从程度副词搭配看分词形容词语义标尺特征

基于Kennedy和McNally的形容词语义标尺理论,结合Sapir、Bolinger和Paradis等对形容词和副词搭配的研究方法,引入FLOB语料库的实证数据,证明了程度副词very和well的选择性搭配可以用来分析分词形容词的语义标尺特征,阐明了两者间搭配机理及规律。发现分词形容词语义标尺取决于源动词动作特征,并受其体性特征影响,两者有强烈的一致性。     

一类大集合p元低相关序列集的线性复杂度研究

构造具有大线性复杂度和大集合容量的p元低相关序列集对码分多址(CDMA)通信系统具有重要的意义。采用Klapper的方法,利用d-型函数,构造了一类具有大集合容量的p元低相关序列集S(r)。该序列集的集合容量为p2n,序列的周期为p n-1,相关函数的最大边峰值为4 p n2-1。利用Key的方法,证明了当p=3或p=5该序列集的最小和最大线性复杂度分别为2n2-2n和3n2-1×2n2-2n;而当p>5时,证明了其线性复杂度的最大和最小值分别大于3n4-1×2n4-2n和2n4-2n。该序列集能极大地提高CDMA通信系统的安全性。     

论教育现代化的逻辑起点和因果链

我们正处在一个全新的以最新科技和人类知识精华为基础的知识经济时代，它给人的全面发展带来了难得的机遇和条件，同时也对人的素质和能力提出了挑战和高要求。从教育现代化的逻辑起点和因果链对我国教育进行分析和阐述，对于改革传统教育，实现教育现代化，培养未来世纪新人具有重要的理论指导作用和现实意义。     

A test for randomness based on a complexity measure

A test for randomness based on a statistic related to the complexity of finite sequences is presented. Simulation of binary sequences under different stochastic models provides estimates of the power of the test. The results show that the test is sensitive to a variety of alternatives to randomness and suggest that the proposed test statistic is a reasonable measure of the stochastic complexity of a finite sequence of discrete random variables.     

Designs with Repeated Measurements on Any Number of Units over Varying Periods

In the usual repeated measurements designs (RMDs), the subjects are all observed for the same number of periods and the optimum RMDs require specified numbers of subjects, usually depending on the number of treatments to be used. In practice, it is sometimes not feasible to meet these requirements. To overcome this problem, alternative designs are suggested where any number of available subjects may be used and they may be observed for different periods. These designs are based on suitable serially balanced sequences which are shown to be optimal. Moreover, besides the usual direct and residual effects, the model considered has an extra term due to the interaction effect between them. The recommended designs are universally optimal in a very general class.     

Forecasting future values of changing sequences

Sequences of independent random variables are observed and on the basis of these observations future values of the process are forecast. The Bayesian predictive density of k future observations for normal, exponential, and binomial sequences which change exactly once are analyzed for several cases. It is seen that the Bayesian predictive densities are mixtures of standard probability distributions. For example, with normal sequences the Bayesian predictive density is a mixture of either normal or t-distributions, depending on whether or not the common variance is known. The mixing probabilities are the same as those occurring in the corresponding posterior distribution of the mean(s) of the sequence. The predictive mass function of the number of future successes that will occur in a changing Bernoulli sequence is computed and point and interval predictors are illustrated.     

语块理论中的搭配对英语教学的指导

针对二语习得者常出现的搭配不恰当的问题,对语块理论中的搭配进行深入的研究,旨在找出提高英语词汇搭配使用水平的有效方法,使语块理论更好的运用于外语教学,特别是词汇教学中。     

An Approximation Algorithm for Alignment of Multiple Sequences using Motif Discovery

Given a set of N sequence, the Multiple Sequence Alignment problem is to align these N sequences, possibly with gaps, that brings out the best commonality of the N sequences. The quality of the alignment is usually measured by penalizing the mis-matches and gaps, and rewarding the matches with appropriate weight functions. However for larger values of N, additional constraints are required to give meaningful alignments. We identify a user-controlled parameter, an alignment number K (2 K N): this additional requirement constrains the alignment to have at least K sequences agree on a character, whenever possible, in the alignment. We identify a natural optimization problem for this approach called the K-MSA problem. We show that the problem is MAX SNP hard. We give a natural extension of this problem that incorporates biological relevance by using motifs (common patterns in the sequences) and give an approximation algorithm for this problem in terms of the motifs in the data. MUSCA is an implementation of this approach and our experimental results indicate that this approach is efficient, particularly on large numbers of long sequences, and gives good alignments when tested on biological data such as DNA and protein sequences.     

基于语料库的高频形容词different词汇知识研究

利用语料库研究中国英语学习者对高频形容词differmt的使用情况,并通过和英语母语者对比,探究两者对该 词的搭配能力运用的异同,从而了解中国英语学习者对该词词汇知识掌握的深度,分析了主要原因,并提出了教学启示。     

Attention一词的搭配研究

以语料库为基础,探讨中国学习者对常用词attention的掌握深度,观察学习者对该词的使用特点,研究发现与本族语使用者的语言使用模式相比,某些搭配模式存在着过度使用、使用不足以及错误搭配。词汇习得中,大多数学习者重视的是扩大词汇量,而忽视了对于熟悉单词使用模式的掌握。文章得出的结论是应当给学生提供更加丰富的语言环境,在强调词汇量的同时,改进学生运用常用词的能力,即对词汇掌握的深度,从而提高其综合语言能力。