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This paper proposes a new method of controlling quantum systems via probability density function (PDF) control. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed for the general case and limited input energy, respectively. Unlike traditional quantum control methods, this method directly controls the probability distribution of the quantum state. It provides an alternative method for quantum control engineering. [ABSTRACT FROM AUTHOR]

In this paper, inspired by the pseudo-fractal networks (PFN) and the delayed pseudo-fractal networks (DPFN), we present a novel delayed pseudo-fractal networks model, denoted by NDPFN. Different from the generation algorithm of those two networks, every edge of the novel model has a time-delay to generate new nodes after producing one node. We derive exactly the main structural properties of the novel networks: degree distribution, clustering coefficient, diameter and average path length. Analytical results show that the novel networks have small-world effect and scale-free topology. Comparing topological parameters of these three networks, we find that the degree exponent of the novel networks is the largest while the clustering coefficient and the average path length are the smallest. It means that this kind of delay could weaken the heterogeneity and the small-world features of the network. Particularly, the delay effect in the NDPFN is contrary to that in the DPFN, which illustrates the variety of delay method could produce different effects on the network structure. These present findings may be helpful for a deeper understanding of the time-delay influence on the network topology. [ABSTRACT FROM AUTHOR]

Utilizing dynamics system to identify community structure has become an important means of research. In this paper, inspired by the relationship between topology structures of networks and the dynamic Potts model, we present a novel method that describes the conditional inequality forming simple community can be transformed into the objective function F which is analogous to the Hamilton function of Potts model. Likewise, to detect the well performance of partitioning we develop improved-EM algorithm to search the optimal value of the objective function F by successively updating the dynamic process of the membership vector of nodes which is also commonly influenced by the weighting function W and the tightness expression T. Via adjusting relevant parameters properly, our method can effectively detect the community structures. Furthermore, stability as the new measure quality method is applied for refining the partitions the improved-EM algorithm detects and mitigating resolution limit brought by modularity. Simulation experiments on benchmark and real-data network all give excellent results. [ABSTRACT FROM AUTHOR]

Nonlinear filtering of local turbulent fluid measurements was an unexplored domain; in this paper, we present original stochastic models and efficient filters to explore it. First, we propose nonlinear filters for processes of a mean-field law and give the convergence of their particle approximations. We then define the acquisition process of a vector field along a random path, and significantly modify the Lagrangian models of fluids proposed by the physicists to make them compatible with the problem of filtering. The closure of these equations is obtained by conditioning the dynamics to the observations and to the acquisition process. Our algorithm allowed us to filter velocity measurements of a real turbulent fluid in 3D flows. [ABSTRACT FROM AUTHOR]

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms. [ABSTRACT FROM AUTHOR]