1 code implementation • 1 Jan 2022 • Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.
no code implementations • 26 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator.
no code implementations • 6 Oct 2020 • Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative
Even though graph convolutional neural network (graph-CNN) has been widely used in deep learning, there is a lack of augmentation methods to generate data on graphs or surfaces.
no code implementations • 7 Nov 2019 • Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo. K. Chung
We also derive the closed-form expression of the spectral decomposition of the Laplace-Beltrami operator and use it to solve heat diffusion on a manifold for the first time.
no code implementations • 3 Jun 2017 • Soo-Chang Pei, Shih-Gu Huang, Jian-Jiun Ding
Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform.
no code implementations • 26 May 2017 • Soo-Chang Pei, Shih-Gu Huang
Simulation results show that the proposed methods have higher accuracy, lower computational complexity and smaller error in the additivity property compared with the previous works.