arXiv:2512.10117v1 Announce Type: new Abstract: Learning the flows of hybrid systems that have both continuous and discrete time dynamics is challenging. The existing method learns the dynamics in each discrete mode, which suffers from the combination of mode switching and…
arXiv:2504.01538v2 Announce Type: replace-cross Abstract: While current AI-driven methods excel at deriving empirical models from individual experiments, a significant challenge remains in uncovering the common fundamental physics that underlie these models — a task at which human physicists are adept.…
arXiv:2512.10133v1 Announce Type: new Abstract: Reliable data-driven estimation of Shannon entropy from small data sets, where the number of examples is potentially smaller than the number of possible outcomes, is a critical matter in several applications. In this paper, we…
arXiv:2511.02620v2 Announce Type: replace-cross Abstract: As large AI models become increasingly valuable assets, the risk of model weight exfiltration from inference servers grows accordingly. An attacker controlling an inference server may exfiltrate model weights by hiding them within ordinary model…
arXiv:2512.10141v1 Announce Type: new Abstract: Traditional feature engineering approaches for molecular sequence classification suffer from sparsity issues and computational complexity, while deep learning models often underperform on tabular biological data. This paper introduces a novel topological approach that transforms molecular…
arXiv:2512.10600v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs), as valuable intellectual property, face unauthorized use. Existing protections, such as digital watermarking, are largely passive; they provide only post-hoc ownership verification and cannot actively prevent the illicit use of a…
arXiv:2512.10147v1 Announce Type: new Abstract: Early detection and characterization of coronavirus disease (COVID-19), caused by SARS-CoV-2, remain critical for effective clinical response and public-health planning. The global availability of large-scale viral sequence data presents significant opportunities for computational analysis; however,…
arXiv:2512.10745v1 Announce Type: cross Abstract: Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compliance (C) are critical for early vascular dysfunction detection. While photoplethysmography (PPG) wearables has gained popularity, existing data-driven methods…
arXiv:2512.10152v1 Announce Type: new Abstract: Causal discovery methods have traditionally been developed under two distinct regimes: independent and identically distributed (i.i.d.) and timeseries data, each governed by separate modelling assumptions. In this paper, we argue that the i.i.d. setting can…
arXiv:2512.10825v1 Announce Type: cross Abstract: We consider the design of smoothings of the (coordinate-wise) max function in $mathbb{R}^d$ in the infinity norm. The LogSumExp function $f(x)=ln(sum^d_iexp(x_i))$ provides a classical smoothing, differing from the max function in value by at most…
