印刷图像中隐藏水印的菲涅耳变换方法

在研究数字全息技术、双随机相位加密技术和数字印刷技术的基础上，提出了一种新的菲涅耳全息术水印隐藏方法．该方法将待隐藏水印信息经过菲涅耳加密面进行随机相位加密，并离轴记录其像全息图像，将离轴记录的菲涅耳变换加密面像全息图像，以适当的强度叠加隐藏在载体数字图像中，通过正确密钥可从含水印图像中恢复水印信息．通过打印或数字印刷技术可以将含水印图像印制在证件等印刷品中；印刷品中的含水印载体数字图像通过扫描输入生成数字图像，就可以从中提取所隐藏信息．理论分析和仿真实验证明，该方法具有安全性、鲁棒性和不可见性．该方法在解密时通过解密模板紧贴解密，且只需进行一次逆菲涅耳变换就可提取隐藏水印信息，若使用光学系统解密，调整简单，有利于提高恢复隐藏信息的质量．     

两区复合油藏二次梯度非线性渗流模型研究

考虑二次压力梯度影响的非线性渗流模型比常规线性模型更能准确反映地下原油在油藏中的渗流规律。针对两区复合油藏，从达西定律、质量守恒原理出发，结合状态方程，推导出了考虑二次压力梯度影响的渗流方程，建立了二次压力梯度两区复合油藏非线性渗流试井模型。通过变量代换，将渗流方程线性化，求得Laplace空间解析解，利用数值反演获得实空间解，从而绘制出带二次压力梯度影响的两区复合油藏试井样版曲线，对不同参数进行了敏感性分析。误差分析表明二次压力梯度影响不应被忽略。     

An approximate entropy test for randomness

We develop an entropy-based test for randomness of binary time series of finite length. The test uses the frequencies of contiguous blocks of different lengths. A simple condition ib the block lengths and the length of the time series enables one to estimate the entropy rate for the data, and this information is used to develop a statistic to test the hypothesis of randomness. This static measures the deviation of the estimated entropy of the observed data from the theoretical maximum under the randomness hypothesis. This test offers a real alternative to the conventional runs test. Critical percentage points, based on simulations, are provided for testing the hypothesis of randomness. Power calculations using dependent data show that the proposed test has higher power against the runs test for short series, and it is similar to the runs test for long series. The test is applied to two published data sets that wree investigated by others with respect to their randomness.     

Partial Identification in Triangular Systems of Equations With Binary Dependent Variables

This paper studies the special case of the triangular system of equations in Vytlacil and Yildiz (2007), where both dependent variables are binary but without imposing the restrictive support condition required by Vytlacil and Yildiz (2007) for identification of the average structural function (ASF) and the average treatment effect (ATE). Under weak regularity conditions, we derive upper and lower bounds on the ASF and the ATE. We show further that the bounds on the ASF and ATE are sharp under some further regularity conditions and an additional restriction on the support of the covariates and the instrument.     

从二元对立到第三空间杂糅——奥立维亚的双重文化身份认定之路

美国当代著名的华裔女作家谭恩美的第三部长篇小说《百种神秘感官》具有浓厚的后殖民主义特色,尤其是东方主义的神秘色彩。其女主人公奥立维亚是中美混血的“香蕉人”,为了全力美国化,她否认自己的华人文化身份,但是美国主流社会却将她视为“他者”,二元文化冲突使她陷入文化身份危机。困境中的奥立维亚放弃了对立的二元文化,接受自己的华人文化身份,用双重文化重塑自我,走出文化身份危机,进入了文化身份的第三空间杂糅,获得新生。奥立维亚的文化身份寻求之路经历了从东方主义的二元对立到第三空间的杂糅,是“他者”的典型身份寻求之路,这个文学人物的心路历程,对越来越多的流散族裔的寻根现象和文化认同经历具有案例价值,为民族文化交流和融合研究提供借鉴。     

Comparisons of computational methods for clustered binary data

Clustered binary data are common in medical research and can be fitted to the logistic regression model with random effects which belongs to a wider class of models called the generalized linear mixed model. The likelihood-based estimation of model parameters often has to handle intractable integration which leads to several estimation methods to overcome such difficulty. The penalized quasi-likelihood (PQL) method is the one that is very popular and computationally efficient in most cases. The expectation–maximization (EM) algorithm allows to estimate maximum-likelihood estimates, but requires to compute possibly intractable integration in the E-step. The variants of the EM algorithm to evaluate the E-step are introduced. The Monte Carlo EM (MCEM) method computes the E-step by approximating the expectation using Monte Carlo samples, while the Modified EM (MEM) method computes the E-step by approximating the expectation using the Laplace's method. All these methods involve several steps of approximation so that corresponding estimates of model parameters contain inevitable errors (large or small) induced by approximation. Understanding and quantifying discrepancy theoretically is difficult due to the complexity of approximations in each method, even though the focus is on clustered binary data. As an alternative competing computational method, we consider a non-parametric maximum-likelihood (NPML) method as well. We review and compare the PQL, MCEM, MEM and NPML methods for clustered binary data via simulation study, which will be useful for researchers when choosing an estimation method for their analysis.     

Selection of Binary Variables and Classification by Boosting

We adopt boosting for classification and selection of high-dimensional binary variables for which classical methods based on normality and non singular sample dispersion are inapplicable. Boosting seems particularly well suited for binary variables. We present three methods of which two combine boosting with the relatively classical variable selection methods developed in Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]). Our primary interest is variable selection in classification with small misclassification error being used as validation of proposed method for variable selection. Two of the new methods perform uniformly better than Wilbur et al. (2002 Wilbur , J. D. , Ghosh , J. K. , Nakatsu , C. H. , Brouder , S. M. , Doerge , R. W. ( 2002 ). Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints . Biometrics 58 : 378 – 386 . [Google Scholar]) in one set of simulated and three real life examples.     

Multiple Marginal Independence Testing for Pick Any/C Variables

Many survey questions allow respondents to pick any number out of c possible categorical responses or “items”. These kinds of survey questions often use the terminology “choose all that apply” or “pick any”. Often of interest is determining if the marginal response distributions of each item differ among r different groups of respondents. Agresti and Liu (1998, 1999) call this a test for multiple marginal independence (MMI). If respondents are allowed to pick only 1 out of c responses, the hypothesis test may be performed using the Pearson chi-square test of independence. However, since respondents may pick more or less than 1 response, the test's assumptions that responses are made independently of each other is violated. Recently, a few MMI testing methods have been proposed. Loughin and Scherer (1998) propose using a bootstrap method based on a modified version of the Pearson chi-square test statistic. Agresti and Liu (1998, 1999) propose using marginal logit models, quasisymmetric loglinear models, and a few methods based on Pearson chi-square test statistics. Decady and Thomas (1999) propose using a Rao-Scott adjusted chi-squared test statistic. There has not been a full investigation of these MMI testing methods. The purpose here is to evaluate the proposed methods and propose a few new methods. Recommendations are given to guide the practitioner in choosing which MMI testing methods to use.     

Weighted Multiple Testing Correction for Correlated Binary Endpoints

Multiple binary endpoints often occur in clinical trials and are usually correlated. Many multiple testing adjustment methods have been proposed to control familywise type I error rates. However, most of them disregard the correlation among the endpoints, for example, the commonly used Bonferroni correction, Bonferroni fixed-sequence (BFS) procedure, and its extension, the alpha-exhaustive fallback (AEF). Extending BFS by taking into account correlations among endpoints, Huque and Alosh proposed a flexible fixed-sequence (FFS) testing method, but this FFS method faces computational difficulty when there are four or more endpoints and the power of the first hypothesis does not depend on the correlations among endpoints. In dealing with these issues, Xie proposed a weighted multiple testing correction (WMTC) for correlated continuous endpoints and showed that the proposed method can easily handle hundreds of endpoints by using the R package and has higher power for testing the first hypothesis compared with the FFS and AEF methods. Since WMTC depends on the joint distribution of the endpoints, it is not clear whether WMTC still keeps those advantages when correlated binary endpoints are used. In this article, we evaluated the statistical power of WMTC method for correlated binary endpoints in comparison with the FFS, the AEF, the prospective alpha allocation scheme (PAAS), and the weighted Holm-Bonferroni methods. Furthermore the WMTC method and others are illustrated on a real dataset examining the circumstance of homicide in New York City.     

The table look-up rule

The table look-up rule problem can be described by the question: what is a good way for the table to represent the decision regions in the N-dimensional measurement space. This paper describes a quickly implementable table look-up rule based on Ashby’s representation of sets in his constraint analysis. A decision region for category c in the N-dimensional measurement space is considered to be the intersection of the inverse projections of the decision regions determined for category c by Bayes rules in smaller dimensional projection spaces. Error bounds for this composite decision rule are derived: any entry in the confusion matrix for the composite decision rule is bounded above by the minimum of that entry taken over all the confusion matrices of the Bayes decision rules in the smaller dimensional projection spaces.

On simulated Gaussian Data, probability of error with the table look-up rule is comparable to the optimum Bayes rule.