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Soil microbial diversity is crucial to sustaining ecosystem productivity and improving carbon sequestration. Global temperature continues to rise, but how climate warming affects microbial diversity and its capacity to sequester soil organic carbon (SOC) remains uncertain. Here, by conducting a global meta-analysis with 251 paired observations from 102 studies, we showed that, on average, warming reduced bacterial and fungal diversity (measured by richness and Shannon index) by 16.0 and 19.7%, respectively, and SOC by 18.1%. The negative responses of both soil bacterial and fungal diversity to warming became more pronounced with increasing warming magnitude, experimental duration, and decreasing soil nitrogen availability. Under the worst-case climate warming scenario (2010 to 2070, 3.4 increase in °C), soil bacterial diversity and fungal diversity are projected to reduce by 56% and 81%, respectively, over 60 y. Importantly, in addition to the direct impact of warming on SOC, warming-induced declines in microbial diversity also contributed to SOC losses. We highlight that prolonged warming could substantially reduce soil microbial diversity and decrease SOC sequestration, accelerating future warming and underscoring the urgent need for decisive actions to mitigate global climate change. 

eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering. 

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model both the internal state transitions of each arm and the influence of external global environmental contexts. Using the dual decomposition method, we develop a scalable index policy algorithm for solving the CRB problem, and theoretically analyze the asymptotical optimality of this algorithm. In the case when the arm models are unknown, we further propose a model-based online learning algorithm based on the index policy to learn the arm models and make decisions simultaneously. Furthermore, we apply the proposed CRB framework and the index policy algorithm specifically to the demand response decision-making problem in smart grids. The numerical simulations demonstrate the performance and efficiency of our proposed CRB approaches. 

A large fraction of total healthcare expenditure occurs due to end-of-life (EOL) care, which means it is important to study the problem of more carefully incentivizing necessary versus unnecessary EOL care because this has the potential to reduce overall healthcare spending. This paper introduces a principal-agent model that integrates a mixed payment system of fee-for-service and pay-for-performance in order to analyze whether it is possible to better align healthcare provider incentives with patient outcomes and cost-efficiency in EOL care. The primary contributions are to derive optimal contracts for EOL care payments using a principal-agent framework under three separate models for the healthcare provider, where each model considers a different level of risk tolerance for the provider. We derive these optimal contracts by converting the underlying principal-agent models from a bilevel optimization problem into a single-level optimization problem that can be analytically solved. Our results are demonstrated using a simulation where an optimal contract is used to price intracranial pressure monitoring for traumatic brain injuries.