We prove that the proposed algorithm converges to the optimal solution at a global geometric rate.

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance, it is crucial to guarantee the safety of an agent during training as well as deployment (e.g., a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision processes (CMDPs), an extension of the standard Markov decision processes (MDPs) augmented with constraints on expected cumulative costs.

Neural Information Processing Systems http://nips.cc/

The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes.

Induction stoves use electromagnetism to heat food more efficiently than any other kind of stovetop. Breakthroughs, discoveries, and DIY tips sent every weekday. Ask someone in the United States about "electric cooking" and they'll probably describe one of those awful coil stoves, the ones that take forever to heat up and then burn your dinner to a crisp the moment you take your eyes off it. This unfortunate association is perhaps one reason why induction cooking hasn't quite taken off in the U.S. the way it has elsewhere in the world--in Europe, for example, where induction stoves are commonplace. How do induction stoves differ from electric stoves?

Plus: Donald Trump wants to outlaw individual states' right to regulate AI The International Energy Agency recently released the latest version of the World Energy Outlook, the annual report that takes stock of the current state of global energy and looks toward the future. It contains some interesting insights and a few surprising figures about electricity, grids, and the state of climate change. Let's dig into some numbers . This article is from The Spark, MIT Technology Review's weekly climate newsletter. Everything is a conspiracy theory now. Our latest series " The New Conspiracy Age " delves into how conspiracies have gripped the White House, turning fringe ideas into dangerous policy, and how generative AI is altering the fabric of truth.

Deep learning models have advanced ETS modeling by effectively capturing sequence dependence. However, learning a generic representation of ETS data for various applications is challenging due to the inherently complex hierarchical structure of ETS data.

Then, we propose FreTS, a simple yet effective architecture built upon Fre quency-domain MLPs for T ime S eries forecasting.

Hu et al. [1] suggested using uncrewed vehicles in civil infrastructure asset management. Similarly, Bechtsis et al. [2] propose using uncrewed ground vehicles (UGVs) in precision farming. One of the emerging areas where such vehicles can prove helpful is assisting in postdisaster evacuation. Natural disasters, including earthquakes, tsunamis, hurricanes, and volcanic eruptions, can severely damage the urban infrastructure, leading to considerable losses. Following such events, providing timely relief and disseminating crucial information, such as safe evacuation routes, becomes essential for affected individuals' safe and organized movement. Recently, among the advanced technologies integrated into disaster response missions include uncrewed aerial vehicles (UAVs) that have been crucial in assessing the state of critical infrastructure essential services, including telecommunications, transportation, and buildings, to facilitate efficient disaster response and evacuation [3]. UAV systems have proven to be increasingly valuable in disaster relief and emergency response (DRER) efforts by enhancing the capabilities of the first responders, offering advanced predictive insights, and enabling early warning systems [4]. UAVs have assisted in diverse tasks, including remote sensing, search and rescue, forest fire detection, survey and surveillance [5].

Forecasting nonlinear time series with multi-scale temporal structures remains a central challenge in complex systems modeling. We present a novel reservoir computing framework that combines delay embedding with random Fourier feature (RFF) mappings to capture such dynamics. Two formulations are investigated: a single-scale RFF reservoir, which employs a fixed kernel bandwidth, and a multi-scale RFF reservoir, which integrates multiple bandwidths to represent both fast and slow temporal dependencies. The framework is applied to a diverse set of canonical systems: neuronal models such as the Rulkov map, Izhikevich model, Hindmarsh-Rose model, and Morris-Lecar model, which exhibit spiking, bursting, and chaotic behaviors arising from fast-slow interactions; and ecological models including the predator-prey dynamics and Ricker map with seasonal forcing, which display multi-scale oscillations and intermittency. Across all cases, the multi-scale RFF reservoir consistently outperforms its single-scale counterpart, achieving lower normalized root mean square error (NRMSE) and more robust long-horizon predictions. These results highlight the effectiveness of explicitly incorporating multi-scale feature mappings into reservoir computing architectures for modeling complex dynamical systems with intrinsic fast-slow interactions.