Taipei, Taiwan – China has launched an antitrust investigation into chip giant Nvidia in what appears to be Beijing's latest act of retaliation against Washington's sanctions on Chinese tech companies. Chinese state media said on Monday that the California-based chipmaker was being investigated by the State Administration for Market Regulation for potentially violating China's antimonopoly laws. Regulators will also review the company's 6.9bn acquisition of Mellanox Technologies, an Israeli-American supplier specialising in computer networking products, state media reports said, without providing further details. Chinese regulators approved the deal in 2020 with several restrictive conditions, including a provision that Nvidia would not discriminate against Chinese suppliers. Nvidia, which designs advanced chips used to power artificial intelligence (AI), is one of the world's most valuable companies, with a market capitalisation of more than 3.4 trillion.

'Fox & Friends Weekend' co-host Rachel Campos-Duffy opens up about seeing drones outside her home. U.S. lawmakers from New Jersey joined in many residents' frustrations over dozens of reports of drones being flown near sensitive sites like a military research facility in recent weeks, and they are now calling on federal agencies to immediately help investigate and address the escalating issue. Rep. Chris Smith, R-N.J., joined law enforcement leaders in his district on Monday on Long Beach Island, having been one of the key figures leading efforts to investigate the source and possible risks associated with the drone activity. "I have been speaking with Ocean County Sheriff Mike Mastronardy, Monmouth County Sheriff Shaun Golden, and national security officials located in the area to discuss the widespread reports of unidentified drone activity across my central New Jersey congressional district and across our state," Smith said in a statement. "Understandably, New Jersey residents are very alarmed at this significant and reoccurring phenomenon – and the tepid response from our state and federal agencies so far is totally unacceptable. As we saw with the Chinese spy balloon last year, our fiercest adversaries will stop at nothing to surveil our homeland and threaten our national security."

President-elect Donald Trump's planned U.S. government efficiency drive involving billionaire Elon Musk could lead to more joint projects between big defense contractors and smaller tech firms in areas such as artificial intelligence, drones and uncrewed submarines, according to interviews with company executives. Musk has indicated that Pentagon spending and priorities will be a target of the efficiency initiative, spreading anxiety at defense heavyweights such as Boeing, Northrop Grumman, Lockheed Martin and General Dynamics. Smaller military technology companies such as AI software firm Palantir and drone-maker Anduril have been buoyed by the prospect of Musk further loosening the grip that defense giants have held on the Pentagon's budget for many decades.

China has opened a probe into Nvidia over suspicions that the U.S. chipmaker broke anti-monopoly laws around a 2020 deal, taking aim at the artificial intelligence heavyweight as Washington ramps up sanctions. The State Administration for Market Regulation opened an investigation into the company's recent behavior as well as the circumstances surrounding the acquisition of Mellanox Technologies, the government said in a statement on Monday. Beijing gave approval for the deal four years ago, on condition that Nvidia not discriminate against Chinese companies. The move against Nvidia is Beijing's latest riposte to escalating U.S. technology curbs, coming just a week after the Chinese government banned exports of several materials with tech and military applications. Nvidia's market value has ballooned this year on demand for chips that can run AI programs, making it one of the most valuable publicly traded companies and China's largest corporate target in the tech trade war so far.

For more than two years, every new AI announcement has lived in the shadow of ChatGPT. No model from any company has eclipsed or matched that initial fever. But perhaps the closest any firm has come to replicating the buzz was this past February, when OpenAI first teased its video-generating AI model, Sora. Tantalizing clips--woolly mammoths kicking up clouds of snow, Pixar-esque animations of adorable fluffy critters--promised a stunning future, one in which anyone can whip up high-quality clips by typing simple text prompts into a computer program. But Sora, which was not immediately available to the public, remained just that: a teaser.

Learning-based solutions have enabled incredible capabilities for autonomous systems. Autonomous vehicles, both aerial and ground, rely on DNN for various integral tasks, including perception. The efficacy of supervised learning solutions hinges on the quality of the training data. Discrepancies between training data and operating conditions result in faults that can lead to catastrophic incidents. However, collecting vast amounts of context-sensitive data, with broad coverage of possible operating environments, is prohibitively difficult. Synthetic data generation techniques for DNN allow for the easy exploration of diverse scenarios. However, synthetic data generation solutions for aerial vehicles are still lacking. This work presents a data augmentation framework for aerial vehicle's perception training, leveraging photorealistic simulation integrated with high-fidelity vehicle dynamics. Safe landing is a crucial challenge in the development of autonomous air taxis, therefore, landing maneuver is chosen as the focus of this work. With repeated simulations of landing in varying scenarios we assess the landing performance of the VTOL type UAV and gather valuable data. The landing performance is used as the objective function to optimize the DNN through retraining. Given the high computational cost of DNN retraining, we incorporated Bayesian Optimization in our framework that systematically explores the data augmentation parameter space to retrain the best-performing models. The framework allowed us to identify high-performing data augmentation parameters that are consistently effective across different landing scenarios. Utilizing the capabilities of this data augmentation framework, we obtained a robust perception model. The model consistently improved the perception-based landing success rate by at least 20% under different lighting and weather conditions.

Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answering (QA) systems can actively manage uncertainty in cybersecurity problem-solving, offering interactive, inquiry-based learning experiences. Large language models (LLMs) have gained prominence in AI-driven QA systems, offering advanced language understanding and user engagement. However, they face challenges like hallucinations and limited domain-specific knowledge, which reduce their reliability in educational settings. To address these challenges, we propose CyberRAG, an ontology-aware retrieval-augmented generation (RAG) approach for developing a reliable and safe QA system in cybersecurity education. CyberRAG employs a two-step approach: first, it augments the domain-specific knowledge by retrieving validated cybersecurity documents from a knowledge base to enhance the relevance and accuracy of the response. Second, it mitigates hallucinations and misuse by integrating a knowledge graph ontology to validate the final answer. Experiments on publicly available cybersecurity datasets show that CyberRAG delivers accurate, reliable responses aligned with domain knowledge, demonstrating the potential of AI tools to enhance education.

Human health is increasingly threatened by exposure to hazardous substances, particularly persistent and toxic chemicals. The link between these substances, often encountered in complex mixtures, and various diseases are demonstrated in scientific studies. However, this information is scattered across several sources and hardly accessible by humans and machines. This paper evaluates current practices for publishing/accessing information on hazardous chemicals and proposes a novel platform designed to facilitate retrieval of critical chemical data in urgent situations. The platform aggregates information from multiple sources and organizes it into a structured knowledge graph. Users can access this information through a visual interface such as Neo4J Bloom and dashboards, or via natural language queries using a Chatbot. Our findings demonstrate a significant reduction in the time and effort required to access vital chemical information when datasets follow FAIR principles. Furthermore, we discuss the lessons learned from the development and implementation of this platform and provide recommendations for data owners and publishers to enhance data reuse and interoperability. This work aims to improve the accessibility and usability of chemical information by healthcare professionals, thereby supporting better health outcomes and informed decision-making in the face of patients exposed to chemical intoxication risks.

Statistical downscaling is a technique used in climate modeling to increase the resolution of climate simulations. High-resolution climate information is essential for various high-impact applications, including natural hazard risk assessment. However, simulating climate at high resolution is intractable. Thus, climate simulations are often conducted at a coarse scale and then downscaled to the desired resolution. Existing downscaling techniques are either simulation-based methods with high computational costs, or statistical approaches with limitations in accuracy or application specificity. We introduce Generative Bias Correction and Super-Resolution (GenBCSR), a two-stage probabilistic framework for statistical downscaling that overcomes the limitations of previous methods. GenBCSR employs two transformations to match high-dimensional distributions at different resolutions: (i) the first stage, bias correction, aligns the distributions at coarse scale, (ii) the second stage, statistical super-resolution, lifts the corrected coarse distribution by introducing fine-grained details. Each stage is instantiated by a state-of-the-art generative model, resulting in an efficient and effective computational pipeline for the well-studied distribution matching problem. By framing the downscaling problem as distribution matching, GenBCSR relaxes the constraints of supervised learning, which requires samples to be aligned. Despite not requiring such correspondence, we show that GenBCSR surpasses standard approaches in predictive accuracy of critical impact variables, particularly in predicting the tails (99% percentile) of composite indexes composed of interacting variables, achieving up to 4-5 folds of error reduction.

This study examines whether large language models (LLMs), such as ChatGPT, specifically the latest GPT-4o-mini, exhibit sensitivity to repeated prompts and whether repeating a question can improve response accuracy. We hypothesize that reiterating a question within a single prompt might enhance the model's focus on key elements of the query. To test this, we evaluate ChatGPT's performance on a large sample of two reading comprehension datasets under both open-book and closed-book settings, varying the repetition of each question to 1, 3, or 5 times per prompt. Our findings indicate that the model does not demonstrate sensitivity to repeated questions, highlighting its robustness and consistency in this context.