Studies on ultraquasi-pseudometrics and orderings

Ultraquasi-pseudometric spaces even though quite simple in concept, as it is easily obtained by altering the usual triangle inequality property, still yield interesting results. Indeed, a natural question that should arise is how does switching to the strong triangle inequality affect some of the results we already know about quasi-pseudometrics. On some points we get similar results to those of when we have the standard triangle inequality, but the general observation is that dealing with the strong triangle inequality is easier. Of course, there are results that cannot be obtained without the "ultra-property". A fast rundown on those effects is then deemed necessary to begin with. Also, since we cannot go through every single result on quasi-pseudometrics we then need to localize our observations, that is why in the first part we restrained our observations on the results from Gaba and Künzi about splitting metrics. Also, we will see some particular algorithms and connections to the bicompletion, joincompact ultraquasi-metric spaces and the old construction of a total order by Herrlich

Analyse Sémantique des Réseaux Sociaux d'Usages et d'Opinions

International audienceNos travaux s'inscrivent dans le cadre d'une Plateforme Régionale d'In-novation dédiée au tourisme du futur. Ils visent à développer un modèle d'analyse sémantique de réseaux sociaux et d'analyse d'opinions destiné à la représentation et à la compréhension des usages territoriaux restitués via des traces numériques, ainsi qu'un système décisionnel basé sur ce modèle. Intégré au sein d'un Système d'In-formation Touristique, ce système de veille et d'analyse constitue un outil d'aide à la gouvernance et à l'identification des produits et services touristiques de demain qui contribueront à l'essor économique territorial

Regulation Bancaire Et Prise De Risque Des Banques De La Cemac

Cet article évalue empiriquement l’effet de la régulation bancaire sur la prise de risques des banques dans la Communauté Economique et Monétaire de l’Afrique Centrale (CEMAC). Les données nécessaires pour réaliser cette étude proviennent de la Banque Mondiale, de la Banque des Etats de l’Afrique Centrale (BEAC) et de la Commission Bancaire de l’Afrique Centrale (COBAC). La période d’étude va de 2010 à 2017 et s’étend sur les six pays de la CEMAC. Afin d’atteindre cet objectif, les moindres carrés généralisés sont retenus à la suite du test de Hausman (1978). Il en ressort que les variations à la hausse de la régulation bancaire encouragent les banques à prendre plus de risques. En effet, les résultats montrent premièrement qu’une hausse du ratio de solvabilité conduit à une plus grande prise de risques des banques. Puis ces auteurs montrent qu’une hausse du ratio du capital réglementaire augmente la prise de risques par les banques. Néanmoins l’action des gouvernements de la région pour la promotion du secteur privé réduit la prise de risques des banques. Enfin, d’après ces résultats une politique monétaire expansionniste et une bonne conjoncture économique réduisent aussi la prise de risques des banques dans la CEMAC. Quelques recommandations de politiques économiques peuvent être faites à la lumière de ces résultats, notamment que les autorités en charge de la régulation bancaire dans la CEMAC doivent réduire le poids de la réglementation dans leur secteur bancaire puisque celleci semble ne pas être optimale pour réduire la prise de risques des banques. Egalement les gouvernements des différents pays de la région doivent accentuer leurs actions sur la promotion du secteur privé car elles contribuent à la réduction de la prise de risques par les banques dans la CEMAC. This article empirically evaluates the effect of banking regulation on banks' risk taking in the Economic and Monetary Community of Central Africa (CEMAC). The data needed to carry out this study come from the World Bank, from “BEAC” and “COBAC”. The study period is from 2010 to 2017 and covers the six CEMAC countries. In order to achieve this goal, generalized least squares are retained following the Hausman test (1978). It shows that upward variations in banking regulation encourage banks to take more risks. Indeed, the results show firstly that an increase in the solvency ratio leads to greater banks’ risk taking. Then they show that an increase in the ratio of regulatory capital increases banks’ risk taking. Nevertheless, the action of the governments of the region for the promotion of the private sector reduces the banks’ risk taking. Finally, according to these results, expansionary monetary policy and good economic conditions also reduce banks' risk taking in CEMAC. Some economic policy recommendations can be made in the light of these results, notably that the authorities in charge of banking regulation in the CEMAC must reduce the weight of regulation in their banking sector since this seems not to be optimal to reduce banks’ risk taking. Also the governments of the different countries of these region must emphasize their actions on the promotion of the private sector because they contribute to the reduction of the banks’ risk taking in CEMAC

Breccia Pipe Estimation: a new approach using non-stationary covariances

International audienceThe El Teniente mine is famous not only as one of the largest known porphyry-copper ore bodies but also, among geologists, for its typical breccia pipe named “Braden”, an almost vertical poorly mineralized cone, located at the center of the mine and surrounded by early-stage mineralizations. As the edge of the pipe constitutes the limit of the deposit and of the mining operation, estimating it accurately is important. In this paper, we are interested in estimation of the elevation of the pipe surface using a geostatistical approach based on non-stationary covariances. Previous approaches have been applied on this dataset by Séguret and Celhay (2013). The proposed estimation method offers an integrated treatment of all aspects of non-stationarity (mean, variance, spatial continuity) in the modelling process. The proposed method has revealed an increased prediction accuracy when compared to standard stationary method, and demonstrated the ability to extract the underlying non-stationarity from a single realization. A comparison of predictions and prediction standard deviations maps indicates that the proposed non-stationary method captures some varying spatial features (such as locally varying anisotropy) in the data that are not present using the stationary method, the outcome appears more realistic

Estimation of Space Deformation Model for Non-stationary Random Functions

Stationary Random Functions have been successfully applied in geostatistical applications for decades. In some instances, the assumption of a homogeneous spatial dependence structure across the entire domain of interest is unrealistic. A practical approach for modelling and estimating non-stationary spatial dependence structure is considered. This consists in transforming a non-stationary Random Function into a stationary and isotropic one via a bijective continuous deformation of the index space. So far, this approach has been successfully applied in the context of data from several independent realizations of a Random Function. In this work, we propose an approach for non-stationary geostatistical modelling using space deformation in the context of a single realization with possibly irregularly spaced data. The estimation method is based on a non-stationary variogram kernel estimator which serves as a dissimilarity measure between two locations in the geographical space. The proposed procedure combines aspects of kernel smoothing, weighted non-metric multi-dimensional scaling and thin-plate spline radial basis functions. On a simulated data, the method is able to retrieve the true deformation. Performances are assessed on both synthetic and real datasets. It is shown in particular that our approach outperforms the stationary approach. Beyond the prediction, the proposed method can also serve as a tool for exploratory analysis of the non-stationarity.Comment: 17 pages, 9 figures, 2 table

A Generalized Convolution Model and Estimation for Non-stationary Random Functions

Standard geostatistical models assume second order stationarity of the underlying Random Function. In some instances, there is little reason to expect the spatial dependence structure to be stationary over the whole region of interest. In this paper, we introduce a new model for second order non-stationary Random Functions as a convolution of an orthogonal random measure with a spatially varying random weighting function. This new model is a generalization of the common convolution model where a non-random weighting function is used. The resulting class of non-stationary covariance functions is very general, flexible and allows to retrieve classes of closed-form non-stationary covariance functions known from the literature, for a suitable choices of the random weighting functions family. Under the framework of a single realization and local stationarity, we develop parameter inference procedure of these explicit classes of non-stationary covariance functions. From a local variogram non-parametric kernel estimator, a weighted local least-squares approach in combination with kernel smoothing method is developed to estimate the parameters. Performances are assessed on two real datasets: soil and rainfall data. It is shown in particular that the proposed approach outperforms the stationary one, according to several criteria. Beyond the spatial predictions, we also show how conditional simulations can be carried out in this non-stationary framework.Comment: 24 pages, 10 figures, 2 table

Robin L. Michael v. Rodney C. Michael : Brief of Appellant

ON APPEAL FROM THE THIRD JUDICIAL DISTRICT COURT IN AND FOR SALT LAKE COUNTY, STATE OF UTAH HONORABLE TIMOTHY R. HANSON, DISTRICT COURT JUDG