The Hilbert Kernel Regression Estimate

Let (X, Y) be an ^d× -valued regression pair, whereXhas a density andYis bounded. Ifni.i.d. samples are drawn from this distribution, the Nadaraya–Watson kernel regression estimate in ^dwith Hilbert kernelK(x)=1/x^dis shown to converge weakly for all such regression pairs. We also show that strong convergence cannot be obtained. This is particularly interesting as this regression estimate does not have a smoothing parameter.     

Robust optimization and portfolio selection: The cost of robustness

Robust optimization is a tractable alternative to stochastic programming particularly suited for problems in which parameter values are unknown, variable and their distributions are uncertain. We evaluate the cost of robustness for the robust counterpart to the maximum return portfolio optimization problem. The uncertainty of asset returns is modelled by polyhedral uncertainty sets as opposed to the earlier proposed ellipsoidal sets. We derive the robust model from a min-regret perspective and examine the properties of robust models with respect to portfolio composition. We investigate the effect of different definitions of the bounds on the uncertainty sets and show that robust models yield well diversified portfolios, in terms of the number of assets and asset weights.     

关于随机选择的一个强极限定理

本文利用似然比和区间剖分法研究相依Ｎ值随机变量序列选择的极限性质,得到关于赌博方法的一个强极限定理的若干推广。     

Indicator space configuration for early warning of violent political conflicts by genetic algorithms

Recognition of preconflict situations has a powerful potential for early warning of violent political conflicts. This paper focuses on the design and application of artificial neural networks as classifiers of preconflict situations. Achieving a desired level of performance of the neural network relies on the appropriate construction of recognition space (selection of indicators) and the choice of network architecture. A fast and effective method for the design of reliable neural recognition systems is described. It is based on genetic algorithm techniques and optimizes both the configuration of input space and the network parameters. The implementation of the methodology provides for increased performance of the classifier in terms of accuracy, generalization capacity, computational and data requirements. This revised version was published online in June 2006 with corrections to the Cover Date.     

超薄超晶格(GaAs)_4/(AlAs)_2的一级拉曼光谱及其选择定则

在超晶格样品（ＧａＡｓ）４／（ＡｌＡｓ）２的一级共振拉曼光谱的界面模的观察中,四个界面模在ＸＸ和ＸＹ两种偏振态中均能被观察到,这与传统的选择定则相矛盾。这是因为传统的理论假设电子波函数是完全限制在势阱当中,而在超薄系统中例如在ＧａＡｓ势阱中的电子波函数会穿透到ＡｌＡｓ势垒中,从而得到新的结果。这表明传统的理论在被应用到超薄系统或超小系统时需要进行一定的修正。     

A robust decentralized reputation management system for service selection

Reputation mechanism is a novel approach to automate QoS-aware service selection in service oriented computing. The reputation system collects ratings on QoS that consumers feedback and aggregates them to derive a reputation value, which can in turn assist other consumers in service selection in future. However, current approaches fail to combat the malicious ratings and hence the calculated reputation values can be biased severely or even manipulated. Moreover, the centralized management of rating data restricts its application to large open environment. In this paper, we present a robust decentralized reputation system which can resist various unfair ratings and manipulation behaviours. It can evolve and become more mature against malicious ratings with the system running continuously. At last, we experimentally verify the robustness of the proposed approach through a simulation study.     

基于可能性分布联合落影的红外图像融合算法选取

针对双模态红外图像实际融合需求中常涉及到异类差异特征协同优化融合,且现有差异特征属性无法针对性地调整融合算法进行有效驱动,导致融合效果差等问题,提出了基于可能性分布联合落影的双模态红外图像融合算法选取的方法。首先计算双模态红外图像差异特征幅值的融合有效度,利用K最近邻法得到差异特征幅值的概率密度分布,得到差异特征频次属性的分布;然后通过差异特征幅值属性与频次属性构造差异特征权重函数,建立异类差异特征权重函数与多融合算法间的可能性分布合成,得到异类差异特征权重函数多融合算法融合有效度的联合落影;最后构建融合性能指标动态选取最优融合算法。实验结果表明,本文方法所选出的最优融合算法在等级得分指标上明显优于其他算法,验证了本文将可能性分布联合落影运用于双模态红外图像最优融合算法选取中具有可行性。     

Three-class classification models of logS and logP derived by using GA–CG–SVM approach

In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class models. A comparison between the performance of GA–CG–SVM models and that of GA–SVM models shows that the SVM parameter optimization has a significant impact on the quality of SVM classification model. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Hui Zhang and Ming-Li Xiang are contributed equally.     

一种机器人支撑臂的力学分析与距离选优

采用Adams虚拟样机仿真技术对一种机器人的主要构件进行建模,分析了模型前后支撑臂的受力情况;分别建立了7种前后两臂之间不同距离的模型,通过数据对比,优选出使电机功率输出最小的两臂间距离.