Titlebook: Advances in Data Science; Ilke Demir,Yifei Lou,Kathrin Welker Book 2021 The Authors and the Association for Women in Mathematics 2021 data

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Osteoporosis

Category Theory and Computer Programminging transformed approximated Heaviside functions (AHFs) for better visualization. In particular, we provide an efficient method for directly computing the scaling and shifting factors of the transformed AHFs, so that blurred edges can be improved accurately. To recover more image structures, we give

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Hyperlipidemia

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Categorical fixed point calculus,have proven very efficient to extract meaningful information from images. Our goal is to learn a mapping from a binary image of a 2D shape to a parametric Bézier curve representation of the medial axis of the shape using a convolutional neural network. We determine the most salient curves in the Blu

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ECG769

An introduction to ,-categories,n ., which is not a polynomial, component-wise to an affine linear functions . + .. The rows of . are referred to as ‘weights’ and . is the ‘bias vector’. The Universal Approximation Theorem for Neural Networks implies that any continuous function on a compact set can be approximated arbitrarily pre

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A calculus for collections and aggregates, widely used in social and data sciences with the goal of learning latent topics that emerge, evolve, and fade over time. Previous work on dynamic topic modeling primarily employ the method of nonnegative matrix factorization (NMF), where slices of the data tensor are each factorized into the produc

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Neil Dewar,Samuel C. Fletcher,Laurenz Hudetzrse signals from a small number of linear measurements, exploiting not only the sparsity but also certain correlations between the signals. Typically, the assumption is that the collection of signals shares a common ., allowing the problem to be solved more efficiently (or with fewer measurements) t