id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.11924 | Make Full Use of Testing Information: An Integrated Accelerated Testing
and Evaluation Method for Autonomous Driving Systems | [
"cs.AI"
] | Testing and evaluation is an important step before the large-scale application of the autonomous driving systems (ADSs). Based on the three level of scenario abstraction theory, a testing can be performed within a logical scenario, followed by an evaluation stage which is inputted with the testing results of each concr... | {
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2501.11926 | Multi-Modal Variable-Rate CSI Reconstruction for FDD Massive MIMO
Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | In frequency division duplex (FDD) systems, acquiring channel state information (CSI) at the base station (BS) traditionally relies on limited feedback from mobile terminals (MTs). However, the accuracy of channel reconstruction from feedback CSI is inherently constrained by the rate-distortion trade-off. To overcome t... | {
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2501.11927 | A Lightweight and Interpretable Deepfakes Detection Framework | [
"cs.CV",
"cs.AI"
] | The recent realistic creation and dissemination of so-called deepfakes poses a serious threat to social life, civil rest, and law. Celebrity defaming, election manipulation, and deepfakes as evidence in court of law are few potential consequences of deepfakes. The availability of open source trained models based on mod... | {
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2501.11929 | ALoFTRAG: Automatic Local Fine Tuning for Retrieval Augmented Generation | [
"cs.LG"
] | Retrieval Augmented Generation (RAG) systems have been shown to improve the accuracy of Large Language Model (LLM) outputs. However, these models can often achieve low accuracy when applied to new data domains. We introduce the Automatic Local Fine Tuning of Retrieval Augmented Generation models (ALoFTRAG) framework,... | {
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2501.11930 | Nocturnal eye inspired liquid to gas phase change soft actuator with
Laser-Induced-Graphene: enhanced environmental light harvesting and
photothermal conversion | [
"cs.RO"
] | Robotic systems' mobility is constrained by power sources and wiring. While pneumatic actuators remain tethered to air supplies, we developed a new actuator utilizing light energy. Inspired by nocturnal animals' eyes, we designed a bilayer soft actuator incorporating Laser-Induced Graphene (LIG) on the inner surface of... | {
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2501.11931 | Construction of Simultaneously Good Polar Codes and Polar Lattices | [
"cs.IT",
"math.IT"
] | In this work, we investigate the simultaneous goodness of polar codes and polar lattices. The simultaneous goodness of a lattice or a code means that it is optimal for both channel coding and source coding simultaneously. The existence of such kind of lattices was proven by using random lattice ensembles. Our work prov... | {
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2501.11935 | Web vs. LLMs: An Empirical Study of Learning Behaviors of CS2 Students | [
"cs.HC",
"cs.AI"
] | LLMs such as ChatGPT have been widely adopted by students in higher education as tools for learning programming and related concepts. However, it remains unclear how effective students are and what strategies students use while learning with LLMs. Since the majority of students' experiences in online self-learning have... | {
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2501.11937 | MeshONet: A Generalizable and Efficient Operator Learning Method for
Structured Mesh Generation | [
"cs.LG",
"cs.AI"
] | Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge, physics-informed intelligent learning methods have recently emerged, significantly imp... | {
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2501.11938 | Navigating Robot Swarm Through a Virtual Tube with Flow-Adaptive
Distribution Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | With the rapid development of robot swarm technology and its diverse applications, navigating robot swarms through complex environments has emerged as a critical research direction. To ensure safe navigation and avoid potential collisions with obstacles, the concept of virtual tubes has been introduced to define safe a... | {
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2501.11945 | Learning to Hop for a Single-Legged Robot with Parallel Mechanism | [
"cs.RO"
] | This work presents the application of reinforcement learning to improve the performance of a highly dynamic hopping system with a parallel mechanism. Unlike serial mechanisms, parallel mechanisms can not be accurately simulated due to the complexity of their kinematic constraints and closed-loop structures. Besides, le... | {
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2501.11949 | GLAM: Global-Local Variation Awareness in Mamba-based World Model | [
"cs.LG"
] | Mimicking the real interaction trajectory in the inference of the world model has been shown to improve the sample efficiency of model-based reinforcement learning (MBRL) algorithms. Many methods directly use known state sequences for reasoning. However, this approach fails to enhance the quality of reasoning by captur... | {
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2501.11951 | HERITAGE: An End-to-End Web Platform for Processing Korean Historical
Documents in Hanja | [
"cs.CL"
] | While Korean historical documents are invaluable cultural heritage, understanding those documents requires in-depth Hanja expertise. Hanja is an ancient language used in Korea before the 20th century, whose characters were borrowed from old Chinese but had evolved in Korea for centuries. Modern Koreans and Chinese cann... | {
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2501.11953 | Proverbs Run in Pairs: Evaluating Proverb Translation Capability of
Large Language Model | [
"cs.CL"
] | Despite achieving remarkable performance, machine translation (MT) research remains underexplored in terms of translating cultural elements in languages, such as idioms, proverbs, and colloquial expressions. This paper investigates the capability of state-of-the-art neural machine translation (NMT) and large language m... | {
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2501.11959 | Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak
Segment Labels | [
"cs.LG"
] | Detecting anomalies in temporal data has gained significant attention across various real-world applications, aiming to identify unusual events and mitigate potential hazards. In practice, situations often involve a mix of segment-level labels (detected abnormal events with segments of time points) and unlabeled data (... | {
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2501.11960 | TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly
Detection | [
"cs.CL",
"cs.AI"
] | Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present T... | {
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2501.11963 | A Contrastive Framework with User, Item and Review Alignment for
Recommendation | [
"cs.IR"
] | Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this... | {
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2501.11967 | A Hybrid Attention Framework for Fake News Detection with Large Language
Models | [
"cs.CL"
] | With the rapid growth of online information, the spread of fake news has become a serious social challenge. In this study, we propose a novel detection framework based on Large Language Models (LLMs) to identify and classify fake news by integrating textual statistical features and deep semantic features. Our approach ... | {
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2501.11968 | Bridging Visualization and Optimization: Multimodal Large Language
Models on Graph-Structured Combinatorial Optimization | [
"cs.AI",
"cs.LG"
] | Graph-structured combinatorial challenges are inherently difficult due to their nonlinear and intricate nature, often rendering traditional computational methods ineffective or expensive. However, these challenges can be more naturally tackled by humans through visual representations that harness our innate ability for... | {
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2501.11971 | SMamba: Sparse Mamba for Event-based Object Detection | [
"cs.CV"
] | Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-event and noisy regions and process them uniformly, leading to high computational overhead. To mitigate computation cost, some researchers prop... | {
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2501.11972 | "FRAME: Forward Recursive Adaptive Model Extraction -- A Technique for
Advance Feature Selection" | [
"cs.LG"
] | Feature selection is a crucial preprocessing step in machine learning, impacting model performance, interpretability, and computational efficiency. This study introduces a novel hybrid approach, the Forward Recursive Adaptive Model Extraction Technique (FRAME), which combines Forward Selection and Recursive Feature Eli... | {
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2501.11977 | Leveraging Graph Structures and Large Language Models for End-to-End
Synthetic Task-Oriented Dialogues | [
"cs.CL",
"cs.AI"
] | Training task-oriented dialogue systems is both costly and time-consuming, due to the need for high-quality datasets encompassing diverse intents. Traditional methods depend on extensive human annotation, while recent advancements leverage large language models (LLMs) to generate synthetic data. However, these approach... | {
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2501.11978 | Weight Distribution of the Weighted Coordinates Poset Block Space and
Singleton Bound | [
"cs.IT",
"math.CO",
"math.IT"
] | In this paper, we determine the complete weight distribution of the space $ \mathbb{F}_q^N $ endowed by the weighted coordinates poset block metric ($(P,w,\pi)$-metric), also known as the $(P,w,\pi)$-space, thereby obtaining it for $(P,w)$-space, $(P,\pi)$-space, $\pi$-space, and $P$-space as special cases. Further, wh... | {
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2501.11979 | Linear Feedback Control Systems for Iterative Prompt Optimization in
Large Language Models | [
"cs.LG"
] | Large Language Models (LLMs) have revolutionized various applications by generating outputs based on given prompts. However, achieving the desired output requires iterative prompt refinement. This paper presents a novel approach that draws parallels between the iterative prompt optimization process in LLMs and feedback... | {
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2501.11980 | A note on the sample complexity of multi-target detection | [
"eess.SP",
"cs.IT",
"math.IT"
] | This work studies the sample complexity of the multi-target detection (MTD) problem, which involves recovering a signal from a noisy measurement containing multiple instances of a target signal in unknown locations, each transformed by a random group element. This problem is primarily motivated by single-particle cryo-... | {
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2501.11992 | Survey on Hand Gesture Recognition from Visual Input | [
"cs.CV",
"cs.AI"
] | Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research de... | {
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2501.11993 | Subcode Ensemble Decoding of Linear Block Codes | [
"cs.IT",
"math.IT"
] | Low-density parity-check (LDPC) codes together with belief propagation (BP) decoding yield exceptional error correction capabilities in the large block length regime. Yet, there remains a gap between BP decoding and maximum likelihood decoding for short block length LDPC codes. In this context, ensemble decoding scheme... | {
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2501.12005 | A note on the relations between mixture models, maximum-likelihood and
entropic optimal transport | [
"stat.ML",
"cs.LG"
] | This note aims to demonstrate that performing maximum-likelihood estimation for a mixture model is equivalent to minimizing over the parameters an optimal transport problem with entropic regularization. The objective is pedagogical: we seek to present this already known result in a concise and hopefully simple manner. ... | {
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2501.12009 | Ratio Attack on G+G Convoluted Gaussian Signature | [
"cs.CR",
"cs.IT",
"math.IT"
] | A lattice-based signature, called G+G convoluted Gaussian signature was proposed in ASIACRYPT 2023 and was proved secure in the quantum random oracle model. In this paper, we propose a ratio attack on the G+G convoluted Gaussian signature to recover the secret key. The attack exploits the fact, proved in this paper, th... | {
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2501.12011 | Reference-free Evaluation Metrics for Text Generation: A Survey | [
"cs.CL"
] | A number of automatic evaluation metrics have been proposed for natural language generation systems. The most common approach to automatic evaluation is the use of a reference-based metric that compares the model's output with gold-standard references written by humans. However, it is expensive to create such reference... | {
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2501.12012 | TabularARGN: A Flexible and Efficient Auto-Regressive Framework for
Generating High-Fidelity Synthetic Data | [
"cs.LG"
] | Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regressive Generative Network (TabularARGN), a flexible framework designed to handle mixed-type, multivariate, and sequential datasets. By train... | {
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2501.12015 | Full Proportional Justified Representation | [
"cs.GT",
"cs.AI"
] | In multiwinner approval voting, forming a committee that proportionally represents voters' approval ballots is an essential task. The notion of justified representation (JR) demands that any large "cohesive" group of voters should be proportionally "represented". The "cohesiveness" is defined in different ways; two com... | {
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2501.12016 | Are Traditional Deep Learning Model Approaches as Effective as a
Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection? | [
"cs.CV",
"cs.LG"
] | Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised... | {
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2501.12020 | On the "Illusion" of Gender Bias in Face Recognition: Explaining the
Fairness Issue Through Non-demographic Attributes | [
"cs.CV"
] | Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthiness of FRS, more recent efforts have tried to find the causes. However, these studies make use of manually selected, correlated, and small-sized sets of facial features to ... | {
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2501.12022 | Foreign object segmentation in chest x-rays through anatomy-guided shape
insertion | [
"cs.CV"
] | In this paper, we tackle the challenge of instance segmentation for foreign objects in chest radiographs, commonly seen in postoperative follow-ups with stents, pacemakers, or ingested objects in children. The diversity of foreign objects complicates dense annotation, as shown in insufficient existing datasets. To addr... | {
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2501.12023 | Comparative Analysis of Pre-trained Deep Learning Models and DINOv2 for
Cushing's Syndrome Diagnosis in Facial Analysis | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Cushing's syndrome is a condition caused by excessive glucocorticoid secretion from the adrenal cortex, often manifesting with moon facies and plethora, making facial data crucial for diagnosis. Previous studies have used pre-trained convolutional neural networks (CNNs) for diagnosing Cushing's syndrome using frontal f... | {
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2501.12025 | Low-Cost 3D printed, Biocompatible Ionic Polymer Membranes for Soft
Actuators | [
"cond-mat.soft",
"cs.RO"
] | Ionic polymer actuators, in essence, consist of ion exchange polymers sandwiched between layers of electrodes. They have recently gained recognition as promising candidates for soft actuators due to their lightweight nature, noise-free operation, and low-driving voltages. However, the materials traditionally utilized t... | {
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2501.12030 | Advancing Earth Observation: A Survey on AI-Powered Image Processing in
Satellites | [
"cs.LG",
"cs.CV"
] | Advancements in technology and reduction in it's cost have led to a substantial growth in the quality & quantity of imagery captured by Earth Observation (EO) satellites. This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing. An approach to addre... | {
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2501.12032 | Multi-Tenant SmartNICs for In-Network Preprocessing of Recommender
Systems | [
"cs.AR",
"cs.DC",
"cs.LG"
] | Keeping ML-based recommender models up-to-date as data drifts and evolves is essential to maintain accuracy. As a result, online data preprocessing plays an increasingly important role in serving recommender systems. Existing solutions employ multiple CPU workers to saturate the input bandwidth of a single training nod... | {
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2501.12033 | Harnessing Generative Pre-Trained Transformer for Datacenter Packet
Trace Generation | [
"cs.NI",
"cs.AI"
] | Today, the rapid growth of applications reliant on datacenters calls for new advancements to meet the increasing traffic and computational demands. Traffic traces from datacenters are essential for further development and optimization of future datacenters. However, traces are rarely released to the public. Researchers... | {
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2501.12040 | Select2Drive: Pragmatic Communications for Real-Time Collaborative
Autonomous Driving | [
"cs.CE"
] | Vehicle-to-Everything communications-assisted Autonomous Driving (V2X-AD) has witnessed remarkable advancements in recent years, with pragmatic communications (PragComm) emerging as a promising paradigm for real-time collaboration among vehicles and other agents.Simultaneously, extensive research has explored the inter... | {
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2501.12043 | High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks:
Bridging Theory and Reality | [
"quant-ph",
"cs.SY",
"eess.SY"
] | Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considera... | {
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2501.12046 | Communication-Efficient and Privacy-Adaptable Mechanism for Federated
Learning | [
"cs.LG"
] | Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work, we address these challenges within the trusted aggregator model by introducing a novel approach called the Communication-Efficient and Priva... | {
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2501.12048 | Adaptive Class Learning to Screen Diabetic Disorders in Fundus Images of
Eye | [
"cs.CV",
"cs.AI"
] | The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention are crucial for averting visual impairment and enhancing patient prognosis. This research introduces a new framework called Class Extension with Limited Data (CELD) to train ... | {
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2501.12050 | Representation Learning with Parameterised Quantum Circuits for
Advancing Speech Emotion Recognition | [
"cs.LG",
"cs.SD",
"eess.AS"
] | Speech Emotion Recognition (SER) is a complex and challenging task in human-computer interaction due to the intricate dependencies of features and the overlapping nature of emotional expressions conveyed through speech. Although traditional deep learning methods have shown effectiveness, they often struggle to capture ... | {
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2501.12051 | MedS$^3$: Towards Medical Small Language Models with Self-Evolved Slow
Thinking | [
"cs.CL"
] | Medical language models (MLMs) have become pivotal in advancing medical natural language processing. However, prior models that rely on pre-training or supervised fine-tuning often exhibit low data efficiency and limited practicality in real-world clinical applications. While OpenAI's o1 highlights test-time scaling in... | {
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2501.12052 | Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease
Management | [
"cs.CV",
"cs.LG"
] | Tomato crop health plays a critical role in ensuring agricultural productivity and food security. Timely and accurate detection of diseases affecting tomato plants is vital for effective disease management. In this study, we propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-establi... | {
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2501.12053 | PINNsAgent: Automated PDE Surrogation with Large Language Models | [
"cs.CE"
] | Solving partial differential equations (PDEs) using neural methods has been a long-standing scientific and engineering research pursuit. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional numerical methods for solving PDEs. However, the gap between domain-specific knowledge ... | {
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2501.12054 | ORCAst: Operational High-Resolution Current Forecasts | [
"cs.CV",
"physics.ao-ph"
] | We present ORCAst, a multi-stage, multi-arm network for Operational high-Resolution Current forecAsts over one week. Producing real-time nowcasts and forecasts of ocean surface currents is a challenging problem due to indirect or incomplete information from satellite remote sensing data. Entirely trained on real satell... | {
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2501.12057 | Unified 3D MRI Representations via Sequence-Invariant Contrastive
Learning | [
"cs.CV",
"physics.med-ph"
] | Self-supervised deep learning has accelerated 2D natural image analysis but remains difficult to translate into 3D MRI, where data are scarce and pre-trained 2D backbones cannot capture volumetric context. We present a sequence-invariant self-supervised framework leveraging quantitative MRI (qMRI). By simulating multip... | {
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2501.12058 | Fractional Subadditivity of Submodular Functions: Equality Conditions
and Their Applications | [
"cs.IT",
"math.IT"
] | Submodular functions are known to satisfy various forms of fractional subadditivity. This work investigates the conditions for equality to hold exactly or approximately in the fractional subadditivity of submodular functions. We establish that a small gap in the inequality implies that the function is close to being mo... | {
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2501.12060 | GSVC: Efficient Video Representation and Compression Through 2D Gaussian
Splatting | [
"cs.CV",
"cs.MM"
] | 3D Gaussian splats have emerged as a revolutionary, effective, learned representation for static 3D scenes. In this work, we explore using 2D Gaussian splats as a new primitive for representing videos. We propose GSVC, an approach to learning a set of 2D Gaussian splats that can effectively represent and compress video... | {
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2501.12061 | Tackling Uncertainties in Multi-Agent Reinforcement Learning through
Integration of Agent Termination Dynamics | [
"cs.LG",
"cs.MA"
] | Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficient and robust policy learning. While Distributional Reinforcement Learning has been successfully appl... | {
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2501.12066 | The Generalized Chernoff-Stein Lemma, Applications and Examples | [
"cs.IT",
"math.IT"
] | In this manuscript we define the notion of "$\delta$-typicality" for both entropy and relative entropy, as well as a notion of $\epsilon$-goodness and provide an extension to Stein's lemma for continuous quantities as well as correlated setups. We apply the derived results on the Gaussian hypothesis testing problem whe... | {
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2501.12067 | EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular
Value Decomposition | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Parameter-efficient fine-tuning methods, such as LoRA, reduces the number of trainable parameters. However, they often suffer from scalability issues and differences between their learning pattern and full fine-tuning. To overcome these limitations, we propose Efficient Weight-Decomposed Low-Rank Adaptation (EDoRA): a ... | {
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2501.12071 | Co-Paced Learning Strategy Based on Confidence for Flying Bird Object
Detection Model Training | [
"cs.CV"
] | To mitigate the adverse effects of hard samples on the training of the Flying Bird Object Detection (FBOD) model for surveillance videos, we propose a Co-Paced Learning Based on Confidence (CPL-BC) strategy and apply this strategy to the training process of the FBOD model. This strategy involves maintaining two models ... | {
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2501.12072 | Fault-tolerance of [[6, 1, 3]] non-CSS code family generated using
measurements on graph states | [
"quant-ph",
"cs.IT",
"math.IT"
] | We construct and analyze the fault tolerance of $[[6,1,3]]$ non-CSS quantum error correcting code under the anisotropic and depolarizing noise models. This rate-optimized code achieves fault-tolerance using a single ancilla qubit for syndrome measurement under anisotropic noise conditions. This method was called fault-... | {
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2501.12073 | Towards autonomous photogrammetric forest inventory using a lightweight
under-canopy robotic drone | [
"cs.RO",
"cs.CV"
] | Drones are increasingly used in forestry to capture high-resolution remote sensing data. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. Inside dense forests, reliance on the Global Navigation Satellite System (GNSS)... | {
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2501.12074 | Optimizing Portfolio Performance through Clustering and Sharpe
Ratio-Based Optimization: A Comparative Backtesting Approach | [
"cs.LG",
"q-fin.PM"
] | Optimizing portfolio performance is a fundamental challenge in financial modeling, requiring the integration of advanced clustering techniques and data-driven optimization strategies. This paper introduces a comparative backtesting approach that combines clustering-based portfolio segmentation and Sharpe ratio-based op... | {
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2501.12076 | From Niche to Mainstream: Community Size and Engagement in Social Media
Conversations | [
"cs.SI",
"cs.CY"
] | The architecture of public discourse has been profoundly reshaped by social media platforms, which mediate interactions at an unprecedented scale and complexity. This study analyzes user behavior across six platforms over 33 years, exploring how the size of conversations and communities influences dialogue dynamics. Ou... | {
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2501.12082 | A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality
Assessment | [
"cs.CV",
"eess.IV"
] | Wide-angle video is favored for its wide viewing angle and ability to capture a large area of scenery, making it an ideal choice for sports and adventure recording. However, wide-angle video is prone to deformation, exposure and other distortions, resulting in poor video quality and affecting the perception and experie... | {
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2501.12085 | Scalable Whole Slide Image Representation Using K-Mean Clustering and
Fisher Vector Aggregation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Whole slide images (WSIs) are high-resolution, gigapixel sized images that pose significant computational challenges for traditional machine learning models due to their size and heterogeneity.In this paper, we present a scalable and efficient methodology for WSI classification by leveraging patch-based feature extract... | {
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2501.12086 | DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with
Semantic-Aware Spatio-Temporal Topology Modeling | [
"cs.CV"
] | Graph convolutional networks (GCNs) have emerged as a powerful tool for skeleton-based action and gesture recognition, thanks to their ability to model spatial and temporal dependencies in skeleton data. However, existing GCN-based methods face critical limitations: (1) they lack effective spatio-temporal topology mode... | {
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2501.12087 | UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer
Model | [
"cs.CV"
] | Disaster recovery and management present significant challenges, particularly in unstable environments and hard-to-reach terrains. These difficulties can be overcome by employing unmanned aerial vehicles (UAVs) equipped with onboard embedded platforms and camera sensors. In this work, we address the critical need for a... | {
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2501.12092 | Data-Aided Regularization of Direct-Estimate Combiner in Distributed
MIMO Systems | [
"eess.SP",
"cs.IT",
"math.IT"
] | This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, t... | {
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2501.12102 | Proxies for Distortion and Consistency with Applications for Real-World
Image Restoration | [
"cs.CV",
"cs.AI",
"cs.LG",
"eess.IV"
] | Real-world image restoration deals with the recovery of images suffering from an unknown degradation. This task is typically addressed while being given only degraded images, without their corresponding ground-truth versions. In this hard setting, designing and evaluating restoration algorithms becomes highly challengi... | {
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2501.12104 | Teacher Encoder-Student Decoder Denoising Guided Segmentation Network
for Anomaly Detection | [
"cs.CV",
"cs.AI"
] | Visual anomaly detection is a highly challenging task, often categorized as a one-class classification and segmentation problem. Recent studies have demonstrated that the student-teacher (S-T) framework effectively addresses this challenge. However, most S-T frameworks rely solely on pre-trained teacher networks to gui... | {
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2501.12106 | Can open source large language models be used for tumor documentation in
Germany? -- An evaluation on urological doctors' notes | [
"cs.CL",
"cs.AI"
] | Tumor documentation in Germany is largely done manually, requiring reading patient records and entering data into structured databases. Large language models (LLMs) could potentially enhance this process by improving efficiency and reliability. This evaluation tests eleven different open source LLMs with sizes ranging ... | {
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2501.12113 | Dual NUP Representations and Min-Maximization in Factor Graphs | [
"stat.ML",
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY"
] | Normals with unknown parameters (NUP) can be used to convert nontrivial model-based estimation problems into iterations of linear least-squares or Gaussian estimation problems. In this paper, we extend this approach by augmenting factor graphs with convex-dual variables and pertinent NUP representations. In particular,... | {
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2501.12115 | Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks
through Meta-learning | [
"cs.LG",
"cs.CV"
] | This paper presents meta-sparsity, a framework for learning model sparsity, basically learning the parameter that controls the degree of sparsity, that allows deep neural networks (DNNs) to inherently generate optimal sparse shared structures in multi-task learning (MTL) setting. This proposed approach enables the dyna... | {
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2501.12116 | Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions
Space | [
"cs.LG",
"cs.AI",
"hep-th",
"math.AP"
] | We present a machine learning framework to facilitate the solution of nonlinear multiscale differential equations and, especially, inverse problems using Physics-Informed Neural Networks (PINNs). This framework is based on what is called multihead (MH) training, which involves training the network to learn a general sp... | {
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2501.12118 | Regularized dynamical parametric approximation of stiff evolution
problems | [
"math.NA",
"cs.LG",
"cs.NA"
] | Evolutionary deep neural networks have emerged as a rapidly growing field of research. This paper studies numerical integrators for such and other classes of nonlinear parametrizations $ u(t) = \Phi(\theta(t)) $, where the evolving parameters $\theta(t)$ are to be computed. The primary focus is on tackling the challeng... | {
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2501.12119 | ENTIRE: Learning-based Volume Rendering Time Prediction | [
"cs.GR",
"cs.CV",
"cs.LG"
] | We present ENTIRE, a novel approach for volume rendering time prediction. Time-dependent volume data from simulations or experiments typically comprise complex deforming structures across hundreds or thousands of time steps, which in addition to the camera configuration has a significant impact on rendering performance... | {
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2501.12121 | Learning Dynamic Representations via An Optimally-Weighted Maximum Mean
Discrepancy Optimization Framework for Continual Learning | [
"cs.LG",
"cs.AI"
] | Continual learning has emerged as a pivotal area of research, primarily due to its advantageous characteristic that allows models to persistently acquire and retain information. However, catastrophic forgetting can severely impair model performance. In this study, we address network forgetting by introducing a novel fr... | {
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2501.12123 | FedCLEAN: byzantine defense by CLustering Errors of Activation maps in
Non-IID federated learning environments | [
"cs.CR",
"cs.AI"
] | Federated Learning (FL) enables clients to collaboratively train a global model using their local datasets while reinforcing data privacy. However, FL is susceptible to poisoning attacks. Existing defense mechanisms assume that clients' data are independent and identically distributed (IID), making them ineffective in ... | {
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2501.12124 | On de Bruijn Array Codes Part II: Linear Codes | [
"cs.IT",
"math.IT"
] | An M-sequence generated by a primitive polynomial has many interesting and desirable properties. A pseudo-random array is the two-dimensional generalization of an M-sequence. Similarly to primitive polynomials, there are irreducible and reducible polynomials whose all nonzero sequences have the same length. In this pap... | {
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2501.12125 | Heterogeneous Federated Learning System for Sparse Healthcare
Time-Series Prediction | [
"cs.LG"
] | In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design dense and sparse feature tensors to deal with the sparsity of data sources. Heterogeneous federated lea... | {
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2501.12128 | Evaluating Efficiency and Engagement in Scripted and LLM-Enhanced
Human-Robot Interactions | [
"cs.RO",
"cs.HC"
] | To achieve natural and intuitive interaction with people, HRI frameworks combine a wide array of methods for human perception, intention communication, human-aware navigation and collaborative action. In practice, when encountering unpredictable behavior of people or unexpected states of the environment, these framewor... | {
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2501.12133 | Distributed Multi-Head Learning Systems for Power Consumption Prediction | [
"cs.LG"
] | As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles (AGVs) in smart factories, which face complex environments and generate large amoun... | {
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2501.12135 | Revisit the AWGN-goodness of Polar-like Lattices | [
"cs.IT",
"math.IT"
] | This paper aims to provide a comprehensive introduction to lattices constructed based on polar-like codes and demonstrate some of their key properties, such as AWGN goodness. We first present polar lattices directly from the perspective of their generator matrix. Next, we discuss their connection with the recently prop... | {
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2501.12136 | Heterogeneous Federated Learning Systems for Time-Series Power
Consumption Prediction with Multi-Head Embedding Mechanism | [
"cs.LG"
] | Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these ... | {
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2501.12147 | Improving Influence-based Instruction Tuning Data Selection for Balanced
Learning of Diverse Capabilities | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Selecting appropriate training data is crucial for effective instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across a diverse range of tasks. Influence-based methods show promise in achieving (1) by estimating the contribution o... | {
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2501.12148 | Deep Unfolding of Fixed-Point Based Algorithm for Weighted Sum Rate
Maximization | [
"cs.IT",
"math.IT"
] | In this paper, we propose a novel approach that harnesses the standard interference function, specifically tailored to address the unique challenges of non-convex optimization in wireless networks. We begin by establishing theoretical guarantees for our method under the assumption that the interference function exhibit... | {
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2501.12149 | On the practical applicability of modern DFT functionals for chemical
computations. Case study of DM21 applicability for geometry optimization | [
"physics.comp-ph",
"cond-mat.mtrl-sci",
"cs.AI"
] | Density functional theory (DFT) is probably the most promising approach for quantum chemistry calculations considering its good balance between calculations precision and speed. In recent years, several neural network-based functionals have been developed for exchange-correlation energy approximation in DFT, DM21 devel... | {
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2501.12150 | DNRSelect: Active Best View Selection for Deferred Neural Rendering | [
"cs.CV"
] | Deferred neural rendering (DNR) is an emerging computer graphics pipeline designed for high-fidelity rendering and robotic perception. However, DNR heavily relies on datasets composed of numerous ray-traced images and demands substantial computational resources. It remains under-explored how to reduce the reliance on h... | {
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2501.12156 | Characterization of Invariance, Periodic Solutions and Optimization of
Dynamic Financial Networks | [
"eess.SY",
"cs.SY",
"math.DS",
"math.OC"
] | Cascading failures, such as bankruptcies and defaults, pose a serious threat for the resilience of the global financial system. Indeed, because of the complex investment and cross-holding relations within the system, failures can occur as a result of the propagation of a financial collapse from one organization to anot... | {
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2501.12157 | Fast-RF-Shimming: Accelerate RF Shimming in 7T MRI using Deep Learning | [
"cs.CV"
] | Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a high signal-to-noise ratio (SNR), enabling exceptional spatial resolution for clinical diagnostics and research. However, higher fields introduce challenges such as transmit radiofrequency (RF) field inhomogeneities, which result in uneven flip angles an... | {
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2501.12162 | AdaServe: SLO-Customized LLM Serving with Fine-Grained Speculative
Decoding | [
"cs.CL",
"cs.AI",
"cs.DC",
"cs.LG"
] | This paper introduces AdaServe, the first LLM serving system to support SLO customization through fine-grained speculative decoding. AdaServe leverages the logits of a draft model to predict the speculative accuracy of tokens and employs a theoretically optimal algorithm to construct token trees for verification. To ac... | {
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2501.12166 | Beyond Window-Based Detection: A Graph-Centric Framework for Discrete
Log Anomaly Detection | [
"cs.SE",
"cs.LG"
] | Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often suffer from context bias and fuzzy localization, which hinder their ability to precisely and efficiently identify anomalies. To address these... | {
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2501.12167 | Soft-Decision Decoding for LDPC Code-Based Quantitative Group Testing | [
"cs.IT",
"math.IT"
] | We consider the problem of identifying defective items in a population with non-adaptive quantitative group testing. For this scenario, Mashauri et al. recently proposed a low-density parity-check (LDPC) code-based quantitative group testing scheme with a hard-decision decoding approach (akin to peeling decoding). This... | {
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2501.12169 | SVGS-DSGAT: An IoT-Enabled Innovation in Underwater Robotic Object
Detection Technology | [
"cs.CV"
] | With the advancement of Internet of Things (IoT) technology, underwater target detection and tracking have become increasingly important for ocean monitoring and resource management. Existing methods often fall short in handling high-noise and low-contrast images in complex underwater environments, lacking precision an... | {
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} |
2501.12173 | ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled
Multimodal Conditions | [
"cs.CV"
] | Building on the success of diffusion models, significant advancements have been made in multimodal image generation tasks. Among these, human image generation has emerged as a promising technique, offering the potential to revolutionize the fashion design process. However, existing methods often focus solely on text-to... | {
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} |
2501.12174 | BiMarker: Enhancing Text Watermark Detection for Large Language Models
with Bipolar Watermarks | [
"cs.LG"
] | The rapid growth of Large Language Models (LLMs) raises concerns about distinguishing AI-generated text from human content. Existing watermarking techniques, like \kgw, struggle with low watermark strength and stringent false-positive requirements. Our analysis reveals that current methods rely on coarse estimates of n... | {
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} |
2501.12175 | Less is More: Information Bottleneck Denoised Multimedia Recommendation | [
"cs.IR"
] | Empowered by semantic-rich content information, multimedia recommendation has emerged as a potent personalized technique. Current endeavors center around harnessing multimedia content to refine item representation or uncovering latent item-item structures based on modality similarity. Despite the effectiveness, we posi... | {
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} |
2501.12176 | DataPro -- A Standardized Data Understanding and Processing Procedure: A
Case Study of an Eco-Driving Project | [
"cs.IR"
] | A systematic pipeline for data processing and knowledge discovery is essential to extracting knowledge from big data and making recommendations for operational decision-making. The CRISP-DM model is the de-facto standard for developing data-mining projects in practice. However, advancements in data processing technolog... | {
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} |
2501.12178 | High-dimensional multimodal uncertainty estimation by manifold
alignment:Application to 3D right ventricular strain computations | [
"cs.CV"
] | Confidence in the results is a key ingredient to improve the adoption of machine learning methods by clinicians. Uncertainties on the results have been considered in the literature, but mostly those originating from the learning and processing methods. Uncertainty on the data is hardly challenged, as a single sample is... | {
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} |
2501.12183 | Extend Adversarial Policy Against Neural Machine Translation via Unknown
Token | [
"cs.CL"
] | Generating adversarial examples contributes to mainstream neural machine translation~(NMT) robustness. However, popular adversarial policies are apt for fixed tokenization, hindering its efficacy for common character perturbations involving versatile tokenization. Based on existing adversarial generation via reinforcem... | {
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} |
2501.12186 | Removal of Small Weight Stopping Sets for Asynchronous Unsourced
Multiple Access | [
"cs.IT",
"math.IT"
] | In this paper, we analyze the formation of small stopping sets in joint factor graphs describing a frame-asynchronous two-user transmission. Furthermore, we propose an algorithm to completely avoid small stopping sets in the joint factor graph over the entire range of symbol delays. The error floor caused by those stop... | {
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} |
2501.12189 | MirrorCBO: A consensus-based optimization method in the spirit of mirror
descent | [
"math.OC",
"cs.LG"
] | In this work we propose MirrorCBO, a consensus-based optimization (CBO) method which generalizes standard CBO in the same way that mirror descent generalizes gradient descent. For this we apply the CBO methodology to a swarm of dual particles and retain the primal particle positions by applying the inverse of the mirro... | {
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} |
2501.12191 | A margin-based replacement for cross-entropy loss | [
"cs.LG",
"cs.CV"
] | Cross-entropy (CE) loss is the de-facto standard for training deep neural networks to perform classification. However, CE-trained deep neural networks struggle with robustness and generalisation issues. To alleviate these issues, we propose high error margin (HEM) loss, a variant of multi-class margin loss that overcom... | {
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} |
2501.12193 | MyDigiTwin: A Privacy-Preserving Framework for Personalized
Cardiovascular Risk Prediction and Scenario Exploration | [
"cs.LG",
"cs.HC"
] | Cardiovascular disease (CVD) remains a leading cause of death, and primary prevention through personalized interventions is crucial. This paper introduces MyDigiTwin, a framework that integrates health digital twins with personal health environments to empower patients in exploring personalized health scenarios while e... | {
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} |
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