Tottenham may have coasted through to the Champions League last 16, but their Premier League form remains a problem for boss Thomas Frank. I was at their draw with Burnley last week and there are a lot of angry Spurs fans out there, said BBC Sport football expert Chris Sutton. Their domestic results are such a contrast to their record in Europe, and it could be another difficult afternoon for Frank when they face Manchester City on Sunday. Sutton is making predictions for all 380 Premier League games this season, against AI, BBC Sport readers and a variety of guests. His guest for week 24 is boxer Francesca Hennessy, who supports Chelsea . Hennessy faces Ellie Bouttell in a WBC title eliminator on Saturday, live on BBC Two from 20:00 GMT.

Is this AI's worst prediction yet? Chris Sutton's guest this week, actor Éanna Hardwicke, plays Roy Keane in Saipan - a new film about the former Manchester United captain's infamous fallout with Republic of Ireland manager Mick McCarthy before the 2002 World Cup. It is in cinemas from Friday. Naturally, we asked AI who would play Sutton if a film were ever made about him. The best fit, apparently, is Hollywood heartthrob Tom Hardy - who is four inches shorter than BBC Sport football expert Sutton but is AI's top choice for the role because he is known for portraying tough, brooding characters with emotional depth. That just shows how way off the mark AI is, said Sutton. But I'm happy with Tom Hardy, even though he is not tall enough.

We only included a candidate variable if the nearest neighbor match was exact, i.e., we could find We compared the "fnlwgt" data to all weight variables "UH_WGTS_A1", which has a similar distribution. Since we did not identify an exact match for "fnlwgt" and the variable is not a property of an individual, we do not utilize it further in We vary the threshold from 6,000 to 72,000. Concretely, for a given threshold, e.g. In our experiments, as the "unconstrained" base classifier, we use the gradient boosted decision tree B.1 ACSIncome Predict whether US working adults' yearly income is above $50,000. T arget: PINCP (Total person's income): an individual's label is 1 if PINCP > 50000, otherwise 0. ACS PUMS data differently, and construct a new prediction task. Features: AGEP (Age): Range of values: - 0 - 99 (integers) - 0 indicates less than 1 year old.

Data duplication within large-scale corpora often impedes large language models' (LLMs) performance and privacy. In privacy-concerned federated learning scenarios, conventional deduplication methods typically rely on trusted third parties to perform uniform deletion, risking loss of informative samples while introducing privacy vulnerabilities. To address these gaps, we propose Federated ReWeighting (FedRW), the first privacy-preserving framework, to the best of our knowledge, that performs soft deduplication via sample reweighting instead of deletion in federated LLM training, without assuming a trusted third party. At its core, FedRW proposes a secure, frequency-aware reweighting protocol through secure multi-party computation, coupled with a parallel orchestration strategy to ensure efficiency and scalability. During training, FedRW utilizes an adaptive reweighting mechanism with global sample frequencies to adjust individual loss contributions, effectively improving generalization and robustness. Empirical results demonstrate that FedRW outperforms the state-of-the-art method by achieving up to 28.78x speedup in preprocessing and approximately 11.42% improvement in perplexity, while offering enhanced security guarantees. FedRW thus establishes a new paradigm for managing duplication in federated LLM training.

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.

We only included a candidate variable if the nearest neighbor match was exact, i.e., we could find We compared the "fnlwgt" data to all weight variables "UH_WGTS_A1", which has a similar distribution. Since we did not identify an exact match for "fnlwgt" and the variable is not a property of an individual, we do not utilize it further in We vary the threshold from 6,000 to 72,000. Concretely, for a given threshold, e.g. In our experiments, as the "unconstrained" base classifier, we use the gradient boosted decision tree B.1 ACSIncome Predict whether US working adults' yearly income is above $50,000. T arget: PINCP (Total person's income): an individual's label is 1 if PINCP > 50000, otherwise 0. ACS PUMS data differently, and construct a new prediction task. Features: AGEP (Age): Range of values: - 0 - 99 (integers) - 0 indicates less than 1 year old.

From sugar shortages to military contracts, World War II helped make M&Ms and Hershey's bars into symbols of American abundance. A 1940s Milky Way ad shows candy keeping pilots smiling through the war. Breakthroughs, discoveries, and DIY tips sent every weekday. Every year, Hershey manufactures 373 million of its signature milk chocolate bars . While the company doesn't release exact stats on Halloween sales, you can bet a lot of those end up in plastic Jack O'Lantern-shaped pails.

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 prediction of contagious disease outbreaks is vital for informed decision-making. Our study addresses the gap between machine learning algorithms and their epidemiological applications, noting that methods optimal for benchmark datasets often underperform with real-world data due to difficulties in incorporating mobility information. We adopt a two-phase approach: first, assessing the significance of mobility data through a pilot study, then evaluating the impact of Graph Convolutional Networks (GCNs) on a transformer backbone. Our findings reveal that while mobility data and GCN modules do not significantly enhance forecasting performance, the inclusion of mortality and hospitalization data markedly improves model accuracy. Additionally, a comparative analysis between GCN-derived spatial maps and lockdown orders suggests a notable correlation, highlighting the potential of spatial maps as sensitive indicators for mobility. Our research offers a novel perspective on mobility representation in predictive modeling for contagious diseases, empowering decision-makers to better prepare for future outbreaks.

Fox News senior congressional correspondent Chad Pergram joins'Fox News Live' to explain how he prepares to report on Congress for the upcoming year. Every two years, the period between the November election and when the new Congress begins is often the busiest swath of time for covering Congress. Reporters are trying to figure out who won their elections and who lost. The existing Congress is back, attempting to prevent a government shutdown and often plowing through a landscape of other major legislation. There are often leadership elections.