TY - GEN A1 - Le Gal, Patrice A1 - Harlander, Uwe A1 - Borcia, Ion-Dan A1 - Le Dizès, Stéphane A1 - Chen, J. A1 - Favier, Benjamin T1 - Instability of vertically stratified horizontal plane Poiseuille flow T2 - Journal of Fluid Mechanics Y1 - 2021 U6 - https://doi.org/10.1017/jfm.2020.917 SN - 1469-7645 SN - 0022-1120 VL - 907 IS - R1 SP - 1 EP - 14 ER - TY - GEN A1 - Liu, Fei A1 - Assaf, George A1 - Chen, Ming A1 - Heiner, Monika T1 - A Petri nets-based framework for whole-cell modeling T2 - Biosystems N2 - Whole-cell modeling aims to incorporate all main genes and processes, and their interactions of a cell in one model. Whole-cell modeling has been regarded as the central aim of systems biology but also as a grand challenge, which plays essential roles in current and future systems biology. In this paper, we analyze whole-cell modeling challenges and requirements and classify them into three aspects (or dimensions): heterogeneous biochemical networks, uncertainties in components, and representation of cell structure. We then explore how to use different Petri net classes to address different aspects of whole-cell modeling requirements. Based on these analyses, we present a Petri nets-based framework for whole-cell modeling, which not only addresses many whole-cell modeling requirements, but also offers a graphical, modular, and hierarchical modeling tool. We think this framework can offer a feasible modeling approach for whole-cell model construction. KW - Whole-cell modeling KW - Systems biology KW - Petri nets KW - Modeling framework Y1 - 2021 UR - https://www.sciencedirect.com/science/article/abs/pii/S0303264721001738?via%3Dihub U6 - https://doi.org/10.1016/j.biosystems.2021.104533 SN - 0303-2647 SN - 1872-8324 VL - 210 ER - TY - GEN A1 - Gao, Yunlong A1 - Luo, Si-Zhe A1 - Pan, Jin-Yan A1 - Chen, Bai-Hua A1 - Zhang, Yi-Song T1 - Robust PCA Using Adaptive Probability Weighting T2 - Acta Automatica Sinica N2 - Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms. KW - Principle component analysis (PCA) KW - weighted principal component analysis (WPCA) KW - dimensionality reduction KW - robustness Y1 - 2021 U6 - https://doi.org/10.16383/j.aas.c180743 SN - 0254-4156 VL - 47 IS - 4 SP - 825 EP - 838 ER -