Recent years have witnessed intense development of randomized methods for low-rank approximation. These methods target principal component analysis and the calculation of truncated singular value decompositions. The present article presents an essentially black-box, foolproof implementation for Mathworks' MATLAB, a popular software platform for numerical computation.
View Article and Find Full Text PDFBackground: Thrombin generation (TG) is a pivotal process in achieving hemostasis. Coagulation profiles during pregnancy and early neonatal period are different from that of normal (non-pregnant) adults. In this ex vivo study, the differences in TG in maternal and cord plasma relative to normal adult plasma were studied.
View Article and Find Full Text PDFA complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors, followed by (2) taking the absolute value of every entry of the resulting vectors, followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as data-driven multiscale windowed power spectra, data-driven multiscale windowed absolute spectra, data-driven multiwavelet absolute values, or (in their most general configuration) data-driven nonlinear multiwavelet packets. Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials.
View Article and Find Full Text PDFBackground: Warfarin is routinely used in the prevention and treatment of prothrombotic events. During initiation of warfarin therapy levels of factor (F) VII and protein C decrease rapidly but prothrombin, FIX and FX decline much slower. Therefore, propagation of thrombin generation (TG) remains unaffected much longer, increasing the risk of inadequate anticoagulation.
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