A ship carrying aid to Gaza in a bid to break Israel's blockade has been hit by drones in international waters off Malta, according to the Freedom Flotilla Coalition (FFC), the group that organised the mission. The FFC said in a statement on Friday that the vessel, now located 14 nautical miles (25km) from Malta, was the target of two drone strikes while on its way to Gaza. The ship had been seeking to deliver aid to the besieged enclave, where aid groups warn people are struggling to survive following a two-month total blockade by Israel. "Armed drones attacked the front of an unarmed civilian vessel twice, causing a fire and a substantial breach in the hull," the group said. The statement did not directly accuse Israel of carrying out the attack.

The NGO appeared to accuse Israel of being behind the incident and called for Israeli ambassadors to be summoned to answer for "violation of international law, including the ongoing blockade and the bombing of our civilian vessel". The Israeli military said it was looking into reports of the attack. The Freedom Flotilla Coalition uploaded a video showing a fire on one of its ships but did not indicate whether anyone had been hurt. It said the attack appeared to have targeted the generator, which left the ship without power and at risk of sinking. The ship was 17 nautical miles (31.5 kilometres) east of Malta when it was hit.

Some European tech workers who might once have headed to the United States are looking at defense startups closer to home. Others are rushing back to Europe from jobs abroad. A sense of patriotism stirred by the war in Ukraine and U.S. President Donald Trump's upending of security alliances is a motivation for many, as well as the opportunity to make money as European governments boost military spending.

Vance made the comments in a wide-ranging interview, in which he defended Trump's approach to the war in Ukraine. "Yes, of course, [the Ukrainians] are angry that they were invaded," Vance added. "But are we going to continue to lose thousands and thousands of soldiers over a few miles of territory this or that way?" Trump this week suggested that Ukraine might be willing to cede Crimea - which Russia invaded in 2014 - in order to reach a truce settlement. But Ukraine's President Volodymyr Zelensky had earlier implied that he would be unable to accept Russian control of the peninsula, citing the Ukrainian constitution. In a separate interview with Fox News on Thursday, US Secretary of State Marco Rubio said there needed to be a "breakthrough" in the conflict soon, otherwise Trump "will have to decide how much time to dedicate to this". Russian president Vladimir Putin this week announced a temporary three-day ceasefire from 8 May, to coincide with anniversary celebrations marking the end of World War Two.

Russia accused Ukraine of deliberately targeting civilians during a recent drone attack that killed at least seven people and wounded more than 20 on Thursday morning in partially occupied Kherson. The drone strike hit a market in the town of Oleshky in Russian-controlled Kherson at approximately 9:30am local time, when many people were outdoors due to the May 1 public holiday, the region's Moscow-appointed governor said. Ukraine's military said the attack targeted Russian troops, and only military personnel were killed, although the claims by either side have not been independently verified. A Russian strike on Ukraine's Odesa killed two people, and a Russian drone attack in the southeastern Ukrainian city of Zaporizhzhia set a building on fire on Thursday night, injuring 14 people, with no fatalities. Ukraine's SBU Security Service said it has thwarted the attempted murder of Sergiy Sternenko, a prominent activist and video blogger, and also detained a suspect.

Alpha School co-founder Mackenzie Price and a junior at the school Elle Kristine join'Fox & Friends' to discuss the benefits of incorporating artificial intelligence into the classroom. At a time when many American students are struggling to keep up, a private school in Texas is doing more with less, much less. At Alpha School, students spend just two hours a day in class, guided by an Artificial Intelligence (AI) tutor. But results are impressive: students are testing in the top 1 to 2% nationally. "We use an AI tutor and adaptive apps to provide a completely personalized learning experience," said Alpha co-founder MacKenzie Price during an interview on Fox & Friends.

Micromobility, which utilizes lightweight mobile machines moving in urban public spaces, such as delivery robots and mobility scooters, emerges as a promising alternative to vehicular mobility. Current micromobility depends mostly on human manual operation (in-person or remote control), which raises safety and efficiency concerns when navigating busy urban environments full of unpredictable obstacles and pedestrians. Assisting humans with AI agents in maneuvering micromobility devices presents a viable solution for enhancing safety and efficiency. In this work, we present a scalable urban simulation solution to advance autonomous micromobility. First, we build URBAN-SIM - a high-performance robot learning platform for large-scale training of embodied agents in interactive urban scenes. URBAN-SIM contains three critical modules: Hierarchical Urban Generation pipeline, Interactive Dynamics Generation strategy, and Asynchronous Scene Sampling scheme, to improve the diversity, realism, and efficiency of robot learning in simulation. Then, we propose URBAN-BENCH - a suite of essential tasks and benchmarks to gauge various capabilities of the AI agents in achieving autonomous micromobility. URBAN-BENCH includes eight tasks based on three core skills of the agents: Urban Locomotion, Urban Navigation, and Urban Traverse. We evaluate four robots with heterogeneous embodiments, such as the wheeled and legged robots, across these tasks. Experiments on diverse terrains and urban structures reveal each robot's strengths and limitations.

Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe de-terministically. In this paper, we propose DetM-CVI, an adaptation of the Monte Carlo V alue Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.

A key objective in spatial analysis is to model spatial relationships and infer spatial processes to generate knowledge from spatial data, which has been largely based on spatial statistical methods. More recently, machine learning offers scalable and flexible approach es that complement traditional methods and has been increasingly applied in spatial data science . Despite its advantages, machine learning is often criticized for being a black box, which limits our understanding of model behavior and output . Recognizing this limitation, XAI has emerged as a pivotal field in AI that provides methods to explain the output of machine learning models to enhance transparency and understanding. These methods are crucial for model diagnosis, bias detection, and ensuring the reliability of results obtained from machine learning models. This chapter introduces key concepts and methods in XAI with a focus on Shapley value - based approach es, which is arguably the most popular XAI method, and their integration with spatial analysis. An empirical example of county - level voting behaviors in the 2020 Presidential election is presented to demonstrate the use of Shapley values and spatial analysis with a comparison to multi - scale geograp hically weighted regression . The chapter concludes with a discussion on the challenges and limitations of current XAI techniques and proposes new directions .

A storm is a type of extreme weather. Therefore, forecasting the path of a storm is extremely important for protecting human life and property. However, storm forecasting is very challenging because storm trajectories frequently change. In this study, we propose an improved deep learning method using a Transformer network to predict the movement trajectory of a storm over the next 6 hours. The storm data used to train the model was obtained from the National Oceanic and Atmospheric Administration (NOAA) [1]. Simulation results show that the proposed method is more accurate than traditional methods. Moreover, the proposed method is faster and more cost-effective