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El. knyga: Advances in Data Science

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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.

Recenzijos

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 masters and PhD levels. Each chapter contains ample references to the related literature. (S. Lakshmivarahan, Computing Reviews, February 21, 2023)

Part I Image Processing
Two-stage Geometric Information Guided Image Reconstruction
3(22)
Jing Qin
Weihong Guo
1 Introduction
3(3)
1.1 Background
3(3)
2 Review of Shearlet Transform
6(1)
3 Proposed Model and Algorithm
7(7)
3.1 Stage I: TV-L1-L2 Model
8(3)
3.2 Stage II: wTV-L1-L2 Model
11(3)
4 Convergence Analysis
14(2)
5 Numerical Examples
16(5)
5.1 Example 1
17(1)
5.2 Example 2
18(2)
5.3 Example 3
20(1)
6 Conclusion and Remarks
21(1)
References
22(3)
Image Edge Sharpening via Heaviside Substitution and Structure Recovery
25(24)
Liang-Jian Deng
Weihong Guo
Ting-Zhu Huang
1 Introduction
25(3)
2 The Proposed Edge Sharpening Method
28(4)
2.1 Heaviside Function
28(1)
2.2 1D Heaviside Function Substitution
29(2)
2.3 2D Image Extension
31(1)
3 Structure Recovery
32(2)
4 Results and Discussions
34(12)
4.1 Application to Image Super-Resolution
35(8)
4.2 Application to Image Deblurring
43(2)
4.3 Application to Edge Sharpening
45(1)
5 Conclusions
46(1)
References
46(3)
Two-Step Blind Deconvolution of UPC-A Barcode Images
49(26)
Bohyun Kim
Yifei Lou
1 Introduction
49(4)
2 Our Approach
53(3)
2.1 Kernel Estimation
53(2)
2.2 Image Deblurring
55(1)
3 Convergence Analysis
56(3)
4 Experiment
59(11)
4.1 Synthetic Data Experiment
59(1)
4.2 Real Data Experiment
60(8)
4.3 Empirical Verification
68(2)
5 Conclusions
70(1)
References
70(5)
Part II Shape and Geometry
An Anisotropic Local Method for Boundary Detection in Images
75(20)
Margaret Lund
Marylesa Howard
Dongsheng Wu
Ryan S. Crum
Dorothy J. Mille
Minta C. Akin
1 Introduction
75(2)
1.1 Related Work
76(1)
2 Anisotropic Locally Adaptive Discriminant Analysis
77(8)
2.1 Visualizing ALADA
81(4)
2.2 Maximum Likelihood Estimation p-Value
85(1)
3 Results
85(7)
3.1 Berkeley Benchmark Images
85(3)
3.2 Real Data
88(4)
4 Conclusions
92(1)
References
93(2)
Towards Learning Geometric Shape Parts
95(18)
Amelie Fondevilla
Geraldine Morin
Kathryn Leonard
1 Motivation
95(1)
2 Background Fundamentals
96(3)
2.1 Blum Medial Axis
96(2)
2.2 Convolutional Neural Networks for Regression
98(1)
3 A Canonical Parametric Medial Axis
99(5)
3.1 Canonical Ordering of Linked Medial Branches
100(2)
3.2 Extracting a Stable Parametric Medial Axis
102(2)
4 Learning a Partial Parametric Medial Axis Using CNN
104(1)
4.1 A Partial Representation of the Shape
104(1)
4.2 Constructing the Neural Network
105(1)
5 Results
105(5)
5.1 General Shape: 1 Branch Model
105(2)
5.2 Adding a Connected Branch: 2 Branches Model
107(1)
5.3 Learning Shape Details: 5 Branch Model
107(3)
6 Discussion and Future Work
110(1)
References
110(3)
Machine Learning in LiDAR 3D Point Clouds
113(24)
F. Patricia Medina
Randy Paffenroth
1 Introduction
113(2)
2 The Data
115(4)
3 Feature Engineering: Nearest Neighbor Matrix
119(2)
4 Machine Learning Frameworks
121(3)
4.1 Dimension Reduction
123(1)
5 Classification Experiments
124(7)
6 Summary and Future Research Directions
131(1)
References
132(5)
Part III Machine Learning
Fitting Small Piece-Wise Linear Neural Network Models to Interpolate Data Sets
137(44)
Linda Ness
1 Introduction
137(2)
2 Paper Overview
139(1)
3 Related Work
139(1)
4 An Example: Xor Is Not Interpolated by a One-Layer Function
140(1)
5 Two Layer One Weight Models 2L1W
141(6)
5.1 Generic, Strictly Generic and Non-generic Weights
141(1)
5.2 Definition of a Two Layer One Weight Model 2L1W
142(3)
5.3 Sequential Variation
145(2)
6 Two Additional Models
147(3)
6.1 The Two Layer Sum Model: 2LS
148(1)
6.2 The Three Layer Binary Model: BIN
149(1)
7 Summary and Research Directions
150(2)
Appendix: Results on Example 2D Data Sets
152(26)
Description of Sequential Variation Results
152(2)
Description of Model Results
154(1)
Model Results for the Generalized Xor Data Set
154(3)
Result Figures
157(21)
References
178(3)
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
181(30)
Miju Ahn
Nicole Eikmeier
Jamie Haddock
Lara Kassab
Alona Kryshchenko
Kathryn Leonard
Deanna Needell
R.W.M.A. Madushani
Elena Sizikova
Chuntian Wang
1 Introduction
181(2)
2 Overview and Notations
183(6)
2.1 NMF-Based Nonnegative Tensor Decompositions
184(2)
2.2 CANDECOMP/PARAFAC (CP) Decomposition and NNCPD
186(3)
3 Comparison of NNCPD and NMF-Based Nonnegative Tensor Decompositions
189(19)
3.1 Synthetic Dataset Numerical Experiments
189(11)
3.2 The 20 Newsgroups Dataset Numerical Experiments
200(4)
3.3 Noise Dataset Robustness Numerical Experiments
204(4)
4 Conclusion
208(1)
References
209(2)
A Simple Recovery Framework for Signals with Time-Varying Sparse Support
211(22)
Natalie Durgin
Rachel Grotheer
Chenxi Huang
Shuang Li
Anna Ma
Deanna Needell
Jing Qin
1 Introduction
211(2)
1.1 Related Work
212(1)
1.2 Organization
213(1)
2 Windowed Framework
213(3)
2.1 Description of Framework
215(1)
3 Example MMV Algorithms
216(3)
3.1 MMV Sparse Randomized Kaczmarz with Prior Information
217(1)
3.2 Weighted L2,1-Minimization
217(1)
3.3 Weighted MMV Stochastic Gradient Matching Pursuit
218(1)
4 Experiments
219(9)
4.1 Experiments with Synthetic Data
220(2)
4.2 Experiments with Real-World Data
222(3)
4.3 Computational Cost
225(3)
5 Conclusion
228(1)
References
228(5)
Part IV Data Analysis
Role Detection and Prediction in Dynamic Political Networks
233(20)
Emily Evans
Weihong Guo
Asli Genctav
Sibel Tani
Carlotta Domeniconi
Anarina Murillo
Julia Chuang
Loulwah AlSumait
Priya Mani
Noha El-Zehiry
1 Introduction
233(1)
2 Related Work
234(2)
3 Methodology
236(4)
3.1 Role Discovery
236(2)
3.2 Dynamic Role Prediction
238(2)
4 Empirical Evaluation
240(8)
4.1 Data Processing and Graph Creation
240(1)
4.2 Feature Calculation
241(2)
4.3 Role Results and Analysis
243(2)
4.4 Prediction and Validation Results
245(3)
5 Conclusion and Future Work
248(1)
References
249(4)
Classifying Sleep States Using Persistent Homology and Markov Chains: A Pilot Study
253(38)
Sarah Tymochko
Kritika Singhal
Giseon Heo
1 Introduction
253(1)
2 Sleep State Analysis Using Persistent Homology
254(11)
2.1 Background
256(2)
2.2 Results
258(7)
3 Visualizing Sleep Patterns of Eight OSA Patients
265(4)
4 Conclusion and Future Research
269(1)
Appendix
270(18)
References
288(3)
A Survey of Statistical Learning Techniques as Applied to Inexpensive Pediatric Obstructive Sleep Apnea Data
291(38)
Emily T. Winn
Marilyn Vazquez
Prachi Loliencar
Kaisa Taipale
Xu Wang
Giseon Heo
1 Introduction
291(2)
2 Pediatric Obstructive Sleep Apnea and Data
293(5)
2.1 Survey Data
293(1)
2.2 Craniofacial Data
294(2)
2.3 Cleaning Data
296(2)
3 Data Exploration
298(7)
3.1 Correlation Networks
299(2)
3.2 Mapper Algorithm
301(2)
3.3 Singular Value Decomposition
303(2)
4 Statistical Learning Methods
305(7)
4.1 Non-Bayesian Supervised Learning
305(3)
4.2 Bayesian Classifiers
308(2)
4.3 Unsupervised Learning
310(2)
5 Results
312(6)
5.1 Results for Survey Data
314(1)
5.2 Results for Craniofacial Data
314(2)
5.3 Results for Combined Survey and Craniofacial Data
316(2)
6 Conclusion and Future Research
318(2)
Appendix
320(6)
References
326(3)
Nonparametric Estimation of Blood Alcohol Concentration from Transdermal Alcohol Measurements Using Alcohol Biosensor Devices
329(32)
Alona Kryshchenko
Melike Sirlanci
Bryan Vader
1 Introduction
329(2)
1.1 Alcohol Biosensor Devices
329(2)
2 Overview
331(1)
3 Methods
332(19)
3.1 Partial Differential Equation Model Simulation
332(10)
3.2 Nonparametric Maximum Likelihood Estimator
342(5)
3.3 Nonparametric Adaptive Grid Algorithm
347(4)
4 Results of the Synthetic Data Experiments
351(4)
5 Conclusions
355(2)
Appendix
357(2)
References
359(2)
Appendix 361
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