At least seven people were killed in Russian strikes across Ukraine overnight, including five in the central city of Dnipro, where officials said an apartment building was hit. Ukrainian President Volodymyr Zelensky said the latest attack lasted practically all night, while rescue workers were still searching for survivors under rubble in Dnipro on Saturday morning. British jets were scrambled from Romania during the heavy attack when Russian drones were detected near the border, though the UK Ministry of Defence rejected a report it had shot some down. Meanwhile, Ukraine carried out some of its longest-distance drone strikes deep inside Russian territory. In Yekaterinburg, almost 1,000 miles (1,600km) from Ukraine's border, the governor said six people were injured when a building was struck - while in nearby Chelyabinsk, a local leader said drones targeting an industrial facility were shot down.

One of the most effectivecontinuous deep reinforcement learning algorithms is normalized advantage functions (NAF).

On a commercial thoroughfare in old town Pasadena, California, a stone's throw from NASA's Jet Propulsion Laboratory (JPL), you'll find the Neon Retro Arcade. Among its collection of vintage video games is the 1979 Atari classic Asteroids, in which a pixelated spaceship shoots down a barrage of space rocks to stave off fatal collisions. After long days of work at JPL, Amy Mainzer used to rack up high scores on that console. "It was a hoot," she says. It was also apt, considering she oversees a space mission designed to spot dangerous asteroids before they crash into Earth. That mission, the Near-Earth Object (NEO) Surveyor, was conceived in the early 2000s and finally got the green light in 2022. Its components are now being built, tested, and assembled in clean rooms across the United States ahead of its planned launch in September 2027. "We're in the thick of building everything," says Mainzer, NEO Surveyor's principal investigator and now an astronomer at the University of California, Los Angeles (UCLA).

Hyabusa2 is already 105.5 million miles on its journey, but new data indicates trouble. Breakthroughs, discoveries, and DIY tips sent every weekday. Japan's Hyabusa2 space probe is currently about 105.5 million miles away, en route to its second asteroid rendezvous . However, revised data collected from a global network of observatories now indicates that the space rock designated as 1998 KY26 will look and behave far differently than astronomers previously theorized--and it may prove disastrous for the tiny explorer. In 2010, the Japanese Aerospace Exploration Agency (JAXA) made history when its Hayabusa probe became the first spacecraft to not only land on and launch from an asteroid (Itokawa), but successfully return to Earth with samples .

Ukraine's European allies marshalled resources this week to provide the besieged country with air defences against drones and ballistic missiles. The European Union also announced an 18th round of sanctions designed to sever all remaining Russian energy imports, and proposed a fivefold increase in the common defence budget to boost EU defence research and procurement. European leaders convinced the United States to symbolically rejoin the 52-nation Ukraine Defence Contact Group coordinating defence donations, but not as a donor. It was the first such meeting attended by US Defense Secretary Pete Hegseth since February, when he told EU members that pushing Russia out of Ukraine's internationally recognised territory was unrealistic. As the ideological chasm between the EU and the US over Ukraine widened, Russia continued to pound Ukrainian defenders, making a few inroads.

This article explores the use of artificial intelligence for the diagnosis of pathologies of the temporomandibular joint (TMJ), in particular, for the segmentation of the articular disc on MRI images. The relevance of the work is due to the high prevalence of TMJ pathologies, as well as the need to improve the accuracy and speed of diagnosis in medical institutions. During the study, the existing solutions (Diagnocat, MandSeg) were analyzed, which, as a result, are not suitable for studying the articular disc due to the orientation towards bone structures. To solve the problem, an original dataset was collected from 94 images with the classes "temporomandibular joint" and "jaw". To increase the amount of data, augmentation methods were used. After that, the models of U-Net, YOLOv8n, YOLOv11n and Roboflow neural networks were trained and compared. The evaluation was carried out according to the Dice Score, Precision, Sensitivity, Specificity, and Mean Average Precision metrics. The results confirm the potential of using the Roboflow model for segmentation of the temporomandibular joint. In the future, it is planned to develop an algorithm for measuring the distance between the jaws and determining the position of the articular disc, which will improve the diagnosis of TMJ pathologies.

--Classifying potentially hazardous asteroids (PHAs) is crucial for planetary defense and deep space navigation, yet traditional methods often overlook the dynamical relationships among asteroids. We introduce a Graph Neural Network (GNN) approach that models asteroids as nodes with orbital and physical features, connected by edges representing their similarities, using a NASA dataset of 958,524 records. Despite an extreme class imbalance with only 0.22% of the dataset with hazardous label, our model achieves an overall accuracy of 99% and an AUC of 0.99, with a recall of 78% and an F1-score of 37% for hazardous asteroids after applying Synthetic Minority Oversampling T echnique. Feature importance analysis highlights albedo, perihelion distance, and semi-major axis as main predictors. This framework supports planetary defense missions and confirm AI's potential in enabling autonomous navigation for future missions such as NASA's NEO Surveyor and ESA's Ramses, offering an interpretable and scalable solution for asteroid hazard assessment. However, a small subset known as potentially hazardous asteroids (PHAs) follow orbits that bring them perilously close to our planet, raising the specter of catastrophic collisions. Historical events, such as the 1908 Tunguska explosion [1], which devastated over 2,000 square kilometers of Siberian forest, and the 2013 Chelyabinsk meteor [2], which injured over 1,000 people and caused widespread property damage, show the destructive potential of these celestial bodies.

The potential for catastrophic collision makes near-Earth asteroids (NEAs) a serious concern. Planetary defense depends on accurately classifying potentially hazardous asteroids (PHAs), however the complexity of the data hampers conventional techniques. This work offers a sophisticated method for accurately predicting hazards by combining machine learning, deep learning, explainable AI (XAI), and anomaly detection. Our approach extracts essential parameters like size, velocity, and trajectory from historical and real-time asteroid data. A hybrid algorithm improves prediction accuracy by combining several cutting-edge models. A forecasting module predicts future asteroid behavior, and Monte Carlo simulations evaluate the likelihood of collisions. Timely mitigation is made possible by a real-time alarm system that notifies worldwide monitoring stations. This technique enhances planetary defense efforts by combining real-time alarms with sophisticated predictive modeling.

Keyphrase selection is a challenging task in natural language processing that has a wide range of applications. Adapting existing supervised and unsupervised solutions for the Russian language faces several limitations due to the rich morphology of Russian and the limited number of training datasets available. Recent studies conducted on English texts show that large language models (LLMs) successfully address the task of generating keyphrases. LLMs allow achieving impressive results without task-specific fine-tuning, using text prompts instead. In this work, we access the performance of prompt-based methods for generating keyphrases for Russian scientific abstracts. First, we compare the performance of zero-shot and few-shot prompt-based methods, fine-tuned models, and unsupervised methods. Then we assess strategies for selecting keyphrase examples in a few-shot setting. We present the outcomes of human evaluation of the generated keyphrases and analyze the strengths and weaknesses of the models through expert assessment. Our results suggest that prompt-based methods can outperform common baselines even using simple text prompts.

The rapid increase in textual information means we need more efficient methods to sift through, organize, and understand it all. While retrieval-augmented generation (RAG) models excel in accessing information from large document collections, they struggle with complex tasks that require aggregation and reasoning over information spanning across multiple documents--what we call holistic reasoning. Long-context language models (LCLMs) have great potential for managing large-scale documents, but their holistic reasoning capabilities remain unclear. In this work, we introduce HoloBench, a novel framework that brings database reasoning operations into text-based contexts, making it easier to systematically evaluate how LCLMs handle holistic reasoning across large documents. Our approach adjusts key factors such as context length, information density, distribution of information, and query complexity to evaluate LCLMs comprehensively. Our experiments show that the amount of information in the context has a bigger influence on LCLM performance than the actual context length. Furthermore, the complexity of queries affects performance more than the amount of information, particularly for different types of queries. Interestingly, queries that involve finding maximum or minimum values are easier for LCLMs and are less affected by context length, even though they pose challenges for RAG systems. However, tasks requiring the aggregation of multiple pieces of information show a noticeable drop in accuracy as context length increases. Additionally, we find that while grouping relevant information generally improves performance, the optimal positioning varies across models. Our findings surface both the advancements and the ongoing challenges in achieving a holistic understanding of long contexts.