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arXiv:2602.22284v1 Announce Type: new Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely …
arXiv:2602.22285v1 Announce Type: new Abstract: Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical …
arXiv:2602.22286v1 Announce Type: new Abstract: Lossless compression is essential for efficient data storage and transmission. Although learning-based lossless compressors achieve strong results, most of them …
arXiv:2602.22288v1 Announce Type: new Abstract: Sudden cardiac death (SCD) is unpredictable, and its prediction in Chagas cardiomyopathy (CC) remains a significant challenge, especially in patients …
arXiv:2602.22291v1 Announce Type: new Abstract: While prior work has focused on projecting adversarial examples back onto the manifold of natural data to restore safety, we …
arXiv:2602.22293v1 Announce Type: new Abstract: River systems operate as inherently interconnected continuous networks, meaning river hydrodynamic simulation ought to be a systemic process. However, widespread …
arXiv:2602.22294v1 Announce Type: new Abstract: Models operating on dynamic physiologic signals must distinguish benign, label-preserving variability from true concept change. Existing concept-drift frameworks are largely …
arXiv:2602.22296v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming …
arXiv:2602.22297v1 Announce Type: new Abstract: Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully …
arXiv:2602.22298v1 Announce Type: new Abstract: Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. …
arXiv:2602.22303v1 Announce Type: new Abstract: Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior …
arXiv:2602.22334v1 Announce Type: new Abstract: Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection …