Virtio network paravirtualization driver: Implementation and performance of a de-facto standard

One of the techniques used to improve I/O performance of virtual machines is paravirtualization. Paravirtualized devices are intended to reduce the performance overhead on full virtualization where all hardware devices are emulated. The interface of a paravirtualized device is not identical to that of the underlying hardware. The OS of the virtual guest machine must be ported in order to use a paravirtualized device. In this paper, the network virtualization done by the Kernel-based Virtual Machine (KVM) is described. The KVM model is different from other Virtual Machines Monitors (VMMs) because the KVM is a Linux kernel model and it depends on hardware support. In this work, the overhead of using such virtual networks is been measured. A paravirtualized model by using the virtio [38] network driver is described, and some performance results of web benchmark on the two models are presented.     

Hierarchies of recursive computations†

A recursive version of the Turing machine model is used to analyze the time and storage complexity of recursive algorithms. Hierarchy theorems are proven for time and for width of recursion (the amount of storage used at a level). A particular language is shown to be the “hardest” language to recognize without recursion. Previous results relating recursive and non-recursive time bounded computations are sharpened.     

Geoadditive expectile regression

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses even in the presence of heteroscedastic errors. Quantile regression estimates are obtained by minimising an asymmetrically weighted sum of absolute deviations from the regression line, a decision theoretic formulation of the estimation problem that avoids a full specification of the error term distribution. Recent advances in mean regression have concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates, random effects or spatial effects. These extensions often rely on penalised least squares or penalised likelihood estimation with quadratic penalties and may therefore be difficult to combine with the linear programming approaches often considered in quantile regression. As a consequence, geoadditive expectile regression based on minimising an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including least asymmetrically weighted squares, boosting and restricted expectile regression. The properties of these procedures are investigated in a simulation study and an analysis on rental fees in Munich is provided where the geoadditive specification allows for an analysis of nonlinear effects of the size of flats or the year of construction and the spatial distribution of rents simultaneously.     

Functional linear regression after spline transformation

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related. The problem of finding such transformations has been rather neglected in the development of functional data analysis tools. In this paper, we consider transformation on the response variable in functional linear regression and propose a nonparametric transformation model in which we use spline functions to construct the transformation function. The functional regression coefficients are then estimated by an innovative procedure called mixed data canonical correlation analysis (MDCCA). MDCCA is analogous to the canonical correlation analysis between two multivariate samples, but is between a multivariate sample and a set of functional data. Here, we apply the MDCCA to the projection of the transformation function on the B-spline space and the functional predictors. We then show that our estimates agree with the regularized functional least squares estimate for the transformation model subject to a scale multiplication. The dimension of the space of spline transformations can be determined by a model selection principle. Typically, a very small number of B-spline knots is needed. Real and simulation data examples are further presented to demonstrate the value of this approach.     

Improved conjugate gradient implementation for least squares support vector machines

As a promising method for pattern recognition and function estimation, least squares support vector machines (LS-SVM) express the training in terms of solving a linear system instead of a quadratic programming problem as for conventional support vector machines (SVM). In this paper, by using the information provided by the equality constraint, we transform the minimization problem with a single equality constraint in LS-SVM into an unconstrained minimization problem, then propose reduced formulations for LS-SVM. By introducing this transformation, the times of using conjugate gradient (CG) method, which is a greatly time-consuming step in obtaining the numerical solution, are reduced to one instead of two as proposed by Suykens et al. (1999). The comparison on computational speed of our method with the CG method proposed by Suykens et al. and the first order and second order SMO methods on several benchmark data sets shows a reduction of training time by up to 44%.     

A simplified multi-class support vector machine with reduced dual optimization

Support vector machine (SVM) was initially designed for binary classification. To extend SVM to the multi-class scenario, a number of classification models were proposed such as the one by Crammer and Singer (2001). However, the number of variables in Crammer and Singer’s dual problem is the product of the number of samples (l) by the number of classes (k), which produces a large computational complexity. This paper presents a simplified multi-class SVM (SimMSVM) that reduces the size of the resulting dual problem from l × k to l by introducing a relaxed classification error bound. The experimental results demonstrate that the proposed SimMSVM approach can greatly speed-up the training process, while maintaining a competitive classification accuracy.     

Minimizing the number of tardy jobs in a single-machine scheduling problem with periodic maintenance

This research focuses on the problem of scheduling jobs on a single machine that requires periodic maintenance with the objective of minimizing the number of tardy jobs. We present a two-phase heuristic algorithm in which an initial solution is obtained first with a method modified from Moore's algorithm for the problem without maintenance and then the solution is improved in the second phase. Performance of the proposed heuristic algorithm is evaluated through computational experiments on randomly generated problem instances and results show that the heuristic gives solutions close to those obtained from a commercial integer programming solver in much shorter time and works better than an existing heuristic algorithm in terms of the solution quality.     

多分部纸机传动控制器的研究

针对造纸机传动控制系统中不同型号变频器之间能否替代的问题,设计出一种多分部纸机传动控制器.传动系统可以用不同型号变频器组成,不论开环和闭环可以任意组合,克服了传统的控制方式中采用集成运算放大器构成模拟式速度链或伺服电动机联动速度链,其在运行时存在漂移、操作不便等缺陷.在实际造纸机传动系统设计中获得了广泛的应用,取得了良好的控制效果.     

现代分析技术在食品添加剂检测中的应用

民以食为天,食以安为先,食品安全牵涉到全民的生活质量和身体健康,加强食品安全检测事关重大.对食品添加剂的检测也成为保证食品安全的重要手段.工欲善其事,必先利其器,借助现代科学仪器和分析技术,使得食品添加剂的检测得以长足的发展.多学科的研究成果的彼此渗透,也促进了分析技术水平的提高,使食品添加剂的检测更为便捷、快速、准确,介绍了现代分析技术在食品添加剂检测中的应用,包括样品预处理技术和测定方法的最新进展.     

基于机器视觉的零件颜色识别系统

以Visual C＋＋6.0作为软件开发平台,以开源的OpenCV作为辅助的图像处理库,组建系统的硬件系统,实现了对工业自动生产线上的零件图像的采集和处理.该系统能对不同颜色的零件进行识别,为后续零件的筛选和分类奠定了基础.实验证明,采用机器视觉对零件颜色进行在线识别,不但检测精度高,而且能够满足生产线上实时性的要求.