1,235,534 research outputs found

    Regional environmental efficiency and economic growth: NUTS2 evidence from Germany, France and the UK

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    This paper by applying nonparametric techniques measures spatial environmental heterogeneities of 98 regions from Germany, France and the UK. Specifically environmental performance indexes are constructed for the 98 regions (NUTS 2 level) identifying their ability to produce higher growth rates and reduce pollution (in the form of municipal waste) generated from regional economic activity. By applying conditional stochastic kernels and local constant estimators it investigates the regional economic activity – environmental quality relationship. The results indicate several spatial environmental heterogeneities among the examined regions. It appears that regions with higher GDP per capita levels tend to have higher environmental performance.Regional environmental efficiency; directional distance function; conditional stochastic kernel; nonparametric regression

    Spanish unemployment: normative versus analytical regionalisation procedures

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    In applied regional analysis, statistical information is usually published at different territorial levels with the aim of providing information of interest for different potential users. When using this information, there are two different choices: first, to use normative regions (towns, provinces, etc.), or, second, to design analytical regions directly related with the analysed phenomena. In this paper, provincial time series of unemployment rates in Spain are used in order to compare the results obtained by applying two analytical regionalisation models (a two stages procedure based on cluster analysis and a procedure based on mathematical programming) with the normative regions available at two different scales: NUTS II and NUTS I. The results have shown that more homogeneous regions were designed when applying both analytical regionalisation tools. Two other obtained interesting results are related with the fact that analytical regions were also more stable along time and with the effects of scale in the regionalisation process. Keywords: Unemployment, normative region, analytical region, regionalisation. JEL Codes: E24, R23, C61.

    Spanish unemployment: Normative versus analytical regionalisation procedures

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    In applied regional analysis, statistical information is usually published at different territorial levels with the aim of providing information of interest for different potential users. When using this information, there are two different choices: first, to use normative regions (towns, provinces, etc.), or, second, to design analytical regions directly related with the analysed phenomena. In this paper, provincial time series of unemployment rates in Spain are used in order to compare the results obtained by applying two analytical regionalisation models (a two stages procedure based on cluster analysis and a procedure based on mathematical programming) with the normative regions available at two different scales: NUTS II and NUTS I. The results have shown that more homogeneous regions were designed when applying both analytical regionalisation tools. Two other obtained interesting results are related with the fact that analytical regions were also more stable along time and with the effects of scale in the regionalisation process.unemployment, regionalisation, analytical region, normative region

    Alignment methods for biased multicanonical sampling

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    The efficiency of the multicanonical procedure can be significantly improved by applying an additional bias to the numerically generated sample space. However, results obtained by biasing in different sampling regions cannot in general be accurately combined, since their relative normalization coefficient is not known precisely. We demonstrate that for overlapping biasing regions a simple iterative procedure can be employed to determine the required coefficients

    Human capital and "club convergence" in Italian regions

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    The aim of the study is to investigate the presence of “convergence clubs†among Italian regions applying the stochastic notion of convergence. Regions are sorted according to some human capital accumulation indicators using the Classification and Regression Tree Analysis (CART). The analysis evidences a strong stochastic convergence process which characterizes all the regions suggesting the presence of different growth patterns. Furthermore, results seem to highlight that human capital accumulation favours regional growth particularly in initially “backwards†regions.

    Prediction of Functional Sites in SCOP Domains using Dynamics Perturbation Analysis

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    Dynamics perturbation analysis (DPA) finds regions in a protein structure where proteins are "ticklish", i.e., where interactions cause a large change in protein dynamics. Previously, such regions were shown to predict the location of native binding sites in a docking test set, but the more general applicability of DPA to the prediction of functional sites in proteins was not shown. Here we describe the results of applying an accelerated algorithm, called Fast DPA, to predict functional sites in over 50,000 SCOP domains

    Foveation-based Mechanisms Alleviate Adversarial Examples

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    We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly. Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to significantly reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation. As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation

    Parameter estimation for the stochastic SIS epidemic model

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    In this paper we estimate the parameters in the stochastic SIS epidemic model by using pseudo-maximum likelihood estimation (pseudo-MLE) and least squares estimation. We obtain the point estimators and 100(1 − α)% confidence intervals as well as 100(1 − α)% joint confidence regions by applying least squares techniques. The pseudo-MLEs have almost the same form as the least squares case. We also obtain the exact as well as the asymptotic 100(1 − α)% joint confidence regions for the pseudo-MLEs. Computer simulations are performed to illustrate our theory
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