Winding clusters in percolation on the Torus and the Moebius strip

Using a simulation technique introduced recently, we study winding clusters in percolation on the torus and the Moebius strip for different aspect ratios. The asynchronous parallelization of the simulation makes very large system and sample sizes possible. Our high accuracy results are fully consistent with predictions from conformal field theory. The numerical results for the Moebius strip and the number distribution of winding clusters on the torus await theoretical explanation. To our knowledge, this study is the first of its kind.Comment: 20 pages, 9 figures, submitted to J. Stat. Phy

Functional-integral based perturbation theory for the Malthus-Verhulst process

We apply a functional-integral formalism for Markovian birth and death processes to determine asymptotic corrections to mean-field theory in the Malthus-Verhulst process (MVP). Expanding about the stationary mean-field solution, we identify an expansion parameter that is small in the limit of large mean population, and derive a diagrammatic expansion in powers of this parameter. The series is evaluated to fifth order using computational enumeration of diagrams. Although the MVP has no stationary state, we obtain good agreement with the associated {\it quasi-stationary} values for the moments of the population size, provided the mean population size is not small. We compare our results with those of van Kampen's $\Omega$-expansion, and apply our method to the MVP with input, for which a stationary state does exist.Comment: 24 pages, 15 figure

Comment on: "Superscaling of Percolation on Rectangular Domains"

In [Watanabe et al., Phys. Rev. Lett. 93 190601 (2004)], the authors show numerically that spanning and percolation probabilities in two-dimensional systems with different aspect ratios obey a form of "superscaling". In this comment, we would like to point out some difficulties with their proposed scaling ansatz and suggest why this remained undetected in their numerical analysis.Comment: 1 page + references, 1 figur

Data-Driven Prediction of Thresholded Time Series of Rainfall and SOC models

We study the occurrence of events, subject to threshold, in a representative SOC sandpile model and in high-resolution rainfall data. The predictability in both systems is analyzed by means of a decision variable sensitive to event clustering, and the quality of the predictions is evaluated by the receiver operating characteristics (ROC) method. In the case of the SOC sandpile model, the scaling of quiet-time distributions with increasing threshold leads to increased predictability of extreme events. A scaling theory allows us to understand all the details of the prediction procedure and to extrapolate the shape of the ROC curves for the most extreme events. For rainfall data, the quiet-time distributions do not scale for high thresholds, which means that the corresponding ROC curves cannot be straightforwardly related to those for lower thresholds.Comment: 19 pages, 10 figure

Analysis of an information-theoretic model for communication

We study the cost-minimization problem posed by Ferrer i Cancho and Sol\'e in their model of communication that aimed at explaining the origin of Zipf's law [PNAS 100, 788 (2003)]. Direct analysis shows that the minimum cost is $\min {\lambda, 1-\lambda}$, where $\lambda$ determines the relative weights of speaker's and hearer's costs in the total, as shown in several previous works using different approaches. The nature and multiplicity of the minimizing solution changes discontinuously at $\lambda=1/2$, being qualitatively different for $\lambda 1/2$, and $\lambda=1/2$. Zipf's law is found only in a vanishing fraction of the minimum-cost solutions at $\lambda = 1/2$ and therefore is not explained by this model. Imposing the further condition of equal costs yields distributions substantially closer to Zipf's law, but significant differences persist. We also investigate the solutions reached by the previously used minimization algorithm and find that they correctly recover global minimum states at the transition.Comment: 19 pages, 4 figures. Important references and new results adde

The perils of thresholding

The thresholding of time series of activity or intensity is frequently used to define and differentiate events. This is either implicit, for example due to resolution limits, or explicit, in order to filter certain small scale physics from the supposed true asymptotic events. Thresholding the birth-death process, however, introduces a scaling region into the event size distribution, which is characterised by an exponent that is unrelated to the actual asymptote and is rather an artefact of thresholding. As a result, numerical fits of simulation data produce a range of exponents, with the true asymptote visible only in the tail of the distribution. This tail is increasingly difficult to sample as the threshold is increased. In the present case, the exponents and the spurious nature of the scaling region can be determined analytically, thus demonstrating the way in which thresholding conceals the true asymptote. The analysis also suggests a procedure for detecting the influence of the threshold by means of a data collapse involving the threshold-imposed scale.Comment: 16 pages, 10 figure

Avalanche Behavior in an Absorbing State Oslo Model

Self-organized criticality can be translated into the language of absorbing state phase transitions. Most models for which this analogy is established have been investigated for their absorbing state characteristics. In this article, we transform the self-organized critical Oslo model into an absorbing state Oslo model and analyze the avalanche behavior. We find that the resulting gap exponent, D, is consistent with its value in the self-organized critical model. For the avalanche size exponent, \tau, an analysis of the effect of the external drive and the boundary conditions is required.Comment: 4 pages, 2 figures, REVTeX 4, submitted to PRE Brief Reports; added reference and some extra information in V

Extreme value and record statistics in heavy-tailed processes with long-range memory

Extreme events are an important theme in various areas of science because of their typically devastating effects on society and their scientific complexities. The latter is particularly true if the underlying dynamics does not lead to independent extreme events as often observed in natural systems. Here, we focus on this case and consider stationary stochastic processes that are characterized by long-range memory and heavy-tailed distributions, often called fractional L\'evy noise. While the size distribution of extreme events is not affected by the long-range memory in the asymptotic limit and remains a Fr\'echet distribution, there are strong finite-size effects if the memory leads to persistence in the underlying dynamics. Moreover, we show that this persistence is also present in the extreme events, which allows one to make a time-dependent hazard assessment of future extreme events based on events observed in the past. This has direct applications in the field of space weather as we discuss specifically for the case of the solar power influx into the magnetosphere. Finally, we show how the statistics of records, or record-breaking extreme events, is affected by the presence of long-range memory.Comment: 15 pages, 20 figures, accepted for publication in AGU Monographs: Complexity and Extreme Events in Geoscienc