Rep. Pat Harrigan is leading a bill to phase out Chinese-manufactured drones from U.S. law enforcement, citing national security concerns tied to the Chinese Communist Party.

Is the war entering a new phase? At least five people have been killed in a Ukrainian attack on Russia and the Crimean peninsula as Kyiv steps up strikes, according to the Russian-appointed governor in the annexed region. Crimea Governor Sergey Aksyonov said two people, including a child, were killed and two others wounded following "overnight enemy attacks" into Thursday. Russia's defence ministry said 269 Ukrainian drones were downed over Russia and Crimea overnight. The head of the Krasnoarmeysk district in Krasnodar Krai said debris from a drone strike triggered an oil depot fire.

Is the war entering a new phase? A multi-storey apartment has been hit with what Russian-installed authorities called a Ukrainian drone strike in the Donetsk region. The area is in a Russian-controlled part of Ukraine. European heatwave, scorching weather triggers UK'red' warning Rubio: US'completely aligned' with Gulf allies on Iran

Is the war entering a new phase? Ukrainian strikes on Russian-occupied Crimea have triggered power outages in its largest city, Sevastopol, according to statements from both sides, as Kyiv intensifies attacks on the peninsula Moscow annexed in 2014. Crimea has been forced to suspend fuel sales to the public as Ukraine's army targets Russian logistics to the region and has hit a series of oil refineries and depots across southern Russia that provide supplies. "The enemy is once again striking treacherously, attempting to deprive us of normal living conditions and sow panic," he posted. He said some areas of the city - where temperatures are approaching 30 degrees Celsius (86 degrees Fahrenheit) - would be without power until at least Wednesday evening.

Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets on which the methods are trained are based on passive cameras, restricting their ability to fully sense the environment. Recently, embodied navigation methods have shown significant potential in precise object detection in interactive scenes. These methods incorporate active camera settings, holding promise in addressing the fundamental issues in crowd counting.

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Unmanned Aerial Vehicle (UAV) object detection faces significant challenges due to complex environmental conditions and different imaging conditions. These factors introduce significant changes in scale and appearance, particularly for small objects that occupy limited pixels and exhibit limited information, complicating detection tasks. To address these challenges, we propose a Multimodel Causal Reasoning framework based on YOLO backbone for UAVObject Detection (MCR-UOD). The key idea is to use the backdoor adjustment to discover the condition-invariant object representation for easy detection. Specifically, the YOLO backbone is first adjusted to incorporate the pre-trained vision-language model.

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While unmanned aerial vehicles (UAVs) offer wide-area, high-altitude coverage for anomaly detection, they face challenges such as dynamic viewpoints, scale variations, and complex scenes. Existing datasets and methods, mainly designed for fixed ground-level views, struggle to adapt to these conditions, leading to significant performance drops in drone-view scenarios. To bridge this gap, we introduce A2Seek (Aerial Anomaly Seek), a large-scale, reasoning-centric benchmark dataset for aerial anomaly understanding. This dataset covers various scenarios and environmental conditions, providing high-resolution real-world aerial videos with detailed annotations, including anomaly categories, frame-level timestamps, region-level bounding boxes, and natural language explanations for causal reasoning. Building on this dataset, we propose A2Seek-R1, a novel reasoning framework that generalizes R1-style strategies to aerial anomaly understanding, enabling a deeper understanding of "Where" anomalies occur and "Why" they happen in aerial frames.

Neuromorphic computing systems are set to revolutionize energy-constrained robotics by achieving orders-of-magnitude efficiency gains, while enabling native temporal processing. Spiking Neural Networks (SNNs) represent a promising algorithmic approach for these systems, yet their application to complex control tasks faces two critical challenges: (1) the non-differentiable nature of spiking neurons necessitates surrogate gradients with unclear optimization properties, and (2) the stateful dynamics of SNNs require training on sequences, which in reinforcement learning (RL) is hindered by limited sequence lengths during early training, preventing the network from bridging its warm-up period. We address these challenges by systematically analyzing surrogate gradient slope settings, showing that shallower slopes increase gradient magnitude in deeper layers but reduce alignment with true gradients. In supervised learning, we find no clear preference for fixed or scheduled slopes. The effect is much more pronounced in RL settings, where shallower slopes or scheduled slopes lead to a 2.1 improvement in both training and final deployed performance. Next, we propose a novel training approach that leverages a privileged guiding policy to bootstrap the learning process, while still exploiting online environment interactions with the spiking policy. Combining our method with an adaptive slope schedule for a real-world drone position control task, we achieve an average return of 400 points, substantially outperforming prior techniques, including Behavioral Cloning and TD3BC, which achieve at most -200 points under the same conditions. This work advances both the theoretical understanding of surrogate gradient learning in SNNs and practical training methodologies for neuromorphic controllers demonstrated in real-world robotic systems.