Eleven skydivers and one pilot have been killed in a plane crash in the US state of Missouri, officials said. The airplane, which was leased by a skydiving company, took off around 11:20 local time on Sunday, according to a Bates County Emergency Management spokesperson. After failing to gain altitude, it made a sharp left turn and crashed about 200 yards away from Butler Memorial Airport, the spokesperson told the BBC. All 12 people on board died, he said. The Federal Aviation Administration (FAA) said a Pacific Aerospace P750 crashed while departing the airport.

Pat McAfee wages war on Omaha's famous Jell-o shot bar after crew gets cold reception at College World Series NASCAR legend Tony Stewart calls mourning fans'a--holes' in tone-deaf rant about Kyle Busch Brewers' Jacob Misiorowski breaks brains and radar guns with hardest pitch ever by a starting pitcher US fans were out in full force ahead of the USMNT's first match of the 2026 FIFA World Cup MLB announces drive-in theater screenings of'The Sandlot' with live games and fireworks for July 4th California Democratic Party under fire for'you're not allowed to watch' World Cup post Victor Wembanyama isn't good or mature enough to be the face of the NBA -- at least not yet Rep. Byron Donalds shares his faith redemption story amid Florida gubernatorial run Iran's foreign minister says peace with US'has never been closer' GOP lawmaker says it's'really important' that US continues cartel crackdown Spencer Pratt's use of AI to boost campaign sparks debate FBI arrests first suspect on'most wanted fraudsters' list Accused Charlie Kirk killer's attorneys seek to BLOCK death penalty Kayleigh McEnany: Capitalism isn't the big evil Bernie Sanders would have you believe Stephen A. Smith says he takes no offense to President Donald Trump's social media criticism, but stands by blaming him for the Knicks' Game 3 loss in the NBA Finals on'Hannity.' Can the San Antonio Spurs bounce back from blowing the biggest lead in NBA Finals history and keep their season alive by beating the New York Knicks in Game 5 Saturday? According to San Antonio phenom Victor Wembanyama: Everybody thinks -- everybody knows -- we're going to do it. Well, Mr. Wembanyama, the Spurs would become just the 16th team in NBA history to win a series after going down 3-1. New York is on the brink of winning its first NBA title in 53 years, thanks to a full team effort.

AB Hernandez advances in California state championship as Save Girls' Sports activists rally nearby Tennis player Rafael Jodar accused of pushing French Open ball girl, but did he really? Steve Hilton rips Steyer for trans athlete support, leads'Save Girls Sports' rally at track title meet Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Greg Sankey insists SEC is'strongest league' despite Big Ten winning three straight national championships Spencer Pratt responds to Newsom's Bass endorsement, calls them'alleged criminal partners' Speaker Johnson outlines plan to defeat'socialist and extremist' Democrats Trump set for'final determination' on Iran nuclear deal NJ governor's'protected protest zone' sparks debate amid violent ICE facility clashes Analyzing how Iran's'shadow oil network' evades US sanctions San Antonio's young core, led by 22-year-old Victor Wembanyama, wasn't supposed to challenge OKC this deep in the playoffs Victor Wembanyama'invisible' in Game 5, Can he still lead the Spurs to the Finals? Victor Wembanyama only scored 20 points on 4-15 FG during the San Antonio Spurs' Game 5 loss to the Oklahoma City Thunder. Jason McIntyre asks if Wemby can still lead the Spurs to the Finals. The NBA had its wishes granted.

Manchester City already hold the record for most consecutive FA Cup semi-finals - eight between 2019 and 2026 - but can they become the first team to reach four finals in a row? That is their target when they play Championship side Southampton at Wembley on Saturday at 17:15 BST, live on BBC One and Radio 5 Live. It will be interesting to see whether City boss Pep Guardiola changes his team up much, said BBC Sport football expert Chris Sutton. They don't play again until they go to Everton on 4 May, so I don't think he will. But, whoever Pep picks, he will be looking for his team to connect again, the way they were playing before they played Burnley . As well as the FA Cup, Sutton is making predictions for all 380 Premier League games this season, against AI, BBC Sport readers and a variety of guests. For all of this weekend's games, he takes on Tottenham fan Alfie Watts, co-host of the Race Across the World: The Detour visual podcast.

All models can be trained entirely on CPUs on consumer grade Laptop machines within minutes orhours. Execution times per epoch for the single-cell data with 529 features are as follows: Base=0.9, Centering the first frame: For golfing and waving, the root point of the first frame is movedtotheorigin(0,0,0). To map putative transcription factor (TF) and target gene relationships, we use as a reference a regulatory network generated using the gene expression and chromatin accessibility features 15 available inthehuman immune cells dataset. Ourruleforsuccessfully mapping aTFtoatargetgene through achromatin peak isthatall TF, chromatin peak, and target gene, have to be simultaneously in the list of features selected in therank_genes_groupsfunction for cell type of interest, and there haveto be TF motifs linked to that transcription factor in the chromatin peak.

Recent curriculum techniques in the post-training stage of LLMs have been widely observed to outperform non-curriculum approaches in enhancing reasoning performance, yet a principled understanding of why and to what extent they work remains elusive. To address this gap, we develop a theoretical framework grounded in the intuition that progressively learning through manageable steps is more efficient than directly tackling a hard reasoning task, provided each stage stays within the model's effective competence. Under mild complexity conditions linking consecutive curriculum stages, we show that curriculum post-training avoids the exponential complexity bottleneck. To substantiate this result, drawing insights from the Chain-of-Thoughts (CoTs) solving mathematical problems such as Countdown and parity, we model CoT generation as a states-conditioned autoregressive reasoning tree, define a uniform-branching base model to capture pretrained behavior, and formalize curriculum stages as either depth-increasing (longer reasoning chains) or hint-decreasing (shorter prefixes) subtasks. Our analysis shows that, under outcome-only reward signals, reinforcement learning finetuning achieves high accuracy with polynomial sample complexity, whereas direct learning suffers from an exponential bottleneck. We further establish analogous guarantees for test-time scaling, where curriculum-aware querying reduces both reward oracle calls and sampling cost from exponential to polynomial order.

The goal of clustering is to group similar objects into meaningful partitions.

Unimodal vision models are known to rely on spurious correlations, but it remains unclear to what extent Multimodal Large Language Models (MLLMs) exhibit similar biases despite language supervision. In this paper, we investigate spurious bias in MLLMs and introduce SpurLens, a pipeline that leverages GPT-4 and open-set object detectors to automatically identify spurious visual cues without human supervision. Our findings reveal that spurious correlations cause two major failure modes in MLLMs: (1) over-reliance on spurious cues for object recognition, where removing these cues reduces accuracy, and (2) object hallucination, where spurious cues amplify the hallucination by over 10x. We validate our findings in various MLLMs and datasets. Beyond diagnosing these failures, we explore potential mitigation strategies, such as prompt ensembling and reasoning-based prompting, and conduct ablation studies to examine the root causes of spurious bias in MLLMs. By exposing the persistence of spurious correlations, our study calls for more rigorous evaluation methods and mitigation strategies to enhance the reliability of MLLMs.

Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in both general-purpose (ChatGPT and Bing Copilot) and task-oriented (customer service chatbot) conversational systems. Existing approaches based on featurized ML models or text embeddings fall short in extracting generalizable patterns and are hard to interpret. In this work, we show that LLMs can extract interpretable signals of user satisfaction from their natural language utterances more effectively than embedding-based approaches. Moreover, an LLM can be tailored for USE via an iterative prompting framework using supervision from labeled examples. The resulting method, Supervised Prompting for User satisfaction Rubrics (SPUR), not only has higher accuracy but is more interpretable as it scores user satisfaction via learned rubrics with a detailed breakdown.

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