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An interpreter broke down in tears at the European Parliament in Brussels while translating for an 11-year-old Ukrainian boy who was injured in a Russian missile strike on a hospital in central Ukraine in 2022. Roman Oleksiv's mother was killed in the attack and he has undergone multiple surgeries since. The aspiring ballroom dancer, who was also the subject of an award-winning film, has also received an award from the Ukraine's President Volodymyr Zelensky. A waterspout is a whirling column of air and mist that can form over oceans, seas or large lakes. 'I don't want to be part of this war machine': Young Germans protest against military service plans Germany is introducing voluntary military service to boost national defences after Russia's full-scale invasion of Ukraine.

On a commercial thoroughfare in old town Pasadena, California, a stone's throw from NASA's Jet Propulsion Laboratory (JPL), you'll find the Neon Retro Arcade. Among its collection of vintage video games is the 1979 Atari classic Asteroids, in which a pixelated spaceship shoots down a barrage of space rocks to stave off fatal collisions. After long days of work at JPL, Amy Mainzer used to rack up high scores on that console. "It was a hoot," she says. It was also apt, considering she oversees a space mission designed to spot dangerous asteroids before they crash into Earth. That mission, the Near-Earth Object (NEO) Surveyor, was conceived in the early 2000s and finally got the green light in 2022. Its components are now being built, tested, and assembled in clean rooms across the United States ahead of its planned launch in September 2027. "We're in the thick of building everything," says Mainzer, NEO Surveyor's principal investigator and now an astronomer at the University of California, Los Angeles (UCLA).

Disney has agreed to invest $1bn (£740m) in OpenAI as part of a deal which will let people use many of its iconic characters in the chatbot ChatGPT and video-generation tool Sora. It is the first major studio to license parts of its catalogue to the tech giant, in a move which could have major implications for the studio's future plans. It means fans will be able to generate and share pictures and videos of more than 200 characters from Disney's franchises, including Pixar, Marvel and Star Wars. The move comes as OpenAI faces mounting questions about how its rapidly advancing tech is used - and as anxiety in Hollywood increases over the impact of AI on the creative industries. According to a blog post announcing the news, the list of eligible characters include those from Disney films Zootopia, Moana and Encanto - as well as characters like Star Wars' Luke Skywalker and Marvel's Deadpool.

'Architects of AI' named Time Magazine's Person of the Year Time Magazine's Person of the Year for 2025 is not a single person. Instead, the magazine has recognised the year's most influential figure as the architects of artificial intelligence (AI). Nvidia boss Jensen Huang, Meta head Mark Zuckerberg, X owner Elon Musk and AI godmother Fei-Fei Li are among those depicted on one of the magazine's two covers. Experts say it highlights how quickly AI, and the firms behind it, are reshaping society. It comes as a boom in the technology, ushered in by OpenAI's launch of ChatGPT in late 2022, continues at pace.

AI has entered the classroom - but is it the solution for overworked teachers? Schools across the UK are trialling the use of deepfake teachers and even employing remote staff to deliver lessons hundreds of miles away from the classroom. It comes as the use of AI is becoming increasingly prevalent in schools. The government says AI has the power to transform education, and improve teacher workload, particularly around admin for teachers. The BBC has spoken to teachers, school leaders and unions who seem divided on what the future of the UK's classrooms should look like.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files I was falsely accused of being the Brown University shooter... Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY Why Conan O'Brien'stopped party guests calling 911' on Nick Reiner: Insiders reveal disturbing new details of final hours before Rob and Michele murders After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Doctors said my hip pain was just tendinitis from sitting all day at work.

Consistency-based methods have emerged as an effective approach to uncertainty quantification (UQ) in large language models. These methods typically rely on several generations obtained via multinomial sampling, measuring their agreement level. However, in short-form QA, multinomial sampling is prone to producing duplicates due to peaked distributions, and its stochasticity introduces considerable variance in uncertainty estimates across runs. We introduce a new family of methods that employ beam search to generate candidates for consistency-based UQ, yielding improved performance and reduced variance compared to multinomial sampling. We also provide a theoretical lower bound on the beam set probability mass under which beam search achieves a smaller error than multinomial sampling. We empirically evaluate our approach on six QA datasets and find that its consistent improvements over multinomial sampling lead to state-of-the-art UQ performance.

Knowledge Distillation (KD) has emerged as a promising technique for model compression but faces critical limitations: (1) sensitivity to hyperparameters requiring extensive manual tuning, (2) capacity gap when distilling from very large teachers to small students, (3) suboptimal coordination in multi-teacher scenarios, and (4) inefficient use of computational resources. We present \textbf{HPM-KD}, a framework that integrates six synergistic components: (i) Adaptive Configuration Manager via meta-learning that eliminates manual hyperparameter tuning, (ii) Progressive Distillation Chain with automatically determined intermediate models, (iii) Attention-Weighted Multi-Teacher Ensemble that learns dynamic per-sample weights, (iv) Meta-Learned Temperature Scheduler that adapts temperature throughout training, (v) Parallel Processing Pipeline with intelligent load balancing, and (vi) Shared Optimization Memory for cross-experiment reuse. Experiments on CIFAR-10, CIFAR-100, and tabular datasets demonstrate that HPM-KD: achieves 10x-15x compression while maintaining 85% accuracy retention, eliminates the need for manual tuning, and reduces training time by 30-40% via parallelization. Ablation studies confirm independent contribution of each component (0.10-0.98 pp). HPM-KD is available as part of the open-source DeepBridge library.

Accurate water quality assessment requires spatially resolved sampling, yet most unmanned surface vehicles (USVs) can collect only a limited number of samples or rely on single-point sensors with poor representativeness. This work presents a solar-powered, fully autonomous USV featuring a novel syringe-based sampling architecture capable of acquiring 72 discrete, contamination-minimized water samples per mission. The vehicle incorporates a ROS 2 autonomy stack with GPS-RTK navigation, LiDAR and stereo-vision obstacle detection, Nav2-based mission planning, and long-range LoRa supervision, enabling dependable execution of sampling routes in unstructured environments. The platform integrates a behavior-tree autonomy architecture adapted from Nav2, enabling mission-level reasoning and perception-aware navigation. A modular 6x12 sampling system, controlled by distributed micro-ROS nodes, provides deterministic actuation, fault isolation, and rapid module replacement, achieving spatial coverage beyond previously reported USV-based samplers. Field trials in Achocalla Lagoon (La Paz, Bolivia) demonstrated 87% waypoint accuracy, stable autonomous navigation, and accurate physicochemical measurements (temperature, pH, conductivity, total dissolved solids) comparable to manually collected references. These results demonstrate that the platform enables reliable high-resolution sampling and autonomous mission execution, providing a scalable solution for aquatic monitoring in remote environments.