At least six people have been killed in a wave of Russia strikes on Kyiv, which the Ukrainian President Volodymyr Zelensky has condemned as a heinous attack. The BBC's James Landale visited the scene of one attack in eastern Kyiv where a drone rammed through a block of flats and left six people dead. Several other regions were also targeted. A drone attack on a market at Chornomorsk in the south of the country killed two people. Catherine Connolly has'never believed more' in the spirit of Ireland New Irish President Catherine Connolly says she has been given a powerful mandate to articulate a vision for a new republic.

At one point, Luis assumes that he and Esteban have been abandoned, only to realize, with a start, that their newfound friends are actually circling back to help. In such moments, we grasp the source of the story's mysterious power: a tough-minded understanding that kindness is rare yet persistent, and quite possibly an affront to the laws of nature. "Sirāt" is a chain of defiantly compassionate acts--noble human improbabilities that take on, in retrospect, an air of fatalistic inevitability. Laxe, a restless wanderer himself, knows Morocco well. He shot his first feature, "You All Are Captains" (2011), in Tangier, where he'd spent several years working at a shelter for disadvantaged children. Several of these children appeared in the movie--a formally playful collision of fiction and documentary in which Laxe, also making an appearance, slyly interrogated his European outsider-artist role. Next came "Mimosas" (2016), an elusive, arrestingly gorgeous drama about a caravan bearing a dying sheikh across Morocco's Atlas Mountains to his homeland. The film had the beauty of a travelogue and the opacity of a parable. Its most dynamic character was a fiery Muslim preacher who warned his fellow-travellers not to stray, geographically or morally.

To help address these issues, a natural approach is to consider games with special structures. In this paper, we focus on congestion games.

However, such "black-box" policies lack interpretability and reliability, and often,

Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. Our contributions are two-fold. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an "expert language" for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce simple to strong baselines to test this problem. Additionally, as a first-pass solution to this novel problem, we design LSKExtractor to benchmark the language-specific knowledge present in a language model and then exploit it during inference. To test our framework, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, LSKExtractor achieves up to 10% relative improvement across datasets, and is competitive against strong baselines, while being feasible in real-world settings. Broadly, our research contributes to the open-source development (https://github.com/agarwalishika/LSKExtractor/tree/main) of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.

Automatic speech recognition (ASR) has advanced in high-resource languages, but most of the world's 7,000+ languages remain unsupported, leaving thousands of long-tail languages behind. Expanding ASR coverage has been costly and limited by architectures that restrict language support, making extension inaccessible to most--all while entangled with ethical concerns when pursued without community collaboration. To transcend these limitations, we introduce Omnilingual ASR, the first large-scale ASR system designed for extensibility. Omnilingual ASR enables communities to introduce unserved languages with only a handful of data samples. It scales self-supervised pre-training to 7B parameters to learn robust speech representations and introduces an encoder-decoder architecture designed for zero-shot generalization, leveraging a LLM-inspired decoder. This capability is grounded in a massive and diverse training corpus; by combining breadth of coverage with linguistic variety, the model learns representations robust enough to adapt to unseen languages. Incorporating public resources with community-sourced recordings gathered through compensated local partnerships, Omnilingual ASR expands coverage to over 1,600 languages, the largest such effort to date--including over 500 never before served by ASR. Automatic evaluations show substantial gains over prior systems, especially in low-resource conditions, and strong generalization. We release Omnilingual ASR as a family of models, from 300M variants for low-power devices to 7B for maximum accuracy. We reflect on the ethical considerations shaping this design and conclude by discussing its societal impact. In particular, we highlight how open-sourcing models and tools can lower barriers for researchers and communities, inviting new forms of participation. Open-source artifacts are available at https://github.com/facebookresearch/omnilingual-asr.

Why should a clinician trust an Artificial Intelligence (AI) prediction? Despite the increasing accuracy of machine learning methods in medicine, the lack of transparency continues to hinder their adoption in clinical practice. In this work, we explore Kolmogorov-Arnold Networks (KANs) for clinical classification tasks on tabular data. In contrast to traditional neural networks, KANs are function-based architectures that offer intrinsic interpretability through transparent, symbolic representations. We introduce \emph{Logistic-KAN}, a flexible generalization of logistic regression, and \emph{Kolmogorov-Arnold Additive Model (KAAM)}, a simplified additive variant that delivers transparent, symbolic formulas. Unlike ``black-box'' models that require post-hoc explainability tools, our models support built-in patient-level insights, intuitive visualizations, and nearest-patient retrieval. Across multiple health datasets, our models match or outperform standard baselines, while remaining fully interpretable. These results position KANs as a promising step toward trustworthy AI that clinicians can understand, audit, and act upon. We release the code for reproducibility in \codeurl.

Billionaire UK businessman Joe Lewis, whose family trust owns Tottenham Hotspur football club, has received a pardon from US President Donald Trump. Lewis, 88, pleaded guilty to insider trading as part of an agreement with prosecutors in 2024 that saw him avoid prison. He was accused of passing on information about his companies to his private pilots, friends, personal assistants and romantic partners in a fraud that authorities said netted millions of dollars in profit. A White House official said Trump approved the pardon for Lewis, who requested it so he could receive medical treatment and visit his grandchildren and great grandchildren in the US. Mr Lewis admitted he made a terrible mistake, did not fight extradition in the case, and paid a $5 million fine, the official told the BBC.

Astronomers illustrated supernova SN 2024ggi in all of its gassy glory. This artist's impression shows a star going supernova. About 22 million light-years away the supernova, SN 2024ggi, exploded in the galaxy NGC 3621. Breakthroughs, discoveries, and DIY tips sent every weekday. According to their study recently published in the journal, their stellar accomplishment thanks to quick thinking and intense geometrical calculations is already providing clues to longstanding questions about some of the universe's most impressive and powerful events .

An independent review of the Sara Sharif case has identified multiple failings from agencies before her murder in Surrey in 2023, following two years of abuse. The child safeguarding practice review, published on Thursday, said there were clearly several points in Sara's life, in particular during the last few months, where different actions could and should have been taken by the authorities. The system failed to keep her safe, it added. Responding to the report, the Children's Commissioner said the case was a catalogue of missed opportunities, poor communication and ill-informed assumptions. The education secretary said there had been the glaring failures across all agencies.