The government plans to submit a bill to revise the economic security promotion law during the current session of parliament that began on Wednesday. The Japanese government plans to revise the economic security promotion law to support companies with economic security-linked projects overseas. This will be the first revision of the law, established in 2022. The move comes amid a rapidly changing international environment, as the Ukraine-Russia war drags on and China continues to flex its economic muscle. Competition is also intensifying in the development of artificial intelligence and other cutting-edge technologies.

It's been nearly 20 years since Apple was this untethered from its tech peers, giving investors an appealing alternative to the artificial intelligence-fueled volatility that has gripped most other corners of the stock market in recent weeks. Apple's 40-day correlation to the Nasdaq 100 Index tumbled to 0.21 last week, the lowest since 2006, according to data compiled by Bloomberg. Its correlation with the benchmark has been on the decline since May, when it reached 0.92, as Apple's decision to mostly sit out the AI arms race has turned it into an outlier compared with many of its rivals. A correlation of 1 means the two securities are moving in perfect unison, while a reading of -1 signals they are moving opposite each other. "Apple's lack of correlation is 100% a positive right now," said Art Hogan, who helps oversee $25 billion as chief market strategist at B. Riley Wealth.

Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks.

More recently, Newton-type methods using sparsified Hessian matrices have demonstrated promising results on OT computation, but there still remain a lot of unresolved open questions.

Longer contexts present a new attack surface for adversarial attacks. In search of a "fruit-fly" of long-context vulnerabilities, we study Many-shot Jailbreaking (MSJ; Figure 1), a simple yet effective and scalable jailbreak.

The application of reinforcement learning (RL) involving interactions with individuals has grown significantly in recent years.

This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

The word embedding space in neural models is skewed, and correcting this can improve task performance.