Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Sir Richard Branson reveals his wife Joan died'quickly and painlessly' while in hospital for a back injury - as he says'life will never be the same' without his'shining star' Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Teenager dragged from car'by migrant gang' and raped in front of her fiancé describes her night of hell and reveals they warned her'if you scream we'll kill you' Virginia Giuffre's family is at war over who gets Andrew's multi-million payout after she died without leaving a will Prince Philip nicknamed Meghan Markle'DOW' and warned Royal Family about her'eerie similarities' with Wallis Simpson, royal author reveals Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Putin'sends top general to Venezuela along with troops tasked with training up President Maduro's forces' as US considers attacking South American country READ MORE: Scientists issue warning over mind-altering'brain weapons' A physicist has proposed a radical new theory of consciousness - and it could finally explain what happens when you die. Consciousness does not emerge from human brains, according to Professor Maria Strømme, a professor of nanotechnology at Uppsala University. Instead, she claims that it exists as a fundamental field. If this is correct, 'mysterious' phenomena such as telepathy, near-death experiences, and even life after death could finally be explained by science.

Warner Music Group (WMG) will begin an artificial intelligence (AI) music venture with technology start-up Suno - a year after it sued the firm in a landmark case. As part of the settlement agreement struck between the two firms, Warner will let users create AI-generated music on Suno using the voices, names and likeness of artists who opt-in to the programme. The record label, which represents artists like Dua Lipa, Coldplay and Ed Sheeran, was among several music giants like Sony Music that sued Suno and a similar platform called Udio. AI-generated content has been controversial, with many artists voicing concerns that it could undermine human songwriters. Starting next year, Suno will roll out new advanced and licensed models to its generative-AI music platform, which allows users to create music based on simple descriptions, said Warner in a statement .

What is in the 28-point US plan for Ukraine? Why is Europe opposing Trump's peace plan? Is the fall of Pokrovsk inevitable? 'A corruption scandal may well end the Ukraine war' At least 18 Ukrainians have been wounded in Russian drone attacks on the Zaporizhzhia area as tens of thousands of invading troops continue their advance on the southeastern region. Ukraine says it supports the "essence" of a United States plan to end its war with Russia, as US President Donald Trump says "progress" is being made and dispatches special envoy Steve Witkoff to Russia for talks with President Vladimir Putin.

Karoline Leavitt's family member'abruptly arrested' by ICE after living in US for decades Residents in liberal Western US city feel'isolated' as state turns extremely red What HAS happened to Beyoncé? Suddenly desperate, I know what's really going on... and it's ugly: CAROLINE BULLOCK LIZ JONES: Sorry, but it's now time for Kate to stop making excuses'I fell for Joan the moment I saw her': The emotional love letter Sir Richard Branson penned to his'rock' on their anniversary - as he announces her death after 50 years together Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air New'Hollywood of the South' emerges as booming industry generates $1bn... but long-time residents are furious University of Minnesota program offers guidelines to'reverse the whiteness pandemic' Emmy-winning CBS anchor reveals her devastating health battle: 'I've been silently struggling' Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train MORE: California squirrels evolving in'shocking' way as scientists investigate key behavioral shift Common rats have learned a shocking and deadly new tactic to kill other animals, which could one day lead to a deadly new pandemic among humans. Scientists witnessed as local brown rats ambushed a colony of bats as they entered two caves in Germany, leaping into the air to catch and kill the nocturnal creatures in droves. Moreover, these rats did this in the middle of the night and without being able to see their surroundings. Researchers from the Leibniz Institute for Evolution and Biodiversity Science said it's the first time common rats have ever been seen in Europe acting with such predatory instincts .

We introduce and analyse active learning markets as a way to purchase labels, in situations where analysts aim to acquire additional data to improve model fitting, or to better train models for predictive analytics applications. This comes in contrast to the many proposals that already exist to purchase features and examples. By originally formalising the market clearing as an optimisation problem, we integrate budget constraints and improvement thresholds into the label acquisition process. We focus on a single-buyer-multiple-seller setup and propose the use of two active learning strategies (variance based and query-by-committee based), paired with distinct pricing mechanisms. They are compared to a benchmark random sampling approach. The proposed strategies are validated on real-world datasets from two critical application domains: real estate pricing and energy forecasting. Results demonstrate the robustness of our approach, consistently achieving superior performance with fewer labels acquired compared to conventional methods. Our proposal comprises an easy-to-implement practical solution for optimising data acquisition in resource-constrained environments.

We therefore pursue a data-driven alternative based on complex-valued V AEs and latent-space OOD scores. In recent years, data-driven approaches have emerged to alleviate the need for precise clutter modeling. Among them, V AEs [4] have demonstrated promising capabilities for anomaly and OOD detection in diverse applications, including radar detection [5], speech enhancement [6], medical imaging [7], industrial monitoring [8], and acoustic signal analysis [9]. These models learn a latent representation of the training data and use reconstruction or probabilistic criteria to detect deviations. Despite their effectiveness, most V AE-based detectors operate in the real domain and often treat complex-valued radar data by separating real and imaginary components into distinct channels. Recent advances in Complex-V alued Neural Networks (CVNNs) have shown the benefits of directly modeling complex-valued signals [10, 11].

This study investigates the limitations of applying Markov Chain Monte Carlo (MCMC) methods to arbitrary objective functions, focusing on a two-block MCMC framework which alternates between Metropolis-Hastings and Gibbs sampling. While such approaches are often considered advantageous for enabling data-driven regularization, we show that their performance critically depends on the sharpness of the employed likelihood form. By introducing a sharpness parameter and exploring alternative likelihood formulations proportional to the target objective function, we demonstrate how likelihood curvature governs both in-sample performance and the degree of regularization inferred by the training data. Empirical applications are conducted on reinforcement learning tasks: including a navigation problem and the game of tic-tac-toe. The study concludes with a separate analysis examining the implications of extreme likelihood sharpness on arbitrary objective functions stemming from the classic game of blackjack, where the first block of the two-block MCMC framework is replaced with an iterative optimization step. The resulting hybrid approach achieves performance nearly identical to the original MCMC framework, indicating that excessive likelihood sharpness effectively collapses posterior mass onto a single dominant mode.

Existing methods are either: (i) DNN-based, which are inherently coupled with network-specific parameters, inevitably introducing architectural bias and compromising generalization; or (ii) DNN-free, which utilize heuristics that lack rigorous theoretical guarantees for stability and accuracy. Neither approach explicitly constrains distributional equivalence of the representative subsets, largely because continuous distribution matching is broadly considered inapplicable to discrete dataset sampling. Furthermore, prevalent distribution metrics (e.g., MSE, KL, MMD, and CE) are often incapable of accurately capturing higher-order moments differences. These deficiencies lead to suboptimal coreset performance, preventing the selected coreset from being truly equivalent to the original dataset. W e propose F AST (Frequency-domain Aligned Sampling via T opology), the first DNN-free distribution-matching coreset selection framework that formulates coreset selection task as a graph-constrained optimization problem grounded in spectral graph theory and employs the Characteristic Function Distance (CFD) to capture full distributional information (i.e., all moments and intrinsic correlations) in the frequency domain. W e further discover that naive CFD suffers from a "vanishing phase gradient" issue in medium and high-frequency regions; to address this, we introduce an Attenuated Phase-Decoupled CFD.

During generation, we use cuboidal control domains of varying dimensionality, location, and shape, to slice out relevant substructures. These local substructures are used to compute differentiable penalty functions that steer the sample towards target constraints. W e control geometric features such as size, shape, and position through voxel-wise moments, while topological features such as connected components, loops, and voids are enforced through persistent homology. Lastly, we implement Anatomica for latent diffusion models, where neural field decoders partially extract substructures, enabling the efficient control of anatomical properties. Anatomica applies flexibly across diverse anatomical systems, composing constraints to control complex structures over arbitrary dimensions and coordinate systems, thereby enabling the rational design of synthetic datasets for virtual trials or machine learning workflows.

Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text labelled with corresponding sentiment classes. One reason for that is that manually labelling text data is time-consuming and expensive. Consequently, automatic and rapid processes are needed to reduce the manual effort as much as possible making the labelling process as efficient as possible. In this paper, we present and analyze an automatic language-independent sentiment labelling method that leverages information from sentiment-bearing emojis and words. Our experiments are conducted with tweets in the languages English, Sepedi and Setswana from SAfriSenti, a multilingual sentiment corpus for South African languages. We show that our sentiment labelling approach is able to label the English tweets with an accuracy of 66%, the Sepedi tweets with 69%, and the Setswana tweets with 63%, so that on average only 34% of the automatically generated labels remain to be corrected.