Critical dynamics of the Potts model: short-time Monte Carlo simulations

We calculate the new dinamic exponent $\theta$ of the 4-state Potts model, using short-time simulations. Our estimates $\theta_{1}=-0.0471(33)$ and $\theta_{2}=$ $-0.0429(11)$ obtained by following the behavior of the magnetization or measuring the evolution of the time correlation function of the magnetization corroborate the conjecture by Okano et. al. In addition, these values agree with previous estimate of the same dynamic exponent for the two-dimensional Ising model with three-spin interactions in one direction, that is known to belong to the same universality class as the 4-state Potts model. The anomalous dimension of initial magnetization $$ is calculated by an alternative way that mixes two different initial conditions. We have also estimated the values of the static exponents $\beta$ and $\nu$. They are in complete agreement with the pertinent results of the literature.Comment: 12 pages, 7 figure

QoS Recommendation in Cloud Services

As cloud computing becomes increasingly popular, cloud providers compete to offer the same or similar services over the Internet. Quality of service (QoS), which describes how well a service is performed, is an important differentiator among functionally equivalent services. It can help a firm to satisfy and win its customers. As a result, how to assist cloud providers to promote their services and cloud consumers to identify services that meet their QoS requirements becomes an important problem. In this paper, we argue for QoS-based cloud service recommendation, and propose a collaborative filtering approach using the Spearman coefficient to recommend cloud services. The approach is used to predict both QoS ratings and rankings for cloud services. To evaluate the effectiveness of the approach, we conduct extensive simulations. Results show that the approach can achieve more reliable rankings, yet less accurate ratings, than a collaborative filtering approach using the Pearson coefficient

Effects of Contrarians in the Minority Game

We study the effects of the presence of contrarians in an agent-based model of competing populations. Contrarians are common in societies. These contrarians are agents who deliberately prefer to hold an opinion that is contrary to the prevailing idea of the commons or normal agents. Contrarians are introduced within the context of the Minority Game (MG), which is a binary model for an evolving and adaptive population of agents competing for a limited resource. Results of numerical simulations reveal that the average success rate among the agents depends non-monotonically on the fraction $a_{c}$ of contrarians. For small $a_{c}$, the contrarians systematically outperform the normal agents by avoiding the crowd effect and enhance the overall success rate. For high $a_{c}$, the anti-persistent nature of the MG is disturbed and the few normal agents outperform the contrarians. Qualitative discussion and analytic results for the small $a_{c}$ and high $a_{c}$ regimes are also presented, and the crossover behavior between the two regimes is discussed.Comment: revtex, 11 pages, 4 figure

Dynamics of opinion formation in a small-world network

The dynamical process of opinion formation within a model using a local majority opinion updating rule is studied numerically in networks with the small-world geometrical property. The network is one in which shortcuts are added to randomly chosen pairs of nodes in an underlying regular lattice. The presence of a small number of shortcuts is found to shorten the time to reach a consensus significantly. The effects of having shortcuts in a lattice of fixed spatial dimension are shown to be analogous to that of increasing the spatial dimension in regular lattices. The shortening of the consensus time is shown to be related to the shortening of the mean shortest path as shortcuts are added. Results can also be translated into that of the dynamics of a spin system in a small-world network.Comment: 10 pages, 5 figure