You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Trump puts a date on when he believes Iran war will end as he looks to rally sinking Republicans: 'They can't hold out' Amazon cargo plane captain, 55, was a US Army Black Hawk helicopter pilot who has chalked up 7,000 flying hours...but experts claim the Miami crash was a royal'screw up' Team Sussex hit back after Uganda pulls out of the Invictus Games over the'Harry-Meghan nonsense'... and claims the move is'clearly a consequence' of King's letter insisting they are'private citizens' The King has slapped down Harry and Meghan... but this is the biggest revelation from his public letter. Pastor and dad of five dies after suffering horror injury in cruise ship swimming pool while celebrating his son's wedding I spoke to Charlie Kirk the night before he died. Lindsay Clancy'copycat's' husband and family lay son, 2, to rest at funeral while she faces judge in court Explicit tour-bus security cam footage that sparked Nashville's latest A-list divorce: Country star'in deep s***' after humiliated wife'saw it all' Two measles patients who died from virus identified... as controversy over counting deaths continues Trump's travel halted as emergency plane slide deployed on problematic new Air Force One My boyfriend's sex request is so degrading... Trump lashes out over claim he made up 9/11 rescue story: 'She's unattractive inside and out' Kim Zolciak's jailed son KJ Biermann, 15, makes legal move in multiple-charge sexual assault case to avoid adult prison sentence Tom Brady's Las Vegas Raiders suffer major blow as star is ruled OUT on eve of new NFL season Truth about the Falling Man: Sister of anonymous 9/11 jumper who became symbol of America's grief tells the heartbreaking full story, clues that led to him... and why she knows God'caught his spirit' and carried it to Heaven Rapist stepson of Norway's new King is shunned by his step-grandmother Queen Sonja at her husband's funeral...and earns a glare from the Bishop Scientists make discovery that matches the Bible's account of how God created the heavens READ MORE: I bled to death. As an atheist, I expected oblivion... but I entered the afterlife for six minutes and saw proof of what God is REALLY like.

Scientists Create the Littlest Big Bang to Study the Universe's Origins The discovery redefines how large atoms need to be to produce the extreme state of matter found in the early universe. In the very first moments of the universe, matter didn't exist as we know today. A millionth of a second or so after the big bang, the universe was a dense, hot soup scientists call quark-gluon plasma (QGP). For several years, particle colliders --which smash molecules together at nearly the speed of light--have been able to replicate this state, but often using heavy elements like lead. Now, a recent experiment by the European Organization for Nuclear Research (also known as CERN from its French acronym) has demonstrated this plasma can be produced by much smaller collisions. Since there's no longer an accessible natural source of this primordial sludge, these micro big bangs can help reveal what happened in the first few minutes of our universe.

Science Physics Particle Physics Scientists smash atoms together to create'little' big bangs Recreating the universe's earliest moments could help us learn more about the elusive quark-gluon plasma. 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. Oxygen and neon nuclei created different particle patterns after colliding with each other. 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 .

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series The Milky Way's center holds the ruins of another ancient galaxy Elisha Sauers writes about space for Mashable, taking deep dives into NASA's moon and Mars missions, chatting up astronauts and history-making discoverers, and jetting above the clouds . Through 17 years of reporting, she's covered a variety of topics, including health, business, and government, with a penchant for public records requests. She previously worked for in Norfolk, Virginia, and in Annapolis, Maryland. Her work has earned numerous state awards, including the Virginia Press Association's top honor, Best in Show, and national recognition for narrative storytelling. For each year she has covered space, Sauers has won National Headliner Awards, including first place for her Sex in Space series.

The big cats can keep deer away from roads, according to new research. 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 . When it comes to living near large carnivores, the drawbacks for humans are pretty obvious.

Crash testing with these massive mammals has come a long way from using real cadavers. 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. The moose crash test dummy helping Volvo engineers in Sweden build cars. 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 .

Appendix A describes the Unity-based interface implemented in EVAAA, including an environment setup, prefab structures, and object instantiation. Appendix B provides a comprehensive introduction to Essential Variables (EVs), including their design, dynamics, and role in internal state regulation. Appendix C explains the implementation of the reward system and its connection to the balance of internal states. Appendix E outlines the modular configuration to generate EVAAA environments, along with the instructions for environment customization. Appendix F presents the structure and progression of naturalistic training environments. Appendix G describes the design of unseen experimental testbeds for evaluation. Appendix I provides analyses of agent behavior across training and test environments, including emergent behavioral patterns. All code and data are publicly available at: https://github.com/cocoanlab/evaaa A.1 Prefabs Environmental elements such as terrain, resources, obstacles, and predators are implemented as reusable and configurable Unity prefabs. Prefabs are grouped into Agents, Environment, and Materials. Each category includes reusable components for constructing and customizing interactive scenes: Agents (main agent and predators), Environment (terrain and containers), and Materials (varied textures and colors for visual distinction). This modular system enables rapid prototyping, task generation, condition randomization, and reproducible scene setup. Prefabs can be customized through the Unity Editor or programmatically at runtime, and reused across scenes without manual rebuilding.

Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous Driving. By leveraging 3DGS techniques, we construct a photorealistic digital replica of the real physical world, enabling the AD policy to extensively explore the state space and learn to handle out-of-distribution scenarios through large-scale trial and error. To enhance safety, we design specialized rewards to guide the policy in effectively responding to safety-critical events and understanding realworld causal relationships. To better align with human driving behavior, we incorporate IL into RL training as a regularization term. We introduce a closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS environments. Compared to IL-based methods, RAD achieves stronger performance in most closed-loop metrics, particularly exhibiting a 3 lower collision rate. Abundant closed-loop results are presented in the supplementary material. Code is available at https://github.com/hustvl/RADfor

The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts.However, existing datasets typically suffer from limitations in data scale or diversity, sanitized layouts lacking small items, and severe object collisions.To address these shortcomings, we introduce \textbf{InternScenes}, a novel large-scale simulatable indoor scene dataset comprising approximately 40,000 diverse scenes by integrating three disparate scene sources, \ie, real-world scans, procedurally generated scenes, and designer-created scenes, including 1.96M 3D objects and covering 15 common scene types and 288 object classes.We particularly preserve massive small items in the scenes, resulting in realistic and complex layouts with an average of 41.5 objects per region.Our comprehensive data processing pipeline ensures simulatability by creating real-to-sim replicas for real-world scans, enhances interactivity by incorporating interactive objects into these scenes, and resolves object collisions by physical simulations.We demonstrate the value of InternScenes with two benchmark applications: scene layout generation and point-goal navigation. Both show the new challenges posed by the complex and realistic layouts. More importantly, InternScenes paves the way for scaling up the model training for both tasks, making the generation and navigation in such complex scenes possible. We commit to open-sourcing the data and benchmarks to benefit the whole community.

Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs. In the first stage, we predict a scene-decoupled, contact-and collision-aware representation--sparse IBS--as the optimization target. Sparse IBS compactly encodes the geometric and contact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining a high grasp success rate across diverse objects and complex scenes.