India's Tata Electronics has signed a deal with Dutch technology giant ASML to build a major semiconductor plant in western India, as Prime Minister Narendra Modi visited the Netherlands during his European tour. The agreement, announced on Saturday, will support the development of Tata's semiconductor facility in Dholera, Gujarat - Modi's home state. The Dutch company said it would help "establish and ramp up" production at the plant by supplying its cutting-edge chipmaking tools. Tata Electronics plans to invest $11bn in the facility, which is expected to manufacture chips for artificial intelligence, the automotive industry and other sectors. ASML chief executive Christophe Fouquet said the company saw "many compelling opportunities" in India's growing semiconductor industry.

In many search applications related to passage retrieval, text entailment, and sub-graph search, the query and each'document' is a set of elements, with a document

The classic Mallows model is a widely-used tool to realize distributions on permutations.

This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifies centralized deliberation with decentralized execution, and supports event-driven replanning. A Task Manager LLM interprets natural-language goals, classifies tasks, and allocates subtasks using static rules plus dynamic contexts (task, history, robot and task status, and events).Each robot runs a local LLM that composes executable Python code from primitive skills (ROS2 nodes, policies), while onboard perception (VLMs/image processing) continuously monitors events and classifies them into relevant or irrelevant to the task. Task failures or user intent changes trigger replanning, allowing robots to assist teammates, resume tasks, or request human help. Hardware studies demonstrate autonomous recovery from disruptive events, filtering of irrelevant distractions, and tightly coordinated transport with emergent human-robot cooperation (e.g., multirobot collaborative object recovery success rate: 9/10, coordinated transport: 8/8, human-assisted recovery: 5/5).Simulation studies show intention-aware replanning. A curated textual benchmark spanning 22 scenarios (3 tasks each, around 20 robots) evaluates task allocation, classification, IoU, executability, and correctness, with high average scores (e.g., correctness up to 0.91) across multiple LLMs, a separate replanning set (5 scenarios) achieves 1.0 correctness. Compared with prior LLM-based systems, CoMuRoS uniquely demonstrates runtime, event-driven replanning on physical robots, delivering robust, flexible multi-robot and human-robot collaboration.

The classic Mallows model is a widely-used tool to realize distributions on permutations.

To benchmark a model's long-form factuality in open domains, we first use GPT -4 to generate LongFact, a prompt set comprising thousands of questions spanning 38 topics. We then propose that LLM agents can be used as automated evaluators for long-form factuality through a method which we call Search-Augmented Factuality Evaluator (SAFE).

Work done as a Research Fellow at Microsoft Research India.

Six new cookbooks bring stellar dishes--and cultures--from around the world into your kitchen. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Think about how difficult cooking from a cookbook from another culture was as little as 10 years ago. Once in a while, you could get your hands on a standout, but the food you could make with it could feel like a compromise with too many substitutions and ingredients you just couldn't find without great effort, or at all.

Efforts to green cities for cooling are succeeding unevenly because the same vegetation that cools surfaces can also intensify how hot the air feels. Previous studies have identified humid heat as a growing urban hazard, yet how physiologically active vegetation governs this trade-off between cooling and moisture accumulation remains poorly understood, leaving mitigation policy and design largely unguided. Here we quantify how vegetation structure and function influence the Heat Index (HI), a combined measure of temperature and humidity in 138 Indian cities spanning tropical savanna, semi-arid steppe, and humid subtropical climates, and across dense urban cores and semi-urban rings. Using an extreme-aware, one kilometre reconstruction of HI and an interpretable machine-learning framework that integrates SHapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE), we isolate vegetation-climate interactions. Cooling generally strengthens for EVI >= 0.4 and LAI >= 0.05, but joint-high regimes begin to reverse toward warming when EVI >= 0.5, LAI >= 0.2, and fPAR >= 0.5,with an earlier onset for fPAR >= 0.25 in humid, dense cores. In such environments, highly physiologically active vegetation elevates near-surface humidity faster than it removes heat, reversing its cooling effect and amplifying perceived heat stress. These findings establish the climatic limits of vegetation-driven cooling and provide quantitative thresholds for climate-specific greening strategies that promote equitable and heat-resilient cities.