A husband and wife have died after being trapped in a private sauna room that caught fire in Japan on Monday. Tokyo police are investigating whether a faulty doorknob trapped the couple inside the room at Sauna Tiger, in the city's Akasaka district, local media has reported. Investigators also found that the facility's emergency alarm system was switched off, and allegedly had been for two years. We offer our deepest condolences... and our heartfelt sympathies for the deep grief and pain that cannot be expressed in words, Sauna Tiger said in a statement on its website. The victims have been named by local media as Yoko Matsuda, a 37-year-old nail artist, and her husband Masanari, 36, who ran a beauty salon.

The model is built to detect when crimes are being "contemplated." A US telecom company trained an AI model on years of inmates' phone and video calls and is now piloting that model to scan their calls, texts, and emails in the hope of predicting and preventing crimes. Securus Technologies president Kevin Elder told that the company began building its AI tools in 2023, using its massive database of recorded calls to train AI models to detect criminal activity. It created one model, for example, using seven years of calls made by inmates in the Texas prison system, but it has been working on building other state-or county-specific models. Over the past year, Elder says, Securus has been piloting the AI tools to monitor inmate conversations in real time (the company declined to specify where this is taking place, but its customers include jails holding people awaiting trial, prisons for those serving sentences, and Immigrations and Customs Enforcement detention facilities). "We can point that large language model at an entire treasure trove [of data]," Elder says, "to detect and understand when crimes are being thought about or contemplated, so that you're catching it much earlier in the cycle."

Joint Senior Corresponding Author: Michael Nercessian Email: michael.nercessian@berkeley.edu Abstract Randomized controlled trials (RCTs) are indispensable for establishing the clinical value of medical artificial-intelligence (AI) tools, yet their high cost and long timelines hinder timely validation as new models emerge rapidly. Here, we propose BRIDGE, a data-reuse RCT design for AI-based risk models. AI risk models support a broad range of interventions, including screening, treatment selection, and clinical alerts. BRIDGE trials recycle participant-level data from completed trials of AI models when legacy and updated models make concordant predictions, thereby reducing the enrollment requirement for subsequent trials. We provide a practical checklist for investigators to assess whether reusing data from previous trials allows for valid causal inference and preserves type I error. Using real-world datasets across breast cancer, cardiovascular disease, and sepsis, we demonstrate concordance between successive AI models, with up to 64.8% overlap in top 5% high-risk cohorts. We then simulate a series of breast cancer screening studies, where our design reduced required enrollment by 46.6%--saving over US$2.8 million--while maintaining 80% power. By transforming trials into adaptive, modular studies, our proposed design makes Level I evidence generation feasible for every model iteration, thereby accelerating cost-effective translation of AI into routine care . Introduction Artificial intelligence (AI) models have the potential to transform patient care by identifying high-risk individuals using high-dimensional data--such as imaging, electronic health records, or time-series data--to personalize screening, prevention, and treatment decisions across a range of diseases, including cancer and heart disease.

This paper considers the problem of selecting a formula for identifying a causal quantity of interest among a set of available formulas.

Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity.

We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In particular, we show that if the hypothesis testing divergence of the one pair dominates over that of the other pair, then this dominance holds for every f -divergence. This approach completely characterizes (ε,δ)-QDP mechanisms by identifying the most informative (ε,δ)-DP quantum state pairs. We apply this to analyze the stability of quantum differentially private learning algorithms, generalizing classical results to the case δ > 0. Additionally, we study precise limits for privatized hypothesis testing and privatized quantum parameter estimation, including tight upper-bounds on the quantum Fisher information under QDP . Finally, we establish near-optimal contraction bounds for differentially private quantum channels with respect to the hockey-stick divergence. I. Introduction A fundamental challenge in modern machine learning is the trade-off between privacy and information extraction. In this work, we explicitly treat both sides: privacy (ensuring that algorithmic outputs do not reveal significant information about the input data of the respondents) and the investigator's goal to extract as much useful information as possible from data for accurate learning and estimation. With the rapid advancement of machine learning, a key concern is about ensuring the privacy of learning algorithms, meaning that their outputs should not reveal significant information about the input data. Differential privacy (DP) provides a rigorous mathematical framework to balance these opposing requirements. Accordingly, we structure our contributions in three steps: first step (privacy), second step (information extraction under privacy constraints), and third step, the quantum channel setup, where the situation is more complicated, and we mark the transition to each step explicitly in the text. This step develops the privacy side of the trade-off from the respondent's perspective by studying the stability [1], [2] of learning algorithms. From the respondent's viewpoint, privacy means that the inclusion or exclusion of their individual data should not materially affect the mechanism's output, so that they can contribute data without fear of singled-out inference. An algorithm is considered stable if its output does not change drastically when a single respondent's data is changed; this point-wise insensitivity is precisely the respondent-centric guarantee we seek.

This research explores the evolution of the detective archetype in French detective fiction through computational analysis. Using quantitative methods and character-level embeddings, we show that a supervised model is able to capture the unity of the detective archetype across 150 years of literature, from M. Lecoq (1866) to Commissaire Adamsberg (2017). Building on this finding, the study demonstrates how the detective figure evolves from a secondary narrative role to become the central character and the "reasoning machine" [35] of the classical detective story. In the aftermath of the Second World War, with the importation of the hardboiled tradition into France, the archetype becomes more complex, navigating the genre's turn toward social violence and moral ambiguity.

Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity.

Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp, which can be an extremely effort-consuming task. Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects' mobile phones, and help prosecutors and investigators search into the data and get valuable insights. Our semantic enrichment process involves extracting message data and modeling it using a knowledge graph, generating transcriptions of voice messages, and annotating the data using an end-to-end entity extraction approach. We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search. The proposed approach ensures that users can verify the information by accessing the original data. While we report about early results and prototypes developed in the context of an ongoing project, our proposal has undergone practical applications with real investigation data. As a consequence, we had the chance to interact closely with prosecutors, collecting positive feedback but also identifying interesting opportunities as well as promising research directions to share with the research community.

Plus: OpenAI's parental controls have come into force Generative AI has enabled the production of child sexual abuse images to skyrocket. Now the leading investigator of child exploitation in the US is experimenting with using AI to distinguish AI-generated images from material depicting real victims, according to a new government filing. The Department of Homeland Security's Cyber Crimes Center, which investigates child exploitation across international borders, has awarded a $150,000 contract to San Francisco-based Hive AI for its software, which can identify whether a piece of content was AI-generated. The need to cut emissions and adapt to our warming world is growing more urgent. This year, we've seen temperatures reach record highs, as they have nearly every year for the last decade. Climate-fueled natural disasters are affecting communities around the world, costing billions of dollars.