The lack of fine-grained, large-scale datasets on water availability presents a critical barrier to applying machine learning (ML) for agricultural water management. Since there are multiple natural and anthropogenic factors that influence water availability, incorporating diverse multimodal features can significantly improve modeling performance. However, integrating such heterogeneous data is challenging due to spatial misalignments, inconsistent formats, semantic label ambiguities, and class imbalances. To address these challenges, we introduce IRRISIGHT, a large-scale, multimodal dataset spanning 20 U.S. states. It consists of 1.4 million pixel-aligned 224 224 patches that fuse satellite imagery with rich environmental attributes. We develop a robust geospatial fusion pipeline that aligns raster, vector, and point-based data on a unified 10m grid, and employ domain-informed structured prompts to convert tabular attributes into natural language. With irrigation type classification as a representative problem, the dataset is AI-ready, offering a spatially disjoint train/test split and extensive benchmarking with both vision and vision-language models.

With the 2026 FIFA World Cup about to get under way, many fans in England and Scotland are honing their strategy to balance late kick-offs with work the next morning. Matches are happening across the US, Canada and Mexico, with England's group games starting at 2100 and 2200 BST and Scotland's even later at 2300 and 0200. Some football fans have already strategically booked annual leave around potential knockout fixtures. Others are hoping to negotiate flexible working - later starts or working from home after late-night matches. Scotland fan Cameron Rae has already booked the Monday after the Haiti game off work so he can attend a Tartan Army fan zone at his local town hall, complete with a bar and DJ running until 4am.

Anthropic, the maker of the Claude artificial intelligence ( AI) models, made a new version of its technology available to the general public on Tuesday while restricting its use in sensitive areas. Dubbed Fable 5, the model is the first to be made widely available from the company's new Mythos class - its most advanced lineup of AI technology, unveiled in April but restricted to a small set of partner institutions for months over cybersecurity concerns. Anthropic promoted Fable 5 as useful for writing and debugging software code, answering complex research questions and analyzing images. Anthropic says the world should have option to'pause' on AI In parallel, Anthropic is offering an unrestricted version, Claude Mythos 5, to companies and organizations that already have access to this model family - including cybersecurity partners enrolled in its Project Glasswing program. That select group was expanded in early June to about 200 organizations in more than 15 countries and is expected to grow further.

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.

World's largest chipmaker does not rule out price rises as costs increase The world's largest chipmaker has told the BBC that inflation is pushing up the cost of doing business, and did not rule out price rises. Taiwan Semiconductor Manufacturing Company (TSMC) makes the most advanced chips designed by companies such as Nvidia, AMD and Apple, so any increase in pricing could ripple through to the cost of AI infrastructure, and potentially over time, the prices customers pay for their electronic devices. However, the firm's chief financial officer, Wendell Huang, said it would not introduce sudden fourfold, fivefold price rises. We reflect our value, he said, pointing to its technology leadership and manufacturing excellence. In an exclusive and wide-ranging interview, Huang also denied that the AI boom was a bubble and that the firm's global expansion was due to geopolitical pressure.

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To meet this moment, we need YOU. For five decades, has been exposing the corruption that the powerful would rather keep buried. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible. To meet this moment, we need YOU. That fight for the truth is at a pivotal point, and it takes readers like you to make it possible.

In the five years since Myanmar's military chief led a coup to overthrow the democratically elected government, civil war has torn the country apart. Thousands have been killed and millions displaced by the conflict between the military and an alliance of ethnic and rebel groups. More than two years ago, the rebels made a series of sweeping gains, but things have taken a turn for the worse for them. Forced conscription and increased drone power has put the military on the offensive in most parts of the country. The BBC's Quentin Sommerville travelled to Myanmar without the permission of the authorities - the only way to report from rebel-held territory.

The automaker today is turning on vehicle-to-grid charging for its GM Energy customers. Will people actually use it? Some 250,000 electric vehicles manufactured by General Motors are driving around the US today--right now!--with an oft-secret capability: Their big, powerful batteries can charge other things. Potentially appliances, homes, and now, thanks to a software update pushed by the automaker this week, an electrical grid . Twelve of GM's EVs have this "bidirectional charging" capability, way more than US competitors' battery-electrics.

In recent years, the alignment between artificial neural network (ANN) embeddings and blood oxygenation level dependent (BOLD) responses in functional magnetic resonance imaging (fMRI) via neural encoding models has significantly advanced research on neural representation mechanisms and interpretability in the brain. However, these approaches remain limited in characterizing the brain's inherently nonlinear response properties. To address this, we propose the Jacobian-based Nonlinearity Evaluation (JNE), an interpretability metric for nonlinear neural encoding models. JNE quantifies nonlinearity by statistically measuring the dispersion of local linear mappings (Jacobians) from model representations to predicted BOLD responses, thereby approximating the nonlinearity of BOLD signals. Centered on proposing JNE as a novel interpretability metric, we validated its effectiveness through controlled simulation experiments on various activation functions and network architectures, and further verified it on real fMRI data, demonstrating a hierarchical progression of nonlinear characteristics from primary to higher-order visual cortices, consistent with established cortical organization. We further extended JNE with Sample-Specificity (JNE-SS), revealing stimulus-selective nonlinear response patterns in functionally specialized brain regions. As the first interpretability metric for quantifying nonlinear responses, JNE provides new insights into brain information processing.