Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models. In many real-world applications, such as energy management and stock investment, agents can access multi-step predictions of future states, which provide additional advantages for decision making. However, multi-step predictions are inherently high-dimensional: naively embedding these predictions into an MDP leads to an exponential blow-up in state space and the curse of dimensionality. Moreover, existing RL theory provides few tools to analyze prediction-augmented MDPs, as it typically works on one-step transition kernels and cannot accommodate multi-step predictions with errors or partial action-coverage. We address these challenges with three key innovations: First, we propose the Bayesian value function to characterize the optimal prediction-aware policy tractably. Second, we develop a novel BellmanJensen Gap analysis on the Bayesian value function, which enables characterizing the value of imperfect predictions. Third, we introduce BOLA (Bayesian Offline Learning with Online Adaptation), a two-stage model-based RL algorithm that separates offline Bayesian value learning from lightweight online adaptation to real-time predictions. We prove that BOLA remains sample-efficient even under imperfect predictions.

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system. China is submerging data centers into the ocean to keep them cool.Photograph: Shanghai Hailanyun Technology China has become the first country in the world to operate an underwater data center, or UDC, powered by wind. Located off the coast of Shanghai, the complex represents a significant advance in the country's strategy to secure energy supplies in the face of the accelerated growth of artificial intelligence, reduce dependence on fossil fuels, and reduce the environmental impact of its technology infrastructure. The initiative is the result of a collaboration between private company HiCloud Technology and state-owned China Communications Construction, which involved an investment of 1.6 billion yuan, equivalent to about $236 million. With an initial capacity of 24 megawatts, the facility is submerged at a depth of 10 meters in the Lin-gang Special Zone, within the China Pilot Free Trade Zone in Shanghai.

For animals, certain colors scream poison. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Although largely safe, turbines still pose a danger to some migratory birds. Breakthroughs, discoveries, and DIY tips sent six days a week. Wind turbines are a net positive for a sustainable society, but that doesn't mean they don't have an environmental impact.

General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studies. This limitation is particularly critical for wind-related applications, such as wind energy, which require spatially coherent, multivariate, and physically plausible near-surface wind fields. Classical statistical downscaling and bias correction methods partly address this issue. Still, they struggle to preserve spatial structure, inter-variable consistency, and robustness under climate change, especially in high-dimensional settings. Recent advances in generative machine learning offer new opportunities for downscaling and bias correction, eliminating the need for explicitly paired low- and high-resolution datasets. However, many existing approaches remain difficult to interpret and challenging to deploy in operational climate impact studies. In this work, we apply SerpentFlow, an interpretable, generative, domain alignment framework, to the multivariate downscaling and bias correction of wind variables from GCM outputs. This is a method that generates low-resolution/high-resolution training data pairs by separating large-scale spatial patterns from small-scale variability. Large-scale components are aligned across climate model and observational domains. Conditional fine-scale variability is then learned using a flow-matching generative model. We apply the approach to multiple wind variables downscaling, including average and maximal wind speed, zonal and meridional components, and compare it with widely used multivariate bias correction methods. Results show improved spatial coherence, inter-variable consistency, and robustness under future climate conditions, highlighting the potential of interpretable generative models for wind and energy applications.

We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. 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. We present an efficient variational approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem. We show results for an artificial data set and real-world data of two wind turbines.

In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.

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

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