论转型期的社会失范与社会控制

自改革开放以来，我国进入了全新的快速转型期。社会转型是社会结构的整体性变迁，是社会由传统型向现代型的转变过程：而社会失范是转型社会的必然现象。正确认识这一点，对于我们把握社会转型和社会控制具有重要的方法论意义。     

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。     

Method in the Madness: The Strategic Uses of Absurdity and the Unexpected in Psychotherapy

This article explores some of the uses of absurdity and the unexpected in psychotherapy. Case examples are used to ground the discussion of some key ideas that inform an understanding of how absurdity and the unexpected might serve as catalysts or primers for change in the psychotherapeutic context. Erickson's confusion technique and some ideas derived from Bateson's understanding of cybernetics also serve an explanatory purpose.     

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。     

商标权的效力范围

商标权的效力范围在商标权域内,被分为专有使用权和禁用权,且有着一定的时区跨度;在商标权域外,也具有一定的排他效力,其主要表现为商标权的禁用权扩张,并延伸至商号、域名、商品名称或者商品装潢等领域。混淆理论是划定商标禁用权范围的理论基础。     

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.