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.

An international university network for online teaching of pathology in Frenchspeaking countries

Introduction: Building online courses is a highly time consuming task for teachers of a single university. Universities working alone create high-quality courses but often cannot cover all pathological fields. Moreover this often leads to duplication of contents among universities, representing a big waste of teacher time and energy. We initiated in 2011 a French university network for building mutualized online teaching pathology cases, and this network has been extended in 2012 to Quebec and Switzerland. Method: Twenty French universities (see & for details), University Laval in Quebec and University of Lausanne in Switzerland are associated to this project. One e-learning Moodle platform (http://moodle.sorbonne-paris-cite.fr/) contains texts with URL pointing toward virtual slides that are decentralized in several universities. Each university has the responsibility of its own slide scanning, slide storage and online display with virtual slide viewers. The Moodle website is hosted by PRES Sorbonne Paris Cité, and financial supports for hardware have been obtained from UNF3S (http://www.unf3s.org/) and from PRES Sorbonne Paris Cité. Financial support for international fellowships has been obtained from CFQCU (http://www.cfqcu.org/). Results: The Moodle interface has been explained to pathology teachers using web-based conferences with screen sharing. The teachers added then contents such as clinical cases, selfevaluations and other media organized in several sections by student levels and pathological fields. Contents can be used as online learning or online preparation of subsequent courses in classrooms. In autumn 2013, one resident from Quebec spent 6 weeks in France and Switzerland and created original contents in inflammatory skin pathology. These contents are currently being validated by senior teachers and will be opened to pathology residents in spring 2014. All contents of the website can be accessed for free. Most contents just require anonymous connection but some specific fields, especially those containing pictures obtained from patients who agreed for a teaching use only, require personal identification of the students. Also, students have to register to access Moodle tests. All contents are written in French but one case has been translated into English to illustrate this communication (http://moodle.sorbonne-pariscite.fr/mod/page/view.php?id=261) (use "login as a guest"). The Moodle test module allows many types of shared questions, making it easy to create personalized tests. Contents that are opened to students have been validated by an editorial committee composed of colleagues from the participating institutions. Conclusions: Future developments include other international fellowships, the next one being scheduled for one French resident from May to October 2014 in Quebec, with a study program centered on lung and breast pathology. It must be kept in mind that these e-learning programs highly depend on teachers' time, not only at these early steps but also later to update the contents. We believe that funding resident fellowships for developing online pathological teaching contents is a win-win situation, highly beneficial for the resident who will improve his knowledge and way of thinking, highly beneficial for the teachers who will less worry about access rights or image formats, and finally highly beneficial for the students who will get courses fully adapted to their practice.

Stand dynamics modelling approaches for multicohort management of eastern Canadian boreal forests

Abstract

The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.

The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.

The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.

Combined approaches in large vestibular schwannomas: surgical and radiosurgical perspective

Purpose: The management of vestibular schwanommas (VS) is challenging, with microsurgery remaining the main treatment option. Planned subtotal resection is now being increasingly considered to reduce the risk of neurological deficits following complete resection. The residual part of the tumor can then be treated with Gamma Knife surgery (GKS) to achieve long-term growth control. Methods: This case series of 11 patients documents early results with planned subtotal resection followed by GKS in Lausanne University Hospital, between July 2010 and March 2012. We analyzed clinical symptoms and signs for all cases, as well as MRI and audiograms. Results: Mean age in this series was 50.3 years (range 24.1-73.4). Two patients (18.2%) had a stereotactic fractionated radiotherapy, which had failed to ensure tumor control, before the microsurgical intervention. The lesions were solid in 9 cases (81.8%), and mixed (solid and cystic) in 2 patients (18.2%). Presurgical tumor volume was of a mean of 18.5 cm3 (range 9.7-34.9 cm3). The mean duration between microsurgery and GKS was 10.5 months (range 4-22.8). The mean tumor volume at the time of GKS treatment was 4.9 cm3 (range 0.5- 12.8). A mean number of 20.7 isocenters was used (range 8-31). Nine patients received 12 Gy and 2 patients with 11 Gy at the periphery (at the 50% prescription isodose). We did not have any major complications in our series. Postoperative status showed no facial nerve deficits. Four patients with useful pre-operative hearing underwent surgery aiming to preserve the cochlear nerve function. Of these patients, the patient who had Gardner-Robertson (GR) class 1 before surgery, remained in GR class 1. Two patients improved after surgery, one changing from GR 5 to GR 3 and the other with slight improvement, remaining in the same GR 3 class. Mean follow-up after surgery was 15.4 months (range 4-31.2). One patient, who presented with secondary trigeminal neuralgia before surgery, had transitory facial hypoesthesia following surgery. No other neurological deficits were encountered. Following GKS, the patients had a mean follow-up of 5.33 months (range 1-13). No new neurological deficits were encountered. Conclusions: Our data suggest that planned subtotal resection followed by GKS has an excellent clinical outcome with respect to preservation of cranial nerves, and other neurological functions, and a good possibility of recovery of many of the pre-operative cranial nerve dysfunctions

Aggregation invariance in general clustering approaches

General clustering deals with weighted objects and fuzzy memberships. We investigate the group- or object-aggregation-invariance properties possessed by the relevant functionals (effective number of groups or objects, centroids, dispersion, mutual object-group information, etc.). The classical squared Euclidean case can be generalized to non-Euclidean distances, as well as to non-linear transformations of the memberships, yielding the c-means clustering algorithm as well as two presumably new procedures, the convex and pairwise convex clustering. Cluster stability and aggregation-invariance of the optimal memberships associated to the various clustering schemes are examined as well.

Online teaching of inflammatory skin pathology by a French-speaking International University Network.

INTRODUCTION: Developments in technology, web-based teaching and whole slide imaging have broadened the teaching horizon in anatomic pathology. Creating online learning material including many types of media such as radiologic images, whole slides, videos, clinical and macroscopic photographs, is now accessible to most universities. Unfortunately, a major limiting factor to maintain and update the learning material is the amount of resources needed. In this perspective, a French-national university network was initiated in 2011 to build joint online teaching modules consisting of clinical cases and tests. The network has since expanded internationally to Québec, Switzerland and Ivory Coast. METHOD: One of the first steps of the project was to build a learning module on inflammatory skin pathology for interns and residents in pathology and dermatology. A pathology resident from Québec spent 6 weeks in France and Switzerland to develop the contents and build the module on an e-learning Moodle platform under the supervision of two dermatopathologists. The learning module contains text, interactive clinical cases, tests with feedback, virtual slides, images and clinical photographs. For that module, the virtual slides are decentralized in 2 universities (Bordeaux and Paris 7). Each university is responsible of its own slide scanning, image storage and online display with virtual slide viewers. RESULTS: The module on inflammatory skin pathology includes more than 50 web pages with French original content, tests and clinical cases, links to over 45 virtual images and more than 50 microscopic and clinical photographs. The whole learning module is being revised by four dermatopathologists and two senior pathologists. It will be accessible to interns and residents in the spring of 2014. The experience and knowledge gained from that work will be transferred to the next international resident whose work will be aimed at creating lung and breast pathology learning modules. CONCLUSION: The challenges of sustaining a project of this scope are numerous. The technical aspect of whole-slide imaging and storage needs to be developed by each university or group. The content needs to be regularly updated and its accuracy reviewed by experts in each individual domain. The learning modules also need to be promoted within the academic community to ensure maximal benefit for trainees. A collateral benefit of the project was the establishment of international partnerships between French-speaking universities and pathologists with the common goal of promoting pathology education through the use of multi-media technology including whole slide imaging.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

Oxidative stress in aging: advances in proteomic approaches

Aging is a gradual, complex process in which cells, tissues, organs, and the whole organism itself deteriorate in a progressive and irreversible manner that, in the majority of cases, implies pathological conditions that affect the individual"s Quality of Life (QOL). Although extensive research efforts in recent years have been made, the anticipation of aging and prophylactic or treatment strategies continue to experience major limitations. In this review, the focus is essentially on the compilation of the advances generated by cellular expression profile analysis through proteomics studies (two-dimensional [2D] electrophoresis and mass spectrometry [MS]), which are currently used as an integral approach to study the aging process. Additionally, the relevance of the oxidative stress factors is discussed. Emphasis is placed on postmitotic tissues, such as neuronal, muscular, and red blood cells, which appear to be those most frequently studied with respect to aging. Additionally, models for the study of aging are discussed in a number of organisms, such as Caenorhabditis elegans, senescence-accelerated probe-8 mice (SAMP8), naked mole-rat (Heterocephalus glaber), and the beagle canine. Proteomic studies in specific tissues and organisms have revealed the extensive involvement of reactive oxygen species (ROS) and oxidative stress in aging.

Faculty-librarians collaboration in an e-learning experience : resources management and training in Open University of Catalonia (UOC)

The traditional model of learning based on knowledge transfer doesn't promote the acquisition of information-related competencies and development of autonomous learning. More needs to be done to embrace learner-centred approaches, based on constructivism, collaboration and co-operation. This new learning paradigm is aligned with the European Higher Education Area (EHEA) requirements. In this sense, a learning experience based in faculty' librarian collaboration was seen as the best option for promoting student engagement and also a way to increase information-related competences in Open University of Catalonia (UOC) academic context. This case study outlines the benefits of teacher-librarian collaboration in terms of pedagogy innovation, resources management and introduction of open educational resources (OER) in virtual classrooms, Information literacy (IL) training and use of 2.0 tools in teaching. Our faculty-librarian's collaboration aims to provide an example of technology-enhanced learning and demonstrate how working together improves the quality and relevance of educational resources in UOC's virtual classrooms. Under this new approach, while teachers change their role from instructors to facilitators of the learning process and extend their reach to students, libraries acquire an important presence in the academic learning communities.

Marketing education, distance learning and hypermedia : teaching 'current issues in marketing' in a virtual campus

This article reports on a project at the Universitat Oberta de Catalunya (UOC: The Open University of Catalonia, Barcelona) to develop an innovative package of hypermedia-based learning materials for a new course entitled 'Current Issues in Marketing'. The UOC is a distance university entirely based on a virtual campus. The learning materials project was undertaken in order to benefit from the advantages which new communication technologies offer to the teaching of marketing in distance education. The article reviews the main issues involved in incorporating new technologies in learning materials, the development of the learning materials, and their functioning within the hypermedia based virtual campus of the UOC. An empirical study is then carried out in order to evaluate the attitudes of students to the project. Finally, suggestions for improving similar projects in the future are put forward.

Time in e-Learning Research: A Qualitative Review of the Empirical Consideration of Time in Research into e-Learning

Peer-reviewed