Trump is imposing new fees on H-1B visas. Trump is imposing new fees on H-1B visas. Trumps's visa fees threaten tech's most prized employees The president's deal on visas could upend Silicon Valley, and will a TikTok purchase finally go through? This week's tech news is all about Donald Trump's deals: with China, with the UK, and with the US tech industry, which is facing steep fines for its favorite visa. Trump's talent tariffs: Visa fines threaten tech's most prized employees US tech giants made a bargain last year: tens of millions of dollars to Trump's presidential campaign in exchange for access to the president and policies that promoted their industry's growth.

Russia's involvement in drone sightings cannot be ruled out, Danish PM says The drone incursion that stopped flights at Copenhagen airport on Monday night was the most severe attack on Danish infrastructure so far, Denmark's Prime Minister Mette Frederiksen said. Kastrup airport in Copenhagen was forced to shut for several hours from around 20:30 (18:30 GMT) on Monday following the sighting of a number of drones. It says something about the times we live in and what we as a society must be prepared to deal with, Frederiksen told reporters. Russian involvement could not be ruled out, Frederiksen added - although Kremlin spokesman Dmitry Peskov called the allegations unfounded. The Danish PM made a link between last night's events in Denmark and the recent Russian drone incursions in Poland and Romania, as well as the violation of Estonian airspace by Russian fighter jets .

What is autism and what are Trump's unproven claims about a Tylenol link? US President Donald Trump has claimed there is a link between the use of painkiller Tylenol by pregnant women and an increased risk of autism in some children. Going against current scientific advice and medical opinion, he said the drug, known as paracetamol in many countries, is no good and women should fight like hell to only take it in extreme cases, such as for high fevers. Medical bodies say the drug is safe and that it remains the best treatment for pain and fever during pregnancy. What is autism and how is it diagnosed?

Google's largest Pixel is a weighty, two-hand hold for big-phone fans. Google's largest Pixel is a weighty, two-hand hold for big-phone fans. Pixel 10 Pro XL review: Google's superphone gets AI and magnetic upgrades The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. G oogle's Pixel superphone is back, packed with a bigger battery, faster charging, magnetic accessories and even more cutting-edge AI tools to try to usurp Apple and Samsung as the monarchs of really big phones.

Porsche's stock tumbled by more than 7% on Monday after warning last week that delays in its electric vehicle (EV) rollout will dent the carmaker's 2025 earnings. Caught between electrification and its iconic petrol-powered sports cars, the German firm said it will slow its push for EVs as demand weakens. Shares of its parent Volkswagen also fell by more than 7% on the same day after saying it will spend billions to overhaul Porsche's line-up of vehicles. The companies' struggles reflect the challenges for European manufacturers, who are faced with intense competition from Chinese rivals and a slowing economy that's dampening demand for luxury cars. Porsche said in a statement on Friday that it has reduced its projected profit margin from up to 7% to 2% or less.

Language models obtain extensive capabilities through pre-training. However, the pre-training process remains a black box. In this work, we track linear interpretable feature evolution across pre-training snapshots using a sparse dictionary learning method called crosscoders. We find that most features begin to form around a specific point, while more complex patterns emerge in later training stages. Feature attribution analyses reveal causal connections between feature evolution and downstream performance. Our feature-level observations are highly consistent with previous findings on Transformer's two-stage learning process, which we term a statistical learning phase and a feature learning phase. Our work opens up the possibility to track fine-grained representation progress during language model learning dynamics.

Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorithms primarily handle scalar or Euclidean outputs, increasingly prevalent complex-structured data, such as distributions, networks, and manifold-valued outputs, present challenges for traditional methods. Such non-Euclidean data lack algebraic structures such as addition, subtraction, or scalar multiplication required by standard gradient boosting frameworks. To address these challenges, we introduce Fréchet geodesic boosting (FGBoost), a novel approach tailored for outputs residing in geodesic metric spaces. FGBoost leverages geodesics as proxies for residuals and constructs ensembles in a way that respects the intrinsic geometry of the output space. Through theoretical analysis, extensive simulations, and real-world applications, we demonstrate the strong performance and adaptability of FGBoost, showcasing its potential for modeling complex data.

Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging Camera System (CSLICS), which uses low cost modular cameras and object detectors trained using human-in-the-loop labeling approaches for automated spawn counting in larval rearing tanks. This paper details the system engineering, dataset collection, and computer vision techniques to detect, classify and count coral spawn. Experimental results from mass spawning events demonstrate an F1 score of 82.4\% for surface spawn detection at different embryogenesis stages, 65.3\% F1 score for sub-surface spawn detection, and a saving of 5,720 hours of labor per spawning event compared to manual sampling methods at the same frequency. Comparison of manual counts with CSLICS monitoring during a mass coral spawning event on the Great Barrier Reef demonstrates CSLICS' accurate measurement of fertilization success and sub-surface spawn counts. These findings enhance the coral aquaculture process and enable upscaling of coral reef restoration efforts to address climate change threats facing ecosystems like the Great Barrier Reef.

Accurate modeling of personalized cardiovascular dynamics is crucial for non-invasive monitoring and therapy planning. State-of-the-art physics-informed neural network (PINN) approaches employ deep, multi-branch architectures with adversarial or contrastive objectives to enforce partial differential equation constraints. While effective, these enhancements introduce significant training and implementation complexity, limiting scalability and practical deployment. We investigate physics-informed neural operator learning models as efficient supervisory signals for training simplified architectures through knowledge distillation. Our approach pre-trains a physics-informed DeepONet (PI-DeepONet) on high-fidelity cuffless blood pressure recordings to learn operator mappings from raw wearable waveforms to beat-to-beat pressure signals under embedded physics constraints. This pre-trained operator serves as a frozen supervisor in a lightweight knowledge-distillation pipeline, guiding streamlined base models that eliminate complex adversarial and contrastive learning components while maintaining performance. We characterize the role of physics-informed regularization in operator learning and demonstrate its effectiveness for supervisory guidance. Through extensive experiments, our operator-supervised approach achieves performance parity with complex baselines (correlation: 0.766 vs. 0.770, RMSE: 4.452 vs. 4.501), while dramatically reducing architectural complexity from eight critical hyperparameters to a single regularization coefficient and decreasing training overhead by 4%. Our results demonstrate that operator-based supervision effectively replaces intricate multi-component training strategies, offering a more scalable and interpretable approach to physiological modeling with reduced implementation burden.