RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.     

A high-precision study of the hopfield model in the phase of broken replica symmetry

Using a multi-spin coding algorithm, the Hopfield model is studied for network sizes up toN=33,968. Thermodynamically stable states are found in a region where the replica-symmetric solution predicts none should exist, but where a one-step replica symmetry-breaking calculation predicts some should exist. Furthermore, the order parameter in this region is found to take on two distinct values, one of which is not predicted by any theory.     

复杂网络病毒传播的局域控制研究

从复杂网络的节点路径长度范围的角度来研究病毒传播的局域控制，分析了在不同拓扑结构的复杂网络中进行局域控制的有效性.研究表明，局域控制对WS小世界网络、BA无标度网络和ER随机网络三类复杂网络均有效，但只有WS小世界网络存在零感染的控制范围最优值d=3；对于长程连边的分布存在距离偏好的Kleinberg小世界网络，随着依赖度的增大，病毒传播率临界值增加，同时局域范围控制的效果得到加强. 关键词： 复杂网络 病毒传播 局域控制 路径长度     

Effective mechanism for social recommendation of news

Recommender systems represent an important tool for news distribution on the Internet. In this work we modify a recently proposed social recommendation model in order to deal with no explicit ratings of users on news. The model consists of a network of users which continually adapts in order to achieve an efficient news traffic. To optimize the network’s topology we propose different stochastic algorithms that are scalable with respect to the network’s size. Agent-based simulations reveal the features and the performance of these algorithms. To overcome the resultant drawbacks of each method we introduce two improved algorithms and show that they can optimize the network’s topology almost as fast and effectively as other not-scalable methods that make use of much more information.     

Network evolution by nonlinear preferential rewiring of edges

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.     

Rich-club and page-club coefficients for directed graphs

Rich-club and page-club coefficients and their null models are introduced for directed graphs. Null models allow for a quantitative discussion of the rich-club and page-club phenomena. These coefficients are computed for four directed real-world networks: Arxiv High Energy Physics paper citation network, Web network (released from Google), Citation network among US Patents, and email network from a EU research institution. The results show a high correlation between rich-club and page-club ordering. For journal paper citation network, we identify both rich-club and page-club ordering, showing that “elite” papers are cited by other “elite” papers. Google web network shows partial rich-club and page-club ordering up to some point and then a narrow declining of the corresponding normalized coefficients, indicating the lack of rich-club ordering and the lack of page-club ordering, i.e. high in-degree (PageRank) pages purposely avoid sharing links with other high in-degree (PageRank) pages. For UC patents citation network, we identify page-club and rich-club ordering providing a conclusion that “elite” patents are cited by other “elite” patents. Finally, for email communication network we show lack of both rich-club and page-club ordering. We construct an example of synthetic network showing page-club ordering and the lack of rich-club ordering.     

Motif structure and cooperation in real-world complex networks

Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.     

Synchronization analysis of delayed complex networks via adaptive time-varying coupling strengths

In this Letter, we study the synchronization for delayed complex networks by adjusting time-varying coupling strengths. Under some assumptions, the update laws of the coupling strengths are obtained to realize the synchronization based on Lassalle-Yoshizawa theorem. For the given delayed complex network, we can always find appropriate coupling strengths to achieve the synchronization. Compared with the existing results, the update laws don't need the information of the characteristics of the identical node and the coupling matrix. The state-dependencies of coupling strengths coupled to the dynamics of the nodes in a way to enhance synchronization. An example shows the proposed theoretical result is feasible and effective.     

Utilization of surface plasmon resonance of Au/Pt nanoparticles for highly photosensitive ZnO nanorods network based plasmon field effect transistor

Hydrothermally processed highly photosensitive ZnO nanorods based plasmon field effect transistors (PFETs) have been demonstrated utilizing the surface plasmon resonance coupling of Au and Pt nanoparticles at Au/Pt and ZnO interface. A significantly enhanced photocurrent was observed due to the plasmonic effect of the metal nanoparticles (NPs). The Pt coated PFETs showed I_on/I_off ratio more than 3 × 10⁴ under the dark condition, with field-effect mobility of 26 cm² V⁻¹ s⁻¹ and threshold voltage of −2.7 V. Moreover, under the illumination of UV light (λ = 350 nm) the PFET revealed photocurrent gain of 10⁵ under off-state (−5 V) of operation. Additionally, the electrical performance of PFETs was investigated in detail on the basis of charge transfer at metal/ZnO interface. The ZnO nanorods growth temperature was preserved at 110 °C which allowed a low temperature, economical and simple method to develop highly photosensitive ZnO nanorods network based PFETs for large scale production.     

基于NW型小世界人工神经网络的污水出水水质预测

为了预测污水处理出水水质,针对污水处理过程具有多变量、非线性、时变性、严重滞后的特点,提出了基于NW型小世界人工神经网络的污水处理出水水质预测模型。首先根据污水处理系统确定模型输入输出变量个数,然后建立了多层前向小世界神经网络模型,并对网络模型的隐层结构进行了优化研究。借助污水处理过程的历史数据进行了仿真研究,结果表明：和同规模的多层前向人工神经网络相比,小世界神经网络对污水出水水质预测具有较高精度和收敛速度,为污水出水水质的实时预测提供了一种有效的新方法。