arXiv:2604.01373v1 Announce Type: new Abstract: Estimating the dissipativity of nonlinear systems from empirical data is useful for the analysis and control of nonlinear systems, especially when an accurate model is unavailable. Based on a Koopman operator model of the nonlinear system on a reproducing kernel Hilbert space (RKHS), the storage function and supply rate functions are expressed as kernel quadratic forms, through which the dissipative inequality is expressed as a linear operator inequality. The RKHS is specified by […]

Ver mais

arXiv:2601.19223v1 Announce Type: cross Abstract: Traditional data-driven methods, effective for deterministic systems or stochastic differential equations (SDEs) with Gaussian noise, fail to handle the discontinuous sample paths and heavy-tailed fluctuations characteristic of L’evy processes, particularly when the noise is state-dependent. To bridge this gap, we establish nonlocal Kramers-Moyal formulas, rigorously generalizing the classical Kramers-Moyal relations to SDEs with multiplicative L’evy noise. These formulas provide a direct link between short-time transition probability densities (or sample path statistics) and the […]

Ver mais

arXiv:2602.07149v1 Announce Type: new Abstract: The rise of generative models has led to increased use of large-scale datasets collected from the internet, often with minimal or no data curation. This raises concerns about the inclusion of sensitive or private information. In this work, we explore the presence of pregnancy ultrasound images, which contain sensitive personal information and are often shared online. Through a systematic examination of LAION-400M dataset using CLIP embedding similarity, we retrieve images containing pregnancy ultrasound […]

Ver mais

arXiv:2601.10905v1 Announce Type: new Abstract: Numerous offline and model-based reinforcement learning systems incorporate world models to emulate the inherent environments. A world model is particularly important in scenarios where direct interactions with the real environment is costly, dangerous, or impractical. The efficacy and interpretability of such world models are notably contingent upon the quality of the underlying training data. In this context, we introduce Action Shapley as an agnostic metric for the judicious and unbiased selection of training […]

Ver mais

The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

Ver mais

If you work in cybersecurity, you are intimately familiar with the phone call. The one that comes at 3 AM or in the middle of a planned vacation. The one where a panicked voice describes a network that’s been compromised, data that’s been exfiltrated, or systems that have been encrypted. Your job, in that moment, is not to ask why. It is to clean up the mess. This article is about what happens after the technical incident is […]

Ver mais

Deploying RL agents on real physical hardware often reveals a catastrophic flaw: hardware drift. I built MicroSafe-RL to act as a real-time safety interceptor that constrains the action space based on hardware stability signatures. Universal Gym Wrapper: I’ve added a MicroSafeWrapper that allows you to apply the same safety shielding and reward shaping during simulation that you will use on the actual hardware. Reward Shaping: The wrapper uses a safety signal to penalize entropy and “Chaos” states, helping […]

Ver mais

arXiv:2601.10418v1 Announce Type: cross Abstract: We study tabular reinforcement learning problems with multiple steps of lookahead information. Before acting, the learner observes $ell$ steps of future transition and reward realizations: the exact state the agent would reach and the rewards it would collect under any possible course of action. While it has been shown that such information can drastically boost the value, finding the optimal policy is NP-hard, and it is common to apply one of two tractable […]

Ver mais