Overview

This volume highlights recent advances in data science, including image processing and enhancement on large data, shape analysis and geometry processing in 2D/3D, exploration and understanding of neural networks, and extensions to atypical data types such as social and biological signals. The contributions are based on discussions from two workshops under Association for Women in Mathematics (AWM), namely the second Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place between July 29 and August 2, 2019 at the Institute for Computational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, and the third Women in Shape (WiSh) Research Collaboration Workshop that took place between July 16 and 20, 2018 at Trier University in Robert-Schuman-Haus, Trier, Germany. These submissions, seeded by working groups at the conference, form a valuable source for readers who are interested in ideas and methods developed in interdisciplinary research fields. The book features ideas, methods, and tools developed through a broad range of domains, ranging from theoretical analysis on graph neural networks to applications in health science. It also presents original results tackling real-world problems that often involve complex data analysis on large multi-modal data sources.

Full Product Details

Author:   Ilke Demir ,  Yifei Lou ,  Xu Wang ,  Kathrin Welker
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2021
Volume:   26
Weight:   0.587kg
ISBN:  

9783030798932

ISBN 10:   3030798933
Pages:   364
Publication Date:   30 November 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Part I: Image Processing.- Two-stage Geometric Information Guided Image Processing (J. Qin and W. Guo).- Image Edge Sharpening via Heaviside Substitution and Structure Recovery (L. Deng, W. Guo, and T. Huang).- Two-step Blind Deconvolution of UPC-A Barcode Images (B. Kim and Y. Lou).- Part II: Shape and Geometry.- An Anisotropic Local Method for Boundary Detection in Images (M. Lund, M. Howard, D. Wu, R. S. Crum, D. J. Miller, and M. C. Akin).- Towards Learning Geometric Shape Parts (A. Fondevilla, G. Morin, and K. Leonard).- Machine Learning in LiDAR 3D Point Clouds (F. P. Medina and R. Paffenroth).- Part III: Machine Learning.- Fitting Small Piece-wise Linear Neural Network Models to Interpolate Data Sets (L. Ness).- On Large-Scale Dynamic Topic Modelling with Nonnegative CP Tensor Decomposition (M. Ahn, N. Eikmeier, J. Haddock, L. Kassab, A. Kryshchenko, K. Leonard, D. Needell, R. W. M. A. Madushani, E. Sizikova, and C. Wang).- A Simple Recovery Framework for Signals with Time-Varying Sparse Support (N. Durgin, R. Grotheer, C. Huang, S. Li, A. Ma, D. Needell, and J. Qin).- Part IV: Data Analysis.- Role Detection and Prediction in Dynamic Political Networks (E. Evans, W. Guo, A. Genctav, S. Tari, C. Domeniconi, A. Murillo, J. Chuang, L. AlSumait, P. Mani, and N. Youssry).- Classifying Sleep States Using Persistent Homology and Markov Chains: A Pilot Study (S. Tymochko, K. Singhal, and G. Heo).- A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data (E. T. Winn, M. Vazquez, P. Loliencar, K. Taipale, X. Wang, and G. Heo).- Nonparametric Estimation of Blood Alcohol Concentration from Transdermal Alcohol Measurements Using Alcohol Biosensor Devices (A. Kryshchenko, M. Sirlanci, and B. Vader).

Reviews

The topics covered are quite interdisciplinary and related to cutting-edge research in data science. ... This book describes results from the forefront of research in data science and would greatly benefit aspiring researchers at the master's and PhD levels. Each chapter contains ample references to the related literature. (S. Lakshmivarahan, Computing Reviews, February 21, 2023)

“The topics covered are quite interdisciplinary and related to cutting-edge research in data science. … This book describes results from the forefront of research in data science and would greatly benefit aspiring researchers at the master’s and PhD levels. Each chapter contains ample references to the related literature.” (S. Lakshmivarahan, Computing Reviews, February 21, 2023)

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