Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The U.K. and Germany scrambled fighter jets to intercept two Russian aircraft flying near Estonia late Tuesday. The Russian aircraft, a Russian Il-78 Midas refueling plane and an Antonov 148 military transport, approached NATO airspace without contacting Estonian authorities. The incident was the first time the U.K. and Germany have conducted a joint air intercept as part of the NATO treaty.

Washington's claim that a Russian fighter jet collided with a US surveillance drone near Crimea causing it to crash is both a rare military incident between the two superpowers and a serious escalation in already tense relations since Moscow's invasion of Ukraine last year. US and Russian officials have given conflicting accounts of what occurred on Tuesday over the Black Sea between the MQ-9 Reaper drone, valued at more than $30m and packed with sensitive US spying technology, and two Russian Su-27 fighter jets that were deployed to intercept the US aircraft. The Pentagon said two Russian Su-27 aircraft intercepted the drone and proceeded to dump fuel on the MQ-9 Reaper model as it conducted routine surveillance over the Black Sea in international airspace. US officials said the Russian jets flew around and in front of the drone several times for 30 to 40 minutes, and then one of the Su-27 fighters "struck the propeller" of the drone, "causing US forces to have to bring the MQ-9 down in international waters". A Pentagon spokesman said the collision likely damaged the Russian fighter jet, though the Su-27 did land.

The U.S. military said a Russian fighter plane clipped the propeller of one its spy drones and made it crash into the Black Sea on Tuesday in the first such direct encounter between the two world powers since Russia invaded Ukraine over a year ago. The Russian Defense Ministry offered a different account, and Moscow's ambassador to Washington said his country "views this incident as a provocation" involving a U.S. MQ-9 drone and Russian Su-27 fighter jet. The United States, which has provided tens of billions of dollars in military aid to Ukraine, has not become directly engaged in the war but it does conduct regular surveillance flights in the region. 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.

"If the message is that they want to deter or dissuade us from flying or operating in international airspace over the Black Sea," Mr Kirby told broadcaster VOA, "then that message will fail because that is not going to happen".

A Russian fighter jet has hit a US military drone over international waters, crashing the drone. The MQ-9 Reaper drone and two SU-27 craft were all flying above the Black Sea, and according to the US military's European Command, the Russian planes dumped fuel on the drone and flew in front of it dangerously, and eventually one of them hit the drone's propeller, forcing the US to bring down the drone. "This unsafe and unprofessional act by the Russians nearly caused both aircraft to crash," said US Air Force commander James B. Hecker in a press release. He also stated that the drone was "conducting routine operations" and that this incident will not stop US aircraft from operating in international airspace. Drones like this one have been operating over the Black Sea since well before the beginning of the Russia-Ukraine war to monitor the situation in Ukraine.

Former U.S. Ambassador to Ukraine John Herbst discusses massive missile attacks launched by Russia as the battle for city of Bakhmut rages on. A Russian Su-27 jet collided with a U.S. MQ-9 Reaper drone over the Black Sea Tuesday, a U.S. defense official told Fox News. It was one of two Su-27's flying. This happened in international airspace over international waters. The propeller to the drone was damaged and the drone landed in the Black Sea, west of Crimea, the U.S. defense official says.

The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies featuring psychological-driven linear models leave some significant limitations in practice. Hence, the present study proposes a new methodology for predicting households' evacuation decisions constructed by easily accessible demographic and resource-related predictors compared to current models with a high reliance on psychological factors. Meanwhile, an enhanced logistic regression (ELR) model that could automatically account for nonlinearities (i.e., univariate and bivariate threshold effects) by an interpretable machine learning approach is developed to secure the accuracy of the results. Specifically, low-depth decision trees are selected for nonlinearity detection to identify the critical thresholds, build a transparent model structure, and solidify the robustness. Then, an empirical dataset collected after Hurricanes Katrina and Rita is hired to examine the practicability of the new methodology. The results indicate that the enhanced logistic regression (ELR) model has the most convincing performance in explaining the variation of the households' evacuation decision in model fit and prediction capability compared to previous linear models. It suggests that the proposed methodology could provide a new tool and framework for the emergency management authorities to improve the estimation of evacuation traffic demands in a timely and accurate manner.

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. Project site with code: https://react-lm.github.io

When robots are deployed in the field for environmental monitoring they typically execute pre-programmed motions, such as lawnmower paths, instead of adaptive methods, such as informative path planning. One reason for this is that adaptive methods are dependent on parameter choices that are both critical to set correctly and difficult for the non-specialist to choose. Here, we show how to automatically configure a planner for informative path planning by training a reinforcement learning agent to select planner parameters at each iteration of informative path planning. We demonstrate our method with 37 instances of 3 distinct environments, and compare it against pure (end-to-end) reinforcement learning techniques, as well as approaches that do not use a learned model to change the planner parameters. Our method shows a 9.53% mean improvement in the cumulative reward across diverse environments when compared to end-to-end learning based methods; we also demonstrate via a field experiment how it can be readily used to facilitate high performance deployment of an information gathering robot.

From Cornwall and Wales to Essex, Blackpool and the Norfolk coast, Britain has experienced a flurry of earthquakes in the past month. The biggest – a 3.8 magnitude tremor that struck Wales on February 24 – sparked panic as locals reported their beds started to move and walls shook. One resident in the small Welsh town of Abertillery not far from the epicentre said the quake was so noticeable'it felt like the roof was falling off'. The Welsh quake was preceded by several more including a 1.5 magnitude quake in Cornwall and a 3.8 magnitude event off the coast of Great Yarmouth. Here's all you need to know about the British tremors – including whether recent tectonic activity suggests a'big one' is soon to hit parts of the country.