Ukraine's Air Force claimed it shot down 93 of 141 drones Russia launched in attacks overnight. The Air Force also said that 47 of the drones were "lost" while two returned to Russia. Russia said it destroyed 31 Ukrainian drones which had primarily targeted industrial sites in Russia's Tatarstan region, located about 1,000km (about 600 miles) from the Ukrainian border. No victims or damage have been reported. The governor of Russia's Bryansk region, Alexander Bogomaz, said 14 Ukrainian drones were neutralised in the region, which borders Ukraine.

Russia's Ministry of Defence said the army gained control of the settlement of Shevchenko, near the logistical centre of Pokrovsk, a key target in its advance through Ukraine's eastern Donetsk region. Ukraine has yet to acknowledge the loss of the town. Ukraine's General Staff of the Armed Forces said it repelled 46 of 56 Russian attacks around a dozen towns in the Pokrovsk sector and several clashes were ongoing. A Ukrainian drone hit one of Russia's largest oil refineries – in Taneko, Tatarstan – according to Russian Telegram channel ASTRA. Fuel oil that spilled from wrecked Russian tankers has spread into the Sea of Azov and reached the shores of Ukraine's partly Russian-occupied Zaporizhia region, a Moscow-installed official said.

We present a novel gradient-free algorithm to solve a convex stochastic optimization problem, such as those encountered in medicine, physics, and machine learning (e.g., adversarial multi-armed bandit problem), where the objective function can only be computed through numerical simulation, either as the result of a real experiment or as feedback given by the function evaluations from an adversary. Thus we suppose that only a black-box access to the function values of the objective is available, possibly corrupted by adversarial noise: deterministic or stochastic. The noisy setup can arise naturally from modeling randomness within a simulation or by computer discretization, or when exact values of function are forbidden due to privacy issues, or when solving non-convex problems as convex ones with an inexact function oracle. By exploiting higher-order smoothness, fulfilled, e.g., in logistic regression, we improve the performance of zero-order methods developed under the assumption of classical smoothness (or having a Lipschitz gradient). The proposed algorithm enjoys optimal oracle complexity and is designed under an overparameterization setup, i.e., when the number of model parameters is much larger than the size of the training dataset. Overparametrized models fit to the training data perfectly while also having good generalization and outperforming underparameterized models on unseen data. We provide convergence guarantees for the proposed algorithm under both types of noise. Moreover, we estimate the maximum permissible adversarial noise level that maintains the desired accuracy in the Euclidean setup, and then we extend our results to a non-Euclidean setup. Our theoretical results are verified on the logistic regression problem.

Video captures the moment and aftermath of what appears to be a drone, allegedly of Ukrainian origin, striking Russian drone production facility. Russian officials claimed that only a worker's dormitory was hit. A Ukrainian "plane-type UAV" on Tuesday struck a Russian weapons plant that allegedly assembled drones, causing an incredible fireball after impact. "This morning, the republic's industrial enterprises in Yelabuga and Nizhnekamsk were attacked by drones," Rustam Minnikhanov, the leader of Russia's autonomous Republic of Tatarstan, said in a post on his Telegram channel. "There is no serious damage, the technological process of the enterprises was not disrupted," Minnikhanov added.

A suspected Ukrainian drone struck a dormitory in Russia's Tatarstan region – sending fireballs into the air and people scattering. At least 7 people were injured in the attack that struck an industrial part of town, where a factory making Iranian-designed drones is reportedly located.

Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model's weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.

Europe's first driverless taxi service has launched in Russia. The fleet of autonomous taxis are operated by Yandex – a Russian search engine that boasts many of the same services as Google, including self-driving vehicles, e-commerce, and a voice-activated AI assistant. Yandex has launched its trial project for the driverless fleet in the new town of Innopolis, which is being developed as a'high tech city' in the Republic of Tatarstan. The taxi service ferries passengers between five designated taxi ranks, including stops at the town's university, sports stadium and popular local apartment blocks. The service will free-of-charge to passengers during the trial and Yandex will have an engineer inside the cab at all times to ensure safety should the system fail.

In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.