Britain is to develop new air defence weapons alongside the EU's four biggest military powers, deepening ties with the European defence sector. The project will invite manufacturers in the UK, Germany, France, Italy and Poland to submit plans to build low-cost missiles and autonomous drones. The allies are pledging a speedy process to build the weapons together, inspired by Ukraine's development of cheap drones to counter attacks from Russia. The UK's Ministry of Defence (MoD) says the programme will prioritise a lightweight, affordable surface-to-air weapon, with the first project to be delivered by next year. The plan, announced at a meeting of the five countries' defence ministers in the Polish city of Krakow, marks a boost to UK-Europe ties after the failure of talks last year over UK participation in the EU's new €150bn (£130bn) defence fund.

Chris Pratt on new film Mercy: I asked to be locked into an executioner's chair Being locked barefoot in an executioner's chair sounds uncomfortable, but that is what Chris Pratt requested for his latest film, Mercy. More familiar as a wisecracking action hero in blockbusters like Guardians of the Galaxy and Jurassic World, this role is quite a departure for him. He plays homicide detective Chris Raven, who's fighting for his life after being accused of murdering his wife. Raven is an alcoholic who wakes in the chair after a drinking binge, with just 90 minutes to convince an AI judge he's innocent, or he'll be executed immediately. The film is set in real time, so we see Raven defend his case - while enduring a crashing hangover.

Traditional and modern machine learning-based path loss models typically assume a constant prediction variance. We propose a neural network that jointly predicts the mean and link-specific variance by minimizing a Gaussian negative log-likelihood, enabling heteroscedastic uncertainty estimates. We compare shared, partially shared, and independent-parameter architectures using accuracy, calibration, and sharpness metrics on blind test sets from large public RF drive-test datasets. The shared-parameter architecture performs best, achieving an RMSE of 7.4 dB, 95.1 percent coverage for 95 percent prediction intervals, and a mean interval width of 29.6 dB. These uncertainty estimates further support link-specific coverage margins, improve RF planning and interference analyses, and provide effective self-diagnostics of model weaknesses.

The remarkable capabilities and easy accessibility of large language models (LLMs) have significantly increased societal risks (e.g., fake news generation), necessitating

The skeletal remains of a man who went missing six years ago were found by a walker in a secluded area in south Wales, an inquest has heard. Jordan Moray, from Cwmbach, near Aberdare in Rhondda Cynon Taf, was reported missing from his flat with his games console still running and mobile phone on charge in July 2019. Despite extensive police searches, his remains were not found until 29 August 2025 . On Friday, an inquest at Pontypridd Coroner's Court heard the discovery was made in a remote area near Merthyr Tydfil. South Wales Police previously said it had received a report of human remains near the Llwyn-on Reservoir in Bannau Brycheiniog National Park, also known as the Brecon Beacons .

The prevalence of Vision-Language Models (VLMs) raises important questions about privacy in an era where visual information is increasingly available. While foundation VLMs demonstrate broad knowledge and learned capabilities, we specifically investigate their ability to infer geographic location from previously unseen image data. This paper introduces a benchmark dataset collected from Google Street View that represents its global distribution of coverage. Foundation models are evaluated on single-image geolocation inference, with many achieving median distance errors of <300 km. We further evaluate VLM "agents" with access to supplemental tools, observing up to a 30.6% decrease in distance error. Our findings establish that modern foundation VLMs can act as powerful image geolocation tools, without being specifically trained for this task. When coupled with increasing accessibility of these models, our findings have greater implications for online privacy. We discuss these risks, as well as future work in this area.

Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information system data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and minimize interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature-based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, maintaining model generalization across a broad range of environments.

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from high-resolution obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.

Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper studies convolutional neural network input representations in more detail. We investigate different methods of representing scalar features in convolutional neural networks. Specifically, we compare using frequency and distance as input channels to convolutional layers or as scalar inputs to regression layers. We assess model performance using three different feature configurations and find that representing scalar features as image channels results in the strongest generalization.