Since his release from a Russian prison, Dmytro Khyliuk has barely been off the phone. The Ukrainian journalist was detained by Russian forces in the first days of their full-scale invasion. Three and a half years later he's been released in a prisoner swap, one of eight civilians freed in a surprise move. While Russia and Ukraine have swapped military prisoners of war before, it is very rare for Russia to release Ukrainian civilians. Dmytro has been catching up frantically on all he's missed.

The world's fourth biggest company, Google-owner Alphabet, has announced a new £5bn ($6.8bn) investment in UK artificial intelligence (AI). The money will be used for infrastructure and scientific research over the next two years - the first of several massive US investments being unveiled ahead of US President Donald Trump's state visit. Google's President and Chief Investment Officer Ruth Porat told BBC News in an exclusive interview that there were profound opportunities in the UK for its pioneering work in advanced science. The company will officially open a vast $1bn (£735m) data centre in Waltham Cross, Hertfordshire, with Chancellor Rachel Reeves on Tuesday. The investment will expand this site and also include funding for London-based DeepMind, run by British Nobel Prize winner Sir Demis Hassabis, which deploys AI to revolutionise advanced scientific research.

Is Chicago the violent crime capital of the US? How did India-US relations decline so fast? A Ukrainian drone attack killed two women in the village of Golovchino in Russia's Belgorod region, Russia's state TASS news agency reports. A man who was seriously injured in a Ukrainian drone attack in Russia's Belgorod region in April has died in hospital, TASS reports. TASS also reported that Russian forces shot down 82 Ukrainian drones in a 24-hour period.

This paper develops a general approach to characterize the long-time trajectory behavior of nonconvex gradient descent in generalized single-index models in the large aspect ratio regime. In this regime, we show that for each iteration the gradient descent iterate concentrates around a deterministic vector called the `Gaussian theoretical gradient descent', whose dynamics can be tracked by a state evolution system of two recursive equations for two scalars. Our concentration guarantees hold universally for a broad class of design matrices and remain valid over long time horizons until algorithmic convergence or divergence occurs. Moreover, our approach reveals that gradient descent iterates are in general approximately independent of the data and strongly incoherent with the feature vectors, a phenomenon previously known as the `implicit regularization' effect of gradient descent in specific models under Gaussian data. As an illustration of the utility of our general theory, we present two applications of different natures in the regression setting. In the first, we prove global convergence of nonconvex gradient descent with general independent initialization for a broad class of structured link functions, and establish universality of randomly initialized gradient descent in phase retrieval for large aspect ratios. In the second, we develop a data-free iterative algorithm for estimating state evolution parameters along the entire gradient descent trajectory, thereby providing a low-cost yet statistically valid tool for practical tasks such as hyperparameter tuning and runtime determination. As a by-product of our analysis, we show that in the large aspect ratio regime, the Gaussian theoretical gradient descent coincides with a recent line of dynamical mean-field theory for gradient descent over the constant-time horizon.

Generative learning has advanced network neuroscience, enabling tasks like graph super-resolution, temporal graph prediction, and multimodal brain graph fusion. However, current methods, mainly based on graph neural networks (GNNs), focus solely on structural and topological properties, neglecting cognitive traits. To address this, we introduce the first cognified generative model, CogGNN, which endows GNNs with cognitive capabilities (e.g., visual memory) to generate brain networks that preserve cognitive features. While broadly applicable, we present CogGNN, a specific variant designed to integrate visual input, a key factor in brain functions like pattern recognition and memory recall. As a proof of concept, we use our model to learn connectional brain templates (CBTs), population-level fingerprints from multi-view brain networks. Unlike prior work that overlooks cognitive properties, CogGNN generates CBTs that are both cognitively and structurally meaningful. Our contributions are: (i) a novel cognition-aware generative model with a visual-memory-based loss; (ii) a CBT-learning framework with a co-optimization strategy to yield well-centered, discriminative, cognitively enhanced templates. Extensive experiments show that CogGNN outperforms state-of-the-art methods, establishing a strong foundation for cognitively grounded brain network modeling.

Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a "default culture" that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI's default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 10 each from South Africa, Taiwan, and the United States, who had integrated AI into their teaching practice. These teachers' experiences informed the development of our three-level cultural distance framework. This work contributes the concept and framework of cultural distance, six illustrative instances spanning in low, mid, high distance levels with teachers' experiences and strategies for addressing them. Empirically, we offer implications to help AI designers, policymakers, and educators create more equitable and culturally responsive GenAI tools for education.

Recently, federated learning (FL) has emerged as a novel framework for distributed model training. In FL, the task publisher (TP) releases tasks, and local model owners (LMOs) use their local data to train models. Sometimes, FL suffers from the lack of training data, and thus workers are recruited for gathering data. To this end, this paper proposes an adaptive incentive mechanism from a service-oriented perspective, with the objective of maximizing the utilities of TP, LMOs and workers. Specifically, a Stackelberg game is theoretically established between the LMOs and TP, positioning TP as the leader and the LMOs as followers. An analytical Nash equilibrium solution is derived to maximize their utilities. The interaction between LMOs and workers is formulated by a multi-agent Markov decision process (MAMDP), with the optimal strategy identified via deep reinforcement learning (DRL). Additionally, an Adaptively Searching the Optimal Strategy Algorithm (ASOSA) is designed to stabilize the strategies of each participant and solve the coupling problems. Extensive numerical experiments are conducted to validate the efficacy of the proposed method.

We introduce AraHealthQA 2025, the Comprehensive Arabic Health Question Answering Shared Task, held in conjunction with ArabicNLP 2025 (co-located with EMNLP 2025). This shared task addresses the paucity of high-quality Arabic medical QA resources by offering two complementary tracks: MentalQA, focusing on Arabic mental health Q&A (e.g., anxiety, depression, stigma reduction), and MedArabiQ, covering broader medical domains such as internal medicine, pediatrics, and clinical decision making. Each track comprises multiple subtasks, evaluation datasets, and standardized metrics, facilitating fair benchmarking. The task was structured to promote modeling under realistic, multilingual, and culturally nuanced healthcare contexts. We outline the dataset creation, task design and evaluation framework, participation statistics, baseline systems, and summarize the overall outcomes. We conclude with reflections on the performance trends observed and prospects for future iterations in Arabic health QA.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Interconnected systems have become the backbone of modern societies. However, the very same critical role played by these systems brings significant challenges: Securing interconnected systems is not merely a technological necessity, but a cornerstone for safeguarding the economic, political, and social stability of countries. While these challenges are global, the Arab World presents a unique landscape that warrants a nuanced exploration of both commonalities and peculiarities within the broader context of securing interconnected systems (see Figure for a brief summary of these challenges). Interconnected systems, including cyber-physical systems, often combine computational and physical processes. They include critical infrastructure such as power grids, transportation networks, and healthcare systems, alongside commercial and industrial applications.

Fighter jets from the UK will join Nato allies in defending Polish airspace after last week's incursion of Russian drones, the defence secretary has confirmed. RAF Typhoon jets will fly air defence missions over Poland as part of the military alliance's mission to bolster the eastern flank. Other allies including Denmark, Germany and France are already taking part - a jet from the latter was scrambled earlier on Monday in response to another potential incursion by Russian drones. Nato said that alert was quickly over. Tensions have risen across Europe since Poland accused Russia of the incident, which saw 19 drones enter its territory.