Kinship, ritual kinship and political milieus in an alpine Valley in 19th century

In my paper I will present some results about ritual kinship and political mobilization of popular groups in an alpine Valley: the Val de Bagnes, in the Swiss canton of Valais. There are two major reasons to choose the Val de Bagnes for our inquiry about social networks: the existence of sharp political and social conflicts during the 18th and the 19th century and the availability of almost systematic genealogical data between 1700 and 1900. The starting point of my research focuses on this question: what role did kinship and ritual kinship play in the political mobilization of popular groups and in the organization of competing factions? This question allows us to shed light on some other uses and meanings of ritual kinship in the local society. Was ritual kinship a significant instrument for economic cooperation? Or was it a channel for patronage or for privileged social contacts? The analysis highlights the importance of kinship and godparentage for the building of homogeneous social and political networks. If we consider transactions between individuals, the analysis of 19th century Val de Bagnes gives the impression of quite open networks. Men and women tried to diversify their relations in order to avoid strong dependency from powerful patrons. Nevertheless, when we consider the family networks, we can notice that most relations took place in a structured social space or a specific "milieu", were intense contacts enhanced trust, although political allegiances and social choices were not fully predictable on the basis of such preferential patterns. In a politically conflictual society, like 19th century Bagnes, ritual kinship interacted with kinship solidarities and ideological factors shaping dense social networks mostly based on a common political orientation. Such milieus sustained the building of political factions, which show surprising stability over time. In this sense, milieus are important factors to understand political and religious polarization in 19th century Switzerland.

Biometrical analysis and selection of tetraploid progenies of Panicum maximum using mixed model methods

The objectives of this work were to estimate the genetic and phenotypic parameters and to predict the genetic and genotypic values of the selection candidates obtained from intraspecific crosses in Panicum maximum as well as the performance of the hybrid progeny of the existing and projected crosses. Seventy-nine intraspecific hybrids obtained from artificial crosses among five apomictic and three sexual autotetraploid individuals were evaluated in a clonal test with two replications and ten plants per plot. Green matter yield, total and leaf dry matter yields and leaf percentage were evaluated in five cuts per year during three years. Genetic parameters were estimated and breeding and genotypic values were predicted using the restricted maximum likelihood/best linear unbiased prediction procedure (REML/BLUP). The dominant genetic variance was estimated by adjusting the effect of full-sib families. Low magnitude individual narrow sense heritabilities (0.02-0.05), individual broad sense heritabilities (0.14-0.20) and repeatability measured on an individual basis (0.15-0.21) were obtained. Dominance effects for all evaluated characteristics indicated that breeding strategies that explore heterosis must be adopted. Less than 5% increase in the parameter repeatability was obtained for a three-year evaluation period and may be the criterion to determine the maximum number of years of evaluation to be adopted, without compromising gain per cycle of selection. The identification of hybrid candidates for future cultivars and of those that can be incorporated into the breeding program was based on the genotypic and breeding values, respectively. The prediction of the performance of the hybrid progeny, based on the breeding values of the progenitors, permitted the identification of the best crosses and indicated the best parents to use in crosses.

GLUT2 in pancreatic and extra-pancreatic gluco-detection (review).

Detection of variations in blood glucose concentrations by pancreatic beta-cells and a subsequent appropriate secretion of insulin are key events in the control of glucose homeostasis. Because a decreased capability to sense glycemic changes is a hallmark of type 2 diabetes, the glucose signalling pathway leading to insulin secretion in pancreatic beta-cells has been extensively studied. This signalling mechanism depends on glucose metabolism and requires the presence of specific molecules such as GLUT2, glucokinase and the K(ATP) channel subunits Kir6.2 and SUR1. Other cells are also able to sense variations in glycemia or in local glucose concentrations and to modulate different physiological functions participating in the general control of glucose and energy homeostasis. These include cells forming the hepatoportal vein glucose sensor, which controls glucose storage in the liver, counterregulation, food intake and glucose utilization by peripheral tissues and neurons in the hypothalamus and brainstem whose firing rates are modulated by local variations in glucose concentrations or, when not protected by a blood-brain barrier, directly by changes in blood glucose levels. These glucose-sensing neurons are involved in the control of insulin and glucagon secretion, food intake and energy expenditure. Here, recent physiological studies performed with GLUT2-/- mice will be described, which indicate that this transporter is essential for glucose sensing by pancreatic beta-cells, by the hepatoportal sensor and by sensors, probably located centrally, which control activity of the autonomic nervous system and stimulate glucagon secretion. These studies may pave the way to a fine dissection of the molecular and cellular components of extra-pancreatic glucose sensors involved in the control of glucose and energy homeostasis.

Behavioural queueing : cellular automata and a laboratory experiment

Dans cette thèse, nous étudions les aspects comportementaux d'agents qui interagissent dans des systèmes de files d'attente à l'aide de modèles de simulation et de méthodologies expérimentales. Chaque période les clients doivent choisir un prestataire de servivce. L'objectif est d'analyser l'impact des décisions des clients et des prestataires sur la formation des files d'attente. Dans un premier cas nous considérons des clients ayant un certain degré d'aversion au risque. Sur la base de leur perception de l'attente moyenne et de la variabilité de cette attente, ils forment une estimation de la limite supérieure de l'attente chez chacun des prestataires. Chaque période, ils choisissent le prestataire pour lequel cette estimation est la plus basse. Nos résultats indiquent qu'il n'y a pas de relation monotone entre le degré d'aversion au risque et la performance globale. En effet, une population de clients ayant un degré d'aversion au risque intermédiaire encoure généralement une attente moyenne plus élevée qu'une population d'agents indifférents au risque ou très averses au risque. Ensuite, nous incorporons les décisions des prestataires en leur permettant d'ajuster leur capacité de service sur la base de leur perception de la fréquence moyenne d'arrivées. Les résultats montrent que le comportement des clients et les décisions des prestataires présentent une forte "dépendance au sentier". En outre, nous montrons que les décisions des prestataires font converger l'attente moyenne pondérée vers l'attente de référence du marché. Finalement, une expérience de laboratoire dans laquelle des sujets jouent le rôle de prestataire de service nous a permis de conclure que les délais d'installation et de démantèlement de capacité affectent de manière significative la performance et les décisions des sujets. En particulier, les décisions du prestataire, sont influencées par ses commandes en carnet, sa capacité de service actuellement disponible et les décisions d'ajustement de capacité qu'il a prises, mais pas encore implémentées. - Queuing is a fact of life that we witness daily. We all have had the experience of waiting in line for some reason and we also know that it is an annoying situation. As the adage says "time is money"; this is perhaps the best way of stating what queuing problems mean for customers. Human beings are not very tolerant, but they are even less so when having to wait in line for service. Banks, roads, post offices and restaurants are just some examples where people must wait for service. Studies of queuing phenomena have typically addressed the optimisation of performance measures (e.g. average waiting time, queue length and server utilisation rates) and the analysis of equilibrium solutions. The individual behaviour of the agents involved in queueing systems and their decision making process have received little attention. Although this work has been useful to improve the efficiency of many queueing systems, or to design new processes in social and physical systems, it has only provided us with a limited ability to explain the behaviour observed in many real queues. In this dissertation we differ from this traditional research by analysing how the agents involved in the system make decisions instead of focusing on optimising performance measures or analysing an equilibrium solution. This dissertation builds on and extends the framework proposed by van Ackere and Larsen (2004) and van Ackere et al. (2010). We focus on studying behavioural aspects in queueing systems and incorporate this still underdeveloped framework into the operations management field. In the first chapter of this thesis we provide a general introduction to the area, as well as an overview of the results. In Chapters 2 and 3, we use Cellular Automata (CA) to model service systems where captive interacting customers must decide each period which facility to join for service. They base this decision on their expectations of sojourn times. Each period, customers use new information (their most recent experience and that of their best performing neighbour) to form expectations of sojourn time at the different facilities. Customers update their expectations using an adaptive expectations process to combine their memory and their new information. We label "conservative" those customers who give more weight to their memory than to the xiv Summary new information. In contrast, when they give more weight to new information, we call them "reactive". In Chapter 2, we consider customers with different degree of risk-aversion who take into account uncertainty. They choose which facility to join based on an estimated upper-bound of the sojourn time which they compute using their perceptions of the average sojourn time and the level of uncertainty. We assume the same exogenous service capacity for all facilities, which remains constant throughout. We first analyse the collective behaviour generated by the customers' decisions. We show that the system achieves low weighted average sojourn times when the collective behaviour results in neighbourhoods of customers loyal to a facility and the customers are approximately equally split among all facilities. The lowest weighted average sojourn time is achieved when exactly the same number of customers patronises each facility, implying that they do not wish to switch facility. In this case, the system has achieved the Nash equilibrium. We show that there is a non-monotonic relationship between the degree of risk-aversion and system performance. Customers with an intermediate degree of riskaversion typically achieve higher sojourn times; in particular they rarely achieve the Nash equilibrium. Risk-neutral customers have the highest probability of achieving the Nash Equilibrium. Chapter 3 considers a service system similar to the previous one but with risk-neutral customers, and relaxes the assumption of exogenous service rates. In this sense, we model a queueing system with endogenous service rates by enabling managers to adjust the service capacity of the facilities. We assume that managers do so based on their perceptions of the arrival rates and use the same principle of adaptive expectations to model these perceptions. We consider service systems in which the managers' decisions take time to be implemented. Managers are characterised by a profile which is determined by the speed at which they update their perceptions, the speed at which they take decisions, and how coherent they are when accounting for their previous decisions still to be implemented when taking their next decision. We find that the managers' decisions exhibit a strong path-dependence: owing to the initial conditions of the model, the facilities of managers with identical profiles can evolve completely differently. In some cases the system becomes "locked-in" into a monopoly or duopoly situation. The competition between managers causes the weighted average sojourn time of the system to converge to the exogenous benchmark value which they use to estimate their desired capacity. Concerning the managers' profile, we found that the more conservative Summary xv a manager is regarding new information, the larger the market share his facility achieves. Additionally, the faster he takes decisions, the higher the probability that he achieves a monopoly position. In Chapter 4 we consider a one-server queueing system with non-captive customers. We carry out an experiment aimed at analysing the way human subjects, taking on the role of the manager, take decisions in a laboratory regarding the capacity of a service facility. We adapt the model proposed by van Ackere et al (2010). This model relaxes the assumption of a captive market and allows current customers to decide whether or not to use the facility. Additionally the facility also has potential customers who currently do not patronise it, but might consider doing so in the future. We identify three groups of subjects whose decisions cause similar behavioural patterns. These groups are labelled: gradual investors, lumpy investors, and random investor. Using an autocorrelation analysis of the subjects' decisions, we illustrate that these decisions are positively correlated to the decisions taken one period early. Subsequently we formulate a heuristic to model the decision rule considered by subjects in the laboratory. We found that this decision rule fits very well for those subjects who gradually adjust capacity, but it does not capture the behaviour of the subjects of the other two groups. In Chapter 5 we summarise the results and provide suggestions for further work. Our main contribution is the use of simulation and experimental methodologies to explain the collective behaviour generated by customers' and managers' decisions in queueing systems as well as the analysis of the individual behaviour of these agents. In this way, we differ from the typical literature related to queueing systems which focuses on optimising performance measures and the analysis of equilibrium solutions. Our work can be seen as a first step towards understanding the interaction between customer behaviour and the capacity adjustment process in queueing systems. This framework is still in its early stages and accordingly there is a large potential for further work that spans several research topics. Interesting extensions to this work include incorporating other characteristics of queueing systems which affect the customers' experience (e.g. balking, reneging and jockeying); providing customers and managers with additional information to take their decisions (e.g. service price, quality, customers' profile); analysing different decision rules and studying other characteristics which determine the profile of customers and managers.

Creation and Validation of Chronojump-Boscosystem: A Free Tool to Measure Vertical Jumps

Measuring the height of the vertical jump is an indicator of the strength and power of the lower body. The technological tools available to measure the vertical jump are black boxes and are not open to third-party verification or adaptation. We propose the creation of a measurement system called Chronojump-Boscosystem, consisting of open hardware and free software. Methods: A microcontroller was created and validated using a square wave generator and an oscilloscope. Two types of contact platforms were developed using different materials. These platforms were validated by the minimum pressure required for activation at different points by a strain gauge, together with the on/off time of our platforms in respect of the Ergojump-Boscosystem platform by a sample of 8 subjects performing submaximal jumps with one foot on each platform. Agile methodologies were used to develop and validate the software. Results: All the tools fall under the free software / open hardware guidelines and are, in that sense, free. The microcontroller margin of error is 0.1%. The validity of the fiberglass platform is 0.95 (ICC). The management software contains nearly 113.000 lines of code and is available in 7 languages.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

Nonpoint source pollution, space, time and asymetric information - a deposit refund approach

The incorporation of space allows the establishment of a more precise relationship between a contaminating input, a contaminating byproduct and emissions that reach the final receptor. However, the presence of asymmetric information impedes the implementation of the first-best policy. As a solution to this problem a site specific deposit refund system for the contaminating input and the contaminating byproduct are proposed. Moreover, the utilization of a successive optimization technique first over space and second over time enables definition of the optimal intertemporal site specific deposit refund system

Reconstruction of the recent history of a large deep prealpine lake (Lake Bourget, France) using subfossil chironomids, diatoms, and organic matter analysis: towards the definition of a lake-specific reference state

This paper presents the recent history of a large prealpine lake (Lake Bourget) using chironomids, diatoms and organic matter analysis, and deals with the ability of paleolimnological approach to define an ecological reference state for the lake in the sense of the European Framework Directive. The study at low resolution of subfossil chironomids in a 4-m-long core shows the remarkable stability over the last 2.5 kyrs of the profundal community dominated by a Micropsectra-association until the beginning of the twentieth century, when oxyphilous taxa disappeared. Focusing on this key recent period, a high resolution and multiproxy study of two short cores reveals a progressive evolution of the lake's ecological state. Until AD 1880, Lake Bourget showed low organic matter content in the deep sediments (TOC less than 1%) and a well-oxygenated hypolimnion that allowed the development of a profundal oxyphilous chironomid fauna (Micropsectra-association). Diatom communities were characteristic of oligotrophic conditions. Around AD 1880, a slight increase in the TOC was the first sign of changes in lake conditions. This was followed by a first limited decline in oligotrophic diatom taxa and the disappearance of two oxyphilous chironomid taxa at the beginning of the twentieth century. The 1940s were a major turning point in recent lake history. Diatom assemblages and accumulation of well preserved planktonic organic matter in the sediment provide evidence of strong eutrophication. The absence of profundal chironomid communities reveals permanent hypolimnetic anoxia. From AD 1995 to 2006, the diatom assemblages suggest a reduction in nutrients, and a return to mesotrophic conditions, a result of improved wastewater management. However, no change in hypolimnion benthic conditions has been shown by either the organic matter or the subfossil chironomid profundal community. Our results emphasize the relevance of the paleolimnological approach for the assessment of reference conditions for modern lakes. Before AD 1900, the profundal Micropsectra-association and the Cyclotella dominated diatom community can be considered as the Lake Bourget reference community, which reflects the reference ecological state of the lake.

Development of Object-Oriented Design and Specifications for Iowa DOT and Urban Standards: Phase I, TR-487, 2004

Currently, individuals including designers, contractors, and owners learn about the project requirements by studying a combination of paper and electronic copies of the construction documents including the drawings, specifications (standard and supplemental), road and bridge standard drawings, design criteria, contracts, addenda, and change orders. This can be a tedious process since one needs to go back and forth between the various documents (paper or electronic) to obtain information about the entire project. Object-oriented computer-aided design (OO-CAD) is an innovative technology that can bring a change to this process by graphical portrayal of information. OO-CAD allows users to point and click on portions of an object-oriented drawing that are then linked to relevant databases of information (e.g., specifications, procurement status, and shop drawings). The vision of this study is to turn paper-based design standards and construction specifications into an object-oriented design and specification (OODAS) system or a visual electronic reference library (ERL). Individuals can use the system through a handheld wireless book-size laptop that includes all of the necessary software for operating in a 3D environment. All parties involved in transportation projects can access all of the standards and requirements simultaneously using a 3D graphical interface. By using this system, users will have all of the design elements and all of the specifications readily available without concerns of omissions. A prototype object-oriented model was created and demonstrated to potential users representing counties, cities, and the state. Findings suggest that a system like this could improve productivity to find information by as much as 75% and provide a greater sense of confidence that all relevant information had been identified. It was also apparent that this system would be used by more people in construction than in design. There was also concern related to the cost to develop and maintain the complete system. The future direction should focus on a project-based system that can help the contractors and DOT inspectors find information (e.g., road standards, specifications, instructional memorandums) more rapidly as it pertains to a specific project.

Inheritance pattern and selection criteria for resistance to soybean cyst nematode races 3 and 9

The objective of this work was to determine soybean resistance inheritance to Heterodera glycines Ichinohe (soybean cyst nematode - SCN) races 3 and 9, as well as to evaluate the efficiency of direct and indirect selection in a soybean population of 112 recombinant inbred lines (RIL) derived from the resistant cultivar Hartwig. The experiment was conducted in a completely randomized design, in Londrina, PR, Brazil. The estimated narrow-sense heritabilities for resistance to races 3 and 9 were 80.67 and 77.97%. The genetic correlation coefficient (r g = 0.17; p<0.01) shows that some genetic components of resistance to these two races are inherited together. The greatest genetic gain by indirect selection was obtained to race 9, selecting to race 3 due to simpler inheritance of resistance to race 9 and not because these two races share common resistance genes. The resistance of cultivar Hartwig to races 3 and 9 is determined by 4 and 2 genes, respectively. One of these genes confers resistance to both races, explaining a fraction of the significant genetic correlation found between resistance to these SCN races. The inheritance pattern described indicates that selection for resistance to SCN must be performed for each race individually.

Vascular Integrins: Therapeutic and Imaging Targets of Tumor Angiogenesis

Cells, including endothelial cells, continuously sense their surrounding environment and rapidly adapt to changes in order to assure tissues and organs homeostasis. The extracellular matrix (ECM) provides a physical scaffold for cell positioning and represents an instructive interface allowing cells to communicate over short distances. Cell surface receptors of the integrin family emerged through evolution as essential mediators and integrators of ECM-dependent communication. In preclinical studies, pharmacological inhibition of vascular integrins suppressed angiogenesis and inhibited tumor progression. alpha(V)beta(3) and alpha(V)beta(5) were the first integrins targeted to suppress tumor angiogenesis. Subsequently, additional integrins, in particular alpha(1)beta(1), alpha(2)beta(1), alpha(5)beta(1), and alpha(6)beta(4), emerged as potential therapeutic targets. Integrin inhibitors are currently tested in clinical trials for their safety and antiangiogenic/antitumor activity. In this chapter, we review the role of integrins in angiogenesis and present recent advances in the use of integrin antagonists as potential therapeutics in cancer and discuss future perspectives.

Liberties and constraints of the normative approach to evaluation and decision in forensic science: a discussion towards overcoming some common misconceptions

At a time when disciplined inference and decision making under uncertainty represent common aims to participants in legal proceedings, the scientific community is remarkably heterogenous in its attitudes as to how these goals ought to be achieved. Probability and decision theory exert a considerable influence, and we think by all reason rightly do so, but they go against a mainstream of thinking that does not embrace-or is not aware of-the 'normative' character of this body of theory. It is normative, in the sense understood in this article, in that it prescribes particular properties, typically (logical) coherence, to which reasoning and decision making ought to conform. Disregarding these properties can result in diverging views which are occasionally used as an argument against the theory, or as a pretext for not following it. Typical examples are objections according to which people, both in everyday life but also individuals involved at various levels in the judicial process, find the theory difficult to understand and to apply. A further objection is that the theory does not reflect how people actually behave. This article aims to point out in what sense these examples misinterpret the analytical framework in its normative perspective. Through examples borrowed mostly from forensic science contexts, it is argued that so-called intuitive scientific attitudes are particularly liable to such misconceptions. These attitudes are contrasted with a statement of the actual liberties and constraints of probability and decision theory and the view according to which this theory is normative.

Heritability and quantitative genetic divergence of serotiny, a fire-persistence plant trait.

BACKGROUND AND AIMS: Although it is well known that fire acts as a selective pressure shaping plant phenotypes, there are no quantitative estimates of the heritability of any trait related to plant persistence under recurrent fires, such as serotiny. In this study, the heritability of serotiny in Pinus halepensis is calculated, and an evaluation is made as to whether fire has left a selection signature on the level of serotiny among populations by comparing the genetic divergence of serotiny with the expected divergence of neutral molecular markers (QST-FST comparison). METHODS: A common garden of P. halepensis was used, located in inland Spain and composed of 145 open-pollinated families from 29 provenances covering the entire natural range of P. halepensis in the Iberian Peninsula and Balearic Islands. Narrow-sense heritability (h(2)) and quantitative genetic differentiation among populations for serotiny (QST) were estimated by means of an 'animal model' fitted by Bayesian inference. In order to determine whether genetic differentiation for serotiny is the result of differential natural selection, QST estimates for serotiny were compared with FST estimates obtained from allozyme data. Finally, a test was made of whether levels of serotiny in the different provenances were related to different fire regimes, using summer rainfall as a proxy for fire regime in each provenance. KEY RESULTS: Serotiny showed a significant narrow-sense heritability (h(2)) of 0·20 (credible interval 0·09-0·40). Quantitative genetic differentiation among provenances for serotiny (QST = 0·44) was significantly higher than expected under a neutral process (FST = 0·12), suggesting adaptive differentiation. A significant negative relationship was found between the serotiny level of trees in the common garden and summer rainfall of their provenance sites. CONCLUSIONS: Serotiny is a heritable trait in P. halepensis, and selection acts on it, giving rise to contrasting serotiny levels among populations depending on the fire regime, and supporting the role of fire in generating genetic divergence for adaptive traits.

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.