Tallers d'Espai Públic. Els barris del marge dret de Sant Adrià i el riu Besòs

Report of one of the workshops developed in 2005 under the process of public participation: Mapping La Mina (2002-2006). http://www.ub.edu/escult/mina

Tallers d'espai públic. Toponímies

Report of one of the workshops developed in 2005 under the process of public participation: Mapping La Mina (2002-2006). http://www.ub.edu/escult/mina

08024-14 Duck Creek Watershed Final Report

River Action is requesting funds for a project that offers design, technical and financial assistance to residential and commercial landowners and municipalities for the installation of buffers along Duck Creek and its tributaries. The buffers will improve water quality, reduce erosion on stream banks and provide habitat for wildlife. The projects will be planned and implemented through public meetings and educational workshops. This method of community involvement will increase awareness and education concerning the impairments in Duck Creek in Davenport and Bettendorf in Scott County, Iowa and promote personal responsibility and stewardship of watersheds.

Methods for Removing Concrete Decks from Bridge Girders, TR-647, 2014

With ever tightening budgets and limitations of demolition equipment, states are looking for cost-effective, reliable, and sustainable methods for removing concrete decks from bridges. The goal of this research was to explore such methods. The research team conducted qualitative studies through a literature review, interviews, surveys, and workshops and performed small-scale trials and push-out tests (shear strength evaluations). Interviews with bridge owners and contractors indicated that concrete deck replacement was more economical than replacing an entire superstructure under the assumption that the salvaged superstructure has adequate remaining service life and capacity. Surveys and workshops provided insight into advantages and disadvantages of deck removal methods, information that was used to guide testing. Small-scale trials explored three promising deck removal methods: hydrodemolition, chemical splitting, and peeling

Good practice for dialogue and communication as strategic principle for policing political manifestations in Europe (GODIAC):field study handbook

This handbook describes the peer review methodology that was applied at the GODIAC project fi eld studies1. The peer review evaluation method as initiated by Otto Adang in the Netherlands and further developed in a European football context (Adang & Brown, 2008) involves experienced police offi cers cooperating with researchers to perform observational fi eld studies to identify good practices and learning points for public order management. The handbook builds on the GODIAC seminars and workshops, for the fi eld study members, which took place in September 2010, January 2012 and January 2013. The handbook has been discussed in the project group and in the steering committee. It is primarily written for the GODIAC fi eld study members as background material for understanding the fi eld study process and for clarifying the different responsibilities that enable active participation in the fi eld study. The handbook has been developed during the project period and incorporates learning points and developments of the peer review method. The handbook aims at promoting the use of fi eld studies for evaluation of policing major events.

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.

Rehabilitación del Parque Recreativo Cariari : propuesta de actividades de educación ambiental

El proyecto de rehabilitación del "Parque Recreativo Cariari" se enmarca en el proyecto más amplio "Proyecto Limón Ciudad Puerto". Se ubica en la zona más desfavorecida de Costa Rica, a nivel social y de infraestructuras. Nuestra labor en el proyecto de rehabilitación, se centra en la preparación de la propuesta de actividades de educación ambiental, aprovechando los recursos biológicos y geológicos del parque, así como las infraestructuras que incluirá una vez rehabilitado. Para la realización del proyecto fue necesaria una gran revisión bibliográfica, una serie de encuestas y reuniones con escuelas, profesores y la responsable del Ministerio de Educación Pública en la región. También se contactó con personal especializado con materias claves para los talleres. Además se acordó con universidades nacionales la realización de prácticas de sus estudiantes de los sectores de turismo ecológico y administración. Así, el resultado, son los 18 talleres, y la preparación de un hormiguero y un mariposario abierto, que se implementarán durante la rehabilitación del parque. De llevarse a cabo correctamente, se espera que transmitan a sus participantes la importancia ambiental de la zona, implicándolos en la conservación de esta. A nivel general, se espera que el parque sea vía de escape y ayude al progreso social y mejora de la calidad de vida de Limón.

Assessing farmers' vulnerability to climate change : a case study in Karnataka, India

In the context of observed climate change impacts and their effect on agriculture and crop production, this study intends to assess the vulnerability of rural livelihoods through a study case in Karnataka, India. The social approach of climate change vulnerability in this study case includes defining and exploring factors that determine farmers’ vulnerability in four villages. Key informant interviews, farmer workshops and structured household interviews were used for data collection. To analyse the data, we adapted and applied three vulnerability indices: Livelihood Vulnerability Index (LVI), LVI-IPCC and the Livelihood Effect Index (LEI), and used descriptive statistical methods. The data was analysed at two scales: whole sample-level and household level. The results from applying the indices for the whole-sample level show that this community's vulnerability to climate change is moderate, whereas the household-level results show that most of the households' vulnerability is high-very high, while 15 key drivers of vulnerability were identified. Results and limitations of the study are discussed under the rural livelihoods framework, in which the indices are based, allowing a better understanding of the social behaviouraltrends, as well as an holistic and integrated view of the climate change, agriculture, and livelihoods processes shaping vulnerability. We conclude that these indices, although a straightforward method to assess vulnerability, have limitations that could account for inaccuracies and inability to be standardised for benchmarking, therefore we stress the need for further research.

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.

IOWATER Sustaining the Wave

Summary of the IOWATER Program and workshops offered.

Recursos naturales y género. El caso de la comunidad Once de Mayo (Campeche, México)

Este estudio pretende realizar una aproximación al papel de la cooperación en el empoderamiento femenino sobre los recursos naturales en la comunidad rural mexicana Once de Mayo. Para ello se analizan las experiencias de doce participantes en proyectos destinados a mujeres utilizando como medios la historia de vida, la observación participativa y los talleres. De todo ello se desprende la importancia de que los programas y/o proyectos aborden las necesidades prácticas de las mujeres, vinculadas a su hogar, sin omitir sus necesidades estratégicas de género como elementos para el empoderamiento. Además, para el empoderamiento femenino a través de la cooperación es fundamental el desarrollo de actitudes de liderazgo por parte de alguna/as de las participantes y el propio interés de todas las involucradas en colaborar, además de sus experiencias previas de capacitación en diferentes temáticas.

A Framework for Building Hypercubes Using MapReduce

The European Space Agency's Gaia mission will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), by providing unprecedented position, parallax, proper motion, and radial velocity measurements for about one billion stars. The resulting catalogue will be made available to the scientific community and will be analyzed in many different ways, including the production of a variety of statistics. The latter will often entail the generation of multidimensional histograms and hypercubes as part of the precomputed statistics for each data release, or for scientific analysis involving either the final data products or the raw data coming from the satellite instruments. In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigmbut without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission. In addition, we put forward the advantages and shortcomings of the deployment of the framework on a public cloud provider, benchmark against other popular solutions available (that are not always the best for such ad-hoc applications), and describe some user experiences with the framework, which was employed for a number of dedicated astronomical data analysis techniques workshops.

A Framework for Building Hypercubes Using MapReduce

The European Space Agency's Gaia mission will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), by providing unprecedented position, parallax, proper motion, and radial velocity measurements for about one billion stars. The resulting catalogue will be made available to the scientific community and will be analyzed in many different ways, including the production of a variety of statistics. The latter will often entail the generation of multidimensional histograms and hypercubes as part of the precomputed statistics for each data release, or for scientific analysis involving either the final data products or the raw data coming from the satellite instruments. In this paper we present and analyze a generic framework that allows the hypercube generation to be easily done within a MapReduce infrastructure, providing all the advantages of the new Big Data analysis paradigmbut without dealing with any specific interface to the lower level distributed system implementation (Hadoop). Furthermore, we show how executing the framework for different data storage model configurations (i.e. row or column oriented) and compression techniques can considerably improve the response time of this type of workload for the currently available simulated data of the mission. In addition, we put forward the advantages and shortcomings of the deployment of the framework on a public cloud provider, benchmark against other popular solutions available (that are not always the best for such ad-hoc applications), and describe some user experiences with the framework, which was employed for a number of dedicated astronomical data analysis techniques workshops.

Early Iron Age Pottery: A Quantitative Approach. Proceedings of the International Round Table organized by the Swiss School of Archaeology in Greece (Athens, Novembre 28-30, 2008).

Quantitative approaches in ceramology are gaining ground in excavation reports, archaeological publications and thematic studies. Hence, a wide variety of methods are being used depending on the researchers' theoretical premise, the type of material which is examined, the context of discovery and the questions that are addressed. The round table that took place in Athens on November 2008 was intended to offer the participants the opportunity to present a selection of case studies on the basis of which methodological approaches were discussed. The aim was to define a set of guidelines for quantification that would prove to be of use to all researchers. Contents: 1) Introduction (Samuel Verdan); 2) Isthmia and beyond. How can quantification help the analysis of EIA sanctuary deposits? (Catherine Morgan); 3) Approaching aspects of cult practice and ethnicity in Early Iron Age Ephesos using quantitative analysis of a Protogeometric deposit from the Artemision (Michael Kerschner); 4) Development of a ceramic cultic assemblage: Analyzing pottery from Late Helladic IIIC through Late Geometric Kalapodi (Ivonne Kaiser, Laura-Concetta Rizzotto, Sara Strack); 5) 'Erfahrungsbericht' of application of different quantitative methods at Kalapodi (Sara Strack); 6) The Early Iron Age sanctuary at Olympia: counting sherds from the Pelopion excavations (1987-1996) (Birgitta Eder); 7) L'aire du pilier des Rhodiens à Delphes: Essai de quantification du mobilier (Jean-Marc Luce); 8) A new approach in ceramic statistical analyses: Pit 13 on Xeropolis at Lefkandi (David A. Mitchell, Irene S. Lemos); 9) Households and workshops at Early Iron Age Oropos: A quantitative approach of the fine, wheel-made pottery (Vicky Vlachou); 10) Counting sherds at Sindos: Pottery consumption and construction of identities in the Iron Age (Stefanos Gimatzidis); 11) Analyse quantitative du mobilier céramique des fouilles de Xombourgo à Ténos et le cas des supports de caisson (Jean-Sébastien Gros); 12) Defining a typology of pottery from Gortyn: The material from a pottery workshop pit, (Emanuela Santaniello); 13) Quantification of ceramics from Early Iron Age tombs (Antonis Kotsonas); 14) Quantitative analysis of the pottery from the Early Iron Age necropolis of Tsikalario on Naxos (Xenia Charalambidou); 15) Finding the Early Iron Age in field survey: Two case studies from Boeotia and Magnesia (Vladimir Stissi); 16) Pottery quantification: Some guidelines (Samuel Verdan).