从“观”、“视”、“听”看上古汉语动词去声别义的方向性功能

本文考察了三个动词"观"、"视"、"听",主要内容是将这几个词读去声与非去声时句法表现进行对比,从而得出它们读去声时都表示和读非去声时相反的方向。也就是说,去声别义起码有一种功能是用来表示方向性。     

Algorithms for the construction of optimizing distributions

We will consider the following problem.Maximise Φ(p)over P={p=(p₁,P₂,…,p_j):P_j≧0,∑p_j=1}". We require to calcute an optimizing distribution. Examples arise in optimal regression design,maximum likelihood estimation and stratified sazmpling problems. A class of multiplicative algorithms, indexed by functions which depend on the derivatives of Φ(·)is considered for solving this problem.Iterations are of the form:p_j ^(r+1)αp_j ^(r)f(x_j ^(r)), where x_{j (r)}=d_j ^(r) or F_j ^(r)and d_j ^(r)=?Φ/?p_j While F_j ^(r)=D_j ^(r)?∑p_i ^(r)d_i ^(r) (a directional derivative)at p=p^(r)f(·)satisfies some suitable properties and may depend on one or more free parameters. These iterations neatly submit to the constraints ofv the problem. Some results will be reported and extensions to problems dependin on two or more distributions and to problems with additional constraints will be considered.     

Robustness of bayesian factor analysis estimates

This paper presents the result of a study of the robustness of posterior estimators of the factor loading matrix, the factor scores, and the disturbance covariance matrix (the main model parameters) in a Bayesian factor analysis with respect to variations in the values of the parameters of their prior distributions (the hyperparameter). We adopt the ε - contamination model of Berger and Berliner(1986) to generate prior distributions whose hyper-paramters reflects small variations in the elements of the uncontaminated hyperparameters, and we use directional derivatives to examine the variation of the uncontaminated estimators with respect to changes in the values of the hyperparameters, in the directions of the main model parameters. Several matrix norms are used to measure the closeness of the resulting values. We illustrate the results with a numerical example.     

基于交互式迭代算法的中国碳减排目标分配研究

众所周知,温室气体的大量排放会引起气候变化和全球变暖等环境问题。其中,二氧化碳作为影响最大的温室气体,主要产生于人类活动。应对气候变化需要来自于全球各个国家的共同行动与努力。作为经济高速发展的发展中国家,中国已于近几年成为世界最大的能耗国和碳排放国。为积极应对温室效应,中国政府计划采取强有力的政策措施,实现节能减排并逐步建立全国碳交易市场。为确保应对气候变化国家方案的切实贯彻实施,如何将全国碳减排目标在各省份间进行合理分配,至关重要。在效率分析的理论框架下,借助于方向性距离函数评价含有非期望产出（碳排放）的绩效表现,在此基础之上提出一种交互式迭代算法,合理有效地将全国碳减排总目标分摊为各省级目标。结果表明,受到地理环境、经济状况、产业结构等因素的共同影响,不同区域相应承担不同水平的减排任务。绝大部分沿海发达地区的省份和直辖市需要大幅减少碳排放量,而中西部经济区则期望减排比例较小。分摊结果不仅显示了方法的有效性和易操作性,也针对各典型区域如何推进可持续发展提供了启发性建议。