Drone attack kills eight children in Haiti's capital NewsFeed Drone attack kills eight children in Haiti's capital At least 11 people, including eight children, were reported killed in a drone attack in a gang-controlled area of Haiti's capital Port-au-Prince. Relatives of the victims said police were targeting a suspected gang leader. Video: Parents carry children's bodies after Israeli attack on Gaza City Protesters outside UN say recognising Palestine isn't enough

Russia's involvement in drone sightings cannot be ruled out, Danish PM says The drone incursion that stopped flights at Copenhagen airport on Monday night was the most severe attack on Danish infrastructure so far, Denmark's Prime Minister Mette Frederiksen said. Kastrup airport in Copenhagen was forced to shut for several hours from around 20:30 (18:30 GMT) on Monday following the sighting of a number of drones. It says something about the times we live in and what we as a society must be prepared to deal with, Frederiksen told reporters. Russian involvement could not be ruled out, Frederiksen added - although Kremlin spokesman Dmitry Peskov called the allegations unfounded. The Danish PM made a link between last night's events in Denmark and the recent Russian drone incursions in Poland and Romania, as well as the violation of Estonian airspace by Russian fighter jets .

Why is Israel still in southern Lebanon? A war to shape Lebanon's future Lebanon is pushing to get more support from the United States after another deadly Israeli drone attack on southern Lebanon, which this time killed five people, including three children, the latest in a series of near-daily violations by Israel of the US-brokered November 2024 ceasefire. President Joseph Aoun and other officials met with a delegation led by US Secretary of State Marco Rubio, the Lebanese presidency said in a statement on Tuesday. The Lebanese president said he wants Israel to stop occupying parts of his country, is looking to gear its army with "equipment and supplies" from the US, and needs Washington's support to hold a conference dedicated to reconstruction in Lebanon. Amid ongoing efforts to disarm Hezbollah, Aoun emphasised that the Lebanese army's mandate includes "all Lebanese regions" as the country tries to seize an opportunity "to achieve just, comprehensive, and lasting peace in the Middle East region". He is also scheduled to address the United Nations General Assembly on Tuesday, where he is expected to denounce Israeli attacks across the region, including in Gaza and Lebanon.

A deadly drone attack in an impoverished area of Haiti's capital, Port-au-Prince, which killed at least 11 people, including eight children, is being blamed on the government, as the country's use of the UAVs in its war on gangs comes under increasing scrutiny. The incident happened on Saturday night in Cite Soleil, one of Port-au-Prince's most dangerous neighbourhoods, in the city's west along the coast, as Albert Steevenson, known as Djouma or "King Jouma", who is a suspected gang leader, was celebrating his birthday. One of the group's leaders and most notorious figures, Jimmy Cherizier, known as Barbecue, promised to avenge the attack. Claudia Bobrun, 30, whose daughter was killed in the attack, showed The Associated Press news agency a video of the eight-year-old in a pool of blood, as she burst into tears. Merika, another four-year-old victim of the attack, was playing with other children at 8pm in the Simon Pele neighbourhood, in Cite Soleil, where the suspected kamikaze drone exploded.

The deployment of unmanned aerial vehicles (UAVs) for reliable and energy-efficient data collection from spatially distributed devices holds great promise in supporting diverse Internet of Things (IoT) applications. Nevertheless, the limited endurance and communication range of UAVs necessitate intelligent trajectory planning. While reinforcement learning (RL) has been extensively explored for UAV trajectory optimization, its interactive nature entails high costs and risks in real-world environments. Offline RL mitigates these issues but remains susceptible to unstable training and heavily rely on expert-quality datasets. To address these challenges, we formulate a joint UAV trajectory planning and resource allocation problem to maximize energy efficiency of data collection. The resource allocation subproblem is first transformed into an equivalent linear programming formulation and solved optimally with polynomial-time complexity. Then, we propose a large language model (LLM)-empowered critic-regularized decision transformer (DT) framework, termed LLM-CRDT, to learn effective UAV control policies. In LLM-CRDT, we incorporate critic networks to regularize the DT model training, thereby integrating the sequence modeling capabilities of DT with critic-based value guidance to enable learning effective policies from suboptimal datasets. Furthermore, to mitigate the data-hungry nature of transformer models, we employ a pre-trained LLM as the transformer backbone of the DT model and adopt a parameter-efficient fine-tuning strategy, i.e., LoRA, enabling rapid adaptation to UAV control tasks with small-scale dataset and low computational overhead. Extensive simulations demonstrate that LLM-CRDT outperforms benchmark online and offline RL methods, achieving up to 36.7\% higher energy efficiency than the current state-of-the-art DT approaches.

Decentralized combinatorial optimization in evolving multi-agent systems poses significant challenges, requiring agents to balance long-term decision-making, short-term optimized collective outcomes, while preserving autonomy of interactive agents under unanticipated changes. Reinforcement learning offers a way to model sequential decision-making through dynamic programming to anticipate future environmental changes. However, applying multi-agent reinforcement learning (MARL) to decentralized combinatorial optimization problems remains an open challenge due to the exponential growth of the joint state-action space, high communication overhead, and privacy concerns in centralized training. To address these limitations, this paper proposes Hierarchical Reinforcement and Collective Learning (HRCL), a novel approach that leverages both MARL and decentralized collective learning based on a hierarchical framework. Agents take high-level strategies using MARL to group possible plans for action space reduction and constrain the agent behavior for Pareto optimality. Meanwhile, the low-level collective learning layer ensures efficient and decentralized coordinated decisions among agents with minimal communication. Extensive experiments in a synthetic scenario and real-world smart city application models, including energy self-management and drone swarm sensing, demonstrate that HRCL significantly improves performance, scalability, and adaptability compared to the standalone MARL and collective learning approaches, achieving a win-win synthesis solution.

Abstract-- This paper proposes an image-based visual ser-voing (IBVS) framework for UA V navigation and collision avoidance using only an RGB camera. While UA V navigation has been extensively studied, it remains challenging to apply IBVS in missions involving multiple visual targets and collision avoidance. The proposed method achieves navigation without explicit path planning, and collision avoidance is realized through AI-based monocular depth estimation from RGB images. Unlike approaches that rely on stereo cameras or external workstations, our framework runs fully onboard a Jetson platform, ensuring a self-contained and deployable system. Experimental results validate that the UA V can navigate across multiple AprilT ags and avoid obstacles effectively in GPS-denied environments. I. INTRODUCTION Most UA V applications depend on position estimation provided by global positioning systems (GPS). However, GPS is often unavailable in indoor, mountainous, or forest environments, motivating the use of computer vision for UA V navigation. This paper focuses on image-based visual servoing (IBVS) with an onboard RGB camera.

The growing threat of low-cost kamikaze drone swarms poses a critical challenge to modern defense systems demanding rapid and strategic decision-making to prioritize interceptions across multiple effectors and high-value target zones. In this work, we present a case study demonstrating the practical advantages of reinforcement learning in addressing this challenge. We introduce a high-fidelity simulation environment that captures realistic operational constraints, within which a decision-level reinforcement learning agent learns to coordinate multiple effectors for optimal interception prioritization. Operating in a discrete action space, the agent selects which drone to engage per effector based on observed state features such as positions, classes, and effector status. We evaluate the learned policy against a handcrafted rule-based baseline across hundreds of simulated attack scenarios. The reinforcement learning based policy consistently achieves lower average damage and higher defensive efficiency in protecting critical zones. This case study highlights the potential of reinforcement learning as a strategic layer within defense architectures, enhancing resilience without displacing existing control systems. All code and simulation assets are publicly released for full reproducibility, and a video demonstration illustrates the policy's qualitative behavior.

Defense tech giant Palantir is selling T-shirts and tote bags as part of a bid to encourage fans to publicly endorse it. Palantir Technologies, which moved from Silicon Valley to Denver in 2020, sells software that immigration authorities use to identify and arrest people, militaries use to organize drone strikes, and corporations use to manage their supply chains. Now, it also sells tote bags. Last year, Palantir re launched an online merchandise store, and its website was recently redesigned with a swanky interface and new payment system . A mock terminal in the lower left corner displays "code" documenting each item you view.

Things to Do in L.A. Tap to enable a layout that focuses on the article. An unmanned Predator drone flies over Kandahar Air Field in southern Afghanistan in 2010. This is read by an automated voice. Please report any issues or inconsistencies here . MQ-9 Predator drones were deployed over Los Angeles to monitor anti-ICE protests in June.