身份认同·英雄血统·史诗架构——刘萧《筸军之城》小说艺术论

刘萧的长篇小说《筸军之城》,究其内部属性而言,实为当代文学制度规约下的宏大题材小说,其以浓厚的女性自我意识、家族观念和边地风情为写作基础,写就了超越的族裔认同、国族想象,并意图获得一种超越的文化认同;在叙事技巧层面,小说以替补与重复的方式塑造英雄,在母性情怀与男子气概之间寻求平衡,以此建构了筸军的英雄血统,进一步丰富了湘西世界的“边地”内涵。     

北美汉学界的《沧浪诗话》研究述评——以华裔族群为中心

北美汉学界的《沧浪诗话》研究在主体构成上包括华裔族群和本土学者两个群体。华裔族群的代表人物是刘若愚、叶维廉、叶嘉莹、欧阳桢等人，本土学者的代表人物是加拿大的林理彰，美国的宇文所安、费维廉等人。整体上看。华裔族群的研究凸显出如下四个主要特点：一是注重影响研究，包括严羽诗论对前代文论的承传及其对后代文论（文学理论和文学批评）的影响；二是对严羽诗论系统中“以禅喻诗”批评方法及“兴趣”、“妙悟”等概念进行了阐释：三是方法上移西释中和中西对话并用；四是注意吸纳中国和日本“严学”家的研究成果。     

东南亚华裔学生中文姓名用字研究

姓名是语言学中一个值得关注的课题,它是一种社会现象,也是一种语言现象,有丰富的内涵。文章通过语料样本对东南亚华裔学生的中文姓名用字进行统计研究,分析用字特点,对华裔学生中文取名方法和对待相关问题应有态度进行探讨。     

参数微调法控制混沌

本文用OGY方法研究了二维映象的混沌控制.计算机模拟表明,OGY参数微调法可以有效地控制混沌运动向周期运动的转变,混沌暂态时间的长短敏感地依赖于系统的初始状态,还用模拟噪声讨论了混沌控制的稳定性问题。     

The ℓ1 oblique procrustes problem

In this paper, we reconsider the well-known oblique Procrustes problem where the usual least-squares objective function is replaced by a more robust discrepancy measure, based on the ₁ norm or smooth approximations of it.We propose two approaches to the solution of this problem. One approach is based on convex analysis and uses the structure of the problem to permit a solution to the ₁ norm problem. An alternative approach is to smooth the problem by working with smooth approximations to the ₁ norm, and this leads to a solution process based on the solution of ordinary differential equations on manifolds. The general weighted Procrustes problem (both orthogonal and oblique) can also be solved by the latter approach. Numerical examples to illustrate the algorithms which have been developed are reported and analyzed.     

论含约束项的降维卡诺图

对含约束项的逻辑函数的一般表达式和卡诺图表示法进行了分析和比较，导出含约束项的降维卡诺图。通过实例分析，讨论了其填写与读出的方法，并归纳出含约束项的降维卡诺图填写与读出规则，该规则可简化实用电路。     

Nonparametric multiple expectile regression via ER-Boost

Expectile regression [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847] is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. Expectile regression at 50% level is the classical conditional mean regression. In many real applications having multiple expectiles at different levels provides a more complete picture of the conditional distribution of the response variable. Multiple linear expectile regression model has been well studied [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847; Efron B. Regression percentiles using asymmetric squared error loss, Stat Sin. 1991;1(93):125.], but it can be too restrictive for many real applications. In this paper, we derive a regression tree-based gradient boosting estimator for nonparametric multiple expectile regression. The new estimator, referred to as ER-Boost, is implemented in an R package erboost publicly available at http://cran.r-project.org/web/packages/erboost/index.html. We use two homoscedastic/heteroscedastic random-function-generator models in simulation to show the high predictive accuracy of ER-Boost. As an application, we apply ER-Boost to analyse North Carolina County crime data. From the nonparametric expectile regression analysis of this dataset, we draw several interesting conclusions that are consistent with the previous study using the economic model of crime. This real data example also provides a good demonstration of some nice features of ER-Boost, such as its ability to handle different types of covariates and its model interpretation tools.     

Lift Every Voice: Voices of African-American Lesbian Elders

Old lesbians of African descent have experienced racism, heterosexism, homophobia, and ageism. This article explores the topics of aging, ageism, heterosexism, and minority stress among older African-American lesbians. The narratives and subsequent analysis offer significant contributions to the dialogue regarding Black aging lesbians in the aging and lesbian, gay, bisexual, and transgender (LGBT) communities generally and in the African-American and African-American lesbian communities specifically.     

L1 for the simple linear regression model

By modifying the direct method to solve the overdetermined linear system we are able to present an algorithm for L₁ estimation which appears to be superior computationally to any other known algorithm for the simple linear regression problem.