Habitat selection in zones of parapatric contact between the common shrew Sorex araneaus and Millet's shrew S. coronatus

(1) The common shrew Sorex araneus and Millet's shrew S. coronatusare sibling species.They are morphologically and genetically very similar but do not hybridize.Their parapatric distribution throughout south-western Europe, with a few narrow zones of distributional overlap, suggests that they are in competitive parapatry. (2) Two of these contact zones were studied; there was evidence of coexistence over periods of 2 years as well as habitat segregation. In both zones, the species segregated on litter thickness and humidity variables. (3) A simple analysis of spatial distribution showed that habitats visible in the field corresponded to the habitats selected by the species. Habitat selection was found throughout the annual life-cycle of the shrews. (4) In one contact zone, a removal experiment was performed to test whether habitat segregation is induced by interspecific interactions. The experiment showed that the species select habitats differentially when both are present and abandon habitat selection when their competitor removed. (5)These results confirm the role of resource partitioning in promoting narrow ranges of distributional overlap between such parapatric species and qualitatively support the prediction of habitat selection theory that, in a two-species system, coexistence may be achieved by differential habitat selection to avoid competition. The results also support the view that the common shrew and Millet's shrew are in competitive parapatry.

Multifractal portrayal of the distribution of the Swiss population

The present research studies the spatial patterns of the distribution of the Swiss population (DSP). This description is carried out using a wide variety of global spatial structural analysis tools such as topological, statistical and fractal measures, which enable the estimation of the spatial degree of clustering of a point pattern. A particular attention is given to the analysis of the multifractality to characterize the spatial structure of the DSP at different scales. This will be achieved by measuring the generalized q-dimensions and the singularity spectrum. This research is based on high quality data of the Swiss Population Census of the Year 2000 at a hectometric resolution (grid 100 x 100 m) issued by the Swiss Federal Statistical Office (FSO).

The social disembedding of business theory and practice. A (neo-)institutional analysis of the homo economicus and corporate social responsibility, and the inherent responsibility of business scholars

Aim Structure of the Thesis In the first article, I focus on the context in which the Homo Economicus was constructed - i.e., the conception of economic actors as fully rational, informed, egocentric, and profit-maximizing. I argue that the Homo Economicus theory was developed in a specific societal context with specific (partly tacit) values and norms. These norms have implicitly influenced the behavior of economic actors and have framed the interpretation of the Homo Economicus. Different factors however have weakened this implicit influence of the broader societal values and norms on economic actors. The result is an unbridled interpretation and application of the values and norms of the Homo Economicus in the business environment, and perhaps also in the broader society. In the second article, I show that the morality of many economic actors relies on isomorphism, i.e., the attempt to fit into the group by adopting the moral norms surrounding them. In consequence, if the norms prevailing in a specific group or context (such as a specific region or a specific industry) change, it can be expected that actors with an 'isomorphism morality' will also adapt their ethical thinking and their behavior -for the 'better' or for the 'worse'. The article further describes the process through which corporations could emancipate from the ethical norms prevailing in the broader society, and therefore develop an institution with specific norms and values. These norms mainly rely on mainstream business theories praising the economic actor's self-interest and neglecting moral reasoning. Moreover, because of isomorphism morality, many economic actors have changed their perception of ethics, and have abandoned the values prevailing in the broader society in order to adopt those of the economic theory. Finally, isomorphism morality also implies that these economic actors will change their morality again if the institutional context changes. The third article highlights the role and responsibility of business scholars in promoting a systematic reflection and self-critique of the business system and develops alternative models to fill the moral void of the business institution and its inherent legitimacy crisis. Indeed, the current business institution relies on assumptions such as scientific neutrality and specialization, which seem at least partly challenged by two factors. First, self-fulfilling prophecy provides scholars with an important (even if sometimes undesired) normative influence over practical life. Second, the increasing complexity of today's (socio-political) world and interactions between the different elements constituting our society question the strong specialization of science. For instance, economic theories are not unrelated to psychology or sociology, and economic actors influence socio-political structures and processes, e.g., through lobbying (Dobbs, 2006; Rondinelli, 2002), or through marketing which changes not only the way we consume, but more generally tries to instill a specific lifestyle (Cova, 2004; M. K. Hogg & Michell, 1996; McCracken, 1988; Muniz & O'Guinn, 2001). In consequence, business scholars are key actors in shaping both tomorrow's economic world and its broader context. A greater awareness of this influence might be a first step toward an increased feeling of civic responsibility and accountability for the models and theories developed or taught in business schools.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Integrated application of geochemical and geophysical methods for hydrogeological impact assessment for tunneling in fractured rock

The interaction of tunneling with groundwater is a problem both from an environmental and an engineering point of view. In fact, tunnel drilling may cause a drawdown of piezometric levels and water inflows into tunnels that may cause problems during excavation of the tunnel. While the influence of tunneling on the regional groundwater systems may be adequately predicted in porous media using analytical solutions, such an approach is difficult to apply in fractured rocks. Numerical solutions are preferable and various conceptual approaches have been proposed to describe and model groundwater flow through fractured rock masses, ranging from equivalent continuum models to discrete fracture network simulation models. However, their application needs many preliminary investigations on the behavior of the groundwater system based on hydrochemical and structural data. To study large scale flow systems in fractured rocks of mountainous terrains, a comprehensive study was conducted in southern Switzerland, using as case studies two infrastructures actually under construction: (i) the Monte Ceneri base railway tunnel (Ticino), and the (ii) San Fedele highway tunnel (Roveredo, Graubiinden). The chosen approach in this study combines the temporal and spatial variation of geochemical and geophysical measurements. About 60 localities from both surface and underlying tunnels were temporarily and spatially monitored during more than one year. At first, the project was focused on the collection of hydrochemical and structural data. A number of springs, selected in the area surrounding the infrastructures, were monitored for discharge, electric conductivity, pH, and temperature. Water samples (springs, tunnel inflows and rains) were taken for isotopic analysis; in particular the stable isotope composition (δ2Η, δ180 values) can reflect the origin of the water, because of spatial (recharge altitude, topography, etc.) and temporal (seasonal) effects on precipitation which in turn strongly influence the isotopic composition of groundwater. Tunnel inflows in the accessible parts of the tunnels were also sampled and, if possible, monitored with time. Noble-gas concentrations and their isotope ratios were used in selected locations to better understand the origin and the circulation of the groundwater. In addition, electrical resistivity and VLF-type electromagnetic surveys were performed to identify water bearing fractures and/or weathered areas that could be intersected at depth during tunnel construction. The main goal of this work was to demonstrate that these hydrogeological data and geophysical methods, combined with structural and hydrogeological information, can be successfully used in order to develop hydrogeological conceptual models of the groundwater flow in regions to be exploited for tunnels. The main results of the project are: (i) to have successfully tested the application of electrical resistivity and VLF-electromagnetic surveys to asses water-bearing zones during tunnel drilling; (ii) to have verified the usefulness of noble gas, major ion and stable isotope compositions as proxies for the detection of faults and to understand the origin of the groundwater and its flow regimes (direct rain water infiltration or groundwater of long residence time); and (iii) to have convincingly tested the combined application of a geochemical and geophysical approach to assess and predict the vulnerability of springs to tunnel drilling. - L'interférence entre eaux souterraines et des tunnels pose des problèmes environnementaux et de génie civile. En fait, la construction d'un tunnel peut faire abaisser le niveau des nappes piézométriques et faire infiltrer de l'eau dans le tunnel et ainsi créer des problème pendant l'excavation. Alors que l'influence de la construction d'un tunnel sur la circulation régionale de l'eau souterraine dans des milieux poreux peut être prédite relativement facilement par des solution analytiques de modèles, ceci devient difficile dans des milieux fissurés. Dans ce cas-là, des solutions numériques sont préférables et plusieurs approches conceptuelles ont été proposées pour décrire et modéliser la circulation d'eau souterraine à travers les roches fissurées, en allant de modèles d'équivalence continue à des modèles de simulation de réseaux de fissures discrètes. Par contre, leur application demande des investigations importantes concernant le comportement du système d'eau souterraine basées sur des données hydrochimiques et structurales. Dans le but d'étudier des grands systèmes de circulation d'eau souterraine dans une région de montagnes, une étude complète a été fait en Suisse italienne, basée sur deux grandes infrastructures actuellement en construction: (i) Le tunnel ferroviaire de base du Monte Ceneri (Tessin) et (ii) le tunnel routière de San Fedele (Roveredo, Grisons). L'approche choisie dans cette étude est la combinaison de variations temporelles et spatiales des mesures géochimiques et géophysiques. Environs 60 localités situées à la surface ainsi que dans les tunnels soujacents ont été suiviès du point de vue temporel et spatial pendant plus de un an. Dans un premier temps le projet se focalisait sur la collecte de données hydrochimiques et structurales. Un certain nombre de sources, sélectionnées dans les environs des infrastructures étudiées ont été suivies pour le débit, la conductivité électrique, le pH et la température. De l'eau (sources, infiltration d'eau de tunnel et pluie) a été échantillonnés pour des analyses isotopiques; ce sont surtout les isotopes stables (δ2Η, δ180) qui peuvent indiquer l'origine d'une eaux, à cause de la dépendance d'effets spatiaux (altitude de recharge, topographie etc.) ainsi que temporels (saisonaux) sur les précipitations météoriques , qui de suite influencent ainsi la composition isotopique de l'eau souterraine. Les infiltrations d'eau dans les tunnels dans les parties accessibles ont également été échantillonnées et si possible suivies au cours du temps. La concentration de gaz nobles et leurs rapports isotopiques ont également été utilisées pour quelques localités pour mieux comprendre l'origine et la circulation de l'eau souterraine. En plus, des campagnes de mesures de la résistivité électrique et électromagnétique de type VLF ont été menées afin d'identifier des zone de fractures ou d'altération qui pourraient interférer avec les tunnels en profondeur pendant la construction. Le but principal de cette étude était de démontrer que ces données hydrogéologiques et géophysiques peuvent être utilisées avec succès pour développer des modèles hydrogéologiques conceptionels de tunnels. Les résultats principaux de ce travail sont : i) d'avoir testé avec succès l'application de méthodes de la tomographie électrique et des campagnes de mesures électromagnétiques de type VLF afin de trouver des zones riches en eau pendant l'excavation d'un tunnel ; ii) d'avoir prouvé l'utilité des gaz nobles, des analyses ioniques et d'isotopes stables pour déterminer l'origine de l'eau infiltrée (de la pluie par le haut ou ascendant de l'eau remontant des profondeurs) et leur flux et pour déterminer la position de failles ; et iii) d'avoir testé d'une manière convainquant l'application combinée de méthodes géochimiques et géophysiques pour juger et prédire la vulnérabilité de sources lors de la construction de tunnels. - L'interazione dei tunnel con il circuito idrico sotterraneo costituisce un problema sia dal punto di vista ambientale che ingegneristico. Lo scavo di un tunnel puô infatti causare abbassamenti dei livelli piezometrici, inoltre le venute d'acqua in galleria sono un notevole problema sia in fase costruttiva che di esercizio. Nel caso di acquiferi in materiale sciolto, l'influenza dello scavo di un tunnel sul circuito idrico sotterraneo, in genere, puô essere adeguatamente predetta attraverso l'applicazione di soluzioni analitiche; al contrario un approccio di questo tipo appare inadeguato nel caso di scavo in roccia. Per gli ammassi rocciosi fratturati sono piuttosto preferibili soluzioni numeriche e, a tal proposito, sono stati proposti diversi approcci concettuali; nella fattispecie l'ammasso roccioso puô essere modellato come un mezzo discreto ο continuo équivalente. Tuttavia, una corretta applicazione di qualsiasi modello numerico richiede necessariamente indagini preliminari sul comportamento del sistema idrico sotterraneo basate su dati idrogeochimici e geologico strutturali. Per approfondire il tema dell'idrogeologia in ammassi rocciosi fratturati tipici di ambienti montani, è stato condotto uno studio multidisciplinare nel sud della Svizzera sfruttando come casi studio due infrastrutture attualmente in costruzione: (i) il tunnel di base del Monte Ceneri (canton Ticino) e (ii) il tunnel autostradale di San Fedele (Roveredo, canton Grigioni). L'approccio di studio scelto ha cercato di integrare misure idrogeochimiche sulla qualité e quantité delle acque e indagini geofisiche. Nella fattispecie sono state campionate le acque in circa 60 punti spazialmente distribuiti sia in superficie che in sotterraneo; laddove possibile il monitoraggio si è temporalmente prolungato per più di un anno. In una prima fase, il progetto di ricerca si è concentrato sull'acquisizione dati. Diverse sorgenti, selezionate nelle aree di possibile influenza attorno allé infrastrutture esaminate, sono state monitorate per quel che concerne i parametri fisico-chimici: portata, conduttività elettrica, pH e temperatura. Campioni d'acqua sono stati prelevati mensilmente su sorgenti, venute d'acqua e precipitazioni, per analisi isotopiche; nella fattispecie, la composizione in isotopi stabili (δ2Η, δ180) tende a riflettere l'origine delle acque, in quanto, variazioni sia spaziali (altitudine di ricarica, topografia, etc.) che temporali (variazioni stagionali) della composizione isotopica delle precipitazioni influenzano anche le acque sotterranee. Laddove possibile, sono state campionate le venute d'acqua in galleria sia puntualmente che al variare del tempo. Le concentrazioni dei gas nobili disciolti nell'acqua e i loro rapporti isotopici sono stati altresi utilizzati in alcuni casi specifici per meglio spiegare l'origine delle acque e le tipologie di circuiti idrici sotterranei. Inoltre, diverse indagini geofisiche di resistività elettrica ed elettromagnetiche a bassissima frequenza (VLF) sono state condotte al fine di individuare le acque sotterranee circolanti attraverso fratture dell'ammasso roccioso. Principale obiettivo di questo lavoro è stato dimostrare come misure idrogeochimiche ed indagini geofisiche possano essere integrate alio scopo di sviluppare opportuni modelli idrogeologici concettuali utili per lo scavo di opere sotterranee. I principali risultati ottenuti al termine di questa ricerca sono stati: (i) aver testato con successo indagini geofisiche (ERT e VLF-EM) per l'individuazione di acque sotterranee circolanti attraverso fratture dell'ammasso roccioso e che possano essere causa di venute d'acqua in galleria durante lo scavo di tunnel; (ii) aver provato l'utilità di analisi su gas nobili, ioni maggiori e isotopi stabili per l'individuazione di faglie e per comprendere l'origine delle acque sotterranee (acque di recente infiltrazione ο provenienti da circolazioni profonde); (iii) aver testato in maniera convincente l'integrazione delle indagini geofisiche e di misure geochimiche per la valutazione della vulnérabilité delle sorgenti durante lo scavo di nuovi tunnel. - "La NLFA (Nouvelle Ligne Ferroviaire à travers les Alpes) axe du Saint-Gothard est le plus important projet de construction de Suisse. En bâtissant la nouvelle ligne du Saint-Gothard, la Suisse réalise un des plus grands projets de protection de l'environnement d'Europe". Cette phrase, qu'on lit comme présentation du projet Alptransit est particulièrement éloquente pour expliquer l'utilité des nouvelles lignes ferroviaires transeuropéens pour le développement durable. Toutefois, comme toutes grandes infrastructures, la construction de nouveaux tunnels ont des impacts inévitables sur l'environnement. En particulier, le possible drainage des eaux souterraines réalisées par le tunnel peut provoquer un abaissement du niveau des nappes piézométriques. De plus, l'écoulement de l'eau à l'intérieur du tunnel, conduit souvent à des problèmes d'ingénierie. Par exemple, d'importantes infiltrations d'eau dans le tunnel peuvent compliquer les phases d'excavation, provoquant un retard dans l'avancement et dans le pire des cas, peuvent mettre en danger la sécurité des travailleurs. Enfin, l'infiltration d'eau peut être un gros problème pendant le fonctionnement du tunnel. Du point de vue de la science, avoir accès à des infrastructures souterraines représente une occasion unique d'obtenir des informations géologiques en profondeur et pour échantillonner des eaux autrement inaccessibles. Dans ce travail, nous avons utilisé une approche pluridisciplinaire qui intègre des mesures d'étude hydrogéochimiques effectués sur les eaux de surface et des investigations géophysiques indirects, tels que la tomographic de résistivité électrique (TRE) et les mesures électromagnétiques de type VLF. L'étude complète a été fait en Suisse italienne, basée sur deux grandes infrastructures actuellement en construction, qui sont le tunnel ferroviaire de base du Monte Ceneri, une partie du susmentionné projet Alptransit, situé entièrement dans le canton Tessin, et le tunnel routière de San Fedele, situé a Roveredo dans le canton des Grisons. Le principal objectif était de montrer comment il était possible d'intégrer les deux approches, géophysiques et géochimiques, afin de répondre à la question de ce que pourraient être les effets possibles dû au drainage causés par les travaux souterrains. L'accès aux galeries ci-dessus a permis une validation adéquate des enquêtes menées confirmant, dans chaque cas, les hypothèses proposées. A cette fin, nous avons fait environ 50 profils géophysiques (28 imageries électrique bidimensionnels et 23 électromagnétiques) dans les zones de possible influence par le tunnel, dans le but d'identifier les fractures et les discontinuités dans lesquelles l'eau souterraine peut circuler. De plus, des eaux ont été échantillonnés dans 60 localités situées la surface ainsi que dans les tunnels subjacents, le suivi mensuelle a duré plus d'un an. Nous avons mesurés tous les principaux paramètres physiques et chimiques: débit, conductivité électrique, pH et température. De plus, des échantillons d'eaux ont été prélevés pour l'analyse mensuelle des isotopes stables de l'hydrogène et de l'oxygène (δ2Η, δ180). Avec ces analyses, ainsi que par la mesure des concentrations des gaz rares dissous dans les eaux et de leurs rapports isotopiques que nous avons effectués dans certains cas spécifiques, il était possible d'expliquer l'origine des différents eaux souterraines, les divers modes de recharge des nappes souterraines, la présence de possible phénomènes de mélange et, en général, de mieux expliquer les circulations d'eaux dans le sous-sol. Le travail, même en constituant qu'une réponse partielle à une question très complexe, a permis d'atteindre certains importants objectifs. D'abord, nous avons testé avec succès l'applicabilité des méthodes géophysiques indirectes (TRE et électromagnétiques de type VLF) pour prédire la présence d'eaux souterraines dans le sous-sol des massifs rocheux. De plus, nous avons démontré l'utilité de l'analyse des gaz rares, des isotopes stables et de l'analyses des ions majeurs pour la détection de failles et pour comprendre l'origine des eaux souterraines (eau de pluie par le haut ou eau remontant des profondeurs). En conclusion, avec cette recherche, on a montré que l'intégration des ces informations (géophysiques et géochimiques) permet le développement de modèles conceptuels appropriés, qui permettant d'expliquer comment l'eau souterraine circule. Ces modèles permettent de prévoir les infiltrations d'eau dans les tunnels et de prédire la vulnérabilité de sources et des autres ressources en eau lors de construction de tunnels.

Network characteristics of film tourism websites in Catalonia: an application of network analysis in tourism

This master thesis presents a research on the analysis of film tourism stakeholders in Catalonia applying the network analysis approach. The research aims to provide an analysis of the relations between local tourism stakeholders with local film offices through their websites. Therefore, the development of the present work involved the review of literature on the themes of film tourism and network analysis. Then the main stakeholders of film and tourism of Catalonia were identified and their websites analyzed. The measures indicators for network analysis such as centrality, closeness and betweenness degree have been applied on the analysis of the websites to determine the extent of the relations of film and tourism stakeholders in Catalonia. Results and conclusions are presented on the referred sections

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.

Spatial learning by rats across visually disconnected environments

Two spatial tasks were designed to test specific properties of spatial representation in rats. In the first task, rats were trained to locate an escape hole at a fixed position in a visually homogeneous arena. This arena was connected with a periphery where a full view of the room environment existed. Therefore, rats were dependent on their memory trace of the previous position in the periphery to discriminate a position within the central region. Under these experimental conditions, the test animals showed a significant discrimination of the training position without a specific local view. In the second task, rats were trained in a radial maze consisting of tunnels that were transparent at their distal ends only. Because the central part of the maze was non-transparent, rats had to plan and execute appropriate trajectories without specific visual feedback from the environment. This situation was intended to encourage the reliance on prospective memory of the non-visited arms in selecting the following move. Our results show that acquisition performance was only slightly decreased compared to that shown in a completely transparent maze and considerably higher than in a translucent maze or in darkness. These two series of experiments indicate (1) that rats can learn about the relative position of different places with no common visual panorama, and (2) that they are able to plan and execute a sequence of visits to several places without direct visual feed-back about their relative position.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Feasibility of direct mapping of cerebral fluorodeoxy-D-glucose metabolism in situ at subcellular resolution using soft X-ray fluorescence.

Glucose metabolism is difficult to image with cellular resolution in mammalian brain tissue, particularly with (18) fluorodeoxy-D-glucose (FDG) positron emission tomography (PET). To this end, we explored the potential of synchrotron-based low-energy X-ray fluorescence (LEXRF) to image the stable isotope of fluorine (F) in phosphorylated FDG (DG-6P) at 1 μm(2) spatial resolution in 3-μm-thick brain slices. The excitation-dependent fluorescence F signal at 676 eV varied linearly with FDG concentration between 0.5 and 10 mM, whereas the endogenous background F signal was undetectable in brain. To validate LEXRF mapping of fluorine, FDG was administered in vitro and in vivo, and the fluorine LEXRF signal from intracellular trapped FDG-6P over selected brain areas rich in radial glia was spectrally quantitated at 1 μm(2) resolution. The subsequent generation of spatial LEXRF maps of F reproduced the expected localization and gradients of glucose metabolism in retinal Müller glia. In addition, FDG uptake was localized to periventricular hypothalamic tanycytes, whose morphological features were imaged simultaneously by X-ray absorption. We conclude that the high specificity of photon emission from F and its spatial mapping at ≤1 μm resolution demonstrates the ability to identify glucose uptake at subcellular resolution and holds remarkable potential for imaging glucose metabolism in biological tissue. © 2012 Wiley Periodicals, Inc.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

The Impact of Housing Market Segmentation between Tourists and Residents on the Hedonic Price for Landscape Quality

Market segmentation is an important issue when estimating the implicit price for an environmental amenity from a surrogate market like property. This paper tests the hypothesis of a segmentation of the housing market between tourists and residents and computes the implicit price for natural landscape quality in Swiss alpine resorts. The results show a clear segmentation between both groups of consumers, although tests also show that the estimated coefficient for landscape is similar in the tourists' model and in the residents'. However, since the functional form is non linear, the nominal - rather than relative - value of a change in natural landscape quality is higher in the tourist housing market than in the residents'. Hence, considering the segmentation of the market between tourists and residents is essential in order to provide valid estimates of the nominal implicit price of natural landscape quality.

Structure and kinematics of the Siviez-Mischabel nappe in the Mattertal (western Alps of Switzerland) = Structure et cinématique de la nappe de Siviez-Mischabel le long de la vallée de Zermatt (Alpes de Suisse occidentale)

Résumé Cette étude porte sur le flanc inverse de la nappe de Siviez-Mischabel et sur les unités tectoniques sous jacentes (zone de Stalden supérieur et zone Houillère) dans la vallée menant à Zermatt. L'étude structurale du granite permien de Randa (orthogneiss oeillé) permet de mieux comprendre les effets de la déformation alpine sur les roches de socle. La cartographie détaillée de l'orthogneiss et de son encaissant, ainsi que l'étude lithostratigraphique des terrains sédimentaires associés permettent de proposer un schéma structural et cinématique du flanc inverse de la nappe de Siviez-Mischabel et de mieux comprendre ses relations avec les unités tectoniques sous-jacentes. L'analyse structurale de l'orthogneiss de Randa et de son encaissant révèle la superposition de plusieurs phases de déformation ductile. Cet orthogneiss formé sous des conditions métamorphiques du faciès schiste vert possède une forte schistosité alpine avec au moins deux linéations d'extension. La première, L1, orientée NW-SE est associée à la mise en place de la nappe. La seconde, L2, orientée SW-NE, se corrèle au cisaillement ductile du Simplon. La quantification de la déformation au moyen de la méthode de Fry sur les faciès porphyriques donne des ellipses à rapports axiaux compris entre 1.9 et 5.3, en accord avec les valeurs obtenues par d'autres marqueurs {tourmalines étirées, fibres). Les valeurs mesurées parallèlement à L1 ou L2 sont très semblables. La méthode de Fry a nécessité une étude théorique préalable afin de vérifier son applicabilité aux orthogneiss oeillés. La méthode requiert une distribution spatiale homogène et isotrope des marqueurs utilisés. Les tests statistiques effectués ont révélé que les phénocristaux de feldspath alcalin satisfont à cette condition et qu'ils peuvent être utilisés comme marqueur de la déformation au moyen de la méthode de Fry. Les valeurs obtenues révèlent l'importance du cisaillement ductile du Simplon sur la géométrie de la nappe dans la région d'étude. Le levé cartographique a permis d'améliorer la lithostratigraphie de la base de la nappe de Siviez-Mischabel. Trois formations en position renversée peuvent être observées sous les gneiss formant le coeur de la nappe. Ces trois formations forment le coeur du synclinal de St-Niklaus qui connecte la nappe de Siviez-Mischabel à la zone de Stalden supérieur. La datation par U-Pb de zircons détritiques et magmatiques par LA-ICP-MS permet de contraindre l'âge des formations observées (probablement Carbonifère à Trias précoce). Ces données ont des répercussions importantes sur la structure de la nappe dans la région, prouvant l'existence de plusieurs plis avec des séries normales et renversées bien préservées. La définition et la datation de ces formations, ainsi que leur identification dans la-Zone- Houillère avoisinante permettent de mieux comprendre la géométrie initiale et les relations tectoniques des nappes du Pennique moyen dans la vallée de Zermatt. Summary This study investigates the overturned limb of the Siviez-Mischabel nappe and underlying tectonic units (Upper Stalden zone and Houillère zone) in the Mattertal area. Detailed structural analysis in the Permian Randa granite (augen orthogneiss) allows a better understanding of the Alpine deformation effects on basement rocks. Detailed mapping of this orthogneiss and surrounding rocks, and the study of the lithostratigraphy in the related sedimentary horizons allow the proposition of a structural and kinematic model for the overturned limb of the Siviez-Mischabel and to better understand the relations with the underlying tectonic units. The structural analysis of the Randa orthogneiss and surrounding rocks revealed the superposition of several phases of ductile deformation. This orthogneiss formed under greenschist facies metamorphic conditions displays a strong Alpine foliation with at least two stretching lineations. The first lineation, L1, is oriented NW-SE and is related to the nappe emplacement northward. The second one, L2, is related to the Simplon ductile shear zone. Strain estimation using the Fry method has been performed on porphyritic facies of the Randa orthogneiss. The obtained ellipses have axial ratios varying between 1.9 and 5.3, in agreement with strain estimation obtained from other markers (stretched turmalines, fringes). The strain values are very similar if measured parallel to L1 or to L2. A theoretical approach was necessary to verify the relevant application of the Fry method to augen orthogneiss. This method requires that the distribution of the used markers has to be homogeneous and isotropic. Statistical tests have been done and revealed that K-feldspar phenocrysts satisfy these conditions and can be used as strain markers with the Fry method. The obtained strain measurements revealed the importance of the Simplon ductile shear zone on the geometry of the nappe in the studied area. Mapping has improved the lithostratigraphy at the base of the Siviez-Mischabel nappe. Three overturned formations can be observed below the gneisses forming the core of the nappe. These three formations form the St-Niklaus syncline, which connects the Siviez-Mischabel nappe to the underlying Upper Stalden zone. U-Pb dating of detrital and magmatic zircons by LA-ICPMS allowed the age of the observed formations to be constrained (presumably Carboniferous to Early Triassic). This data has critical implications for nappe structure in the region, composed of few recumbent folds with well preserved normal and overturned limbs. The definition and dating of these formations, as well as their identification in the adjacent "Houillère Zone" improve the understanding of the geometry and tectonic relations of the Middle Penninic nappes in the Mattertal.

Reconciling phonological neighborhood effects in speech production through single trial analysis

A crucial step for understanding how lexical knowledge is represented is to describe the relative similarity of lexical items, and how it influences language processing. Previous studies of the effects of form similarity on word production have reported conflicting results, notably within and across languages. The aim of the present study was to clarify this empirical issue to provide specific constraints for theoretical models of language production. We investigated the role of phonological neighborhood density in a large-scale picture naming experiment using fine-grained statistical models. The results showed that increasing phonological neighborhood density has a detrimental effect on naming latencies, and re-analyses of independently obtained data sets provide supplementary evidence for this effect. Finally, we reviewed a large body of evidence concerning phonological neighborhood density effects in word production, and discussed the occurrence of facilitatory and inhibitory effects in accuracy measures. The overall pattern shows that phonological neighborhood generates two opposite forces, one facilitatory and one inhibitory. In cases where speech production is disrupted (e.g. certain aphasic symptoms), the facilitatory component may emerge, but inhibitory processes dominate in efficient naming by healthy speakers. These findings are difficult to accommodate in terms of monitoring processes, but can be explained within interactive activation accounts combining phonological facilitation and lexical competition.

GRW as an ontology of dispositions

The paper argues that the formulation of quantum mechanics proposed by Ghirardi, Rimini and Weber (GRW) is a serious candidate for being a fundamental physical theory and explores its ontological commitments from this perspective. In particular, we propose to conceive of spatial superpositions of non-massless microsystems as dispositions or powers, more precisely propensities, to generate spontaneous localizations. We set out five reasons for this view, namely that (1) it provides for a clear sense in which quantum systems in entangled states possess properties even in the absence of definite values; (2) it vindicates objective, single-case probabilities; (3) it yields a clear transition from quantum to classical properties; (4) it enables to draw a clear distinction between purely mathematical and physical structures, and (5) it grounds the arrow of time in the time-irreversible manifestation of the propensities to localize.