10,000 publications from this institution
Abstract During the Space Telescope and Optical Reverberation Mapping Project observations of NGC 5548, the continuum and emission-line variability became decorrelated during the second half of the six-month-long observing campaign. Here we present Swift and Chandra X-ray spectra of NGC 5548 obtained as part of the campaign. The Swift spectra show that excess flux (relative to a power-law continuum) in the soft X-ray band appears before the start of the anomalous emission-line behavior, peaks during the period of the anomaly, and then declines. This is a model-independent result suggesting that the soft excess is related to the anomaly. We divide the Swift data into on- and off-anomaly spectra to characterize the soft excess via spectral fitting. The cause of the spectral differences is likely due to a change in the intrinsic spectrum rather than to variable obscuration or partial covering. The Chandra spectra have lower signal-to-noise ratios, but are consistent with the Swift data. Our preferred model of the soft excess is emission from an optically thick, warm Comptonizing corona, the effective optical depth of which increases during the anomaly. This model simultaneously explains all three observations: the UV emission-line flux decrease, the soft-excess increase, and the emission-line anomaly.
Ensemble methods using the same underlying algorithm trained on different subsets of observations have recently received increased attention as practical prediction tools for massive datasets. We propose Subsemble: a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a clever form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. We give an oracle result that provides a theoretical performance guarantee for Subsemble. Through simulations, we demonstrate that Subsemble can be a beneficial tool for small to moderate sized datasets, and often has better prediction performance than the underlying algorithm fit just once on the full dataset. We also describe how to include Subsemble as a candidate in a SuperLearner library, providing a practical way to evaluate the performance of Subsemlbe relative to the underlying algorithm fit just once on the full dataset.
A chiral metal-organic framework, MOF-520, was used to coordinatively bind and align molecules of varying size, complexity, and functionality.The reduced motional degrees of freedom obtained with this coordinative alignment method allowed the structures of molecules to be determined by single-crystal x-ray diffraction techniques.The chirality of the MOF backbone also served as a reference in the structure solution for an unambiguous assignment of the absolute configuration of bound molecules.Sixteen molecules representing four common functional groups (primary alcohol, phenol, vicinal diol, and carboxylic acid), ranging in complexity from methanol to plant hormones (gibberellins, containing eight stereocenters), were crystallized and had their precise structure determined.We distinguished single and double bonds in gibberellins, and we enantioselectively crystallized racemic jasmonic acid, whose absolute configuration had only been inferred from derivatives.
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
Abstract The strengths of H 2 and CO chemisorption (by TPD) and the catalytic activity of Pd/SiO2 for hydrogenation of CO to CH 4 have been investigated as a function of dispersion.
This paper introduces a novel synaptic model called a comparative synapse. Compared with traditional synapses, the new model is multiplication free, being thus attractive for digital implementations. Our results suggests that in an adaptive layer with binary outputs, the synaptic model does not significantly affect the system performances, provided that the input data is properly projected via a nonlinear preprocessor into a separable space. A set of benchmark classification problems were considered to illustrate this property for the case of the comparative synapse and a nonlinear preprocessor defined by fuzzy membership functions.
10. Community Health Assessment or Healthy Community Assessment: Whose Community? Whose Health? Whose Assessment? was published in Community Organizing and Community Building for Health and Social Equity, 4th edition on page 147.