论当代恐怖主义的政治特点

政治目的性是恐怖主义的本质特征之一。但由于政治本身的复杂性,所以恐怖主义的政治特点也较难把握。恐怖主义的政治目的在性质上表现为某些政治利益的代表,但如果进行具体的分析又会发现其政治目的性质往往是比较模糊,而且当代恐怖主义政治目的的内涵和外延都在扩展,特别是在向社会领域扩展,所以在内容上就表现出较强的复合性。而在暴力手段和政治目的关系上,暴力破坏正在逐步成为政治目的的内容之一。     

论人口的组织结构

本文认为组织是现代社会的一个重要人口特征,可以分别从组织类型、组织规模、组织密度等方面对人口组织结构进行探讨;研究人口组织结构有助于增强人口学的学术和应用价值.对基本单位普查和经济普查资料的初步分析表明,广州就业人口的组织结构正处于一个由传统的行政为主型向企业为主型的转变过程中.     

谈"中国情境"下的波谱艺术

艺术总是摆脱不了时代的制约,波谱艺术也是如此。这个在西方市场经济和消费文化的语境下产生的艺术形式,当它登陆中国大陆时,由于其生存的土壤发生了变化,因此,也在中国的土地上勾勒出了一条有着"中国特色"的发展轨迹。     

中国道路文化的特征及其研究意义

鉴于自古以来中国道路建设都取得了巨大成就,而中国道路文化研究相对滞后,分析了道路文化的含义、特征、研究范围和意义。分析认为,道路是人工建设的工程,它与政治、经济、生态、环境等联系密切,具有丰富的历史文化内涵;道路的与时俱进的时代特征、辐射连通的开放特征、利在社会的公益特征,呈现出文化上的广泛联系和多元价值取向;它是文明的纽带、历史的见证和文化的载体;道路文化研究在中国道路建设和交通事业发展中具有基础性和战略性作用。     

荒诞派戏剧的深层特质探析

荒诞派戏剧是后现代主义文学的重要流派,后现代主义文学是一种无视任何既定规范的极度自由的“破坏性”文学,是某种意义上的“反文学”。     

Adaptive Estimation of the Integral of Squared Regression Derivatives

A problem of estimating the integral of a squared regression function and of its squared derivatives has been addressed in a number of papers. For the case of a heteroscedastic model where smoothness of the underlying regression function, the design density, and the variance of errors are known, the asymptotically sharp minimax lower bound and a sharp estimator were found in Pastuchova & Khasminski (1989). However, there are apparently no results on the either rate optimal or sharp optimal adaptive, or data-driven, estimation when neither the degree of regression function smoothness nor design density, scale function and distribution of errors are known. After a brief review of main developments in non-parametric estimation of non-linear functionals, we suggest a simple adaptive estimator for the integral of a squared regression function and its derivatives and prove that it is sharp-optimal whenever the estimated derivative is sufficiently smooth.     

Robust location estimation with missing data

In a missing data setting, we have a sample in which a vector of explanatory variables ${\bf x}_i$ is observed for every subject i, while scalar responses $y_i$ are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at random (MAR) data, under a semiparametric regression model. Our approach allows the consistent estimation of any weakly continuous functional of the response's distribution. In particular, strongly consistent estimators of any continuous location functional, such as the median, L‐functionals and M‐functionals, are proposed. A robust fit for the regression model combined with the robust properties of the location functional gives rise to a robust recipe for estimating the location parameter. Robustness is quantified through the breakdown point of the proposed procedure. The asymptotic distribution of the location estimators is also derived. The proofs of the theorems are presented in Supplementary Material available online. The Canadian Journal of Statistics 41: 111–132; 2013 © 2012 Statistical Society of Canada     

Predicting infectious disease outbreak risk via migratory waterfowl vectors

The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.     

Functional principal component analysis for the explorative analysis of multisite–multivariate air pollution time series with long gaps

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.     

Segmentation,alignment and statistical analysis of biosignals with application to disease classification

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.