Food and agricultural consumption are responsible for nearly a quarter of the world’s greenhouse gas (GHG) emissions, so eating is a critical element in slowing climate change. Accurate estimation of meals carbon price is necessary to promote sustainable food intakes, but current methods rely heavily on self-administered dietary questionnaires, nutrition databases, or manual input, which are time-consuming, subject to errors, and difficult to scale up. In response to these challenges, we provide a novel machine learning model that […]

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Physical neural networks typically train linear synaptic weights while treating device nonlinearities as fixed. We show the opposite – by training the synaptic nonlinearity itself, as in Kolmogorov-Arnold Network (KAN) architectures, we yield markedly higher task performance per physical resource and improved performance-parameter scaling than conventional linear weight-based networks, demonstrating ability of KAN topologies to exploit reconfigurable nonlinear physical dynamics. We experimentally realise physical KANs in silicon-on-insulator devices we term ‘Synaptic Nonlinear Elements’ (SYNEs), operating at room temperature, […]

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arXiv:2601.01223v1 Announce Type: cross Abstract: Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves […]

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Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due to data scarcity, constrained workspaces, and the need for an exceptional level of safety and predictability. We present a supervised Mixture-of-Experts (MoE) architecture designed for phase-structured surgical manipulation tasks, which can be added on top of any autonomous policy. Unlike prior surgical robot learning approaches that rely on multi-camera setups or thousands of demonstrations, we show that a lightweight action […]

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Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal patterns at one subsystem relate to others. This is challenging in decentralized settings where raw measurements cannot be shared and client observations are heterogeneous. In practical deployments each subsystem (client) operates a fixed proprietary model that cannot be modified or retrained, limiting existing […]

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I have just recently released a free-to-use open source, local python implementation of a Multi Method Reinforcement Learning pipeline with no 3rd party paid requirements or sign-ups. It’s as simple as clone, configure, run. The repo contains full documentation and pipeline explanations, is made purely for consumer hardware compatibility, and works with any existing codebase or projects.Setup is as straightforward with extremely customizable configurations alongside the entire pipeline is one python file. Context and Motivations: I’m doing this […]

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Hey guys i have recently started learning about RL, dont know much in depth but focused more on implementing it in the stock market. I am not looking for some crazy unrealistic returns… just want to make something that can perform better than the market and want to learn along the way. My current roadmap is to just test how different models are performing on a basic level. I’d appreciate any kind of help or suggestion come my […]

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