Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited abilit...Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited ability to deal with all structures accurately and automatically in the big materials database because different material resources and various measurement errors lead to variation of bond length and bond angle. To address this issue, we propose a new paradigm based on graph theory(GTscheme) to improve the efficiency and accuracy of material identification, which focuses on processing the "topological relationship" rather than the value of bond length and bond angle among different structures. By using this method, automatic deduplication for big materials database is achieved for the first time, which identifies 626,772 unique structures from 865,458 original structures.Moreover, the graph theory scheme has been modified to solve some advanced problems such as identifying highly distorted structures, distinguishing structures with strong similarity and classifying complex crystal structures in materials big data.展开更多
基于虚拟隐含网络的虚假数据注入攻击(False data injection attack,FDIA)防御控制策略,本文提出了一种基于图论的拓扑优化算法来提高其防御性能.首先,提出了一种图的等效变换方法—权值分配法,实现二分图连接拓扑与二分图拉普拉斯矩阵...基于虚拟隐含网络的虚假数据注入攻击(False data injection attack,FDIA)防御控制策略,本文提出了一种基于图论的拓扑优化算法来提高其防御性能.首先,提出了一种图的等效变换方法—权值分配法,实现二分图连接拓扑与二分图拉普拉斯矩阵的一一对应;进而基于网络拓扑的连通度以及连通图的可去边理论,给出了虚拟隐含网络和二分图连接网络的拓扑选择依据;在考虑拓扑权值的基础上,给出了权值拓扑优化的指标评价函数;通过求解指标评价函数的最小化代价实现拓扑优化选择,从而改善基于虚拟隐含网络的虚假数据注入攻击防御方法的性能.最后,通过在IEEE-14总线电网系统上的仿真验证了所提算法的有效性.展开更多
Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization o...Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods： The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results： Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions： All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.展开更多
For a bipartite graph G on m and n vertices, respectively, in its vertices classes, and for integers s andt such that 2≤ s ≤ t, 0≤ m-s ≤ n-t, andre＋n≤ 2s＋t-1, we prove that if G has at least mn- （2（m - s） ＋...For a bipartite graph G on m and n vertices, respectively, in its vertices classes, and for integers s andt such that 2≤ s ≤ t, 0≤ m-s ≤ n-t, andre＋n≤ 2s＋t-1, we prove that if G has at least mn- （2（m - s） ＋ n - t） edges then it contains a subdivision of the complete bipartite K（s,t） with s vertices in the m-class and t vertices in the n-class. Furthermore, we characterize the corresponding extremal bipartite graphs with mn- （2（m - s） ＋ n - t ＋ 1） edges for this topological Turan type problem.展开更多
基金supported by the National Key R&D Program of China (2016YFB0700600)the National Natural Science Foundation of China (21603007, 51672012)+1 种基金Soft Science Research Project of Guangdong Province (2017B030301013)New Energy Materials Genome Preparation & Test Key-Laboratory Project of Shenzhen (ZDSYS201707281026184)
文摘Material identification technique is crucial to the development of structure chemistry and materials genome project. Current methods are promising candidates to identify structures effectively, but have limited ability to deal with all structures accurately and automatically in the big materials database because different material resources and various measurement errors lead to variation of bond length and bond angle. To address this issue, we propose a new paradigm based on graph theory(GTscheme) to improve the efficiency and accuracy of material identification, which focuses on processing the "topological relationship" rather than the value of bond length and bond angle among different structures. By using this method, automatic deduplication for big materials database is achieved for the first time, which identifies 626,772 unique structures from 865,458 original structures.Moreover, the graph theory scheme has been modified to solve some advanced problems such as identifying highly distorted structures, distinguishing structures with strong similarity and classifying complex crystal structures in materials big data.
文摘基于虚拟隐含网络的虚假数据注入攻击(False data injection attack,FDIA)防御控制策略,本文提出了一种基于图论的拓扑优化算法来提高其防御性能.首先,提出了一种图的等效变换方法—权值分配法,实现二分图连接拓扑与二分图拉普拉斯矩阵的一一对应;进而基于网络拓扑的连通度以及连通图的可去边理论,给出了虚拟隐含网络和二分图连接网络的拓扑选择依据;在考虑拓扑权值的基础上,给出了权值拓扑优化的指标评价函数;通过求解指标评价函数的最小化代价实现拓扑优化选择,从而改善基于虚拟隐含网络的虚假数据注入攻击防御方法的性能.最后,通过在IEEE-14总线电网系统上的仿真验证了所提算法的有效性.
基金The work was supported by the grants from:The National High-tech Research and Development Program of China，the National Natural Science Foundation of China，the Clinical Medicine Technology Foundation of Jiangsu Province，the Natural Science Foundation of Jiangsu Province，State Key Clinical Specialty，Provincial Medical Key Discipline
文摘Background： Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder （MDD）. Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. Methods： The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. Results： Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. Conclusions： All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.
文摘For a bipartite graph G on m and n vertices, respectively, in its vertices classes, and for integers s andt such that 2≤ s ≤ t, 0≤ m-s ≤ n-t, andre＋n≤ 2s＋t-1, we prove that if G has at least mn- （2（m - s） ＋ n - t） edges then it contains a subdivision of the complete bipartite K（s,t） with s vertices in the m-class and t vertices in the n-class. Furthermore, we characterize the corresponding extremal bipartite graphs with mn- （2（m - s） ＋ n - t ＋ 1） edges for this topological Turan type problem.
