One civilian was killed and three more were reportedly injured in one of the biggest Ukrainian drone attacks on Moscow in months. Moscow's Mayor Sergei Sobyanin said Russian air defence units destroyed at least 69 drones flying towards Moscow in a "massive" attack that later reports said involved more than 90 drones. Four airports in the Moscow region and the Domodedovo train network were forced to suspend services due to the attack. Several apartments were also damaged while Russia's TASS news agency reported a large fire in a car park near the Russian capital. Pro-Russian war bloggers said Kremlin forces have advanced further into the country's Kursk region as part of a major encirclement operation to push out thousands of Ukrainian soldiers holding territory inside Russia.

Ukraine has launched a "massive" early morning drone attack against the Russian capital that killed at least one person, injured several others and saw the shutdown of airports and damaged residential buildings, Moscow officials and aviation authorities said. The drone raid, the largest against Moscow in months, comes as Ukraine is poised to present the United States with a plan for a partial ceasefire with Russia during talks on Tuesday in Saudi Arabia. Andrei Vorobyov, governor of the Moscow region, said that one person was killed and three more wounded as a result of the raid, which began at 4am local time (01:00 GMT). The wave of attack drones damaged seven apartments in a residential building in the Moscow region's Ramenskoye district, Vorobyov said. Russia's Ministry of Defence said that air defences destroyed a total of 337 Ukrainian drones overnight, with 91 of them over the Moscow region.

At least one person has been killed and three injured in a "massive" overnight drone attack on Moscow and the capital region, local officials say. Regional Governor Andrei Vorobyev says the casualties were in the towns of Vidnoye and Domodedovo, just outside the capital. Seven apartments in a residential building were damaged. Moscow Mayor Sergei Sobyanin says 73 drones heading towards the city were shot down. The roof of one building was damaged by drone wreckage.

A sprawling Russian disinformation network is manipulating Western AI chatbots to spew pro-Kremlin propaganda, researchers say, at a time when the United States is reported to have paused its cyber operations against Moscow. The Pravda network, a well-resourced Moscow-based operation to spread pro-Russian narratives globally, is said to be distorting the output of chatbots by flooding large language models (LLM) with pro-Kremlin falsehoods. A study of 10 leading AI chatbots by the disinformation watchdog NewsGuard found that they repeated falsehoods from the Pravda network more than 33% of the time, advancing a pro-Moscow agenda.

Prescribed-time reach-avoid-stay (PT-RAS) specifications are critical in applications that involve guiding a system to reach a desired state within a specified time, avoiding unsafe regions, and respecting state constraints [1]. PT-RAS tasks also serve as fundamental building blocks in the design of complex specifications [2, 3] for autonomous systems involving temporal and spatial constraints. Effective design control strategies ensuring PT-RAS task is crucial in applications like robotics, autonomous vehicles, and aerospace to ensure reliability and safety with precise timing. Several control techniques have been proposed in the literature to address these specifications, including model predictive control (MPC) [4] and potential field methods [5, 6]. While these approaches can handle time-bound tasks and obstacle avoidance, they often suffer from difficulty in ensuring safety guarantees over the entire mission duration. These limitations highlight the need for more efficient and reliable methods that can provide formal safety guarantees. Symbolic control techniques [7] have emerged as powerful tools for specifying and solving complex tasks. However, these techniques typically rely on state space abstraction, which can lead to increased computational complexity.

We propose a Coefficient-to-Basis Network (C2BNet), a novel framework for solving inverse problems within the operator learning paradigm. C2BNet efficiently adapts to different discretizations through fine-tuning, using a pre-trained model to significantly reduce computational cost while maintaining high accuracy. Unlike traditional approaches that require retraining from scratch for new discretizations, our method enables seamless adaptation without sacrificing predictive performance. Furthermore, we establish theoretical approximation and generalization error bounds for C2BNet by exploiting low-dimensional structures in the underlying datasets. Our analysis demonstrates that C2BNet adapts to low-dimensional structures without relying on explicit encoding mechanisms, highlighting its robustness and efficiency. To validate our theoretical findings, we conducted extensive numerical experiments that showcase the superior performance of C2BNet on several inverse problems. The results confirm that C2BNet effectively balances computational efficiency and accuracy, making it a promising tool to solve inverse problems in scientific computing and engineering applications.

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weather conditions, and incident-specific factors. By adapting machine learning models, we identify key risk factors influencing fire occurrence and consequences. Our findings show that vulnerable communities, characterized by socioeconomic disparities or the prevalence of outdated or vacant buildings, face higher fire risks. Incident-specific factors, such as fire origins and safety features, strongly influence fire consequences. Buildings equipped with fire detectors and automatic extinguishing systems experience significantly lower fire spread and injury risks. By pinpointing high-risk areas and populations, this research supports targeted interventions, including mandating fire safety systems and providing subsidies for disadvantaged communities. These measures can enhance fire prevention, protect vulnerable groups, and promote safer, more equitable communities.

The constant increase of devices connected to the Internet, and therefore of cyber-attacks, makes it necessary to analyze network traffic in order to recognize malicious activity. Traditional packet-based analysis methods are insufficient because in large networks the amount of traffic is so high that it is unfeasible to review all communications. For this reason, flows is a suitable approach for this situation, which in future 5G networks will have to be used, as the number of packets will increase dramatically. If this is also combined with unsupervised learning models, it can detect new threats for which it has not been trained. This paper presents a systematic review of the literature on unsupervised learning algorithms for detecting anomalies in network flows, following the PRISMA guideline. A total of 63 scientific articles have been reviewed, analyzing 13 of them in depth. The results obtained show that autoencoder is the most used option, followed by SVM, ALAD, or SOM. On the other hand, all the datasets used for anomaly detection have been collected, including some specialised in IoT or with real data collected from honeypots.

In today's media landscape, propaganda distribution has a significant impact on society. It sows confusion, undermines democratic processes, and leads to increasingly difficult decision-making for news readers. We investigate the lasting effect on critical thinking and propaganda awareness on them when using a propaganda detection and contextualization tool. Building on inoculation theory, which suggests that preemptively exposing individuals to weakened forms of propaganda can improve their resilience against it, we integrate Kahneman's dual-system theory to measure the tools' impact on critical thinking. Through a two-phase online experiment, we measure the effect of several inoculation doses. Our findings show that while the tool increases critical thinking during its use, this increase vanishes without access to the tool. This indicates a single use of the tool does not create a lasting impact. We discuss the implications and propose possible approaches to improve the resilience against propaganda in the long-term.

Autonomous vehicles (AVs) are transforming modern transportation, but their reliability and safety are significantly challenged by harsh weather conditions such as heavy rain, fog, and snow. These environmental factors impair the performance of cameras, LiDAR, and radar, leading to reduced situational awareness and increased accident risks. Conventional cloud-based AI systems introduce communication delays, making them unsuitable for the rapid decision-making required in real-time autonomous navigation. This paper presents a novel Edge AI-driven real-time decision-making framework designed to enhance AV responsiveness under adverse weather conditions. The proposed approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for improved perception, alongside reinforcement learning (RL)-based strategies to optimize vehicle control in uncertain environments. By processing data at the network edge, this system significantly reduces decision latency while improving AV adaptability. The framework is evaluated using simulated driving scenarios in CARLA and real-world data from the Waymo Open Dataset, covering diverse weather conditions. Experimental results indicate that the proposed model achieves a 40% reduction in processing time and a 25% enhancement in perception accuracy compared to conventional cloud-based systems.