertainty a... | {
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2501.01642 | iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice
Embeddings | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Current methods for searching brain MR images rely on text-based approaches, highlighting a significant need for content-based image retrieval (CBIR) systems. Directly applying 3D brain MR images to machine learning models offers the benefit of effectively learning the brain's structure; however, building the generaliz... | {
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2501.01644 | Multimodal Contrastive Representation Learning in Augmented Biomedical
Knowledge Graphs | [
"cs.CL",
"cs.LG"
] | Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential novel drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from ... | {
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2501.01645 | HLV-1K: A Large-scale Hour-Long Video Benchmark for Time-Specific Long
Video Understanding | [
"cs.CV",
"cs.AI"
] | Multimodal large language models have become a popular topic in deep visual understanding due to many promising real-world applications. However, hour-long video understanding, spanning over one hour and containing tens of thousands of visual frames, remains under-explored because of 1) challenging long-term video anal... | {
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2501.01648 | Dual Mutual Learning Network with Global-local Awareness for RGB-D
Salient Object Detection | [
"cs.CV",
"cs.MM"
] | RGB-D salient object detection (SOD), aiming to highlight prominent regions of a given scene by jointly modeling RGB and depth information, is one of the challenging pixel-level prediction tasks. Recently, the dual-attention mechanism has been devoted to this area due to its ability to strengthen the detection process.... | {
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2501.01649 | AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time
Series Generation | [
"cs.LG",
"cs.AI"
] | Data augmentation can significantly enhance the performance of machine learning tasks by addressing data scarcity and improving generalization. However, generating time series data presents unique challenges. A model must not only learn a probability distribution that reflects the real data distribution but also captur... | {
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2501.01652 | MIRAGE: Exploring How Large Language Models Perform in Complex Social
Interactive Environments | [
"cs.CL"
] | Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly in interactive role-playing contexts. This paper introduces the Multiverse Interactive Role-play Ability General Evaluation (MIRAGE), a compr... | {
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2501.01653 | Look Back for More: Harnessing Historical Sequential Updates for
Personalized Federated Adapter Tuning | [
"cs.LG",
"cs.DC"
] | Personalized federated learning (PFL) studies effective model personalization to address the data heterogeneity issue among clients in traditional federated learning (FL). Existing PFL approaches mainly generate personalized models by relying solely on the clients' latest updated models while ignoring their previous up... | {
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2501.01658 | EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical
image segmentation | [
"cs.CV",
"cs.AI"
] | Weakly-supervised medical image segmentation is gaining traction as it requires only rough annotations rather than accurate pixel-to-pixel labels, thereby reducing the workload for specialists. Although some progress has been made, there is still a considerable performance gap between the label-efficient methods and fu... | {
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2501.01664 | BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat
Prediction | [
"cs.CR",
"cs.AI"
] | The integration of Internet of Things (IoT) technology in various domains has led to operational advancements, but it has also introduced new vulnerabilities to cybersecurity threats, as evidenced by recent widespread cyberattacks on IoT devices. Intrusion detection systems are often reactive, triggered by specific pat... | {
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2501.01665 | FairSense: Long-Term Fairness Analysis of ML-Enabled Systems | [
"cs.LG",
"cs.CY",
"cs.SE"
] | Algorithmic fairness of machine learning (ML) models has raised significant concern in the recent years. Many testing, verification, and bias mitigation techniques have been proposed to identify and reduce fairness issues in ML models. The existing methods are model-centric and designed to detect fairness issues under ... | {
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2501.01668 | CoT-based Synthesizer: Enhancing LLM Performance through Answer
Synthesis | [
"cs.CL"
] | Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily on the quality of candidate responses and are unable to produce correct answers when all candidates are incorrect. In this pa... | {
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2501.01669 | Inversely Learning Transferable Rewards via Abstracted States | [
"cs.LG",
"cs.RO"
] | Inverse reinforcement learning (IRL) has progressed significantly toward accurately learning the underlying rewards in both discrete and continuous domains from behavior data. The next advance is to learn {\em intrinsic} preferences in ways that produce useful behavior in settings or tasks which are different but align... | {
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2501.01677 | PG-SAG: Parallel Gaussian Splatting for Fine-Grained Large-Scale Urban
Buildings Reconstruction via Semantic-Aware Grouping | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a transformative method in the field of real-time novel synthesis. Based on 3DGS, recent advancements cope with large-scale scenes via spatial-based partition strategy to reduce video memory and optimization time costs. In this work, we introduce a parallel Gaussian splatting... | {
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2501.01679 | Adaptive Few-shot Prompting for Machine Translation with Pre-trained
Language Models | [
"cs.CL",
"cs.AI"
] | Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translation tasks. To address... | {
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2501.01681 | SNeRV: Spectra-preserving Neural Representation for Video | [
"eess.IV",
"cs.CV"
] | Neural representation for video (NeRV), which employs a neural network to parameterize video signals, introduces a novel methodology in video representations. However, existing NeRV-based methods have difficulty in capturing fine spatial details and motion patterns due to spectral bias, in which a neural network learns... | {
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2501.01685 | IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks | [
"cs.CV"
] | Image segmentation is a vital task for providing human assistance and enhancing autonomy in our daily lives. In particular, RGB-D segmentation-leveraging both visual and depth cues-has attracted increasing attention as it promises richer scene understanding than RGB-only methods. However, most existing efforts have pri... | {
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2501.01689 | Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input
Transformer-Based Network | [
"cs.CV"
] | Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly ... | {
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2501.01690 | Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case
Study Using Socrata Datasets | [
"cs.LG"
] | This study explores the application of topic modelling techniques Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA) on the Socrata dataset spanning from 1908 to 2009. Categorized by operator type (military, commercial, and private), the analysis... | {
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2501.01691 | VidFormer: A novel end-to-end framework fused by 3DCNN and Transformer
for Video-based Remote Physiological Measurement | [
"cs.CV",
"cs.AI"
] | Remote physiological signal measurement based on facial videos, also known as remote photoplethysmography (rPPG), involves predicting changes in facial vascular blood flow from facial videos. While most deep learning-based methods have achieved good results, they often struggle to balance performance across small and l... | {
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2501.01692 | Recursive decoding of projective Reed-Muller codes | [
"cs.IT",
"math.IT"
] | We give a recursive decoding algorithm for projective Reed-Muller codes making use of a decoder for affine Reed-Muller codes. We determine the number of errors that can be corrected in this way, which is the current highest for decoders of projective Reed-Muller codes. We show when we can decode up to the error correct... | {
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2501.01693 | Denoising and Adaptive Online Vertical Federated Learning for Sequential
Multi-Sensor Data in Industrial Internet of Things | [
"cs.LG",
"cs.NI"
] | With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motiv... | {
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2501.01694 | Comparative Study of Deep Learning Architectures for Textual Damage
Level Classification | [
"cs.LG"
] | Given the paramount importance of safety in the aviation industry, even minor operational anomalies can have significant consequences. Comprehensive documentation of incidents and accidents serves to identify root causes and propose safety measures. However, the unstructured nature of incident event narratives poses a ... | {
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2501.01695 | CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene
Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction, leveraging densely distributed Gaussian primitives to enable real-time rendering of high-resolution images. While existing 3DGS methods perform well in scenes with minor view variation, large view changes in cros... | {
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2501.01696 | Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient
Descent | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT"
] | Tensors, which give a faithful and effective representation to deliver the intrinsic structure of multi-dimensional data, play a crucial role in an increasing number of signal processing and machine learning problems. However, tensor data are often accompanied by arbitrary signal corruptions, including missing entries ... | {
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2501.01699 | Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels | [
"cs.CV",
"cs.MM"
] | Cross-modal hashing (CMH) has appeared as a popular technique for cross-modal retrieval due to its low storage cost and high computational efficiency in large-scale data. Most existing methods implicitly assume that multi-modal data is correctly labeled, which is expensive and even unattainable due to the inevitable im... | {
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2501.01700 | Aesthetic Matters in Music Perception for Image Stylization: A
Emotion-driven Music-to-Visual Manipulation | [
"cs.CV"
] | Emotional information is essential for enhancing human-computer interaction and deepening image understanding. However, while deep learning has advanced image recognition, the intuitive understanding and precise control of emotional expression in images remain challenging. Similarly, music research largely focuses on t... | {
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2501.01702 | AgentRefine: Enhancing Agent Generalization through Refinement Tuning | [
"cs.AI",
"cs.CL",
"cs.RO"
] | Large Language Model (LLM) based agents have proved their ability to perform complex tasks like humans. However, there is still a large gap between open-sourced LLMs and commercial models like the GPT series. In this paper, we focus on improving the agent generalization capabilities of LLMs via instruction tuning. We f... | {
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2501.01704 | Optimal Fiducial Marker Placement for Satellite Proximity Operations
Using Observability Gramians | [
"eess.SY",
"cs.CV",
"cs.RO",
"cs.SY",
"math.OC"
] | This paper investigates optimal fiducial marker placement on the surface of a satellite performing relative proximity operations with an observer satellite. The absolute and relative translation and attitude equations of motion for the satellite pair are modeled using dual quaternions. The observability of the relative... | {
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2501.01705 | The Essence of Contextual Understanding in Theory of Mind: A Study on
Question Answering with Story Characters | [
"cs.CL",
"cs.AI"
] | Theory-of-Mind (ToM) is a fundamental psychological capability that allows humans to understand and interpret the mental states of others. Humans infer others' thoughts by integrating causal cues and indirect clues from broad contextual information, often derived from past interactions. In other words, human ToM heavil... | {
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2501.01707 | Catch Causal Signals from Edges for Label Imbalance in Graph
Classification | [
"cs.LG"
] | Despite significant advancements in causal research on graphs and its application to cracking label imbalance, the role of edge features in detecting the causal effects within graphs has been largely overlooked, leaving existing methods with untapped potential for further performance gains. In this paper, we enhance th... | {
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2501.01708 | $(\Theta, \Delta_\Theta, \mathbf{a})$-cyclic codes over $\mathbb{F}_q^l$
and their applications in the construction of quantum codes | [
"cs.IT",
"math.IT"
] | In this article, for a finite field $\mathbb{F}_q$ and a natural number $l,$ let $\mathcal{R}$ denote the product ring $\mathbb{F}_q^l.$ Firstly, for an automorphism $\Theta$ of $\mathcal{R},$ a $\Theta$-derivation $\Delta_\Theta$ of $\mathcal{R}$ and for a unit $\mathbf{a}$ in $\mathcal{R},$ we study $(\Theta, \Delta_... | {
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2501.01709 | MoVE-KD: Knowledge Distillation for VLMs with Mixture of Visual Encoders | [
"cs.CV",
"cs.AI"
] | Visual encoders are fundamental components in vision-language models (VLMs), each showcasing unique strengths derived from various pre-trained visual foundation models. To leverage the various capabilities of these encoders, recent studies incorporate multiple encoders within a single VLM, leading to a considerable inc... | {
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2501.01710 | Enhancing Large Vision Model in Street Scene Semantic Understanding
through Leveraging Posterior Optimization Trajectory | [
"cs.CV",
"cs.LG",
"cs.RO"
] | To improve the generalization of the autonomous driving (AD) perception model, vehicles need to update the model over time based on the continuously collected data. As time progresses, the amount of data fitted by the AD model expands, which helps to improve the AD model generalization substantially. However, such ever... | {
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2501.01711 | LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User
Queries | [
"cs.HC",
"cs.AI"
] | The paper presents a preliminary analysis of an experiment conducted by Frank Bold, a Czech expert group, to explore user interactions with GPT-4 for addressing legal queries. Between May 3, 2023, and July 25, 2023, 1,252 users submitted 3,847 queries. Unlike studies that primarily focus on the accuracy, factuality, or... | {
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2501.01715 | Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision | [
"cs.CV",
"cs.RO"
] | We introduce Cloth-Splatting, a method for estimating 3D states of cloth from RGB images through a prediction-update framework. Cloth-Splatting leverages an action-conditioned dynamics model for predicting future states and uses 3D Gaussian Splatting to update the predicted states. Our key insight is that coupling a 3D... | {
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2501.01716 | Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for
Online Linear Programming | [
"math.OC",
"cs.DS",
"cs.LG",
"math.PR"
] | The Certainty Equivalent heuristic (CE) is a widely-used algorithm for various dynamic resource allocation problems in OR and OM. Despite its popularity, existing theoretical guarantees of CE are limited to settings satisfying restrictive fluid regularity conditions, particularly, the non-degeneracy conditions, under t... | {
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2501.01717 | KeyNode-Driven Geometry Coding for Real-World Scanned Human Dynamic Mesh
Compression | [
"cs.CV",
"cs.MM",
"eess.SP"
] | The compression of real-world scanned 3D human dynamic meshes is an emerging research area, driven by applications such as telepresence, virtual reality, and 3D digital streaming. Unlike synthesized dynamic meshes with fixed topology, scanned dynamic meshes often not only have varying topology across frames but also sc... | {
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2501.01720 | Interpretable Face Anti-Spoofing: Enhancing Generalization with
Multimodal Large Language Models | [
"cs.CV"
] | Face Anti-Spoofing (FAS) is essential for ensuring the security and reliability of facial recognition systems. Most existing FAS methods are formulated as binary classification tasks, providing confidence scores without interpretation. They exhibit limited generalization in out-of-domain scenarios, such as new environm... | {
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2501.01721 | Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and
Modulation Design for Random ISAC Signals | [
"eess.SP",
"cs.IT",
"math.IT"
] | Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of... | {
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2501.01722 | AR4D: Autoregressive 4D Generation from Monocular Videos | [
"cs.CV"
] | Recent advancements in generative models have ignited substantial interest in dynamic 3D content creation (\ie, 4D generation). Existing approaches primarily rely on Score Distillation Sampling (SDS) to infer novel-view videos, typically leading to issues such as limited diversity, spatial-temporal inconsistency and po... | {
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2501.01723 | IGAF: Incremental Guided Attention Fusion for Depth Super-Resolution | [
"cs.CV"
] | Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become esse... | {
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} |
2501.01725 | Subject Specific Deep Learning Model for Motor Imagery Direction
Decoding | [
"eess.SP",
"cs.NE"
] | Hemispheric strokes impair motor control in contralateral body parts, necessitating effective rehabilitation strategies. Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) promote neuroplasticity, aiding the recovery of motor functions. While deep learning has shown promise in decoding MI actions for stroke rehabi... | {
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} |
2501.01726 | Sensor Placement on a Cantilever Beam Using Observability Gramians | [
"eess.SY",
"cs.SY",
"math.AP",
"math.OC"
] | Working from an observability characterization based on output energy sensitivity to changes in initial conditions, we derive both analytical and empirical observability Gramian tools for a class of continuum material systems. Using these results, optimal sensor placement is calculated for an Euler-Bernoulli cantilever... | {
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} |
2501.01727 | Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal
Reinforcement Learning | [
"cs.AI",
"cs.LG"
] | Humanoid robots must master numerous tasks with sparse rewards, posing a challenge for reinforcement learning (RL). We propose a method combining RL and automated planning to address this. Our approach uses short goal-conditioned policies (GCPs) organized hierarchically, with Monte Carlo Tree Search (MCTS) planning usi... | {
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} |
2501.01728 | Multi-modal classification of forest biodiversity potential from 2D
orthophotos and 3D airborne laser scanning point clouds | [
"cs.CV"
] | Accurate assessment of forest biodiversity is crucial for ecosystem management and conservation. While traditional field surveys provide high-quality assessments, they are labor-intensive and spatially limited. This study investigates whether deep learning-based fusion of close-range sensing data from 2D orthophotos (1... | {
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} |
2501.01732 | Combined Hyper-Extensible Extremely-Secured Zero-Trust CIAM-PAM
architecture | [
"cs.CR",
"cs.AI",
"cs.NI"
] | Customer Identity and Access Management (CIAM) systems play a pivotal role in securing enterprise infrastructures. However, the complexity of implementing these systems requires careful architectural planning to ensure positive Return on Investment (RoI) and avoid costly delays. The proliferation of Active Persistent c... | {
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} |
2501.01733 | Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item
Detection under Noisy Annotations | [
"cs.CV",
"cs.AI"
] | Automatic X-ray prohibited item detection is vital for public safety. Existing deep learning-based methods all assume that the annotations of training X-ray images are correct. However, obtaining correct annotations is extremely hard if not impossible for large-scale X-ray images, where item overlapping is ubiquitous.A... | {
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} |
2501.01741 | How Toxic Can You Get? Search-based Toxicity Testing for Large Language
Models | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Language is a deep-rooted means of perpetration of stereotypes and discrimination. Large Language Models (LLMs), now a pervasive technology in our everyday lives, can cause extensive harm when prone to generating toxic responses. The standard way to address this issue is to align the LLM, which, however, dampens the is... | {
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} |
2501.01743 | Automating Legal Concept Interpretation with LLMs: Retrieval,
Generation, and Evaluation | [
"cs.CL",
"cs.AI"
] | Legal articles often include vague concepts for adapting to the ever-changing society. Providing detailed interpretations of these concepts is a critical and challenging task even for legal practitioners. It requires meticulous and professional annotations and summarizations by legal experts, which are admittedly time-... | {
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} |
2501.01752 | Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma
Probe Signals in Minimally Invasive Cancer Surgery | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Cancer remains a significant health challenge worldwide, with a new diagnosis occurring every two minutes in the UK. Surgery is one of the main treatment options for cancer. However, surgeons rely on the sense of touch and naked eye with limited use of pre-operative image data to directly guide the excision of cancerou... | {
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} |
2501.01760 | From Age Estimation to Age-Invariant Face Recognition: Generalized Age
Feature Extraction Using Order-Enhanced Contrastive Learning | [
"cs.CV"
] | Generalized age feature extraction is crucial for age-related facial analysis tasks, such as age estimation and age-invariant face recognition (AIFR). Despite the recent successes of models in homogeneous-dataset experiments, their performance drops significantly in cross-dataset evaluations. Most of these models fail ... | {
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} |
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