Epidemic Spread in Networks Induced by Deactivation Mechanism

We have studied the topology and epidemic spreading behaviors on the networks in which deactivation mechanism and long-rang connection are coexisted. By means of numerical simulation, we find that the clustering coefficient C and the Pearson correlation coefficient r decrease with increasing long-range connection μ and the topological state of the network changes into that of BA model at the end （when μ = 1）. For the Susceptible-Infect-Susceptible model of epidemics, the epidemic threshold can reach maximum value at μ = 0.4 and presents two different variable states around μ= 0.4.     

Exponential Transformations in Lattice Dynamics

An exponential transformation for the Hamiltonian of a perturbed harmonic lattice with arbitrary point defects is presented. It decouples one mode and leads to a derivation of the equations of the well known Lifshitz formalism in a pure quantum mechanical manner. For illustration, the method is applied to a linear chain with a single anomalous force constant.     

Two-Layer Feedback Neural Networks with Associative Memories

We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly.     

Modular Epidemic Spreading in Small-World Networks

We study the epidemic spreading of the susceptible-infected-susceptible model on small-world networks with modular structure. It is found that the epidemic threshold increases linearly with the modular strength. Furthermore, the modular structure may influence the infected density in the steady state and the spreading velocity at the beginning of propagation. Practically, the propagation can be hindered by strengthening the modular structure in the view of network topology. In addition, to reduce the probability of reconnection between modules may also help to control the propagation.     

Epidemic Diffusion on Complex Networks

Both diffusion and epidemic are well studied in the stochastic systems and complex networks, respectively. Here we combine these two fields and study epidemic diffusion in complex networks. Instead of studying the threshold of infection, which was focused on in previous works, we focus on the diffusion behayiour. We find that the epidemic diffusion in a complex network is an anomalous superdiffusion with varying diffusion exponent and that γ is influenced seriously by the network structure, such as the clustering coefficient and the degree distribution. Numerical simulations have confirmed the theoretical predictions.     

Exponential Relaxation of Glauber Dynamics with Some Special Boundary Conditions

We consider attractive finite-range Glauber dynamics and show that if a certain mixing condition is satisfied, then the system evolving on arbitrary subsets of the lattice, with appropriate boundary conditions, converges to equilibrium exponentially fast, in the uniform sense, uniformly over the subsets of the lattice. This result applies, for instance, to the ferromagnetic nearest neighbor Ising model in the so-called “Basuev region,” where complete analyticity is expected to fail. Technically the result in this paper is an extension of a result of Martinelli and Olivieri, who proved that under a weaker form of mixing the infinite system approaches equilibrium exponentially fast. Conceptually this paper may be seen as a step towards developing and exploiting a restricted notion of complete analyticity in which the boundary conditions, rather than the shapes of the regions under consideration, are being restricted. Received: 24 May 1996 / Accepted: 24 May 1996     

Exponential Synchronization of Uncertain Complex Dynamical Networks with Delay Coupling

This paper studies the global exponential synchronization of uncertain complex delayed dynamical networks. The network model considered is general dynamical delay networks with unknown network structure and unknown coupling functions but bounded. Novel delay-dependent linear controllers are designed via the Lyapunov stability theory. Especially, it is shown that the controlled networks are globally exponentially synchronized with a given convergence rate. An example of typical dynamical network of this class, having the Lorenz system at each node, has been used to demonstrate and verify the novel design proposed. And, the numerical simulation results show the effectiveness of proposed synchronization approaches.     

Opinion Dynamics on Complex Networks with Communities

The Ising or Potts models of ferromagnetism have been widely used to describe locally interacting social or economic systems. We consider a related model, introduced by Sznajd to describe the evolution of consensus in the scale-free networks with the tunable strength （noted by Q） of community structure. In the Sznajd model, the opinion or state of any spins can only be changed by the influence of neighbouring pairs of similar connection spins. Such pairs can polarize their neighbours. Using asynchronous updating, it is found that the smaller the community strength Q, the larger the slope of the exponential relaxation time distribution. Then the effect of the initial upspin concentration p as a function of the final all up probability E is investigated by taking different initialization strategies, the random node-chosen initialization strategy has no difference under different community strengths, while the strategies of community node-chosen initialization and hub node-chosen initialization are different in fina/probability under different Q, and the latter one is more effective in reaching final state.     

Feedback Loops in Opinion Dynamics of Agent-Based Models with Multiplicative Noise

We introduce an agent-based model for co-evolving opinions and social dynamics, under the influence of multiplicative noise. In this model, every agent is characterized by a position in a social space and a continuous opinion state variable. Agents’ movements are governed by the positions and opinions of other agents and similarly, the opinion dynamics are influenced by agents’ spatial proximity and their opinion similarity. Using numerical simulations and formal analyses, we study this feedback loop between opinion dynamics and the mobility of agents in a social space. We investigate the behaviour of this ABM in different regimes and explore the influence of various factors on the appearance of emerging phenomena such as group formation and opinion consensus. We study the empirical distribution, and, in the limit of infinite number of agents, we derive a corresponding reduced model given by a partial differential equation (PDE). Finally, using numerical examples, we show that a resulting PDE model is a good approximation of the original ABM.     

BEC Dynamics in a Double‐Well with Interferometric Feedback

The feedback control scheme for a Bose‐Einstein condensate (BEC) in a double‐well trapping potential located in one arm of Mach‐Zehnder interferometer (MZI) is investigated. The off‐resonant light beam performs the phase probing in one of the wells, thus creating information about the number of atoms in this well. The parameters of the trapping potential are controlled via a feedback loop based on the measured output of the MZI. The problem is analyzed in the framework of master equations for hybrid quantum‐classical systems. Significant modifications of the stationary distribution of atoms over the wells are predicted. These distributions can effectively be controlled by the tunable phase shift in the other arm of the MZI.     

Delay-Dependent Exponential Synchronization Criteria for Chaotic Neural Networks with Time-Varying Delays

The problem of exponentially synchronizing class of delayed neural networks is studied. Both constant and time-varying delays are considered, to obtain the delay-dependent state feedback synchronization gain matrix. By means of the method of Lyapunov–Krasovskii functional, combined with linear matrix inequalities, exponential synchronization of the master–slave structure of neural networks is achieved. The delay interval is decomposed into multiple nonequidistant subintervals, on which Lyapunov–Krasovskii functionals are constructed. On the basis of these functionals, a new exponential synchronization condition, one that is time-delay dependent, is proposed in terms of linear matrix inequalities. A numerical example showing the effectiveness of the proposed method is presented.     

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals＇ relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each ease. Finally, we present numerical simulations for each case to verify our results.     

Different Epidemic Models on Complex Networks

Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can besorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemicmodels on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.     

Periodic Wave of Epidemic Spreading in Community Networks

It was reported by Cummings ef al. [Nature 427 （2004） 344] that there are periodic waves in the spatiotemporal data of epidemics. For understanding the mechanism, we study the epidemic spreading on community networks by both the SIS model and the SIRS model. We find that with the increase of infection rate, the number of total infected nodes may be stabilized at a fixed point, oscillatory waves, and periodic cycles. Moreover, the epidemic spreading in the SIS model can be explained by an analytic map.     

Dynamics of a Single-Mode Semiconductor Laser with Incoherent Optical Feedback

Radiophysics and Quantum Electronics - A new model of an injection semiconductor laser with optical feedback, which generalizes the Lang–Kobayashi equations to the case of incoherent...     

Predicting Quantum Many-Body Dynamics with Transferable Neural Networks

Advanced machine learning(ML)approaches such as transfer learning have seldom been applied to approximate quantum many-body systems.Here we demonstrate that a simple recurrent unit(SRU)based efficient and transferable sequence learning framework is capable of learning and accurately predicting the time evolution of the one-dimensional(ID)Ising model with simultaneous transverse and parallel magnetic fields,as quantitatively corroborated by relative entropy measurements between the predicted and exact state distributions.At a cost of constant computational complexity,a larger many-body state evolution is predicted in an autoregressive way from just one initial state,without any guidance or knowledge of any Hamiltonian.Our work paves the way for future applications of advanced ML methods in quantum many-body dynamics with knowledge only from a smaller system.     

Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

We investigate cooperative behaviors of lattice-embedded scale-free networking agents in the prisoner＇s dilemma game model by employing two initial strategy distribution mechanisms, which are specific distribution to the most connected sites （hubs） and random distribution. Our study indicates that the game dynamics crucially depends on the underlying spatial network structure with different strategy distribution mechanism. The cooperators＇ specific distribution contributes to an enhanced level of cooperation in the system compared with random one, and cooperation is robust to cooperators＇ specific distribution but fragile to defectors＇ specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.     

Cooperative Dynamics in Lattice-Embedded Scale-Free Networks

We investigate cooperative behaviors of lattice-embeddedscale-free networking agents in the prisoner's dilemma game model byemploying two initial strategy distribution mechanisms, which are specificdistribution to the most connected sites (hubs) and random distribution. Ourstudy indicates that the game dynamics crucially depends on the underlyingspatial network structure with different strategy distribution mechanism.The cooperators' specific distribution contributes to an enhanced level ofcooperation in the system compared with random one, and cooperation isrobust to cooperators' specific distribution but fragile to defectors' specific distribution. Especially, unlike the specific case, increasing heterogeneity of network does not always favor the emergence of cooperation under random mechanism. Furthermore, we study the geographical effects and find that the graphically constrained network structure tends to improve the evolution of cooperation in random case and in specific one for a large temptation to defect.