Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian and Ukrainian troops have fought battles in the ruins of Pokrovsk, a transport and logistics hub in eastern Ukraine, with Ukraine's military reporting fierce fighting under way in a part of the city that was key for Kyiv's front-line logistics. Ukrainian President Volodymyr Zelenskyy said he visited troops fighting near the eastern city of Dobropillia, where Ukrainian forces are conducting a counteroffensive against Russian troops. Russia struck civilian energy and port infrastructure in a massive overnight drone attack on Ukraine's southern region of Odesa, the region's governor said in a post on the Telegram messaging app, adding that rescuers extinguished fires and there were no casualties.

It's not clear that everyone is singing from the same hymn sheet though, meaning the AGM in Austin, Texas is set to become a referendum on Musk himself, after a rightward political turn which has made him one of the most polarising chief executives in recent memory. Musk himself has taken to X - which he owns - to raise the stakes higher still, saying the fate of Tesla could affect the future of civilization. He's also used his social media megaphone to amplify some of the deal's high-profile backers, including Dell Technologies' Michael Dell, Ark Invest CEO Cathie Wood, and his brother, Kimbal, who sits on the Tesla board. There is no one remotely close to my brother, Kimbal said, extolling his sibling's leadership qualities.

Now he's dead, here's the full story of what happened that day... and the ghastly aftermath no one knows about Wake up and see he's the master of the dark arts: MEGYN KELLY blows the lid on the REAL Mamdani... how are they missing this? 'Screaming' Sydney Sweeney'hates' that she was caught hiding in ex-fiancé's car: Now insiders spill truth about backseat rendezvous and lingering'frustrations' Mom is an Oscar winner who has acted with Selena Gomez and Nicole Kidman, who is this nepo kid who came out last year? Bella Thorne continues swimsuit season as she works sexy bikini for Los Cabo trip with her'love' Mark Emms Experts pinpoint typical life expectancy from initial dementia diagnosis - and there's a huge variation between different subtypes Diddy's male prison protector unmasked: How disgraced mogul has repaid him... and turned to God for repentance Teachers threatened over bloody'Problem Solved' T-shirts over claims they mocked murder of Charlie Kirk Astonishing moment Miss Universe winner storms out of this year's event after pageant president reprimands Miss Mexico and tells security to remove her for not showing'respect' Mystery of the'golfer's curse' is SOLVED: Scientists pinpoint why golf balls'lip out' after appearing to enter the hole READ MORE: Golf balls are a'product of colonial exploitation', exhibition says Experts have finally solved the mystery of one of the most infuriating occurrences in golf - the dreaded lip out. The phenomenon occurs when the golf ball appears to enter the hole, only to immediately pop back out again. Scientists have finally pinpointed the physics behind the'curse', which has plagued everyone from amateur hobbyists to PGA professionals. Best of all, they've revealed the best way to avoid it - keeping your score intact.

AI'godmother' Fei-Fei Li says she is'proud to be different' The'godmother' of AI, Professor Fei-Fei Li has told the BBC that being the only woman amongst seven pioneers of artificial Intelligence being presented with a top engineering prize by the King today makes her proud to be different. The King will present the 2025 Queen Elizabeth Prize for Engineering to Prof Li and six others during a ceremony at St James's Palace. Those honoured alongside her are Prof Yoshua Bengio, Dr Bill Dally, Dr Geoffrey Hinton, Prof John Hopfield, Nvidia founder Jensen Huang and Meta's Chief AI Scientist Dr Yann LeCun. They are being recognised for their contributions to the development of modern machine learning, a field that underpins the rapid advancement of AI. Who are the Godparents of AI? Dr Hinton, Prof Bengio and Yann LeCun, currently Chief AI Scientist at Meta have widely been recognised as the Godfathers of AI since they were jointly awarded the 2018 Turing Award.

The number of sick and disabled people out of work is putting the UK is at risk of an economic inactivity crisis that threatens the country's prosperity, according to a new report. There were 800,000 more people out of work now than in 2019 due to health conditions, costing employers £85bn a year, according to the review by former John Lewis boss Sir Charlie Mayfield. The problem could worsen without intervention, but Sir Charlie, who will lead a taskforce aimed at helping people return to work, said this was not inevitable. The move has been broadly welcomed, but some business groups said Labour's Employment Rights Bill included some disincentives to hiring people with existing illnesses. One in five working age people were out of work, and not seeking work, according to the report, which was commissioned by the Department for Work and Pensions by produced independently.

Research has suggested super-recognisers look at more areas across a face than typical people. Research has suggested super-recognisers look at more areas across a face than typical people. Research uses eye-tracking data to examine some people's extraordinary recognition ability They have been used in the search for the Salisbury novichok poisoners, finding murder suspects and even spotting sexual predators. Now, research has revealed fresh insights into why super-recognisers are so good at identifying faces. Previous research has suggested people with an extraordinary ability to recognise people look at more areas across a face than typical people.

In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the patch size during model training, resulting in an improved class balance for smaller patch sizes and accelerated convergence of the training process. We evaluate our curriculum approach in two settings: a resource-efficient mode and a performance mode, both regarding Dice score performance and computational costs across 15 diverse and popular 3D medical image segmentation tasks. The resource-efficient mode matches the Dice score performance of the conventional constant patch size sampling baseline with a notable reduction in training time to only 44%. The performance mode improves upon constant patch size segmentation results, achieving a statistically significant relative mean performance gain of 1.28% in Dice Score. Remarkably, across all 15 tasks, our proposed performance mode manages to surpass the constant patch size baseline in Dice Score performance, while simultaneously reducing training time to only 89%. The benefits are particularly pronounced for highly imbalanced tasks such as lesion segmentation tasks. Rigorous experiments demonstrate that our performance mode not only improves mean segmentation performance but also reduces performance variance, yielding more trustworthy model comparison. Furthermore, our findings reveal that the proposed curriculum sampling is not tied to a specific architecture but represents a broadly applicable strategy that consistently boosts performance across diverse segmentation models, including UNet, UNETR, and SwinUNETR. In summary, we show that this simple yet elegant transformation on input data substantially improves both Dice Score performance and training runtime, while being compatible across diverse segmentation backbones.

Early detection of heart arrhythmia can prevent severe future complications in cardiac patients. While manual diagnosis still remains the clinical standard, it relies heavily on visual interpretation and is inherently subjective. In recent years, deep learning has emerged as a powerful tool to automate arrhythmia detection, offering improved accuracy, consistency, and efficiency. Several variants of convolutional and recurrent neural network architectures have been widely explored to capture spatial and temporal patterns in physiological signals. However, despite these advancements, current models often struggle to generalize well in real-world scenarios, especially when dealing with small or noisy datasets, which are common challenges in biomedical applications. In this paper, a novel CNN-H-Infinity-LSTM architecture is proposed to identify arrhythmic heart signals from heart sound recordings. This architecture introduces trainable parameters inspired by the H-Infinity filter from control theory, enhancing robustness and generalization. Extensive experimentation on the PhysioNet CinC Challenge 2016 dataset, a public benchmark of heart audio recordings, demonstrates that the proposed model achieves stable convergence and outperforms existing benchmarks, with a test accuracy of 99.42% and an F1 score of 98.85%.

Knowledge Tracing (KT) has been an established problem in the educational data mining field for decades, and it is commonly assumed that the underlying learning process being modeled remains static. Given the ever-changing landscape of online learning platforms (OLPs), we investigate how concept drift and changing student populations can impact student behavior within an OLP through testing model performance both within a single academic year and across multiple academic years. Four well-studied KT models were applied to five academic years of data to assess how susceptible KT models are to concept drift. Through our analysis, we find that all four families of KT models can exhibit degraded performance, Bayesian Knowledge Tracing (BKT) remains the most stable KT model when applied to newer data, while more complex, attention based models lose predictive power significantly faster.

Purpose: To evaluate deep learning (DL) models for enhancing vitreous optical coherence tomography (OCT) image quality and reducing acquisition time. Methods: Conditional Denoising Diffusion Probabilistic Models (cDDPMs), Brownian Bridge Diffusion Models (BBDMs), U-Net, Pix2Pix, and Vector-Quantised Generative Adversarial Network (VQ-GAN) were used to generate high-quality spectral-domain (SD) vitreous OCT images. Inputs were SD ART10 images, and outputs were compared to pseudoART100 images obtained by averaging ten ART10 images per eye location. Model performance was assessed using image quality metrics and Visual Turing Tests, where ophthalmologists ranked generated images and evaluated anatomical fidelity. The best model's performance was further tested within the manually segmented vitreous on newly acquired data. Results: U-Net achieved the highest Peak Signal-to-Noise Ratio (PSNR: 30.230) and Structural Similarity Index Measure (SSIM: 0.820), followed by cDDPM. For Learned Perceptual Image Patch Similarity (LPIPS), Pix2Pix (0.697) and cDDPM (0.753) performed best. In the first Visual Turing Test, cDDPM ranked highest (3.07); in the second (best model only), cDDPM achieved a 32.9% fool rate and 85.7% anatomical preservation. On newly acquired data, cDDPM generated vitreous regions more similar in PSNR to the ART100 reference than true ART1 or ART10 B-scans and achieved higher PSNR on whole images when conditioned on ART1 than ART10. Conclusions: Results reveal discrepancies between quantitative metrics and clinical evaluation, highlighting the need for combined assessment. cDDPM showed strong potential for generating clinically meaningful vitreous OCT images while reducing acquisition time fourfold. Translational Relevance: cDDPMs show promise for clinical integration, supporting faster, higher-quality vitreous imaging. Dataset and code will be made publicly available.