Does pop music exist? Hierarchical structure in phonographic markets

I find a topological arrangement of assets traded in phonographic markets which has associated a meaningful economic taxonomy. I continue using the Minimal Spanning Tree and the correlations between assets, but now outside the stock markets. This is the first attempt to use these methods on phonographic markets where we have artists instead of stocks. The value of an artist is defined by record sales. The graph is obtained starting from the matrix of correlation coefficients computed between the world’s most popular 30 artists by considering the synchronous time evolution of the difference of the logarithm of weekly record sales. This method provides the hierarchical structure of the phonographic market and information on which music genre is meaningful according to customers. Statistical properties (including the Hurst exponent) of weekly record sales in the phonographic market are also discussed.     

The impact of human activity patterns on asymptomatic infectious processes in complex networks

The study of the impact of human activity patterns on network dynamics has attracted a lot of attention in recent years. However, individuals’ knowledge of their own physical states has rarely been incorporated into modeling processes. In real life, for certain infectious processes, infected agents may not have any visible or physical signs and symptoms; therefore, they may believe that they are uninfected even when they have been infected asymptomatically. This infection awareness factor is covered neither in the classical epidemic models such as SIS nor in network propagation studies. In this article, we propose a novel infectious process model that differentiates between the infection awareness states and the physical states of individuals and extend the SIS model to deal with both asymptomatic infection characteristics and human activity patterns. With regards to the latter, we focus particularly on individuals’ testing action, which is to determine whether an individual is infected by an epidemic. The simulation results show that less effort is required in controlling the disease when the transmission probability is either very small or large enough and that Poisson activity patterns are more effective than heavy-tailed patterns in controlling and eliminating asymptomatic infectious diseases due to the long-tail characteristic.     

Feedback between node and network dynamics can produce real-world network properties

Real-world networks are characterized by common features, including among others a scale-free degree distribution, a high clustering coefficient and a short typical distance between nodes. These properties are usually explained by the dynamics of edge and node addition and deletion.     

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate.     

Derivative-free optimization and neural networks for robust regression

Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression.     

Autonomous control methods in logistics – A mathematical perspective

We model general autonomously controlled production networks by means of nonlinear differential equations and implement autonomous control methods, where transportation times and disturbances in the transportation times are taken into account. Autonomous control enables intelligent logistic objects to route themselves through a logistic network. Based on this model we investigate a certain scenario of a production network, where we show advantages and disadvantages of the implementation of autonomous control methods from a mathematical perspective in view of robustness and stability.     

The split-step θ-methods for stochastic delay Hopfield neural networks

In this paper, we construct a new split-step numerical method for stochastic delay Hopfield neural networks. The main aim of this paper is to investigate the mean-square stability of this split-step θ-methods for stochastic delay Hopfield neural networks. It is proved that the split-step θ-methods are mean-square stable under suitable conditions. Numerical experiments verify the numerical stability results obtained from theory. A comparison between this work and Ronghua et al. [8] is also discussed in the example.     

供应链网络可靠性的多层Bayes估计模型

对供应链网络可靠性进行界定和分析,以节点企业间的协同为基础,得到可靠度计算模型,以此为依据采集实证样本无失效运行的数据.根据供应链网络可靠性统计特性,建立一种多层Bayes估计方法,应用于样本可靠性评估中.在估计失效率的基础上,对供应链网络可靠度进行估计,对仿真结果进行分析,显示多层Bayes估计方法应用效果较好,精确度高,反映了参数不确定性对供应链网络可靠性的影响,能够较好地解决依据无失效数据判定供应链网络可靠性水平的问题.     

基于模糊聚类分析的小波神经网络模型在径流预测中的应用

提出了一种在对预报因子集进行模糊聚类分析基础上构建径流预测模型的新方法:先通过模糊C-均值聚类将历史径流数据进行分类,然后利用小波神经网络分别建立预报因子集类别变量特征值与观测值之间的局部预测模型,并设计了特征值分类识别器,自动搜寻相适应的局部网络模型进行预测.通过西南某水库2011年日平均入库来流的计算实例对简单小波神经网络预测模型和所建的基于FCM与小波神经网络的预测模型进行了比较,结果较为满意.