Recombinant workfare, across the Americas: Transnationalizing “fast” social policy

The paper presents a genealogy of the Bloomberg administration’s Opportunity NYC program, launched in 2007 as part of New York City’s explicitly experimental anti-poverty strategy. Opportunity NYC was modeled on “conditional cash transfer” programs, currently operating in more than thirty countries across the Global South, drawing direct inspiration from Mexico’s widely touted Oportunidades program. This striking case of South-North policy emulation calls attention to some distinctive features of what is characterized here as a transnationalizing “fast-policy” regime, based on technocratic forms of program evaluation and development, dense expert networks, and orchestrated communities of practice, within which a range of policy intermediaries—particularly those connected with multilateral agencies—are assuming significant new roles. What appears to be a pragmatic form of policy learning in fact operates in the context of narrow ideological parameters, within which there is a concerted technocratic “push” toward favored solutions.     

北京“7.21”暴雨的不稳定性及其触发机制分析

本文利用WRF模拟的高分辨率资料对2012年7月21日北京特大暴雨过程的对流不稳定和条件对称不稳定性及其触发和维持机制进行了诊断分析。分析结果表明:(1)在临近暴雨发生时刻及暴雨初期, 大气低层主要以对流不稳定为主, 随后对流触发, 不稳定性减弱, 而低空急流和湿斜压性的增强, 使得条件性对称不稳定加强, 维持和加强了暴雨的不稳定性。(2)分析表明, 在暴雨过程中主要由于较强的水平风的垂直切变造成湿位涡的斜压分量异常, 从而导致条件性对称不稳定的产生。(3)本文分别对暴雨发生过程中的对流不稳定与条件对称不稳定的触发机制进行了分析, 主要结论如下:暴雨初期对流性降水阶段, 切变线上有利的垂直上升环境与地形的强迫抬升相互配合, 触发了对流性降水。另外, 北京上空的干冷空气入侵, 也增强了大气的对流不稳定性, 更易触发对流;对称不稳定导致的降水阶段, 主要是由于北京上空冷暖空气的长期对峙, 冷空气逐渐深入到暖湿空气下方, 使得暖湿气团沿冷气团爬升, 从而触发对称不稳定, 造成持续性降水。此次暴雨过程中0900～1300 UTC时刻暴雨增幅的重要原因是0900 UTC北京风向突变, 转为偏东风, 且风速骤增, 北京西北侧的喇叭口状的地形的强迫抬升作用, 与上空750 hPa移来的切变线上的垂直运动相互叠加, 形成中尺度涡旋, 产生了强烈的上升运动, 触发不稳定, 产生大暴雨。     

Conditional components for simulation of vector random fields

Conditional component random fields (CC) based on Cholesky decomposition of the multivariate spectra are introduced in this study to develop a new method for conditional simulation of vector attributes in environmental and geological phenomena. The CC are independent random fields with covariance models obtained from projections and conditioning in the frequency domain. The approach is to simulate one attribute in the physical space and use the results to estimate the other attributes in the frequency domain. Then, a CC for the next attribute is simulated and projected on the other attributes. In general, any attribute is built as the sum of inverse Fourier transform of the orthogonal projection of previous simulated CC plus a last CC simulated in the physical space. This simulation approach continues in this fashion for several attributes and the order of them may be changed for different realizations. This method allows for data conditioning and simulation. A simplified version for intrinsically correlated random fields allows for an approach that avoids the frequency domain.     

Multigaussian kriging for point-support estimation: incorporating constraints on the sum of the kriging weights

In the geostatistical analysis of regionalized data, the practitioner may not be interested in mapping the unsampled values of the variable that has been monitored, but in assessing the risk that these values exceed or fall short of a regulatory threshold. This kind of concern is part of the more general problem of estimating a transfer function of the variable under study. In this paper, we focus on the multigaussian model, for which the regionalized variable can be represented (up to a nonlinear transformation) by a Gaussian random field. Two cases are analyzed, depending on whether the mean of this Gaussian field is considered known or not, which lead to the simple and ordinary multigaussian kriging estimators respectively. Although both of these estimators are theoretically unbiased, the latter may be preferred to the former for practical applications since it is robust to a misspecification of the mean value over the domain of interest and also to local fluctuations around this mean value. An advantage of multigaussian kriging over other nonlinear geostatistical methods such as indicator and disjunctive kriging is that it makes use of the multivariate distribution of the available data and does not produce order relation violations. The use of expansions into Hermite polynomials provides three additional results: first, an expression of the multigaussian kriging estimators in terms of series that can be calculated without numerical integration; second, an expression of the associated estimation variances; third, the derivation of a disjunctive-type estimator that minimizes the variance of the error when the mean is unknown.     

Conditional Wavelet Technique Applied to Aircraft Data Measured in the Thermal Internal Boundary Layer During Sea-Breeze Events

We describe a wavelet-based technique to determine the spectral turbulentcontribution to the vertical flux of sensible heat in a position-wavelength representation. This technique combines awavelet transform (Morlet wavelet) with conditional sampling. We apply this methodto aircraft datacollected during a sea-breeze circulation (BEMA97 experiment) with heterogeneousturbulence conditions horizontally and vertically as well. The turbulent fluxes are analysed with the conditional wavelet techniqueas a function of the wavelength and the horizontal distance.The turbulent processes within the thermal internal boundary layer associatedwith the sea breeze are clearly identified. The results exhibit the wavelength bands through which the upward flux (originating fromthe surface) and the downward flux (originating from the top of theboundary layer) are important.     

基于Open GL条件随机模拟三维模型可视化

利用两阶段建模思路建立条件随机模拟模型。第一阶段在地质体表面利用Denaunay三角网剖分算法实现三角网网格化，第二阶段用Denaunay四面体剖分算实现地质体的四面体网格化，并对四面体单元进行三角面插值，进而得到三角网，最后用OpenGL实现地质体三维模型的可视化。     

Substitution Random Fields with Gaussian and Gamma Distributions: Theory and Application to a Pollution Data Set

This paper presents random field models with Gaussian or gamma univariate distributions and isofactorial bivariate distributions, constructed by composing two independent random fields: a directing function with stationary Gaussian increments and a stationary coding process with bivariate Gaussian or gamma distributions. Two variations are proposed, by considering a multivariate directing function and a coding process with a separable covariance, or by including drift components in the directing function. Iterative algorithms based on the Gibbs sampler allow one to condition the realizations of the substitution random fields to a set of data, while the inference of the model parameters relies on simple tools such as indicator variograms and variograms of different orders. A case study in polluted soil management is presented, for which a gamma model is used to quantify the risk that pollutant concentrations over remediation units exceed a given toxicity level. Unlike the multivariate Gaussian model, the proposed gamma model accounts for an asymmetry in the spatial correlation of the indicator functions around the median and for a spatial clustering of high pollutant concentrations.     

Worth of secondary data compared to piezometric data for the probabilistic assessment of radionuclide migration

A common approach for the performance assessment of radionuclide migration from a nuclear waste repository is by means of Monte-Carlo techniques. Multiple realizations of the parameters controlling radionuclide transport are generated and each one of these realizations is used in a numerical model to provide a transport prediction. The statistical analysis of all transport predictions is then used in performance assessment. In order to reduce the uncertainty on the predictions is necessary to incorporate as much information as possible in the generation of the parameter fields. In this regard, this paper focuses in the impact that conditioning the transmissivity fields to geophysical data and/or piezometric head data has on convective transport predictions in a two-dimensional heterogeneous formation. The Walker Lake data based is used to produce a heterogeneous log-transmissivity field with distinct non-Gaussian characteristics and a secondary variable that represents some geophysical attribute. In addition, the piezometric head field resulting from the steady-state solution of the groundwater flow equation is computed. These three reference fields are sampled to mimic a sampling campaign. Then, a series of Monte-Carlo exercises using different combinations of sampled data shows the relative worth of secondary data with respect to piezometric head data for transport predictions. The analysis shows that secondary data allows to reproduce the main spatial patterns of the reference transmissivity field and improves the mass transport predictions with respect to the case in which only transmissivity data is used. However, a few piezometric head measurements could be equally effective for the characterization of transport predictions.     

Uncertainty reduction and sampling efficiency in slope designs using 3D conditional random fields

A method of combining 3D Kriging for geotechnical sampling schemes with an existing random field generator is presented and validated. Conditional random fields of soil heterogeneity are then linked with finite elements, within a Monte Carlo framework, to investigate optimum sampling locations and the cost-effective design of a slope. The results clearly demonstrate the potential of 3D conditional simulation in directing exploration programmes and designing cost-saving structures; that is, by reducing uncertainty and improving the confidence in a project’s success. Moreover, for the problems analysed, an optimal sampling distance of half the horizontal scale of fluctuation was identified.     

Markov Chain Random Fields for Estimation of?Categorical Variables

Multi-dimensional Markov chain conditional simulation (or interpolation) models have potential for predicting and simulating categorical variables more accurately from sample data because they can incorporate interclass relationships. This paper introduces a Markov chain random field (MCRF) theory for building one to multi-dimensional Markov chain models for conditional simulation (or interpolation). A MCRF is defined as a single spatial Markov chain that moves (or jumps) in a space, with its conditional probability distribution at each location entirely depending on its nearest known neighbors in different directions. A general solution for conditional probability distribution of a random variable in a MCRF is derived explicitly based on the Bayes’ theorem and conditional independence assumption. One to multi-dimensional Markov chain models for prediction and conditional simulation of categorical variables can be drawn from the general solution and MCRF-based multi-dimensional Markov chain models are nonlinear.