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A single day was the difference between success and disaster. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . The weather forecast did not inspire confidence for Allied forces ahead of June 6, 1944.

To play this video you need to enable JavaScript in your browser. A destructive tornado has torn through villages in southern France, injuring more than 40 people. An eyewitness from the village of Verzeille recorded the tornado forming beneath a thunderstorm. Winds of more than 100km/h (62mph) ripped the roofs off homes in Pomas. Local authorities say about 2,800 households were still without electricity on Monday evening, while several roads were blocked.

Cities across north-eastern Canada and the US are suffering from intense smoke brought on by wildfires burning across Ontario and Minnesota. Residents in New York, Boston and Toronto have been encouraged to avoid strenuous activity over potential health impacts caused by the pollution. Canada wildfires leave train'encased in flames' as smoke drifts towards US Where are the wildfires and how did they start? There are currently 858 wildfires actively burning across Canada - nearly 200 of those in Ontario - according to the Canadian Interagency Forest Fire Centre. Along the northern edge of Minnesota there are 17 fires that are still burning and an emergency declaration is in place to help mobilise suppression efforts.

The public will be urged to take small but important steps to prepare for food or water shortages in the event of a cyber-attack or severe weather, the government has said as it updated Parliament on its national resilience plans. Cabinet Office Minister Darren Jones said a public awareness campaign would be launched later this year to help people prepare for emergencies. He also said the government would carry out the largest UK home defence exercise in several decades next year to ensure the UK is ready should the worst ever happen. Separately, the national risk register, external has been updated with seven new risks including the threat of a cyber-attack on water infrastructure. The threat of digital resilience failure - such as the global CrowdStrike outage which crippled more than eight million computers - has also been added to the list of the most acute risks facing the UK, which totals 95.

California couple's desperate bid to save man, 28, from crocodile attack ends in tragedy after they heard screams coming from beach while on vacation in Mexico'Most beautiful girl in the world' Thylane Blondeau is married: Model stuns as she ties the knot with French DJ Ben Attal in Paris three months after getting engaged Sordid marriage secrets of country star Sam Hunt: Insiders reveal wife's brutal ultimatum... as singer's strange disappearance fuels Nashville whispers The signs I missed that I was sleeping next to a killer: My husband dismembered his secret girlfriend with a machete. Blue collar Democrat's VERY kinky history is exposed as she desperately grasps on to rural Washington seat World's first'pregnant man' Thomas Beatie reveals astonishing full story for the first time as his daughter turns 18... and confronts a hard truth about trans teens'Super, well done you!' Moment Kate stops to chat to 11-year-old boy in wheelchair during her Three Peaks Challenge as he's ...

The process of editing an image can be naturally modeled as evaluating a counterfactual query: "What would an image look like if a particular feature had changed?" While recent advances in text-guided image editing leverage powerful pre-trained models to produce visually appealing images, they often lack counterfactual consistency - ignoring how features are causally related and how changing one may affect others. In contrast, existing causal-based editing approaches offer solid theoretical foundations and perform well in specific settings, but remain limited in scalability and often rely on labeled data. In this work, we aim to bridge the gap between causal editing and large-scale text-to-image generation through two main contributions. First, we introduce Backdoor Disentangled Causal Latent Space (BD-CLS), a new class of latent spaces that allows for the encoding of causal inductive biases. One desirable property of this latent space is that, even under weak supervision, it can be shown to exhibit counterfactual consistency. Second, and building on this result, we develop BD-CLS-Edit, an algorithm capable of learning a BD-CLS from a (non-causal) pre-trained Stable Diffusion model. This enables counterfactual image editing without retraining. Our method ensures that edits respect the causal relationships among features, even when some features are unlabeled or unprompted and the original latent space is oblivious to the environment's underlying cause-and-effect relationships.

The increasing energy demands and carbon footprint of large-scale AI require intelligent workload management in globally distributed data centers. Yet progress is limited by the absence of benchmarks that realistically capture the interplay of time-varying environmental factors (grid carbon intensity, electricity prices, weather), detailed data center physics (CPUs, GPUs, memory, HVAC energy), and geo-distributed network dynamics (latency and transmission costs). To bridge this gap, we present DCcluster-Opt: an open-source, high-fidelity simulation benchmark for sustainable, geo-temporal task scheduling. DCcluster-Opt combines curated real-world datasets, including AI workload traces, grid carbon intensity, electricity markets, weather across 20 global regions, cloud transmission costs, and empirical network delay parameters with physics-informed models of data center operations, enabling rigorous and reproducible research in sustainable computing. It presents a challenging scheduling problem where a top-level coordinating agent must dynamically reassign or defer tasks that arrive with resource and service-level agreement requirements across a configurable cluster of data centers to optimize multiple objectives. The environment also models advanced components such as heat recovery. A modular reward system enables an explicit study of trade-offs among carbon emissions, energy costs, service level agreements, and water use. It provides a Gymnasium API with baseline controllers, including reinforcement learning and rule-based strategies, to support reproducible ML research and a fair comparison of diverse algorithms. By offering a realistic, configurable, and accessible testbed, DCcluster-Opt accelerates the development and validation of next-generation sustainable computing solutions for geo-distributed data centers.

Can cloud seeding save us from water bankruptcy? We've long tried to control the weather by engineering rainfall. Now such cloud-seeding efforts are escalating, creating conflict between countries and stoking conspiracy theories. On a cold, windy night in November 2025, a quadcopter drone took off from a farm field at the foot of the Bannock mountain range north of Salt Lake City, rising 4000 metres into thick clouds. A fan with anti-icing propellers kicked into action, blowing yellow dust out of a cannister attached to the back of the drone. Cloud-seeding company Rainmaker was trying to fight dust with dust, spreading silver iodide powder to encourage precipitation and end the deadly dust storms plaguing Utah's capital.

Unlike MLPs, Kolmogorov-Arnold Networks (KANs) expose explicit learnable edge functions on every connection, enabling mechanistic explanation in time-series forecasting. This paper introduces Temporal Functional Circuits, a framework that transforms KAN edge functions from latent visualizations into faithful, temporally grounded explanations. Built on a gated residual KAN that decomposes forecasts into a linear base and a sparsely activated KAN correction, the framework (i) maps each edge to input lags via output-aware attribution, (ii) ranks edges by learned activation range, and (iii) validates faithfulness through edge-level interventions including zeroing and spline removal. Removing the learned B-spline component while retaining the base SiLU term degrades forecasts, providing evidence that the spline shape itself carries predictive value beyond the base activation. On four synthetic regimes of increasing complexity, the learned gate opens progressively wider as signal complexity grows. On regime-switching signals, gated KAN achieves 59% lower MSE than linear-only models. Across eight benchmarks, the gated architecture is competitive with linear, attention, and MLP alternatives, while providing interpretable edge functions that MLP-based corrections cannot offer.