Team GB Olympic and Paralympic athletes are being offered a new form of artificial intelligence-based protection from online abuse. UK Sport, the body that funds Olympic and Paralympic sports, has signed a contract worth more than £300,000 to give thousands of athletes access to an app that detects and hides abusive posts sent by other users on social media. Athletes are able to sign up for free and can protect their accounts throughout the Games cycle up to Los Angeles 2028. The level of abuse our athletes are facing online is unacceptable - to do nothing about this is not an option, UK Sport director of performance Kate Baker said about a deal that is the first of its kind in British sport. The app, called Social Protect, uses AI to try to ensure athletes see as few abusive messages sent their way as possible.

Police accused of'homophobic assumptions' over victims of blackmail on Grindr Police failed to properly investigate allegations that a gang was blackmailing men on the gay dating app Grindr, the BBC can reveal. Our investigation has learned of five cases of suspected blackmail involving victims targeted on Grindr in one area, with at least four of them connected to the same gang, which remains at large. In one instance, a suspected victim killed himself 24 hours after a group of men turned up at his home demanding he hand over his new Range Rover. The Independent Office for Police Conduct (IOPC) watchdog has told Hertfordshire Police - the investigating force - to examine whether homophobic assumptions could have contributed to failures in the investigation. Hertfordshire Police said it was unable to discuss specific points about the case, which has now been reopened, but said it is committed to building and maintaining good working relationships with the LGBTQ+ communities.

The Metropolitan Police Department arrested a 17-year-old boy on Thursday for allegedly carrying out a cyberattack on the operator of the Kaikatsu Club internet cafe chain, sources said. Tokyo police served an arrest warrant on a 17-year-old boy on Thursday for allegedly carrying out a cyberattack on the operator of the Kaikatsu Club internet cafe chain, investigative sources said. The Metropolitan Police Department arrested the second-year high school student from the city of Osaka over an alleged violation of the law against unauthorized computer access and fraudulent obstruction of business. According to the sources, the boy fraudulently obtained about 7.25 million sets of Kaikatsu Club membership information with a computer program he created using the ChatGPT artificial intelligence chatbot. The boy is said to have skills strong enough to have won awards in cybersecurity competitions, as reported by TBS.

Facial recognition technology could be used more often by UK police forces, according to new plans announced by the Home Office. Policing and crime minister Sarah Jones said a widespread rollout of the equipment could mark the biggest breakthrough in catching criminals since DNA matching. People are being asked for their views on its use, as part of a 10-week consultation launched on Thursday, possibly paving the way for new laws. Jones credited the technology for helping to arrest thousands of criminals, but campaign group Big Brother Watch said increased use would make George Orwell roll in his grave. Facial recognition is used to locate wanted suspects and find vulnerable people.

Things to Do in L.A. Tap to enable a layout that focuses on the article. The Audrey Irmas Pavilion, left, at the Wilshire Boulevard Temple, center in background, in 2021. This is read by an automated voice. Please report any issues or inconsistencies here . Two were arrested during a pro-Palestinian protest at Wilshire Boulevard Temple that ended in confrontation.

Canadian military's cyber chief touts AI's advantages but warns against'unquestioned' use AI-infused military systems are enabling operators and commanders to not only identify anomalies or threats that might be missed by analysts or traditional systems, but also to quickly analyze large volumes of data to support critical decision-making on the battlefield. Artificial intelligence has begun providing critical advantages for armed forces in an era where the speed of decision-making could be the deciding factor between victory and defeat, the Canadian military's cybercommand chief told The Japan Times. Yet despite the advantages in operational efficiency, no country should be adopting these cutting-edge technologies in "an unquestioned or unlimited manner," Maj. Gen. Dave Yarker warned in an exclusive interview on Wednesday in Tokyo, amid concerns that doing so may present unforeseeable risks. "We are using AI to make our defenses stronger and improve our ability to protect ourselves," Yarker said.

Fuzzy simplicial sets have become an object of interest in dimensionality reduction and manifold learning, most prominently through their role in UMAP. However, their definition through tools from algebraic topology without a clear probabilistic interpretation detaches them from commonly used theoretical frameworks in those areas. In this work we introduce a framework that explains fuzzy simplicial sets as marginals of probability measures on simplicial sets. In particular, this perspective shows that the fuzzy weights of UMAP arise from a generative model that samples Vietoris-Rips filtrations at random scales, yielding cumulative distribution functions of pairwise distances. More generally, the framework connects fuzzy simplicial sets to probabilistic models on the face poset, clarifies the relation between Kullback-Leibler divergence and fuzzy cross-entropy in this setting, and recovers standard t-norms and t-conorms via Boolean operations on the underlying simplicial sets. We then show how new embedding methods may be derived from this framework and illustrate this on an example where we generalize UMAP using Čech filtrations with triplet sampling. In summary, this probabilistic viewpoint provides a unified probabilistic theoretical foundation for fuzzy simplicial sets, clarifies the role of UMAP within this framework, and enables the systematic derivation of new dimensionality reduction methods.

Infinite hidden Markov models provide a flexible framework for modelling time series with structural changes and complex dynamics, without requiring the number of latent states to be specified in advance. This flexibility is achieved through the hierarchical Dirichlet process prior, while efficient Bayesian inference is enabled by the beam sampler, which combines dynamic programming with slice sampling to truncate the infinite state space adaptively. Despite extensive methodological developments, the role of initialization in this framework has received limited attention. This study addresses this gap by systematically evaluating initialization strategies commonly used for finite hidden Markov models and assessing their suitability in the infinite setting. Results from both simulated and real datasets show that distance-based clustering initializations consistently outperform model-based and uniform alternatives, the latter being the most widely adopted in the existing literature.

Probabilistic Graphical Models (PGMs) encode conditional dependencies among random variables using a graph -nodes for variables, links for dependencies- and factorize the joint distribution into lower-dimensional components. This makes PGMs well-suited for analyzing complex systems and supporting decision-making. Recent advances in topological signal processing highlight the importance of variables defined on topological spaces in several application domains. In such cases, the underlying topology shapes statistical relationships, limiting the expressiveness of canonical PGMs. To overcome this limitation, we introduce Colored Markov Random Fields (CMRFs), which model both conditional and marginal dependencies among Gaussian edge variables on topological spaces, with a theoretical foundation in Hodge theory. CMRFs extend classical Gaussian Markov Random Fields by including link coloring: connectivity encodes conditional independence, while color encodes marginal independence. We quantify the benefits of CMRFs through a distributed estimation case study over a physical network, comparing it with baselines with different levels of topological prior.

Online A/B testing, the gold standard for evaluating new advertising policies, consumes substantial engineering resources and risks significant revenue loss from deploying underperforming variations. This motivates the use of Off-Policy Evaluation (OPE) for rapid, offline assessment. However, applying OPE to ad auctions is fundamentally more challenging than in domains like recommender systems, where stochastic policies are common. In online ad auctions, it is common for the highest-bidding ad to win the impression, resulting in a deterministic, winner-takes-all setting. This results in zero probability of exposure for non-winning ads, rendering standard OPE estimators inapplicable. We introduce the first principled framework for OPE in deterministic auctions by repurposing the bid landscape model to approximate the propensity score. This model allows us to derive robust approximate propensity scores, enabling the use of stable estimators like Self-Normalized Inverse Propensity Scoring (SNIPS) for counterfactual evaluation. We validate our approach on the AuctionNet simulation benchmark and against 2-weeks online A/B test from a large-scale industrial platform. Our method shows remarkable alignment with online results, achieving a 92\% Mean Directional Accuracy (MDA) in CTR prediction, significantly outperforming the parametric baseline. MDA is the most critical metric for guiding deployment decisions, as it reflects the ability to correctly predict whether a new model will improve or harm performance. This work contributes the first practical and validated framework for reliable OPE in deterministic auction environments, offering an efficient alternative to costly and risky online experiments.