id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.10879 | A Benchmark of French ASR Systems Based on Error Severity | [
"cs.CL"
] | Automatic Speech Recognition (ASR) transcription errors are commonly assessed using metrics that compare them with a reference transcription, such as Word Error Rate (WER), which measures spelling deviations from the reference, or semantic score-based metrics. However, these approaches often overlook what is understand... | {
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2501.10884 | Fixed Point Computation: Beating Brute Force with Smoothed Analysis | [
"cs.GT",
"cs.DS",
"cs.LG"
] | We propose a new algorithm that finds an $\varepsilon$-approximate fixed point of a smooth function from the $n$-dimensional $\ell_2$ unit ball to itself. We use the general framework of finding approximate solutions to a variational inequality, a problem that subsumes fixed point computation and the computation of a N... | {
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2501.10885 | CEReBrO: Compact Encoder for Representations of Brain Oscillations Using
Efficient Alternating Attention | [
"cs.LG"
] | Electroencephalograph (EEG) is a crucial tool for studying brain activity. Recently, self-supervised learning methods leveraging large unlabeled datasets have emerged as a potential solution to the scarcity of widely available annotated EEG data. However, current methods suffer from at least one of the following limita... | {
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2501.10891 | OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for
Global High-Resolution Land Cover Mapping | [
"eess.IV",
"cs.AI",
"cs.CV",
"eess.SP"
] | High-resolution land cover mapping plays a crucial role in addressing a wide range of global challenges, including urban planning, environmental monitoring, disaster response, and sustainable development. However, creating accurate, large-scale land cover datasets remains a significant challenge due to the inherent com... | {
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2501.10893 | Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in
Realistic Environments | [
"cs.LG",
"cs.AI"
] | Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis. The abilities of existing LLMs at such tasks are often hindered by the lack of high-quality agent data from the corresponding environm... | {
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2501.10895 | Classical and Deep Reinforcement Learning Inventory Control Policies for
Pharmaceutical Supply Chains with Perishability and Non-Stationarity | [
"cs.AI",
"cs.LG",
"math.OC"
] | We study inventory control policies for pharmaceutical supply chains, addressing challenges such as perishability, yield uncertainty, and non-stationary demand, combined with batching constraints, lead times, and lost sales. Collaborating with Bristol-Myers Squibb (BMS), we develop a realistic case study incorporating ... | {
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2501.10896 | Robust Joint Message and State Transmission under Arbitrarily Varying
Jamming | [
"cs.IT",
"math.IT"
] | Joint message and state transmission under arbitrarily varying jamming attack is investigated. An inner bound of the robust capacity-distortion region is provided, which includes the worst-case communication rate and the worst-case estimation rate. The bound is optimal for the joint message and lossless state communica... | {
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2501.10897 | Unfolding Tensors to Identify the Graph in Discrete Latent Bipartite
Graphical Models | [
"math.ST",
"cs.LG",
"stat.TH"
] | We use a tensor unfolding technique to prove a new identifiability result for discrete bipartite graphical models, which have a bipartite graph between an observed and a latent layer. This model family includes popular models such as Noisy-Or Bayesian networks for medical diagnosis and Restricted Boltzmann Machines in ... | {
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2501.10900 | A Generative Security Application Engineering Curriculum | [
"cs.CY",
"cs.AI"
] | Generative AI and large language models (LLMs) are transforming security by automating many tasks being performed manually. With such automation changing the practice of security as we know it, it is imperative that we prepare future students for the technology landscape they will ultimately face. Towards this end, we ... | {
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2501.10901 | ARD-VAE: A Statistical Formulation to Find the Relevant Latent
Dimensions of Variational Autoencoders | [
"cs.LG"
] | The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to its simple yet effective formulation for modeling the data distribution. Moreover, optimizing the VAE objective function is more manageable than other DLVMs. The bottleneck dimension of the VAE is a crucial design choice, and it h... | {
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2501.10905 | A Remote Sensing Image Change Detection Method Integrating Layer
Exchange and Channel-Spatial Differences | [
"cs.CV"
] | Change detection in remote sensing imagery is a critical technique for Earth observation, primarily focusing on pixel-level segmentation of change regions between bi-temporal images. The essence of pixel-level change detection lies in determining whether corresponding pixels in bi-temporal images have changed. In deep ... | {
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2501.10906 | Explainable Adversarial Attacks on Coarse-to-Fine Classifiers | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage c... | {
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2501.10909 | Fine-Grained Appropriate Reliance: Human-AI Collaboration with a
Multi-Step Transparent Decision Workflow for Complex Task Decomposition | [
"cs.AI",
"cs.HC"
] | In recent years, the rapid development of AI systems has brought about the benefits of intelligent services but also concerns about security and reliability. By fostering appropriate user reliance on an AI system, both complementary team performance and reduced human workload can be achieved. Previous empirical studies... | {
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2501.10910 | DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample
Attention within Contrastive Framework | [
"cs.LG",
"stat.ML"
] | Missing values of varying patterns and rates in real-world tabular data pose a significant challenge in developing reliable data-driven models. Existing missing value imputation methods use statistical and traditional machine learning and are ineffective when the missing rate is high and not at random. This paper explo... | {
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2501.10913 | Know "No" Better: A Data-Driven Approach for Enhancing Negation
Awareness in CLIP | [
"cs.CV",
"cs.CL"
] | While CLIP has significantly advanced multimodal understanding by bridging vision and language, the inability to grasp negation - such as failing to differentiate concepts like "parking" from "no parking" - poses substantial challenges. By analyzing the data used in the public CLIP model's pre-training, we posit this l... | {
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2501.10914 | Green Video Camouflaged Object Detection | [
"cs.CV"
] | Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and u... | {
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2501.10915 | LegalGuardian: A Privacy-Preserving Framework for Secure Integration of
Large Language Models in Legal Practice | [
"cs.CL",
"cs.CR",
"cs.IR"
] | Large Language Models (LLMs) hold promise for advancing legal practice by automating complex tasks and improving access to justice. However, their adoption is limited by concerns over client confidentiality, especially when lawyers include sensitive Personally Identifiable Information (PII) in prompts, risking unauthor... | {
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2501.10917 | Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal
for Multi-Sensor Wearable Human Activity Recognition | [
"cs.CV",
"cs.AI",
"cs.HC"
] | Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction ac... | {
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2501.10920 | Data Enrichment Opportunities for Distribution Grid Cable Networks using
Variational Autoencoders | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Electricity distribution cable networks suffer from incomplete and unbalanced data, hindering the effectiveness of machine learning models for predictive maintenance and reliability evaluation. Features such as the installation date of the cables are frequently missing. To address data scarcity, this study investigates... | {
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2501.10924 | Adaptive Target Localization under Uncertainty using Multi-Agent Deep
Reinforcement Learning with Knowledge Transfer | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Target localization is a critical task in sensitive applications, where multiple sensing agents communicate and collaborate to identify the target location based on sensor readings. Existing approaches investigated the use of Multi-Agent Deep Reinforcement Learning (MADRL) to tackle target localization. Nevertheless, t... | {
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2501.10926 | A Semantic Approach to Successive Interference Cancellation for Multiple
Access Networks | [
"cs.IT",
"math.IT"
] | Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from ... | {
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2501.10928 | Generative Physical AI in Vision: A Survey | [
"cs.CV",
"cs.AI"
] | Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D or 4D content. Traditionally... | {
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2501.10929 | Issues with Neural Tangent Kernel Approach to Neural Networks | [
"stat.ML",
"cs.LG"
] | Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of work is the theorem of equivalence between a trained neural network and kernel regression with the corresponding NTK. This theorem allows for ... | {
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2501.10933 | BeST -- A Novel Source Selection Metric for Transfer Learning | [
"cs.LG",
"stat.ML"
] | One of the most fundamental, and yet relatively less explored, goals in transfer learning is the efficient means of selecting top candidates from a large number of previously trained models (optimized for various "source" tasks) that would perform the best for a new "target" task with a limited amount of data. In this ... | {
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2501.10935 | TSVC:Tripartite Learning with Semantic Variation Consistency for Robust
Image-Text Retrieval | [
"cs.CV",
"cs.AI"
] | Cross-modal retrieval maps data under different modality via semantic relevance. Existing approaches implicitly assume that data pairs are well-aligned and ignore the widely existing annotation noise, i.e., noisy correspondence (NC). Consequently, it inevitably causes performance degradation. Despite attempts that empl... | {
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2501.10937 | Leveraging Chain of Thought towards Empathetic Spoken Dialogue without
Corresponding Question-Answering Data | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Empathetic dialogue is crucial for natural human-computer interaction, allowing the dialogue system to respond in a more personalized and emotionally aware manner, improving user satisfaction and engagement. The emergence of large language models (LLMs) has revolutionized dialogue generation by harnessing their powerfu... | {
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2501.10938 | Blockchain-assisted Demonstration Cloning for Multi-Agent Deep
Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Multi-Agent Deep Reinforcement Learning (MDRL) is a promising research area in which agents learn complex behaviors in cooperative or competitive environments. However, MDRL comes with several challenges that hinder its usability, including sample efficiency, curse of dimensionality, and environment exploration. Recent... | {
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2501.10940 | Influence- and Interest-based Worker Recruitment in Crowdsourcing using
Online Social Networks | [
"cs.SI"
] | Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS). Current recruitment systems assume that a pre-defined pool of workers is available. However, this assumption is not always true, especially ... | {
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2501.10943 | InsQABench: Benchmarking Chinese Insurance Domain Question Answering
with Large Language Models | [
"cs.CL",
"cs.AI"
] | The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance industry remains underexplored. The complexity of insurance knowledge, encompassing specialized terminology and diverse data types, poses significa... | {
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2501.10945 | Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories,
Applications, and Beyond | [
"cs.LG",
"stat.ML"
] | Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize multiple conflicting objectives, a challenge frequently encountered in areas like multi-task learning and multi-criteria learning. Recent advancements in gradient-based MOO methods have enabled the discovery of diverse types of solution... | {
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2501.10950 | Factor Graph-Based Active SLAM for Spacecraft Proximity Operations | [
"cs.RO"
] | We investigate a scenario where a chaser spacecraft or satellite equipped with a monocular camera navigates in close proximity to a target spacecraft. The satellite's primary objective is to construct a representation of the operational environment and localize itself within it, utilizing the available image data. We f... | {
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2501.10953 | Channel Coding for Gaussian Channels with Mean and Variance Constraints | [
"cs.IT",
"math.IT"
] | We consider channel coding for Gaussian channels with the recently introduced mean and variance cost constraints. Through matching converse and achievability bounds, we characterize the optimal first- and second-order performance. The main technical contribution of this paper is an achievability scheme which uses rando... | {
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2501.10956 | Multimodal Techniques for Malware Classification | [
"cs.CR",
"cs.LG"
] | The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with multimodal machine learning approaches for malware classification, based on the structured nature of the Windows Portable Executable (PE) file ... | {
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2501.10957 | MARIO: A Mixed Annotation Framework For Polyp Segmentation | [
"cs.CV",
"cs.AI"
] | Existing polyp segmentation models are limited by high labeling costs and the small size of datasets. Additionally, vast polyp datasets remain underutilized because these models typically rely on a single type of annotation. To address this dilemma, we introduce MARIO, a mixed supervision model designed to accommodate ... | {
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2501.10958 | Rethinking Early-Fusion Strategies for Improved Multimodal Image
Segmentation | [
"cs.CV"
] | RGB and thermal image fusion have great potential to exhibit improved semantic segmentation in low-illumination conditions. Existing methods typically employ a two-branch encoder framework for multimodal feature extraction and design complicated feature fusion strategies to achieve feature extraction and fusion for mul... | {
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2501.10963 | Open FinLLM Leaderboard: Towards Financial AI Readiness | [
"cs.CE"
] | Financial large language models (FinLLMs) with multimodal capabilities are envisioned to revolutionize applications across business, finance, accounting, and auditing. However, real-world adoption requires robust benchmarks of FinLLMs' and agents' performance. Maintaining an open leaderboard of models is crucial for en... | {
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2501.10966 | DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided
Quantization | [
"cs.CV",
"cs.AI"
] | Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completion have been developed. Despite achieving encouraging results, a significant issue remains: these methods often overlook the variability in p... | {
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2501.10967 | Advancing General Multimodal Capability of Vision-language Models with
Pyramid-descent Visual Position Encoding | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Vision-language Models (VLMs) have shown remarkable capabilities in advancing general artificial intelligence, yet the irrational encoding of visual positions persists in inhibiting the models' comprehensive perception performance across different levels of granularity. In this work, we propose Pyramid-descent Visual P... | {
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2501.10969 | AI Based Font Pair Suggestion Modelling For Graphic Design | [
"cs.CV",
"cs.CL"
] | One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. Previous efforts involved manually mapping design intent to fonts. Though this was high quality, this method does not scale for a large number of fonts (3000+) ... | {
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2501.10970 | The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically
Justify Replacing Human Annotators with LLMs | [
"cs.CL",
"cs.AI",
"cs.HC"
] | The "LLM-as-a-judge" paradigm employs Large Language Models (LLMs) as annotators and evaluators in tasks traditionally performed by humans. LLM annotations are widely used, not only in NLP research but also in fields like medicine, psychology, and social science. Despite their role in shaping study results and insights... | {
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2501.10974 | Sequential Change Detection for Learning in Piecewise Stationary Bandit
Environments | [
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT",
"stat.OT"
] | A finite-horizon variant of the quickest change detection problem is investigated, which is motivated by a change detection problem that arises in piecewise stationary bandits. The goal is to minimize the \emph{latency}, which is smallest threshold such that the probability that the detection delay exceeds the threshol... | {
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2501.10977 | SMARTe-VR: Student Monitoring and Adaptive Response Technology for
e-learning in Virtual Reality | [
"cs.HC",
"cs.CV"
] | This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR is aimed to gather data for adaptive learning, focusing on facial biometrics and learning metadata. The platform allows instructors to create tailored learning sessions ... | {
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2501.10979 | Control LLM: Controlled Evolution for Intelligence Retention in LLM | [
"cs.LG"
] | Large Language Models (LLMs) demand significant computational resources, making it essential to enhance their capabilities without retraining from scratch. A key challenge in this domain is \textit{catastrophic forgetting} (CF), which hampers performance during Continuous Pre-training (CPT) and Continuous Supervised Fi... | {
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2501.10980 | An analysis of the combination of feature selection and machine learning
methods for an accurate and timely detection of lung cancer | [
"cs.LG"
] | One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer using sophisticated machine learning techniques like Random Forest (RF) and Support... | {
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2501.10984 | Self-CephaloNet: A Two-stage Novel Framework using Operational Neural
Network for Cephalometric Analysis | [
"cs.CV",
"math.OC"
] | Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, c... | {
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2501.10985 | GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models | [
"cs.LG",
"cs.CR"
] | Graph neural networks (GNNs) have exhibited superior performance in various classification tasks on graph-structured data. However, they encounter the potential vulnerability from the link stealing attacks, which can infer the presence of a link between two nodes via measuring the similarity of its incident nodes' pred... | {
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2501.10990 | Societal citations undermine the function of the science reward system | [
"cs.DL",
"cs.SI",
"physics.soc-ph"
] | Citations in the scientific literature system do not simply reflect relationships between knowledge but are influenced by non-objective and societal factors. Citation bias, irresponsible citation, and citation manipulation are widespread and have become a serious and growing problem. However, it has been difficult to a... | {
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2501.10991 | Front Hair Styling Robot System Using Path Planning for Root-Centric
Strand Adjustment | [
"cs.RO"
] | Hair styling is a crucial aspect of personal grooming, significantly influenced by the appearance of front hair. While brushing is commonly used both to detangle hair and for styling purposes, existing research primarily focuses on robotic systems for detangling hair, with limited exploration into robotic hair styling.... | {
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2501.11002 | pMixFed: Efficient Personalized Federated Learning through Adaptive
Layer-Wise Mixup | [
"cs.LG",
"cs.DC"
] | Traditional Federated Learning (FL) methods encounter significant challenges when dealing with heterogeneous data and providing personalized solutions for non-IID scenarios. Personalized Federated Learning (PFL) approaches aim to address these issues by balancing generalization and personalization, often through parame... | {
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2501.11003 | Building low-resource African language corpora: A case study of
Kidawida, Kalenjin and Dholuo | [
"cs.CL"
] | Natural Language Processing is a crucial frontier in artificial intelligence, with broad applications in many areas, including public health, agriculture, education, and commerce. However, due to the lack of substantial linguistic resources, many African languages remain underrepresented in this digital transformation.... | {
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2501.11006 | GREEN-CODE: Optimizing Energy Efficiency in Large Language Models for
Code Generation | [
"cs.DC",
"cs.AI",
"cs.PF",
"cs.SE"
] | Large Language Models (LLMs) are becoming integral to daily life, showcasing their vast potential across various Natural Language Processing (NLP) tasks. Beyond NLP, LLMs are increasingly used in software development tasks, such as code completion, modification, bug fixing, and code translation. Software engineers wide... | {
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2501.11007 | HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based
Action Recognition | [
"cs.CV",
"cs.LG"
] | In recent years, action recognition has received much attention and wide application due to its important role in video understanding. Most of the researches on action recognition methods focused on improving the performance via various deep learning methods rather than the classification of skeleton points. The topolo... | {
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2501.11009 | Efficient Reconciliation of Continuous Variable Quantum Key Distribution
with Multiplicatively Repeated Non-Binary LDPC Codes | [
"quant-ph",
"cs.IT",
"math.IT"
] | Continuous variable quantum key distribution bears the promise of simple quantum key distribution directly compatible with commercial off the shelf equipment. However, for a long time its performance was hindered by the absence of good classical postprocessing capable of distilling secret-keys in the noisy regime. Adva... | {
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2501.11012 | GenAI Content Detection Task 1: English and Multilingual
Machine-Generated Text Detection: AI vs. Human | [
"cs.CL"
] | We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025. The task consists of two subtasks: Monolingual (English) and Multilingual. The shared task attracted many participants: 36 teams made official submissions ... | {
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2501.11014 | Transfer Learning Strategies for Pathological Foundation Models: A
Systematic Evaluation in Brain Tumor Classification | [
"eess.IV",
"cs.CV"
] | Foundation models pretrained on large-scale pathology datasets have shown promising results across various diagnostic tasks. Here, we present a systematic evaluation of transfer learning strategies for brain tumor classification using these models. We analyzed 252 cases comprising five major tumor types: glioblastoma, ... | {
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2501.11015 | Wireless Control over Edge Networks: Joint User Association and
Communication-Computation Co-Design | [
"cs.IT",
"math.IT"
] | This paper studies a wireless networked control system with multiple base stations (BSs) cooperatively coordinating the wireless control of a number of subsystems each consisting of a plant, a sensor, and an actuator. In this system, each sensor first offloads the sensing data to its associated BS, which then employs m... | {
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2501.11020 | Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D
Car Reconstruction | [
"cs.CV"
] | 3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as highly reflective and transparent surface materials, existing methods often struggle to achieve accurate 3D car reconstruction.To address thes... | {
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2501.11023 | Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment
Analysis in Hausa Language Using AfriBERTa | [
"cs.CL"
] | Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by ~identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This st... | {
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2501.11024 | Laplacian Eigenvector Centrality | [
"cs.SI",
"cs.GT",
"physics.soc-ph"
] | Networks significantly influence social, economic, and organizational outcomes, with centrality measures serving as crucial tools to capture the importance of individual nodes. This paper introduces Laplacian Eigenvector Centrality (LEC), a novel framework for network analysis based on spectral graph theory and the eig... | {
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2501.11030 | Tracking Mouse from Incomplete Body-Part Observations and Deep-Learned
Deformable-Mouse Model Motion-Track Constraint for Behavior Analysis | [
"cs.CV"
] | Tracking mouse body parts in video is often incomplete due to occlusions such that - e.g. - subsequent action and behavior analysis is impeded. In this conceptual work, videos from several perspectives are integrated via global exterior camera orientation; body part positions are estimated by 3D triangulation and bundl... | {
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2501.11031 | AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration
of Large and Small Language Model | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Automated log analysis is crucial to ensure high availability and reliability of complex systems. The advent of LLMs in NLP has ushered in a new era of language model-driven automated log analysis, garnering significant interest. Within this field, two primary paradigms based on language models for log analysis have be... | {
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2501.11034 | Generative Retrieval for Book search | [
"cs.IR"
] | In book search, relevant book information should be returned in response to a query. Books contain complex, multi-faceted information such as metadata, outlines, and main text, where the outline provides hierarchical information between chapters and sections. Generative retrieval (GR) is a new retrieval paradigm that c... | {
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2501.11035 | From Arabic Text to Puzzles: LLM-Driven Development of Arabic
Educational Crosswords | [
"cs.CL"
] | We present an Arabic crossword puzzle generator from a given text that utilizes advanced language models such as GPT-4-Turbo, GPT-3.5-Turbo and Llama3-8B-Instruct, specifically developed for educational purposes, this innovative generator leverages a meticulously compiled dataset named Arabic-Clue-Instruct with over 50... | {
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2501.11036 | LF-Steering: Latent Feature Activation Steering for Enhancing Semantic
Consistency in Large Language Models | [
"cs.CL"
] | Large Language Models (LLMs) often generate inconsistent responses when prompted with semantically equivalent paraphrased inputs. Recently, activation steering, a technique that modulates LLMs' behaviours by adjusting their latent representations during inference time, has been explored to improve the semantic consiste... | {
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2501.11039 | Beyond Any-Shot Adaptation: Predicting Optimization Outcome for
Robustness Gains without Extra Pay | [
"cs.LG"
] | The foundation model enables general-purpose problem-solving and enjoys desirable rapid adaptation due to its adopted cross-task generalization paradigms, e.g., pretraining, meta-training, and finetuning. Recent advances in these paradigms show the crucial role of challenging tasks' prioritized sampling in enhancing ad... | {
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2501.11041 | Enhancing Semantic Consistency of Large Language Models through Model
Editing: An Interpretability-Oriented Approach | [
"cs.CL"
] | A Large Language Model (LLM) tends to generate inconsistent and sometimes contradictory outputs when presented with a prompt that has equivalent semantics but is expressed differently from the original prompt. To achieve semantic consistency of an LLM, one of the key approaches is to finetune the model with prompt-outp... | {
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2501.11043 | BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity
Spatial-Temporal Video Super-Resolution | [
"cs.CV",
"cs.AI"
] | Enhancing low-resolution, low-frame-rate videos to high-resolution, high-frame-rate quality is essential for a seamless user experience, motivating advancements in Continuous Spatial-Temporal Video Super Resolution (C-STVSR). While prior methods employ Implicit Neural Representation (INR) for continuous encoding, they ... | {
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2501.11053 | Learning with Open-world Noisy Data via Class-independent Margin in Dual
Representation Space | [
"cs.LG",
"cs.CV"
] | Learning with Noisy Labels (LNL) aims to improve the model generalization when facing data with noisy labels, and existing methods generally assume that noisy labels come from known classes, called closed-set noise. However, in real-world scenarios, noisy labels from similar unknown classes, i.e., open-set noise, may o... | {
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2501.11054 | Temporal Analysis of Adversarial Attacks in Federated Learning | [
"cs.LG",
"cs.CR"
] | In this paper, we experimentally analyze the robustness of selected Federated Learning (FL) systems in the presence of adversarial clients. We find that temporal attacks significantly affect model performance in the FL models tested, especially when the adversaries are active throughout or during the later rounds. We c... | {
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2501.11057 | Machine Learning Surrogates for Optimizing Transportation Policies with
Agent-Based Models | [
"cs.CE"
] | Rapid urbanization and growing urban populations worldwide present significant challenges for cities, including increased traffic congestion and air pollution. Effective strategies are needed to manage traffic volumes and reduce emissions. In practice, traditional traffic flow simulations are used to test those strateg... | {
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2501.11063 | Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With
Subgroup-Based Positive-Pair Selection | [
"cs.CV"
] | The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving the robustness towards noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains under-explored. Existing... | {
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2501.11065 | Enhancing Neural Spoken Language Recognition: An Exploration with
Multilingual Datasets | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | In this research, we advanced a spoken language recognition system, moving beyond traditional feature vector-based models. Our improvements focused on effectively capturing language characteristics over extended periods using a specialized pooling layer. We utilized a broad dataset range from Common-Voice, targeting te... | {
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2501.11067 | IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI
Systems | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) are transforming artificial intelligence, evolving into task-oriented systems capable of autonomous planning and execution. One of the primary applications of LLMs is conversational AI systems, which must navigate multi-turn dialogues, integrate domain-specific APIs, and adhere to strict po... | {
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2501.11069 | Refinement Module based on Parse Graph of Feature Map for Human Pose
Estimation | [
"cs.CV"
] | Parse graphs of the human body can be obtained in the human brain to help humans complete the human pose estimation (HPE). It contains a hierarchical structure, like a tree structure, and context relations among nodes. Many researchers predefine the parse graph of body structure to design HPE frameworks. However, these... | {
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2501.11079 | Federated Deep Reinforcement Learning for Energy Efficient
Multi-Functional RIS-Assisted Low-Earth Orbit Networks | [
"cs.LG",
"cs.AI",
"eess.SP"
] | In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MF-RIS) in low-Earth orbit (LEO) is proposed. Unlike traditional RIS with only signal reflection capability, the MF-RIS can reflect, refract, and amplify signals, as well as harvest energy from wireless sig... | {
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2501.11084 | B-Call: Integrating Ideological Position and Political Cohesion in
Legislative Voting Models | [
"cs.SI",
"stat.AP"
] | This paper combines two significant areas of political science research: measuring individual ideological position and cohesion. Although both approaches help analyze legislative behaviors, no unified model currently integrates these dimensions. To fill this gap, the paper proposes a methodology called B-Call that comb... | {
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2501.11086 | Can LLM Generate Regression Tests for Software Commits? | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate the opportunities of LLMs for automatic regression test generation for programs that take highly structured, human-readable inputs, such as XML parsers or JavaScript interpreters. Concretely, we e... | {
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2501.11087 | Leveraging counterfactual concepts for debugging and improving CNN model
performance | [
"cs.CV",
"cs.AI"
] | Counterfactual explanation methods have recently received significant attention for explaining CNN-based image classifiers due to their ability to provide easily understandable explanations that align more closely with human reasoning. However, limited attention has been given to utilizing explainability methods to imp... | {
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2501.11088 | Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration
Framework | [
"cs.RO"
] | Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each other must be known. With Multi-LiCa, we present a novel approach for the alignment, e.g. calibration. We present an automatic motion- and tar... | {
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2501.11090 | Dynamic semantic networks for exploration of creative thinking | [
"cs.CL"
] | Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring crea... | {
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2501.11094 | Enhanced Suicidal Ideation Detection from Social Media Using a
CNN-BiLSTM Hybrid Model | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Suicidal ideation detection is crucial for preventing suicides, a leading cause of death worldwide. Many individuals express suicidal thoughts on social media, offering a vital opportunity for early detection through advanced machine learning techniques. The identification of suicidal ideation in social media text is i... | {
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2501.11096 | Reproducibility review of "Why Not Other Classes": Towards
Class-Contrastive Back-Propagation Explanations | [
"cs.CV",
"cs.LG"
] | "Why Not Other Classes?": Towards Class-Contrastive Back-Propagation Explanations (Wang & Wang, 2022) provides a method for contrastively explaining why a certain class in a neural network image classifier is chosen above others. This method consists of using back-propagation-based explanation methods from after the so... | {
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2501.11097 | Unit Region Encoding: A Unified and Compact Geometry-aware
Representation for Floorplan Applications | [
"cs.CV"
] | We present the Unit Region Encoding of floorplans, which is a unified and compact geometry-aware encoding representation for various applications, ranging from interior space planning, floorplan metric learning to floorplan generation tasks. The floorplans are represented as the latent encodings on a set of boundary-ad... | {
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2501.11102 | RDG-GS: Relative Depth Guidance with Gaussian Splatting for Real-time
Sparse-View 3D Rendering | [
"cs.CV"
] | Efficiently synthesizing novel views from sparse inputs while maintaining accuracy remains a critical challenge in 3D reconstruction. While advanced techniques like radiance fields and 3D Gaussian Splatting achieve rendering quality and impressive efficiency with dense view inputs, they suffer from significant geometri... | {
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2501.11107 | ChaosEater: Fully Automating Chaos Engineering with Large Language
Models | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.DC",
"cs.NI"
] | Chaos Engineering (CE) is an engineering technique aimed at improving the resiliency of distributed systems. It involves artificially injecting specific failures into a distributed system and observing its behavior in response. Based on the observation, the system can be proactively improved to handle those failures. R... | {
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2501.11109 | Estimation Error: Distribution and Pointwise Limits | [
"cs.IT",
"math.IT"
] | In this paper, we examine the distribution and convergence properties of the estimation error $W = X - \hat{X}(Y)$, where $\hat{X}(Y)$ is the Bayesian estimator of a random variable $X$ from a noisy observation $Y = X +\sigma Z$ where $\sigma$ is the parameter indicating the strength of noise $Z$. Using the conditional... | {
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2501.11110 | Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large
Language Models via a Multi-Paradigm Perspective | [
"cs.CL"
] | Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet they often rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. In this paper, we introduce Chain-of-Reasoning (CoR), a novel unified framework that integrates multiple reasoning paradigms--Natural ... | {
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2501.11111 | OpenLiDARMap: Zero-Drift Point Cloud Mapping using Map Priors | [
"cs.RO"
] | Accurate localization is a critical component of mobile autonomous systems, especially in Global Navigation Satellite Systems (GNSS)-denied environments where traditional methods fail. In such scenarios, environmental sensing is essential for reliable operation. However, approaches such as LiDAR odometry and Simultaneo... | {
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2501.11112 | A Novel Pearson Correlation-Based Merging Algorithm for Robust
Distributed Machine Learning with Heterogeneous Data | [
"cs.LG"
] | Federated learning faces significant challenges in scenarios with heterogeneous data distributions and adverse network conditions, such as delays, packet loss, and data poisoning attacks. This paper proposes a novel method based on the SCAFFOLD algorithm to improve the quality of local updates and enhance the robustnes... | {
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2501.11114 | Clinical trial cohort selection using Large Language Models on n2c2
Challenges | [
"cs.CL",
"cs.AI"
] | Clinical trials are a critical process in the medical field for introducing new treatments and innovations. However, cohort selection for clinical trials is a time-consuming process that often requires manual review of patient text records for specific keywords. Though there have been studies on standardizing the infor... | {
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} |
2501.11120 | Tell me about yourself: LLMs are aware of their learned behaviors | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | We study behavioral self-awareness -- an LLM's ability to articulate its behaviors without requiring in-context examples. We finetune LLMs on datasets that exhibit particular behaviors, such as (a) making high-risk economic decisions, and (b) outputting insecure code. Despite the datasets containing no explicit descrip... | {
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"cs.SY": 0
} |
2501.11122 | Optimal Functional $2^{s-1}$-Batch Codes: Exploring New Sufficient
Conditions | [
"cs.IT",
"math.IT"
] | A functional $k$-batch code of dimension $s$ consists of $n$ servers storing linear combinations of $s$ linearly independent information bits. These codes are designed to recover any multiset of $k$ requests, each being a linear combination of the information bits, by $k$ disjoint subsets of servers. A recent conjectur... | {
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} |
2501.11123 | Assessing Semantic Annotation Activities with Formal Concept Analysis | [
"cs.CL"
] | This paper describes an approach to assessing semantic annotation activities based on formal concept analysis (FCA). In this approach, annotators use taxonomical ontologies created by domain experts to annotate digital resources. Then, using FCA, domain experts are provided with concept lattices that graphically displa... | {
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} |
2501.11124 | Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal
Action Localization from the Perspective of Noise Correction | [
"cs.CV"
] | Pseudo-label learning methods have been widely applied in weakly-supervised temporal action localization. Existing works directly utilize weakly-supervised base model to generate instance-level pseudo-labels for training the fully-supervised detection head. We argue that the noise in pseudo-labels would interfere with ... | {
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} |
2501.11126 | SIC-free Multicast Scheduling for Multi-antenna Coded Caching | [
"cs.IT",
"math.IT"
] | Multi-antenna coded caching (CC) with multicast beamforming typically relies on a complex successive interference cancellation (SIC) structure to decode a superposition of multiple streams received by each user. Signal-level CC schemes require the regeneration and cancellation of interfering signals at the physical lay... | {
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} |
2501.11127 | A Regularized Online Newton Method for Stochastic Convex Bandits with
Linear Vanishing Noise | [
"math.OC",
"cs.LG",
"stat.ML"
] | We study a stochastic convex bandit problem where the subgaussian noise parameter is assumed to decrease linearly as the learner selects actions closer and closer to the minimizer of the convex loss function. Accordingly, we propose a Regularized Online Newton Method (RONM) for solving the problem, based on the Online ... | {
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} |
2501.11128 | A Collection of Question Answering Datasets for Norwegian | [
"cs.CL",
"cs.AI"
] | This paper introduces a new suite of question answering datasets for Norwegian; NorOpenBookQA, NorCommonSenseQA, NorTruthfulQA, and NRK-Quiz-QA. The data covers a wide range of skills and knowledge domains, including world knowledge, commonsense reasoning, truthfulness, and knowledge about Norway. Covering both of the ... | {
"Other": 0,
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"cs.RO": 0,
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"cs.SY": 0
} |
2501.11129 | Optimal Binary Variable-Length Codes with a Bounded Number of 1's per
Codeword: Design, Analysis, and Applications | [
"cs.IT",
"cs.DS",
"math.IT"
] | In this paper, we consider the problem of constructing optimal average-length binary codes under the constraint that each codeword must contain at most $D$ ones, where $D$ is a given input parameter. We provide an $O(n^2D)$-time complexity algorithm for the construction of such codes, where $n$ is the number of codewor... | {
"Other": 1,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.11130 | Efficient and accurate simulation of the Smith-Zener pinning mechanism
during grain growth using a front-tracking numerical framework | [
"cs.CE"
] | This study proposes a new full-field approach for modeling grain boundary pinning by second phase particles in two-dimensional polycrystals. These particles are of great importance during thermomechanical treatments, as they produce deviations from the microstructural evolution that the alloy produces in the absence of... | {
"Other": 0,
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"cs.SY": 0
} |
2501.11131 | Spatio-temporal characterisation of underwater noise through semantic
trajectories | [
"stat.AP",
"cs.DB"
] | Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by ves... | {
"Other": 0,
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"cs.SI": 0,
"cs.SY": 0
} |
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