id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.14646 | SyncAnimation: A Real-Time End-to-End Framework for Audio-Driven Human
Pose and Talking Head Animation | [
"cs.CV"
] | Generating talking avatar driven by audio remains a significant challenge. Existing methods typically require high computational costs and often lack sufficient facial detail and realism, making them unsuitable for applications that demand high real-time performance and visual quality. Additionally, while some methods ... | {
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2501.14649 | Investigating the (De)Composition Capabilities of Large Language Models
in Natural-to-Formal Language Conversion | [
"cs.CL"
] | To achieve generalized and robust natural-to-formal language conversion (N2F), large language models (LLMs) need to have strong capabilities of decomposition and composition in N2F when faced with an unfamiliar formal language and be able to cope with compositional gaps and counter-intuitive symbolic names. To investig... | {
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2501.14652 | Decoupled SGDA for Games with Intermittent Strategy Communication | [
"cs.LG"
] | We focus on reducing communication overhead in multiplayer games, where frequently exchanging strategies between players is not feasible and players have noisy or outdated strategies of the other players. We introduce Decoupled SGDA, a novel adaptation of Stochastic Gradient Descent Ascent (SGDA). In this approach, pla... | {
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2501.14653 | Federated Domain Generalization with Data-free On-server Gradient
Matching | [
"cs.LG",
"cs.AI",
"cs.DC",
"cs.MA"
] | Domain Generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. One of the key approaches in DG is training an encoder which generates domain-invariant representations. However, this approach is not applicable in Federated Domain Generalization (FD... | {
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2501.14654 | MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical
LLM Agents | [
"cs.LG",
"cs.AI",
"cs.MA"
] | Recent large language models (LLMs) have demonstrated significant advancements, particularly in their ability to serve as agents thereby surpassing their traditional role as chatbots. These agents can leverage their planning and tool utilization capabilities to address tasks specified at a high level. However, a standa... | {
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2501.14659 | Towards Unified Structured Light Optimization | [
"cs.CV"
] | Structured light (SL) 3D reconstruction captures the precise surface shape of objects, providing high-accuracy 3D data essential for industrial inspection and robotic vision systems. However, current research on optimizing projection patterns in SL 3D reconstruction faces two main limitations: each scene requires separ... | {
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2501.14660 | Mean-field limit from general mixtures of experts to quantum neural
networks | [
"math-ph",
"cs.LG",
"math.MP",
"math.PR"
] | In this work, we study the asymptotic behavior of Mixture of Experts (MoE) trained via gradient flow on supervised learning problems. Our main result establishes the propagation of chaos for a MoE as the number of experts diverges. We demonstrate that the corresponding empirical measure of their parameters is close to ... | {
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2501.14661 | Neural-Symbolic Message Passing with Dynamic Pruning | [
"cs.LG",
"cs.AI"
] | Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs) is a challenging task. Recently, a line of message-passing-based research has been proposed to solve CQA. However, they perform unsatisfactorily on negative queries and fail to address the noisy messages between variable nodes in the query graph. More... | {
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2501.14663 | End-to-end workflow for machine learning-based qubit readout with QICK
and hls4ml | [
"quant-ph",
"cs.LG"
] | We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to ... | {
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2501.14664 | Predictive Position Estimation for Remote Surgery under Packet Loss
Using the Informer Framework | [
"eess.SY",
"cs.SY"
] | Accurate and real-time position estimation of the robotic arm on the patient's side is crucial for the success of remote robotic surgery in Tactile Internet environments. This paper proposes a predictive approach using the computationally efficient Transformer-based Informer model for position estimation, combined with... | {
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2501.14672 | Gaussian-Process-based Adaptive Tracking Control with Dynamic Active
Learning for Autonomous Ground Vehicles | [
"eess.SY",
"cs.RO",
"cs.SY"
] | This article proposes an active-learning-based adaptive trajectory tracking control method for autonomous ground vehicles to compensate for modeling errors and unmodeled dynamics. The nominal vehicle model is decoupled into lateral and longitudinal subsystems, which are augmented with online Gaussian Processes (GPs), u... | {
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2501.14673 | State Space Models for Extractive Summarization in Low Resource
Scenarios | [
"cs.CL",
"cs.AI"
] | Extractive summarization involves selecting the most relevant sentences from a text. Recently, researchers have focused on advancing methods to improve state-of-the-art results in low-resource settings. Motivated by these advancements, we propose the MPoincareSum method. This method applies the Mamba state space model ... | {
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2501.14677 | MatAnyone: Stable Video Matting with Consistent Memory Propagation | [
"cs.CV"
] | Auxiliary-free human video matting methods, which rely solely on input frames, often struggle with complex or ambiguous backgrounds. To address this, we propose MatAnyone, a robust framework tailored for target-assigned video matting. Specifically, building on a memory-based paradigm, we introduce a consistent memory p... | {
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2501.14678 | A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery
Using Informer Model | [
"cs.RO",
"cs.AI"
] | Precise and real-time estimation of the robotic arm's position on the patient's side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation. Addi... | {
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2501.14679 | Surface Vision Mamba: Leveraging Bidirectional State Space Model for
Efficient Spherical Manifold Representation | [
"cs.CV",
"cs.AI"
] | Attention-based methods have demonstrated exceptional performance in modelling long-range dependencies on spherical cortical surfaces, surpassing traditional Geometric Deep Learning (GDL) models. However, their extensive inference time and high memory demands pose challenges for application to large datasets with limit... | {
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2501.14685 | Rethinking Foundation Models for Medical Image Classification through a
Benchmark Study on MedMNIST | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Foundation models are widely employed in medical image analysis, due to their high adaptability and generalizability for downstream tasks. With the increasing number of foundation models being released, model selection has become an important issue. In this work, we study the capabilities of foundation models in medica... | {
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2501.14687 | Decoding Generalization from Memorization in Deep Neural Networks | [
"cs.LG",
"cs.AI"
] | Overparameterized Deep Neural Networks that generalize well have been key to the dramatic success of Deep Learning in recent years. The reasons for their remarkable ability to generalize are not well understood yet. It has also been known that deep networks possess the ability to memorize training data, as evidenced by... | {
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2501.14689 | Approach to Designing CV Systems for Medical Applications: Data,
Architecture and AI | [
"cs.CV",
"cs.AI"
] | This paper introduces an innovative software system for fundus image analysis that deliberately diverges from the conventional screening approach, opting not to predict specific diagnoses. Instead, our methodology mimics the diagnostic process by thoroughly analyzing both normal and pathological features of fundus stru... | {
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2501.14693 | Rethinking Table Instruction Tuning | [
"cs.CL",
"cs.AI"
] | Recent advances in table understanding have focused on instruction-tuning large language models (LLMs) for table-related tasks. However, existing research has overlooked the impact of hyperparameter choices and lacks a comprehensive evaluation of the out-of-domain table understanding ability and the general capabilitie... | {
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2501.14694 | Towards Automated Self-Supervised Learning for Truly Unsupervised Graph
Anomaly Detection | [
"cs.LG",
"cs.AI"
] | Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph anomaly detection. However, we empirically found that three important factors can substantially impact detection performance across dataset... | {
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2501.14696 | Predictor-Feedback Stabilization of Globally Lipschitz Nonlinear Systems
with State and Input Quantization | [
"math.OC",
"cs.SY",
"eess.SY"
] | We develop a switched nonlinear predictor-feedback control law to achieve global asymptotic stabilization for nonlinear systems with arbitrarily long input delay, under state quantization. The proposed design generalizes the nonlinear predictor-feedback framework by incorporating quantized measurements of both the plan... | {
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2501.14700 | An Attentive Graph Agent for Topology-Adaptive Cyber Defence | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.NI"
] | As cyber threats grow increasingly sophisticated, reinforcement learning (RL) is emerging as a promising technique to create intelligent and adaptive cyber defense systems. However, most existing autonomous defensive agents have overlooked the inherent graph structure of computer networks subject to cyber attacks, pote... | {
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2501.14701 | NLP-based assessment of prescription appropriateness from Italian
referrals | [
"cs.CL",
"cs.LG"
] | Objective: This study proposes a Natural Language Processing pipeline to evaluate prescription appropriateness in Italian referrals, where reasons for prescriptions are recorded only as free text, complicating automated comparisons with guidelines. The pipeline aims to derive, for the first time, a comprehensive summar... | {
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2501.14704 | Stroke classification using Virtual Hybrid Edge Detection from in silico
electrical impedance tomography data | [
"math.AP",
"cs.CV",
"cs.NA",
"math.NA"
] | Electrical impedance tomography (EIT) is a non-invasive imaging method for recovering the internal conductivity of a physical body from electric boundary measurements. EIT combined with machine learning has shown promise for the classification of strokes. However, most previous works have used raw EIT voltage data as n... | {
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2501.14705 | The Karp Dataset | [
"cs.LG",
"cs.CL"
] | Understanding the mathematical reasoning capabilities of Large Language Models (LLMs) is a central topic in the study of artificial intelligence. This new domain necessitates the creation of datasets of reasoning tasks for both training and benchmarking the performance of LLMs. To this end, we introduce the Karp datase... | {
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2501.14708 | Decision-Focused Learning for Complex System Identification: HVAC
Management System Application | [
"eess.SY",
"cs.LG",
"cs.SY"
] | As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parame... | {
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2501.14709 | Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics
Informed Deep Autoencoders | [
"cond-mat.mtrl-sci",
"cs.CV",
"eess.IV"
] | We present a physics-informed deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), such as diffraction limited resolution, noise, and undersampling due to low laser power conditions. The optical system's point spread function (PSF) and common CLSM image degradation mechani... | {
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2501.14710 | Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective | [
"stat.ML",
"cs.LG"
] | Training machine learning models for fair decisions faces two key challenges: The \emph{fairness-accuracy trade-off} results from enforcing fairness which weakens its predictive performance in contrast to an unconstrained model. The incompatibility of different fairness metrics poses another trade-off -- also known as ... | {
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2501.14713 | FlexiGPT: Pruning and Extending Large Language Models with Low-Rank
Weight Sharing | [
"cs.CL",
"cs.LG"
] | The rapid proliferation of large language models (LLMs) in natural language processing (NLP) has created a critical need for techniques that enable efficient deployment on memory-constrained devices without compromising performance. We present a method to prune LLMs that selectively prunes model blocks based on an impo... | {
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2501.14717 | Towards Better Understanding Table Instruction Tuning: Decoupling the
Effects from Data versus Models | [
"cs.CL"
] | Recent advances in natural language processing have leveraged instruction tuning to enhance Large Language Models (LLMs) for table-related tasks. However, previous works train different base models with different training data, lacking an apples-to-apples comparison across the result table LLMs. To address this, we fin... | {
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2501.14719 | Do LLMs Provide Consistent Answers to Health-Related Questions across
Languages? | [
"cs.CL",
"cs.AI",
"cs.HC",
"cs.IR"
] | Equitable access to reliable health information is vital for public health, but the quality of online health resources varies by language, raising concerns about inconsistencies in Large Language Models (LLMs) for healthcare. In this study, we examine the consistency of responses provided by LLMs to health-related ques... | {
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2501.14720 | Communication-Based Distributed Control of Large-Scale District Heating
Networks | [
"eess.SY",
"cs.SY"
] | This paper presents a non-cooperative distributed model predictive controller for the control of large-scale District Heating Networks. To enable the design of this controller a novel information passing scheme and feasibility restoration method are created, allowing the local controllers to achieve a global consensus ... | {
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2501.14721 | Comparable Corpora: Opportunities for New Research Directions | [
"cs.CL"
] | Most conference papers present new results, but this paper will focus more on opportunities for the audience to make their own contributions. This paper is intended to challenge the community to think more broadly about what we can do with comparable corpora. We will start with a review of the history, and then suggest... | {
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2501.14723 | CodeMonkeys: Scaling Test-Time Compute for Software Engineering | [
"cs.LG"
] | Scaling test-time compute is a promising axis for improving LLM capabilities. However, test-time compute can be scaled in a variety of ways, and effectively combining different approaches remains an active area of research. Here, we explore this problem in the context of solving real-world GitHub issues from the SWE-be... | {
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2501.14724 | MLPs at the EOC: Concentration of the NTK | [
"cs.LG",
"stat.ML"
] | We study the concentration of the Neural Tangent Kernel (NTK) $K_\theta : \mathbb{R}^{m_0} \times \mathbb{R}^{m_0} \to \mathbb{R}^{m_l \times m_l}$ of $l$-layer Multilayer Perceptrons (MLPs) $N : \mathbb{R}^{m_0} \times \Theta \to \mathbb{R}^{m_l}$ equipped with activation functions $\phi(s) = a s + b \vert s \vert$ fo... | {
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2501.14726 | Relightable Full-Body Gaussian Codec Avatars | [
"cs.CV",
"cs.GR"
] | We propose Relightable Full-Body Gaussian Codec Avatars, a new approach for modeling relightable full-body avatars with fine-grained details including face and hands. The unique challenge for relighting full-body avatars lies in the large deformations caused by body articulation and the resulting impact on appearance c... | {
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2501.14728 | Mitigating GenAI-powered Evidence Pollution for Out-of-Context
Multimodal Misinformation Detection | [
"cs.MM",
"cs.CL",
"cs.CV",
"cs.CY"
] | While large generative artificial intelligence (GenAI) models have achieved significant success, they also raise growing concerns about online information security due to their potential misuse for generating deceptive content. Out-of-context (OOC) multimodal misinformation detection, which often retrieves Web evidence... | {
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2501.14729 | HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene
Understanding and Generation | [
"cs.CV"
] | Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we present a unified D... | {
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2501.14731 | From Critique to Clarity: A Pathway to Faithful and Personalized Code
Explanations with Large Language Models | [
"cs.SE",
"cs.AI",
"cs.CL"
] | In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical professionals benefit from enhanced understanding and improved problem-solving skills, while business stakeholders gain insights into project align... | {
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2501.14733 | LLM as HPC Expert: Extending RAG Architecture for HPC Data | [
"cs.DC",
"cs.AI"
] | High-Performance Computing (HPC) is crucial for performing advanced computational tasks, yet their complexity often challenges users, particularly those unfamiliar with HPC-specific commands and workflows. This paper introduces Hypothetical Command Embeddings (HyCE), a novel method that extends Retrieval-Augmented Gene... | {
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2501.14734 | Research on the Application of Spark Streaming Real-Time Data Analysis
System and large language model Intelligent Agents | [
"cs.DC",
"cs.AI"
] | This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static workflows, inefficient stateful computations, and lack of human intervention by leveraging LangGraph's graph-based workflow construct... | {
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2501.14735 | ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural
Knowledge | [
"cs.SE",
"cs.AI"
] | Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals. Manual assessment against intricate sets of rules often leads to significant project delays and exp... | {
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2501.14736 | NEAT Algorithm-based Stock Trading Strategy with Multiple Technical
Indicators Resonance | [
"cs.NE",
"cs.LG",
"q-fin.PM"
] | In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to g... | {
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2501.14737 | EvalSVA: Multi-Agent Evaluators for Next-Gen Software Vulnerability
Assessment | [
"cs.SE",
"cs.AI"
] | Software Vulnerability (SV) assessment is a crucial process of determining different aspects of SVs (e.g., attack vectors and scope) for developers to effectively prioritize efforts in vulnerability mitigation. It presents a challenging and laborious process due to the complexity of SVs and the scarcity of labeled data... | {
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2501.14738 | On strict ranking by pairwise comparisons | [
"cs.IT",
"math.IT"
] | We attack the problem of getting a strict ranking (i.e. a ranking without equally ranked items) of $n$ items from a pairwise comparisons matrix. Basic structures are described, a first heuristical approach based on a condition, the $\mathcal{R}-$condition, is proposed. Analyzing the limits of this ranking procedure, we... | {
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2501.14739 | Reproduction Research of FSA-Benchmark | [
"cs.DC",
"cs.LG"
] | In the current landscape of big data, the reliability and performance of storage systems are essential to the success of various applications and services. as data volumes continue to grow exponentially, the complexity and scale of the storage infrastructures needed to manage this data also increase. a significant chal... | {
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2501.14741 | On Design Choices in Similarity-Preserving Sparse Randomized Embeddings | [
"cs.NE",
"cs.LG",
"q-bio.NC"
] | Expand & Sparsify is a principle that is observed in anatomically similar neural circuits found in the mushroom body (insects) and the cerebellum (mammals). Sensory data are projected randomly to much higher-dimensionality (expand part) where only few the most strongly excited neurons are activated (sparsify part). Thi... | {
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2501.14742 | Evaluating the effectiveness, reliability and efficiency of a
multi-objective sequential optimization approach for building performance
design | [
"cs.NE",
"math.OC"
] | The complexity of performance-based building design stems from the evaluation of numerous candidate design options, driven by the plethora of variables, objectives, and constraints inherent in multi-disciplinary projects. This necessitates optimization approaches to support the identification of well performing designs... | {
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2501.14743 | KVDirect: Distributed Disaggregated LLM Inference | [
"cs.DC",
"cs.LG",
"cs.PF"
] | Large Language Models (LLMs) have become the new foundation for many applications, reshaping human society like a storm. Disaggregated inference, which separates prefill and decode stages, is a promising approach to improving hardware utilization and service quality. However, due to inefficient inter-node communication... | {
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2501.14744 | FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking
Neural Networks | [
"cs.NE",
"cs.CV",
"cs.LG"
] | Spiking Neural Networks (SNNs) are emerging as a promising alternative to Artificial Neural Networks (ANNs) due to their inherent energy efficiency. Owing to the inherent sparsity in spike generation within SNNs, the in-depth analysis and optimization of intermediate output spikes are often neglected. This oversight si... | {
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2501.14745 | AI-Driven Health Monitoring of Distributed Computing Architecture:
Insights from XGBoost and SHAP | [
"cs.DC",
"cs.LG"
] | With the rapid development of artificial intelligence technology, its application in the optimization of complex computer systems is becoming more and more extensive. Edge computing is an efficient distributed computing architecture, and the health status of its nodes directly affects the performance and reliability of... | {
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2501.14746 | Neuromorphic Spiking Neural Network Based Classification of COVID-19
Spike Sequences | [
"cs.NE",
"cs.LG"
] | The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magnitude, opening research doors to analyze its behavior. Various studies are conducted by researchers to gain a deeper understanding of the virus, like genomic surveillance, ... | {
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2501.14747 | Enhancing Green Economy with Artificial Intelligence: Role of Energy Use
and FDI in the United States | [
"econ.GN",
"cs.AI",
"q-fin.EC"
] | The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial ... | {
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2501.14750 | Engineering Carbon Credits Towards A Responsible FinTech Era: The
Practices, Implications, and Future | [
"cs.CY",
"cs.LG"
] | Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explore... | {
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2501.14751 | Optimizing LPB Algorithms using Simulated Annealing | [
"cs.NE"
] | Learner Performance-based Behavior using Simulated Annealing (LPBSA) is an improvement of the Learner Performance-based Behavior (LPB) algorithm. LPBSA, like LPB, has been proven to deal with single and complex problems. Simulated Annealing (SA) has been utilized as a powerful technique to optimize LPB. LPBSA has provi... | {
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2501.14753 | ABACUS: A FinOps Service for Cloud Cost Optimization | [
"cs.DC",
"cs.AI",
"cs.NI",
"cs.SE"
] | In recent years, as more enterprises have moved their infrastructure to the cloud, significant challenges have emerged in achieving holistic cloud spend visibility and cost optimization. FinOps practices provide a way for enterprises to achieve these business goals by optimizing cloud costs and bringing accountability ... | {
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2501.14755 | Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for Foundation
Models | [
"cs.DC",
"cs.AI"
] | The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast valuable data with varied types utilized by these models. Nevertheless, the current landscape presents unique challenges that traditional data processing frameworks cannot handle effectively, especially... | {
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2501.14756 | Towards An Automated AI Act FRIA Tool That Can Reuse GDPR's DPIA | [
"cs.CY",
"cs.AI"
] | The AI Act introduces the obligation to conduct a Fundamental Rights Impact Assessment (FRIA), with the possibility to reuse a Data Protection Impact Assessment (DPIA), and requires the EU Commission to create of an automated tool to support the FRIA process. In this article, we provide our novel exploration of the DPI... | {
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2501.14759 | LPBSA: Enhancing Optimization Efficiency through Learner
Performance-based Behavior and Simulated Annealing | [
"cs.NE",
"math.OC"
] | This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence,... | {
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2501.14762 | Linked Data on Geo-annotated Events and Use Cases for the Resilience of
Ukraine | [
"cs.CY",
"cs.SI"
] | The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. Linked Data technology can be used to unify, enrich, and integrate data from mul... | {
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2501.14765 | Hybrid Cooperative Co-Evolution Algorithm for Deadlock-prone Distributed
Assembly Flowshop Scheduling with Limited buffers Using Petri nets | [
"cs.DC",
"cs.SY",
"eess.SY"
] | The distributed assembly flowshop scheduling problem (DAFSP) can be applied to immense manufacturing environments. In DAFSP, jobs are first processed in distributed flowshops, and then assembled into final products by an assembly machine, which usually has limited buffers in practical application. This limited capacity... | {
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2501.14766 | Artificial Intelligence for Sustainable Urban Biodiversity: A Framework
for Monitoring and Conservation | [
"cs.CY",
"cs.AI"
] | The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detec... | {
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2501.14767 | Leveraging Social Media Data and Artificial Intelligence for Improving
Earthquake Response Efforts | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.SI"
] | The integration of social media and artificial intelligence (AI) into disaster management, particularly for earthquake response, represents a profound evolution in emergency management practices. In the digital age, real-time information sharing has reached unprecedented levels, with social media platforms emerging as ... | {
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2501.14768 | Equation discovery framework EPDE: Towards a better equation discovery | [
"cs.NE",
"cs.AI",
"cs.LG"
] | Equation discovery methods hold promise for extracting knowledge from physics-related data. However, existing approaches often require substantial prior information that significantly reduces the amount of knowledge extracted. In this paper, we enhance the EPDE algorithm -- an evolutionary optimization-based discovery ... | {
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2501.14769 | A survey on pioneering metaheuristic algorithms between 2019 and 2024 | [
"cs.NE",
"cs.AI"
] | This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the pro... | {
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2501.14770 | Optimizing SSD Caches for Cloud Block Storage Systems Using Machine
Learning Approaches | [
"cs.DC",
"cs.LG",
"cs.OS"
] | The growing demand for efficient cloud storage solutions has led to the widespread adoption of Solid-State Drives (SSDs) for caching in cloud block storage systems. The management of data writes to SSD caches plays a crucial role in improving overall system performance, reducing latency, and extending the lifespan of s... | {
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2501.14771 | Dynamic Adaptation in Data Storage: Real-Time Machine Learning for
Enhanced Prefetching | [
"cs.DC",
"cs.LG",
"cs.OS"
] | The exponential growth of data storage demands has necessitated the evolution of hierarchical storage management strategies [1]. This study explores the application of streaming machine learning [3] to revolutionize data prefetching within multi-tiered storage systems. Unlike traditional batch-trained models, streaming... | {
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2501.14772 | DropMicroFluidAgents (DMFAs): Autonomous Droplet Microfluidic Research
Framework Through Large Language Model Agents | [
"cs.CY",
"cs.AI"
] | Applying Large language models (LLMs) within specific domains requires substantial adaptation to account for the unique terminologies, nuances, and context-specific challenges inherent to those areas. Here, we introduce DropMicroFluidAgents (DMFAs), an advanced language-driven framework leveraging state-of-the-art pre-... | {
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2501.14775 | Hybrid Firefly-Genetic Algorithm for Single and Multi-dimensional 0-1
Knapsack Problems | [
"cs.NE",
"cs.AI"
] | This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploratio... | {
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2501.14776 | Green AI: Which Programming Language Consumes the Most? | [
"cs.CY",
"cs.AI",
"cs.PL"
] | AI is demanding an evergrowing portion of environmental resources. Despite their potential impact on AI environmental sustainability, the role that programming languages play in AI (in)efficiency is to date still unknown. With this study, we aim to understand the impact that programming languages can have on AI environ... | {
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2501.14777 | Enhancing Supply Chain Resilience with Metaverse and ChatGPT
Technologies | [
"cs.CY",
"cs.AI"
] | Global supply lines have been severely disrupted by the COVID-19 epidemic and the conflict between Russia and Ukraine, which has sharply increased the price of commodities and generated inflation. These incidents highlight how critical it is to improve supply chain resilience (SCRES) in order to fend off unforeseen set... | {
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2501.14778 | Advancing Trustworthy AI for Sustainable Development: Recommendations
for Standardising AI Incident Reporting | [
"cs.CY",
"cs.AI",
"cs.HC"
] | The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents... | {
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2501.14779 | The Use of Generative Artificial Intelligence for Upper Secondary
Mathematics Education Through the Lens of Technology Acceptance | [
"cs.CY",
"cs.AI",
"cs.HC"
] | This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high school students to represent how key constructs of the Technology Acceptance Model (Perceived Usefulness, Perceived Ease of Use, Perceived ... | {
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2501.14780 | Perspective Chapter: MOOCs in India: Evolution, Innovation, Impact, and
Roadmap | [
"cs.CY",
"cs.AI",
"cs.DL"
] | With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. ... | {
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2501.14784 | DeServe: Towards Affordable Offline LLM Inference via Decentralization | [
"cs.DC",
"cs.AI"
] | The rapid growth of generative AI and its integration into everyday workflows have significantly increased the demand for large language model (LLM) inference services. While proprietary models remain popular, recent advancements in open-source LLMs have positioned them as strong contenders. However, deploying these mo... | {
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2501.14785 | ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification | [
"stat.ML",
"cs.AI",
"cs.LG",
"cs.SI"
] | Eating disorders (ED) are critical psychiatric problems that have alarmed the mental health community. Mental health professionals are increasingly recognizing the utility of data derived from social media platforms such as Twitter. However, high dimensionality and extensive feature sets of Twitter data present remarka... | {
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2501.14786 | Punch Out Model Synthesis: A Stochastic Algorithm for Constraint Based
Tiling Generation | [
"cs.DC",
"cs.LG"
] | As an artistic aid in tiled level design, Constraint Based Tiling Generation (CBTG) algorithms can help to automatically create level realizations from a set of tiles and placement constraints. Merrell's Modify in Blocks Model Synthesis (MMS) and Gumin's Wave Function Collapse (WFC) have been proposed as Constraint Bas... | {
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2501.14787 | Matrix Calculus (for Machine Learning and Beyond) | [
"math.HO",
"cs.LG",
"cs.NA",
"math.NA",
"stat.ML"
] | This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even ... | {
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2501.14788 | Methods to Increase the Amount of Data for Speech Recognition for Low
Resource Languages | [
"cs.SD",
"cs.CL",
"eess.AS"
] | This study explores methods to increase data volume for low-resource languages using techniques such as crowdsourcing, pseudo-labeling, advanced data preprocessing and various permissive data sources such as audiobooks, Common Voice, YouTube. While these methods are well-explored for highresource languages, their appli... | {
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2501.14790 | Towards Dynamic Neural Communication and Speech Neuroprosthesis Based on
Viseme Decoding | [
"q-bio.NC",
"cs.AI",
"cs.SD",
"eess.AS"
] | Decoding text, speech, or images from human neural signals holds promising potential both as neuroprosthesis for patients and as innovative communication tools for general users. Although neural signals contain various information on speech intentions, movements, and phonetic details, generating informative outputs fro... | {
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} |
2501.14794 | HeteroLLM: Accelerating Large Language Model Inference on Mobile SoCs
platform with Heterogeneous AI Accelerators | [
"cs.DC",
"cs.AI",
"cs.LG"
] | With the rapid advancement of artificial intelligence technologies such as ChatGPT, AI agents and video generation,contemporary mobile systems have begun integrating these AI capabilities on local devices to enhance privacy and reduce response latency. To meet the computational demands of AI tasks, current mobile SoCs ... | {
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2501.14802 | DNN-Powered MLOps Pipeline Optimization for Large Language Models: A
Framework for Automated Deployment and Resource Management | [
"cs.DC",
"cs.LG"
] | The exponential growth in the size and complexity of Large Language Models (LLMs) has introduced unprecedented challenges in their deployment and operational management. Traditional MLOps approaches often fail to efficiently handle the scale, resource requirements, and dynamic nature of these models. This research pres... | {
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2501.14808 | HyGen: Efficient LLM Serving via Elastic Online-Offline Request
Co-location | [
"cs.DC",
"cs.LG"
] | Large language models (LLMs) have facilitated a wide range of applications with distinct service-level objectives (SLOs), from latency-sensitive online tasks like interactive chatbots to throughput-oriented offline workloads like document summarization. The existing deployment model, which dedicates machines to each wo... | {
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} |
2501.14809 | Towards Foundation Models: Evaluation of Geoscience Artificial
Intelligence with Uncertainty | [
"cs.LG",
"cs.AI",
"physics.geo-ph"
] | Artificial intelligence (AI) has transformed the geoscience community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. This success has led to the development of geoscience foundation models (FMs), which promise to accomplish multiple tasks within a workflow or replace the ... | {
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2501.14813 | Dissertation Machine Learning in Materials Science -- A case study in
Carbon Nanotube field effect transistors | [
"physics.app-ph",
"cond-mat.mes-hall",
"cs.LG",
"physics.data-an"
] | In this thesis, I explored the use of several machine learning techniques, including neural networks, simulation-based inference, and generative flow networks, on predicting CNTFETs performance, probing the conductivity properties of CNT network, and generating CNTFETs processing information for target performance. | {
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2501.14815 | A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs | [
"cs.DC",
"cs.AI",
"cs.AR",
"cs.NI"
] | PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. Changes to the hardware design require a time-consuming FPGA synthesis process, and modification to the software, especially the operating sy... | {
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2501.14816 | Jump Point Search Pathfinding in 4-connected Grids | [
"cs.RO"
] | This work introduces JPS4, a novel pathfinding algorithm for 4-connected grid maps. JPS4 builds upon the Jump Point Search (JPS8) algorithm, originally designed for 8-connected environments. To achieve efficient pathfinding on 4-connected grids, JPS4 employs a canonical ordering and a successor function that enable onl... | {
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2501.14817 | A Cutting Mechanics-based Machine Learning Modeling Method to Discover
Governing Equations of Machining Dynamics | [
"cs.LG",
"cs.CE"
] | This paper proposes a cutting mechanics-based machine learning (CMML) modeling method to discover governing equations of machining dynamics. The main idea of CMML design is to integrate existing physics in cutting mechanics and unknown physics in data to achieve automated model discovery, with the potential to advance ... | {
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2501.14818 | Eagle 2: Building Post-Training Data Strategies from Scratch for
Frontier Vision-Language Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most open-source models only publish their final model weights, leaving the critical details of data strategies and implementation largely opaqu... | {
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} |
2501.14819 | A Comprehensive Mathematical and System-Level Analysis of Autonomous
Vehicle Timelines | [
"cs.MA",
"cs.RO"
] | Fully autonomous vehicles (AVs) continue to spark immense global interest, yet predictions on when they will operate safely and broadly remain heavily debated. This paper synthesizes two distinct research traditions: computational complexity and algorithmic constraints versus reliability growth modeling and real-world ... | {
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} |
2501.14822 | Controlling Ensemble Variance in Diffusion Models: An Application for
Reanalyses Downscaling | [
"stat.AP",
"cs.AI",
"cs.LG"
] | In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate that a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relat... | {
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} |
2501.14823 | Quantifying Energy and Cost Benefits of Hybrid Edge Cloud: Analysis of
Traditional and Agentic Workloads | [
"cs.DC",
"cs.AI"
] | This paper examines the workload distribution challenges in centralized cloud systems and demonstrates how Hybrid Edge Cloud (HEC) [1] mitigates these inefficiencies. Workloads in cloud environments often follow a Pareto distribution, where a small percentage of tasks consume most resources, leading to bottlenecks and ... | {
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.14824 | A causal learning approach to in-orbit inertial parameter estimation for
multi-payload deployers | [
"eess.SY",
"astro-ph.IM",
"cs.LG",
"cs.RO",
"cs.SY"
] | This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different spacecraft configurations (inertial parameter sets) are simulated under different actuation profile... | {
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"cs.SD": 0,
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"cs.SY": 1
} |
2501.14826 | Multi-Modality Transformer for E-Commerce: Inferring User Purchase
Intention to Bridge the Query-Product Gap | [
"cs.IR",
"cs.AI",
"cs.LG"
] | E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data s... | {
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"cs.SD": 0,
"cs.SI": 0,
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} |
2501.14828 | An Ensemble Model with Attention Based Mechanism for Image Captioning | [
"cs.CV",
"cs.AI"
] | Image captioning creates informative text from an input image by creating a relationship between the words and the actual content of an image. Recently, deep learning models that utilize transformers have been the most successful in automatically generating image captions. The capabilities of transformer networks have ... | {
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} |
2501.14830 | Sharp exact recovery threshold for two-community Euclidean random graphs | [
"cs.SI",
"math.PR"
] | This paper considers the problem of label recovery in random graphs and matrices. Motivated by transitive behavior in real-world networks (i.e., ``the friend of my friend is my friend''), a recent line of work considers spatially-embedded networks, which exhibit transitive behavior. In particular, the Geometric Hidden ... | {
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"cs.SD": 0,
"cs.SI": 1,
"cs.SY": 0
} |
2501.14836 | Symbolic Knowledge Extraction and Injection with Sub-symbolic
Predictors: A Systematic Literature Review | [
"cs.AI",
"cs.LG",
"cs.LO"
] | In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and injection (SKI) from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both hum... | {
"Other": 1,
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"cs.MA": 0,
"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.14837 | A Semiparametric Bayesian Method for Instrumental Variable Analysis with
Partly Interval-Censored Time-to-Event Outcome | [
"stat.ME",
"cs.LG",
"stat.AP",
"stat.CO",
"stat.ML"
] | This paper develops a semiparametric Bayesian instrumental variable analysis method for estimating the causal effect of an endogenous variable when dealing with unobserved confounders and measurement errors with partly interval-censored time-to-event data, where event times are observed exactly for some subjects but le... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.14844 | Unmasking Conversational Bias in AI Multiagent Systems | [
"cs.CL",
"cs.AI",
"cs.MA"
] | Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings. However, the majority of existing methodologies for identifying biases in generated text consider the models in isolation and neglect their contextual applicat... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.14846 | Wormhole Memory: A Rubik's Cube for Cross-Dialogue Retrieval | [
"cs.LG",
"cs.AI",
"cs.CL"
] | In view of the gap in the current large language model in sharing memory across dialogues, this research proposes a wormhole memory module (WMM) to realize memory as a Rubik's cube that can be arbitrarily retrieved between different dialogues. Through simulation experiments, the researcher built an experimental framewo... | {
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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