Missouri State University trained over 1,500 Chinese executives tied to China's military-industrial complex through an MBA program, a new watchdog report claims.

U.S. defense firm Anduril in talks for Nissan plant to build drones in Japan, sources say U.S. defense firm Anduril Industries is in talks to acquire the plant to build military drones in Japan, sources say. U.S. defense firm Anduril Industries is in talks to acquire Nissan Motor's Oppama assembly plant near Tokyo as the maker of autonomous weapons looks to build military drones in Japan, according to three sources familiar with the matter. While they say no decision has been made, any deal could transform one of Japan's first large-scale postwar car factories, long a symbol of its industrial revival, into an arms-making hub. The talks over Oppama, which are being reported for the first time, come as Prime Minister Sanae Takaichi's government seeks to expand defense manufacturing amid growing concern that a Taiwan Strait crisis could draw in Japan and run down weapons stocks. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Anthropic accuses Alibaba of'illicitly' accessing AI models Alibaba's American depositary receipts sank to a session low on the news, falling more than 3% to $99.10 at 3:38 p.m. in New York on Wednesday. Anthropic accused Chinese technology giant Alibaba Group Holding of waging a large-scale effort to "illicitly" access its Claude artificial intelligence model using thousands of fraudulent accounts that undermine the U.S. AI developer's decision to keep its products out of China. Anthropic claimed that a campaign by operators linked to Alibaba's Qwen AI lab targeted Claude's most prized capabilities, including software engineering and agentic reasoning, according to a letter that the AI startup sent to several U.S. senators and White House officials. The company said it was the biggest attempt so far by a Chinese company to piggyback on the work of top U.S. labs. In its letter, Anthropic claimed that the effort involved 28.8 million exchanges with Claude between April and June through almost 25,000 fraudulent accounts, according to people familiar with the document and a copy seen by Bloomberg News. The company said the Alibaba campaign resembled past efforts by other Chinese developers that Anthropic flagged in a blog post earlier this year.

People visit the Delta Electronics booth during the annual Computex trade show in Taipei, Taiwan, on June 3, 2026. Thailand's stock market is having the best year among Southeast Asian peers, as investors discover an unlikely source of exposure to the global artificial-intelligence boom. Much of that gain has come from Delta Electronics (Thailand). The maker of power systems for AI data centers has surged more than 80% this year and became Thailand's first $100 billion company, large enough to be worth more than the next four largest Thai stocks combined. While the country lacks the semiconductor champions of Taiwan or South Korea, investors are increasingly recognizing its role in supplying the infrastructure behind AI. "Thailand isn't a pure AI market, but its exposure to data centers, electronics, power systems and digital infrastructure gives investors a new way to view Thai equities beyond the traditional tourism, banks and domestic consumption cycle," Bloomberg Intelligence Strategist Sufianti said in a note. Delta's rise is the clearest evidence of that shift.

An artificial intelligence-equipped camera system shows a person trapped inside a railway crossing gate during a test by Kintetsu Railway. A growing number of Japanese railway operators are introducing artificial intelligence (AI)-based systems to help prevent accidents involving trains at level crossings. The technology can automatically detect and report abnormalities, such as stalled vehicles or people trapped on the tracks, enabling train drivers and other railway staff to respond more quickly. Industry officials view AI as an effective tool for improving crossing safety, while the government has started offering financial support to encourage wider adoption. Kintetsu Railway, based in the city of Osaka, has tested an AI-equipped camera system at a crossing on the Kyoto Line in the town of Seika, Kyoto Prefecture.

First global rules adopted for self-driving cars, U.N. says Safety concerns and costs have long slowed progress on autonomous vehicles. The first global regulations for fully autonomous vehicles were adopted Wednesday, a U.N. agency said, establishing uniform international safety requirements that could pave the way for larger-scale rollouts of self-driving cars. Safety concerns and the cost of developing next-level systems have long slowed progress on autonomous vehicles. As self-driving cars have begun to hit the road in a growing number of cities, the fragmented national approaches to regulation have spurred manufacturer fears that vehicles developed for one market could be blocked from others. In a bid to address that issue, a meeting of the World Forum for Harmonization of Vehicle Regulations at the United Nations in Geneva decided to introduce a global regulatory framework for vehicles equipped with fully autonomous driving systems (ADS).

Classification and Regression Trees (CART) constitute one of the most influential paradigms in statistical learning. Although a variety of impurity measures have been proposed for different statistical models, these criteria are typically introduced on a case-by-case basis and analyzed separately. In this paper, we study CART through the lens of Bregman divergences. This perspective places the classical least-squares criterion, Poisson deviance, Kullback-Leibler-type losses, and other impurity measures associated with exponential-family models within a common framework. As a result, key ingredients of the CART methodology -- including node representatives, impurity measures, and split selection rules -- can be expressed and analyzed through general properties of convex functions rather than through separate model-specific constructions. Beyond the algorithmic formulation, we investigate theoretical properties of Bregman-based CART procedures. In particular, we analyze how geometric properties of the generating convex function influence impurity reductions and stability of recursive partitions. We also establish consistency results within the proposed framework, providing a unified theoretical treatment for a broad family of CART type procedures. Our results provide a geometric interpretation of impurity-based tree construction and show that many classical CART impurity criteria admit a common interpretation within a Bregman framework.

Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis, and financial transactions. Existing autoregressive methods suffer from error accumulation during multi-step generation, while non-autoregressive diffusion methods are typically limited to fixed-length output sequences. In this paper, we propose Latent Block-Diffusion Temporal Point Processes (LBDTPP), a novel semi-autoregressive TPP framework that introduces a latent block diffusion mechanism for high-quality and variable-length event sequence generation. The core idea is to define an autoregressive probability distribution over event blocks in latent space and perform Gaussian diffusion within each block. By sequentially generating blocks while simultaneously sampling events in each block, LBDTPP preserves the length flexibility of autoregressive TPPs and inherits the parallel high-quality generation capability of diffusion models. Theoretically, we derive Wasserstein error bounds showing that, under suitable local approximation and prefix-stability assumptions, block-wise generation can reduce error accumulation compared with event-wise autoregressive generation. Extensive experiments on six real-world benchmark datasets demonstrate that LBDTPP outperforms state-of-the-art TPP baselines in both unconditional and conditional generation tasks. Further empirical analyses verify the benefits of latent-space diffusion and block-wise generation, and reveal the trade-off between generation quality and block size. Our code is available at https://github.com/Zh-Shuai/LBDTPP.

Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity naturally coexist, and the mismatch between local and global imbalances exacerbates the performance degradation of the aggregated model. The agnosticism of global class distribution poses significant challenges for data-level methods, especially under extreme conditions with severe class absence across clients. In this paper, we propose FedReLa, a novel data-level approach that tackles the coexistence of data heterogeneity and class imbalance in federated learning. By re-labeling samples with a feature-dependent label re-allocator, FedReLa corrects biased global decision boundaries without requiring knowledge of the global class distribution. This modular, model-agnostic approach can be integrated with algorithmic methods to deliver consistent improvements without additional communication overhead. Through extensive experiments, our method significantly improves the accuracy of minority classes and the overall accuracy on stepwise-imbalanced and long-tailed datasets, outperforming the previous state of the art.

Mean Field Variational Inference (MFVI) is widely understood to underestimate posterior variance. By analysing conjugate Bayesian Linear Regression (BLR), we show that this characterization is incomplete: while MFVI underestimates the variance in parameter space, it can overestimate the predictive variance compared to the exact posterior. We show that if the MFVI posterior underestimates predictive variances in some directions, it necessarily overestimates them in others. Crucially, this overestimation occurs in directions where the training data concentrates. This leads to the surprising result that, for a test point drawn from the training distribution, MFVI's expected predictive variance exceeds that of the exact posterior. We demonstrate a pathological case of this effect, where the MFVI posterior fails to reduce predictive variance compared to the prior on in distribution data. We connect these results to the Cold Posterior Effect, arguing that varying the temperature can correct this overestimation, yielding predictions closer to those of the exact posterior. We validate our theory on synthetic and real-world regression tasks.