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12,667 results on '"Tang , Yu"'

224. Quality assurance in a phase III, multicenter, randomized trial of POstmastectomy radioThErapy in Node posiTive breast cancer with or without Internal mAmmary nodaL irradiation (POTENTIAL): a planning benchmark case

Author

Song, Yu-Chun, Hu, Zhi-Hui, Yan, Xue-Na, Fang, Hui, Tang, Yu, Jing, Hao, Men, Kuo, Zhang, Na, Zhang, Jun, Jin, Jing, Zhong, Qiu-Zi, Ma, Jun, Yang, Wei-Fang, Zhong, Ya-Hua, Dong, Li-Hua, Wang, Xiao-Hong, Wu, Hong-Fen, Du, Xiang-Hui, Hou, Xiao-Rong, Tie, Jian, Lu, Yu-Fei, Zhao, Li-Na, Li, Ye-Xiong, and Wang, Shu-Lian

Published
2023
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249. Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

Author

Gao, Peiyuan, Yang, Xiu, Tang, Yu-Hang, Zheng, Muqing, Anderson, Amity, Murugesan, Vijayakumar, Hollas, Aaron, and Wang, Wei

Subjects
Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Physics - Chemical Physics
Abstract

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.

Published
2021
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250. Nonlinear Matrix Approximation with Radial Basis Function Components

Author

Rebrova, Elizaveta and Tang, Yu-Hang

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We introduce and investigate matrix approximation by decomposition into a sum of radial basis function (RBF) components. An RBF component is a generalization of the outer product between a pair of vectors, where an RBF function replaces the scalar multiplication between individual vector elements. Even though the RBF functions are positive definite, the summation across components is not restricted to convex combinations and allows us to compute the decomposition for any real matrix that is not necessarily symmetric or positive definite. We formulate the problem of seeking such a decomposition as an optimization problem with a nonlinear and non-convex loss function. Several modern versions of the gradient descent method, including their scalable stochastic counterparts, are used to solve this problem. We provide extensive empirical evidence of the effectiveness of the RBF decomposition and that of the gradient-based fitting algorithm. While being conceptually motivated by singular value decomposition (SVD), our proposed nonlinear counterpart outperforms SVD by drastically reducing the memory required to approximate a data matrix with the same L2 error for a wide range of matrix types. For example, it leads to 2 to 6 times memory save for Gaussian noise, graph adjacency matrices, and kernel matrices. Moreover, this proximity-based decomposition can offer additional interpretability in applications that involve, e.g., capturing the inner low-dimensional structure of the data, retaining graph connectivity structure, and preserving the acutance of images.

Published
2021

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