“无损失的翻译”与翻译的虚无——再论传统翻译理论的“忠实观”

我们只要问一句：有没有一种不具有翻译性的翻译存在？如果没有,那么“翻译性”或“变异性”就是翻译的本质属性。一切文本一旦遭遇翻译,它就获得了自己的翻译性。如果说原文所要求于翻译的是忠实,那就应该是一种充分忠实于翻译的本质属性,并同时让译者在翻译时履行对自我显现行为的忠实。     

浅论我国会计诚信危机的成因与对策

由于我国市场经济正处于建立和完善之中,与之相适应的信用体系尚未建立,使近年来的会计诚信问题成为被关注的焦点。本文从会计诚信危机的表现、危害入手,分析会计诚信危机形成的原因,并提出相应的对策。     

浅谈翻译目的论

翻译目的论反映了翻译理论的全面转向 ,即由原先占主导地位的语言学以及侧重形式的翻译理论转向更加注重功能和社会文化因素的翻译观。目的论认为 ,译本的预期目的决定翻译的方法和策略。在翻译过程中应遵循三个总体原则 ,即目的原则、连贯原则和精当原则     

On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

Non parametric mixture priors based on an exponential random scheme

We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions, the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion ofFeller-type approximation, with a random scheme based on the natural exponential family, in order to construct a large class of distribution functions. We show how one can assign a probability to such a class and discuss the main properties of the proposed prior, namedFeller prior. Feller priors are related to mixture models with unknown number of components or, more generally, to mixtures with unknown weight distribution. Two illustrations relative to the estimation of a density and of a mixing distribution are carried out with respect to well known data-set in order to evaluate the performance of our procedure. Computations are performed using a modified version of an MCMC algorithm which is briefly described.     

Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects

The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients'' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called ‘HLnet’) in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.     

智能控制系统研究现状与展望

控制理论的发展正面临着新的突破，智能控制将成为控制理论发展中的一个新阶段．本文以近年来智能控制领域的研究成果为基础，结合国际上一些比较值得注意的动向，分析了智能控制系统的结构特点，对其研究现状进行了综述，进一步探讨了相关问题，并提出了智能控制系统今后可能的发展方向．     

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation

Children represent a large underserved population of “therapeutic orphans,” as an estimated 80% of children are treated off‐label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or “borrowing”) of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure‐response information for antiepileptic drugs to pediatrics.     

区域电力市场结构、所有权结构对发电企业效率影响的实证研究

以我国电力产业放松规制的实践为背景,基于产业组织理论构建了区域电力市场结构、所有权结构对发电企业效率影响的综合模型。以1999~2006年电力行业上市公司的统计数据为样本,采用分层线性模型回归方法对市场结构和所有权结构对发电企业效率的影响效果和影响路径进行了研究。实证结果表明:电力行业市场集中度下降有利于我国电力企业效率提升;电力行业国有所有权比重下降有利于企业效率的提升;电力行业企业数量和国有所有权比重对企业效率存在负向交互影响作用,但不显著。     

Citizen Support for Hospital Privatization: A Hierarchical Cross-National Analysis

The movement for a new public management (NPM) holds that citizens will put aside partisan and ideological differences to support more efficient service delivery. Focusing on hospital privatization, a multi-national sample is utilized to investigate this assertion. Generalized hierarchical linear modeling (GHLM) is employed as the method of statistical analysis. The findings with regard to privatizing hospitals reveal that citizens develop their policy position by reference to a combination of symbolic political variables, service demand, self-interest, information levels, and political efficacy. The results cast doubt on the assertion that partisan differences will be set aside as citizens consider privatization reforms.