单相流体在锅炉并联管组中流量分配规律研究

本文首先对单相流体在并联管组中流量分配研究的重要性作了论述，综合评述了国内外在单相流体流量分配方面的研究工作，在此基础上，结合我国大容量锅炉的运行现状，提出了进一步研究流量分配问题的意见．     

Banach空间中线性流形的单值度量投影算子部分

刘晶和王玉文于2001年引入并研究了Banach空间中线性流形的单值度量投影算子部分，但他们是在空间X和Y均自反、严格凸的强几何假定下来进行讨论的，这极不利于应用．我们在X和Y均是一般Banach空间的弱假定下，讨论并研究了Banach空间中线性流形的单值度量投影算子部分，并给出了该算子部分的结构的刻划．这为将比Lee S.J.与Nashed M.Z.所引进并研究的Hilbert空间集值线性映射包含的最小二乘解推广到Banach空间奠定了理论基础，所得的本质地推广了刘晶与王玉文的结果。     

Gauss-Bonnet公式的另一种表示形式

设M为n=2P维的紧致定向Riemann流形,本文将证明Gauss-Bonnet公式可表示成 x(M)=((-1)~p/2~pπ~p)∫_(mΩ_(1…n)) 其中,对任意偶数m≤n。 Ω_(i_1…i_m)=(sum from k)ε((1K2…K-1K+1…m)(1…m)Ω_(i_1i_k)∧Ω_(i_2…i_(k-1)i_(k+1)…i_m))     

Formulas for stationary planar fibre processes III – intersections with fibre systems

In this paper we investigate the distribution of the periodogram, respectively, the periodogram matrix for stationary random sequences. These .distributions are consid¬ered in the case of a fixed frequency as well as in the case of a finite number of frequencies for Gaussian sequences and for sequences of independent random variables. The exact distribution is obtained in the case of a fixed frequence for one-dimensional GAUSsian sequences. Asymptotic expansions, respectively, the rate of convergence to the asymptotic distribution are given in the case mentioned above     

流形概念的起源与发展

流形概念是20世纪数学的最具代表性的基本观念之一。文章分析与探讨流形概念的起源与发展。特别地,研究了流形概念从产生到其概念的精确的数学描述这一历史过程。     

COMBINING MULTIPLE MARKERS FOR MULTI‐CATEGORY CLASSIFICATION: AN ROC SURFACE APPROACH

The Receiver Operating Characteristic (ROC) curve and the Area Under the ROC Curve (AUC) are effective statistical tools for evaluating the accuracy of diagnostic tests for binary‐class medical data. However, many real‐world biomedical problems involve more than two categories. The Volume Under the ROC Surface (VUS) and Hypervolume Under the ROC Manifold (HUM) measures are extensions for the AUC under three‐class and multiple‐class models. Inference methods for such measures have been proposed recently. We develop a method of constructing a linear combination of markers for which the VUS or HUM of the combined markers is maximized. Asymptotic validity of the estimator is justified by extending the results for maximum rank correlation estimation that are well known in econometrics. A bootstrap resampling method is then applied to estimate the sampling variability. Simulations and examples are provided to demonstrate our methods.     

Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo

In this article, we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian approach using traditional Metropolis-Hastings methods, Hamiltonian Monte Carlo (HMC), and Riemann manifold HMC (RMHMC) methods to obtain the approximations to the posterior marginal distributions of interest. Applications to real datasets and simulation studies provide evidence that the extra analytical work involved in Hamiltonian Monte Carlo algorithms is compensated by a more efficient exploration of the parameter space.     

GIPSCAL revisited. A projected gradient approach

A model for analysis and visualization of asymmetric data—GIPSCAL—is reconsidered by means of the projected gradient approach. GIPSCAL problem is formulated as initial value problem for certain first order matrix ordinary differential equations. This results in a globally convergent algorithm for solving GIPSCAL. Additionally, first and second order optimality conditions for the solutions are established. A generalization of the GIPSCAL model for analyzing three-way arrays is also considered. Finally, results from simulation experiments are reported.     

Geodesic Monte Carlo on Embedded Manifolds

Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton–Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices.     

Morse理论的基本定理

Morse理论的基本定理是利用流形上的Morse函数重建黎曼流形的代数拓扑结构,即给每个流形一个较几何复形更为一般的CW─复形的伦型,然后在重建的结构里（CW─复型）展开流形的研究。