Synchronization performance in time-delayed random networks induced by diversity in system parameter

Synchronization rhythm and oscillating in biological systems can give clues to understanding the cooperation and competition between cells under appropriate biological and physical conditions. As a result, the network setting is appreciated to detect the stability and transition of collective behaviors in a network with different connection types. In this paper, the synchronization performance in time-delayed excitable homogeneous random networks（EHRNs） induced by diversity in system parameters is investigated by calculating the synchronization parameter and plotting the spatiotemporal evolution pattern, and distinct impacts induced by parameter-diversity are detected by setting different time delays. It is found that diversity has no distinct effect on the synchronization performance in EHRNs with small time delay being considered. When time delay is increased greatly, the synchronization performance of EHRN degenerates remarkably as diversity is increased. Surprisingly, by setting a moderate time delay, appropriate parameter-diversity can promote the synchronization performance in EHRNs, and can induce the synchronization transition from the asynchronous state to the weak synchronization. Moreover, the bistability phenomenon, which contains the states of asynchronous state and weak synchronization,is observed. Particularly, it is confirmed that the parameter-diversity promoted synchronization performance in time-delayed EHRN is manifested in the enhancement of the synchronization performance of individual oscillation and the increase of the number of synchronization transitions from the asynchronous state to the weak synchronization. Finally, we have revealed that this kind of parameter-diversity promoted synchronization performance is a robust phenomenon.

Fully Distributed Learning for Deep Random Vector Functional-Link Networks

In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.

Comprehensive analysis of two-dimensional charge transport mechanism in thin-film transistors based on random networks of single-wall carbon nanotubes using transient measurements

Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectronic applications.Herein,a comprehensive investigation of the two-dimensional(2D)charge transport mechanism in SWCNT-TFTs is reported by analyzing the temperature-dependent electrical characteristics determined from the direct-current and non-quasi-static transient measurements at 80-300 K.To elucidate the time-domain charge transport characteristics of the random networks in the SWCNTs,an empirical equation was derived from a theoretical trapping model,and a carrier velocity distribution was determined from the differentiation of the transient response.Furthermore,charge trapping and de-trapping in shallow-and deep-traps in SWCNT-TFTs were analyzed by investigating charge transport based on their trapping/de-trapping rate.The comprehensive analysis of this study provides fundamental insights into the 2D charge transport mechanism in TFTs based on random networks of nanomaterial channels.

On Latency Reductions in Vehicle-to-Vehicle Networks by Random Linear Network Coding

In the Internet of vehicles(IoV),direct communication between vehicles,i.e.,vehicle-tovehicle(V2V)may have lower latency,compared to the schemes with help of Road Side Unit(RSU)or base station.In this paper,the scenario where the demands of a vehicle are satisfied by cooperative transmissions from those one in front is considered.Since the topology of the vehicle network is dynamic,random linear network coding is applied in such a multisource single-sink vehicle-to-vehicle network,where each vehicle is assumed to broadcast messages to others so that the intermediate vehicles between sources and sink can reduce the latency collaboratively.It is shown that the coding scheme can significantly reduce the time delay compared with the non-coding scheme even in the channels with high packet loss rate.In order to further optimize the coding scheme,one can increase the generation size,where the generation size means the number of raw data packets sent by the source node to the sink node in each round of communication.Under the premise of satisfying the coding validity,we can dynamically select the Galois field size according to the number of intermediate nodes.It is not surprised that the reduction in the Galois field size can further reduce the transmission latency.

Control of random Boolean networks via average sensitivity of Boolean functions

In this paper, we discuss how to transform the disordered phase into an ordered phase in random Boolean networks. To increase the effectiveness, a control scheme is proposed, which periodically freezes a fraction of the network based on the average sensitivity of Boolean functions of the nodes. Theoretical analysis is carried out to estimate the expected critical value of the fraction, and shows that the critical value is reduced using this scheme compared to that of randomly freezing a fraction of the nodes. Finally, the simulation is given for illustrating the effectiveness of the proposed method.

Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images

AIM:To analyze ultrasound biomicroscopy(UBM)images using random forest network to find new features to make predictions about vault after implantable collamer lens(ICL)implantation.METHODS:A total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient’s preoperative parameters as well as the vault of the ICL after implantation.The vault was set as the prediction target,and the input elements were mainly ciliary sulcus shape parameters,which included 6 angular parameters,2 area parameters,and 2 parameters,distance between ciliary sulci,and anterior chamber height.A random forest regression model was applied to predict the vault,with the number of base estimators(n_estimators)of 2000,the maximum tree depth(max_depth)of 17,the number of tree features(max_features)of Auto,and the random state(random_state)of 40.0.RESULTS:Among the parameters selected in this study,the distance between ciliary sulci had a greater importance proportion,reaching 52%before parameter optimization is performed,and other features had less influence,with an importance proportion of about 5%.The importance of the distance between the ciliary sulci increased to 53% after parameter optimization,and the importance of angle 3 and area 1 increased to 5% and 8%respectively,while the importance of the other parameters remained unchanged,and the distance between the ciliary sulci was considered the most important feature.Other features,although they accounted for a relatively small proportion,also had an impact on the vault prediction.After parameter optimization,the best prediction results were obtained,with a predicted mean value of 763.688μm and an actual mean value of 776.9304μm.The R²was 0.4456 and the root mean square error was 201.5166.CONCLUSION:A study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.

Weighted Scaling in Non-growth Random Networks

We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its totM number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scMe-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

Synchronization of coupled logistic maps on random community networks

The collective synchronization of a system of coupled logistic maps on random community networks is investigated. It is found that the synchronizability of the community network is affected by two factors when the size of the network and the number of connections are fixed. One is the number of communities denoted by the parameter rn, and the other is the ratio σ of the connection probability p of each pair of nodes within each community to the connection probability q of each pair of nodes among different communities. Theoretical analysis and numerical results indicate that larger rn and smaller σ are the key to the enhancement of network synchronizability. We also testify synchronous properties of the system by analysing the largest Lyapunov exponents of the system.

Interference Coordination for Secrecy Enhancement in Random Wireless Networks

The co-channel interference modeling is vital for evaluating the secrecy performance in random wireless networks,where the legitimate nodes and eavesdroppers are randomly distributed.In this paper,a new interference model is proposed from the userdominant perspective.The model can provide a better analytical assessment of secrecy performance with interference coordination for the presence of eavesdroppers.The typical legitimate is assumed to be located at the origin,and chooses the closest base station(BS) as its serving BS.The field of interferers is obtained by excluding the desired BSs(including the serving BS and its cooperative BS(s)).In contract with the exiting interference model,it is assumed that desired BSs and interferers belong to the same Poisson Point Process(PPP),and eavesdroppers are distributed according to another independent PPP.Based on this model,the average secrecy transmission capacity is derived in simply analytical forms with interference coordination.Analysis and simulation results show that the secrecy performance can be significantly enhanced by exploiting interference coordination.Furthermore,the average secrecy transmission capacity increases with increasing number of cooperative BSs.

Epidemic spreading on random surfer networks with infected avoidance strategy

In this paper, we study epidemic spreading on random surfer networks with infected avoidance （IA） strategy. In particular, we consider that susceptible individuals＇ moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy＇s effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive.

Algebraic Approach to Random Convolutional Network Coding over Wireless Packet Networks

To characterize the algebraic structure of wireless network coding, a hypergragh is utilized to model wireless packet networks from network layer. The algebraic description of random convolutional network coding is deduced, and the coding condition is also presented. Analyses and simulations show that random convolutional coding is capacity-achieving with probability approaching 1.

Efficient learning for decomposing and optimizing random networks

In this study,we consider the problem of node ranking in a random network.A Markov chain is defined for the network,and its transition probability matrix is unknown but can be learned by sampling random interactions among nodes.Our objective is to decompose the Markov chain into several ergodic classes and select the best node in each ergodic class.We propose a dynamic sampling procedure,which gives a probability guarantee on correct decomposition and maximizes a weighted probability of correct selection of the best node in each ergodic class.Numerical experiment results demonstrate the efficiency of the proposed sampling procedure.

Exact Solution for Clustering Coefficient of Random Apollonian Networks

By means of the series method, we obtain the exact analytical solution of clustering coefficient in random Apollonian networks [Phys. Rev. E 71 （2005）046141]. Our exact analytical result is identical with the simulation, whereas in the original work, there is a deviation of about 4% between their approximate analytical result and the simulation.

Interpreting Randomly Wired Graph Models for Chinese NER

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)tasks.However,most existing approaches only focus on improving the performance of models but ignore their interpretability.In this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ChineseNER.Besides,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial Attacks.Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

Grouping Decision Algorithm for Dynamic Terminals in Random Access Networks

This paper proposes a grouping decision algorithm for random access networks with the carrier sense multiple access (CSMA) mechanism, which can balance the traffic load and solve the hidden terminal issue. Considering the arrival characteristics of terminals and quality of service (QoS) requirements, the traffic load is evaluated based on the effective bandwidth theory. Additionally, a probability matrix of hidden terminals is constructed to take into account the dynamic nature of hidden terminal relations. In the grouping process, an income function is established with a view to the benefits of decreasing the probability of hidden terminal collisions and load balancing. Then, we introduce the grey wolf optimization (GWO) algorithm to implement the grouping decision. Simulation results demonstrate that the grouping algorithm can effectively alleviate the performance degradation and facilitate the management of network resources.

Design and performance analysis of networked control systems with random delay

The design and performance analysis of networked control systems with random network delay in the forward channel is proposed, which are described in a state-space form. A new control scheme is used to overcome the effects of network transmission delay, which is termed networked predictive control （NPC）. Furthermore, three different ways to choose control input are discussed and the performances are analyzed, respectively. Both real-time simulations and practical experiments show the effectiveness of the control scheme.

Degree distribution of random birth-and-death network with network size decline

In this paper,we provide a general method to obtain the exact solutions of the degree distributions for random birthand-death network（RBDN） with network size decline.First,by stochastic process rules,the steady state transformation equations and steady state degree distribution equations are given in the case of m ≥ 3 and 0 〈 p 〈 1/2,then the average degree of network with n nodes is introduced to calculate the degree distributions.Specifically,taking m = 3 for example,we explain the detailed solving process,in which computer simulation is used to verify our degree distribution solutions.In addition,the tail characteristics of the degree distribution are discussed.Our findings suggest that the degree distributions will exhibit Poisson tail property for the declining RBDN.

Influence of Inhomogeneity on Critical Behavior of Earthquake Model on Random Graph

We consider the earthquake model on a random graph. A detailed analysis of the probability distribution of the size of the avalanches will be given. The model with different inhomogeneities is studied in order to compare the critical behavior of different systems. The results indicate that with the increase of the inhomogeneities, the avalanche exponents reduce, i.e., the different numbers of defects cause different critical behaviors of the system. This is virtually ascribed to the dynamical perturbation.

Effects of refractory period on dynamical range in excitable networks

Effects of refractory period on the dynamical range in excitable networks are studied by computer simulations and theoretical analysis. The first effect is that the maximum or peak of the dynamical range appears when the largest eigenvalue of adjacent matrix is larger than one. We present a modification of the theory of the critical point by considering the correlation between excited nodes and their neighbors, which is brought by the refractory period. Our analysis provides the interpretation for the shift of the peak of the dynamical range. The effect is negligible when the average degree of the network is large. The second effect is that the dynamical range increases as the length of refractory period increases, and it is independent of the average degree. We present the mechanism of the second effect. As the refractory period increases,the saturated response decreases. This makes the bottom boundary of the dynamical range smaller and the dynamical range extend.

Self-organized Criticality in an Earthquake Model on Random Network

A simplified Olami-Feder-Christensen model on a random network has been studied. We propose a new toppling rule -- when there is an unstable site toppling, the energy of the site is redistributed to its nearest neighbors randomly not averagely. The simulation results indicate that the model displays self-organized criticality when the system is conservative, and the avalanche size probability distribution of the system obeys finite size scaling. When the system is nonconservative, the model does not display scaling behavior. Simulation results of our model with different nearest neighbors q is also compared, which indicates that the spatial topology does not alter the critical behavior of the system.