We introduce a new computer program, SYNAPPS, for forward-modeling of supernova (SN) spectroscopy data sets. SYNAPPS is a spectrum fitter embedding a highly parameterized synthetic SN spectrum calculation within a parallel asynchronous optimizer. This open-source code is primarily aimed at the problem of systematically interpreting large sets of SN spectroscopy data. While SYNAPPS should be useful to current SN spectroscopy efforts like the Nearby Supernova Factory, Lick Observatory Supernova Search, Palomar Transient Factory, Harvard Center for Astrophysics SN program, and so on, it could also benefit future similar efforts connected to the Dark Energy Survey, Panoramic Survey Telescope and Rapid Response System, or the Large Synoptic Survey Telescope. Smaller programs are also potential users. SYNAPPS illustrates the potential for data-driven discovery enabled by high-performance computing, where complex physical systems are directly constrained by large information-rich sets of scientific observations. Here, we discuss the motivation of our approach, outline the structure of the code, present some example calculations, and describe a few enhancements in terms of physics modeling, optimization, and computing that we will be pursuing for the future.
Abstract. Identifying small groups of lines, whose removal would cause a severe blackout, is critical for the secure operation of the electric power grid. We show how power grid vulnerability analysis can be studied as a mixed integer nonlinear programming (minlp) problem. Our analysis reveals a special structure in the formulation that can be exploited to avoid nonlinearity and approximate the original problem as a pure combinatorial problem. The key new observation behind our analysis is the correspondence between the Jacobian matrix (a representation of the feasibility boundary of the equations that describe the flow of power in the network) and the Laplacian matrix in spectral graph theory (a representation of the graph of the power grid). The reduced combinatorial problem is known as the network inhibition problem, for which we present a mixed integer linear programming formulation. Our experiments on benchmark power grids show that the reduced combinatorial model provides an accurate approximation, to enable vulnerability analyses of real-sized problems with more than 10,000 power lines.
We describe the design and implementation of KSSOLV, a MATLAB toolbox for solving a class of nonlinear eigenvalue problems known as the Kohn-Sham equations. These types of problems arise in electronic structure calculations, which are nowadays essential for studying the microscopic quantum mechanical properties of molecules, solids and other nanoscale materials. KSSOLV is well suited for developing new algorithms for solving the Kohn-Sham equations and is designed to enable researchers in computational and applied mathematics to investigate the convergence properties of the existing algorithms. The toolbox makes use of the object-oriented programming features available in MATLAB so that the process of setting up a physical system is straightforward and the amount of coding effort required to prototype, test and compare new algorithms is significantly reduced. All of these features should also make this package attractive to other computational scientists and students who wish to study small to medium size systems.
The development of reliable methods for restoring susceptibility after antibiotic resistance arises has proven elusive. A greater understanding of the relationship between antibiotic administration and the evolution of resistance is key to overcoming this challenge. Here we present a data-driven mathematical approach for developing antibiotic treatment plans that can reverse the evolution of antibiotic resistance determinants. We have generated adaptive landscapes for 16 genotypes of the TEM β-lactamase that vary from the wild type genotype “TEM-1” through all combinations of four amino acid substitutions. We determined the growth rate of each genotype when treated with each of 15 β-lactam antibiotics. By using growth rates as a measure of fitness, we computed the probability of each amino acid substitution in each β-lactam treatment using two different models named the Correlated Probability Model (CPM) and the Equal Probability Model (EPM). We then performed an exhaustive search through the 15 treatments for substitution paths leading from each of the 16 genotypes back to the wild type TEM-1. We identified optimized treatment paths that returned the highest probabilities of selecting for reversions of amino acid substitutions and returning TEM to the wild type state. For the CPM model, the optimized probabilities ranged between 0.6 and 1.0. For the EPM model, the optimized probabilities ranged between 0.38 and 1.0. For cyclical CPM treatment plans in which the starting and ending genotype was the wild type, the probabilities were between 0.62 and 0.7. Overall this study shows that there is promise for reversing the evolution of resistance through antibiotic treatment plans.
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