基于桥梁动力响应的车-桥接触力识别方法

将桥梁划分为欧拉梁单元建立桥梁的振动方程，基于拉格拉日原理建立汽车的振动方程，根据接触点处的接触力将汽车和桥梁系统耦合在一起。求解汽车一桥梁系统耦合方程得到桥梁节点动力响应，由广义正交函数和模态叠加原理确定模态响应及其导数，用正则化方法得到稳定的识别结果。数值模拟结果表明，该方法用于识别车桥接触力是有效的、可行的。     

编辑工作规范化的问题及对策刍议

编辑工作规范是实现编辑工作现代化的前提和保证。作者从编辑工作规范化对提高编辑工作效率和刊物的质量、实现网上编辑、保护“著作权”以及信息检索系统建立等方面的影响,论述了编辑工作规范化的意义。并针对现状提出了相应的建议,以提高编辑工作规范化的水平,促进期刊质量的全面提升。     

中国古代承揽契约论略--兼论明清江南棉布字号产业之法律调整

至迟出现于宋代的承揽契约是中国古代商品经济发达的重要表征,其外延与内涵与今时之承揽契约略有差异,但其实质功能则别无二致.本文集约相关史料,从名义考辩、法律关系、法律调整三方面探讨传统中国(主要集中于明、清时期江南棉布行业)承揽契约产生之历史前提及其协调功能,进而说明承揽契约实施过程中官府、定作人、承揽人三者之间的互动关系以及民间习惯法与官府成文法之间既相互包容又相互拒斥的历史成因.     

工程项目审计问题探析

提高工程项目的质量和建设经费的使用效益是决策者普遍关心的问题。本文对实现高等学校基建工程审计法制化、制度化、规范化的重要性、必要性以及对策进行了初步的探讨。     

Learning rates of multi-kernel regularized regression

Learning the kernel function has recently received considerable attention in machine learning. In this paper, we consider the multi-kernel regularized regression (MKRR) algorithm associated with least square loss over reproducing kernel Hilbert spaces. We provide an error analysis for the MKRR algorithm based on the Rademacher chaos complexity and iteration techniques. The main result is an explicit learning rate for the MKRR algorithm. Two examples are given to illustrate that the learning rates are much improved compared to those in the literature.     

Adaptive wavelet series estimation in separable nonparametric regression models

It is well-known that multivariate curve estimation suffers from the curse of dimensionality. However, reasonable estimators are possible, even in several dimensions, under appropriate restrictions on the complexity of the curve. In the present paper we explore how much appropriate wavelet estimators can exploit a typical restriction on the curve such as additivity. We first propose an adaptive and simultaneous estimation procedure for all additive components in additive regression models and discuss rate of convergence results and data-dependent truncation rules for wavelet series estimators. To speed up computation we then introduce a wavelet version of functional ANOVA algorithm for additive regression models and propose a regularization algorithm which guarantees an adaptive solution to the multivariate estimation problem. Some simulations indicate that wavelets methods complement nicely the existing methodology for nonparametric multivariate curve estimation.     

Robustness of automated data choices of smoothing parameter in image regularization

Statistical image restoration techniques are oriented mainly toward modelling the image degradation process in order to recover the original image. This usually involves formulating a criterion function that will yield some optimal estimate of the desired image. Often these techniques assume that the point spread function is known when the image is restored and indeed when we estimate the smoothing parameter. However in practice this assumption may not hold. This paper investigates empirically the effect of mis-specifying the point spread function on some data-based estimates of the regularization parameter and hence on the image reconstructions. Comparisons of image reconstruction quality are based on the mean absolute difference in pixel intensities between the true and reconstructed images.     

A unified class of penalties with the capability of producing a differentiable alternative to l1 norm penalty

Abstract

This article presents a class of novel penalties that are defined under a unified framework, which includes lasso, SCAD and ridge as special cases, and novel functions, such as the asymmetric quantile check function. The proposed class of penalties is capable of producing alternative differentiable penalties to lasso. We mainly focus on this case and show its desirable properties, propose an efficient algorithm for the parameter estimation and prove the theoretical properties of the resulting estimators. Moreover, we exploit the differentiability of the penalty function by deriving a novel Generalized Information Criterion (GIC) for model selection. The method is implemented in the R package DLASSO freely available from CRAN, http://CRAN.R-project.org/package=DLASSO.     

A comparison of regularization methods applied to the linear discriminant function with high-dimensional microarray data

Classification of gene expression microarray data is important in the diagnosis of diseases such as cancer, but often the analysis of microarray data presents difficult challenges because the gene expression dimension is typically much larger than the sample size. Consequently, classification methods for microarray data often rely on regularization techniques to stabilize the classifier for improved classification performance. In particular, numerous regularization techniques, such as covariance-matrix regularization, are available, which, in practice, lead to a difficult choice of regularization methods. In this paper, we compare the classification performance of five covariance-matrix regularization methods applied to the linear discriminant function using two simulated high-dimensional data sets and five well-known, high-dimensional microarray data sets. In our simulation study, we found the minimum distance empirical Bayes method reported in Srivastava and Kubokawa [Comparison of discrimination methods for high dimensional data, J. Japan Statist. Soc. 37(1) (2007), pp. 123–134], and the new linear discriminant analysis reported in Thomaz, Kitani, and Gillies [A Maximum Uncertainty LDA-based approach for Limited Sample Size problems – with application to Face Recognition, J. Braz. Comput. Soc. 12(1) (2006), pp. 1–12], to perform consistently well and often outperform three other prominent regularization methods. Finally, we conclude with some recommendations for practitioners.