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.     

Analysis of nonstationary,Gaussian and non-Gaussian,generalized Langevin equations using methods of multiplicative stochastic processes

Using the methods of multiplicative stochastic processes, a thorough analysis of non-Markovian, generalized Langevin equations is presented. For the Gaussian case, these methods are used to show that the nonstationary Fokker-Planck equation already found by Adelman and others is also obtainable from van Kampen's lemma for stochastic probability flows. Here, results applicable to an arbitraryn-component process are obtained and the specific two-component case of the Brownian harmonic oscillator is presented in detail in order to explicitly exhibit the matrix algebraic methods. The non-Gaussian case is presented at the end of the paper and shows that the methods already used in the Gaussian case lead directly to results for the non-Gaussian case. In order to use the methods of multiplicative stochastic processes analysis, it is necessary to transform the non-Markovian, generalized Langevin equation using a stochastic extension of a transformation discussed by Adelman. This transformation removes the memory kernel term in the usual generalized Langevin equation and in the Gaussian case leads to the result that the original process was in fact not non-Markovian but actually nonstationary,Markovian.Supported through a fellowship from the Alfred P. Sioan Foundation.     

Variational Bayesian Inference for Nonlinear Hawkes Process with Gaussian Process Self-Effects

Traditionally, Hawkes processes are used to model time-continuous point processes with history dependence. Here, we propose an extended model where the self-effects are of both excitatory and inhibitory types and follow a Gaussian Process. Whereas previous work either relies on a less flexible parameterization of the model, or requires a large amount of data, our formulation allows for both a flexible model and learning when data are scarce. We continue the line of work of Bayesian inference for Hawkes processes, and derive an inference algorithm by performing inference on an aggregated sum of Gaussian Processes. Approximate Bayesian inference is achieved via data augmentation, and we describe a mean-field variational inference approach to learn the model parameters. To demonstrate the flexibility of the model we apply our methodology on data from different domains and compare it to previously reported results.     

Measurement-induced decoherence and Gaussian smoothing of the Wigner distribution function

We study the problem of measurement-induced decoherence using the phase-space approach employing the Gaussian-smoothed Wigner distribution function. Our investigation is based on the notion that measurement-induced decoherence is represented by the transition from the Wigner distribution to the Gaussian-smoothed Wigner distribution with the widths of the smoothing function identified as measurement errors. We also compare the smoothed Wigner distribution with the corresponding distribution resulting from the classical analysis. The distributions we computed are the phase-space distributions for simple one-dimensional dynamical systems such as a particle in a square-well potential and a particle moving under the influence of a step potential, and the time-frequency distributions for high-harmonic radiation emitted from an atom irradiated by short, intense laser pulses.     

随机高斯型相位板的束匀滑特性

 针对激光系统对光束匀滑的需求,设计了高斯型连续相位板,并对其远场特性进行了研究。分别计算了相干长度为39,30,10和3 mm的高斯相位板远场光斑分布,结果显示相位板自身相关长度是决定远场能量分布的重要因素,当相干长度大于10 mm时,由于不满足各态历经条件,远场光斑分布能量分散。当相干长度小于10 mm后,由于满足各态历经条件,远场光斑能量将接近理想的高斯分布特性。通过数值计算模拟了相干长度为3 mm的连续相位板对畸变光束进行匀滑处理的过程,演示了束匀滑处理结果。通过比较匀滑前后远场光斑的能量分布,显示了畸变光束通过连续相位板后远场光斑能量分布变化情况,通过相位板后,光斑形状明显接近理想高斯分布情况。     

Study of various few-body systems using Gaussian expansion method (GEM)

We review our calculation method, Gaussian expansion method (GEM), to solve accurately the Schrödinger equations for bound, resonant and scattering states of few-body systems. Use is made of the Rayleigh-Ritz variational method for bound states, the complex-scaling method for resonant states and the Kohn-type variational principle to S-matrix for scattering states. GEM was proposed 30 years ago and has been applied to a variety of subjects in few-body (3- to 5-body) systems, such as 1) few-nucleon systems, 2) few-body structure of hypernuclei, 3) clustering structure of light nuclei and unstable nuclei, 4) exotic atoms/molecules, 5) cold atoms, 6) nuclear astrophysics and 7) structure of exotic hadrons. Showing examples in our published papers, we explain i) high accuracy of GEM calculations and its reason, ii) wide applicability of GEM to various few-body systems, iii) successful predictions by GEM calculations before measurements. The total bound-state wave function is expanded in terms of few-body Gaussian basis functions spanned over all the sets of rearrangement Jacobi coordinates. Gaussians with ranges in geometric progression work very well both for shortrange and long-range behavior of the few-body wave functions. Use of Gaussians with complex ranges gives much more accurate solution than in the case of real-range Gaussians, especially, when the wave function has many nodes (oscillations). These basis functions can well be applied to calculations using the complex-scaling method for resonances. For the few-body scattering states, the amplitude of the interaction region is expanded in terms of those few-body Gaussian basis functions.     

RPA calculations with Gaussian expansion method

The Gaussian expansion method (GEM) is applied to calculations of the nuclear excitations in the random-phase approximation (RPA). We adopt the mass-independent basis-set that is successful in the mean-field calculations. The RPA results obtained by the GEM are compared with those obtained by several other available methods in Ca isotopes, by using a density-dependent contact interaction along with the Woods–Saxon single-particle states. It is confirmed that energies, transition strengths and widths of their distribution are described by the GEM with good precision, for the 1⁻, 2⁺ and 3⁻ collective states. The GEM is then applied to the self-consistent RPA calculations with the finite-range Gogny D1S interaction. The spurious center-of-mass motion is well separated from the physical states in the E1 response, and the energy-weighted sum rules for the isoscalar transitions are fulfilled reasonably well. Properties of low-energy transitions in ⁶⁰Ca are investigated in some detail.     

高功率微波高斯馈源口面场分析

 用波导本征模展开方法对用于高功率微波发射系统的方角锥高斯馈源口面场进行分析，提出结合馈源远场辐射特性和避免高功率击穿折衷选定相应的高斯模注腰半径，进而确定多个波导模幅值，从而为运用模匹配或耦合波理论设计高斯馈源提供依据。     

Fast Gaussian wavepacket transforms and Gaussian beams for the Schrödinger equation

This paper introduces a wavepacket-transform-based Gaussian beam method for solving the Schrödinger equation. We focus on addressing two computational issues of the Gaussian beam method: how to generate a Gaussian beam representation for general initial conditions and how to perform long time propagation for any finite period of time. To address the first question, we introduce fast Gaussian wavepacket transforms and develop on top of them an efficient initialization algorithm for general initial conditions. Based on this new initialization algorithm, we address the second question by reinitializing the beam representation when the beams become too wide. Numerical examples in one, two, and three dimensions demonstrate the efficiency and accuracy of the proposed algorithms. The methodology can be readily generalized to deal with other semi-classical quantum mechanical problems.     

Restoration of Decentered Gaussian Beam with the Combination of a Lens and a Gaussian Aperture

1　Ｉｎｔｒｏｄｕｃｔｉｏｎ　　ＴｈｅＧａｕｓｓｉａｎｂｅａｍｉｓｔｈｅｆｕｎｄａｍｅｎｔａｌｓｈａｐｅｏｆｔｈｅｌａｓｅｒｗｉｔｈａｓｐｈｅｒｉｃａｌ ｍｉｒｒｏｒｒｅｓｏｎａｔｏｒａｎｄｗｉｄｅｌｙｕｓｅｄｔｏｄｅｓｃｒｉｂｅａｓｉｎｇｌｅｍｏｄｅｌａｓｅｒ.Ｉｎｔｈｅｐａｒａｘｉａｌａｐｐｒｏｘｉｍａｔｉｏｎ ,ｔｈｅｄｉｓｔｒｉｂｕｔｉｏｎｏｆｃｏｍｐｌｅｘａｍｐｌｉｔｕｄｅｏｆｉｔｒｅｍａｉｎｓｕｎｃｈａｎｇｅｄｗｈｅｎｉｔｕｎｄｅｒｇｏｅｓｔｈｅＦｏｕｒｉｅｒｔｒａｎｓｆｏｒｍｏｒｔｈｅ…     

Null test for cosmic curvature using Gaussian process

The cosmic curvature \begin{document}$ \Omega_{K,0} $\end{document}, which determines the spatial geometry of the universe, is an important parameter in modern cosmology. Any deviation from \begin{document}$ \Omega_{K,0}=0 $\end{document} would have a profound impact on the primordial inflation paradigm and fundamental physics. In this work, we adopt a cosmological model-independent method to test whether \begin{document}$ \Omega_{K,0} $\end{document} deviates from zero. We use the Gaussian process to reconstruct the reduced Hubble parameter \begin{document}$ E(z) $\end{document} and the derivative of the distance \begin{document}$ D'(z) $\end{document} from observational data and then determine \begin{document}$ \Omega_{K,0} $\end{document} with a null test relation. The cosmic chronometer (CC) Hubble data, baryon acoustic oscillation (BAO) Hubble data, and supernovae Pantheon sample are considered. Our result is consistent with a spatially flat universe within the domain of reconstruction \begin{document}$ 0<z<2.3 $\end{document}, at the \begin{document}$ 1\sigma $\end{document} confidence level. In the redshift interval \begin{document}$ 0<z<1 $\end{document}, the result favors a flat universe, while at \begin{document}$ z>1 $\end{document}, it tends to favor a closed universe. In this sense, there is still a possibility for a closed universe. We also carry out the null test of the cosmic curvature at \begin{document}$ 0<z<4.5 $\end{document} using the simulated gravitational wave standard sirens, CC+BAO, and redshift drift Hubble data. The result indicates that in the future, with the synergy of multiple high-quality observations, we can tightly constrain the spatial geometry or exclude the flat universe.     

Quantifying Discriminating Strength for Gaussian States

The discriminating strength D_S(ρ_AB) induced by local Gaussian unitary operators for any (n+m)-mode Gaussian state ρ_AB is introduced in[Phys. Rev. A 83 (2011) 042325]. In this paper, we further discuss the quantity by restricting to Hilbert-Schmidt norm. The analytic formulas of DS for two-mode squeezed thermal states and mixed thermal states are given. Then, the relationship between D_S(ρ_AB) and D_S((I ⊗ Φ)(ρ_AB)) for some special Gaussian channels Φ is discussed. In addition, D_S is compared with Gaussian entanglement for symmetric squeezed thermal states.     

Quantum fidelity for Gaussian states describing the evolution of open systems

Using the expression of the fidelity for the most general Gaussian quantum states, the quantum fidelity is studied for the states of a harmonic oscillator interacting with an environment, in particular with a thermal bath. The time evolution of the considered system is described in the framework of the theory of open systems based on quantum dynamical semigroups. By taking a correlated squeezed Gaussian state as initial state, we calculate the quantum fidelity for both undisplaced and displaced states. The time evolution of the quantum fidelity is analyzed depending on the squeezing and correlation parameters characterizing the initial Gaussian state and on the dissipation constant and temperature of the thermal bath.     

On the Classical Capacity of General Quantum Gaussian Measurement

In this paper, we consider the classical capacity problem for Gaussian measurement channels. We establish Gaussianity of the average state of the optimal ensemble in the general case and discuss the Hypothesis of Gaussian Maximizers concerning the structure of the ensemble. Then, we consider the case of one mode in detail, including the dual problem of accessible information of a Gaussian ensemble. Our findings are relevant to practical situations in quantum communications where the receiver is Gaussian (say, a general-dyne detection) and concatenation of the Gaussian channel and the receiver can be considered as one Gaussian measurement channel. Our efforts in this and preceding papers are then aimed at establishing full Gaussianity of the optimal ensemble (usually taken as an assumption) in such schemes.     

Gaussian mixture model-based gradient field reconstruction for infrared image detail enhancement and denoising

Infrared images are characterized by low signal-to-noise ratio and low contrast. Therefore, the edge details are easily immerged in the background and noise, making it much difficult to achieve infrared image edge detail enhancement and denoising. This article proposes a novel method of Gaussian mixture model-based gradient field reconstruction, which enhances image edge details while suppressing noise. First, by analyzing the gradient histogram of noisy infrared image, Gaussian mixture model is adopted to simulate the distribution of the gradient histogram, and divides the image information into three parts corresponding to faint details, noise and the edges of clear targets, respectively. Then, the piecewise function is constructed based on the characteristics of the image to increase gradients of faint details and suppress gradients of noise. Finally, anisotropic diffusion constraint is added while visualizing enhanced image from the transformed gradient field to further suppress noise. The experimental results show that the method possesses unique advantage of effectively enhancing infrared image edge details and suppressing noise as well, compared with the existing methods. In addition, it can be used to effectively enhance other types of images such as the visible and medical images.     

1维线阵离轴高斯光束的分数傅里叶变换

 为研究非相干的1维线阵离轴高斯光束通过分数傅里叶变换(FRFT)系统的传输特性,利用Collins积分公式,导出了其在FRFT面上的光强分布解析式,并利用此解析式作数值计算和分析。研究表明：非相干的1维线阵离轴高斯光束在FRFT面上的光强分布由FRFT的阶数和子光束数目共同决定,其归一化的光强分布随FRFT的阶数周期性变化,周期为2;子光束数目的大小及其奇偶性对归一化光强分布的影响取决于FRFT的阶数;轴上归一化光强分布也随FRFT的阶数周期性变化,变化周期也为2。     

数字波面恢复中的高斯门限法

在工程干涉度量中获得的干涉图往往信噪比低且不为常值,这给采用条纹细化和拟合方法恢复波面带来困难,并导致较大的测量误差.本文提出的高斯门限法不仅克服了常用技术的不足,简便易行,而且经计算机模拟分析和测试实例分析表明,本法具有很高的精度和可靠性.文中详述了高斯门限的原理、算法,提供了实例分析结果.