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.     

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.     

组合拍卖竞胜标确定问题的混沌搜索算法

组合拍卖能够提高拍卖的效率,还能降低竞标人的风险. 但竞胜标确定问题是一个NP 难题. 在分析该问题特性的基础上,设计了一种嵌入优先适合启发式规则的混沌搜索算法. 与 传统算法相比,该算法具有实现方便,寻优效果好的优点. 实例计算结果表明了算法在解决该 问题的有效性和广阔的应用前景.     

公共行政中的混沌与复杂性理论

混沌理论被引入公共行政后,公共行政领域已初步形成自己的混沌观———公共行政系统中存在着混沌现象。但这只是一个保守的结论,事实上公共行政系统本身就是一个混沌与复杂系统,遵循从简单到复杂、从复杂到混沌的发展规律;它也是线性与非线性因素并存、有序与无序统一的系统,具有自我组织能力,在演化过程中存在分叉。因此,公共行政活动应在混沌与复杂性理论的指导下树立混杂思维,包括:树立大系统思维;确立“不可预见”哲学观;保持复杂性阶段;鼓励创建有利于公务员发展的“小生境”以及利用自组织协同实现行政系统从混沌到有序的转变等。     

Henon混沌系统的控制研究

运用数值仿真的方法,研究了两种Henon混沌的控制方法,一种以不确定差分方程为预测模型,预测模型参数通过折息法在线辨识得到,一种带有终端滑模等式约束的预测控制算法。仿真结果表明这两种方法的有效性。     

Asymptotic properties of randomly indexed sequences of random variables

Conditions are given for a randomly indexed sequence of random variables to converge weakly. The key concept employed is the so-called generalized Anscombe condition. The results give a method of determining sequential stopping rules, which have the required accuracy of estimation of an unknown parameter in the case when the observations are not necessarily independent and identically distributed.     

一类混沌经济模型的控制方法的研究

就经济系统中的一类多参数混沌模型 ,应用微分动力学理论 ,研究了变参数结构反馈控制方法 ,成功地进行了多参数混沌控制 .并讨论小波滤波法 ,成功实现了混沌不稳定周期轨道的稳定控制方法 ,并以具体的财产管理混沌模型为例验证所提出的方法     

Efficient Algorithms for Local Alignment Search

We present efficient algorithms for local alignment search in biological sequences. These algorithms identify maximal segment pairs (MSPs). Our algorithms have the potential of performing better than BLAST (Basic Local Alignment Search Tool) and also are efficiently parallelizable. We employ Fast Fourier Transforms (FFTs). Though several attempts have been made in the past to employ FFTs in sequence analysis, they fail to capture local similarities. Our algorithms employ FFTs in a novel way to identify local similarities. FFT-based techniques have the attractive feature of benefiting from ultrafast special purpose hardware available for digital signal processing.