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02/18/2023
Heterogeneous Graph Convolutional Neural Network via Hodge-Laplacian for Brain Functional Data
This study proposes a novel heterogeneous graph convolutional neural net...
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09/21/2022
Modeling and Simulating Dependence in Networks Using Topological Data Analysis
Topological data analysis (TDA) approaches are becoming increasingly pop...
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04/28/2022
Topological Data Analysis for Multivariate Time Series Data
Over the last two decades, topological data analysis (TDA) has emerged a...
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01/01/2022
Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering
We present the novel Wasserstein graph clustering for dynamically changi...
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05/01/2021
Topological Data Analysis of COVID-19 Virus Spike Proteins
Topological data analysis, including persistent homology, has undergone ...
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02/17/2021
Reviews: Topological Distances and Losses for Brain Networks
Almost all statistical and machine learning methods in analyzing brain n...
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02/05/2021
Cosine Series Representation
This short paper is based on Chung et al. (2010), where the cosine serie...
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11/25/2020
Topological Learning for Brain Networks
This paper proposes a novel topological learning framework that can inte...
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10/26/2020
Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering
This paper revisits spectral graph convolutional neural networks (graph-...
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10/06/2020
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering
Deep neural networks have recently been recognized as one of the powerfu...
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08/28/2020
Introduction to logistic regression
For random field theory based multiple comparison corrections In brain i...
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08/01/2020
Sparse Network Modeling
There have been many attempts to identify high-dimensional network featu...
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07/27/2020
Diffusion Equations on Graphs
In brain imaging, the image acquisition and processing processes themsel...
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07/19/2020
Introduction to Random Fields
General linear models (GLM) are often constructed and used in statistica...
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07/19/2020
Gaussian kernel smoothing
Image acquisition and segmentation are likely to introduce noise. Furthe...
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11/07/2019
Dynamic Connectivity without Sliding Windows
Objective: Sliding and tapered sliding window methods are the most often...
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11/07/2019
Fast Polynomial Approximation of Heat Diffusion on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis
Heat diffusion has been widely used in brain imaging for surface fairing...
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12/17/2018
Rapid Acceleration of the Permutation Test via Slow Random Walks in the Permutation Group
The permutation test is an often used test procedure in brain imaging. U...
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09/09/2018
Topological Brain Network Distances
Existing brain network distances are often based on matrix norms. The el...
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07/08/2018
Exact Combinatorial Inference for Brain Images
The permutation test is known as the exact test procedure in statistics....
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06/30/2018
Hill Climbing Optimized Twin Classification Using Resting-State Functional MRI
Twin imaging studies are an important part of human brain research that ...
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10/21/2017
Heat Kernel Smoothing in Irregular Image Domains
We present the discrete version of heat kernel smoothing on graph data s...
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09/15/2015
Mapping Heritability of Large-Scale Brain Networks with a Billion Connections via Persistent Homology
In many human brain network studies, we do not have sufficient number (n...
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09/23/2014
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
We present a novel kernel regression framework for smoothing scalar surf...
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08/31/2014
Persistent Homology in Sparse Regression and Its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that dete...
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09/25/2013
