arXiv:2501.03746v2 Announce Type: replace Abstract: An accurate AI-based diagnostic system for induction motors (IMs) holds the potential to enhance proactive maintenance, mitigating unplanned downtime and curbing overall maintenance costs within an industrial environment. Notably, among the prevalent faults in IMs,…
arXiv:2510.15211v1 Announce Type: new Abstract: The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in the model’s main responses, we argue that it is…
arXiv:2502.15646v2 Announce Type: replace Abstract: High-throughput preclinical perturbation screens, where the effects of genetic, chemical, or environmental perturbations are systematically tested on disease models, hold significant promise for machine learning-enhanced drug discovery due to their scale and causal nature. Predictive…
arXiv:2510.15216v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) can elicit strong reasoning in large language models (LLMs), while their performance after RLVR varies dramatically across different base models. This raises a fundamental question: what microscopic property of…
arXiv:2504.19530v2 Announce Type: replace Abstract: This paper proposes and analyzes a gradient-type algorithm based on Burer-Monteiro factorization, called the Asymmetric Projected Gradient Descent (APGD), for reconstructing the point set configuration from partial Euclidean distance measurements, known as the Euclidean Distance…
arXiv:2510.15217v1 Announce Type: new Abstract: The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley,…
arXiv:2506.09080v2 Announce Type: replace Abstract: Financial decision-making presents unique challenges for language models, demanding temporal reasoning, adaptive risk assessment, and responsiveness to dynamic events. While large language models (LLMs) show strong general reasoning capabilities, they often fail to capture behavioral…
arXiv:2510.15218v1 Announce Type: new Abstract: We utilized a cohort of 214 meningitis patients and 46,303 non-meningitis patients from the MIMIC-III database. After extensive data preprocessing, which included ICD-based cohort selection, one-hot encoding of coding, and a two-stage feature selection process…
arXiv:2503.21473v2 Announce Type: replace-cross Abstract: Gaussian Processes (GPs) provide a flexible and statistically principled foundation for modelling spatiotemporal phenomena, but their $O(N^3)$ scaling makes them intractable for large datasets. Approximate methods such as variational inference (VI), inducing points (sparse GPs),…
arXiv:2510.12328v3 Announce Type: replace Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (GNNs) combined with extreme-value analysis techniques to improve gauge-station rainfall…
