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。     

中国信访工作面临问题的成因探析

我国信访制度的运行效率低下，是由一系列的重大制度性缺陷造成的。具体包括制度运行机制缺陷、制度运行环境缺陷以及制度运行法律缺陷等。     

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。     

论语言普遍性的多维度存在

目前语言学界对语言普遍性的研究主要有两条思路，一条是以Chomsky为代表的生成语法理论，主张通过对一种语言作深入的研究，采用推演法找出语言的共性；另一条是以Greenberg为代表的功能类型学理论，主张通过对多种语言进行比较，采用归纳法找出语言的共性。这两种思路的研究都已经取得了丰硕的成果，语言中存在普遍性的观点也得到了越来越多学者的认同。但仍有学者对这一观点持质疑的态度。我们认为，语言普遍性是语言学存在的理论前提，并在语音、句法、语义和语用等多个维度中存在。从句法和语用两个维度，根据语言现实，结合生成语法及普遍语用学理论，进一步论证语言普遍性在语言中的多维度存在。     

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。     

Racial Formation Theory and Systemic Racism in Hip‐Hop Fans’ Perceptions

This work contributes empirical research to racial formation theory (RFT) and systemic racism (SR), demonstrating how these theories complement each other. There are few practical applications of these theories. This research examines RFT and SR from the perspective of hip‐hop fans. I qualitatively examine how 23 nonblack women articulate the relationships of race, class, and gender through discussion of hip‐hop music and videos that accompany it. Findings suggest that hip‐hop is a site of racial formation. Participants spoke from a color‐blind perspective and white racial frame so that they perpetuated ideals of systemic racism theory.     

P2P网络借贷机构的系统重要性研究

互联网金融发展得如火如荼,其蕴藏的风险不容忽视。由于P2P网络借贷是互联网金融的一项重要业态,且其风险溢出效应较大,研究其风险监管有着非常重要的意义。通过借鉴巴塞尔委员会对系统重要性银行识别的指标体系,基于指标法的思想联系我国互联网金融发展现状以及P2P网络借贷机构的特征对指标体系进行了合理改进。对国内比较重要的P2P网络借贷机构进行了系统重要性排名,并且和以前的研究成果进行了比较。对于推动互联网金融分类适度的监管工作有着一定的意义。     

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.