28 research outputs found
Numerical convergence of the block-maxima approach to the Generalized Extreme Value distribution
In this paper we perform an analytical and numerical study of Extreme Value
distributions in discrete dynamical systems. In this setting, recent works have
shown how to get a statistics of extremes in agreement with the classical
Extreme Value Theory. We pursue these investigations by giving analytical
expressions of Extreme Value distribution parameters for maps that have an
absolutely continuous invariant measure. We compare these analytical results
with numerical experiments in which we study the convergence to limiting
distributions using the so called block-maxima approach, pointing out in which
cases we obtain robust estimation of parameters. In regular maps for which
mixing properties do not hold, we show that the fitting procedure to the
classical Extreme Value Distribution fails, as expected. However, we obtain an
empirical distribution that can be explained starting from a different
observable function for which Nicolis et al. [2006] have found analytical
results.Comment: 34 pages, 7 figures; Journal of Statistical Physics 201
Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network
This study reports a statistical analysis of monthly sunspot number time
series and observes non homogeneity and asymmetry within it. Using Mann-Kendall
test a linear trend is revealed. After identifying stationarity within the time
series we generate autoregressive AR(p) and autoregressive moving average
(ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of
p and q respectively. In the next phase, autoregressive neural network
(AR-NN(3)) is generated by training a generalized feedforward neural network
(GFNN). Assessing the model performances by means of Willmott's index of second
order and coefficient of determination, the performance of AR-NN(3) is
identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure
Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses
Copyright: © 2015 Sudhakar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedNeurons of the cerebellar nuclei convey the final output of the cerebellum to their targets in various parts of the brain. Within the cerebellum their direct upstream connections originate from inhibitory Purkinje neurons. Purkinje neurons have a complex firing pattern of regular spikes interrupted by intermittent pauses of variable length. How can the cerebellar nucleus process this complex input pattern? In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. Pause beginning synchronization produced a unique effect on nuclei neuron firing, while the effect of pause ending and pause overlapping synchronization could not be distinguished from each other. Pause beginning synchronization produced better time-locking of nuclear neurons for short length pauses. We also characterize the effect of pause length and spike jitter on the nuclear neuron firing. Additionally, we find that the rate of rebound responses in nuclear neurons after a synchronous pause is controlled by the firing rate of Purkinje neurons preceding it.Peer reviewedFinal Published versio
Sampling Bias in BitTorrent Measurements
Real-world measurements play an important role in understanding the characteristics and in improving the operation of BitTorrent, which is currently a popular Internet application. Much like measuring the Internet, the complexity and scale of the BitTorrent network make a single, complete measurement impractical. While a large number of measurements have already employed diverse sampling techniques to study parts of BitTorrent network, until now there exists no investigation of their sampling bias, that is, of their ability to objectively represent the characteristics of BitTorrent. In this work we present the first study of the sampling bias in BitTorrent measurements. We first introduce a novel taxonomy of sources of sampling bias in BitTorrent measurements. We then investigate the sampling among fifteen long-term BitTorrent measurements completed between 2004 and 2009, and find that different data sources and measurement techniques can lead to significantly different measurement results. Last, we formulate three recommendations to improve the design of future BitTorrent measurements, and estimate the cost of using these recommendations in practice