Acoustic emission monitoring of the Syracuse Athena temple: scale invariance in the timing of ruptures

We perform a comparative statistical analysis between the acoustic-emission time series from the ancient Greek Athena temple in Syracuse and the sequence of nearby earthquakes. We find an apparent association between acoustic-emission bursts and the earthquake occurrence. The waiting-time distributions for acoustic-emission and earthquake time series are described by a unique scaling law indicating self-similarity over a wide range of magnitude scales. This evidence suggests a correlation between the aging process of the temple and the local seismic activity.     

Statistical universality and the method of Poissonian randomizations

In this paper we demonstrate the remarkable effectiveness of Poissonian randomizations in the generation of statistical universality. We do so via a highly versatile spatio-statistical model in which points are randomly scattered, according to a Poisson process, across a general metric space. The points have general independent and identically distributed random physical characteristics. A probe is positioned in space, and is affected by the points. The effect of a given point on the probe is a function of the physical characteristic of the point and the distance of the point from the probe. We determine the classes of Poissonian randomizations – i.e., the spatial Poissonian scatterings of the points – that render the effects of the points invariant with respect to the physical characteristics of the points. These Poissonian randomizations have intrinsic power-law structures, yield statistical robustness, and generate universal statistics including Lévy distributions and extreme-value distributions. In effect, our results establish how “fractal” spatial geometries lead to statistical universality.     

Hall Conductivity in the presence of repulsive magnetic impurities

The Hall conductivity of disordered magnetic systems consisting of hard-core point vortices randomly dropped on the plane with a Poissonian distribution, has a behavior analogous to the one observed experimentally by Haug, Gerhardts, Klitzling and Ploog, with repulsive scatterers [#!1!#]. We also argue that models of homogeneous magnetic field with disordered potential, have necessarily vanishing Hall conductivities when their Hilbert space is restricted to a given Landau level subspace. Received: 29 September 1998 / Accepted: 21 December 1998     

Correlation integral as a tool for distinguishing between dynamics and statistics in time series data

We propose a new test for time series data for proper choice of processing technique: dynamical or statistical. It is based upon the normalized slope of the correlation integral (, m) = m⁻¹d(ln C()) /d ln , where m is the embedding dimension. It is shown that when does not tend to 0 on the resolved range of scales as m grows, then there will be serious limitations for dynamical methods even if the data are dynamical by nature. In the latter case it means that the length of time series does not allow to resolve small scales, and on large scales the delay reconstruction for any m mixes true and false neighbours of points and therefore restricts the application of dynamical techniques, such as estimating Lyapunov exponents or predicting time series.     

Detecting dynamical nonstationarity in time series data

Nonlinear time series analysis is becoming an ever more powerful tool to explore complex phenomena and uncover underlying patterns from irregular data recorded from experiments. However, the existence of dynamical nonstationarity in time series data causes many results of such analysis to be questionable and inconclusive. It is increasingly recognized that detecting dynamical nonstationarity is a crucial precursor to data analysis. In this paper, we present a test procedure to detect dynamical nonstationarity by directly inspecting the dependence of nonlinear statistical distributions on absolute time along a trajectory in phase space. We test this method using a broad range of data, chaotic, stochastic and power-law noise, both computer-generated and observed, and show that it provides a reliable test method in analyzing experimental data. (c) 1999 American Institute of Physics.     

Scaling and universality in rock fracture

We present a detailed statistical analysis of acoustic emission time series from laboratory rock fracture obtained from different experiments on different materials including acoustic emission controlled triaxial fracture and punch-through tests. In all considered cases, the waiting time distribution can be described by a unique scaling function indicating its universality. This scaling function is even indistinguishable from that for earthquakes suggesting its general validity for fracture processes independent of time, space, and magnitude scales.     

The distribution and scaling of fluctuations for Hang Seng index in Hong Kong stock market

The statistical properties of the Hang Seng index in the Hong Kong stock market are analyzed. The data include minute by minute records of the Hang Seng index from January 3, 1994 to May 28, 1997. The probability distribution functions of index returns for the time scales from 1 minute to 128 minutes are given. The results show that the nature of the stochastic process underlying the time series of the returns of Hang Seng index cannot be described by the normal distribution. It is more reasonable to model it by a truncated Lévy distribution with an exponential fall-off in its tails. The scaling of the maximium value of the probability distribution is studied. Results show that the data are consistent with scaling of a Lévy distribution. It is observed that in the tail of the distribution, the fall-off deviates from that of a Lévy stable process and is approximately exponential, especially after removing daily trading pattern from the data. The daily pattern thus affects strongly the analysis of the asymptotic behavior and scaling of fluctuation distributions. Received 9 August 2000 and Received in final form 28 August 2000     

Electromagnetic instability of a homogeneous plasma in interstellar space

A study is made of an electromagnetic instability of a homogeneous plasma with an arbitrary velocity distribution in interstellar space on long time scales typical of gas-dust clouds without a significant magnetic field. It is shown that such instabilities develop on time scales of a few months or years and that it is only a narrow class of nearly spherical solutions that conforms to the stability requirements.     

Effect of persistence on the significance of Kendall’s tau as a measure of correlation between natural time series

Although persistence in natural data is generally admitted, its effect on the significance of various statistical tests has not been extensively studied and is sometimes overlooked or simply ignored in practice. In particular, modified tests that are robust in the presence of persistence are still lacking. In many situations, need may arise to test the significance of correlation between two observed natural time series. Although the estimation of the classical product-moment correlation coefficient is a straightforward task, classical significance testing depends on two major assumptions. The first assumption is that the data are Gaussian, which is violated by many natural time series. In this case, a distribution-free measure of correlation, such as Kendall’s tau should be used. The second, and often overlooked assumption, is that the observations in each time series are not autocorrelated, which is also violated by most natural time series. Similar to the case of trend testing (e.g. Mann-Kendall trend test), which has received some attention recently, the existence of persistence increases the chance of falsely detecting significant correlation when the two series are actually uncorrelated. In this paper, the effect of both short- and long-term persistence (STP and LTP) on the distribution of Kendall’s tau as a distribution-free measure of correlation between two time series is investigated, and an exact expression for its variance under persistence is derived. The implications of these results for the analysis of natural data are illustrated through the study of spurious correlation between a 133-year Nile flow time series from A.D. 1871 to A.D. 2003 and independent segments of a reconstruction of the North Hemisphere temperature time series from A.D. 1000 to A.D. 1980, both of which exhibit LTP. It is shown that spurious significant correlation between completely unrelated segments of the two time series is on average three times as common as in random series of the same length at the 10% significance level, which is consistent with the theoretical results. It is also shown that accounting for LTP by using the correct variance of the test statistic effectively reduces the probability of false identification to near its expected nominal value of 10%. Similar results were obtained at other significance levels.     

Persistence in random walk in composite media

We consider a class of inhomogeneous media known as composite media that is often encountered in experimental sciences, and investigate the persistence probability of a random walker in such a system. Analytical and numerical results for the crossover time scales are obtained for a composite system with two homogeneous components and three homogeneous components respectively.     

Sampling the posterior: An approach to non-Gaussian data assimilation

The viewpoint taken in this paper is that data assimilation is fundamentally a statistical problem and that this problem should be cast in a Bayesian framework. In the absence of model error, the correct solution to the data assimilation problem is to find the posterior distribution implied by this Bayesian setting. Methods for dealing with data assimilation should then be judged by their ability to probe this distribution. In this paper we propose a range of techniques for probing the posterior distribution, based around the Langevin equation; and we compare these new techniques with existing methods.

When the underlying dynamics is deterministic, the posterior distribution is on the space of initial conditions leading to a sampling problem over this space. When the underlying dynamics is stochastic the posterior distribution is on the space of continuous time paths. By writing down a density, and conditioning on observations, it is possible to define a range of Markov Chain Monte Carlo (MCMC) methods which sample from the desired posterior distribution, and thereby solve the data assimilation problem. The basic building-blocks for the MCMC methods that we concentrate on in this paper are Langevin equations which are ergodic and whose invariant measures give the desired distribution; in the case of path space sampling these are stochastic partial differential equations (SPDEs).

Two examples are given to show how data assimilation can be formulated in a Bayesian fashion. The first is weather prediction, and the second is Lagrangian data assimilation for oceanic velocity fields. Furthermore the relationship between the Bayesian approach outlined here and the commonly used Kalman filter based techniques, prevalent in practice, is discussed. Two simple pedagogical examples are studied to illustrate the application of Bayesian sampling to data assimilation concretely. Finally a range of open mathematical and computational issues, arising from the Bayesian approach, are outlined.     

Time-Reversibility,Causality and Compression-Complexity

Detection of the temporal reversibility of a given process is an interesting time series analysis scheme that enables the useful characterisation of processes and offers an insight into the underlying processes generating the time series. Reversibility detection measures have been widely employed in the study of ecological, epidemiological and physiological time series. Further, the time reversal of given data provides a promising tool for analysis of causality measures as well as studying the causal properties of processes. In this work, the recently proposed Compression-Complexity Causality (CCC) measure (by the authors) is shown to be free of the assumption that the "cause precedes the effect", making it a promising tool for causal analysis of reversible processes. CCC is a data-driven interventional measure of causality (second rung on the Ladder of Causation) that is based on Effort-to-Compress (ETC), a well-established robust method to characterize the complexity of time series for analysis and classification. For the detection of the temporal reversibility of processes, we propose a novel measure called the Compressive Potential based Asymmetry Measure. This asymmetry measure compares the probability of the occurrence of patterns at different scales between the forward-time and time-reversed process using ETC. We test the performance of the measure on a number of simulated processes and demonstrate its effectiveness in determining the asymmetry of real-world time series of sunspot numbers, digits of the transcedental number π and heart interbeat interval variability.     

Distance statistics in random media

Consider an unlimited homogeneous medium disturbed by points generated via Poisson process. The neighborhood of a point plays an important role in spatial statistics problems. Here, we obtain analytically the distance statistics to kth nearest neighbor in a d-dimensional media. Next, we focus our attention in high dimensionality and high neighborhood order limits. High dimensionality makes distance distribution behavior as a delta sequence, with mean value equal to Cerf’s conjecture. Distance statistics in high neighborhood order converges to a Gaussian distribution. The general distance statistics can be applied to detect departures from Poissonian point distribution hypotheses as proposed by Thompson and generalized here.     

Long-range dependence in North Atlantic sea level

Sea level is an important parameter in climate and oceanographic applications. In this work the scaling behavior of sea level is analyzed from time series of sea level observations. The wavelet domain is particularly attractive for the identification of scaling behavior in an observed time series. The wavelet spectrum from a scale-by-scale wavelet analysis of variance reproduces in the wavelet domain the power laws underlying a scaling process, allowing the estimation of the scaling exponent from the slope of the wavelet spectrum. Here the scaling exponent is estimated in the wavelet domain for time series of sea level observations in the North Atlantic: at coastal sites from tide gauges, covering 50 years of monthly measurements, and in the open ocean from satellite altimetry, covering 12 years of satellite measurements at 10 days intervals. Both tide gauge and altimetry time series exhibit scaling behavior. Furthermore, the degree of stochastic persistence is spatially coherent and distinct at the coast and in the open ocean. Near the coast, the stochastic structure of the sea level observations is characterized by long-range dependence with a moderate degree of persistence. Larger values of the scaling exponent, consistent with weaker persistence, are concentrated in the northern Atlantic. At mid-latitudes the stochastic dependence of sea level observations is characterized by strong persistence in the form of strong long-range and 1/f dependence.     

Photon distribution function in blinking fluorescence of individual molecules

A photon distribution function w_N(T) for blinking fluorescence with bright on- and dark off-intervals is derived. The function w_N(T) is expressed via few Poissonian functions each of which relates to corresponding exponential process in quantum dynamics of a given individual molecule. The distribution of photons is calculated for short, middle and long time intervals as compared to off-intervals. The distributions are much broader than Poissonian distribution and have rather complicated shape. If time resolution of an experiment does not permit us to see off-interval and, therefore, fluorescence looks like CW emission, the distribution of photons gives a signal about existence of hidden off- intervals in such CW fluorescence.     

Perturbation Theory for Continuous Stochastic Equations

The various general perturbational schemes for continuous stochastic equations are considered. These schemes have many analogous features with the iterational solution of Schwinger equation for S-matrix. The following problems are discussed: continuous stochastic evolution equations for probaibility distribution functionals, evolution equations for equal time correlators, perturbation theory for Gaussian and Poissonian additive noise, perturbation theory for birth and death processes. stochastic properties of systems with multiplicative noise. The general results are illustrated by diffusion - controlled reactions, fluctuations in closed systems with chemical processes, propagation of waves in random media in parabolic equation approximation, and nonequilibrium phase transitions in systems with Poissonian breeding centers. The rate of irreversible reaction X + X → A (Smoluchowski process) is calculated with the use of general theory based on continuous stochastic equations for birth and death processes. The threshold criterion and range of fluctuational region for synergetic phase transition in system with Poissonian breeding centers are also considered.     

Femtosecond dynamics of spin-dependent SHG response from NiO (001)

Based on a parameterized electronic many-body theory we calculate the spectrum of optical second-harmonic generation (SHG) on an antiferromagnetic (AF) surface: NiO (001). The occurrence of AF spectral lines is further exploited for the calculation of ultra-fast spin dynamics. This spin dynamics is observable in SHG. It is characterized by both a sharp drop of the AF signal within a few femtoseconds and a long persistence (up to nanoseconds) of the spin coherence. These two features constitute an ideal system for possible applications in both magnetic recording and quantum computing. Received: 16 October 2001 / Revised version: 21 March 2002 / Published online: 6 June 2002