Miss Israel Melanie Shiraz said she received death and rape threats after controversial video appears to show her glaring at Palestinian contestant during Miss Universe pageant in Thailand.

In 1996 Nasa and the White House made the explosive announcement that the rock contained traces of Martian bugs. The meteorite, catalogued as Allen Hills (ALH) 84001, crashed onto the frozen wastes of Antarctica 13,000 years ago and was recovered in 1984. Photographs were released showing elongated segmented objects that appeared strikingly lifelike.

UK's sweeping asylum law changes: How will they impact refugees? Shabana Mahmood, the United Kingdom's home secretary, has said the country's asylum system is "not working" and is placing "intense strain on communities" ahead of proposals for major government reforms that would end refugees' automatic right to settle permanently in the UK. Speaking to the BBC on Sunday, Mahmood said undocumented migration is "tearing the country apart". First, they would end the automatic path to settled status for refugees after five years. And second, they would remove state benefits from those who have the right to work and can support themselves.

Trump turns to Epstein's lawyer to prove he has'nothing to hide' as he orders GOP to vote on releasing ALL documents to avert MAGA mutiny If I had to start over, here's how I'd make millions again! KEVIN O'LEARY reveals best investments, the career with soaring salaries and worst mistake he made My Montecito mole tells me why Me-Me-Meghan'threw a fit' after Kris Jenner's birthday party... this Kardashian drama just won't go away: KENNEDY Jeff Bezos's ex MacKenzie Scott contributes more than $700MILLION to'historically black colleges' Boy, 9, accused of raping and brutally attacking girl, 5, is allowed home with ankle monitor...despite victim's mom pleading with judge Smiling girl, 14, who vanished without a trace is found dead in RV... as cops arrest her family member Marjorie Taylor Greene's lookalike daughter defends mom against'fake MAGA' attacks amid fallout with Trump Trump crashes Mar-a-Lago wedding to talk about getting into heaven... but MAGA Christians are left angry Terrifying rise of'taboo cancer': Doctors reveal subtle signs ALL women must know... the most common cause... and a game-changer shot that could save your life She runs the anonymous real-life Gossip Girl account outing Hollywood scandals now we expose HER identity and the secret life she's desperate to hide Ariana Grande and Cynthia Erivo cause a stir with ANOTHER'ridiculous' red carpet moment Residents of city dubbed the'Birthplace of Silicon Valley' that's home to Mark Zuckerberg are sick of sleepless nights Nigerian gunmen abduct'dozens' of girls from boarding school after killing deputy head teacher in chilling echo of Chibok kidnappings Emily Blunt's asymmetrical frock horror and Kate Hudson's drab dress lead worst dressed stars at 16th Governors Awards Timothee Chalamet reveals his'true feelings' about ex Kylie Jenner after avoiding the'Kardashian curse' that has destroyed the lives and careers of the sisters' famous exes Iconic O.J. Simpson witness looks VERY different 30 years after legendary murder trial... see him now Most parents try to get them over and done with as soon as possible - but woke scientists now claim that nappy changes should be used as an opportunity to teach babies consent. Dr Nicole Downs and Dr Katherine Bussey, lecturers in Early Childhood at Deakin University in Australia, maintain that parents should not wait until their kids are teenagers to talk about appropriate touching. Instead, consent should be a'normal, everyday part of life' that teaches babies what is acceptable when it comes to their bodies. Parents should take their children's views into account, according to the pair - even when it comes to dealing with a dreaded nappy disaster.

There are no catchy melodies in McCartney's first release in five years, only quiet hiss and the odd clatter. There are no catchy melodies in McCartney's first release in five years, only quiet hiss and the odd clatter. But Paul McCartney's first new recording in five years lacks the sing-along tune and jaunty guitar chops because there's barely anything there. In place of catchy melodies and evocative lyrics there is only quiet hiss and the odd clatter. It suggests that if AI companies unfairly exploit musicians' intellectual property to train their generative AI models, the creative ecosystem will be wrecked and original music silenced.

Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to assess open-ended, economically consequential tasks in high-stakes domains like Legal and Finance, where practical returns are paramount. To address this, we introduce Professional Reasoning Bench (PRBench), a realistic, open-ended, and difficult benchmark of real-world problems in Finance and Law. We open-source its 1,100 expert-authored tasks and 19,356 expert-curated criteria, making it, to our knowledge, the largest public, rubric-based benchmark for both legal and finance domains. We recruit 182 qualified professionals, holding JDs, CFAs, or 6+ years of experience, who contributed tasks inspired by their actual workflows. This process yields significant diversity, with tasks spanning 114 countries and 47 US jurisdictions. Our expert-curated rubrics are validated through a rigorous quality pipeline, including independent expert validation. Subsequent evaluation of 20 leading models reveals substantial room for improvement, with top scores of only 0.39 (Finance) and 0.37 (Legal) on our Hard subsets. We further catalog associated economic impacts of the prompts and analyze performance using human-annotated rubric categories. Our analysis shows that models with similar overall scores can diverge significantly on specific capabilities. Common failure modes include inaccurate judgments, a lack of process transparency and incomplete reasoning, highlighting critical gaps in their reliability for professional adoption.

The "Vision Zero" policy, introduced by the Swedish Parliament in 1997, aims to eliminate fatalities and serious injuries resulting from traffic accidents. To achieve this goal, the use of self-driving vehicles in traffic is envisioned and a roadmap for the certification of self-driving vehicles is aimed to be determined. However, it is still unclear how the basic safety requirements that autonomous vehicles must meet will be verified and certified, and which methods will be used. This paper focuses on the comparative evaluation of the prominent methods planned to be used in the certification process of autonomous vehicles. It examines the prominent methods used in the certification process, develops a pipeline for the certification process of autonomous vehicles, and determines the stages, actors, and areas where the addressed methods can be applied.

There has been a prevalence of applying AI software in both high-stakes public-sector and industrial contexts. However, the lack of transparency has raised concerns about whether these data-informed AI software decisions secure fairness against people of all racial, gender, or age groups. Despite extensive research on emerging fairness-aware AI software, up to now most efforts to solve this issue have been dedicated to binary classification tasks. Fairness in regression is relatively underexplored. In this work, we adopted a mutual information-based metric to assess separation violations. The metric is also extended so that it can be directly applied to both classification and regression problems with both binary and continuous sensitive attributes. Inspired by the Reweighing algorithm in fair classification, we proposed a FairReweighing pre-processing algorithm based on density estimation to ensure that the learned model satisfies the separation criterion. Theoretically, we show that the proposed FairReweighing algorithm can guarantee separation in the training data under a data independence assumption. Empirically, on both synthetic and real-world data, we show that FairReweighing outperforms existing state-of-the-art regression fairness solutions in terms of improving separation while maintaining high accuracy.

Large Vision-Language Models (LVLMs) exhibit powerful reasoning capabilities but suffer sophisticated jailbreak vulnerabilities. Fundamentally, aligning LVLMs is not just a safety challenge but a problem of economic efficiency. Current alignment methods struggle with the trade-off between safety, utility, and operational costs. Critically, a focus solely on final outputs (process-blindness) wastes significant computational budget on unsafe deliberation. This flaw allows harmful reasoning to be disguised with benign justifications, thereby circumventing simple additive safety scores. To address this, we propose EcoAlign, an inference-time framework that reframes alignment as an economically rational search by treating the LVLM as a boundedly rational agent. EcoAlign incrementally expands a thought graph and scores actions using a forward-looking function (analogous to net present value) that dynamically weighs expected safety, utility, and cost against the remaining budget. To prevent deception, path safety is enforced via the weakest-link principle. Extensive experiments across 3 closed-source and 2 open-source models on 6 datasets show that EcoAlign matches or surpasses state-of-the-art safety and utility at a lower computational cost, thereby offering a principled, economical pathway to robust LVLM alignment.

Multimodal Large Language Models (MLLMs) achieve impressive performance once optimized on massive datasets. Such datasets often contain sensitive or copyrighted content, raising significant data privacy concerns. Regulatory frameworks mandating the 'right to be forgotten' drive the need for machine unlearning. This technique allows for the removal of target data without resource-consuming retraining. However, while well-studied for text, visual concept unlearning in MLLMs remains underexplored. A primary challenge is precisely removing a target visual concept without disrupting model performance on related entities. To address this, we introduce AUVIC, a novel visual concept unlearning framework for MLLMs. AUVIC applies adversarial perturbations to enable precise forgetting. This approach effectively isolates the target concept while avoiding unintended effects on similar entities. To evaluate our method, we construct VCUBench. It is the first benchmark designed to assess visual concept unlearning in group contexts. Experimental results demonstrate that AUVIC achieves state-of-the-art target forgetting rates while incurs minimal performance degradation on non-target concepts.