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The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the Gaia satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the whole wavelength range, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels to all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from Gaia and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.
This population-based cohort study examines the appropriateness of antibiotic prescribing in South Carolina via aggregated pharmacy claims data matched with diagnosis codes from medical claims. Inappropriate antibiotic prescribing decreased from 30.2% in 2012 to 22.6% in 2017 (P < 0.001) and was more common in adults >40 years old.
We examine the least squares approximation C to a symmetric matrix B, when all diagonal elements get weight w relative to all nondiagonal elements. WhenB has positivity p and C is constrained to be positive semi-definite, our main result states that, when w ≥1/2, then the rank of C is never greater than p, and when w ≤1/2 then the rank of C is at least p. For the problem of approximating a given n × n matrix with a zero diagonal by a squared-distance matrix, it is shown that the sstress criterion leads to a similar weighted least squares solution with w =(n+2)/4; the main result remains true. Other related problems and algorithmic consequences are briefly discussed.
This chapter provides an overview of the impacts of crisis and trauma on the LGBTQ+ populations. Additionally, the authors review barriers to seeking mental health services as they particularly apply to sexual and gender-expansive clients. Discussion of suicide and self-harm and of individual, group, and community violence are also provided. Finally, as a specific example, a case study from the Pulse nightclub shooting is presented.
Single crystal X-ray diffraction patterns reveal that the structure of selected anauxite crystals is the same as the structure of macroscopic kaolinite crystals. Anauxite and kaolinite crystals are intergrowths on a domain scale of units in pseudotwin orientations. Individual domains in anauxite have the triclinic geometry of kaolinite, and give X-ray reflections that compare closely in intensity with those calculated from the atomic parameters of kaolinite. Large crushed crystals of anauxite give powder patterns identical with that of kaolinite. Because it has been shown recently that the chemical composition of anauxite is also identical with that of kaolinite, it is recommended that the term “anauxite” no longer be used.
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Eigenvalues of the Laplacian matrix of a graph have been widely used in studying connectivity and expansion properties of networks, and also in analyzing random walks on a graph. Independently, statisticians introduced various optimality criteria in experimental design, the goal being to obtain more accurate estimates of quantities of interest in an experiment. It turns out that the most popular of these optimality criteria for block designs are determined by the Laplacian eigenvalues of the concurrence graph, or of the Levi graph, of the design. The most important optimality criteria, called A (average), D (determinant) and E (extreme), are related to the conductance of the graph as an electrical network, the number of spanning trees, and the isoperimetric properties of the graphs, respectively. The number of spanning trees is also an evaluation of the Tutte polynomial of the graph, and is the subject of the Merino–Welsh conjecture relating it to acyclic and totally cyclic orientations, of interest in their own right. This chapter ties these ideas together, building on the work in [4] and [5].
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
Edited by
R. A. Bailey, University of St Andrews, Scotland,Peter J. Cameron, University of St Andrews, Scotland,Yaokun Wu, Shanghai Jiao Tong University, China
This collection of four short courses looks at group representations, graph spectra, statistical optimality, and symbolic dynamics, highlighting their common roots in linear algebra. It leads students from the very beginnings in linear algebra to high-level applications: representations of finite groups, leading to probability models and harmonic analysis; eigenvalues of growing graphs from quantum probability techniques; statistical optimality of designs from Laplacian eigenvalues of graphs; and symbolic dynamics, applying matrix stability and K-theory. An invaluable resource for researchers and beginning Ph.D. students, this book includes copious exercises, notes, and references.