This study examines the lexical and syntactic interventions of human and LLM proofreading aimed at improving overall intelligibility in identical second language writings, and evaluates the consistency of outcomes across three LLMs (ChatGPT-4o, Llama3.1-8b, Deepseek-r1-8b). Findings show that both human and LLM proofreading enhance bigram lexical features, which may contribute to better coherence and contextual connectedness between adjacent words. However, LLM proofreading exhibits a more generative approach, extensively reworking vocabulary and sentence structures, such as employing more diverse and sophisticated vocabulary and incorporating a greater number of adjective modifiers in noun phrases. The proofreading outcomes are highly consistent in major lexical and syntactic features across the three models.

Agentic LLM AI agents are often little more than autonomous chatbots: actors following scripts, often controlled by an unreliable director. This work introduces a bottom-up framework that situates AI agents in their environment, with all behaviors triggered by changes in their environments. It introduces the notion of aspects, similar to the idea of umwelt, where sets of agents perceive their environment differently to each other, enabling clearer control of information. We provide an illustrative implementation and show that compared to a typical architecture, which leaks up to 83% of the time, aspective agentic AI enables zero information leakage. We anticipate that this concept of specialist agents working efficiently in their own information niches can provide improvements to both security and efficiency.

We study policy evaluation of offline contextual bandits subject to unobserved confounders. Sensitivity analysis methods are commonly used to estimate the policy value under the worst-case confounding scenario within a given uncertainty set. However, existing work often resorts to some coarse relaxation of the uncertainty set for the sake of tractability, leading to overly conservative estimation of the policy value. In this paper, we propose a general estimator that provides a sharp lower bound of the policy value using convex programming. The generality of our estimator enables various extensions such as sensitivity analysis using f-divergence, model selection with cross validation and information criterion, and robust policy learning with the sharp lower bound. Furthermore, our estimation method can be reformulated as an empirical risk minimization problem thanks to the strong duality, which enables us to provide strong theoretical guarantees of the proposed estimator using M-estimation techniques.

Kernel methods have been among the most popular techniques in machine learning, where learning tasks are solved using the property of reproducing kernel Hilbert space (RKHS). In this paper, we propose a novel data analysis framework with reproducing kernel Hilbert $C^*$-module (RKHM) and kernel mean embedding (KME) in RKHM. Since RKHM contains richer information than RKHS or vector-valued RKHS (vv RKHS), analysis with RKHM enables us to capture and extract structural properties in multivariate data, functional data and other structured data. We show a branch of theories for RKHM to apply to data analysis, including the representer theorem, and the injectivity and universality of the proposed KME. We also show RKHM generalizes RKHS and vv RKHS. Then, we provide concrete procedures for employing RKHM and the proposed KME to data analysis.

Actress and freelance writer Yumi Ishikawa, whose campaign against mandatory high heels in the workplace saw broad public support, has stepped up her fight against strict corporate dress codes in Japan to include a ban at some firms on women wearing glasses. Ishikawa shot to prominence this year with her drive against Japanese office culture, in which high heels are seen as near-obligatory when job-hunting or working in the office. Known by the hashtag #KuToo -- a play on words from the Japanese word "kutsu," meaning "shoes," and "kutsuu," meaning "pain" -- the campaign was recognized Monday for having generated one of the buzzwords of the year. Ishikawa's latest petition to relax the rules, delivered to the labor ministry on Tuesday, has attracted more than 31,000 signatures. "The root cause of the problem is that (there are companies) that have rules for women only -- such as a ban on wearing glasses or a requirement to wear make-up," the 32-year-old told reporters.

Actress and freelance writer Yumi Ishikawa, whose campaign against mandatory high heels in the workplace won broad public support, has stepped up her fight against strict corporate dress codes in Japan -- including a ban at some firms on women wearing glasses. Ishikawa shot to prominence this year with her drive against Japanese office culture, in which high heels are seen as near-obligatory when job-hunting or working in the office. Known by the hashtag #KuToo -- a play on words from the Japanese word "kutsu", meaning "shoes," and "kutsuu," meaning "pain" -- the campaign was chosen on Monday as one of the buzzwords of the year. Ishikawa's latest petition to relax the rules, delivered to the labor ministry on Tuesday, has attracted more than 31,000 signatures. "The root cause of the problem is that (there are companies) that have rules for women only -- such as a ban on wearing glasses or a requirement to wear make-up," the 32-year-old told reporters.

A new technology uses artificial intelligence to generate synthetic images that can pass as real. The technology was developed by a team led by Hiroshi Ishikawa, a professor at Japan's Waseda University. It uses convolutional neural networks, a type of deep learning, to predict missing parts of images. The technology could be used in photo-editing apps. It can also be used to generate 3-D images from real 2-D images.

But "Saya" was a different kind of star, because she is the product of a Tokyo computer lab. And like all "parents", her creators have big ambitions for her, writes the BBC's Yvette Tan. "'I think I've seen her somewhere' or'She looks like someone I know' are what people usually say when they see Saya," says Yuka Ishikawa, one half of the husband and wife graphic artist team behind Saya. When the couple first posted pictures of the hyper-realistic schoolgirl online last year, it was a revelation about what can be achieved with computer design. Her slightly askew school tie, heavily fringed hair, freckled skin and teenage pout left thousands trying to work out whether or not she was a real person.

The fitness class at the Do Life Shinagawa nursing home begins at 10:30 a.m., drawing about two dozen eager participants -- many of them pushing 70, 80 and even 90 years of age. At the front of the room is a human instructor, but it is the 16 inch tall robot standing on a table to his right that the crowd is paying attention to. The sleek, white humanoid robot is turned on and with fluid motion, stands up and spreads out its arms. "Why are you waking me up?" the robot asks in Japanese in an almost child-like voice. "Oh, you want to exercise?"

Since making its first public appearance last October, Sharp Corp.'s humanoid robot smartphone, RoBoHoN, has created a lot of buzz. But the eye-catching 19.5-cm-tall robot, which can walk, talk and dance but still works as a phone, has also left many people wondering why a phone and a robot should be bundled together, and what Sharp's long game is. As it turns out, Sharp is dead serious about the project. Officials say the firm thinks a robot phone could be the next big thing -- in fact, as big as the smartphone boom. The company is even envisioning a science fiction-like future in which everyone will carry his or her own robot phone that doubles as a personal assistant.