A bear warning sign is displayed in Shirakawa-go, a popular tourist spot in Gifu Prefecture. A town in Miyagi Prefecture has retracted its social media post warning of a bear sighting after discovering an image submitted to it had been generated using artificial intelligence. A Japanese town has deleted a social media post warning of a bear sighting after discovering that a picture it had received showing the fearsome creature was generated using artificial intelligence. Similar fake images have been circulating online as fear of bears runs high in the country, where the animals have killed a record 13 people this year. "The town prioritized informing residents to avoid danger, but we apologize for causing any anxiety or confusion," the town of Onagawa, Miyagi Prefecture, said on its official X social media account on Wednesday.

Recent advances in multimodal large language models (MLLMs) have significantly enhanced video understanding capabilities, opening new possibilities for practical applications. Yet current video benchmarks focus largely on indoor scenes or short-range outdoor activities, leaving the challenges associated with long-distance travel largely unexplored. Mastering extended geospatial-temporal trajectories is critical for next-generation MLLMs, underpinning real-world tasks such as embodied-AI planning and navigation. To bridge this gap, we present VIR-Bench, a novel benchmark consisting of 200 travel videos that frames itinerary reconstruction as a challenging task designed to evaluate and push forward MLLMs' geospatial-temporal intelligence. Experimental results reveal that state-of-the-art MLLMs, including proprietary ones, struggle to achieve high scores, underscoring the difficulty of handling videos that span extended spatial and temporal scales. Moreover, we conduct an in-depth case study in which we develop a prototype travel-planning agent that leverages the insights gained from VIR-Bench. The agent's markedly improved itinerary recommendations verify that our evaluation protocol not only benchmarks models effectively but also translates into concrete performance gains in user-facing applications.

Android robots shown at the Osaka Expo in a pavilion produced by University of Osaka professor Hiroshi Ishiguro will be relocated to Kyoto Prefecture. OSAKA - Seven android robots shown at the 2025 World Exposition in Osaka in a pavilion produced by University of Osaka professor Hiroshi Ishiguro will be relocated to Kyoto Prefecture after the end of the event on Monday. In addition, the Dutch pavilion will be moved to Awaji Island, Hyogo Prefecture. People involved in the use of expo assets after the event hope that they will be loved as tourist attractions in their new places. The prefectural government of Kyoto was chosen as the new owner of the androids in an open tender held by the expo organizer, the Japan Association for the 2025 World Exposition, in September. The robots will be shown to the public at a research facility in the Keihanna Science City research district straddling the Kyoto municipalities of Seika and Kizugawa.

High-dimensional functional time series (HDFTS) are often characterized by nonlinear trends and high spatial dimensions. Such data poses unique challenges for modeling and forecasting due to the nonlinearity, nonstationarity, and high dimensionality. We propose a novel probabilistic functional neural network (ProFnet) to address these challenges. ProFnet integrates the strengths of feedforward and deep neural networks with probabilistic modeling. The model generates probabilistic forecasts using Monte Carlo sampling and also enables the quantification of uncertainty in predictions. While capturing both temporal and spatial dependencies across multiple regions, ProFnet offers a scalable and unified solution for large datasets. Applications to Japan's mortality rates demonstrate superior performance. This approach enhances predictive accuracy and provides interpretable uncertainty estimates, making it a valuable tool for forecasting complex high-dimensional functional data and HDFTS.

Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a networked Susceptible-Infectious-Recovered (SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo (MCMC) approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the networked SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts). Additionally, incorporating uncertainty quantification through conformal prediction enhances the model's practical utility for guiding targeted public health interventions.

Japanese regional banking group Senshu Ikeda Holdings' entry into the reservation-based transit bus business is aimed at stimulating the regional economy, President and CEO Atsushi Ukawa said in a recent interview. "Even regional banks in urban areas must think about serving the local community," Ukawa said of the first reservation bus operations by a regional bank in Japan. He said that the Osaka-based company will work with local governments to expand the operation area to complement public transport. Senshu Ikeda operates an "on-demand bus," which uses artificial intelligence to run according to users' desired dates, times and locations. It partnered with companies, including auto parts maker Aisin, to launch the bus operations on a trial basis in four municipalities in Osaka Prefecture in January 2023.

Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attract much attention but remain extremely challenging. To address this challenge, we propose a framework named Interventional Dynamical Causality (IntDC) for such non-intervention systems, along with its computational criterion, Interventional Embedding Entropy (IEE), to quantify causality. The IEE criterion theoretically and numerically enables the deciphering of IntDC solely from observational (non-interventional) time-series data, without requiring any knowledge of dynamical models or real interventions in the considered system. Demonstrations of performance showed the accuracy and robustness of IEE on benchmark simulated systems as well as real-world systems, including the neural connectomes of C. elegans, COVID-19 transmission networks in Japan, and regulatory networks surrounding key circadian genes.

We empirically demonstrate that a transformer pre-trained on country-scale unlabeled human mobility data learns embeddings capable, through fine-tuning, of developing a deep understanding of the target geography and its corresponding mobility patterns. Utilizing an adaptation framework, we evaluate the performance of our pre-trained embeddings in encapsulating a broad spectrum of concepts directly and indirectly related to human mobility. This includes basic notions, such as geographic location and distance, and extends to more complex constructs, such as administrative divisions and land cover. Our extensive empirical analysis reveals a substantial performance boost gained from pre-training, reaching up to 38% in tasks such as tree-cover regression. We attribute this result to the ability of the pre-training to uncover meaningful patterns hidden in the raw data, beneficial for modeling relevant Figure 1: A transformer pre-trained from scratch on countryscale high-level concepts. The pre-trained embeddings emerge as robust unlabeled human mobility data is adapted to model a representations of regions and trajectories, potentially valuable for variety of high-level concepts manifesting at different levels a wide range of downstream applications.

Shin-Etsu Chemical said Tuesday that it will build a new semiconductor materials plant in the city of Isesaki, Gunma Prefecture, at a cost of some 83 billion. The plant, slated to be completed by 2026, will make photoresists, including extreme ultraviolet resists used for state-of-the-art chips for generative artificial intelligence systems, and other semiconductor-related materials. The investment includes the cost to buy a 150,000-square-meter site for the factory. It will be the Japanese company's first new domestic production base since its plant in the city of Kamisu, Ibaraki Prefecture, was built in 1970. The Isesaki plant will also carry out research and development in the future. Currently, the company makes photoresists and related products at its plants in the prefectures of Niigata and Fukui, both along the Sea of Japan, and in Taiwan.

A U.S. military unit operating MQ-9 spy drones has completed its withdrawal from the Maritime Self-Defense Force's Kanoya air base in Kagoshima Prefecture for relocation to Okinawa Prefecture, the Japanese government said Sunday. The Defense Ministry's Kyushu Defense Bureau announced the unit's withdrawal from the Japanese base, where eight MQ-9 aircraft were operated for a limited period of one year from November last year. Up to 200 U.S. military personnel related to the operations were stationed there. The unit will be transferred to the U.S. military's Kadena Air Base in Okinawa Prefecture, near Kagoshima. The drones are set to be used to strengthen surveillance of Chinese military ships in the East China Sea.