Sterile neutrino dark matter: A tale of weak interactions in the strong coupling epoch

We perform a detailed study of the weak interactions of standard model neutrinos with the primordial plasma and their effect on the resonant production of sterile neutrino dark matter. Motivated by issues in cosmological structure formation on small scales, and reported X-ray signals that could be due to sterile neutrino decay, we consider $7$ keV-scale sterile neutrinos. Oscillation-driven production of such sterile neutrinos occurs at temperatures $T \gtrsim 100$ MeV, where we study two significant effects of weakly charged species in the primordial plasma: (1) the redistribution of an input lepton asymmetry; (2) the opacity for active neutrinos. We calculate the redistribution analytically above and below the quark-hadron transition, and match with lattice QCD calculations through the transition. We estimate opacities due to tree level processes involving leptons and quarks above the quark-hadron transition, and the most important mesons below the transition. We report final sterile neutrino dark matter phase space densities that are significantly influenced by these effects, and yet relatively robust to remaining uncertainties in the nature of the quark-hadron transition. We also provide transfer functions for cosmological density fluctuations with cutoffs at $k \simeq 10 \ h \ {\rm Mpc}^{-1}$, that are relevant to galactic structure formation.Comment: 28 pages, 11 figures, code repository at https://github.com/ntveem/sterile-d

Maritime Politics and Policy in the City of Ships

Along the Kennebec River, over 2,500 vessels were launched in Bath, “The City of Ships,” from the 18th century to the present day. Shipbuilding transformed the town—its economy, global prominence, and architecture. This article examines Bath as a case study of the role of politics and policy in Maine’s maritime history. Shipbuilding positioned Bath at the center of national politics and at the center of the nation’s imperial ambitions, while also heavily influencing its architectural fabric and workforce

Forest management is driving the eastern North American boreal forest outside its natural range of variability

Fire is fundamental to the natural dynamics of the North American boreal forest. It is therefore often suggested that the impacts of anthropogenic disturbances (eg logging) on a managed landscape are attenuated if the patterns and processes created by these events resemble those of natural disturbances (eg fire). To provide forest management guidelines, we investigate the long-term variability in the mean fire interval (MFI) of a boreal landscape in eastern North America, as reconstructed from lacustrine (lake-associated) sedimentary charcoal. We translate the natural variability in MFI into a range of landscape age structures, using a simple modeling approach. Although using the array of possible forest age structures provides managers with some flexibility, an assessment of the current state of the landscape suggests that logging has already caused a shift in the age-class distribution toward a stronger representation of young stands with a concurrent decrease in old-growth stands. Logging is indeed quickly forcing the studied landscape outside of its long-term natural range of variability, implying that substantial changes in management practices are required, if we collectively decide to maintain these fundamental attributes of the boreal forest

Spatially varying embedded stochastic galerkin methods for steady-state PDEs.

Existing discretizations for stochastic PDEs, based on a tensor product between the deterministic basis and the stochastic basis, treat the required resolution of uncertainty as uniform across the physical domain. However, solutions to many PDEs of interest exhibit spatially localized features that may result in uncertainty being severely over or under-resolved by existing discretizations. In this report, we explore the mechanics and accuracy of using a spatially varying stochastic expansion. This is achieved through an adaptive refinement algorithm where simple error estimates are used to independently drive refinement of the stochastic basis at each point in the physical domain. Results are presented comparing the accuracy of the adaptive techinque to the accuracy achieved using uniform refinement

Strategies for Managing Water Resources in Saltwater Intrusion Environments

2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Graph Neural Networks and Applied Linear Algebra

Sparse matrix computations are ubiquitous in scientific computing. With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can leverage neural networks (NN). Unfortunately, multi-layer perceptron (MLP) neural networks are typically not natural for either graph or sparse matrix computations. The issue lies with the fact that MLPs require fixed-sized inputs while scientific applications generally generate sparse matrices with arbitrary dimensions and a wide range of nonzero patterns (or matrix graph vertex interconnections). While convolutional NNs could possibly address matrix graphs where all vertices have the same number of nearest neighbors, a more general approach is needed for arbitrary sparse matrices, e.g. arising from discretized partial differential equations on unstructured meshes. Graph neural networks (GNNs) are one approach suitable to sparse matrices. GNNs define aggregation functions (e.g., summations) that operate on variable size input data to produce data of a fixed output size so that MLPs can be applied. The goal of this paper is to provide an introduction to GNNs for a numerical linear algebra audience. Concrete examples are provided to illustrate how many common linear algebra tasks can be accomplished using GNNs. We focus on iterative methods that employ computational kernels such as matrix-vector products, interpolation, relaxation methods, and strength-of-connection measures. Our GNN examples include cases where parameters are determined a-priori as well as cases where parameters must be learned. The intent with this article is to help computational scientists understand how GNNs can be used to adapt machine learning concepts to computational tasks associated with sparse matrices. It is hoped that this understanding will stimulate data-driven extensions of classical sparse linear algebra tasks

Focal Adhesion Kinase-mediated Phosphorylation of Beclin1 Protein Suppresses Cardiomyocyte Autophagy and Initiates Hypertrophic Growth

Autophagy is an evolutionarily conserved intracellular degradation/recycling system that is essential for cellular homeostasis but is dysregulated in a number of diseases, including myocardial hypertrophy. Although it is clear that limiting or accelerating autophagic flux can result in pathological cardiac remodeling, the physiological signaling pathways that fine-tune cardiac autophagy are poorly understood. Herein, we demonstrated that stimulation of cardiomyocytes with phenylephrine (PE), a well known hypertrophic agonist, suppresses autophagy and that activation of focal adhesion kinase (FAK) is necessary for PE-stimulated autophagy suppression and subsequent initiation of hypertrophic growth. Mechanistically, we showed that FAK phosphorylates Beclin1, a core autophagy protein, on Tyr-233 and that this post-translational modification limits Beclin1 association with Atg14L and reduces Beclin1-dependent autophagosome formation. Remarkably, although ectopic expression of wild-type Beclin1 promoted cardiomyocyte atrophy, expression of a Y233E phosphomimetic variant of Beclin1 failed to affect cardiomyocyte size. Moreover, genetic depletion of Beclin1 attenuated PE-mediated/FAK-dependent initiation of myocyte hypertrophy in vivo. Collectively, these findings identify FAK as a novel negative regulator of Beclin1-mediated autophagy and indicate that this pathway can facilitate the promotion of compensatory hypertrophic growth. This novel mechanism to limit Beclin1 activity has important implications for treating a variety of pathologies associated with altered autophagic flux

"Libraries model sustainability": The results of an OCLC survey on library contributions to the Sustainable Development Goals

Ce document constitue un modèle de durabilité en bibliothèque. OCLC a mené une enquête auprès de 1700 employés de bibliothèques dans le monde entier pour déterminer comment les bibliothèques contribuent à cinq des objectifs de développement durable des Nations Unies. Dans cet article, du Journal de l’IFLA, nous découvrons des résultats détaillés, montrant que les bibliothèques non seulement mettent en œuvre elles-mêmes des pratiques de durabilité, mais promeuvent également activement un comportement durable parmi leurs utilisateurs