Xiuling Zhou

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Hepatocellular carcinoma (HCC), accounting for the majority of liver cancer, is a highly aggressive malignancy with poor prognosis and therefore adds up the financial burden. Incidence data of HCC in three decades during 1983-2012 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database with incidence rates of 1.9, 3.1 and 4.9 per(More)
The CHSO algorithm is a fast algorithm for computing the contribution of a point to the hypervolume of the whole set directly. In this paper an improved CHSO is described. And it is explained by theory why not only the points in the first nondominated front, but also the points in the second nondominated front which are dominated only by one of points in(More)
A new and efficient Pd(II)/AgNO₃-cocatalyzed homocoupling of aromatic terminal alkynes is described. Various symmetrical 1,4-disubstituted-1,3-diynes are obtained in good to excellent yields. This protocol employs a loading with relatively low palladium(II) in aqueous media under aerobic conditions.
Saussurea involucrata Kar. et Kir. tolerates severe abiotic stresses including cold, and the level of membrane fatty acid desaturation is associated with its cold acclimation. We discovered and characterized a full-length cDNA of stearoyl acyl-carrier-protein desaturase (sikSACPD) which encodes a protein consisted of 396 amino acids. A sequence alignment of(More)
The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the(More)
Regularization is a solution for the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. In many applications such as image restoration, sparse representation, we have to deal with multi-regularization parameters problem. In this paper, the case of covariance matrix estimation with multi-regularization(More)
Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM_L covariance matrix estimation is derived theoretically based on MDL (minimum description length)(More)
The computational complexity of kernel 2DPCA is mainly determined by the product of the number of input image samples and rows of matrix. For the large number of this product, it is computational intractable to directly calculate the eigenvalues and eigenvectors of kernel matrix in kernel 2DPCA. In this paper, a method called M2D-PCA is proposed to(More)