We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning. As a result, structured physics knowledge can be embedded into larger systems, allowing them, for example, to match observations by performing precise simulations, while achieves high sample efficiency. Specifically, in this paper we demonstrate how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem. Unlike traditional finite difference methods, such gradients can be computed analytically, which allows for greater flexibility of the engine. Through experiments in diverse domains, we highlight the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods. Code for the engine and experiments is included with the paper.

How much do you know about friction? Jennifer R. Vail's charming, if sometimes technical, biography of the force showcases its amazing and largely overlooked role in everything from climate change to dark matter, says Karmela Padavic-Callaghan IN 2009, World Aquatics banned a specific type of swimsuit from all international competitions in water sports, ruling that it gave athletes an unfair advantage. The development of this swimsuit included using NASA's testing facilities and sophisticated computer software. Some versions had ultrasonically welded seams instead of traditional stitches. Swimmers who wore the suit broke 23 of the 25 world records set at the Beijing Olympics in 2008.

It isn't always easy to detect cheating in chess Just three judiciously deployed cheats can turn an otherwise equal chess game into a near-certain victory, a new analysis shows - and systems designed to crack down on cheating might not notice the foul play. Daniel Keren at the University of Haifa in Israel simulated 100,000 matches using the powerful Stockfish chess engine - a computer system that, at its maximum power, is better at playing chess than any human world champion. The matches were played between two computer engines competing at the level of an average chess player - 1500 on the Elo rating scale typically used to calculate skill level in chess. Half the games were logged without any further intervention, while the other half allowed occasional intervention by a stronger computer chess "player" with an Elo score of 3190 - a higher rating than any human player has ever achieved. Competitors usually have a slim advantage when playing white, with a 51 per cent chance of winning, on average, tied to the fact that they make the game's first move.

Lamborghini's new hybrid supercar includes a three-level drift mode and three axial flux motors The supercar pulls out the stops with a screaming 10,000 revolutions per minute at the redline. With a top speed of 213 miles per hour and a 10,000 rpm redline, the Lamborghini Temerario is a wild machine. Breakthroughs, discoveries, and DIY tips sent every weekday. Lamborghini's legacy gas-only machines have been unapologetically loud, brash, and in your face with sonorous symphonies conducted by fuel-guzzling V12 and V10 engines. Today, the brand is in its electrification age, with three plug-in hybrids: the Urus SE SUV, the top-tier Revuelto, and the newest Raging Bull, the Temerario.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? How can Ukraine rebuild China ties scarred by Russia's war? Back in the 1990s, China's nascent capitalism triggered demand for Ukrainian steel slabs and iron ore, corn and sunflower oil.

When you purchase through links in our articles, we may earn a small commission. Picochess is a chess program for the Raspberry Pi that you can use to carry out analyses, train openings, and master games. The Picochess chess program already has a long and storied history behind it--something you should be aware of if you're looking to download and use it to play chess with on Raspberry Pi. After years of development, version 1.0 was released in 2019, but only offered minor improvements compared to 0.9N. This was followed by version 2.01 at the beginning of 2020 and 3.0 towards the end of the year.

The rise of multi-agent systems powered by large language models (LLMs) and specialized reasoning agents exposes fundamental limitations in today's data management architectures. Traditional databases and data fabrics were designed for static, well-defined workloads, whereas agentic systems exhibit dynamic, context-driven, and collaborative behaviors. Agents continuously decompose tasks, shift attention across modalities, and share intermediate results with peers - producing non-deterministic, multi-modal workloads that strain conventional query optimizers and caching mechanisms. We propose an Agent-Centric Data Fabric, a unified architecture that rethinks how data systems serve, optimize, coordinate, and learn from agentic workloads. To achieve this we exploit the concepts of attention-guided data retrieval, semantic micro-caching for context-driven agent federations, predictive data prefetching and quorum-based data serving. Together, these mechanisms enable agents to access representative data faster and more efficiently, while reducing redundant queries, data movement, and inference load across systems. By framing data systems as adaptive collaborators, instead of static executors, we outline new research directions toward behaviorally responsive data infrastructures, where caching, probing, and orchestration jointly enable efficient, context-rich data exchange among dynamic, reasoning-driven agents.