With the recent development of new geometric and angular-radial frameworks for multivariate extremes, reliably simulating from angular variables in moderate-to-high dimensions is of increasing importance. Empirical approaches have the benefit of simplicity, and work reasonably well in low dimensions, but as the number of variables increases, they can lack the required flexibility and scalability. Classical parametric models for angular variables, such as the von Mises-Fisher (vMF) distribution, provide an alternative. Exploiting mixtures of vMF distributions increases their flexibility, but there are cases where even this is not sufficient to capture the intricate features that can arise in data. Owing to their flexibility, generative deep learning methods are able to capture complex data structures; they therefore have the potential to be useful in the simulation of angular variables. In this paper, we explore a range of deep learning approaches for this task, including generative adversarial networks, normalizing flows and flow matching. We assess their performance via a range of metrics and make comparisons to the more classical approach of using a mixture of vMF distributions. The methods are also applied to a metocean data set, demonstrating their applicability to real-world, complex data structures.

A Russian drone attack on the Black Sea port city of Odesa overnight injured three people, sparked fires and damaged homes and civilian infrastructure, regional governor Oleh Kiper said. Russia's Ministry of Defence said its units destroyed 26 Ukrainian drones overnight. Nine of the drones were shot down over the southern Voronezh region, while eight were taken down over the border region of Belgorod. The remaining drones were downed over the Kursk, Lipetsk and Moscow regions, as well as over the Russian-annexed Crimean peninsula, the ministry said. The Defence Ministry also said Russia has taken control of Kalynove village in Ukraine's eastern Donetsk region.

It can have serious implications in critical application scenarios like that of Knowledge Graph-based multilingual data integration. In view of the above, the thesis argues that the current understanding of the problem of semantic heterogeneity as the'existence of variance', while being crucially necessary, is not sufficient and under-characterized. There can be no variance without a prior notion of a unifying reference taken as the basis for computing the variance itself. To that end, the thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneity. The thesis also includes proof-of-concepts of the language and knowledge representations developed for two international research projects - DataScientia (data catalogs) and JIDEP (materials modelling). Finally, the thesis concludes with future lines of research.

Russia's territorial gains in Ukraine are slowing down dramatically, two analyses have found, continuing a pattern from 2024 at a time when both nations are trying to project strength in the face of United States-mediated negotiations aimed at ending the war. Britain's Ministry of Defence last week estimated that Russian forces seized 143sq km (55sq miles) of Ukrainian land in March, compared with 196sq km (76sq miles) in February and 326sq km (126sq miles) in January. The Institute for the Study of War, a Washington, DC-based think tank, spotted the same trend, estimating Russian gains at 203sq km (78sq miles) in March, 354sq km (137sq miles) in February and 427sq km (165sq miles) in January. These estimates are based on satellite imagery and geolocated open-source photography rather than claims by either side. Should this trend continue, Russian forces could come to a standstill by early summer, roughly coinciding with US President Donald Trump's self-imposed early deadline for achieving a ceasefire.

It has been 113 years since the Titanic sank beneath the waves, claiming the lives of more than 1,500 passengers and crew. But new evidence has finally cleared the tragedy's Scottish scapegoat: First Officer William Murdoch. For years, Officer Murdoch has been accused of taking bribes, abandoning his post, and was even depicted shooting a passenger in the James Cameron movie. Now, more than a century later, 3D scans show that Officer Murdoch did not flee his position, but died while helping passengers escape until the very end. Deep sea scanning company Magellan has snapped 715,000 photos of the Titanic wreck 12,500 feet beneath the Atlantic.

In a video game year dominated by dark, bloody fantasy adventures – and continued job losses and studio closures – it was a cute robot that stole the night at the 2025 Bafta video game awards. Sony's family-friendly platformer Astro Bot won in five categories at yesterday evening's ceremony, including best game and game design. The rest of the awards were evenly spread across a range of Triple A and independent titles. Oil rig thriller Still Wakes the Deep was the next biggest winner with three awards: new intellectual property, performer in a leading role and performer in a supporting role. Clearly actors looking for Bafta-winning roles need look no further than the North Sea.

The RMS Titanic sank in the North Atlantic Ocean on April 15, 1912, after colliding with an iceberg during her maiden voyage from Southampton to New York. More than 1,500 people died when the ship, which was carrying 2,224 passengers and crew, sank under the command of Captain Edward Smith. Some of the wealthiest people in the world were on board, including property tycoon John Jacob Astor IV, great grandson of John Jacob Astor, founder of the Waldorf Astoria Hotel. Millionaire Benjamin Guggenheim, heir to his family's mining business, also perished, along with Isidor Straus, the German-born co-owner of Macy's department store. The ship was the largest afloat at the time and was designed in such a way that it was meant to be'unsinkable'.

The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) took place in Philadelphia from Tuesday 25 February to Tuesday 4 March 2025. The programme featured eight invited talks. Susan works at the intersection of computer science and economics. In the past she has researched problems relating to mechanism design, auctions, pricing, and causal inference, but recently she has turned her attention to modelling worker career transitions using transformer models. In her talk, Susan described the research in a few of her recent papers covering topics such as the gender wage gap and economic prediction of labour sequence data.

Kyiv, Ukraine – Thread-thin, glistening in the sun and kilometres long, optical fibres wind through the branches of trees on the frontlines of eastern Ukraine. The cords were – sometimes still are – attached to Russian drones, making them immune to radio-electronic jamming. The drones may have been shot down. Some are still operational, waylaid and replete with danger. "When somebody is passing by, they just fly up and attack," Oleh, a military officer deployed in eastern Ukraine, told Al Jazeera.

Rip currents are strong, localized and narrow currents of water that flow outwards into the sea, causing numerous beach-related injuries and fatalities worldwide. Accurate identification of rip currents remains challenging due to their amorphous nature and the lack of annotated data, which often requires expert knowledge. To address these issues, we present RipVIS, a large-scale video instance segmentation benchmark explicitly designed for rip current segmentation. RipVIS is an order of magnitude larger than previous datasets, featuring $184$ videos ($212,328$ frames), of which $150$ videos ($163,528$ frames) are with rip currents, collected from various sources, including drones, mobile phones, and fixed beach cameras. Our dataset encompasses diverse visual contexts, such as wave-breaking patterns, sediment flows, and water color variations, across multiple global locations, including USA, Mexico, Costa Rica, Portugal, Italy, Greece, Romania, Sri Lanka, Australia and New Zealand. Most videos are annotated at $5$ FPS to ensure accuracy in dynamic scenarios, supplemented by an additional $34$ videos ($48,800$ frames) without rip currents. We conduct comprehensive experiments with Mask R-CNN, Cascade Mask R-CNN, SparseInst and YOLO11, fine-tuning these models for the task of rip current segmentation. Results are reported in terms of multiple metrics, with a particular focus on the $F_2$ score to prioritize recall and reduce false negatives. To enhance segmentation performance, we introduce a novel post-processing step based on Temporal Confidence Aggregation (TCA). RipVIS aims to set a new standard for rip current segmentation, contributing towards safer beach environments. We offer a benchmark website to share data, models, and results with the research community, encouraging ongoing collaboration and future contributions, at https://ripvis.ai.