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

Classification of Marine Vessels with Multi-Feature Structure Fusion

The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result

Decoupling Control of an Aviation Remote Sensing Stabilization Platform Based on a Cerebellar Model Articulation Controller

To obtain high-resolution and high-precision images, the aviation remote sensing stabilization platform (ARSSP) is used, which enables the isolation of unpredictable aerial camera movements during aerial photography. However, because of the interaxle coupling interference and other nonlinear interferences in the ARSSP system, the imaging quality of the aerial camera is adversely affected. Therefore, we derived the dynamic model of moment coupling between shafts to illustrate the problem. On the basis of the former proportion integration differentiation (PID) controller based on cerebellar neural network, a nonlinear cross feedback decoupling scheme is adopted to reduce the adverse effects of these interferences. The cerebellar model articulation controller (CMAC) based on synovial membrane control (SMC) is used to reduce the nonlinear interferences caused by the new cross-decoupling module. To verify the effectiveness of the scheme, simulation, indoor and outdoor experiments were conducted. The results showed that the SMC-CMAC significantly reduced the interaxle coupling effect and effectively suppressed the nonlinear interference, resulting in a good tracking performance of the ARSSP system

The Prognostic Significance of CD44V6, CDH11, and β-Catenin Expression in Patients with Osteosarcoma

This study aimed to examine the expression of and the relationship between CD44V6, CDH11, and β-catenin. The expression of these cell adhesion molecules was detected in 90 osteosarcoma and 20 osteochondroma specimens using immunohistochemistry. Associations between these parameters and clinicopathological data were also examined. The expression rates of CD44V6, CDH11, and β-catenin were 25.0% (5/20), 70.0% (14/20), and 20.0% (4/20) in osteochondroma specimens, respectively. Compared to osteochondromas, the proportions of expression of CD44V6 and β-catenin in osteosarcoma specimens increased to 65.6% (59/90) and 60.0% (54/90), respectively. However, the expression rate of CDH11 in osteosarcomas was reduced to 40.0% (36/90). The expression of these markers was significantly associated with metastasis and overall survival (P<0.05). Survival analysis revealed that patients with increased expression of CD44V6 and β-catenin as well as decreased expression of CDH11 were correlated with a shorter survival time. Multivariate analysis indicated that clinical stage, metastasis status, and the expression of CD44V6, CDH11, and β-catenin were found to be associated with overall survival. Further, the expression of β-catenin and that of CD44V6 were positively correlated with each other. Thus, our results indicated abnormal expression of CD44V6, CDH11, and β-catenin in osteosarcomas and osteochondromas, which may provide important indicators for further research

HRCT imaging features of systemic sclerosis-associated interstitial lung disease

Background: The aim of the study was to evaluate radiographic features of systemic sclerosis-associated interstitial lung disease. Patients and methods: 116 patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) from 2010 to 2019 comprised our retrospective study. All patients were subject to high resolution computed tomography (HRCT). ILD patterns were classified into 7 patterns as IIPs and analyzed with pathology. We chose two staging method and two semi-quantitative score methods to evaluate the HRCT performance and analyzed with pulmonary function tests. Results: Ground-glass opacities were the most common presentation on HRCT, followed by interlobular septal thickening, reticular opacities, intralobular interstitial thickening; honeycombing, traction bronchiectasis and nodules can also be observed. The most common pattern of SSc-ILD was nonspecific interstitial pneumonia (NSIP), secondly was UIP. There was no difference in ILD pattern between HRCT and pathology, and revealed a high congruence. The four HRCT evaluating methods presented in this study all had significant relationships with PETs. Conclusion: The most common pattern of SSc-ILD was nonspecific interstitial pneumonia (NSIP). The ILD patterns of HRCT coincide very well with histology, and will replace pathology as the gold standard for diagnosis and evaluation of SSc-ILD

Fusion of 2D CNN and 3D DenseNet for Dynamic Gesture Recognition

Gesture recognition has been applied in many fields as it is a natural human&ndash;computer communication method. However, recognition of dynamic gesture is still a challenging topic because of complex disturbance information and motion information. In this paper, we propose an effective dynamic gesture recognition method by fusing the prediction results of a two-dimensional (2D) motion representation convolution neural network (CNN) model and three-dimensional (3D) dense convolutional network (DenseNet) model. Firstly, to obtain a compact and discriminative gesture motion representation, the motion history image (MHI) and pseudo-coloring technique were employed to integrate the spatiotemporal motion sequences into a frame image, before being fed into a 2D CNN model for gesture classification. Next, the proposed 3D DenseNet model was used to extract spatiotemporal features directly from Red, Green, Blue (RGB) gesture videos. Finally, the prediction results of the proposed 2D and 3D deep models were blended together to boost recognition performance. The experimental results on two public datasets demonstrate the effectiveness of our proposed method

A Pipeline to Call Multilevel Expression Changes between Cancer and Normal Tissues and Its Applications in Repurposing Drugs Effective for Gastric Cancer

Differential gene analyses on gastric cancer usually focus on expression change of single genes between tumor and adjacent normal tissues. However, besides changes on single genes, there are also coexpression and expression network module changes during the development of gastric cancer. In this study, we proposed a pipeline to investigate various levels of changes between gastric cancer and adjacent normal tissues, which were used to repurpose potential drugs for treating gastric cancer. Specifically, we performed a series of analyses on 242 gastric cancer samples (33-normal, 209-cancer) downloaded from the cancer genome atlas (TCGA), including data quality control, differential gene analysis, gene coexpression network analysis, module function enrichment analysis, differential coexpression analysis, differential pathway analysis, and screening of potential therapeutic drugs. In the end, we discovered some genes and pathways that are significantly different between cancer and adjacent normal tissues (such as the interleukin-4 and interleukin-13 signaling pathway) and screened perturbed genes by 2703 drugs that have a high overlap with the identified differentially expressed genes. Our pipeline might be useful for understanding cancer pathogenesis as well as gastric cancer treatment