Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian forces launched 645 attacks on Ukraine's Zaporizhia region in the past day, killing one person in the Polohivskyi district, Governor Ivan Fedorov wrote in a post on Telegram. A Russian drone attack on a railway facility killed a security guard in Ukraine's Kherson region, Governor Oleksandr Prokudin wrote in a post on Facebook.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Ukraine's top military commander, Oleksandr Syrskii, said the army's situation has "significantly worsened" in parts of the southeastern Zaporizhia region amid fierce fighting with Russian forces. Ukrainian President Volodymyr Zelenskyy said in a post on X that he had received an update from Syrskii, which conveyed that the situation" remains difficult" in the Zaporizhia region, as well as in the direction of the embattled city of Pokrovsk .

Obstacle avoidance and path planning are essential for guiding unmanned ground vehicles (UGVs) through environments that are densely populated with dynamic obstacles. This paper develops a novel approach that combines tangentbased path planning and extrapolation methods to create a new decision-making algorithm for local path planning. In the assumed scenario, a UGV has a prior knowledge of its initial and target points within the dynamic environment. A global path has already been computed, and the robot is provided with waypoints along this path. As the UGV travels between these waypoints, the algorithm aims to avoid collisions with dynamic obstacles. These obstacles follow polynomial trajectories, with their initial positions randomized in the local map and velocities randomized between O and the allowable physical velocity limit of the robot, along with some random accelerations. The developed algorithm is tested in several scenarios where many dynamic obstacles move randomly in the environment. Simulation results show the effectiveness of the proposed local path planning strategy by gradually generating a collision free path which allows the robot to navigate safely between initial and the target locations.

Urban environments are continuously mapped and modeled by various data collection platforms, including satellites, unmanned aerial vehicles and street cameras. The growing availability of 3D geospatial data from multiple modalities has introduced new opportunities and challenges for integrating spatial knowledge at scale, particularly in high-impact domains such as urban planning and rapid disaster management. Geospatial entity resolution is the task of identifying matching spatial objects across different datasets, often collected independently under varying conditions. Existing approaches typically rely on spatial proximity, textual metadata, or external identifiers to determine correspondence. While useful, these signals are often unavailable, unreliable, or misaligned, especially in cross-source scenarios. To address these limitations, we shift the focus to the intrinsic geometry of 3D spatial objects and present 3dSAGER (3D Spatial-Aware Geospatial Entity Resolution), an end-to-end pipeline for geospatial entity resolution over 3D objects. 3dSAGER introduces a novel, spatial-reference-independent featurization mechanism that captures intricate geometric characteristics of matching pairs, enabling robust comparison even across datasets with incompatible coordinate systems where traditional spatial methods fail. As a key component of 3dSAGER, we also propose a new lightweight and interpretable blocking method, BKAFI, that leverages a trained model to efficiently generate high-recall candidate sets. We validate 3dSAGER through extensive experiments on real-world urban datasets, demonstrating significant gains in both accuracy and efficiency over strong baselines. Our empirical study further dissects the contributions of each component, providing insights into their impact and the overall design choices.

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells are turned off due to NES or incidents, e.g., disasters, hardware failures, or outages. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, with a reduction of approximately 24\% compared to the conventional all GBS ON configuration, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5,000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Comprehensive analyses and experiments demonstrate the dataset's scale, diversity, annotation quality, and effectiveness for training video generation models. We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications. The project page is https://lixsp11.github.io/sekai-project/.

Are we closer to a Gaza international peace force? How Israel is using'no war, no peace' model in Gaza How is Israel using PR firms to frame its war? At least two people including a child have been killed in an Israeli drone strike east of Khan Younis in southern Gaza, according to Al Jazeera reporters in the besieged Palestinian territory. Hamas condemned Israel's "daily and continuous violations" since a truce came into effect last month, accusing it of maintaining a campaign of bombardments and demolitions across the besieged enclave. The Israeli military said the Palestinians killed on Monday posed "an immediate threat" to its forces. Israeli forces have also been systematically destroying homes inside the so-called "yellow line", a temporary withdrawal boundary agreed in the ceasefire.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Most regions of Ukraine are undergoing scheduled power outages amid a new wave of attacks on energy sites by Russian drones and missiles. Ukrenergo, the state-run electricity transmission systems operator in Ukraine, said the blackouts will last at least until the end of Monday as repairs are conducted on infrastructure damaged over the weekend and demand remains high as the onset of winter approaches. According to Ukraine's military, Russian forces used two air-launched ballistic missiles, five surface-to-air guided missiles and 67 drones, including those of Iranian design, during their attacks overnight into Monday. The Ukrainian army did not report shooting down any of the missiles, but it said 52 of the drones were intercepted and the remaining 15 conducted strikes on nine locations.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Is the fall of Pokrovsk, Ukraine's key eastern stronghold, inevitable? Pokrovsk, a key fortress and logistical hub for Ukrainian forces in the eastern region of Donbas, has been under siege for almost two years. But in recent weeks, tens of thousands of Russian soldiers have been storming the town around the clock, taking over the streets where buildings are mostly reduced to bombed-out, deserted ruins. They use reconnaissance drones and satellite images to identify gaps in Ukrainian defences and use tiny groups of soldiers who are attacked and killed in droves by Ukrainian drones .

To perform outdoor visual navigation and search, a robot may leverage satellite imagery to generate visual priors. This can help inform high-level search strategies, even when such images lack sufficient resolution for target recognition. However, many existing informative path planning or search-based approaches either assume no prior information, or use priors without accounting for how they were obtained. Recent work instead utilizes large Vision Language Models (VLMs) for generalizable priors, but their outputs can be inaccurate due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework with a flexible plug-and-play interface compatible with various input modalities (e.g., image, text, sound) and planning methods (e.g., RL-based). First, we pretrain a satellite image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our TTA framework dynamically refines CLIP's predictions during search using uncertainty-weighted gradient updates inspired by Spatial Poisson Point Processes. To train and evaluate Search-TTA, we curate AVS-Bench, a visual search dataset based on internet-scale ecological data containing 380k images and taxonomy data. We find that Search-TTA improves planner performance by up to 30.0%, particularly in cases with poor initial CLIP predictions due to domain mismatch and limited training data. It also performs comparably with significantly larger VLMs, and achieves zero-shot generalization via emergent alignment to unseen modalities. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.