Russia has suspended its participation in a landmark agreement that allowed vital grain exports from Ukraine after what it said was a drone attack on Russian ships in occupied Crimea. Russia's defence ministry said Ukraine attacked the Black Sea Fleet near Sevastopol in the annexed Crimean Peninsula with 16 drones in the early hours of Saturday, and that British navy "specialists" had helped coordinate the "terrorist" attack. London bluntly rejected Moscow's claim. The Turkey and UN-brokered deal to unlock grain exports signed between Russia and Ukraine in July is critical to easing the global food crisis caused by the conflict. The agreement has already allowed more than 9 million tonnes of Ukrainian grain to be exported and was due to be renewed on November 19.

Fox News contributor Mike Pompeo joined'America Reports' to discuss Putin alleging Ukraine will use a'dirty bomb' in the war and the latest on Hunter Biden's business dealings. Russia announced it is withdrawing from the UN-facilitated Black Sea grain export agreement after an attack on its naval forces in Sevastopol, Crimea. "We've seen the reports from the Russian Federation regarding the suspension of their participation in the Black Sea Grain Initiative following an attack on the Russian Black Sea Fleet," Stéphane Dujarric, spokesman for the U.N. Secretary General, said in a press release Saturday morning. "We are in touch with the Russian authorities on this matter." "It is vital that all parties refrain from any action that would imperil the Black Sea Grain Initiative which is a critical humanitarian effort that is clearly having a positive impact on access to food for millions of people around the world," Dujarric added.

British navy personnel planted explosives and blew up the Nord Stream gas pipelines last month, Russia's defence ministry says, a claim London called false and designed to distract from Moscow's military failures in Ukraine. Russia did not give evidence for its allegation that a leading NATO member had sabotaged critical Russian infrastructure amid the worst crisis in relations between the West and Moscow since the depths of the Cold War. The Russian ministry alleged "British specialists" from the same unit that directed Ukrainian drone attacks on ships from the Russian Black Sea fleet in Crimea earlier on Saturday were responsible for the Nord Stream pipeline sabotage. "According to available information, representatives of this unit of the British Navy took part in the planning, provision and implementation of a terrorist attack in the Baltic Sea on September 26 this year – blowing up the Nord Stream 1 and Nord Stream 2 gas pipelines," the ministry said. The United Kingdom denied the accusation.

The Russian army accused Ukraine of a "massive" drone attack on its Black Sea Fleet in Crimea on Saturday, claiming the UK helped in the strike that damaged a ship. Sevastopol in Moscow-annexed Crimea, which has been targeted several times in recent months, serves as the headquarters for the fleet and a logistical hub for operations in Ukraine. The Russian army claimed to have "destroyed" nine aerial drones and seven maritime ones, in an attack early Saturday in the port. Moscow's forces alleged British "specialists", whom they said were based in the southern Ukrainian city of Ochakiv, had helped prepare and train Kyiv to carry out the strike. In a further singling out of the UK -- which Moscow sees as one of the most unfriendly Western countries -- Moscow said the same British unit was involved in explosions of the Nord Stream gas pipeline last month.

"In the course of repelling a terrorist attack on the outer roadstead of Sevastopol, the use of naval weapons and naval aviation of the Black Sea Fleet destroyed four marine unmanned vehicles, three more devices were destroyed on the internal roadstead," a statement from the ministry read.

The Russian navy has "repelled" a drone attack in the bay of Sevastopol, home to Moscow's Black Sea Fleet in Moscow-annexed Crimea, according to a statement by a Russian-installed governor, as a battle rages for the control of southeastern Ukrainian cities Kherson and Bakhmut. "Today, starting at 04:30am for several hours, various air defence systems in Sevastopol repelled drone attacks," Sevastopol Governor Mikhail Razvozhayev said on Telegram early on Saturday. "All UAVs (unmanned aerial vehicles) have been shot down," he added. "Nothing has been hit in the city. The situation is under control."

Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors. The background knowledge improves the model prediction performance by up to 40\% in terms of $R^2$ (\ie coefficient of determination). We use the KG and KG embeddings to provide quantitative and qualitative insights into the predictions. These insights are expected to improve the confidence in effect prediction. Larger scale implementation of such extrapolation models should be expected to support hazard and risk assessment, by simplifying and reducing testing needs.

Chest X-Ray imaging is one of the most common radiological tools for detection of various pathologies related to the chest area and lung function. In a clinical setting, automated assessment of chest radiographs has the potential of assisting physicians in their decision making process and optimize clinical workflows, for example by prioritizing emergency patients. Most work analyzing the potential of machine learning models to classify chest X-ray images focuses on vision methods processing and predicting pathologies for one image at a time. However, many patients undergo such a procedure multiple times during course of a treatment or during a single hospital stay. The patient history, that is previous images and especially the corresponding diagnosis contain useful information that can aid a classification system in its prediction. In this study, we analyze how information about diagnosis can improve CNN-based image classification models by constructing a novel dataset from the well studied CheXpert dataset of chest X-rays. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin. We provide code to replicate the dataset creation and model training.

Potential advancements in artificial intelligence (AI) could have profound implications for how countries research and develop weapons systems, and how militaries deploy those systems on the battlefield. The idea of AI-enabled military systems has motivated some activists to call for restrictions or bans on some weapon systems, while others have argued that AI may be too diffuse to control. This paper argues that while a ban on all military applications of AI is likely infeasible, there may be specific cases where arms control is possible. Throughout history, the international community has attempted to ban or regulate weapons or military systems for a variety of reasons. This paper analyzes both successes and failures and offers several criteria that seem to influence why arms control works in some cases and not others. We argue that success or failure depends on the desirability (i.e., a weapon's military value versus its perceived horribleness) and feasibility (i.e., sociopolitical factors that influence its success) of arms control. Based on these criteria, and the historical record of past attempts at arms control, we analyze the potential for AI arms control in the future and offer recommendations for what policymakers can do today.

Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are "small" but "realistic" semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts, or are limited to image classification problems with few classes. In this paper, we overcome this by generating Diffusion Visual Counterfactual Explanations (DVCEs) for arbitrary ImageNet classifiers via a diffusion process. Two modifications to the diffusion process are key for our DVCEs: first, an adaptive parameterization, whose hyperparameters generalize across images and models, together with distance regularization and late start of the diffusion process, allow us to generate images with minimal semantic changes to the original ones but different classification. Second, our cone regularization via an adversarially robust model ensures that the diffusion process does not converge to trivial non-semantic changes, but instead produces realistic images of the target class which achieve high confidence by the classifier.