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  1. Free, publicly-accessible full text available May 1, 2024
  2. The past 15–20 years has seen a remarkable shift in our understanding of astrocyte contributions to central nervous system (CNS) function. Astrocytes have emerged from the shadows of neuroscience and are now recognized as key elements in a broad array of CNS functions. Astrocytes comprise a substantial fraction of cells in the human CNS. Nevertheless, fundamental questions surrounding their basic biology remain poorly understood. While recent studies have revealed a diversity of essential roles in CNS function, from synapse formation and function to blood brain barrier maintenance, fundamental mechanisms of astrocyte development, including their expansion, migration, and maturation, remain to be elucidated. The coincident development of astrocytes and synapses highlights the need to better understand astrocyte development and will facilitate novel strategies for addressing neurodevelopmental and neurological dysfunction. In this review, we provide an overview of the current understanding of astrocyte development, focusing primarily on mammalian astrocytes and highlight outstanding questions that remain to be addressed. We also include an overview of Drosophila glial development, emphasizing astrocyte-like glia given their close anatomical and functional association with synapses. Drosophila offer an array of sophisticated molecular genetic tools and they remain a powerful model for elucidating fundamental cellular and molecular mechanisms governing astrocyte development. Understanding the parallels and distinctions between astrocyte development in Drosophila and vertebrates will enable investigators to leverage the strengths of each model system to gain new insights into astrocyte function. 
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  3. The BUQEYE collaboration (Bayesian Uncertainty Quantification: Errors in Your effective field theory) presents a pedagogical introduction to projection-based, reduced-order emulators for applications in low-energy nuclear physics. The term emulator refers here to a fast surrogate model capable of reliably approximating high-fidelity models. As the general tools employed by these emulators are not yet well-known in the nuclear physics community, we discuss variational and Galerkin projection methods, emphasize the benefits of offline-online decompositions, and explore how these concepts lead to emulators for bound and scattering systems that enable fast and accurate calculations using many different model parameter sets. We also point to future extensions and applications of these emulators for nuclear physics, guided by the mature field of model (order) reduction. All examples discussed here and more are available as interactive, open-source Python code so that practitioners can readily adapt projection-based emulators for their own work. 
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  4. Abstract The field of model order reduction (MOR) is growing in importance due to its ability to extract the key insights from complex simulations while discarding computationally burdensome and superfluous information. We provide an overview of MOR methods for the creation of fast & accurate emulators of memory- and compute-intensive nuclear systems, focusing on eigen-emulators and variational emulators. As an example, we describe how ‘eigenvector continuation’ is a special case of a much more general and well-studied MOR formalism for parameterized systems. We continue with an introduction to the Ritz and Galerkin projection methods that underpin many such emulators, while pointing to the relevant MOR theory and its successful applications along the way. We believe that this guide will open the door to broader applications in nuclear physics and facilitate communication with practitioners in other fields. 
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  5. Active inference proposes a unifying principle for perception and action as jointly minimizing the free energy of an agent’s internal world model. In the active inference literature, world models are typically pre-specified or learned through interacting with an environment. This paper explores the possibility of learning world models of active inference agents from recorded demonstrations, with an application to human driving behavior modeling. The results show that the presented method can create models that generate human-like driving behavior but the approach is sensitive to input features. 
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