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Over the last decade a wave of U.S. states began reimbursing doula services through Medicaid, hoping to improve infant health and narrow stark racial gaps in birth outcomes. I evaluate these mandates using the staggered 2021-2024 rollout, a panel of 32.1 million births from CDC WONDER (2016-2024), and a newly assembled measure of the state doula workforce drawn from the national provider registry. Identification comes from the policy's timing rather than from comparing doula users to non-users, addressing the selection problem that limits the existing observational literature. On average I find no detectable effect on low birth weight (LBW). Consistent with the heterogeneity emphasized by Peet (2022) and the maternal-health-disparities literature, however, the effect concentrates among the group at greatest risk: Black mothers, for whom LBW falls by roughly half a percentage point (about 5% of the baseline) in the states with the longest exposure, with flat pre-trends and a coherent upward shift in the birth-weight distribution. The estimate is marginal once I use inference valid for few treated clusters, and the binding constraint is statistical power: most mandates took effect in

Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance. Retrieval-Augmented Generation (RAG) mitigates these risks by grounding responses in an explicitly maintained corpus, but end-to-end performance depends critically on retrieval configuration and on evaluation beyond multiple-choice formats. We extend PubHealthBench, a question answering (QA) benchmark of 7,929 questions derived from UK Government public health guidance, into a retrieval-augmented setting and systematically evaluate retrieval and generation choices. We compare dense, sparse, and hybrid retrieval across multiple embedding models and corpus variants, and show that hybrid retrieval consistently improves recall and ranking quality, with chunk length and topic interacting with ranking performance. Providing retrieved context substantially increases multiple-choice accuracy across a diverse set of LLMs, enabling smaller open-weight models to match or outperform larger models used without retrieval, with gains primarily driven by retrieval quality and careful context se

Semiclassical gravity couples classical gravity to the quantized matter in meanfield approximation. The meanfield coupling is problematic for two reasons. First, it ignores the quantum fluctuation of matter distribution. Second, it violates the linearity of the quantum dynamics. The first problem can be mitigated by allowing stochastic fluctuations of the geometry but the second problem lies deep in quantum foundations. Restoration of quantum linearity requires a conceptual approach to hybrid classical-quantum coupling. Studies of the measurement problem and the quantum-classical transition point to the solution. It is based on a postulated mechanism of spontaneous quantum monitoring plus feedback. This approach eliminates Schrödinger cat states, takes quantum fluctuations into account, and restores the linearity of quantum dynamics. Such a captivating conceptionally `healthier' semiclassical theory exists in the Newtonian limit, but its relativistic covariance hits a wall. Here we will briefly recapitulate the concept and its realization in the nonrelativistic limit. We emphasize that the long-known obstacles to the relativistic extension lie in quantum foundations.

The recent emergence of extreme climate events has significantly raised awareness about sustainable living. In addition to developing energy-saving materials and technologies, existing research mainly relies on traditional methods that encourage behavioral shifts towards sustainability, which can be overly demanding or only passively engaging. In this work, we propose to employ recommendation systems to actively nudge users toward more sustainable choices. We introduce Green Recommender Aligned with Personalized Eating (GRAPE), which is designed to prioritize and recommend sustainable food options that align with users' evolving preferences. We also design two innovative Green Loss functions that cater to green indicators with either uniform or differentiated priorities, thereby enhancing adaptability across a range of scenarios. Extensive experiments on a real-world dataset demonstrate the effectiveness of our GRAPE.

Social media platforms increasingly employ proactive moderation techniques, such as detecting and curbing toxic and uncivil comments, to prevent the spread of harmful content. Despite these efforts, such approaches are often criticized for creating a climate of censorship and failing to address the underlying causes of uncivil behavior. Our work makes both theoretical and practical contributions by proposing and evaluating two types of emotion monitoring dashboards to users' emotional awareness and mitigate hate speech. In a study involving 211 participants, we evaluate the effects of the two mechanisms on user commenting behavior and emotional experiences. The results reveal that these interventions effectively increase users' awareness of their emotional states and reduce hate speech. However, our findings also indicate potential unintended effects, including increased expression of negative emotions (Angry, Fear, and Sad) when discussing sensitive issues. These insights provide a basis for further research on integrating proactive emotion regulation tools into social media platforms to foster healthier digital interactions.

This study presents a dual-mode interface design concept for social media platforms aimed at reducing social comparison in health-related content among Korean MZ (Millennials and Gen-Z) users. The proposed "Inspiration" and "Reality" modes allow users to toggle between curated, idealized posts and more realistic, candid content. This approach aims to alleviate negative psychological effects, such as decreased self-esteem and body dissatisfaction. The pre-study outlines the design framework and discusses potential implications for user satisfaction, perceived authenticity, and mental well-being.

With the increasing popularity of cryptocurrencies and blockchain technology, smart contracts have become a prominent feature in developing decentralized applications. However, these smart contracts are susceptible to vulnerabilities that hackers can exploit, resulting in significant financial losses. In response to this growing concern, various initiatives have emerged. Notably, the SWC vulnerability list played an important role in raising awareness and understanding of smart contract weaknesses. However, the SWC list lacks maintenance and has not been updated with new vulnerabilities since 2020. To address this gap, this paper introduces the Smart Contract Weakness Enumeration (SWE), a comprehensive and practical vulnerability list up until 2023. We collect 273 vulnerability descriptions from 86 top conference papers and journal papers, employing open card sorting techniques to deduplicate and categorize these descriptions. This process results in the identification of 40 common contract weaknesses, which are further classified into 20 sub-research fields through thorough discussion and analysis. SWE provides a systematic and comprehensive list of smart contract vulnerabilities,

The physical environment you navigate strongly determines which communities and people matter most to individuals. These effects drive both personal access to opportunities and the social capital of communities, and can often be observed in the personal mobility traces of individuals. Traditional social media feeds underutilize these mobility-based features, or do so in a privacy exploitative manner. Here we propose a consent-first private information sharing paradigm for driving social feeds from users' personal private data, specifically using mobility traces. This approach designs the feed to explicitly optimize for integrating the user into the local community and for social capital building through leveraging mobility trace overlaps as a proxy for existing or potential real-world social connections, creating proportionality between whom a user sees in their feed, and whom the user is likely to see in person. These claims are validated against existing social-mobility data, and a reference implementation of the proposed algorithm is built for demonstration. In total, this work presents a novel technique for designing feeds that represent real offline social connections through

A global shift toward healthier diets could sharply reduce livestock farming, free up agricultural land, and cut emissions from land-use change by 85% by 2050。 At the same time, fruit, vegetable, nut, and legume production could soar, creating a sweeping transformation of what the world grows and eats

Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Bayesian experimental design, that identifies critically important information within a dataset, while ignoring data that is either misleading or brings unnecessary complexity to the surrogate model of choice. Our method improves sample-wise error convergence and eliminates instances where more data leads to worse performance and instabilities of the surrogate model, often termed sample-wise ``double descent''. We find these instabilities are a result of the complexity of the underlying map and linked to extreme events and heavy tails. Our approach has two key features. First, the selection algorithm dynamically couples the chosen model and data. Data is chosen based on its merits towards improving the selected model, rather than being compared strictly against other data. Second, a natural convergence of the method removes the need for dividing the data into training, testing, and validation sets. Instead, the selection metric inherently assesses testing and validation error through global statistics of the