Activist fund Palliser Capital has taken a stake in Japanese washlet maker Toto, and is urging the company to boost promotion of its little-known chip parts business. Activist fund Palliser Capital has taken a stake in Japanese washlet maker Toto and is pushing the firm to ramp up promotion of its little-known chip parts business in a bid to unlock value from the artificial intelligence boom. The U.K.-based fund sent a letter to Toto's board last week calling for more disclosure about its advanced ceramics segment. The segment produces electrostatic chucks used in the manufacturing of NAND memory chips, and Palliser views the toilet maker as "the most undervalued and overlooked AI memory beneficiary," the documents show. A representative for Toto declined to comment. Insatiable demand for AI infrastructure has sent memory prices skyrocketing in recent months, boosting shares of chipmakers such as Kioxia Holdings to record highs.

Foreign workers in Japan became one of the main topics of all parties in the Feb. 8 Lower House election, which took place just after a Jan. 23 Cabinet decision calling for 1,231,900 foreign workers by March 2029 in 19 sectors facing acute labor shortages. While some parties argued for strictly monitoring foreign nationals or setting quotas on their numbers, especially at the local level, an artificial-intelligence engineer-led party that went into the election with no seats and emerged with 11 proportional representation seats proposed the increased use of AI to replace workers, including foreign nationals, as a solution to concerns about more foreign workers. Team Mirai, founded in May and led by Takahiro Anno, won four seats in the Tokyo block and three in the South Kanto block, along with one seat each in the Tohoku, North Kanto, Tokai, and Kyushu blocks. 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. With your current subscription plan you can comment on stories.

An oligopoly is a market in which the price of goods is controlled by a few firms. Cournot introduced the simplest game-theoretic model of oligopoly, where profit-maximizing behavior of each firm results in market failure. Furthermore, when the Cournot oligopoly game is infinitely repeated, firms can tacitly collude to monopolize the market. Such tacit collusion is realized by the same mechanism as direct reciprocity in the repeated prisoner's dilemma game, where mutual cooperation can be realized whereas defection is favorable for both prisoners in a one-shot game. Recently, in the repeated prisoner's dilemma game, a class of strategies called zero-determinant strategies attracts much attention in the context of direct reciprocity. Zero-determinant strategies are autocratic strategies which unilaterally control payoffs of players by enforcing linear relationships between payoffs. There were many attempts to find zero-determinant strategies in other games and to extend them so as to apply them to broader situations. In this paper, first, we show that zero-determinant strategies exist even in the repeated Cournot oligopoly game, and that they are quite different from those in the repeated prisoner's dilemma game. Especially, we prove that a fair zero-determinant strategy exists, which is guaranteed to obtain the average payoff of the opponents. Second, we numerically show that the fair zero-determinant strategy can be used to promote collusion when it is used against an adaptively learning player, whereas it cannot promote collusion when it is used against two adaptively learning players. Our findings elucidate some negative impact of zero-determinant strategies in the oligopoly market.

To accelerate therapeutic antibody discovery, computational methods, especially machine learning, have attracted considerable interest for predicting specific interactions between antibody candidates and target antigens such as viruses and bacteria.

Breakthroughs, discoveries, and DIY tips sent every weekday. Humans don't need to blast off into space to find some truly alien-looking wonders. The deepest depths of our ocean are like another planet, teeming with the charismatic "Casper" octopus, the carnivorous (aka the flying spaghetti monster), and even some sharks . A team from Kumamoto University in Japan recently uncovered a deep-sea anemone that has a tight bond with hermit crabs. These wispy pink invertebrates build shell-like "homes" for the crabs.

In football, attacking teams attempt to break through the opponent's defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players' spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.