教师话语的多模态隐喻架构分析--以“外教社杯”全国大学英语教学大赛为例

以多模态隐喻和架构理论为依据,选取“外教社杯”全国大学英语教学大赛中同一主题6个教学视频为语料,归纳贯穿教师话语的3个多模态隐喻,对比分析隐喻背后的架构。研究发现,教师话语中的多模态隐喻和架构能反映教师的教育哲学观;对同一主题的教学,教师因教学理念和教学目标不同,选择了不同的多模态隐喻和架构,这些架构决定学生对教学内容接受度的高低;即使采用相同的架构和隐喻,因选择不同的模态,突显的方面亦不同。     

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.     

Penalized contrast estimation in functional linear models with circular data

Our aim is to estimate the unknown slope function in the functional linear model when the response Y is real and the random function X is a second-order stationary and periodic process. We obtain our estimator by minimizing a standard (and very simple) mean-square contrast on linear finite dimensional spaces spanned by trigonometric bases. Our approach provides a penalization procedure which allows to automatically select the adequate dimension, in a non-asymptotic point of view. In fact, we can show that our penalized estimator reaches the optimal (minimax) rate of convergence in the sense of the prediction error. We complete the theoretical results by a simulation study and a real example that illustrates how the procedure works in practice.     

震后应急物资多方式供应的模糊动态LRP

研究震后应急物资多方式供应中的多层次设施定位-路线规划问题(LRP),综合考虑应急物流网络中的多周期应急物资模糊需求、时间窗限制、部分路网损毁与动态恢复、车辆随机行驶时间、大需求点采用需求分割策略同时进行运输与配送等特性,以应急物资总供应时间最短为目标,构建了一个震后应急物资多方式供应的多周期模糊LRP优化模型,并根据模型特点设计了一种贪婪算法结合蚁群算法的混合启发式算法予以求解.最后,通过算例验证了本文模型和算法的可行性与有效性.     

《警察与赞美诗》两个中译本的历时语料对比分析

经典的作品一经译介,往往会有许多重译本.受译作产生年代、所处社会环境及译入语语言发展变化等因素的影响,同一著作的不同译本往往会有所不同.本文以奈达的功能对等理论为基础,对《警察与赞美诗》两个中译本——李文俊和潘明元译本进行比较研究,兼探讨功能对等理论在翻译实践中的应用和意义.本文尝试以这一理论为基础,通过对翻译文本和例句的分析,重点探讨两译本中选词、句式以及风格等方面的差异及在翻译实践中的一些规律,力图从不同的角度丰富翻译理论及实践.     

从文体学视角对《葛底斯堡演说》两译本的分析

林肯总统的传世精品——《葛底斯堡演说》,是演讲中将文体学运用到极致的典型代表,也是这篇文章能得以传承的重要原因之一.演讲词措辞精练,句式有层次感,语言真挚,感人肺腑.该文有很多版本的译本,文章将从文体学的视角出发去分析其中广受欢迎的两个译本,张培基译本和许渊冲的译作.语音、词汇、修辞和句法这些表层的形式能服务于特定的效果,因此这些层面的文体学能适用于评判译本的水准.这也为赏析译本提供了新的视角,恰当领悟和使用文体学特点是可以为好的译本服务的.     

论给付行政裁量的规制完善

讨论法治思维、法治政府评价的问题,就离不开对行政裁量的考察。考察行政裁量的本质论,就不能绕开对要件裁量说和效果裁量说这种两分法的再认识。探讨自由(便宜)裁量和羁束(法规)裁量的区别问题,不仅有助于把握行政裁量司法统制的界限和基准,而且有助于促进法规范的制定和解释之完善。结合给付行政的增大和裁量权的扩展这种世界性发展趋势,在给付行政领域建构符合该领域特点和规律性的行政责任论,裁量规制和裁量机制本身的完善必不可少。在具有较强裁量性的给付行政领域,宜进行功能性、程序法性的合目的性裁量,而运用和"羁束裁量行为"相对应意义以外的"自由裁量行为"观念,则是不适宜的。在给付行政领域,需要根据每个受给者的具体情形展开具有针对性的、各种各样的灵活应对,这种特点决定了该领域的裁量需要更广阔的空间,同时也需要完善相应的程序和准则,确立基层工作人员状况判断的优位等独具特色的权力运用规则,重视和强调规制手段的活用。     

功能属性和品牌个性对购买决策的影响——以家用轿车行业为例

以两个中档轿车品牌——帕萨特和马自达6为例,收集7个城市消费者的1 440份调查问卷,采用结构方程模型实证研究功能属性、品牌个性和品牌资产对消费者购买汽车决策的影响。研究发现:（1）功能属性越强,消费者感知的品牌资产越高;（2）功能属性和品牌个性对购买决策的作用,完全被品牌资产中介;（3）消费者感知的品牌资产越高,购买意愿就越强;（4）品牌个性对于品牌资产的影响因品牌不同而存在差异。