论文学符号的本质与功能性结构关系

符号的本质是在特定结构关系中承担着某种功能并产生着一定意义的媒介物。其最核心的概念是“系统”、“功能”和“意义”。文学符号与语言符号一样 ,也是“能指”和“所指”的统一。在文学符号的内部 ,又包含着各种代码 ,其中最重要的文学代码有“性格代码”、“背景代码”和“情感代码”三种。这就决定着文学符号又是一个具有着强烈的自主功能性、清晰的层次性和内在的编码性的有机运作过程。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

关于文化差异与翻译问题的思考

语言和文化的密不可分性 ,决定了文化因素在翻译中的重要地位。目前 ,对文化差异的翻译方法存在同化与异化之争。本文从“功能等值”的角度 ,探讨了这个问题。     

词的非常规功能义释义模式探析

词的功能义,是指因词性(或语法功能)不同而导致词的意义变化而形成的词义.研究词的功能义,重点是认识词在非常规功能下意义的变化.探讨词的非常规功能义的释义模式要注意区分逻辑语义模式与语言表达变式,以期在语文辞书中准确地、规范地说解词的非常规功能义.     

高阶混合型周期系统的周期解

应用Ｆｏｕｒｉｅｒ分析的方法，结合一些不等式技巧得到了ｎ阶常系数带有滞后与超前偏差变元的混合型线性周期系统存在周期解的充要条件．     

市场的制度性分割:经济适用房制度的博弈分析

为低收入者提供基本住房保障是政府的基本职责,经济适用房是实现该职责的一个制度体现.但是中国的经济适用房制度并没有实现其设计目标.那么是什么原因导致了这个结果,如何实现为低收入者提供基本住房保障的目标?从政府、开发商、购买者三方博弈的角度,可以发现经济适用房制度失败的关键在于它造成了市场的制度性分割,由此产生的市场套利行为瓦解了经济适用房的制度基础.改革方向在于撤销经济适用房制度,实施住房补贴制度,重新建立统一的住房市场.     

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).