An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients

We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a Multi-Branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present

An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients

We propose improvements to the artificial neural network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a multi-branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present

ACUTE CORONARY SYNDROMES AND DEPRESSION: ROLE OF CIRCULATING ENDOTHELIAL PROGENITOR CELLS

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Frequency and clinical correlates of bipolar features in acute coronary syndrome patients

Depression and acute coronary syndrome (ACS) are both extremely prevalent diseases. Studies aimed at evaluating whether depression is an independent risk factor for cardiac events provided no definitive results. In most of these studies, depression has been broadly defined with no differentiation between unipolar (MDD) versus bipolar forms (BD). The aim of this study was to evaluate the frequency of DSM-IV BD (bipolar I and bipolar II subtypes, cyclothymia), as well as temperamental or isolated bipolar features in a sample of 171 patients hospitalized for ACS. We also explored whether these psychopathological conditions were associated with some clinical characteristics of ACS