Development of an urban intensity index to facilitate urban ecosystem studies in Trinidad and Tobago

Urban ecosystems, considered centres of economic, social and cultural development, face a multitude of environmental and socio-economic challenges, which impact on quality of life. Effective management of the urbanization process is believed critical to improving quality of life and realizing sustainable development. The ecosystem perspective provides a holistic approach, needed to address the complexly interconnected issues, which arise from urban development. Central to the mapping and characterization of urban ecosystems is the delineation of their boundaries, which are made less transparent by growing urbanization. This exposes the limitations of a dichotomous approach. An urban intensity index is a critical tool which supports urban ecosystem studies by facilitating analysis of effects along the urban–rural gradient. In this study, Urban Intensity is estimated and ranked from most to least intense for communities across Trinidad and Tobago, using multivariate statistical analysis of physical data from the built environment. This statistically validated index, designed for Trinidad and Tobago, should have wider applicability to other disciplines and countries.     

On parallel policies for ranking and selection problems

In this paper we develop and test experimental methodologies for selection of the best alternative among a discrete number of available treatments. We consider a scenario where a researcher sequentially decides which treatments are assigned to experimental units. This problem is particularly challenging if a single measurement of the response to a treatment is time-consuming and there is a limited time for experimentation. This time can be decreased if it is possible to perform measurements in parallel. In this work we propose and discuss asynchronous extensions of two well-known Ranking & Selection policies, namely, Optimal Computing Budget Allocation (OCBA) and Knowledge Gradient (KG) policy. Our extensions (Asynchronous Optimal Computing Budget Allocation (AOCBA) and Asynchronous Knowledge Gradient (AKG), respectively) allow for parallel asynchronous allocation of measurements. Additionally, since the standard KG method is sequential (it can only allocate one experiment at a time) we propose a parallel synchronous extension of KG policy – Synchronous Knowledge Gradient (SKG). Computer simulations of our algorithms indicate that our parallel KG-based policies (AKG, SKG) outperform the standard OCBA method as well as AOCBA, if the number of evaluated alternatives is small or the computing/experimental budget is limited. For experimentations with large budgets and big sets of alternatives, both the OCBA and AOCBA policies are more efficient.     

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.     

近代广东商业人群在广西的分布面及其梯度差考详——粤港商业经济对广西经济辐射研究之二

文章从中国近代史上城镇经济主体——商业经济的角度切入,运用各类历史资料,分区逐县地确证广东商业人群以及所造成的“无东不成市”格局在全广西的分布范围,并揭示其在广西不同地域的程度等差和梯度分布状态。从而,以原始的生活场景原生态地呈现出粤港澳地区对广西经济辐射由东而西逐步推进、由城市而圩市逐层次渗透的基本节奏和客观规律。     

婚姻挤压对中国农村不同群体安全感的影响——基于全国百村调查数据的分析

中国的男性婚姻挤压问题,已成为目前社会转型期的“社会风险”之一,给包括不同婚姻状态下男女两性在内的社会各群体的安全感均带来不利影响,成为影响人口社会可持续发展的隐患。从个人微观层次分析婚姻挤压下农村各群体的安全感现状及婚姻挤压对不同年龄和地区的农村男性安全感的影响发现:农村大龄未婚男性群体的安全感最低,婚姻状况对个人的安全感具有显著影响,婚姻挤压对男性安全感的影响存在显著的时代差异以及地区差异。提高中国农村各群体的安全感需从根本上改善农村各群体的生存状况,统筹解决性别失衡引起的婚姻挤压问题,从国家政策与制度创新的层面关注婚姻挤压及农村各群体的安全感问题。     

间接挤压铸造浇口形状对充型形态的影响

在间接挤压铸造过程中,充型液面的稳定性是铸件质量的保证。以平板铸件为研究对象,利用专业铸造软件 ProCAST进行数值模拟,分析了不同浇口形状与液面稳定性的关系。结果表明：带扩张角的浇道充型效果明显优于直浇 道,且随着扩张角的增大,其充型形态越平稳,液面凸起也越小;双浇道的浇注系统其充型效果不佳,液面波动加剧。     

位移边界对2D-FGM板热应力影响研究

考虑不同的位移边界条件,建立了常物性2D-FGM板研究模型,并采用有限元法,得到其冷却瞬态热应力场分布图形。位移边界条件对常物性2D-FGM平面区域冷却瞬态热应力分布影响颇大,在简支和一端固定情况下,2D-FGM板自由边界上的热应力σ_x=0;两端固定情况下,其左右两边界均形成上中下三个应力聚集,应力分布形状和数值都发生了巨大的变化;将四周均设置呈固定约束后,热应力数值增长幅度较两端固定情况时更为明显。此结果为该材料的设计、制备提供了准确的理论计算依据。     

Adaptive stochastic gradient boosting tree with composite criterion

ABSTRACT

In this paper, we propose an adaptive stochastic gradient boosting tree for classification studies with imbalanced data. The adjustment of cost-sensitivity and the predictive threshold are integrated together with a composite criterion into the original stochastic gradient boosting tree to deal with the issues of the imbalanced data structure. Numerical study shows that the proposed method can significantly enhance the classification accuracy for the minority class with only a small loss in the true negative rate for the majority class. We discuss the relation of the cost-sensitivity to the threshold manipulation using simulations. An illustrative example of the analysis of suboptimal health-state data in traditional Chinese medicine is discussed.     

对共轭梯度法中标量β_k的一种修正

共轭梯度法是优化算法中最常用的方法之一,适于解决大规模问题,因而有着广泛的应用,而标量kβ不同的选取可以构成不同的共轭梯度法。修正了共轭梯度法中的标量kβ,将其推广到一般情况,并在wolfe线搜索下证明了它的全局收敛性。     

Dimension reduction via local rank regression

Outer product of gradients (OPG) achieves dimension reduction via estimating the gradients of the regression function. In this paper, we propose two novel OPG estimators via local rank regression: the rank OPG estimator and the Walsh-average OPG estimator. Both proposals guard against a wide range of error distributions, and are safe alternatives to existing OPG estimators based on local linear regression or local L1 regression. The effectiveness of the new proposals are demonstrated via extensive numerical studies.