框架理论与等值翻译——认知语言学视角下的翻译

框架理论是认知语言学理论的一个重要组成部分。框架理论认为：对任何一个概念的理解必然会激发一整套相关经验概念，包括它的隐含意义和社会文化意义。在翻译时，如何处理中西方读者因生活经验和文化背景差异而造成的框架差异或框架冲突至关重要。从等值翻译入手，提出译文与原文存在三种对应方法。     

从功能对等的角度析英语电影片名的汉译

将英语电影片名译成汉语,以功能对等为目标,按照片名的信息价值、文化价值、审美价值和商业价值的要求进行翻译。翻译过程中,译者应对片名进行分析、转换和重组,根据不同情况采用不同的翻译方法,使汉语译名形式与英文电影内容达到辩证统一。既注意到标题的字面意义,又注意发掘其深层意义。从功能对等理论的角度对不同的翻译方法进行分析。     

A Spatio-Temporal Model for Functional Magnetic Resonance Imaging Data – with a View to Resting State Networks

Abstract.  Functional magnetic resonance imaging (fMRI) is a technique for studying the active human brain. During the fMRI experiment, a sequence of MR images is obtained, where the brain is represented as a set of voxels. The data obtained are a realization of a complex spatio-temporal process with many sources of variation, both biological and technical. We present a spatio-temporal point process model approach for fMRI data where the temporal and spatial activation are modelled simultaneously. It is possible to analyse other characteristics of the data than just the locations of active brain regions, such as the interaction between the active regions. We discuss both classical statistical inference and Bayesian inference in the model. We analyse simulated data without repeated stimuli both for location of the activated regions and for interactions between the activated regions. An example of analysis of fMRI data, using this approach, is presented.     

A Non-parametric Test for Generalized First-order Autoregressive Models

We derive a non-parametric test for testing the presence of V(X_i,ε_i) in the non-parametric first-order autoregressive model X_i+1=T(X_i)+V(X_i,ε_i)+U(X_i)ε_i+1, where the function T(x) is assumed known. The test is constructed as a functional of a basic process for which we establish a weak invariance principle, under the null hypothesis and under stationarity and mixing assumptions. Bounds for the local and non-local powers are provided under a condition which ensures that the power tends to one as the sample size tends to infinity.The testing procedure can be applied, e.g. to bilinear models, ARCH models, EXPAR models and to some other uncommon models. Our results confirm the robustness of the test constructed in Ngatchou Wandji (1995) and in Diebolt & Ngatchou Wandji (1995).     

论法院依职权调查取证

在审理对象的形成和证据的提出层面实行辩论主义是我国民事司法改革的方向，但是在强调当事人自我责任的同时，也不应忽略法院的作用。如果过分限制法院依职权调查取证的权力，既不利于法院在诉讼中发现真实，也不利于充分保障当事人的程序权利与实体权利，可能造成诉讼能力薄弱者无法实现实体公正。从有利于发现真实、保护当事人合法权益、实现实体公正的角度考虑，有必要适当拓宽我国法院依职权调查取证的范围。     

从功能语言学的视角论《尤利西斯》中语码转换的翻译

爱尔兰著名作家乔伊斯的《尤利西斯》被公认为20世纪最晦涩难懂的英文小说。小说中大量的语码转换是《尤利西斯》的一大特色,也是造成《尤利西斯》晦涩难懂的主要原因之一。本文从韩礼德系统功能语言学的语言功能观入手,不仅探讨了《尤利西斯》中存在的大量的语码转换现象及其诗学价值,而且分析了两个汉译本对原作中的语码转换所采取的不同翻译策略及其功能的再现。     

城市地域结构理论与实践——以广州为例

科学、和谐、宜居城市的形成离不开科学的城市规划理论。在本文中作者首先介绍了几种有代表性的城市地域结构理论,包括同心圆理论、扇形理论、多核心学说等。然后对它们的一一进行了评述,指出了它们的科学性与局限性。进而结合广州城市规划的实践以及广州城市发展过程中所形成的具有一定规模的功能区分析,探讨了这几种主要理论在实践中的指导意义与不足之处,以期能对其他城市的发展提供借鉴,建设更加美满的人类城市生活空间。     

Generalized Sobol sensitivity indices for dependent variables: numerical methods

The hierarchically orthogonal functional decomposition of any measurable function η of a random vector X=(X₁,?…?, X_p) consists in decomposing η(X) into a sum of increasing dimension functions depending only on a subvector of X. Even when X₁,?…?, X_p are assumed to be dependent, this decomposition is unique if the components are hierarchically orthogonal. That is, two of the components are orthogonal whenever all the variables involved in one of the summands are a subset of the variables involved in the other. Setting Y=η(X), this decomposition leads to the definition of generalized sensitivity indices able to quantify the uncertainty of Y due to each dependent input in X [Chastaing G, Gamboa F, Prieur C. Generalized Hoeffding–Sobol decomposition for dependent variables – application to sensitivity analysis. Electron J Statist. 2012;6:2420–2448]. In this paper, a numerical method is developed to identify the component functions of the decomposition using the hierarchical orthogonality property. Furthermore, the asymptotic properties of the components estimation is studied, as well as the numerical estimation of the generalized sensitivity indices of a toy model. Lastly, the method is applied to a model arising from a real-world problem.     

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.     

Variable selection in partial linear regression with functional covariate

The problem of variable selection is considered in high-dimensional partial linear regression under some model allowing for possibly functional variable. The procedure studied is that of nonconcave-penalized least squares. It is shown the existence of a √n/s_n-consistent estimator for the vector of p_n linear parameters in the model, even when p_n tends to ∞ as the sample size n increases (s_n denotes the number of influential variables). An oracle property is also obtained for the variable selection method, and the nonparametric rate of convergence is stated for the estimator of the nonlinear functional component of the model. Finally, a simulation study illustrates the finite sample size performance of our procedure.