Daily Arxiv

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Surgical Agent Orchestration Platform for Voice-directed Patient Data Interaction

FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation

Hard vs. Noise: Resolving Hard-Noisy Sample Confusion in Recommender Systems via Large Language Models

Learning Quantized Continuous Controllers for Integer Hardware

Differentiated Directional Intervention A Framework for Evading LLM Safety Alignment

TiS-TSL: Image-Label Supervised Surgical Video Stereo Matching via Time-Switchable Teacher-Student Learning

Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling

Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention

Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendation

SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?

A Remarkably Efficient Paradigm to Multimodal Large Language Models for Sequential Recommendation

EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion

Enhancing Diffusion Model Guidance through Calibration and Regularization

DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning

Report from Workshop on Dialogue alongside Artificial Intelligence

Token Is All You Need: Cognitive Planning through Belief-Intent Co-Evolution

Selective Diabetic Retinopathy Screening with Accuracy-Weighted Deep Ensembles and Entropy-Guided Abstention

SWE-Compass: Towards Unified Evaluation of Agentic Coding Abilities for Large Language Models

Alternative Fairness and Accuracy Optimization in Criminal Justice

I Detect What I Don't Know: Incremental Anomaly Learning with Stochastic Weight Averaging-Gaussian for Oracle-Free Medical Imaging

OMPILOT: Harnessing Transformer Models for Auto Parallelization to Shared Memory Computing Paradigms

Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices

How to Evaluate Speech Translation with Source-Aware Neural MT Metrics

GUI-AIMA: Aligning Intrinsic Multimodal Attention with a Context Anchor for GUI Grounding

Normalization in Attention Dynamics

Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options

Language over Content: Tracing Cultural Understanding in Multilingual Large Language Models

TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding

TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration

Bee: A High-Quality Corpus and Full-Stack Suite to Unlock Advanced Fully Open MLLMs

Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions

Evolutionary Profiles for Protein Fitness Prediction

Epistemic Diversity and Knowledge Collapse in Large Language Models

MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

A Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models

TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding

RSVG-ZeroOV: Exploring a Training-Free Framework for Zero-Shot Open-Vocabulary Visual Grounding in Remote Sensing Images

Instance Generation for Meta-Black-Box Optimization through Latent Space Reverse Engineering

Decoding Latent Attack Surfaces in LLMs: Prompt Injection via HTML in Web Summarization

Towards Methane Detection Onboard Satellites

OPERA: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study

Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction

CoCoLIT: ControlNet-Conditioned Latent Image Translation for MRI to Amyloid PET Synthesis

Beyond Algorethics: Addressing the Ethical and Anthropological Challenges of AI Recommender Systems

Imbalance in Balance: Online Concept Balancing in Generation Models

ReCode: Updating Code API Knowledge with Reinforcement Learning

Rethinking Losses for Diffusion Bridge Samplers

Zeroth-Order Optimization Finds Flat Minima

UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection

A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control

BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations

FB-RAG: Improving RAG with Forward and Backward Lookup

FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models

Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning

RL in Name Only? Analyzing the Structural Assumptions in RL post-training for LLMs

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learning

FALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Alignment

How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference

FaSDiff: Balancing Perception and Semantics in Face Compression via Stable Diffusion Priors

On the generalization of language models from in-context learning and finetuning: a controlled study

Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation

A Multimodal Recaptioning Framework to Account for Perceptual Diversity Across Languages in Vision-Language Modeling

WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada

MULTI-LF: A Continuous Learning Framework for Real-Time Malicious Traffic Detection in Multi-Environment Networks

STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

COPA: Comparing the incomparable in multi-objective model evaluation

CLEV: LLM-Based Evaluation Through Lightweight Efficient Voting for Free-Form Question-Answering

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples

Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance

Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees

MA-GTS: A Multi-Agent Framework for Solving Complex Graph Problems in Real-World Applications

On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers

A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses

Generalizing Weisfeiler-Lehman Kernels to Subgraphs

SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins

GeMID: Generalizable Models for IoT Device Identification

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

Selection of LLM Fine-Tuning Data based on Orthogonal Rules

Benchmarking Domain Generalization Algorithms in Computational Pathology

Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form QA

Identifying treatment response subgroups in observational time-to-event data

Informed Correctors for Discrete Diffusion Models

Integrating Artificial Intelligence into Operating Systems: A Survey on Techniques, Applications, and Future Directions

Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships

Spikingformer: A Key Foundation Model for Spiking Neural Networks

DeepPersona: A Generative Engine for Scaling Deep Synthetic Personas

Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture

A Theoretical Analysis of Detecting Large Model-Generated Time Series

Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts

Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives

Reasoning with Confidence: Efficient Verification of LLM Reasoning Steps via Uncertainty Heads

When Object-Centric World Models Meet Policy Learning: From Pixels to Policies, and Where It Breaks

ScRPO: From Errors to Insights

Deep Value Benchmark: Measuring Whether Models Generalize Deep Values or Shallow Preferences

Glia: A Human-Inspired AI for Automated Systems Design and Optimization

Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors

Created by

Haebom

저자

Farida Mohsen, Zubair Shah

개요

본 연구는 심전도(ECG) 특징과 임상 위험 요인(CRF)을 통합한 다중 모드 딥러닝 모델 ECG-DiaNet을 제시하여 2형 당뇨병(T2DM) 발병 예측을 향상시키고자 하였다. 카타르 바이오뱅크(QBB) 데이터를 사용하여 개발 코호트(n=2043)에서 모델을 훈련 및 검증하고, 5년 추적 관찰을 통해 종단적 테스트 세트(n=395)에서 성능을 평가하였다. 그 결과, ECG-DiaNet은 단일 모드 ECG 모델 및 CRF 모델보다 우수한 성능을 보였으며, CRF 모델보다 높은 AUROC (0.845 vs 0.8217, DeLong p<0.001)를 달성하였다. 재분류 지표 또한 개선됨을 확인하였고(NRI=0.0153, IDI=0.0482), 고위험군에서 우수한 양성 예측 값(PPV)을 달성하였다. 비침습적이고 광범위하게 사용 가능한 ECG 신호를 사용하여 임상 및 지역 사회 건강 환경에서의 실현 가능성을 보여주었다. 심장 전기 생리학 및 전신 위험 프로파일을 결합함으로써 T2DM의 다요인적 특성을 다루고 정밀 예방을 지원한다. 특히 중동 지역 소외 인구에서 T2DM의 조기 진단 및 예방 전략 발전에 다중 모드 AI의 가치를 강조한다.

시사점, 한계점

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시사점:

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ECG와 CRF를 통합한 다중 모드 딥러닝 모델 ECG-DiaNet이 T2DM 발병 예측 성능을 향상시켰다.

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비침습적이고 광범위하게 사용 가능한 ECG를 활용하여 임상 적용 가능성을 높였다.

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고위험군에서 우수한 양성 예측 값을 보여 정밀 예방에 기여할 수 있다.

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중동 지역 소외 인구를 포함한 다양한 인구 집단에 적용 가능성을 제시하였다.

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다중 모드 AI를 활용한 T2DM 조기 진단 및 예방 전략의 발전 가능성을 보여주었다.

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한계점:

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QBB 데이터셋에 대한 의존성으로 인한 일반화 가능성의 제한.

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다른 인종 및 민족 집단으로의 일반화 가능성에 대한 추가 연구 필요.

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장기적인 추적 관찰을 통한 모델 성능의 지속적인 평가 필요.

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모델의 해석 가능성 및 임상적 적용을 위한 추가 연구 필요.

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