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FedAvg-Based CTMC Hazard Model for Federated Bridge Deterioration Assessment

arXiv:2602.20194v1 Announce Type: new Abstract: Bridge periodic inspection records contain sensitive information about public infrastructure, making cross-organizational data sharing impractical under existing data governance constraints. We propose a federated framework for estimating a Continuous-Time Markov Chain (CTMC) hazard model of…

CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models

arXiv:2602.02620v2 Announce Type: replace-cross Abstract: Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling near-atomic-level visualization of biomolecular assemblies. However, the exponential growth in cryo-EM data throughput and complexity, coupled with diverse downstream analytical tasks, necessitates unified computational frameworks that…

Empirically Calibrated Conditional Independence Tests

arXiv:2602.21036v1 Announce Type: cross Abstract: Conditional independence tests (CIT) are widely used for causal discovery and feature selection. Even with false discovery rate (FDR) control procedures, they often fail to provide frequentist guarantees in practice. We highlight two common failure…

Exploring Anti-Aging Literature via ConvexTopics and Large Language Models

arXiv:2602.20224v1 Announce Type: new Abstract: The rapid expansion of biomedical publications creates challenges for organizing knowledge and detecting emerging trends, underscoring the need for scalable and interpretable methods. Common clustering and topic modeling approaches such as K-means or LDA remain…

Predicting Subway Passenger Flows under Incident Situation with Causality

arXiv:2412.06871v2 Announce Type: replace Abstract: In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction…

Coupled Cluster con M=oLe: Molecular Orbital Learning for Neural Wavefunctions

arXiv:2602.20232v1 Announce Type: new Abstract: Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond…

Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning

arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this…