On the relation between Phi(1,2) and Phi(1,5) perturbed minimal models

We consider the RSOS S-matrices of the Phi(1,5) perturbed minimal models which have recently been found in the companion paper [hep-th/9604098]. These S-matrices have some interesting properties, in particular, unitarity may be broken in a stronger sense than seen before, while one of the three classes of Phi(1,5) perturbations (to be described) shares the same Thermodynamic Bethe Ansatz as a related Phi(1,2) perturbation. We test these new S-matrices by the standard Truncated Conformal Space method, and further observe that in some cases the BA equations for two particle energy levels may be continued to complex rapidity to describe (a) single particle excitations and (b) complex eigenvalues of the Hamiltonian corresponding to non-unitary S-matrix elements. We make some comments on identities between characters in the two related models following from the fact that the two perturbed theories share the same breather sector.Comment: LaTeX, 23 pages, 12 figures. Substantial revision of introductory section, new discussion of complex eigenvalues and non-unitary S-matrice

Cavity approach for real variables on diluted graphs and application to synchronization in small-world lattices

We study XY spin systems on small world lattices for a variety of graph structures, e.g. Poisson and scale-free, superimposed upon a one dimensional chain. In order to solve this model we extend the cavity method in the one pure-state approximation to deal with real-valued dynamical variables. We find that small-world architectures significantly enlarge the region in parameter space where synchronization occurs. We contrast the results of population dynamics performed on a truncated set of cavity fields with Monte Carlo simulations and find excellent agreement. Further, we investigate the appearance of replica symmetry breaking in the spin-glass phase by numerically analyzing the proliferation of pure states in the message passing equations.Comment: 10 pages, 3 figure

Evaluation of be-38 percent al alloy final report, 27 jun. 1964 - 28 feb. 1965

Mechanical properties, microstructural features, and general metallurgical quality of beryllium- aluminum allo

Monte Carlo simulation of the transmission of measles: Beyond the mass action principle

We present a Monte Carlo simulation of the transmission of measles within a population sample during its growing and equilibrium states by introducing two different vaccination schedules of one and two doses. We study the effects of the contact rate per unit time $\xi$ as well as the initial conditions on the persistence of the disease. We found a weak effect of the initial conditions while the disease persists when $\xi$ lies in the range 1/L-10/L ($L$ being the latent period). Further comparison with existing data, prediction of future epidemics and other estimations of the vaccination efficiency are provided. Finally, we compare our approach to the models using the mass action principle in the first and another epidemic region and found the incidence independent of the number of susceptibles after the epidemic peak while it strongly fluctuates in its growing region. This method can be easily applied to other human, animals and vegetable diseases and includes more complicated parameters.Comment: 15 pages, 4 figures, 1 table, Submitted to Phys.Rev.

Diffusive transport in networks built of containers and tubes

We developed analytical and numerical methods to study a transport of non-interacting particles in large networks consisting of M d-dimensional containers C_1,...,C_M with radii R_i linked together by tubes of length l_{ij} and radii a_{ij} where i,j=1,2,...,M. Tubes may join directly with each other forming junctions. It is possible that some links are absent. Instead of solving the diffusion equation for the full problem we formulated an approach that is computationally more efficient. We derived a set of rate equations that govern the time dependence of the number of particles in each container N_1(t),N_2(t),...,N_M(t). In such a way the complicated transport problem is reduced to a set of M first order integro-differential equations in time, which can be solved efficiently by the algorithm presented here. The workings of the method have been demonstrated on a couple of examples: networks involving three, four and seven containers, and one network with a three-point junction. Already simple networks with relatively few containers exhibit interesting transport behavior. For example, we showed that it is possible to adjust the geometry of the networks so that the particle concentration varies in time in a wave-like manner. Such behavior deviates from simple exponential growth and decay occurring in the two container system.Comment: 21 pages, 18 figures, REVTEX4; new figure added, reduced emphasis on graph theory, additional discussion added (computational cost, one dimensional tubes

Irreversible growth of binary mixtures on small-world networks

Binary mixtures growing on small-world networks under far-from-equilibrium conditions are studied by means of extensive Monte Carlo simulations. For any positive value of the shortcut fraction of the network ($p>0$), the system undergoes a continuous order-disorder phase transition, while it is noncritical in the regular lattice limit ($p=0$). Using finite-size scaling relations, the phase diagram is obtained in the thermodynamic limit and the critical exponents are evaluated. The small-world networks are thus shown to trigger criticality, a remarkable phenomenon which is analogous to similar observations reported recently in the investigation of equilibrium systems.Comment: 7 pages, 7 figures; added/removed references and modified presentation. To appear in PR

Community Aliveness: Discovering Interaction Decay Patterns in Online Social Communities

Online Social Communities (OSCs) provide a medium for connecting people, sharing news, eliciting information, and finding jobs, among others. The dynamics of the interaction among the members of OSCs is not always growth dynamics. Instead, a $\textit{decay}$ or $\textit{inactivity}$ dynamics often happens, which makes an OSC obsolete. Understanding the behavior and the characteristics of the members of an inactive community help to sustain the growth dynamics of these communities and, possibly, prevents them from being out of service. In this work, we provide two prediction models for predicting the interaction decay of community members, namely: a Simple Threshold Model (STM) and a supervised machine learning classification framework. We conducted evaluation experiments for our prediction models supported by a $\textit{ground truth}$ of decayed communities extracted from the StackExchange platform. The results of the experiments revealed that it is possible, with satisfactory prediction performance in terms of the F1-score and the accuracy, to predict the decay of the activity of the members of these communities using network-based attributes and network-exogenous attributes of the members. The upper bound of the prediction performance of the methods we used is $0.91$ and $0.83$ for the F1-score and the accuracy, respectively. These results indicate that network-based attributes are correlated with the activity of the members and that we can find decay patterns in terms of these attributes. The results also showed that the structure of the decayed communities can be used to support the alive communities by discovering inactive members.Comment: pre-print for the 4th European Network Intelligence Conference - 11-12 September 2017 Duisburg, German

Periodic Neural Activity Induced by Network Complexity

We study a model for neural activity on the small-world topology of Watts and Strogatz and on the scale-free topology of Barab\'asi and Albert. We find that the topology of the network connections may spontaneously induce periodic neural activity, contrasting with chaotic neural activities exhibited by regular topologies. Periodic activity exists only for relatively small networks and occurs with higher probability when the rewiring probability is larger. The average length of the periods increases with the square root of the network size.Comment: 4 pages, 5 figure

Cascade Failure in a Phase Model of Power Grids

We propose a phase model to study cascade failure in power grids composed of generators and loads. If the power demand is below a critical value, the model system of power grids maintains the standard frequency by feedback control. On the other hand, if the power demand exceeds the critical value, an electric failure occurs via step out (loss of synchronization) or voltage collapse. The two failures are incorporated as two removal rules of generator nodes and load nodes. We perform direct numerical simulation of the phase model on a scale-free network and compare the results with a mean-field approximation.Comment: 7 pages, 2 figure