The annual holiday commemorates the Soviet Union's victory over Nazi Germany during World War Two, a conflict the Kremlin has baselessly tried to draw parallels with since launching its invasion of Ukraine last year.

Artificial Intelligence is revolutionizing the way military INCE the early days of cyber space technology strides in enhancing its strategic capabilities. Today, we and government organizations operate. These advanced find ourselves at the precipice of a new technological technologies enable machines to learn and reason revolution: Artificial Intelligence (AI). As a strategic autonomously, with applications ranging from situational imperative for national security, AI presents unparalleled awareness to decision-making support. In particular, the opportunities for strengthening our defense capabilities, advent of Large Language Models (LLMs) has significantly similar to how space and cyberspace technology transformed impacted the field of natural language processing, providing our approach to warfare and reconnaissance.

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner. In this study, we demonstrate how to overcome this limitation by pairing traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning model performance related to dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases to power model selection and targeted generalization. Second, the gradient connection between machine learning task model and data model allows advanced, precise tolerancing of task model sensitivity to changes in the data generation. These drift forensics can be used to precisely specify the acceptable data environments in which a task model may be run. Third, drift optimization opens up the possibility to create drifts that can help the task model learn better faster, effectively optimizing the data generating process itself. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.

An apparent drone attack on the Kremlin this week has sparked fears of an escalation in Russia's brutal war in Ukraine. On Wednesday night, two remotely-operated devices flew towards the domed roof of the Kremlin before being shot down by Russian air defences, exploding but harming no one. After the incident, Moscow Mayor Sergey Sobyanin declared that flying drones by private citizens was now banned in Moscow. Russia said the United States masterminded the attack, claiming Ukraine carried it out. Washington and Kyiv have denied responsibility, insisting that Ukraine's war efforts are purely defensive.

Kyiv, Ukraine – What happened over the vermilion walls of the Kremlin early on Wednesday could have been a dream come true for many Ukrainians, who have been suffering at the hands of invading Russian troops for more than a year. What could showcase Ukraine's resilience better than a drone attack on Russian President Vladimir Putin's residence in the medieval fortress-turned government seat of power, a centuries-old symbol of Russia's imperial power that stretched from the Baltic to the Pacific? But analysts told Al Jazeera that the details of the attack, which Russia blamed on Washington and Kyiv without providing any evidence, remain unclear and unverified. Both the United States and Ukraine have denied those allegations while the European Union warned Moscow against using the apparent assault as reason to further escalate its brutal war. Around 2:30am on Wednesday [23:30 GMT on Tuesday], a small drone flying from the south crashed into the dome of the Senate Palace, an 18th-century building that serves as Putin's official workplace.

London – The humiliating spectacle for Russia of two drones flying over the walls of the Kremlin, its historic seat of power, has spawned conflicting theories about who did it and why -- but for Vladimir Putin the incident could yet prove useful politically. Although the drones were destroyed before causing serious damage, the incident highlighted the apparent vulnerability of central Moscow to enemy drones, and prompted angry commentators to query the efficacy of Russia's air defenses. Inside Russia, it helped reinforce the Kremlin-backed narrative that its war in Ukraine is an existential one for the Russian state and people. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Agile autonomous drones are becoming increasingly popular in research due to the challenges they represent in fields like control, state estimation, or perception at high speeds. When all algorithms are computed onboard the uav, the computational limitations make the task of agile and robust flight even more difficult. One of the most computationally expensive tasks in agile flight is the generation of optimal trajectories that tackles the problem of planning a minimum time trajectory for a quadrotor over a sequence of specified waypoints. When these trajectories must be updated online due to changes in the environment or uncertainties, this high computational cost can leverage to not reach the desired waypoints or even crash in cluttered environments. In this paper, a fast lightweight dynamic trajectory modification approach is presented to allow modifying computational heavy trajectories using Local Gaussian Modifiers (LGMs), when recalculating a trajectory is not possible due to the time of computation. Our approach was validated in simulation, being able to pass through a race circuit with dynamic gates with top speeds up to 16.0 m/s, and was also validated in real flight reaching speeds up to 4.0 m/s in a fully autonomous onboard computing condition.

Air raid sirens sounded in Ukraine's Kyiv after residents were subjected to drone attacks, spasms of gunfire and explosions during the fourth attack on the capital in as many days, according to officials. Officials said at least one drone was downed after anti-aircraft units went into action during the raid on Thursday evening, which began just after 8pm (17:00 GMT) and lasted about 20 minutes. Kyiv Mayor Vitali Klitschko said there had been two impacts from downed drones. "During the last air alert, an unmanned aerial vehicle was spotted over Kyiv. The object was shot down by air defence forces," Kyiv city military administration head Serhiy Popko said on Telegram.

Kyiv and Washington deny involvement in what Russia says was an assassination attempt on President Putin.

So, who fired two drones at the Kremlin in the wee hours of Wednesday morning, and what does the act say about the course of the war in Ukraine? The whole business is a mystery, and may remain so forever, except to those who did the deed. The two questions are, of course, related; the intended impact on the war depends on who launched the weapons. But there might also be separate impacts, depending on who the combatants think fired the shots. So, it may be worth scurrying down the rabbit holes of straight or madcap logic to examine the possibilities.