topology structure
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2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Yixiao Tian ◽  
Jing Li ◽  
Xinping Tian ◽  
Xiaofeng Zeng

Abstract Background There have been lacking reliable serum biomarkers in assessing the disease activity of Takayasu’s arteritis (TAK). This study aimed to assess the disease activity of TAK by assayed gene expression levels in peripheral mononuclear cells (PBMCs). Methods The expression level of genes that essential in T cell activation in PBMCs in active TAK patients, inactive TAK patients, and healthy controls were detected by real-time fluorescence quantitative polymerase chain reaction, including TCR, CD28, CD40, CD40L, PD-1, PD-L1, PD-L2, CTLA4, TIGIT, TIM3, LAG3, CCL5, T-bet, RORC, and FOXP3. Gene co-expression network was established, and the signature of the topology structure in active TAK patients compared to the inactive TAK patients were extracted and described by formulas. Respectively, the disease activity was assessed by the routine serum biomarkers, including ESR, CRP, IL-6, and TNF-α, the gene expression level of TCR, CD28, T-bet, and RORC, as well as the signature of the topology structure, and the diagnostic efficacies were compared. Results Compared with the inactive TAK patient group, the active TAK patient group had a greater clustering coefficient in the network consisting of genes that essential in T cell activation. When assessing the disease activity used this signature of topology structure, the sensitivity was 90.9%, the specificity was 100%, and the AUC was 0.98, which was greater than the AUCs of these biomarkers. Conclusions The signature of the topology structure could distinguish the active TAK patients from inactive TAK patients. This maybe is a novel evaluation algorithm of disease activity.


2021 ◽  
Author(s):  
Longjie Zhang ◽  
Yong Chen ◽  
Ikram Ali

Abstract Deep learning plays an important role in the development of artificial intelligence (AI) technology. The security of deep networks has become the crucial thing to be considered. When the deep learning algorithms are implemented in the hardware platform, the interference for topology structure will appear because of cyber-attacks. We analyze the working capacity of acyclic deep networks under the topology attacks and injection attacks. Considering the topology structure of the deep network, the maximum working capacity is studied under the topology attacks and injection attacks. Furthermore, the robustness of the random networks is researched and the structural robustness index (SRI) is proposed to measure the toleration for the topology attacks. This work supplies some suggestions for building a robust deep network and improving the endogenous safety and security (ESS) of the deep networks.


Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2096
Author(s):  
Mingcong Zhou ◽  
Zhaoyan Wu

Topology structure and system parameters have a great influence on the dynamical behavior of dynamical networks. However, they are sometimes unknown or uncertain in advance. How to effectively identify them has been investigated in various network models, from integer-order networks to fractional-order networks with the same order. In the real world, many systems consist of subsystems with different fractional orders. Therefore, the structure identification of a dynamical network with different fractional orders is investigated in this paper. Through designing proper adaptive controllers and parameter updating laws, two network estimators are well constructed. One is for identifying only the unknown topology structure. The other is for identifying both the unknown topology structure and system parameters. Based on the Lyapunov function method and the stability theory of fractional-order dynamical systems, the theoretical results are analytically proved. The effectiveness is verified by three numerical examples as well. In addition, the designed estimators have a good performance in monitoring switching topology. From the practical viewpoint, the designed estimators can be used to monitor the change of current and voltage in the fractional-order circuit systems.


Author(s):  
Jinlong Du ◽  
Senzhang Wang ◽  
Hao Miao ◽  
Jiaqiang Zhang

Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hanlin Dong

Tourism has developed into an industry with a powerful momentum of development in the world today. With the development of information technology, information has become a powerful driving force to promote the prosperity and development of the tourism industry and the entire society. The introduction of the tourism information system can significantly improve the service level, operation level, and management level of the tourism industry, thereby accelerating tourism development. The research of this article is to help villages establish a set of MVC-based rural tourism information service systems that can promote the development of rural tourism. First of all, this article conducts demand research on tourists, rural scenic spots, and ancient villages to discover the problems in rural tourism development. Secondly, this paper combines the problems existing in constructing the various subsystems of the rural tourism information system, combined with the fuzzy comprehensive evaluation method. In this paper, we propose the rural tourism system architecture based on MVC. The system architecture consists of the user layer, service layer, business layer, and data layer. It describes the system’s implementation process from two aspects: the system’s interface design and its deployment model. Finally, the network topology structure of the rural tourism information service system based on MVC is drawn. Finally, the system’s deployment is implemented according to the network topology structure.


2021 ◽  
Vol 564 ◽  
pp. 384-395
Author(s):  
Le Zhang ◽  
Ying Wang ◽  
HaiShun Chen ◽  
Jie Li ◽  
ZhenXi Zhang
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