Ubisoft, one of the world's largest games developers, says it's working to fix an apparent hack on popular online shooter Rainbow Six Siege. Servers for the tactical multiplayer game were taken offline on Saturday and Sunday after in-game currency thought to be worth millions of pounds was distributed to players. The company has since restored service, but suspended the game's marketplace until further notice and warned players they may face queues when trying to log on. In a statement on X, Ubisoft said it would continue to make investigations and corrections over the next two weeks. Rainbow Six Siege, commonly referred to as R6, has been a success story for Ubisoft, which is also behind the Assassin's Creed and Far Cry series.

Budget 2025: What's the best and worst that could happen for Labour? Any big red box moment is risky. Now the chancellor's big choices are out there, what's the best-case scenario for Reeves and Starmer, and what's the worst that could happen next? On the positive side of the ledger, Labour MPs have gone off to their constituencies in a better mood this week. That is in large part down to the chancellor's decision to scrap the limit on bigger families getting some extra benefits.

How do language models use information provided as context when generating a response?

How Joseph Pilates went from circus performer to exercise expert. Pilates is one of the fastest growing exercises in America, but it all started in an unlikely place. Breakthroughs, discoveries, and DIY tips sent every weekday. Pilates is having a moment. According to a recent report from the Sports and Fitness Industry Association, Pilates participation has shot up from 9.2 million participants to 12.9 million since 2019, a jump of nearly 40% and the largest of any workout type across the United States.

Large Language Models (LLMs) are rapidly being adopted in conservation to address the biodiversity crisis, yet their reliability for species evaluation is uncertain. This study systematically validates five leading models on 21,955 species across four core IUCN Red List assessment components: taxonomy, conservation status, distribution, and threats. A critical paradox was revealed: models excelled at taxonomic classification (94.9%) but consistently failed at conservation reasoning (27.2% for status assessment). This knowledge-reasoning gap, evident across all models, suggests inherent architectural constraints, not just data limitations. Furthermore, models exhibited systematic biases favoring charismatic vertebrates, potentially amplifying existing conservation inequities. These findings delineate clear boundaries for responsible LLM deployment: they are powerful tools for information retrieval but require human oversight for judgment-based decisions. A hybrid approach is recommended, where LLMs augment expert capacity while human experts retain sole authority over risk assessment and policy.

Machine translators have made it easier than ever to create error-plagued Wikipedia articles in obscure languages. What happens when AI models get trained on junk pages? When Kenneth Wehr started managing the Greenlandic-language version of Wikipedia four years ago, his first act was to delete almost everything. It had to go, he thought, if it had any chance of surviving. Wehr, who's 26, isn't from Greenland--he grew up in Germany--but he had become obsessed with the island, an autonomous Danish territory, after visiting as a teenager. He'd spent years writing obscure Wikipedia articles in his native tongue on virtually everything to do with it. He even ended up moving to Copenhagen to study Greenlandic, a language spoken by some 57,000 mostly Indigenous Inuit people scattered across dozens of far-flung Arctic villages. The Greenlandic-language edition was added to Wikipedia around 2003, just a few years after the site launched in English. By the time Wehr took its helm nearly 20 years later, hundreds of Wikipedians had contributed to it and had collectively written some 1,500 articles totaling over tens of thousands of words.

W e present a novel and detailed dataset on origin-destination annual migration flows and stocks between 230 countries and regions, spanning the period from 1990 to the present. Our flow estimates are further disaggregated by country of birth, providing a comprehensive picture of migration over the last 35 years. The estimates are obtained by training a deep recurrent neural network to learn flow patterns from 18 covariates for all countries, including geographic, economic, cultural, societal, and political information. The recurrent architecture of the neural network means that the entire past can influence current migration patterns, allowing us to learn long-range temporal correlations. By training an ensemble of neural networks and additionally pushing uncertainty on the covariates through the trained network, we obtain confidence bounds for all our estimates, allowing researchers to pinpoint the geographic regions most in need of additional data collection. W e validate our approach on various test sets of unseen data, demonstrating that it significantly outperforms traditional methods estimating five-year flows while delivering a significant increase in temporal resolution. The model is fully open source: all training data, neural network weights, and training code are made public alongside the migration estimates, providing a valuable resource for future studies of human migration.

Accurate and reliable selection of the appropriate acetabular cup size is crucial for restoring joint biomechanics in total hip arthroplasty (THA). This paper proposes a novel framework that integrates square-root velocity function (SRVF)-based elastic shape registration technique with an embedded deformation (ED) graph approach to reconstruct the 3D articular surface of the acetabulum by fusing multiple views of 2D pre-operative pelvic X-ray images and a hemispherical surface model. The SRVF-based elastic registration establishes 2D-3D correspondences between the parametric hemispherical model and X-ray images, and the ED framework incorporates the SRVF-derived correspondences as constraints to optimize the 3D acetabular surface reconstruction using nonlinear least-squares optimization. Validations using both simulation and real patient datasets are performed to demonstrate the robustness and the potential clinical value of the proposed algorithm. The reconstruction result can assist surgeons in selecting the correct acetabular cup on the first attempt in primary THA, minimising the need for revision surgery. Code and data will be released upon acceptance.

Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multi-hop question answering (QA). However, their retrieval process faces two key challenges: (1) it can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are not designed to dynamically adapt to the evolving information needs in multi-step reasoning, making it difficult to identify and retrieve the missing information required at each iterative step. Therefore, we propose KiRAG, which uses a knowledge-driven iterative retriever model to enhance the retrieval process of iRAG. Specifically, KiRAG decomposes documents into knowledge triples and performs iterative retrieval with these triples to enable a factually reliable retrieval process. Moreover, KiRAG integrates reasoning into the retrieval process to dynamically identify and retrieve knowledge that bridges information gaps, effectively adapting to the evolving information needs. Empirical results show that KiRAG significantly outperforms existing iRAG models, with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA.