多元弱奇异积分方程近似解直接方法的优化

本文研究了多元弱奇异第二类Ｆｒｅｄｈｏｌｍ积分方程数ψ在适应直接方法意义下的最优近似解产 们推广了文献「１」中的相关结果。     

The average dimension of a multidimensional function for quasi-Monte Carlo estimates of an integral

The effective dimension of a multidimensional function was previously introduced to measure the complexity of the function with respect to the evaluation of an integral by quasi-Monte Carlo methods. For the same goal, the concept of the average dimension is introduced, which, in contrast to the effective dimension, is independent of an arbitrary confidence level.     

Error expansions for multidimensional trapezoidal rules with Sidi transformations

In 1993, Sidi introduced a set of trigonometric transformations x = ψ(t) that improve the effectiveness of the one-dimensional trapezoidal quadrature rule for a finite interval. In this paper, we extend Sidi's approach to product multidimensional quadrature over [0,1] ^N . We establish the Euler–Maclaurin expansion for this rule, both in the case of a regular integrand function f(x) and in the cases when f(x) has homogeneous singularities confined to vertices. This revised version was published online in August 2006 with corrections to the Cover Date.     

Assessment and robust reconstruction of laser radar signals

In direct-detection imaging, laser radar speckle noise, drop-outs, and outliers have to be considered in order to ensure high accuracy and reliability of measurement data. The most common approach for the stabilization of laser radar data is temporal averaging over several shots. This, however, is not in all cases the best method for the reconstruction of noisy image data. It is shown that principal component reconstruction can yield a remarkable improvement of accuracy and robustness of range data.     

Bayesian estimation of the intraclass correlation coefficients in the mixed linear model

The method of determining Bayesian estimators for the special ratios of variance components called the intraclass correlation coefficients is presented. The exact posterior distribution for these ratios of variance components is obtained. The approximate posterior mean of this distribution is also derived. All computations are non-iterative and avoid numerical integration.     

Completely integrable nonlinear dynamical systems of the Langmuir chains type

The solution of the Cauchy problem for semi-infinite chains of ordinary differential equations, studied first by O. I. Bogoyavlenskii in 1987, is obtained in terms of the decomposition in a multidimensional continuous fraction of Markov vector functions (the resolvent functions) related to the chain of a nonsymmetric operator; the decomposition is performed by the Euler-Jacobi-Perron algorithm. The inverse spectral problem method, based on Lax pairs, on the theory of joint Hermite-Padé approximations, and on the Sturm-Liouville method for finite difference equations is used. Translated fromMatematicheskie Zametki, Vol. 62, No. 4, pp. 588–602, October, 1997. Translated by A. M. Chebotarev     

基于改进Hopfield神经网络的自适应二维噪声对消器

提出了基于连续型Hopfield神经网络(CHNN)的自适应二维噪声对消器,讨论了神经网络的结构和原理及相应的自适应滤波算法,并从理论上进行了论证.仿真结果表明相对于采用最小均方算法的二维线性噪声对消器,CHNN噪声对消器能更有效实现二维噪声的消除,保持原信号的完整性,获得较好的去噪声效果.     

Examining the Capacity of Text Mining and Software Metrics in Vulnerability Prediction

Software security is a very important aspect for software development organizations who wish to provide high-quality and dependable software to their consumers. A crucial part of software security is the early detection of software vulnerabilities. Vulnerability prediction is a mechanism that facilitates the identification (and, in turn, the mitigation) of vulnerabilities early enough during the software development cycle. The scientific community has recently focused a lot of attention on developing Deep Learning models using text mining techniques for predicting the existence of vulnerabilities in software components. However, there are also studies that examine whether the utilization of statically extracted software metrics can lead to adequate Vulnerability Prediction Models. In this paper, both software metrics- and text mining-based Vulnerability Prediction Models are constructed and compared. A combination of software metrics and text tokens using deep-learning models is examined as well in order to investigate if a combined model can lead to more accurate vulnerability prediction. For the purposes of the present study, a vulnerability dataset containing vulnerabilities from real-world software products is utilized and extended. The results of our analysis indicate that text mining-based models outperform software metrics-based models with respect to their F₂-score, whereas enriching the text mining-based models with software metrics was not found to provide any added value to their predictive performance.