Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architectures: (1) a UNet with residual encoder blocks, reflecting commonly used baselines in vascular labeling; (2) CS-Net, an attention-augmented UNet incorporating channel and spatial attention mechanisms for enhanced curvilinear structure recognition; and (3) nnUNet, a self-configuring framework that automates preprocessing, training, and architectural adaptation based on dataset characteristics. Among these, nnUNet achieved the highest labeling performance (average Dice score: 0.922; average surface distance: 0.387 mm), with improved robustness in anatomically complex vessels. To assess predictive confidence, we implemented test-time augmentation (TT A) and introduced a novel coordinate-guided strategy to reduce interpolation errors during augmented inference. The resulting uncertainty maps reliably indicated regions of anatomical ambiguity, pathological variation, or manual labeling inconsistency. We further validated clinical utility by comparing flow velocities derived from automated and manual labels in co-registered 4D Flow MRI datasets, observing close agreement with no statistically significant differences. Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration. Introduction The intracranial arterial system plays a critical role in brain perfusion to maintain normal cognitive function.

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.

We present our submission to Task 3 (Discourse Relation Classification) of the DISRPT 2025 shared task. Task 3 introduces a unified set of 17 discourse relation labels across 39 corpora in 16 languages and six discourse frameworks, posing significant multilingual and cross-formalism challenges. We first benchmark the task by fine-tuning multilingual BERT-based models (mBERT, XLM-RoBERTa-Base, and XLM-RoBERTa-Large) with two argument-ordering strategies and progressive unfreezing ratios to establish strong baselines. We then evaluate prompt-based large language models (namely Claude Opus 4.0) in zero-shot and few-shot settings to understand how LLMs respond to the newly proposed unified labels. Finally, we introduce HiDAC, a Hierarchical Dual-Adapter Contrastive learning model. Results show that while larger transformer models achieve higher accuracy, the improvements are modest, and that unfreezing the top 75% of encoder layers yields performance comparable to full fine-tuning while training far fewer parameters. Prompt-based models lag significantly behind fine-tuned transformers, and HiDAC achieves the highest overall accuracy (67.5%) while remaining more parameter-efficient than full fine-tuning.

This research examines how Artificial Intelligence (AI) can improve participatory budgeting processes within smart cities. In response to challenges like declining civic participation and resource allocation conflicts, the study explores how online political participation can be improved by AI. It investigates the state capacity governments need to implement AI-enhanced participatory tools, considering technological dependencies and vulnerabilities. It analyzes technological and administrative structures, actors, interests, and strategies to understand the dynamics of online political participation technologies in the case of Sao Paulo, Brazil. The study contributes to understanding how technological advancements can reshape participatory budgeting processes. In a broader sense, the research highlights how AI can transform participatory institutions by offering new tools for citizens and also for government officials in charge of participatory processes within smart cities.

Vehicle Routing Problems (VRP) are an extension of the Traveling Salesperson Problem and are a fundamental NP - hard challenge in combinatorial optimization. Solving VRP in real - time at large scale has become critical in numerous applications, from growing markets like last - mile delivery to emerging use - cases like interactive logistics planning. In many applications, one has to repeatedly solv e VRP instances dr a wn from the same distribution, yet current state - of - the - art solvers treat each instance on its own without leveraging previous examples . We introduce a n optimization framework where a reinforcement learning agent is trained on prior instances and quickly generate s initial solutions, which are then further optimized by a genetic algorithm. This framework, Evolutionary Algorithm with Reinforcement Learning Initialization ( EARLI), consistently outperforms current state - of - the - art solvers across various time budgets . For example, EARLI handles vehicle routing with 500 locations within one second, 10x faster than current solvers for the same solution quality, enabling real - time and interactive routing at scale . EARLI can generalize to new data, as we demonstrate on real e - commerce delivery data of a previously unseen city . By combin ing reinforcement learning and genetic algorithms, o ur hybrid framework takes a step forward to closer interdisciplinary collaboration between AI and optimization communities towards real - time optimization in diverse domains .

Nvidia's $100 billion investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. Nvidia will invest as much as $100 billion in OpenAI to support new data centers and other artificial intelligence infrastructure, a blockbuster deal that underscores booming demand for AI tools like ChatGPT and the computing power needed to make them run. The companies announced the agreement Monday, saying they'd signed a letter of intent for a strategic deal. The investment is meant to help OpenAI build data centers with a capacity of at least 10 gigawatts of power -- equipped with Nvidia's advanced chips to train and deploy AI models. The money will be provided in stages, with the first $10 billion coming when the deal is signed, according to people familiar with the matter. Nvidia is making the investment in cash and will receive OpenAI equity as part of the deal, said the people, who asked not to be identified because the talks were private.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Health Secretary Robert F. Kennedy Jr. speaks about autism in the White House on Monday as President Trump and Centers for Medicare & Medicaid Services Administrator Dr. Mehmet Oz look on. This is read by an automated voice. Please report any issues or inconsistencies here . On Monday, President Trump led a White House press event where he and many of his administration's health leaders told the public that taking Tylenol during pregnancy increases the risk of autism in children.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Copenhagen airport shut after sighting of'unidentified drones' Authorities in Denmark have closed Copenhagen airport after unidentified drones were sighted nearby, causing about 15 flights to be diverted, police and airport officials told the AFP news agency. "The airspace over Copenhagen airport has been closed since 8:30pm (18:30 GMT) due to two to three unidentified drones. No aircraft can take off or land," airport spokeswoman Lise Agerley Kurstein said.

'Done properly, AI releases human capacity,' Simon Stiell said. 'Done properly, AI releases human capacity,' Simon Stiell said. AI'carries risks' but will help tackle global heating, says UN's climate chief Mon 22 Sep 2025 15.54 EDTLast modified on Mon 22 Sep 2025 16.04 EDT Harnessing artificial intelligence will help the world to tackle the climate crisis, but governments must step in to regulate the technology, the UN's climate chief has said. AI is being used to make energy systems more efficient, and to develop tools to reduce carbon from industrial processes. The UN is also using AI as an aid to climate diplomacy.

US tech giant Nvidia will invest up to $100bn (£73bn) in OpenAI, the firm behind ChatGPT, the companies announced. Nvidia said it will supply high-performance chips needed for the processing power required by artificial intelligence (AI), of which OpenAI is a specialist. Described as a strategic partnership by Nvidia, it is the latest move by two high profile tech firms in the global AI race, where China is an emerging rival. The announcement comes after a series of high-profile investments by Nvidia, including a $5bn investment in Intel and a £2bn investment in the UK's AI sector. Nvidia said its latest investment will go towards growing data centres for OpenAI's next-generation AI infrastructure.