no code implementations • 8 Jan 2023 • Shuai Wang, ChiYung Yam, Shuguang Chen, Lihong Hu, Liping Li, Faan-Fung Hung, Jiaqi Fan, Chi-Ming Che, Guanhua Chen
Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration.
no code implementations • 10 Nov 2021 • Kwok-Leung Chan, Liping Li, Arthur Wing-Tak Leung, Ho-Yin Chan
In this paper, we propose a method that can be used to improve the visibility of the images, and eventually reduce the errors of the 3D scene model.
no code implementations • 27 Sep 2021 • Xuyan Tan, Yuhang Wang, Bowen Du, Junchen Ye, Weizhong Chen, Leilei Sun, Liping Li
Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis.
no code implementations • 11 Feb 2021 • Zhenxing Di, Liping Li, Li Liang, Fei Xu
This paper focuses on a question raised by Holm and J{\o}rgensen, who asked if the induced cotorsion pairs $(\Phi({\sf X}),\Phi({\sf X})^{\perp})$ and $(^{\perp}\Psi({\sf Y}),\Psi({\sf Y}))$ in $\mathrm{Rep}(Q,{\sf{A}})$ -- the category of all $\sf{A}$-valued representations of a quiver $Q$ -- are complete whenever $(\sf X,\sf Y)$ is a complete cotorsion pair in an abelian category $\sf{A}$ satisfying some mild conditions.
Representation Theory K-Theory and Homology
no code implementations • 24 Dec 2019 • Yang Liu, Yan Kang, Xinwei Zhang, Liping Li, Yong Cheng, Tianjian Chen, Mingyi Hong, Qiang Yang
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.
1 code implementation • 9 Sep 2019 • Weiyu Li, Tianyi Chen, Liping Li, Zhaoxian Wu, Qing Ling
Specifically, in CSGD, the latest mini-batch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative.
1 code implementation • 9 Nov 2018 • Liping Li, Wei Xu, Tianyi Chen, Georgios B. Giannakis, Qing Ling
In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers.