A conditional Granger causality model approach for group analysis in functional magnetic resonance imaging

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed to identify effective connectivity in the human brain with functional magnetic resonance imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pair-wise GCM has commonly been applied based on single-voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of fMRI data with GCM. To compare the effectiveness of our approach with traditional pair-wise GCM models, we applied a well-established conditional GCM to preselected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis of an fMRI data set in the temporal domain. Data sets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM-detected brain activation regions in the emotion-related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state data set, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network that can be characterized as both afferent and efferent influences on the medial prefrontal cortex and posterior cingulate cortex. These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive model can achieve greater accuracy in detecting network connectivity than the widely used pair-wise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI.     

移动僵尸网络的设计及分析

为了更好的对移动僵尸病毒传播方式及命令与控制网络进行研究,提出一种基于SMS-HTTP的移动僵尸网络模型。该文利用多差树结构和混合P2P结构建立高效的命令与控制网络,给出移动僵尸病毒传播算法、命令与控制网络构建算法和僵尸节点加入算法。通过对网络模型的度和高度的平衡,对网络的节点规模、联通性和安全性进行控制。实验环境中,当模型高度为6、度为8时,模型节点规模最多可达到21万。当模型节点规模为10万时,模型的连通率可在5跳内达到100%。结果表明:模型具有较好的可扩展性,可使模型具有较高的连通率和安全性。     

基于粒子群算法的车载自组网服务节点部署

为了提高车载自组网( VANETs)覆盖率,该文提出了一种城市场景下VANETs中基于连通性的服务节点部署方案.通过理论分析得出服务节点扩展覆盖某一区域的概率,依据该概率和VANETs特点,提出了VANETs中的服务节点优化部署模型,并基于粒子群算法(PSO)提出了解决方案.将服务节点的位置信息抽象为粒子进行解的搜索.给出了利用PSO进行最优解搜索的计算步骤.仿真实验结果表明,该文算法能实现服务节点的最优部署,并具有较快的收敛速度和较好的收敛性.     

工序网络的拓扑结构与配置性

以实际的汽轮机制造工序网络为研究对象,分析通用的工序对模体形式和网络的拓扑连通性,以揭示工序对的配置规律.基于复杂网络理论,从微观角度揭示工序网络的模体结构,并从宏观角度分析工序网络的度分布特性.结果表明：复杂的工序网络是简单模体类型的多样化拓扑组合,其拓扑连通性具有衰减幂律的无标度特性,只有少数拥有较大度值的工序才具有较多的配置关系;工序网络的拓扑连通性还具有非协调性,且一部分配置关系具有随机性,表现出较弱的配置性,而另一部分具有技术约束性,表现出很强的配置性.所提出的基于非协调性的度相关系数可以评价任意工序对或整条工序链的配置性.     

图Kcr∨Ks的邻点可区别全色数

利用组合分析方法研究r阶空图与s阶完全图的联图K^c_r∨K_s的邻点可区别全色数问题, 得到了当r+s为奇数且s>r²+2r-1时, χ_at(K^c_r∨K_s)=r+s+2, 其中χ_at(G)表示图G的邻点可区别全色数.     

基于Fiedler矢量的分布式自适应分簇算法

针对无线传感器网络分簇(clustering)问题,提出一种基于Fiedler矢量的分布式分簇改进算法.该算法利用Fiedler矢量的元素符号特性对网络进行递归分簇处理,引入网络拓扑信息,根据网络自身的内部连接自适应决定分簇数目,通过Fiedler矢量的元素数值选出簇头,并且算法给簇头子集筛选合适的网关节点以确保簇头子集的连通性.仿真实验表明,在共识频谱感知的基础上,该算法生成的簇头子集与全网络共识所收敛的结果相同,簇头子集共识收敛速度相对更快,耗时短,能够以更好的时效性、更高的能效达到与全网络共识收敛相同的效果.     

Algebraic connectivity analysis in molecular electronic structure theory I: coulomb potential,tensor connectivity, ε-approximation

In this (first) paper we attempt to generalize the notion of tensor connectivity, subsequently studying how this property is affected in different tensorial operations. We show that the often implied corollary of the linked diagram theorem, namely individual size-extensivity of arbitrary connected closed diagrams, can be violated in Coulomb systems. In particular, the assumption of the existence of localized Hartree–Fock orbitals is generally incompatible with the individual size-extensivity of connected closed diagrams when the interaction tensor is generated by the true two-body part of the electronic Hamiltonian. Thus, in general, size-extensivity of a many-body method may originate in specific cancellations of super-extensive quantities, breaking the convenient one-to-one correspondence between the connectivity of arbitrary many-body equations and the size-extensivity of the expectation values evaluated by those equations (for example, when certain diagrams are discarded from the method). Nevertheless, assuming that many-body equations are evaluated for a stable many-particle system, it is possible to introduce a workaround, called the ε-approximation, which restores the individual size-extensivity of an arbitrary connected closed diagram, without qualitatively affecting the asymptotic behavior of the computed expectation values. No assumptions concerning the periodicity of the system and its strict electrical neutrality are made.     

fMRI assessment of thalamocortical connectivity during attentional performance

Functional magnetic resonance imaging (fMRI) studies have shown dysfunction in key areas associated with the thalamocortical circuit in patients with schizophrenia. This study examined the functional connectivity involving the frontal-thalamic circuitry during a spatial focusing-of-attention task in 18 unmedicated patients with schizophrenia and 38 healthy controls. Functional connectivity was analyzed by assigning seed regions (in the thalamic nuclei (mediodorsal nucleus (MDN), pulvinar, anterior nucleus (AN)), the dorsolateral prefrontal cortex (Brodmann areas 9 and 46), and the caudate), and correlating their respective activity with that in the non-seed regions voxel-wise. Functional connectivity analysis demonstrated that functional connectivity was significantly impaired in patients, e.g., between the right pulvinar and regions such as the prefrontal and temporal cortices and the cerebellum. On the other hand, enhanced functional connectivity was found in patients e.g., between the AN and regions such as the prefrontal and temporal cortices. In addition, the patients had significantly lower task performance and less (but non-significant) brain activation than those of controls. These results revealed disturbed functional integration in schizophrenia, and suggested that the functional connectivity abnormalities in the thalamocortical circuitry, especially the frontal-thalamic circuitry, may underlie the attention deficits in schizophrenia patients. Further, this study suggested that functional connectivity analysis might be more sensitive than brain activation analysis in detecting the functional abnormalities in schizophrenia.