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Winter Storm Fern death toll climbs to 34 after brutal freeze batters the US... and meteorologists warn even colder weather is on the way Top lawyer, event planner and pilot identified as three of six killed in private jet crash while taking'girls' trip' to Paris Insidious secret life of promiscuous neurosurgeon found dead in his $2.5m mansion'He has no loyalty': The bitter secret fallout between One Direction star Harry Styles and his former bandmates - as insiders reveal for the first time what really happened at Liam Payne's funeral Nicola Peltz was raised by billionaire'bully' Nelson who became the most feared investor on Wall Street before starting his own dynasty with his 10 children Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Influencer shares haunting 911 call after crash that killed her son known for viral'Okay Baby' video Matthew Stafford's wife Kelly shares emotional moment NFL star returned home after heartbreaking playoff defeat Martha Stewart breaks political silence after being urged by teenage granddaughter: 'Things must change' Insiders reveal the REAL misstep that got Kristi Noem humiliatingly ditched by Trump... and the weak excuse she's peddling to try and save herself Defiant Trump dismisses Alzheimer's fears as he struggles to recall name of disease in interview How AI cops will be used to patrol Britain's streets: From live facial recognition to virtual chatbots - the Orwellian technologies that are set to tackle crime Britain's police forces are getting a high-tech upgrade, as artificial intelligence ( AI) tools are rolled out to tackle crime . As part of major police reforms, Home Secretary Shabana Mahmood has announced that over £140 million will be invested in new technology . Police will be given access to facial recognition vans, tools for rapid CCTV analysis, and a suite of digital forensics tools. How the public interacts with the police is also set to change, as 999 control rooms use'AI-assisted operator services' to filter'non-policing calls'.

An Australian tourist describes seeing one of the landslides behind him and running from it.

A flotilla of uncrewed wolf pack drone boats has successfully been used to escort warships in a Royal Navy and Army trial. The Navy said it was a milestone demonstration of how it could utilise such technology in a real-life scenario. With camera and sensor data being fed back to Patrick Blackett, five 7.2m autonomous Rattler boats safely escorted the two ships playing the role of foreign warships during the 72-hour milestone training exercise, it said. The demonstration was a culmination of months of trials by the Navy's Disruptive Capabilities and Technology Office (DCTO) and the Fleet Experimentation Squadron (FXS). Each of the Rattler boats were operated by a two-person team, with one responsible for piloting the drone and the other monitoring and operating onboard systems, as well as helping to manage live data streams.

We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effectively separate informative covariates from unimportant ones. Through simulations, we demonstrate and evaluate the efficacy of the proposed method in identifying important covariates in the presence of non-Gaussian model errors. Finally, we apply the proposed method to analyze two datasets arising in genetic and epigenetic studies.

Virginia's seventeenth- and eighteenth-century land patents survive primarily as narrative metes-and-bounds descriptions, limiting spatial analysis. This study systematically evaluates current-generation large language models (LLMs) in converting these prose abstracts into geographically accurate latitude/longitude coordinates within a focused evaluation context. A digitized corpus of 5,471 Virginia patent abstracts (1695-1732) is released, with 43 rigorously verified test cases serving as an initial, geographically focused benchmark. Six OpenAI models across three architectures (o-series, GPT-4-class, and GPT-3.5) were tested under two paradigms: direct-to-coordinate and tool-augmented chain-of-thought invoking external geocoding APIs. Results were compared with a GIS-analyst baseline, the Stanford NER geoparser, Mordecai-3, and a county-centroid heuristic. The top single-call model, o3-2025-04-16, achieved a mean error of 23 km (median 14 km), outperforming the median LLM (37.4 km) by 37.5%, the weakest LLM (50.3 km) by 53.5%, and external baselines by 67% (GIS analyst) and 70% (Stanford NER). A five-call ensemble further reduced errors to 19 km (median 12 km) at minimal additional cost (approx. USD 0.20 per grant), outperforming the median LLM by 48.6%. A patentee-name-redaction ablation increased error by about 9%, indicating reliance on textual landmark and adjacency descriptions rather than memorization. The cost-efficient gpt-4o-2024-08-06 model maintained a 28 km mean error at USD 1.09 per 1,000 grants, establishing a strong cost-accuracy benchmark; external geocoding tools offered no measurable benefit in this evaluation. These findings demonstrate the potential of LLMs for scalable, accurate, and cost-effective historical georeferencing.

Recent advances in Large Language Models (LLMs) have positioned them as a prominent solution for Natural Language Processing tasks. Notably, they can approach these problems in a zero or few-shot manner, thereby eliminating the need for training or fine-tuning task-specific models. However, LLMs face some challenges, including hallucination and the presence of outdated knowledge or missing information from specific domains in the training data. These problems cannot be easily solved by retraining the models with new data as it is a time-consuming and expensive process. To mitigate these issues, Knowledge Graphs (KGs) have been proposed as a structured external source of information to enrich LLMs. With this idea, in this work we use KGs to enhance LLMs for zero-shot Entity Disambiguation (ED). For that purpose, we leverage the hierarchical representation of the entities' classes in a KG to gradually prune the candidate space as well as the entities' descriptions to enrich the input prompt with additional factual knowledge. Our evaluation on popular ED datasets shows that the proposed method outperforms non-enhanced and description-only enhanced LLMs, and has a higher degree of adaptability than task-specific models. Furthermore, we conduct an error analysis and discuss the impact of the leveraged KG's semantic expressivity on the ED performance.

'Bella the robot waitress won't replace our staff' 4 days agoShareSaveSophie CridlandReporting fromPortlandShareSaveBBCMike Deadman, from The View Cafe and Bar, said Bella was not being used to replace staff Bella carries multiple trays packed with food and drinks, deftly swerving any obstacles and delivering orders day in and day out to her customers. This is the latest recruit at The View Cafe and Bar at Portland's Heights hotel in Dorset. But Bella is no normal member of the waiting staff - she is a state-of-the art robot programmed to serve and even interact with the eatery's patrons. And costing a little under 9,000, it is hoped it can be an economical idea, as well as a novel one. But assistant manager Mike Deadman insists Bella - built by Chinese technology company Pudu - will not result in any job losses.

Childhood vaccination is a cornerstone of public health, yet disparities in vaccination coverage persist across England. These disparities are shaped by complex interactions among various factors, including geographic, demographic, socioeconomic, and cultural (GDSC) factors. Previous studies mostly rely on cross-sectional data and traditional statistical approaches that assess individual or limited sets of variables in isolation. Such methods may fall short in capturing the dynamic and multivariate nature of vaccine uptake. In this paper, we conducted a longitudinal machine learning analysis of childhood vaccination coverage across 150 districts in England from 2021 to 2024. Using vaccination data from NHS records, we applied hierarchical clustering to group districts by vaccination coverage into low- and high-coverage clusters. A CatBoost classifier was then trained to predict districts' vaccination clusters using their GDSC data. Finally, the SHapley Additive exPlanations (SHAP) method was used to interpret the predictors' importance. The classifier achieved high accuracies of 92.1, 90.6, and 86.3 in predicting districts' vaccination clusters for the years 2021-2022, 2022-2023, and 2023-2024, respectively. SHAP revealed that geographic, cultural, and demographic variables, particularly rurality, English language proficiency, the percentage of foreign-born residents, and ethnic composition, were the most influential predictors of vaccination coverage, whereas socioeconomic variables, such as deprivation and employment, consistently showed lower importance, especially in 2023-2024. Surprisingly, rural districts were significantly more likely to have higher vaccination rates. Additionally, districts with lower vaccination coverage had higher populations whose first language was not English, who were born outside the UK, or who were from ethnic minority groups.

LLMs often adopt an assertive language style also when making false claims. Such ``overconfident hallucinations'' mislead users and erode trust. Achieving the ability to express in language the actual degree of uncertainty around a claim is therefore of great importance. We find that ``verbal uncertainty'' is governed by a single linear feature in the representation space of LLMs, and show that this has only moderate correlation with the actual ``semantic uncertainty'' of the model. We apply this insight and show that (1) the mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone and (2) we can intervene on verbal uncertainty at inference time and reduce hallucinations on short-form answers, achieving an average relative reduction of 32%.