United States President Joe Biden has reportedly lifted restrictions on Kyiv on the use of long-range missiles, which means Ukrainian forces may fire American-made missiles inside Russian territory for the first time. The move, which comes weeks before Biden leaves office and hours after massive Russian missile and drone attacks, has angered the Kremlin, which accused Washington of "throwing oil on the fire". Kremlin spokesman Dmitry Peskov said the decision would mean Washington's direct involvement in the conflict, echoing a similar sentiment expressed by President Vladimir Putin in September. The White House and President-elect Donald Trump have not commented yet, but Trump's eldest son, Donald Trump Jr, said: "The military industrial complex seems to want to make sure they get World War III going before my father has a chance to create peace and save lives." The elder Trump, who takes office on January 20, repeatedly pledged during his campaign to negotiate an end to the Ukraine war.

Russia has launched a huge strike on Ukraine's energy facilities and military infrastructure, Russian news agencies report, citing the Ministry of Defence. Ukrainian President Volodymyr Zelenskyy on Sunday said Russian forces launched about 120 missiles and 90 drones in a "massive" combined air attack – one of the largest barrages of the near-three-year war. In a message on his Telegram channel, Zelenskyy added that Ukrainian defence forces shot down more than 140 Russian projectiles. A Russian drone attack on the southern Ukrainian city of Mykolaiv killed at least two people and wounded six others, including children, Zelenskyy said, adding that "all areas" were left without power. Explosions were heard across Ukraine on Sunday, including the capital, Kyiv, the key southern port of Odesa, and the country's west and central regions, according to local reports.

This work investigates the self-organization of multi-agent systems into closed trajectories, a common requirement in unmanned aerial vehicle (UAV) surveillance tasks. In such scenarios, smooth, unbiased control signals save energy and mitigate mechanical strain. We propose a decentralized control system architecture that produces a globally stable emergent structure from local observations only; there is no requirement for agents to share a global plan or follow prescribed trajectories. Central to our approach is the formulation of an injective virtual embedding induced by rotations from the actual agent positions. This embedding serves as a structure-preserving map around which all agent stabilize their relative positions and permits the use of well-established linear control techniques. We construct the embedding such that it is topologically equivalent to the desired trajectory (i.e., a homeomorphism), thereby preserving the stability characteristics. We demonstrate the versatility of this approach through implementation on a swarm of Quanser QDrone quadcopters. Results demonstrate the quadcopters self-organize into the desired trajectory while maintaining even separation.

The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth. To overcome this issue, spectrum-sharing or opportunistic spectrum utilization by secondary users stands out as a promising solution. However, both approaches must minimize interference to primary users. Therefore, spectrum sensing becomes vital for such opportunistic usage, ensuring the proper operation of the primary users. Although this problem has been investigated for 2D networks, unmanned aerial vehicle (UAV) networks need different points of view concerning 3D space, its challenges, and opportunities. For this purpose, we propose a federated learning (FL)-based method for spectrum sensing in UAV networks to account for their distributed nature and limited computational capacity. FL enables local training without sharing raw data while guaranteeing the privacy of local users,lowering communication overhead, and increasing data diversity. Furthermore, we develop a federated aggregation method, namely FedSNR, that considers the signal-to-noise ratio observed by UAVs to acquire a global model. The numerical results show that the proposed architecture and the aggregation method outperform traditional methods.

The first time that Terry Vandenbos watched a bear run from a drone, on a spring day two years ago, he was chasing the animal himself. After he saw the grizzly cross a road near his property, the Montana rancher hopped on his all-terrain vehicle, planning to scare it away from his cattle if needed. But the bear began sprinting away when he was still far from it, looking over its shoulder as it ran, and Vandenbos looked up too; overhead, a small drone was following the bear, its four propellers emitting a high-pitched whine as it sent the animal towards a nearby lake. "I don't think I need to be here," Vandenbos remembers thinking. The bear never touched his cows.

In perhaps the most adorable hacker story of the year, a trio of technologists in India found an innovative way to circumvent Apple's location restrictions on AirPod Pro 2s so they could enable the earbuds' hearing aid feature for their grandmas. The hack involved a homemade Faraday cage, a microwave, and a lot of trial and error. On the other end of the tech-advancements spectrum, the US military is currently testing an AI-enabled machine gun that is capable of auto-targeting swarms of drones. The Bullfrog, built by Allen Control Systems, is one of several advanced weapons technologies in the works to combat the growing threat of cheap, small drones on the battlefield. The US Department of Justice announced this week that an 18-year-old from California has admitted to making or orchestrating more than 375 swatting attacks across the United States.

A laser beam downs drones by heating up and "frying" their electronics – each invisible, soundless "shot" is more precise and less costly than an air defence missile. Hanwha Aerospace, a South Korean defence company, has fire-tested and is about to start mass-producing the world's first-ever optical fibre laser weapon. And Hanwha is ready to supply it to Ukraine if Seoul lifts a ban on the export of lethal weapons to Kyiv "in light of North Korean military activities", South Korean President Yoon Suk Yeol said in late October. His statement followed North Korea's deployment of about 10,000 soldiers to western Russia, becoming the first foreign power to step into the Russia-Ukrainian war. However, the US president-elect may stand in the way.

North Korean leader Kim Jong Un has called for accelerating the mass production of attack drones, according to state media, as international concerns mount over the country's deepening military cooperation with Russia. The official Korean Central News Agency (KCNA) reported on Friday that Kim supervised the latest tests of "various types of suicide attack drones" produced by Pyongyang's Unmanned Aerial Technology Complex. The unmanned drones can hit land and sea targets, effectively acting as guided missiles. Kim "underscored the need to build a serial production system as early as possible and go into full-scale mass production", noting how drones are becoming crucial in modern warfare as he oversaw the tests on Thursday, KCNA said. North Korea first unveiled its suicide drones in August and military experts said the capability could be attributed to the country's growing alliance with Russia, with both sides signing a mutual defence pact.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

Uncrewed aerial systems have tightly coupled energy and motion dynamics which must be accounted for by onboard planning algorithms. This work proposes a strategy for coupled motion and energy planning using model predictive control (MPC). A reduced-order linear time-invariant model of coupled energy and motion dynamics is presented. Constrained zonotopes are used to represent state and input constraints, and hybrid zonotopes are used to represent non-convex constraints tied to a map of the environment. The structures of these constraint representations are exploited within a mixed-integer quadratic program solver tailored to MPC motion planning problems. Results apply the proposed methodology to coupled motion and energy utilization planning problems for 1) a hybrid-electric vehicle that must restrict engine usage when flying over regions with noise restrictions, and 2) an electric package delivery drone that must track waysets with both position and battery state of charge requirements. By leveraging the structure-exploiting solver, the proposed mixed-integer MPC formulations can be implemented in real time.