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This paper puts forward a novel image encryption algorithm that is based on permutation-diffusion architecture. In pixels' permutation stage, algorithm takes full advantage of the idea of magic cube's scrambling. There is only simple cyclic shift operation in each sub-block's permutation, but when the algorithm has disposed the current sub-block, the adjacent sub-blocks will be dealt with, too. In the cyclic shift of each row, stable points will help to decrease the correlation of adjacent pixels. To make encryption procedure uncertain, this paper brings in a parameter named delay-time that is generated by chaotic map. In the diffusion stage, by combining multiple operations and dynamic look-up table together, the proposed algorithm highly increases the uncertainty of the encryption procedure. At last, the experiment results of key space analysis, information entropy analysis, histogram analysis and etc. show that the encryption algorithm has well performance and it can be used in image encryption and transmission. [ABSTRACT FROM AUTHOR]

Link prediction is a significant and fundamental research issue in the field of network science. Numerous similarity-based algorithms have been widely applied due to low computational cost and high prediction accuracy. The topological features of networks like community structure are beneficial to link prediction. In this paper, we first introduce a Weighted Two-level Neighborhood Similarity (WTNS) index that integrates the resource allocation index and local path index. Then we define the Strength of Community Link (SCL) as a quantitative index to evaluate the close relationship among communities. Based on this, the connection likelihood between two nodes can be calculated and a new link prediction algorithm called SCL-WTNS is presented. The proposed algorithm has been compared with nine popular similarity methods on 12 real-world networks to verify the performance. SCL-WTNS is also compared with two groups of community-based link prediction methods. Experiments indicate that the proposed algorithm is better than comparison algorithms both in prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

In this paper, the exact solutions of the logarithmic nonlinear Schrödinger equation (log-NLS) types, namely, the logarithmic perturbed nonlinear Schrödinger equation (log-pert-NLS), logarithmic improved nonlinear Schrödinger (log-imp-NLS) and logarithmic perturbed improved nonlinear Schrödinger (log-pert-imp-NLS) are examined. These solutions are attained by eliminating logarithmic nonlinearity. The solutions show different Gaussian solitary patterns in different conditions. The dynamics of the obtained solutions are analyzed by means of two-dimensional (2D) and three-dimensional (3D) graphs. [ABSTRACT FROM AUTHOR]

This paper proposes a new symmetric key encryption algorithm based on one-dimensional chaotic map. This algorithm uses the random-like property and ergodicity of chaotic systems. In the process of encryption or decryption, this algorithm generates a chaotic pseudo-random sequence, changes the initial iterative times and the increment to encrypt the plaintext, and realizes fast encryption and decryption of all kinds of files. When analyzing the algorithm's performance and security, the result shows that, compared with the method of Baptista, the proposed method is safer, faster and more powerful. [ABSTRACT FROM AUTHOR]

The flows around a typical open cavity with a store are numerically simulated in this paper. A fully implicit unfactored method is employed in the solver of RANS equations, where the S-A turbulent model is implemented to calculate the turbulent eddy viscosity. The flow-induced oscillation of the free shear layer around the cavity lip is captured and analyzed. Comparisons for the different cases of the cavity flow are made. The calculated results show that the pressure fluctuation is pertinent to the different positions of the store inside the cavity. The power spectral density (PSD) of the pressure by fast Fourier transformations (FFT) is also displayed in the paper. For the Rossiter's oscillatory mode, the numerical results compare well with the experimental data. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the problem of heat exchange in composite plane wall whose heat transmitting coefficient is not a locally periodic function. For that problem, we present a method of multiscale asymptotic expansions and then propose its corresponding FE algorithm. Of course, we give comprehensive theoretical analysis for the FE algorithm. Finally, we use numerical experiments to investigate them. [ABSTRACT FROM AUTHOR]

Database search has wide applications and is used as a subroutine in many important algorithms. In this paper, we will consider a database with a single target item. Quantum algorithm (Grover) locates the target item faster than any classical algorithm. In addition to a full (Grover) search, it frequently occurs that one is looking for a group of items (a block) containing the target item, rather than the target item itself. This problem is known as partial search. As a generalization of the full search, partial search is of particular importance in practice. Partial search trades accuracy for speed, i.e., it works faster than a full search. There exists different versions of partial search. We will study the optimized version of the algorithm discovered by Grover and Radhakrishnan and call it GRK. GRK can be applied successively (in a sequence). First, the database is partitioned into blocks and GRK is applied to find the target block. This target block is then partitioned into subblocks and GRK is used again to find the target subblock. This procedure can be repeated if the database is large enough. (This sequence of GRK's is called a hierarchy.) Another possibility is to partition the database into subblocks directly and use GRK to find the target subblock once. In this paper, we will prove that the latter is faster (makes less queries to the Oracle). [ABSTRACT FROM AUTHOR]

In this paper, we consider the problems of identifying the most appropriate model for a given physical system and of assessing the model contribution to the measurement uncertainty. The above problems are studied in terms of Bayesian model selection and model averaging. As the evaluation of the 'evidence' , i.e., the integral of Likelihood × Prior over the space of the measurand and the parameters, becomes impracticable when this space has dimensions, it is necessary to consider an appropriate numerical strategy. Among the many algorithms for calculating , we have investigated the ellipsoidal nested sampling, which is a technique based on three pillars: The study of the iso-likelihood contour lines of the integrand, a probabilistic estimate of the volume of the parameter space contained within the iso-likelihood contours and the random samplings from hyperellipsoids embedded in the integration variables. This paper lays out the essential ideas of this approach. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines. [ABSTRACT FROM AUTHOR]

Label propagation algorithm (LPA) has been proven to be an extremely fast method for community detection in large complex networks. But an important issue of the algorithm has not yet been properly addressed that random update orders in label propagation process hamper the algorithm robustness of algorithm. We note that when there are multiple maximal labels among a node neighbors' labels, choosing a node' label from which there is a local cycle to the node instead of a random node' label can avoid the labels propagating among communities at random. In this paper, an improved LPA based on local cycles is given. We have evaluated the proposed algorithm on computer-generated networks with planted partition and some real-world networks whose community structure are already known. The result shows that the performance of the proposed approach is even significantly improved. [ABSTRACT FROM AUTHOR]

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Almost all existing approaches for community detection only make use of the network topology information, which completely ignore the background information of the network. However, in many real world applications, we may know some prior information that could be useful in detecting the community structures. Specifically, the true community assignments of certain nodes are known in advance. In this paper, a novel semi-supervised community detection approach is proposed based on label propagation, which can utilize prior information to guide the discovery process of community structure. Our algorithm can propagate the labels from the labeled nodes to the whole network nodes. The algorithm is evaluated on several artificial and real-world networks and shows that it is highly effective in recovering communities. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the fractal pattern in spatial structure of urban road networks. By introducing sub-domain, an improved box-counting algorithm is proposed to obtain the fractal pattern. Numerical experiments explore the realistic urban road network at Dalian city in China. In order to clearly show the performance of our proposed box-counting algorithm, two other measures for urban road networks, i.e., density pattern and accessibility pattern are introduced, compared and discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a new block encryption algorithm. The chaotic trajectories are computed by weighting. Then the result is used to mask the plaintext. Multiple blocks of plaintext are encrypted at the same time and this decreases the chaotic iterations. So the encryption speed is improved to some extent. The proposed algorithm is flexible. When the number of weights is increased, the number of the encrypted plaintext block at the same time is increased and the encryption speed is improved. The simulation result shows that the proposed algorithm has fast encryption speed and fine security. [ABSTRACT FROM AUTHOR]

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]

This paper investigates properties of synchronization of trigonometric coupled maps. These maps have some advantages, such as an invariant measure, ergodicity and the possibility of Kolmogorov-Sinai (KS) entropy calculation. Moreover, an encryption algorithm based on a synchronized coupled map is suggested and described in detail. Also, some security analyses are presented to illustrate the effectiveness of the proposed scheme. The cryptosystem's speed is analyzed as well. Results of the various types of analysis are encouraging and it can be concluded that the proposed image encryption technique is a suitable choice for practical applications. [ABSTRACT FROM AUTHOR]

This paper reports progress on the simulation of metallic clusters embedded in a quantum fluid matrix such as 4He. In previous work we have reported progress developing a real-space density functional method. The core of the method is a diffusion algorithm that extracts the low-lying eigenfunctions of the Kohn-Sham Hamiltonian by propagating the wave functions (which are represented on a real-space grid) in imaginary time. Due to the diffusion character of the kinetic energy operator in imaginary time, algorithms developed so far are at most fourth order in the time-step. The first part of this paper discusses further progress, in particular we show that for a grid based algorithm, imaginary time propagation of any even order can be devised on the basis of multi-product splitting. The new propagation method is particularly suited for modern parallel computing environments. The second part of this paper addresses a yet unsolved problem, namely a consistent description of the interaction between helium atoms and a metallic cluster that can bridge the whole range from a single atom to a metal. Using a combination of DFT calculations to determine the response of the valence electrons, and phenomenological acounts of Pauli repulsion and short-ranged correlations that are poorly described in DFT, we show how such an interaction can be derived. [ABSTRACT FROM AUTHOR]

The paper analyses the limitation of Baptista cryptography in partitioning an attractor, and proposes two new attractor-partition methods: uniform-partition algorithm and dynamic-partition algorithm. They are different from the equal-interval-partition algorithm in Baptista cryptography, but dividing the attractor according to the attractor's natural invariant density and plaintext density, respectively. The comparative experiment indicates: the two new methods improve the unbalanced cryptograph distribution and cryptograph length in Baptista cryptography, they are also able to reduce the iteration time in chaotic systems effectively, which has improved decryption time and cryptographies' security. [ABSTRACT FROM AUTHOR]

In this paper, the dynamical behavior of the FitzHugh–Nagumo (FHN) neural system with time delay driven by Lévy noise is studied from two aspects: the mean first-passage time (MFPT) and the probability density function (PDF) of the first-passage time (FPT). Using the Janicki–Weron algorithm to generate the Lévy noise, and through the order-4 Runge–Kutta algorithm to simulate the FHN system response, the time that the system needs from one stable state to the other one is tracked in the process. Using the MATLAB software to simulate the process above 20,000 times and recording the PFTs, the PDF of the FPT and the MFPT is obtained. Finally, the effects of the Lévy noise and time-delay on the FPT are discussed. It is found that the increase of both time-delay feedback intensity and Lévy noise intensity can promote the transition of the particle from the resting state to the excited state. However, the two parameters produce the opposite effects in the other direction. [ABSTRACT FROM AUTHOR]

Identifying critical nodes in complex networks has gained increasing attention in recent years. However, how to design an algorithm that has low computational complexity but can accurately identify important network nodes is still a challenge. Considering the role of structural holes in shaping communication channels, this paper presents an effective method based on local characteristics to identify critical nodes that play important roles in maintaining network connectivity. Our method considers the connections of a node as well as the connectivity of the neighborhood of the node. Through numerical simulations on various real-world networks, we have demonstrated that the proposed approach outperforms some other well-known heuristic algorithms in identifying vital nodes and leads to faster network collapse in target destruction. [ABSTRACT FROM AUTHOR]

With the development of economy and society, network analysis is widely used in more and more fields. Signed network has a good effect in the process of representation and display. As an important part of network analysis, fuzzy community detection plays an increasingly important role in analyzing and visualizing the real world. Fuzzy community detection helps to detect nodes that belong to some communities but are still closely related to other communities. These nodes are helpful for mining information from the network more realistically. However, there is little research in this field. This paper proposes a fuzzy community detection algorithm based on pointer and adjacency list. The model adopts a new ICALF network data structure, which can achieve the effect of storing community partition structure and membership value between community and node at the same time, with low time complexity and storage space. Experiments on real networks verify the correctness of the method, and prove that the method is suitable for large-scale network applications. [ABSTRACT FROM AUTHOR]

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage. [ABSTRACT FROM AUTHOR]

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken into account. Based on this similarity measure, we propose a novel link community detection algorithm for link clustering on line graphs. The detection algorithm combines the novel link similarity measure with the classic Markov Cluster (MCL) Algorithm and determines the link community partitions by calculating an extended modularity measure. Extensive experiments on two types of complex networks demonstrate the effectiveness, reliability and rationality of our solution in contrast to the other two classical algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we sketch and scrutinize the solitonic wave solution of Camassa–Holm equation by applying Kudryashov's new method. We promote the algorithm of our new method to find the new solutions of this essential model. Camassa–Holm equation is a recent model in the point of distortion of hierarchies composition of integrability systems. It has been displayed that these solutions have the shape of dark, bright and singular solitons solutions of Camassa–Holm nonlinear Schrodinger equation. Graphically changing of extracted results of this model (CH) has been separated to grasp the substantial evolution. We analyze the sensitivity of the obtained solutions on behalf of different boundary conditions. It is fair that our model furnishes an impressive mathematical mechanism for manufacturing the solutions of the traveling wave for many models in physics and mathematics. The strategy utilized here is straightforward and succinct. [ABSTRACT FROM AUTHOR]

Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or clusters. However, the existing algorithms are with high complexity, which cost much computational time. In this paper, an efficient graph clustering algorithm based on spectral coarsening is proposed, to deal with the large time complexity of the traditional spectral algorithm. We first find the subset most possibly belonged to the same cluster in the original network, and merge them into a single node. The scale of the network will decrease with the network being coarsened. Then, the spectral clustering algorithm is performed on the coarsened network with the maintained advantages and the improved time efficiency. Finally, the experimental results on the multiple datasets demonstrate that the proposed algorithm, compared with the current state-of-the-art methods, has superior performance. [ABSTRACT FROM AUTHOR]

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods. [ABSTRACT FROM AUTHOR]

In order to solve the low efficiency of public opinion influence analysis of social media, a new public opinion influence algorithm K-adaboost has been proposed in this paper according to adaboost and K-means algorithms. We first group the training samples and calculate the clustering center of all types of users in the group using the K-means algorithm, and then train the weak classifier of public opinion data and confirm the influence of public opinion on all types of users using the adaboost algorithm, so as to get the total influence of public opinions. Finally, we compare and analyze the performance of K-adaboost, K-means and adaboost algorithms through simulation experiments. The results show that K-adaboost has good adaptability in convergence time and accuracy. [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]

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms. [ABSTRACT FROM AUTHOR]

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better. [ABSTRACT FROM AUTHOR]

Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best. [ABSTRACT FROM AUTHOR]

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field. [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]

In this paper, an image encryption algorithm based on couple multiple chaotic systems is presented. It made the one-dimensional Coupled Map Lattice (CML) formed by Skew Tent map as spatiotemporal chaotic system and made its output sequence as the initial value of logistic and meanwhile did iterative of specific times to get the final key sequence, and then did XOR operations with corresponding pixels to finish the encryption. Numerical analysis expresses that this algorithm has large enough space and high security. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach for monitoring power consumption in several processes. The generalization of the envSOM algorithm, a variant of Self-Organizing Map (SOM), is used to build an electrical model and visualize the information. The envSOM extended to n hierarchical phases allows us to obtain a more accurate model from real past data. The model is conditioned hierarchically on environmental variables. In this way, time variables can be used to consider seasonality and weekday/hour periodicity. Time variable maps and electrical component planes make it possible to visualize and analyze power consumption. The representation of the Best Matching Unit (BMU) or its trajectory on these maps enables the on-line monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for one-way hash function construction based on the chaotic look-up table with changeable parameter. First, the original message with padding is divided into block messages as groups and then further broken into sub-block messages, which are translated into corresponding ASCII codes by groups. Then the group ASCII codes are successively inputted into the chaotic look-up table updated by changeable parameter to permute corresponding values. Finally, based on the calculation of the values, 128-bit hash value is generated. Performance analysis indicates that our proposed algorithm satisfies sensitive requirements and can resist all kinds of attacks. [ABSTRACT FROM AUTHOR]

This paper investigates antisynchronization of identical and nonidentical Φ6 Van der Pol oscillators (VDPOs) and Φ6 Duffing oscillators (DOs) with both parametric and external excitations based on adaptive backstepping technique. The technique is applied to achieve complete antisynchronization between identical Φ6 (VDPOs), identical Φ6 (DOs), and nonidentical Φ6 oscillators comprising the Φ6 (VDPO) and Φ6 (DO). Numerical simulations are implemented to verify the feasibility and effectiveness of the antisynchronization technique. [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]

A Monte Carlo simulation of the dynamics of diffusion process is presented in the framework of the lattice gas model. The considered lattice is of square geometry with lateral interaction energy between diffusive adsorbates. The paper is entirely devoted to first JFN and second JSN neighboring repulsive interactions with the ratio R=JSN/JFN in the [0, 1] interval. It is shown that repulsive second neighboring interaction allows emergency of new ordered phases at the coverages theta = 1/4 and 3/4 and induces anisotropy when R is greater than the magnitude 0.5. The present situation is similar to that of Sadiq and Binder, Surf. Sci.128, 350 (1985) except for the choice of transition probability. Particular attention is given to both the behavior of the tracer diffusion coefficient and to the static of the half-full lattice for which a complete phase diagram is derived from calculation of the apparent activation energy. Our dynamic calculations suggest that in the temperature range examined, the jump algorithm gives rise to acceptable results, even its criticable normalization factor. [ABSTRACT FROM AUTHOR]

In this paper, the generalized projective synchronization of chaotic neural networks is investigated. Based on the modified nonlinear state observer algorithm and the pole placement technique, a synchronization scheme is designed. The generalized projective synchronization of different chaotic neural networks can be achieved by using the proposed method. Numerical simulations further demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Non-linear filtering of local turbulent fluid measurements was an unexplored domain, in this paper we present original stochastic models and efficient filters to perform it. First we propose non-linear filters for processes of a mean-field law and give the convergence of their particle approximations. Then we define the acquisition process of a vector field along a random path. We deeply 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 we discuss a noisy mean field model for the genetic toggle switch. We show that this model approximates very well the characteristics of the system, observed using the exact Gillespie stochastic simulation algorithm. Also, we show that the system can be made exponentially stable depending on reaction parameters. [ABSTRACT FROM AUTHOR]

A new modified Grassberger and Precaccia Algorithm (GPA), called Δr neighborhood GPA, is investigated to estimate the correlation dimension (D[sub 2]) in this paper. Comparison of time cost between the new algorithm and other GPAs exhibits its efficiency with scaling as O(N * N[sub ref]/q[sup 2]), whereas the original algorithm's is O(N[sup 2]), the box-assisted correlation algorithm's is O(Nlog N). The D[sub 2] result of Lorenz model calculated by the new algorithm satisfies the expectation well and thus tests its accuracy. Finally, a theoretical evaluation of the time cost of the Δr neighborhood GPA is discussed. [ABSTRACT FROM AUTHOR]

The unique characteristic of quantum mechanics may be used in the near future to create a quantum associative memory with a capacity exponential in the number of neurons. In this paper we discuss some quantum computational ideas and algorithms necessary to develop a quantum associative memory. [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]

The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a link density clustering (LDC) method for overlapping community detection based on density peaks. We firstly use an extended cosine link distance metric to reflect the relationship of links. Then we introduce a clustering algorithm with fast search for solving the link clustering (LC) problem by density peaks with box plot strategy to determine the cluster centers automatically. Finally, we acquire both the link communities and the node communities. Our algorithm is compared with other representative algorithms through substantial experiments on real-world networks. The experimental results show that our algorithm consistently outperforms other algorithms in terms of modularity and coverage. [ABSTRACT FROM AUTHOR]

Community detection based on label propagation algorithm (LPA) has attracted widespread concern because of its high efficiency. But it is difficult to guarantee the accuracy of community detection as the label spreading is random in the algorithm. In response to the problem, an improved LPA based on random walk (RWLPA) is proposed in this paper. Firstly, a matrix measuring similarity among various nodes in the network is obtained through calculation. Secondly, during the process of label propagation, when a node has more than a neighbor label with the highest frequency, not the label of a random neighbor but the label of the neighbor with the highest similarity will be chosen to update. It can avoid label propagating randomly among communities. Finally, we test LPA and the improved LPA in benchmark networks and real-world networks. The results show that the quality of communities discovered by the improved algorithm is improved compared with the traditional algorithm. [ABSTRACT FROM AUTHOR]

Compared with the low dimension chaotic systems (LDCS), the high dimension chaotic systems (HDCS) have many advantages as more complex mechanism, increasing the key space, more ruleless for the time series of the system variable, etc. Based on these reasons, a new encryption cryptosystem based on Lorenz system is proposed. The theoretical and experimental results show that, this algorithm can resist the local linear attack, the phase space reconstruct attack, the entropy attack, etc. The proposed cryptosystem has higher security. [ABSTRACT FROM AUTHOR]

A novel measurement method of parallel-plate waveguide (PPW) system is introduced to detect the characteristics of left-handed metamaterials (LHMs). A sample of LHM composed of triangular open-loop resonator plus wire structures is proposed and measured by using this PPW system. The measurement results are in good agreement with the simulation results. Furthermore, the parametric retrieval algorithm, the calculated negative phase shift and the simulated subwavelength imaging result are given to validate the authenticity of PPW measurement system, which effectively demonstrates the measured transmission passband is indeed a left-handed passband. [ABSTRACT FROM AUTHOR]