US and coalition troops are also stationed in the Red Sea after the Iran-backed Houthis began attacking commercial ships in the region. The Yemen-based group says it is targeting vessels in the region in support of Palestinians in Gaza, where Israel is fighting Hamas.

A UK vessel shot down a Houthi drone in the Red Sea, the United Kingdom's Ministry of Defence has said, as tensions in the Middle East soar amid the ongoing war in Gaza. "Yesterday HMS Diamond successfully repelled a drone attack from the Iranian-backed Houthis in the Red Sea," read a statement from the ministry published on Sunday on X. "Diamond destroyed a drone targeting her, with no injuries or damage sustained to Diamond or her crew," it added. There was no immediate comment from the Houthis. The Yemen-based group previously pledged to target Israel-linked vessels in the region as part of an effort to pressure the country's government to end its bombardment of Gaza and allow more humanitarian aid supplies into the coastal Palestinian enclave. Gaza has been under heavy bombardment by Israeli forces since October 7, when Hamas fighters stormed communities in southern Israel, killing at least 1,139 people and taking about 240 others captive, according to Israeli officials.

Three US service members have been killed and "many" others wounded during an unmanned aerial drone attack on US forces stationed in northeastern Jordan near the Syrian border, President Joe Biden has said, blaming Iran-backed groups for the attack. The United States military said in a statement that at least 25 people were injured. "While we are still gathering the facts of this attack, we know it was carried out by radical Iran-backed militant groups operating in Syria and Iraq," Biden said in a statement on Sunday. Biden said the US "will hold all those responsible to account at a time and in a manner [of] our choosing." Jordanian state television quoted Muhannad Mubaidin, a spokesperson for Jordan's government, as saying the attack happened outside of the kingdom across the border in Syria. There was no immediate comment from Iran.

Fox News reporter Stephanie Bennett has more on the rising tension in the Middle East on'Fox News Live.' Three U.S. service members were killed and 25 others were injured in a drone attack on an outpost in northeast Jordan near the Syria border, U.S. Central Command confirmed on Sunday. "On Jan. 28, three U.S. service members were killed and 25 injured from a one-way attack UAS that impacted at a base in northeast Jordan, near the Syria border. As a matter of respect for the families and in accordance with DoD policy, the identities of the servicemembers will be withheld until 24 hours after their next of kin have been notified," CENTCOM said. "Updates will be provided as they become available," it added. The White House says President Biden was briefed Sunday morning by Defense Secretary Lloyd Austin, National Security Advisor Jake Sullivan, and Principal Deputy National Security Advisor Jon Finer about the attack, which marked a significant escalation in the Middle East as the first time American troops have been killed by enemy fire in the region since the Israel-Hamas war began.

Japan and the United States have recently agreed to begin joint research on artificial intelligence in the hope of using the technology for drones that would work in tandem with the Asian nation's next fighter jet. Japan plans to co-develop a next-generation fighter aircraft with Britain and Italy by 2035. While the United States, Japan's key security ally, is not part of the fighter jet project, it has sought to bolster defense cooperation with Tokyo, including in autonomous systems capabilities. The objective of the joint AI study is to "revolutionize airborne combat by merging state-of-the-art artificial intelligence and machine learning with advanced unmanned air vehicles," the U.S. Air Force said in a press release issued last month upon the signing of the agreement. "The AI developed in this joint research is expected to be applied to UAVs operated alongside Japan's next fighter aircraft," it said, emphasizing that the collaboration will be beneficial for maintaining "technological advantages" of the Japan-U.S. alliance.

Many European citizens become targets of the Kremlin propaganda campaigns, aiming to minimise public support for Ukraine, foster a climate of mistrust and disunity, and shape elections (Meister, 2022). To address this challenge, we developed ''Check News in 1 Click'', the first NLP-empowered pro-Kremlin propaganda detection application available in 7 languages, which provides the lay user with feedback on their news, and explains manipulative linguistic features and keywords. We conducted a user study, analysed user entries and models' behaviour paired with questionnaire answers, and investigated the advantages and disadvantages of the proposed interpretative solution.

The paper presents an approach to the modelling of epistemic uncertainty in Conjunction Data Messages (CDM) and the classification of conjunction events according to the confidence in the probability of collision. The approach proposed in this paper is based on Dempster-Shafer Theory (DSt) of evidence and starts from the assumption that the observed CDMs are drawn from a family of unknown distributions. The Dvoretzky-Kiefer-Wolfowitz (DKW) inequality is used to construct robust bounds on such a family of unknown distributions starting from a time series of CDMs. A DSt structure is then derived from the probability boxes constructed with DKW inequality. The DSt structure encapsulates the uncertainty in the CDMs at every point along the time series and allows the computation of the belief and plausibility in the realisation of a given probability of collision. The methodology proposed in this paper is tested on a number of real events and compared against existing practices in the European and French Space Agencies. We will show that the classification system proposed in this paper is more conservative than the approach taken by the European Space Agency but provides an added quantification of uncertainty in the probability of collision.

Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to LLMs, it relies heavily on the relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. To this end, we propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. Specifically, a lightweight retrieval evaluator is designed to assess the overall quality of retrieved documents for a query, returning a confidence degree based on which different knowledge retrieval actions can be triggered. Since retrieval from static and limited corpora can only return sub-optimal documents, large-scale web searches are utilized as an extension for augmenting the retrieval results. Besides, a decompose-then-recompose algorithm is designed for retrieved documents to selectively focus on key information and filter out irrelevant information in them. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches. Experiments on four datasets covering short- and long-form generation tasks show that CRAG can significantly improve the performance of RAG-based approaches.

This paper investigates a novel algorithmic vulnerability when imperceptible image layers confound multiple vision models into arbitrary label assignments and captions. We explore image preprocessing methods to introduce stealth transparency, which triggers AI misinterpretation of what the human eye perceives. The research compiles a broad attack surface to investigate the consequences ranging from traditional watermarking, steganography, and background-foreground miscues. We demonstrate dataset poisoning using the attack to mislabel a collection of grayscale landscapes and logos using either a single attack layer or randomly selected poisoning classes. For example, a military tank to the human eye is a mislabeled bridge to object classifiers based on convolutional networks (YOLO, etc.) and vision transformers (ViT, GPT-Vision, etc.). A notable attack limitation stems from its dependency on the background (hidden) layer in grayscale as a rough match to the transparent foreground image that the human eye perceives. This dependency limits the practical success rate without manual tuning and exposes the hidden layers when placed on the opposite display theme (e.g., light background, light transparent foreground visible, works best against a light theme image viewer or browser). The stealth transparency confounds established vision systems, including evading facial recognition and surveillance, digital watermarking, content filtering, dataset curating, automotive and drone autonomy, forensic evidence tampering, and retail product misclassifying. This method stands in contrast to traditional adversarial attacks that typically focus on modifying pixel values in ways that are either slightly perceptible or entirely imperceptible for both humans and machines.

The HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission serves as an in-orbit demonstration of a constellation of nanosatellites whose primary scientific purpose is to discover intense high-energy transients, such as gamma-ray bursts, across a broad energy range (few keV to few MeV) with unparalleled temporal precision and exact localisation. By 2024, the first constellation of six nanosatellites is expected to be launched. To fully exploit satellite data and allow faint astronomical events to emerge, a precise estimation of satellite background count rates is required to determine whether the event is statistically valid or not. The dynamics of the background are related to the satellite's orbital information, which varies in the order of minutes, potentially hiding long transient events. This work introduces two main contributions I have brought ahead; first a novel background estimator is presented that could potentially be fitted to any type of X/Gamma-ray satellite space telescope, capable of capturing long-term dynamics and accurate enough to detect faint transients. This estimator is built using a Neural Network and tested on data from the Fermi Gamma-ray Space Telescope's Gamma Burst Monitor (GBM). As a second objective, it is employed a trigger algorithm, called FOCuS (Functional Online CUSUM), to extract events from the background using the background estimator. The resulting framework, DeepGRB, can identify astronomical events that are both present and absent from the Fermi-GBM catalog. The analysis of the discovered events reveals the strengths and weaknesses of the framework.