Spatial Warped Gaussian Processes: Estimation and Efficient Field Reconstruction

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we study both the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators, the multi-point Maximum A-Posteriori (MAP) estimators, an efficient class of multi-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators, and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.     

Toward the detection of gravitational waves under non-Gaussian noises I. Locally optimal statistic

After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping where the observed marginal distribution and the two-body correlation function are fully taken into account. We then apply these two approaches to Student’s t-distribution which has a larger tails than Gaussian. It is shown that while both methods work well in the case the non-Gaussianity is small, only the latter method works well for highly non-Gaussian case.     

Ising Model for Interpolation of Spatial Data on Regular Grids

We apply the Ising model with nearest-neighbor correlations (INNC) in the problem of interpolation of spatially correlated data on regular grids. The correlations are captured by short-range interactions between “Ising spins”. The INNC algorithm can be used with label data (classification) as well as discrete and continuous real-valued data (regression). In the regression problem, INNC approximates continuous variables by means of a user-specified number of classes. INNC predicts the class identity at unmeasured points by using the Monte Carlo simulation conditioned on the observed data (partial sample). The algorithm locally respects the sample values and globally aims to minimize the deviation between an energy measure of the partial sample and that of the entire grid. INNC is non-parametric and, thus, is suitable for non-Gaussian data. The method is found to be very competitive with respect to interpolation accuracy and computational efficiency compared to some standard methods. Thus, this method provides a useful tool for filling gaps in gridded data such as satellite images.     

三模高斯态光场非局域性的增强

提出了增强三模高斯态光场非局域性的方案. 结果表明：通过一个IPS（inconlusive photon subtraction）过程的非高斯操作,可以将三模高斯态退变为非高斯态,利用Bell不等式检测发现对于较弱的输入高斯态非局域性能够得到加强. 关键词： 非局域性 三模高斯态     

Computing communities in complex networks using the Dirichlet processing Gaussian mixture model with spectral clustering

Community detection becomes a significant tool for the complex network analysis. The study of the community detection algorithms has received an enormous amount of attention. It is still an open question whether a highly accurate and efficient algorithm is found in most data sets. We propose the Dirichlet Processing Gaussian Mixture Model with Spectral Clustering algorithm for detecting the community structures. The combination of traditional spectral algorithm and new non-parametric Bayesian model provides high accuracy and quality. We compare the proposed algorithm with other existing community detecting algorithms using different real-world data sets and computer-generated synthetic data sets. We show that the proposed algorithm results in high modularity, and better accuracy in a wide range of networks. We find that the proposed algorithm works best for the large size of the data sets.     

Passive source range estimation with a single receiver in shallow water

An approach for long-range passive impulsive source ranging with a single receiver in shallow water is proposed,which utilizes the frequency spectrum of the warped signal autocorrelation function via warping transform.For an ideal waveguide,there are invariable frequency features both in the frequency spectrum of the warped signal corresponding to modal cut-off frequencies and the warped signal autocorrelation function due to modal interference.These intrinsic frequency features can be used to passive source ranging.So,the approximate relationship between the frequency of warped signal at an unknown source range and the intrinsic frequency extracted by the time warping transform is derived.These rules can be generalized to an actual shallow water waveguide.Employing an acoustic model to offer the invariable frequency spectrum features,the impulsive signal data collected by a single hydrophone in the North Yellow Sea in December 2011 are analyzed to verify the proposed source ranging approach.The estimated ranges are in good agreement with the ranges measured by GPS,and the mean relative error of range estimation is less than 10%.     

浅海中利用单水听器的声源被动测距

针对浅海声波导中远距离脉冲声源被动测距问题,提出了一种利用单水听器接收信号自相关函数进行warping变换的声源被动测距方法。理想水下声波导中,接收信号warping变换输出的傅里叶变换频谱中具有不变性频率特征,即与声源距离无关的各简正波截止频率;信号自相关函数中不同简正波相干成分也存在不变性频率特征;推导了未知声源距离时特征频率提取值与不变性频率特征之间的近似关系式。这些规律可推广到实际浅海声波导,并用于声源被动测距。利用声场计算模型来提供具有不变性频率特征的频谱,对2011年12月北黄海海域水声实验中单水听器接收的脉冲声数据进行了处理,验证了方法的有效性,测距结果和实际距离符合良好,平均测距误差在10%以内。      

Frequency invariability of acoustic field and passive source range estimation in shallow water

The frequency invariability of the warped modal signal and the warped signal autocorrelation function in shallow water is discussed.A method is proposed for passive source-range estimation based on the frequency invariability and warping transform of signal autocorrelation function received by a single hydrophone in a range-independent or weak range-dependent shallow water environment.In the method,a guided source with a known range is employed to provide the crucial and relative invariant scaled features.The experimental data in shallow water with an iso-speed profile and a fluctuated thermocline are used to verify this approach.The relative errors of the source range estimation are basically less than 10%.     

Stationary properties of a single-mode laser system with non-Gaussian and Gaussian noise

A single-mode laser system with non-Gaussian and Gaussian noise is investigated. The stationary mean value and the normalized variance of the laser intensity are numerically calculated under the condition that the stationary probability distribution function (SPDF) is derived. The SPDF as a function of the laser intensity exhibits a maximum. The maximum becomes smaller with the increase of the correlation intensity or the non-Gaussian parameter, where the later is a measure of the deviation from the Gaussian characteristic. The maximum becomes larger as the correlation time increases. The laser intensity stationary mean value decreases with the increase of the correlation intensity or the non-Gaussian parameter while increases with the correlation time increasing. The laser intensity normalized variance increases with the increase of the correlation intensity or the non-Gaussian parameter while decreases as the correlation time increases.     

Identification of inherent noise components of semiconductor devices on an example of optocouplers

In the paper, a method of estimation of parameters of Gaussian and non-Gaussian components in the noise signal of semiconductor devices in a frequency domain is proposed. The method is based on composing estimators of two spectra, corresponding to 1/f^α noise (Gaussian component) and two-level RTS noise (non-Gaussian component). The proposed method can be applied for precise evaluation of the corner RTS frequency fRTS in the noise spectrum.     

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribution BPA functions with padding terms are used. After that, nrBPAs are determined by two processes, reassigning the high blur degree BPA and transforming them into the form of negation. In addition, evidence of preliminary fusion is obtained using the entropy weight method based on the improved belief entropy of nrBPAs. The final fusion results are calculated from the preliminary fused evidence through the Dempster’s combination rule. In the experimental section, the UCI iris data set and the wine data set are used for validating the arithmetic processes of the proposed method. In the comparative analysis, the effectiveness of the BPA determination using a padded Gaussian function is verified by discussing the classification task with the iris data set. Subsequently, the comparison with other methods using the cross-validation method proves that the proposed method is robust. Notably, the classification accuracy of the iris data set using the proposed method can reach an accuracy of 97.04%, which is higher than many other methods.     

Mean First-Passage Time of an Asymmetric Kinetic Model Driven by Two Different Kinds of Colored Noises

The mean first-passage time （MFPT） of an asymmetric bistable system between multiplicative non-Gaussian noise and additive Gaussian white noise with nonzero cross-correlation time is investigated. Firstly, the non-Markov process is reduced to the Markov process through a path-integral approach; Secondly, the approximate Fokker Planck equation is obtained by applying the unified colored noise approximation and the.Novikov Theorem. The steady-state probability distribution （SPD） is also obtained. The basal functional analysis and simplification are employed to obtain the approximate expressions of MFPT T^±. The effects of the asymmetry parameter β, the non-Gaussian parameter （measures deviation from Gaussian character） r, the noise correlation times τ and τ2, the coupling coefficient A, the intensities D and a of noise on the MFPT are discussed. It is found that the asymmetry parameter β, the non-Gaussian parameter r and the coupling coefficient A can induce phase transition. Moreover, the main findings are that the effect of self-existent parameters （D, α, and τ） of noise and cross-correlation parameters （A, 7-2） between noises on MFPT T^± is different.     

Flexible and Efficient Inference with Particles for the Variational Gaussian Approximation

Variational inference is a powerful framework, used to approximate intractable posteriors through variational distributions. The de facto standard is to rely on Gaussian variational families, which come with numerous advantages: they are easy to sample from, simple to parametrize, and many expectations are known in closed-form or readily computed by quadrature. In this paper, we view the Gaussian variational approximation problem through the lens of gradient flows. We introduce a flexible and efficient algorithm based on a linear flow leading to a particle-based approximation. We prove that, with a sufficient number of particles, our algorithm converges linearly to the exact solution for Gaussian targets, and a low-rank approximation otherwise. In addition to the theoretical analysis, we show, on a set of synthetic and real-world high-dimensional problems, that our algorithm outperforms existing methods with Gaussian targets while performing on a par with non-Gaussian targets.     

Stochastic properties coupling between of tumor growth with non-Gaussian and Gaussian noise terms

Dynamical behavior of a tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the active state, induce the minimum of the average tumor-cell population to move toward a smaller non-Gaussian noise, but also decrease the mean first-passage time. The increase of white-noise intensity can increase the tumor-cell population and shorten the mean first-passage time, while the coupling strength between noise terms has opposite effects, and the noise correlation time has a very small effect.     

Stochastic properties of tumor growth with coupling between non-Gaussian and Gaussian noise terms

Dynamical behavior of tumor-growth model with coupling between non-Gaussian and Gaussian noise terms is investigated. The departure from the Gaussian noise can not only reduce the probability of tumor cells in the active state, induce the minimum of the average tumor-cell population to move toward a smaller non-Gaussian noise, but also decrease the mean first-passage time. The increase of white-noise intensity can increase the tumor-cell population and shorten the mean first-passage time, while the coupling strength between noise terms has opposite effects, and the noise correlation time has a very small effect.     

Stochastic simulation of infrared non-Gaussian natural terrain imagery

We describe a new technique for the fast simulation of natural IR imagery with non-Gaussian statistics. We pass samples of the imagery through an invertible pointwise nonlinear transform, to give samples of a new random field with Gaussian marginal probability density. We compute the correlation function for the transformed samples, and simulate Gaussian random fields (GRFs) with these correlations. Passing the simulated GRFs through the inverse transform gives our simulations of the original imagery. We discuss the effectiveness of this approach for simulating natural terrain backgrounds.     

Data-Driven Analysis of Nonlinear Heterogeneous Reactions through Sparse Modeling and Bayesian Statistical Approaches

Heterogeneous reactions are chemical reactions that occur at the interfaces of multiple phases, and often show a nonlinear dynamical behavior due to the effect of the time-variant surface area with complex reaction mechanisms. It is important to specify the kinetics of heterogeneous reactions in order to elucidate the microscopic elementary processes and predict the macroscopic future evolution of the system. In this study, we propose a data-driven method based on a sparse modeling algorithm and sequential Monte Carlo algorithm for simultaneously extracting substantial reaction terms and surface models from a number of candidates by using partial observation data. We introduce a sparse modeling approach with non-uniform sparsity levels in order to accurately estimate rate constants, and the sequential Monte Carlo algorithm is employed to estimate time courses of multi-dimensional hidden variables. The results estimated using the proposed method show that the rate constants of dissolution and precipitation reactions that are typical examples of surface heterogeneous reactions, necessary surface models, and reaction terms underlying observable data were successfully estimated from only observable temporal changes in the concentration of the dissolved intermediate products.     

Fractional diffusion and reflective boundary condition

Scaling analysis of the magnitude series (volatile series) has been proposed recently to identify possible non-linear/multifractal signatures in the given data [Y. Ashkenazy, et al. Phys. Rev. Lett. 86 (2001) 1900; Y. Ashkenazy, et al. Physica A 323 (2003) 19; T. Kalisky, Y. Ashkenazy, S. Havlin. Phys. Rev. E 72 (2005) 011913]. In this article, correlations of volatile series generated from stationary first-order linear feedback process with Gaussian and non-Gaussian innovations are investigated. While volatile correlations corresponding to Gaussian innovations exhibited uncorrelated behavior across all time scales, those of non-Gaussian innovations showed significant deviation from uncorrelated behavior even at large time scales. The results presented raise the intriguing question whether non-Gaussian innovations can be sufficient to realize long-range volatile correlations.     

Generalized Ornstein–Uhlenbeck process by Doob’s theorem and the time evolution of financial prices

We generalize the Ornstein–Uhlenbeck (OU) process using Doob’s theorem. We relax the Gaussian and stationary conditions, assuming a linear and time-homogeneous process. The proposed generalization retains much of the simplicity of the original stochastic process, while exhibiting a somewhat richer behavior. Analytical results are obtained using transition probability and the characteristic function formalism and compared with empirical stock market data, which are notorious for the non-Gaussian behavior. The analysis focus on the decay patterns and the convergence study of the first four cumulants considering the logarithmic returns of stock prices. It is shown that the proposed model offers a good improvement over the classical OU model.     

基于频带分解和距离加权的单矢量水听器浅海被动测距方法研究

本文针对浅海环境下中低频宽带脉冲声源被动测距问题, 提出了一种声压和水平振速联合处理的被动测距方法. 在浅海波导中, 声压和质点振速的自相关函数卷绕(warping)谱具有稳定的频率特征. 声压和水平振速的自相关卷绕谱具有相同的准线谱特征, 垂直振速自相关卷绕谱具有宽谱叠加线谱的特征, 与声压自相关卷绕谱相比, 其尖峰个数比更多, 且尖峰宽度更宽. 利用引导源, 本文提出了基于频带分解和距离加权的声压和水平振速联合被动测距方法. 利用该方法对2008年冬季青岛海域综合实验中单矢量水听器接收的气枪信号进行处理, 结果表明, 该方法能够实现气枪声源的有效测距. 与传统单声压水听器被动测距方法相比, 该方法可以有效减小代价函数的主瓣宽度、提高测距精度.