Edge consensus on complex networks: a structural analysis

This paper investigates the consensus taking place on the edges of networks by defining dynamics to edges. Discrete-time and continuous-time edge consensus protocols are proposed in which each edge regulates itself according to the information of itself and its neighbouring edges. By mapping the original graph to its corresponding line graph, conditions guarantee the reaching of edge consensus are presented. Furthermore, the influence of network structure on the speed of reaching edge consensus is analysed by considering a scale-free network with a tunable exponent γ between 2 and ∞. It is found that the convergence speed increases as the network becomes more heterogeneous. In addition, as original network becomes more homogeneous, i.e. as the exponent γ increases from 2, the corresponding line graph becomes more heterogeneous till γ reaches 2.5, after that, the line graph becomes more homogeneous.     

Multidimensional neural growing networks and computer intelligence

The paper examines information-computation processes in time and in space and some aspects of computer intelligence using multidimensional matrix neural growing networks. In particular, issues of object-oriented “thinking” of computers are considered. Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 41–55, July–August, 1994.     

Using neural networks to integrate structural analysis package and optimization package

To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. Since most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we propose a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling employ artificial neural networks to build model Y = f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables X*. Optimization of truss structures was used to validate the DAMDO approach. The empirical results show that the truss optimization problems can be solved by the DAMDO approach, which employ neural networks to integrate the structural analysis package and optimization package without requiring direct integration of the two packages. This approach is promising in many engineering optimization domains which need to couple an analysis package and an optimization one to obtain the optimum solutions.     

Modeling and qualitative analysis of continuous-time neural networks under pure structural variations

A qualitative analysis is developed for continuous-time neural networks subjected to random pure structural variations. Simple algebraic conditions are established for both structural exponential stability of x = 0 of the neural network and for estimates of its domain of attraction. Bounds on motions of the neural network in a forced regime are provided. They do not require any information about its actual structure, which can be completely unknown and may vary unpredictably.     

Multifocal electroretinogram diagnosis of glaucoma applying neural networks and structural pattern analysis

Glaucoma is a chronic ophthalmological disease that affects 5% of the 40-60-year-old population and can lead to irreversible blindness. The multifocal electroretinogram (mfERG) is a recently developed diagnostic technique that provides objective spatial data on the visual pathway and may be of potential benefit in early diagnosis of glaucoma. This paper analyses 13 morphological characteristics that define mfERG recordings and classifies them using a radial basis function network trained with the Extreme Learning Machine algorithm. When used to detect glaucomatous sectors, the method proposed produces sensitivity and specificity values of over 0.8.     

Visual analysis of large heterogeneous social networks by semantic and structural abstraction

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies     

Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks

State-of-the-art molecular dynamics (MD) simulations generate massive datasets involving billion-vertex chemical bond networks, which makes data mining based on graph algorithms such as K-ring analysis a challenge. This paper proposes an algorithm to improve the efficiency of ring analysis of large graphs, exploiting properties of K-rings and spatial correlations of vertices in the graph. The algorithm uses dual-tree expansion (DTE) and spatial hash-function tagging (SHAFT) to optimize computation and memory access. Numerical tests show nearly perfect linear scaling of the algorithm. Also a parallel implementation of the DTE + SHAFT algorithm achieves high scalability. The algorithm has been successfully employed to analyze large MD simulations involving up to 500 million atoms.     

Similarity of personal preferences: Theoretical foundations and empirical analysis

We study the problem of defining similarity measures on preferences from a decision-theoretic point of view. We propose a similarity measure, called probabilistic distance, that originates from the Kendall's tau function, a well-known concept in the statistical literature. We compare this measure to other existing similarity measures on preferences. The key advantage of this measure is its extensibility to accommodate partial preferences and uncertainty. We develop efficient methods to compute this measure, exactly or approximately, under all circumstances. These methods make use of recent advances in the area of Markov chain Monte Carlo simulation. We discuss two applications of the probabilistic distance: in the construction of the Decision-Theoretic Video Advisor (diva), and in robustness analysis of a theory refinement technique for preference elicitation.     

Topological alternatives and structural modeling of infinite grillages on elastic foundations

Several infinite spatially periodic grillages on elastic supports, subjected to lateral static loading, are studied. The topologies of the repeating patterns of the grillages that are considered herein are the only possibilities for dividing a plane into regular polygons. These are grillages composed of triangular, square, or hexagonal cells. A general scheme for the analysis of arbitrary periodic infinite grillages on elastic supports is presented. The compliance and robustness, which is understood as the response of a system when one of its elements is absent, are calculated analytically for the case of discrete elastic spring supports and of a Winkler elastic foundation. A parametric study is carried out in order to compare the three topologies. The results are discussed and concluding remarks are presented.     

Multidimensional knapsack problem: a fitness landscape analysis.

Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Five representations are investigated for the multidimensional knapsack problem. Common mutation operators, such as bit-flip mutation, are employed to generate fitness landscapes. Measures such as fitness distance correlation and autocorrelation are applied to examine the landscapes associated with the tested genetic encodings. Furthermore, additional experiments are made to observe the effects of adding heuristics and local optimization to the representations. Encodings with a strong heuristic bias are more efficient, and the addition of local optimization techniques further enhances their performance.     

Interactive structural learning of Bayesian networks

We propose an hybrid approach for structure learning of Bayesian networks, in which a computer system and a human expert cooperate to search for the best structure. The system builds an initial tree structure which is graphically presented to the expert, and then the expert can modify this structure according to his knowledge of the domain. The system has several tools for aiding the human in this task: it allows for graphical editing (adding, deleting, inverting arcs) of the network, it shows graphically the correlation between variables, and it gives a measure of the quality and complexity for each structure. A measure which combines both quality and complexity, that we call quality, is defined. We have tested the tool in two domains: atmospheric pollution and car insurance, with good results.     

Multidimensional analysis in problems of education qualimetry

A wide range of problems is solved in the field of educational qualimetry. They are student education quality control, ranking of departments of educational institutions, comparison and evaluation of the quality of the educational process in basic centers and branches of remote education, establishment of equivalent qualifications, mutual acknowledgment of education certificates of the Russian Federation and other countries, and so on. It is reasonable to use statistical methods of multidimensional analysis (principal component and factor analysis) to create a system of objective monitoring of the quality of the educational process. The problem of interpretation of principal components in problems of education qualimetry is considered. Principal components of educational-methodical unions of a particular profession are proposed as the criterion of evaluation of the quality of the educational process of a university under study.     

Identification of structural systems by neural networks

A method based on the use of neural networks is developed for the identification of systems encountered in the field of structural dynamics. The methodology is applied to the identification of linear and nonlinear dynamic systems such as the damped Duffing oscillator and the Van der Pol equation. The “generalization” ability of the neural networks is used to predict the response of the identified systems under deterministic and stochastic excitations. It is shown that neural networks provide high fidelity models of unknown structural dynamic systems, which are used in applications such as structural control, health monitoring of structures, earthquake engineering, etc.     

Evolving ICT and governance in organizational networks - Conceptual and theoretical foundations

Both private and public sector organizations tend to participate in networks in order to gain access to knowledge, skills and resources of other organizations and to create synergies to achieve highly demanding and complex goals they cannot attain individually. The governance of these networks has been recognized as being an important variable influencing organizational network performance. This Special Issue aims to contribute to the investigation and understanding of the relationships between ICT and governance of organizational networks. This introductory paper provides the conceptual and theoretical background. The relationship between network governance and technology is bi-directional; evolutions in ICT enable the development of new types of network collaborations and governance, whereas governance of collaboration networks is critical for the development of complex ICT infrastructures. We argue that network governance should be conceptualized as an evolving socio-technical process shaped by actors and aimed at tackling complex and dynamic contemporary challenges.     

Resisting structural re-identification in anonymized social networks

We identify privacy risks associated with releasing network datasets and provide an algorithm that mitigates those risks. A network dataset is a graph representing entities connected by edges representing relations such as friendship, communication or shared activity. Maintaining privacy when publishing a network dataset is uniquely challenging because an individual’s network context can be used to identify them even if other identifying information is removed. In this paper, we introduce a parameterized model of structural knowledge available to the adversary and quantify the success of attacks on individuals in anonymized networks. We show that the risks of these attacks vary based on network structure and size and provide theoretical results that explain the anonymity risk in random networks. We then propose a novel approach to anonymizing network data that models aggregate network structure and allows analysis to be performed by sampling from the model. The approach guarantees anonymity for entities in the network while allowing accurate estimates of a variety of network measures with relatively little bias.     

高校财务多维查询分析系统

为了能够从多个角度查询财务数据,以支持分析和决策,提出了基于MDX(多维表达式)技术的财务分析器,实现从源数据库建立多维数据集,并利用MDX技术从多个维度查询财务数据,以及对结果进行数据分析的功能.在此基础上实现了结果导出Excel、用户和组管理、可访问数据库设置、表项别称设定等模块,以帮助管理、使用和支持决策.并通过实例测试与传统SQL查询进行比较,结果表明该分析器在效率上更加优秀且更适合于帮助分析决策.     

Vibration analysis of Timoshenko beam-columns on two-parameter elastic foundations

This paper presents a finite element technique for determining the vibration characteristics of a uniform Timoshenko beam-column supported on a two-parameter elastic foundation. The beam-column is discretized into a number of simple elements with four degrees of freedom each. The effects of axial force, foundation stiffness parameters, transverse shear deformation and rotatory inertia are incorporated into a finite element model. The matrix equation governing the free vibrations of the beam-column on the elastic foundation is derived from Hamilton's principle. The numerical results for the natural frequencies and the associated mode shapes of the classical Euler-Bernoulli and Timoshenko beam-columns on the elastic foundation are presented and compared with the exact or available solutions, wherever possible. It is shown that the present technique provides a unified approach for the vibration analysis of beam-columns with any end conditions, resting on the elastic foundation.     

Duration calculus: Logical foundations

The Duration Calculus (abbreviated DC) represents a logical approach for formal design of real-time systems, where real numbers are used to model time and Boolean valued functions over time are used to model states and events of real-time systems. Since its introduction, DC has been applied to many case studies and it has been extended in several directions. The aim of this paper is to provide a thorough presentation of the logic.on leave of absence from Software Institute, Academia Sinica, Beijing     

Topological structural analysis

Topological structural analysis is a terminology proposed for studying the behaviour of structures when the constitutive law is reduced to a least formulation. In addition to the equilibrium and deformation compatibility equations we only require that the extensional and contractile strains correspond, respectivley, to tensile and compressive stresses, without further specifying the nature of the constitutive law. The only analysis parameter in these equations is the equilibrium matrix which incorporates pure topological information such as node positions and bar connectivities. It is shown that in a topological context the internal forces, which can be realized by a structure, are bounded by a convex combination of the internal forces of its embedded statically determinate substructures. It is also shown that this structural equilibrium space is nonconvex. Consequently, the internal forces are bounded, component by component, by the internal forces in the statically determinate solutions. Having established that the structural equilibrium space is a small subset of the equilibrium space it is shown that pure equilibrium solutions, such as are obtained in plastic analysis and design, are not always feasible. It is conjectured that topological design of structures may benefit from using topological analysis rather than a pure equilibrium analysis.