Visual grounding is essential for precise perception and reasoning in multimodal large language models (MLLMs), especially in medical imaging domains. While existing medical visual grounding benchmarks primarily focus on single-image scenarios, real-world clinical applications often involve sequential images, where accurate lesion localization across different modalities and temporal tracking of disease progression (e.g., pre-vs.

Clinical decision making (CDM) is a complex, dynamic process crucial to healthcare delivery, yet it remains a significant challenge for artificial intelligence systems. While Large Language Model (LLM)-based agents have been tested on general medical knowledge using licensing exams and knowledge question-answering tasks, their performance in the CDM in real-world scenarios is limited due to the lack of comprehensive benchmark that mirror actual medical practice. To address this gap, we present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow.

Use BBC.com or the new BBC App to listen to BBC podcasts, Radio 4 and the World Service outside the UK. The world's largest data centre (62sq miles) has been approved in Utah, but there is growing opposition towards the project. At twice the size of Manhattan with promises to create thousands of jobs, we look at the bi partisan opposition against it. In this episode, Justin and Anthony discuss the enormous buildings being built across rural America, to house the huge amounts of data that A.I companies work with. Tech bosses say the centres are essential to the growth of Artificial Intelligence.

The remains include a rare 70-million-year-old T. rex relative. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Gobi Desert is one of the biggest troves of dinosaur fossils on Earth. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The rapid advancement of Large Language Models (LLMs) in code generation has raised significant attribution and intellectual property concerns. Code watermarking offers a potential solution but faces unique challenges due to programming languages' strict syntactic constraints and semantic requirements. To address these challenges, we introduce ACW (AST-guided Code Watermarking), a novel adaptive framework that leverages Abstract Syntax Tree (AST) analysis during training to learn watermark embedding strategies. Our framework identifies substitutable code components and strategically biases token selections to embed watermarks. We also propose a novel sampling scheme that distributes tokens between green/red lists according to semantic context, ensuring statistical distinguishability while preserving code functionality. Extensive experiments demonstrate that ACW achieves a significant improvement in watermark detection accuracy compared to existing methods, with negligible impact on code functionality. This adaptive framework offers a promising solution for effective and practical code watermarking in the age of LLMs.

PCWorld reports that Microsoft is expanding its Copilot+ AI features beyond NPU-only requirements to include Nvidia RTX GPUs through experimental Windows App SDK updates. This shift enables millions of older PCs with powerful GPUs to access AI tools like text summarization and image upscaling previously exclusive to newer Copilot+ devices. The change represents Microsoft's more inclusive AI strategy, allowing broader Windows 11 device compatibility for local AI processing tasks. An under-the-hood change in Windows seems to signal the further deterioration of Microsoft's Copilot+ branding, which, at least historically, depended solely on NPUs as the engine of local PC AI. Now, PCs with dedicated GPUs will have access to those features. An experimental release of the Windows App SDK on Github now allows certain AI-specific features to run on Nvidia RTX GPUs, rather than solely depending on an integrated NPU.

State space models (SSMs) have become a cornerstone for unraveling brain dynamics, capturing how latent neural states evolve over time and give rise to observed signals. By combining deep learning's flexibility with SSMs' principled dynamical structure, recent studies have achieved powerful fits to functional neuroimaging data. However, most approaches still view the brain as a set of loosely connected regions or impose oversimplified network priors, falling short of a truly holistic, self organized dynamical system perspective. Brain functional connectivity (FC) at each time point naturally forms a symmetric positive definite (SPD) matrix, which lives on a curved Riemannian manifold rather than in Euclidean space. Capturing the trajectories of these SPD matrices is key to understanding how coordinated networks support cognition and behavior. To this end, we introduce, a geometric state space neural network that tracks latent brain state trajectories directly on the high dimensional SPD manifold.

We present UniFoil, the largest publicly available universal airfoil database based on Reynolds-Averaged Navier-Stokes (RANS) simulations. It contains over 500,000 samples spanning a wide range of Reynolds and Mach numbers, capturing both transitional and fully turbulent flows across incompressible to compressible regimes. UniFoil is designed to support machine learning research in fluid dynamics, particularly for modeling complex aerodynamic phenomena.Most existing datasets are limited to incompressible, fully turbulent flows with smooth field characteristics, thus overlooking the critical physics of laminar-turbulent transition and shock-wave interactions--features that exhibit strong nonlinearity and sharp gradients. UniFoil fills this gap by offering a broad spectrum of realistic flow conditions.In the database, turbulent simulations utilize the Spalart-Allmaras (SA) model, while transitional flows are modeled using an $e^N$-based transition prediction method coupled with the SA model. The database includes a comprehensive geometry set comprising over 4,800 natural laminar flow (NLF) airfoils and 30,000 fully turbulent (FT) airfoils, effectively covering the diversity of airfoil designs relevant to aerospace, wind energy, and marine applications.This database is also highly valuable for scientific machine learning (SciML), enabling the development of data-driven models that more accurately capture the transport processes associated with laminar-turbulent transition. UniFoil is freely available under a permissive CC-BY-SA license.

Cool Routes helps users find the coolest paths and reduce exposure to dangerous heat. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The mean radiant temperature in Phoenix in the sun can go over 150 degrees Fahrenheit. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The Time-Dependent Traveling Salesman Problem (TDTSP) extends the classical TSP by allowing dynamic edge weights that vary with departure time, reflecting real-world scenarios such as transportation networks, where travel times fluctuate due to congestion patterns. TDTSP violates symmetry, triangle inequality, and cyclic invariance properties of classical TSP, creating unique computational challenges. In this paper, we propose a neural model that extends MatNet from static asymmetric TSP to time-dependent settings by using an adjacency tensor to capture temporal variations, followed by a time-aware decoder. Our architecture addresses the unique challenge of asymmetry and triangle inequality violations that change dynamically over time. Beyond architectural innovations, our research reveals a critical evaluation insight: many practical TDTSP instances maintain the same optimal solution regardless of time-dependent edge weights.