Annette Witt - Academia.edu (original) (raw)

Papers by Annette Witt

Hydrology and Earth System Sciences Discussions, 2016

In this paper we present a unique 9.5 m palaeo-lacustrine record of 771 palaeofloods which... more In this paper we present a unique 9.5 m palaeo-lacustrine record of 771 palaeofloods which occurred over a period of 10 kyr in the Piànico-Sèllere basin (southern Alps) during an interglacial period in the Pleistocene (sometime 400–800 ka) and analyse its correlation, clustering and cyclicity properties. We first examine correlations, by applying power-spectral analysis and detrended fluctuation analysis (DFA) to a time series of the number of floods per year and find weak long-range persistence: power-spectral exponent βPS ≈ 0.39 and equivalent power-spectra exponent from DFA, βDFA≈ 0.25. We then examine clustering using the one-point probability distribution of the inter-flood intervals and find that the occurrence of palaeofloods do cluster in time as they are Weibull distributed with shape parameter kW = 0.78. We then exami...

Computers in Cardiology 1995

ABSTRACT

Biomedizinische Technik/Biomedical Engineering, 1994

Common non-invasive diagnostic methods like Holter monitoring or the analysis of high-resolution ... more Common non-invasive diagnostic methods like Holter monitoring or the analysis of high-resolution ECG and heart rate variability are unable to accurately assess the individual risk for sudden cardiac death, since they describe only statistical, linear or strictly periodic parameters. Using new methods of non-linear dynamics one can now calculate parameters which much better describe the dynamic behaviour of complex systems. The application of these new methods should therefore lead to an improved identification of high-risk patients. The results of this first pilot investigation show on the one hand that ventricular arrhythmias are quick detectable using phase space plots, and on the other hand that the new methods of non-linear dynamics could lead to a new classification of high-risk patients.

Physical Review E, 2003

The Roberts flow, a helical flow in the form of convectionlike rolls, is known to be capable of b... more The Roberts flow, a helical flow in the form of convectionlike rolls, is known to be capable of both kinematic and nonlinear dynamo action. We study the Roberts dynamo with particular attention being paid to the spatial structure of the generated magnetic field and its back-reaction on the flow. The dynamo bifurcation is decisively determined by the symmetry group of the problem, which is given by a subgroup of discrete transformations and a continuous translational invariance of the flow. In the bifurcation the continuous symmetry is broken while the discrete subgroup symmetry completely survives. Its actions help in understanding the spatial structures of the magnetic field and of the modified flow. In accordance with experimental observations, the magnetic field component perpendicular to the originally invariant direction is much stronger than the component in this direction. Furthermore, the magnetic field is largely concentrated in layers separating the convectionlike rolls of the flow and containing, in particular, its stagnation points, which are isolated for the modified flow while they are line filling for the original Roberts flow. The magnetic field is strongest near ␤-type stagnation points, with a two-dimensional unstable and a one-dimensional stable manifold, and is weak near ␣-type stagnation points, with a two-dimensional stable and a one-dimensional unstable manifold. This contrasts with the usual picture that dynamo action is promoted at the ␣ points and impeded at the ␤ points. Both the creation of isolated stagnation points and the concentration of strong fields at the ␤ points may be understood as a result of the way in which the Roberts dynamo saturates. It is also found that, while the original Roberts flow is regular, the modified flow is chaotic in the layers between the convectionlike rolls where the magnetic field is concentrated. This chaoticity, which results from the back-reaction of the magnetic field on the flow, appears to merely enhance magnetic diffusion rather than to strengthen the dynamo effect.

Dynamic oscillatory coherence is believed to play a central role in flexible communication betwee... more Dynamic oscillatory coherence is believed to play a central role in flexible communication between brain circuits. To test this communication-through-coherence hypothesis, experimental protocols that allow a reliable control of phase-relations between neuronal populations are needed. In this modeling study, we explore the potential of closed-loop optogenetic stimulation for the control of functional interactions mediated by oscillatory coherence. The theory of non-linear oscillators predicts that the efficacy of local stimulation will depend not only on the stimulation intensity but also on its timing relative to the ongoing oscillation in the target area. Induced phase-shifts are expected to be stronger when the stimulation is applied within specific narrow phase intervals. Conversely, stimulations with the same or even stronger intensity are less effective when timed randomly. Stimulation should thus be properly phased with respect to ongoing oscillations (in order to optimally perturb them) and the timing of the stimulation onset must be determined by a real-time phase analysis of simultaneously recorded local field potentials (LFPs). Here, we introduce an electrophysiologically calibrated model of Channelrhodopsin 2 (ChR2)-induced photocurrents, based on fits holding over two decades of light intensity. Through simulations of a neural population which undergoes coherent gamma oscillations-either spontaneously or as an effect of continuous optogenetic driving-we show that precisely-timed photostimulation pulses can be used to shift the phase of oscillation, even at transduction rates smaller than 25%. We consider then a canonic circuit with two interconnected neural populations oscillating with gamma frequency in a phase-locked manner. We demonstrate that photostimulation pulses applied locally to a single population can induce, if precisely phased, a lasting reorganization of the phase-locking pattern and hence modify functional interactions between the two populations.

Physical Review E, 1999

Recently, searches for unstable periodic orbits in biological and medical applications have becom... more Recently, searches for unstable periodic orbits in biological and medical applications have become of interest. The motivations for this research range, in order of ascending complexity, from efforts to understand the dynamics of simple sensory neurons, through speculations regarding neural coding, to the hopeful development of new diagnostic and/or control techniques for cardiac and epileptic pathologies. Biological and medical data are, however, noisy and nonstationary. Findings of unstable periodic orbits in such data thus require convincing assessments of their statistical significance. Such tests are accomplished by comparison with surrogate data files designed to test an appropriate null hypothesis. In this paper we test surrogates generated by three different algorithms against correlated noise as well as stable periodic orbits. One of the surrogates is new, and has been specifically designed to preserve the shape of the attractor. We discuss the suitability of these surrogates and argue that the simple shuffled one correctly tests the appropriate null hypothesis. ͓S1063-651X͑99͒00505-X͔

Nonlinear Processes in Geophysics, 2011

We review work on extreme events, their causes and consequences, by a group of European and Ameri... more We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socioeconomic events, along with their prediction. Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socioeconomic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.

The Journal of the Acoustical Society of America, 2012

Although human musical performances represent one of the most valuable achievements of mankind, t... more Although human musical performances represent one of the most valuable achievements of mankind, the best musicians perform imperfectly. Musical rhythms are not entirely accurate and thus inevitably deviate from the ideal beat pattern. Nevertheless, computer generated perfect beat patterns are frequently devalued by listeners due to a perceived lack of human touch. Professional audio editing software therefore offers a humanizing feature which artificially generates rhythmic fluctuations. However, the built-in humanizing units are essentially random number generators producing only simple uncorrelated fluctuations. Here, for the first time, we establish long-range fluctuations as an inevitable natural companion of both simple and complex human rhythmic performances [1,2]. Moreover, we demonstrate that listeners strongly prefer long-range correlated fluctuations in musical rhythms. Thus, the favorable fluctuation type for humanizing interbeat intervals coincides with the one generical...

Earth Surface Processes and Landforms, 2010

ABSTRACT This paper examines temporal correlations and temporal clustering of a proxy historical ... more ABSTRACT This paper examines temporal correlations and temporal clustering of a proxy historical landslide time series, 2255 reported landslides 1951–2002, for an area in the Emilia-Romagna Region, Italy. Landslide intensity is measured by the number of reported landslides in a day (DL) and in an ‘event’ (Sevent) of consecutive days with landsliding. The non-zero values in both time series DL and Sevent are unequally spaced in time, and have heavy-tailed frequency-size distributions. To examine temporal correlations, we use power-spectral analysis (Lomb periodogram) and surrogate data analysis, confronting our original DL and Sevent time series with 1000 shuffled (uncorrelated) versions. We conclude that the landslide intensity series DL has strong temporal correlations and Sevent has likely temporal correlations. To examine temporal clustering in DL and Sevent, we consider extremes over different landslide intensity thresholds. We first examine the statistical distribution of interextreme occurrence times, τ, and find Weibull distributions with parameter γ << 1·0 [DL] and γ < 1·0 [Sevent]; thus DL and Sevent each have temporal correlations, but Sevent to a lesser degree. We next examine correlations between successive interextreme occurrence times, τ. Using autocorrelation analysis applied to τ, combined with surrogate data analysis, we find for DL linear correlations in τ, but for Sevent inconclusive results. However, using Kendall's rank correlation analysis we find for both DL and Sevent the series of τ are strongly correlated. Finally, we apply Fano Factor analysis, finding for both DL and Sevent the timings of extremes over a given threshold exhibit a fractal structure and are clustered in time. In this paper, we provide a framework for examining time series where the non-zero values are strongly unequally spaced and heavy-tailed, particularly important in the Earth Sciences due to their common occurrence, and find that landslide intensity time series exhibit temporal correlations and clustering. Many landslide models currently are designed under the assumption that landslides are uncorrelated in time, which we show is false. Copyright © 2010 John Wiley & Sons, Ltd.

Chaos, Solitons & Fractals, 2005

We study Hamiltonian chaos generated by the dynamics of passive tracers moving in a two-dimension... more We study Hamiltonian chaos generated by the dynamics of passive tracers moving in a two-dimensional fluid flow and describe the complex structure formed in a chaotic layer that separates a vortex region from the shear flow. The stable and unstable manifolds of unstable periodic orbits are computed. It is shown that their intersections in the Poincaré map as an invariant set of homoclinic points constitute the backbone of the chaotic layer. Special attention is paid to the finite time properties of the chaotic layer. In particular, finite time Lyapunov exponents are computed and a scaling law of the variance of their distribution is derived. Additionally, the box counting dimension as an effective dimension to characterize the fractal properties of the layer is estimated for different duration times of simulation. Its behavior in the asymptotic time limit is discussed. By computing the Lyapunov exponents and by applying methods of symbolic dynamics, the formation of the layer as a function of the external forcing strength, which in turn represents the perturbation of the originally integrable system, is characterized. In particular, it is shown that the capture of KAM tori by the layer has a remarkable influence on the averaged Lyapunov exponents.

Cardiovascular Research, 1996

Objectives: This study introduces new methods of non-linear dynamics (NLD) and compares these wit... more Objectives: This study introduces new methods of non-linear dynamics (NLD) and compares these with traditional methods of heart rate variability (HRV) and high resolution ECG (HRECG) analysis in order to improve the reliability of high risk stratification. Methods: Simultaneous 30 min high resolution ECG's and long-term ECG's were recorded from 26 cardiac patients after myocardial infarction (MI). They were divided into two groups depending upon the electrical risk, a low risk group (group 2, n = 10) and a high risk group (group 3, n = 16). The control group consisted of 35 healthy persons (group 1). From these electrocardiograms we extracted standard measures in time and frequency domain as well as measures from the new non-linear methods of symbolic dynamics and renormalized entropy. Results: Applying discriminant function techniques on HRV analysis the parameters of non-linear dynamics led to an acceptable differentiation between healthy persons and high risk patients of 96%. The time domain and frequency domain parameters were successful in less than 90%. The combination of parameters from all domains and a stepwise discriminant function separated these groups completely (100%). Use of this discriminant function classified three patients with apparently low (no) risk into the same cluster as high risk patients. The combination of the HRECG and HRV analysis showed the same individual clustering but increased the positive value of separation. Conclusions: The methods of NLD describe complex rhythm fluctuations and separate structures of non-linear behavior in the heart rate time series more successfully than classical methods of time and frequency domains. This leads to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The methods of symbolic dynamics and renormalized entropy were particularly useful measures for classifying the dynamics of HRV.

Surveys in Geophysics, 2013

Time series in the Earth Sciences are often characterized as self-affine longrange persistent, wh... more Time series in the Earth Sciences are often characterized as self-affine longrange persistent, where the power spectral density, S, exhibits a power-law dependence on frequency, f, S(f) * f-b , with b the persistence strength. For modelling purposes, it is important to determine the strength of self-affine long-range persistence b as precisely as possible and to quantify the uncertainty of this estimate. After an extensive review and discussion of asymptotic and the more specific case of self-affine long-range persistence, we compare four common analysis techniques for quantifying self-affine long-range persistence: (a) rescaled range (R/S) analysis, (b) semivariogram analysis, (c) detrended fluctuation analysis, and (d) power spectral analysis. To evaluate these methods, we construct ensembles of synthetic self-affine noises and motions with different (1) time series lengths N = 64, 128, 256, …, 131,072, (2) modelled persistence strengths b model =-1.0,-0.8,-0.6, …, 4.0, and (3) one-point probability distributions (Gaussian, log-normal: coefficient of variation c v = 0.0 to 2.0, Levy: tail parameter a = 1.0 to 2.0) and evaluate the four techniques by statistically comparing their performance. Over 17,000 sets of parameters are produced, each characterizing a given process; for each process type, 100 realizations are created. The four techniques give the following results in terms of systematic error (bias = average performance test results for b over 100 realizations minus modelled b) and random error (standard deviation of measured b over 100 realizations): (1) Hurst rescaled range (R/S) analysis is not recommended to use due to large systematic errors. (2) Semivariogram analysis shows no systematic errors but large random errors for self-affine noises with 1.2 B b B 2.8. (3) Detrended fluctuation analysis is well suited for time series with Electronic supplementary material The online version of this article (