U.K.'s Starmer escalates threats against X, calls Grok'shameful' U.K.'s prime minister escalated threats against Elon Musk's X, vowing to enforce a law that bans the sexualization of people's images without consent and calling such content generated by Grok disgusting and shameful. U.K. Prime Minister Keir Starmer escalated his threats against Elon Musk's X on Monday, vowing to enforce a law that banned the sexualization of people's images without their consent and calling such content generated by Grok "disgusting and shameful." From this week, the government will enforce the offense established in last year's Data Act, which made the creation of nonconsensual intimate images illegal. Starmer told members of Parliament on Monday, "if X cannot control Grok, we will -- and we'll do it fast because if you profit from harm and abuse, you lose the right to self regulate." Starmer accused X of protecting "abusive users" instead of the women and children whose images have been exploited, describing it as a "total distortion of priorities."

A thermometer displays 42 degrees Celsius in the city of Isesaki, Gunma Prefecture, on Aug. 5. | JIJI The Environment Ministry plans to step up efforts to prevent elderly people from suffering heatstroke indoors, including at home. It has requested ¥1 billion for related measures under the government's fiscal 2026 budget. The government has set a target of halving the average annual number of heatstroke deaths by 2030 from some 1,300 marked during the five years through 2022, but fatalities hit a record high above 2,000 in 2024. According to the Fire and Disaster Management Agency, 57.4% of people taken to hospital by ambulance due to heatstroke in May-September 2024 were aged 65 or older. Of the total cases, 38.0% occurred at houses, making up the largest share. While elderly people are at higher risk of heatstroke due to their declining thermoregulation and ability to sweat, some refrain from using air conditioners even on very hot days.

This work investigates whether time series of natural phenomena can be understood as being generated by sequences of latent states which are ordered in systematic and regular ways. We focus on clinical time series and ask whether clinical measurements can be interpreted as being generated by meaningful physiological states whose succession follows systematic principles. Uncovering the underlying compositional structure will allow us to create synthetic data to alleviate the notorious problem of sparse and low-resource data settings in clinical time series forecasting, and deepen our understanding of clinical data. We start by conceptualizing compositionality for time series as a property of the data generation process, and then study data-driven procedures that can reconstruct the elementary states and composition rules of this process. We evaluate the success of this methods using two empirical tests originating from a domain adaptation perspective. Both tests infer the similarity of the original time series distribution and the synthetic time series distribution from the similarity of expected risk of time series forecasting models trained and tested on original and synthesized data in specific ways. Our experimental results show that the test set performance achieved by training on compositionally synthesized data is comparable to training on original clinical time series data, and that evaluation of models on compositionally synthesized test data shows similar results to evaluating on original test data, outperforming randomization-based data augmentation. An additional downstream evaluation of the prediction task of sequential organ failure assessment (SOFA) scores shows significant performance gains when model training is entirely based on compositionally synthesized data compared to training on original data.

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods which focused on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks focused on fast data processing and model adaptation. AAD has been researched in the literature extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on the current research within this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS studies from 2013 to 2023 (November). We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our analysis indicates, among other findings, that reviewed works focused on a single aspect of adaptation (either data processing or model adaptation) but rarely in both at the same time. We aim to help researchers to advance the state of the art and help practitioners to become familiar with recent progress in this field. We identify the limitations of the state of the art and provide recommendations for future research directions.

In recent years, researchers have increasingly sought batteries as an efficient and cost-effective solution for energy storage and supply, owing to their high energy density, low cost, and environmental resilience. However, the issue of dendrite growth has emerged as a significant obstacle in battery development. Excessive dendrite growth during charging and discharging processes can lead to battery short-circuiting, degradation of electrochemical performance, reduced cycle life, and abnormal exothermic events. Consequently, understanding the dendrite growth process has become a key challenge for researchers. In this study, we investigated dendrite growth mechanisms in batteries using a combined machine learning approach, specifically a two-dimensional artificial convolutional neural network (CNN) model, along with computational methods. We developed two distinct computer models to predict dendrite growth in batteries. The CNN-1 model employs standard convolutional neural network techniques for dendritic growth prediction, while CNN-2 integrates additional physical parameters to enhance model robustness. Our results demonstrate that CNN-2 significantly enhances prediction accuracy, offering deeper insights into the impact of physical factors on dendritic growth. This improved model effectively captures the dynamic nature of dendrite formation, exhibiting high accuracy and sensitivity. These findings contribute to the advancement of safer and more reliable energy storage systems.

The problem of identifying to which of a given set of classes objects belong is ubiquitous, occurring in many research domains and application areas, including medical diagnosis, financial decision making, online commerce, and national security. But such assignments are rarely completely perfect, and classification errors occur. This means it is necessary to compare classification methods and algorithms to decide which is ``best'' for any particular problem. However, just as there are many different classification methods, so there are many different ways of measuring their performance. It is thus vital to choose a measure of performance which matches the aims of the research or application. This paper is a contribution to the growing literature on the relative merits of different performance measures. Its particular focus is the critical importance of matching the properties of the measure to the aims for which the classification is being made.

Mitsubishi Electric said Tuesday that it has developed a prototype for a multilingual system that shows words on a screen in different languages. The Japanese company hopes that the system will be used on occasions such as morning assembly meetings at factories where information needs to be related accurately to a large number of workers, including non-Japanese ones. Mitsubishi Electric aims to put the system into commercial use as early as fiscal 2025, which begins in April next year. The company also expects the system to be used for tourism purposes. The system translates a prepared script written in Japanese into 17 other languages, with the screen showing sentences in four languages, including original Japanese sentences, at once.

In recent years, the deterioration of artificial materials used in structures has become a serious social issue, increasing the importance of inspections. Non-destructive testing is gaining increased demand due to its capability to inspect for defects and deterioration in structures while preserving their functionality. Among these, Laser Ultrasonic Visualization Testing (LUVT) stands out because it allows the visualization of ultrasonic propagation. This makes it visually straightforward to detect defects, thereby enhancing inspection efficiency. With the increasing number of the deterioration structures, challenges such as a shortage of inspectors and increased workload in non-destructive testing have become more apparent. Efforts to address these challenges include exploring automated inspection using machine learning. However, the lack of anomalous data with defects poses a barrier to improving the accuracy of automated inspection through machine learning. Therefore, in this study, we propose a method for automated LUVT inspection using an anomaly detection approach with a diffusion model that can be trained solely on negative examples (defect-free data). We experimentally confirmed that our proposed method improves defect detection and localization compared to general object detection algorithms used previously.

Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking. However, previous advanced self-supervised dense blocking approaches require domain-specific training on the target domain, which limits the benefits and rapid adaptation of these methods. To address this issue, we propose UniBlocker, a dense blocker that is pre-trained on a domain-independent, easily-obtainable tabular corpus using self-supervised contrastive learning. By conducting domain-independent pre-training, UniBlocker can be adapted to various downstream blocking scenarios without requiring domain-specific fine-tuning. To evaluate the universality of our entity blocker, we also construct a new benchmark covering a wide range of blocking tasks from multiple domains and scenarios. Our experiments show that the proposed UniBlocker, without any domain-specific learning, significantly outperforms previous self- and unsupervised dense blocking methods and is comparable and complementary to the state-of-the-art sparse blocking methods.

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.