是达到了最高学术水平,还是罕见的假冒伪劣--评荆贵生主编的《古代汉语》

河南师范大学教授荆贵生宣称由他主编的<古代汉语>达到了最高学术水平,本文则以大量无可辩驳的事实揭露该书是一部罕见的假冒伪劣之作.此外,作者谴责了庸俗的书评,并对如何制止践踏学术道德、违背学术规范的不良现象,提出了自己的看法.     

Left divergence,right convergence: anarchists,Marxists, and nationalist polarization in the Ukrainian conflict, 2013–2014

ABSTRACT

The article traces nationalist polarization and divergence within the Ukrainian new left in response to the Maidan and Anti-Maidan protests in 2013–2014, and the military conflict in Eastern Ukraine. The ideological left-wing groups in the protests were too weak to push forward any independent progressive agenda. Instead of moving the respective campaigns to the left, they were increasingly converging with the right themselves and degraded into marginal supporters of either pro-Ukrainian or pro-Russian camps in the conflict. The liberal and libertarian left supported the Maidan movement on the basis of abstract self-organization, liberal values and anti-authoritarianism. In contrast, the Marxist-Leninists attempted to seize political opportunities from supporting more plebeian and decentralized Anti-Maidan protests and reacting to the far-right threat after the Maidan victory. They deluded themselves that Russian nationalists were not as reactionary as their Ukrainian counterparts and that the world-system crisis allowed them to exploit Russian anti-American politics for progressive purposes.     

Quantile regression methods for left-truncated and right-censored data

Left-truncated and right-censored (LTRC) data are encountered frequently due to a prevalent cohort sampling in follow-up studies. Because of the skewness of the distribution of survival time, quantile regression is a useful alternative to the Cox's proportional hazards model and the accelerated failure time model for survival analysis. In this paper, we apply the quantile regression model to LTRC data and develops an unbiased estimating equation for regression coefficients. The proposed estimation methods use the inverse probabilities of truncation and censoring weighting technique. The resulting estimator is uniformly consistent and asymptotically normal. The finite-sample performance of the proposed estimation methods is also evaluated using extensive simulation studies. Finally, analysis of real data is presented to illustrate our proposed estimation methods.     

（λ,μ）-模糊子半环与（λ,μ）-模糊理想

引入了（λ,μ）-模糊子半环及半环的（λ,μ）-模糊左理想、（λ,μ）-模糊右理想、（λ,μ）-模糊双边理想、（λ,μ）模糊双理想的概念，讨论了它们的一些较好的性质，并给出各种（λ,μ）-模糊理想的等价刻划．     

Reconstruction of the past failure times for the proportional reversed hazard rate model

The problem of reconstruction of the past failure times in the left-censored set-up is considered. Various reconstructors of time to failure of units censored in a left-censored sample from the proportional reversed hazard rate models are demonstrated. The maximum-likelihood, best unbiased and conditional median reconstructors are obtained. We also present two methods, non-Bayesian and Bayesian, for obtaining reconstruction intervals for the past failure times. Numerical example and a Monte Carlo simulation study are given to illustrate all the reconstruction methods discussed in this paper.     

论邓小平反对“左”、右错误倾向的思想

“左”、右倾的干扰是危害改革开放和现代化建设事业的重要因素。正确认识并克服“左”、右两种错误倾向,是顺利推进现代化建设的重要保证,关系着建设中国特色社会主义目标的顺利实现。邓小平在领导全党开辟中国特色社会主义道路的过程中,分析了改革开放和现代化建设中的“左”、右倾错误的主要表现、危害和根源,提出了克服这些错误的基本思路及主要措施,强调全党要警惕右,但主要是防止“左”,排除各种干扰,坚持党的基本路线不动摇。     

一种脱机手写体汉字识别的容错编码方法研究

手写体汉字识别是字符识别领域中的难点。为了使机器识别汉字适应于手写体汉字的变形等因素,基于人类认识汉字的容错机理,提出了一种用于机器识字的汉字容错编码方法,以提高手写体汉字识别率。该编码方法首先对横竖撇捺笔划形态给出了模糊化表示;然后定义了仿人拆字的字元集,并给出了易混淆笔划字元的多归类容错编码;接着给出了笔划字元的顺序判断规则和归结了36类简单常用字的部首子结构,并给出冗余的容错编码;进而建立了仿人构字的汉字编码规则和具有容错性的多模板字典,并对《新华字典》中收录的10000余个单字汉字进行了标准编码,重码率为0.48%;最后对HCCORG和NKIM手写体汉字库中的100个手写体汉字进行了仿真识别,识别正确率为96%。试验结果表明,这种编码方法可生成多模板字典,不仅对手写体汉字变形具有较好的容错性,且重码率和误识率较低。     

“周人尚左”与“右手的优越”——中国古代分类体系的人类学解释

作为原始二元分类的重要范畴,左、右在传统文化的分类体系中扮演着重要角色。周代是传统左右观形成的关键时期,＂尚左＂的周人却实行着＂右手的优越＂,同一时期集体观念与行为方式的相悖及不同历史时期左右观的无常变化在揭示出矛盾的同时,也清楚地表明了传统分类体系具有对立功能的转换、对立之间不存在必然的一致性等特征,以及设立秩序、维系平衡的功能。     

Conditional Quantile Estimation for Truncated and Associated Data

Abstract

In survival or reliability studies, it is common to have data which are not only incomplete but weakly dependent too. Random truncation and censoring are two common forms of such data when they are neither independent nor strongly mixing but rather associated. The focus of this paper is on estimating conditional distribution and conditional quantile functions for randomly left truncated data satisfying association condition. We aim at deriving strong uniform consistency rates and asymptotic normality for the estimators and thereby, extend to association case some results stated under iid and α-mixing hypotheses. The performance of the quantile function estimator is evaluated on simulated data sets.     

Clustered Survival Data with Left‐truncation

Left‐truncation occurs frequently in survival studies, and it is well known how to deal with this for univariate survival times. However, there are few results on how to estimate dependence parameters and regression effects in semiparametric models for clustered survival data with delayed entry. Surprisingly, existing methods only deal with special cases. In this paper, we clarify different kinds of left‐truncation and suggest estimators for semiparametric survival models under specific truncation schemes. The large‐sample properties of the estimators are established. Small‐sample properties are investigated via simulation studies, and the suggested estimators are used in a study of prostate cancer based on the Finnish twin cohort where a twin pair is included only if both twins were alive in 1974.