Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

Structural and Morphological Evolution of Lead Dendrites during Electrochemical Migration

The electrochemical deposition and dissolution of lead on gold electrodes immersed in an aqueous solution of lead nitrate were studied in situ using a biasing liquid cell by transmission electron microscopy (TEM). We investigate in real time the growth mechanisms of lead dendrites as deposited on the electrodes under an applied potential. TEM images reveal that lead dendrites are developed by the fast protrusion of lead branches in the electrolyte and tip splitting. And, the fast growing tip of the dendritic branch is composed of polycrystalline nanograins and it develops into a single crystalline branch eventually. This study demonstrated unique electrochemical growth of single crystal dendrites through nucleation, aggregation, alignment and attachment of randomly oriented small grains. Additionally, we found the lead concentration in the electrolyte drastically influences the morphology of dendritic formation

Full-reference image quality assessment by combining features in spatial and frequency domains

Objective image quality assessment employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations