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8,034 results on '"Zhou, Ting"'

160. Human iPSC modeling recapitulates in vivo sympathoadrenal development and reveals an aberrant developmental subpopulation in familial neuroblastoma

Author

Van Haver, Stéphane, Fan, Yujie, Bekaert, Sarah-Lee, Everaert, Celine, Van Loocke, Wouter, Zanzani, Vittorio, Deschildre, Joke, Maestre, Inés Fernandez, Amaro, Adrianna, Vermeirssen, Vanessa, De Preter, Katleen, Zhou, Ting, Kentsis, Alex, Studer, Lorenz, Speleman, Frank, and Roberts, Stephen S.

Published
2024
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170. A comprehensive predictive model for radiation-induced brain injury in risk stratification and personalized radiotherapy of nasopharyngeal carcinoma

Author

He, Yong-Qiao, Wang, Tong-Min, Yang, Da-Wei, Xue, Wen-Qiong, Deng, Chang-Mi, Li, Dan-Hua, Zhang, Wen-Li, Liao, Ying, Xiao, Ruo-Wen, Luo, Lu-Ting, Diao, Hua, Tong, Xia-Ting, Wu, Yan-Xia, Chen, Xue-Yin, Zhang, Jiang-Bo, Zhou, Ting, Li, Xi-Zhao, Zhang, Pei-Fen, Zheng, Xiao-Hui, Zhang, Shao-Dan, Hu, Ye-Zhu, Zhou, Guan-Qun, Ma, Jun, Sun, Ying, and Jia, Wei-Hua

Published
2024
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173. Deep Learning based Antenna Selection and CSI Extrapolation in Massive MIMO Systems

Author

Lin, Bo, Gao, Feifei, Zhang, Shun, Zhou, Ting, and Alkhateeb, Ahmed

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

Published
2021

184. Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived β-like cells

Author

Xue, Dongxiang, Narisu, Narisu, Taylor, D. Leland, Zhang, Meili, Grenko, Caleb, Taylor, Henry J., Yan, Tingfen, Tang, Xuming, Sinha, Neelam, Zhu, Jiajun, Vandana, J. Jeya, Nok Chong, Angie Chi, Lee, Angela, Mansell, Erin C., Swift, Amy J., Erdos, Michael R., Zhong, Aaron, Bonnycastle, Lori L., Zhou, Ting, Chen, Shuibing, and Collins, Francis S.

Published
2023
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189. Inverse problems for nonlinear Maxwell's equations with second harmonic generation

Author

Assylbekov, Yernat and Zhou, Ting

Subjects
Mathematics - Analysis of PDEs
Abstract

In the current paper we consider an inverse boundary value problem of electromagnetism with nonlinear Second Harmonic Generation (SHG) process. We show the unique determination of the electromagnetic material parameters and the SHG susceptibility parameter of the medium by making electromagnetic measurements on the boundary. We are interested in the case when a frequency is fixed., Comment: 19 pages

Published
2020

193. Partial Data Inverse Problems for Nonlinear Magnetic Schr\'odinger Equations

Author

Lai, Ru-Yu and Zhou, Ting

Subjects
Mathematics - Analysis of PDEs
Abstract

We prove that the knowledge of the Dirichlet-to-Neumann map, measured on a part of the boundary of a bounded domain in $\mathbb{R}^n, n\geq2$, can uniquely determine, in a nonlinear magnetic Schr\"odinger equation, the vector-valued magnetic potential and the scalar electric potential, both being nonlinear in the solution., Comment: 20 pages

Published
2020

195. Digital resolution enhancement in low transverse sampling optical coherence tomography angiography using deep learning

Author

Zhou, Ting, Zhou, Kang, Yang, Jianlong, Fang, Liyang, Hu, Yan, Zhao, Yitian, Cheng, Jun, Chen, Xiangping, Gao, Shenghua, and Liu, Jiang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Optics
Abstract

Optical coherence tomography angiography (OCTA) requires high transverse sampling density for visualizing retinal and choroidal capillaries. Low transverse sampling causes resolution degradation, such as the angiograms in wide-field OCTA. In this paper, we propose to address this problem using deep learning. We conducted extensive experiments on converting the centrally cropped 3 x 3 mm2 field of view (FOV) of the 8 x 8 mm2 foveal OCTA images (a sampling density of 22.9 $\mu$m) to the native 3 x 3 mm2 en face OCTA images (a sampling density of 12.2 $\mu$m). We employed a cycle-consistent adversarial network architecture in this conversion. The quantitative analysis using the perceptual similarity measures shows the generated OCTA images are closer to the native 3 x 3 mm2 scans. Besides, the results show the proposed method could also enhance signal-to-noise ratio. We further applied our method to enhance diseased cases and calculate vascular biomarkers, which demonstrates its generalization performance and clinical perspective.

Published
2019

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