Russia's Federal Security Service (FSB) has detained a man for plotting a rail bombing in Crimea as a drone was downed over the Moscow-occupied peninsula. Crimea, which Russia seized from Ukraine in 2014, has been targeted by Ukrainian drone raids and sabotage attacks as Kyiv tries to retake the peninsula. The FSB said on Friday the suspect – a Russian citizen in his mid-40s – had been "collecting information on the deployment of Russian defence ministry facilities and units" and was preparing a railway bombing. "In a hiding place he had organised [we] found and seized an improvised explosive device made using foreign-made plastic explosives," it said. It said the man had been acting on the "instructions of Ukrainian military intelligence" and had been remanded in custody. Russia's TASS news agency said the man was a resident of the Crimean city of Sevastopol.

Ukrainian presidential adviser Mykhailo Podolyak has slammed Elon Musk for indirectly allowing Russian forces to attack Ukrainian cities after it was revealed his Starlink satellite communications interfered with a drone operation. Details of the incident are laid out in a biography of Musk by Walter Isaacson, due out on Tuesday. The book describes how the network turned off communications near the coast of the Russian-occupied Crimean peninsula as Ukrainian drones were approaching Russian warships, resulting in "lost connectivity". Musk allegedly ordered Starlink engineers to turn off the communications as he feared Russian President Vladimir Putin would respond with nuclear weapons to a Ukrainian attack on Crimea, according to Isaacson's book. "I think if the Ukrainian attacks had succeeded in sinking the Russian fleet, it would have been like a mini Pearl Harbor and led to a major escalation," Musk is quoted as saying.

On 22 February1946, George Kennan, an American diplomat stationed in Moscow, dictated a 5,000-word cable to Washington. In this famous telegram, Kennan warned that the Soviet Union's commitment to communism meant that it was inherently expansionist, and urged the US government to resist any attempts by the Soviets to increase their influence. This strategy quickly became known as "containment" – and defined American foreign policy for the next 40 years. The Coming Wave is Suleyman's book-length warning about technological expansionism: in close to 300 pages, he sets out to persuade readers that artificial intelligence (AI) and synthetic biology (SB) threaten our very existence and we only have a narrow window within which to contain them before it's too late. Unlike communism during the cold war, however, AI and SB are not being forced on us.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on'Special Report.' Lawmakers on the Senate Energy Committee were warned on Thursday about both the threats and opportunities that come with artificial intelligence being integrated into the U.S. energy sector and everyday life as a whole. The committee held a hearing on the rapidly advancing technology, and experts present spent a significant amount of time not only discussing AI but the ever-looming threat of China and its efforts to steal and recreate emerging U.S. capabilities. "China released their new generation of AI Development Plan, which includes [research and development] and infrastructure targets. The U.S. currently does not have a strategic AI plan like this," Committee Chair Joe Manchin, D-W.Va., said at the hearing's outset.

Russia continued its attacks on Ukraine's Danube ports. Governor Oleh Kiper said Russian drone attacks lasting three hours damaged port infrastructure, a grain silo and administrative buildings in the Izmail district of Ukraine's Odesa region. NATO chief Jens Stoltenberg said there were no indications that drone debris found on Romanian territory was caused by a deliberate Russian attack, but that air attacks close to NATO borders posed a risk. Romania lies just across the river from Izmail. Stoltenberg also said that Ukraine was making progress in its counteroffensive and started to reclaim territory seized by Russia.

We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first application is to build a classification model for histopathological cancer detection using the quantum transfer learning strategy. The second application is to test the performance of this model for various adversarial attacks. Rather than using a single transfer learning model, the hybrid classical-quantum models are tested using multiple transfer learning models, especially ResNet18, VGG-16, Inception-v3, and AlexNet as feature extractors and integrate it with several quantum circuit-based variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of classical models and hybrid classical-quantum transfer learning models for histopathological cancer detection under several adversarial attacks. We compared the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator. We also observed that for histopathological cancer detection under several adversarial attacks, Hybrid Classical-Quantum (HCQ) models provided better accuracy than classical image classification models.

Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. Yet, FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. Preventing backdoors proves especially challenging due to their stealthy nature. Prominent mitigation techniques against poisoning attacks rely on monitoring certain metrics and filtering malicious model updates. While shown effective in evaluations, we argue that previous works didn't consider realistic real-world adversaries and data distributions. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. Through extensive empirical tests, we show that existing defense methods can be easily circumvented in this adversary model. We also demonstrate, that existing defenses have limited effectiveness when no assumptions are made about underlying data distributions. We introduce Metric-Cascades (MESAS), a novel defense method for more realistic scenarios and adversary models. MESAS employs multiple detection metrics simultaneously to identify poisoned model updates, creating a complex multi-objective optimization problem for adaptive attackers. In our extensive evaluation featuring nine backdoors and three datasets, MESAS consistently detects even strong adaptive attackers. Furthermore, MESAS outperforms existing defenses in distinguishing backdoors from data distribution-related distortions within and across clients. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.

We present the Newspaper Bias Dataset (NewB), a text corpus of more than 200,000 sentences from eleven news sources regarding Donald Trump. While previous datasets have labeled sentences as either liberal or conservative, NewB covers the political views of eleven popular media sources, capturing more nuanced political viewpoints than a traditional binary classification system does. We train two state-of-the-art deep learning models to predict the news source of a given sentence from eleven newspapers and find that a recurrent neural network achieved top-1, top-3, and top-5 accuracies of 33.3%, 61.4%, and 77.6%, respectively, significantly outperforming a baseline logistic regression model's accuracies of 18.3%, 42.6%, and 60.8%. Using the news source label of sentences, we analyze the top n-grams with our model to gain meaningful insight into the portrayal of Trump by media sources.We hope that the public release of our dataset will encourage further research in using natural language processing to analyze more complex political biases. Our dataset is posted at https://github.com/JerryWeiAI/NewB .

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.

Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual classified as y -- the factual -- we seek actions such that their prediction becomes the desired class y' -- the counterfactual. This process offers algorithmic recourse that is (1) easy to customise and interpret, and (2) directly aligned with the goals of each individual. However, the properties of a "good" counterfactual are still largely debated; it remains an open challenge to effectively locate a counterfactual along with its corresponding recourse. Some strategies use gradient-driven methods, but these offer no guarantees on the feasibility of the recourse and are open to adversarial attacks on carefully created manifolds. This can lead to unfairness and lack of robustness. Other methods are data-driven, which mostly addresses the feasibility problem at the expense of privacy, security and secrecy as they require access to the entire training data set. Here, we introduce LocalFACE, a model-agnostic technique that composes feasible and actionable counterfactual explanations using locally-acquired information at each step of the algorithmic recourse. Our explainer preserves the privacy of users by only leveraging data that it specifically requires to construct actionable algorithmic recourse, and protects the model by offering transparency solely in the regions deemed necessary for the intervention.