Nonlinear Complexity Analysis of Brain fMRI Signals in Schizophrenia

We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.     

Evaluation of Scaling Invariance Embedded in Short Time Series

Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length . Calculations with specified Hurst exponent values of show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias () and sharp confidential interval (standard deviation ). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.     

Complexity-entropy causality plane as a complexity measure for two-dimensional patterns

Complexity measures are essential to understand complex systems and there are numerous definitions to analyze one-dimensional data. However, extensions of these approaches to two or higher-dimensional data, such as images, are much less common. Here, we reduce this gap by applying the ideas of the permutation entropy combined with a relative entropic index. We build up a numerical procedure that can be easily implemented to evaluate the complexity of two or higher-dimensional patterns. We work out this method in different scenarios where numerical experiments and empirical data were taken into account. Specifically, we have applied the method to [Formula: see text] fractal landscapes generated numerically where we compare our measures with the Hurst exponent; [Formula: see text] liquid crystal textures where nematic-isotropic-nematic phase transitions were properly identified; [Formula: see text] 12 characteristic textures of liquid crystals where the different values show that the method can distinguish different phases; [Formula: see text] and Ising surfaces where our method identified the critical temperature and also proved to be stable.     

The US stock market leads the federal funds rate and treasury bond yields

Using a recently introduced method to quantify the time-varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be anti-correlated; (ii) the change in central bank rates, as a proxy of the monetary policy of the central bank, should be a predictor of the future stock market direction. Using both monthly and weekly data, we found very similar lead-lag dependence between the S&P 500 stock market index and the yields of bonds inside two groups: bond yields of short-term maturities (Federal funds rate (FFR), 3M, 6M, 1Y, 2Y, and 3Y) and bond yields of long-term maturities (5Y, 7Y, 10Y, and 20Y). In all cases, we observe the opposite of (i) and (ii). First, the stock market and yields move in the same direction. Second, the stock market leads the yields, including especially the FFR. Moreover, we find that the short-term yields in the first group lead the long-term yields in the second group before the financial crisis that started in mid-2007 and the inverse relationship holds afterwards. These results suggest that the Federal Reserve is increasingly mindful of the stock market behavior, seen as key to the recovery and health of the economy. Long-term investors seem also to have been more reactive and mindful of the signals provided by the financial stock markets than the Federal Reserve itself after the start of the financial crisis. The lead of the S&P 500 stock market index over the bond yields of all maturities is confirmed by the traditional lagged cross-correlation analysis.     

Patterns of body temperatures: Is lizard thermoregulation chaotic?

We surveyed body temperature (T_b) fluctuations of 23 species of thermoregulating lizards (9 families, 305 individuals) for pattern similarities. Here we report that the T_b time-series patterns of all species and almost all individuals met both qualitative criteria and quantitative criteria (positive Lyapunov exponent and capacity and/or correlation dimensions less than 5, and Hurst exponent not equal to 0.5) for deterministic chaos. Chaotic thermoregulation appears controlled, but at unpredictable times becomes disturbed and exhibits nonlinear behaviors before returning to control. This chaotic pattern of regulation is common to sufficiently diverse species to suggest that it is likely a primitive lacertilian, and perhaps reptilian, physiological characteristic.     

DNA序列的多重分形Hurst分析

为了深入研究基因组序列的多重分形性质,首先选取12条较长的DNA序列,并根据此12条DNA序列的编码／非编码片段将DNA序列转换成相应的12条时间序列,其次对这12个时间序列进行多重分形Hurst分析,计算它们的Hurst指数,并且利用Hurst指数分析序列的自相似性,进一步将得到的Hurst指数与DNA一维游走模型相比较,发现12条序列均具有长程相关性,这说明DNA序列中确实存在着长程相关现象。     

Memory effects on scale-free dynamics in foraging Drosophila

The fruit fly, Drosophila melanogaster, displays a scale-free behavior in foraging, i.e., the dwell time on food exhibits a power law distribution. The scaling exponent is generally believed to be stable and the significance of the exponent itself with respect to the scale-free behavior remains elusive. We propose a model whereby the scaling exponent of the scale-free behavior of an animal depends on the memory of the individual. The proposed model is based on the premise that animal behaviors are associated with internal states of the animal. The changes in the scaling exponent are derived by considering losing memory as increasing uncertainty, which is expressed in terms of information entropy of the internal states. Predicted model behaviors agree with experimental results of foraging behavior in wild-type and learning/memory Drosophila mutants. The concept of changes in the scaling exponent due to the amount of memory provides a novel insight into the emergence of a scale-free behavior and the meaning of the scaling exponent.     

Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy

1) ObjectivePulmonary optical endomicroscopy (POE) is an imaging technology in real time. It allows to examine pulmonary alveoli at a microscopic level. Acquired in clinical settings, a POE image sequence can have as much as 25% of the sequence being uninformative frames (i.e. pure-noise and motion artifacts). For future data analysis, these uninformative frames must be first removed from the sequence. Therefore, the objective of our work is to develop an automatic detection method of uninformative images in endomicroscopy images.2) Material and methodsWe propose to take the detection problem as a classification one. Considering advantages of deep learning methods, a classifier based on CNN (Convolutional Neural Network) is designed with a new loss function based on Havrda-Charvat entropy which is a parametrical generalization of the Shannon entropy. We propose to use this formula to get a better hold on all sorts of data since it provides a model more stable than the Shannon entropy.3) ResultsOur method is tested on one POE dataset including 3895 distinct images and is showing better results than using Shannon entropy and behaves better with regard to the problem of overfitting. We obtain 70% of accuracy with Shannon entropy versus 77 to 79% with Havrda-Charvat.4) ConclusionWe can conclude that Havrda-Charvat entropy is better suited for restricted and or noisy datasets due to its generalized nature. It is also more suitable for classification in endomicroscopy datasets.     

基于Agent模型的生态公平指数构建与模拟

伴随着社会经济的发展,社会公平逐渐成为热点话题。社会公平问题不但体现在社会现象上,还对生态系统起着重要的影响。通过构建资本交换自主体模型,模拟社会财富分配动态过程,可观测资本交换熵指数变化情况,从而解释熵增原理。除此以外,不同系统的最终状态能达到的最大熵指数不同,用熵增原理与本文构建的最大熵指数模型可以对系统的生态公平进行纵向或横向评价。构建的资本交换熵模型也可以证明最大熵原理,同时,财富集中的现象将会使生态环境的选择权完全交予富人群体,影响可持续发展。     

Financial Crisis: A New Measure for Risk of Pension Fund Portfolios

It has been argued that pension funds should have limitations on their asset allocation, based on the risk profile of the different financial instruments available on the financial markets. This issue proves to be highly relevant at times of market crisis, when a regulation establishing limits to risk taking for pension funds could prevent defaults. In this paper we present a framework for evaluating the risk level of a single financial instrument or a portfolio. By assuming that the log asset returns can be described by a multifractional Brownian motion, we evaluate the risk using the time dependent Hurst parameter H(t) which models volatility. To provide a measure of the risk, we model the Hurst parameter with a random variable with mixture of beta distribution. We prove the efficacy of the methodology by implementing it on different risk level financial instruments and portfolios.     

Fractal modeling of human isochronous serial interval production

The Hurst exponent (H) was estimated for series of 256 time intervals produced by human participants, collected in 5 sessions performed on different days. Each series was obtained during the continuation phase following synchronization with 25 isochronous intervals generated by a computer and presented through headphones. Dispersional analysis yielded estimates of H > 0.5. These were sufficiently stable to yield statistically significant differences between participants and between each target interval duration (0.5, 0.8, 1.1, and 1.5s). The results indicate that variability in isochronous serial interval production (ISIP) can be modeled as fractional Gaussian noise, which corroborates and qualifies previous research indicating positive serial dependency or long memory in ISIP data in terms of drift and 1/f noise characteristics. It is concluded that ISIP is a more complex process than is assumed by influential timing models and theories, and that realistic modeling of human timing must account for nonlinear variability patterns.Abbreviations Bm Brownian motion - f frequency - fGn fractional Gaussian noise - H Hurst exponent - ISIP isochronous serial interval production - IOI inter onset interval     

HIV-1 GP120 V3 conformational and informational entropies

In an attempt to analyze structure, function and evolution of HIV-1 GP120 V3, interactions among the Hartree–Fock energy, the conformational entropy and the Shannon entropy were determined for the 1NJ0 set of antibody-bound V3 loop conformers. The Hartree–Fock energy of each conformer was determined at the MINI level with GAMESS. The conformational entropy was determined per conformer and per residue from the mass-weighted covariance matrices. The Shannon entropy per residue was determined from sequence-substitution frequencies. Correlations were determined by linear regression analysis. There was a negative correlation between the Hartree–Fock energy and the conformational entropy (R=−0.4840, p=0.0078, df =28) that enhanced the negative Helmholtz-free-energy change for the binding of the GP120 ligand to target CD4. The Shannon entropy of V3 was a function of the conformational entropy variance (R=0.7225, p=0.00157, df=15) and of the V3 Hartree–Fock energy. Biological implications of this work are that (1) conformational entropy interacts with V3 Hartree–Fock energy to enhance GP120 binding to CD4 cell receptors and that (2) the Hartree–Fock energy of V3 interacts with the evolutionary system to participate in the regulation of V3 diversity.     

Persistent behavior in a phase-shift sequence of periodical biochemical oscillations

We present the analysis of a phase-shift sequence obtained from random transitions between periodic solutions of a biochemical dynamical model, formed by a system of three differential equations and which represent an instability-generating multienzymatic mechanism. The phase-shift series was studied in terms of Hurst’s rescaled range analysis. We found that the data were characterized by a Hurst exponent H = 0.69, which was clearly indicative of long-term trends. This result had a high significance level, as was confirmed through Monte Carlo simulations in which the data were scrambled in the series, destroying its original ordering. For these series we obtained a Hurst exponent which was consistent with the expectation of H = 0.5 for a random independent process. This clearly showed that, although the transitions between the periodic solutions were provoked randomly, the stochastic process obtained exhibited long-term persistence. The fractal dimension was also estimated and found to be consistent with the value of the Hurst exponent.     

近20年辽宁省植被动态特征及其对气候变化的响应

区域归一化植被指数(NDVI)变化特征对环境容量和生态发展方向有重要指示作用。基于SPOT/VEGETATION NDVI数据和ESA CCI-LC植被分类数据,利用Theil-Sen+Mann-Kendall、变异系数、Hurst指数和相关性分析方法,对辽宁省2000—2019年不同植被类型归一化植被指数时空变化特征和气候因子之间的响应关系进行分析。结果表明：(1)NDVI均值呈现从乔木到草原逐渐降低的趋势,不同植被类型在生长季具有不同的生长习性;(2)各植被类型都呈增加趋势,结合Hurst指数和Sen趋势,辽宁省36.26%的植被将趋于改善,约有61.51%的植被将趋于退化;(3)变异系数结果表明：所有植被类型中以乔木植被最为稳定,草原型植被最不稳定。(4)辽宁省各植被类型NDVI与降水显著正相关,与气温相关性相对较低。结果可为辽宁省生态评价和碳循环研究提供植被覆盖动态参考。     

Fractal dimension of birds population sizes time series

Information about fractal dimension is collected so that it can be applied to time series interpreting Hurst coefficient. The population size of a species is modelled as a dynamic system. The Hurst coefficient is calculated for these times series. A computer programme has been elaborated to compute the Hurst exponent of time series using the algorithms of range increment, second order moment increment and local second order moment increment. It has been applied to time series of birds' populations.     

Temporal Structure of Human Gaze Dynamics Is Invariant During Free Viewing

We investigate the dynamic structure of human gaze and present an experimental study of the frequency components of the change in gaze position over time during free viewing of computer-generated fractal images. We show that changes in gaze position are scale-invariant in time with statistical properties that are characteristic of a random walk process. We quantify and track changes in the temporal structure using a well-defined scaling parameter called the Hurst exponent, H. We find H is robust regardless of the spatial complexity generated by the fractal images. In addition, we find the Hurst exponent is invariant across all participants, including those with distinct changes to higher order visual processes due to neural degeneration. The value we find for H of 0.57 shows that the gaze dynamics during free viewing of fractal images are consistent with a random walk process with persistent movements. Our research suggests the human visual system may have a common strategy that drives the dynamics of human gaze during exploration.     

Interaction between Fiscal and Monetary Policy in a Dynamic Nonlinear Model

The objective of this study is to verify the dynamics between fiscal policy, measured by public debt, and monetary policy, measured by a reaction function of a central bank. Changes in monetary policies due to deviations from their targets always generate fiscal impacts. We examine two policy reaction functions: the first related to inflation targets and the second related to economic growth targets. We find that the condition for stable equilibrium is more restrictive in the first case than in the second. We then apply our simulation model to Brazil and United Kingdom and find that the equilibrium is unstable in the Brazilian case but stable in the UK case.     

Expected Shannon Entropy and Shannon Differentiation between Subpopulations for Neutral Genes under the Finite Island Model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information (“Shannon differentiation”) between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.