Nvidia has said it expects a 5.5bn ( 4.1bn) hit after Donald Trump's administration barred the chip designer from selling crucial artificial intelligence chips in China, sending shares in one of the US's most valuable companies plunging in after-hours trading. The company said in an official filing late on Tuesday that its H20 AI chip, which was designed specifically for the Chinese market to comply with export controls, would now require a special licence to sell there for the "indefinite future". The US government, which is battling China in the race for AI supremacy, told Nvidia the new rules were designed to address the risk that its products might be "used in, or diverted to, a supercomputer in China". The chip designer now expects to report 5.5bn in charges in its financial quarter that ends on 27 April, because of stocks of H20 chips and sales commitments. Nvidia, whose chips have helped drive huge developments in artificial intelligence technology in recent years, has produced extraordinary returns for its investors.

President Trump announces the U.S. Stargate investment alongside three artificial intelligence industry leaders. President Donald Trump's high-tech moonshot may hit a Texas-sized speed bump -- and it's coming from his own party. Trump's AI initiative, dubbed "Stargate," aims to build 20 ultra-powerful data centers across the country. Backed by heavyweights like OpenAI, Oracle, SoftBank, and the UAE-funded MGX, the project represents a 500 billion bet on the future with Texas chosen as ground zero for the first 10 centers. But a new Texas bill, Senate Bill 6, could delay or derail that momentum.

Kuala Lumpur, Malaysia – China's President Xi Jinping has arrived in Malaysia as part of a Southeast Asian tour which is seen as delivering a personal message that Beijing is a more reliable trading partner than the United States amid a bruising trade war with Washington. Xi arrived in the capital, Kuala Lumpur, on Tuesday evening in what is his first visit to Malaysia since 2013. He flew in from Vietnam where he had signed dozens of trade cooperation agreements in Hanoi on everything from artificial intelligence to rail development. On touching down, Xi said that deepening "high-level strategic cooperation" was good for the common interests of both China and Malaysia, and good for peace, stability and prosperity in the region and the world", according to the official Malaysian news agency Bernama. Xi's three-country tour and his "message" that Beijing is Southeast Asia's better friend than the truculent administration of US President Donald Trump comes as many countries in the 10-member Association of Southeast Asian Nations (ASEAN) bloc are unhappy with their treatment after the US imposed huge tariffs on countries around the world. "This is a very significant visit.

Nvidia announced on Tuesday that the US government had told it last week that the H20 chip required a permit to be sold to China, including Hong Kong. The tech giant said federal officials had advised them the licence requirement "will be in effect for the indefinite future". "The [government] indicated that the license requirement addresses the risk that the covered products may be used in, or diverted to, a supercomputer in China," Nvidia said. The company declined to comment further when contacted by the BBC. Marc Einstein from the Counterpoint Research consultancy said the 5.5bn hit estimated by Nvidia was in line with his estimates.

The advertising watchdog says it has been using AI to proactively search for online ads that might break the rules. Three of the clinics - Beautyjenics, Bomb Doll Aesthetics and Ccskinlondondubai -did not respond to the ASA's inquiries. Rejuvenate Clinics said it has reviewed ASA guidance and will remove all references to time-limited offers and state in ads that the surgery is carried out by a medical professional with ultrasound, to minimise risks and enhance safety. EME Aesthetics said all its clients are given a full consultation and are under no obligation to book any procedures, and it therefore considers that its ad had not pressured consumers or trivialised the risks of cosmetic procedures. Dr Ducu said it will ensure it follows the ASA's rules and guidance, that the time-limited Black Friday offer was intended to provide consumers with an opportunity to access the company's services at a discounted rate, and it always encourages consumers to make informed decisions without pressure.

Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (i) the cultural context, (ii) the masculine characteristics or behaviors, and (iii) the reward for, or simply acceptance of, those masculine characteristics or behaviors. In this work, we study discourse-based masculine defaults, and propose a twofold framework for (i) the large-scale discovery and analysis of gendered discourse words in spoken content via our Gendered Discourse Correlation Framework (GDCF); and (ii) the measurement of the gender bias associated with these gendered discourse words in LLMs via our Discourse Word-Embedding Association Test (D-WEAT). We focus our study on podcasts, a popular and growing form of social media, analyzing 15,117 podcast episodes. We analyze correlations between gender and discourse words -- discovered via LDA and BERTopic -- to automatically form gendered discourse word lists. We then study the prevalence of these gendered discourse words in domain-specific contexts, and find that gendered discourse-based masculine defaults exist in the domains of business, technology/politics, and video games. Next, we study the representation of these gendered discourse words from a state-of-the-art LLM embedding model from OpenAI, and find that the masculine discourse words have a more stable and robust representation than the feminine discourse words, which may result in better system performance on downstream tasks for men. Hence, men are rewarded for their discourse patterns with better system performance by one of the state-of-the-art language models -- and this embedding disparity is a representational harm and a masculine default.

Risk-averse reinforcement learning (RARL) is critical for decision-making under uncertainty, which is especially valuable in high-stake applications. However, most existing works focus on risk measures, e.g., conditional value-at-risk (CVaR), while measures of variability remain underexplored. In this paper, we comprehensively study nine common measures of variability, namely Variance, Gini Deviation, Mean Deviation, Mean-Median Deviation, Standard Deviation, Inter-Quantile Range, CVaR Deviation, Semi_Variance, and Semi_Standard Deviation. Among them, four metrics have not been previously studied in RARL. We derive policy gradient formulas for these unstudied metrics, improve gradient estimation for Gini Deviation, analyze their gradient properties, and incorporate them with the REINFORCE and PPO frameworks to penalize the dispersion of returns. Our empirical study reveals that variance-based metrics lead to unstable policy updates. In contrast, CVaR Deviation and Gini Deviation show consistent performance across different randomness and evaluation domains, achieving high returns while effectively learning risk-averse policies. Mean Deviation and Semi_Standard Deviation are also competitive across different scenarios. This work provides a comprehensive overview of variability measures in RARL, offering practical insights for risk-aware decision-making and guiding future research on risk metrics and RARL algorithms.

Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advancements in network alignment research, focusing on analyzing network alignment characteristics and progress in various domains such as social network analysis, bioinformatics, computational linguistics and privacy protection. It provides a detailed analysis of various methods' implementation principles, processes, and performance differences, including structure consistency-based methods, network embedding-based methods, and graph neural network-based (GNN-based) methods. Additionally, the methods for network alignment under different conditions, such as in attributed networks, heterogeneous networks, directed networks, and dynamic networks, are presented. Furthermore, the challenges and the open issues for future studies are also discussed.

A Large Language Model (LLM) powered GUI agent is a specialized autonomous system that performs tasks on the user's behalf according to high-level instructions. It does so by perceiving and interpreting the graphical user interfaces (GUIs) of relevant apps, often visually, inferring necessary sequences of actions, and then interacting with GUIs by executing the actions such as clicking, typing, and tapping. To complete real-world tasks, such as filling forms or booking services, GUI agents often need to process and act on sensitive user data. However, this autonomy introduces new privacy and security risks. Adversaries can inject malicious content into the GUIs that alters agent behaviors or induces unintended disclosures of private information. These attacks often exploit the discrepancy between visual saliency for agents and human users, or the agent's limited ability to detect violations of contextual integrity in task automation. In this paper, we characterized six types of such attacks, and conducted an experimental study to test these attacks with six state-of-the-art GUI agents, 234 adversarial webpages, and 39 human participants. Our findings suggest that GUI agents are highly vulnerable, particularly to contextually embedded threats. Moreover, human users are also susceptible to many of these attacks, indicating that simple human oversight may not reliably prevent failures. This misalignment highlights the need for privacy-aware agent design. We propose practical defense strategies to inform the development of safer and more reliable GUI agents.

The ability to reconstruct fine-grained network session data, including individual packets, from coarse-grained feature vectors is crucial for improving network security models. However, the large-scale collection and storage of raw network traffic pose significant challenges, particularly for capturing rare cyberattack samples. These challenges hinder the ability to retain comprehensive datasets for model training and future threat detection. To address this, we propose a machine learning approach guided by formal methods to encode and reconstruct network data. Our method employs autoencoder models with domain-informed penalties to impute PCAP session headers from structured feature representations. Experimental results demonstrate that incorporating domain knowledge through constraint-based loss terms significantly improves reconstruction accuracy, particularly for categorical features with session-level encodings. By enabling efficient reconstruction of detailed network sessions, our approach facilitates data-efficient model training while preserving privacy and storage efficiency.