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arXiv:2602.16749v1 Announce Type: cross Abstract: Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions. Accurate edge-based diagnosis across geographically...
arXiv:2602.16753v1 Announce Type: cross Abstract: We present an analytic approximation model for non-rigid point set registration, grounded in the multivariate Taylor expansion of vector-valued functions....
arXiv:2602.16759v1 Announce Type: cross Abstract: We investigate the influence of a relatively small perturbation on various generalized inverses functions or quantities derived from a tensor...
arXiv:2602.16794v1 Announce Type: cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains...
arXiv:2602.16830v1 Announce Type: cross Abstract: This study addresses a central tactical dilemma for football coaches: whether to employ a defensive strategy, colloquially known as "parking...
arXiv:2602.16841v1 Announce Type: cross Abstract: Envelope extraction in nuclear magnetic resonance (NMR) is a fundamental step for processing the data space generated by this technique....
arXiv:2602.16908v1 Announce Type: cross Abstract: Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural...
arXiv:2602.16914v1 Announce Type: cross Abstract: Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been...
arXiv:2602.16923v1 Announce Type: cross Abstract: We study dynamic joint assortment and pricing where a seller updates decisions at regular accounting/operating intervals to maximize the cumulative...
arXiv:2602.16951v1 Announce Type: cross Abstract: Developing foundation models for electroencephalography (EEG) remains challenging due to the signal's low signal-to-noise ratio and complex spectro-temporal non-stationarity. Existing...
arXiv:2602.17010v1 Announce Type: cross Abstract: Evaluating perceived video quality is essential for ensuring high Quality of Experience (QoE) in modern streaming applications. While existing subjective...
arXiv:2602.17023v1 Announce Type: cross Abstract: Existing studies on generalized pinching-antenna systems are predominantly link-level, which optimize system parameters for a given user set with objectives...
arXiv:2602.17032v1 Announce Type: cross Abstract: This two-part paper aims to develop an environment-aware network-level design framework for generalized pinching-antenna systems to overcome the limitations of...
arXiv:2602.17065v1 Announce Type: cross Abstract: Quantum communication holds the potential to revolutionize information transmission by enabling secure data exchange that exceeds the limits of classical...
arXiv:2602.17070v1 Announce Type: cross Abstract: Probabilities of causation (PoCs), such as the probability of necessity and sufficiency (PNS), are important tools for decision making but...
arXiv:2602.17086v1 Announce Type: cross Abstract: Dynamic decision-making under model uncertainty is central to many economic environments, yet existing bandit and reinforcement learning algorithms rely on...
arXiv:2602.17098v1 Announce Type: cross Abstract: Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of...
arXiv:2602.17115v1 Announce Type: cross Abstract: Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they...
arXiv:2602.17120v1 Announce Type: cross Abstract: In Video on Demand (VoD) scenarios, traditional codecs are the industry standard due to their high decoding efficiency. However, they...
arXiv:2602.17176v1 Announce Type: cross Abstract: Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central...
arXiv:2602.17187v1 Announce Type: cross Abstract: The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously...
arXiv:2602.17205v1 Announce Type: cross Abstract: The detection limit of astronomical imaging observations is limited by several noise sources. Some of that noise is correlated between...
arXiv:2602.17211v1 Announce Type: cross Abstract: Generating samples from limited information is a fundamental problem across scientific domains. Classical maximum entropy methods provide principled uncertainty quantification...
arXiv:2602.17213v1 Announce Type: cross Abstract: We present an AI-assisted framework for predicting individual runs of complex quantum experiments, including contextuality and causality (adaptive measurements), within...