Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

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.

Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?

We propose a multivariate approach to the study of geographic species distribution which does not require absence data. Building on Hutchinson's concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in Situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.

Le livre sous la loupe : Nouvelles formes d’écriture électronique

Les fichiers qui accompagnent le document incluent une archive .jar du zoom-éditeur (qui peut être lancé via un browser) et des exemples de z-textes réalisés avec ce logiciel.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Análise da aceitação de aguardentes de cana por testes afetivos e mapa de preferência interno

A aceitação de 11 amostras de aguardentes de cana envelhecidas e não envelhecidas foi avaliada por testes sensoriais afetivos e análises estatísticas uni e multivariada. As aguardentes estudadas compreenderam seis amostras comerciais de diferentes marcas, (sendo três não envelhecidas e três envelhecidas) e ainda outras cinco amostras correspondentes a zero, 12, 24, 36 e 48 meses de envelhecimento em um tonel de carvalho de 200L. As amostras foram avaliadas por 100 provadores consumidores do produto, recrutados por questionário de avaliação quanto à afetividade. Para os testes afetivos foi utilizada escala hedônica não estruturada de 9cm, sendo os dados obtidos avaliados por dois métodos estatísticos distintos: o Mapa de Preferência Interno (MDPREF) e a análise de variância univariada (ANOVA) com comparação de médias pelo teste de Tukey e análise de correlação. As amostras de aguardente envelhecidas por 12, 36 e 48 meses obtiveram maior aceitação, com médias ao redor de 7,0 na escala hedônica. A amostra com menor aceitação foi a correspondente ao tempo zero de envelhecimento (controle). As demais amostras obtiveram aceitação intermediária. A análise por MDPREF gerou em espaço multidimensional (onde as variações com relação aos dados de preferência foram extraídas em eixos ortogonais e para cada dimensão de preferência), coordenadas relativas aos produtos, que foram geradas em função da resposta dos consumidores. Os dados de aceitação de cada provador foram utilizados para o desenvolvimento de vetores individuais de preferência, resultando na construção de um mapa mutidimensional das amostras, em função dos dados de aceitação. No presente estudo o MDPREF foi gerado pelas primeira e segunda dimensões de preferência, as quais explicaram em conjunto 89,83% das variações observadas entre as amostras com relação à aceitação. O MDPREF confirmou os resultados da ANOVA, indicando uma maior preferência dos provadores pelas amostras de aguardentes envelhecidas. Os resultados sugerem também que aguardentes envelhecidas por mais de 24 meses em tonel de carvalho de 200L são preferidas pelos consumidores, em detrimento das comerciais não envelhecidas e mesmo das comerciais envelhecidas, que podem ser adicionadas de aguardente não envelhecida (processo denominado corte) e também ter correção da cor, conforme permite a Legislação Brasileira. O conteúdo de polifenóis totais e a intensidade de cor também foram determinados, e ambos apresentaram correlação linear positiva significativa (p<=0,05) com o aumento do tempo de envelhecimento das amostras.

Knowledge extraction using visualization of hemoglobin parameters to identify thalassemia

The analysis of large amounts of data is better performed by humans when represented in a graphical format. Therefore, a new research area called the Visual Data Mining is being developed endeavoring to use the number crunching power of computers to prepare data for visualization, allied to the ability of humans to interpret data presented graphically.This work presents the results of applying a visual data mining tool, called FastMapDB to detect the behavioral pattern exhibited by a dataset of clinical information about hemoglobinopathies known as thalassemia. FastMapDB is a visual data mining tool that get tabular data stored in a relational database such as dates, numbers and texts, and by considering them as points in a multidimensional space, maps them to a three-dimensional space. The intuitive three-dimensional representation of objects enables a data analyst to see the behavior of the characteristics from abnormal forms of hemoglobin, highlighting the differences when compared to data from a group without alteration.

Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

The International Trans-Antarctic Scientific Expedition (ITASE): An Overview

From its original formulation in 1990 the International Trans-Antarctic Scientific Expedition (ITASE) has had as its primary aim the collection and interpretation of a continent-wide array of environmental parameters assembled through the coordinated efforts of scientists from several nations. ITASE offers the ground-based opportunities of traditional-style traverse travel coupled with the modern technology of CPS, crevasse detecting radar, satellite communications and multidisciplinary research. By operating predominantly in the mode of an oversnow traverse, ITASE offers scientists the opportunity to experience the dynamic range of the Antarctic environment. ITASE also offers an important interactive venue for research similar to that afforded by oceanographic research vessels and large polar field camps, without the cost of the former or the lack of mobility of the latter. More importantly, the combination of disciplines represented by ITASE provides a unique, multidimensional (space and time) view of the ice sheet and its history. ITASE has now collected > 20 000 km of snow radar, recovered more than 240 firn/ice cores (total length 7000m), remotely penetrated to similar to 4000m into the ice sheet, and sampled the atmosphere to heights of > 20 km.

A multiple pheromone ant clustering algorithm

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis. © 2014 Springer International Publishing Switzerland.

A Bimodality Test in High Dimensions

We present a test for identifying clusters in high dimensional data based on the k-means algorithm when the null hypothesis is spherical normal. We show that projection techniques used for evaluating validity of clusters may be misleading for such data. In particular, we demonstrate that increasingly well-separated clusters are identified as the dimensionality increases, when no such clusters exist. Furthermore, in a case of true bimodality, increasing the dimensionality makes identifying the correct clusters more difficult. In addition to the original conservative test, we propose a practical test with the same asymptotic behavior that performs well for a moderate number of points and moderate dimensionality. ACM Computing Classification System (1998): I.5.3.

La institucionalidad rural en Colombia : Reflexiones para su análisis y fortalecimiento

La cuestión rural constituye uno de los grandes desafíos para la institucionalidad en Colombia. La discusión respecto a la eficiencia y eficacia institucional para el sector rural debe adelantarse fundamentada en aspectos coyunturales que a su vez median en la dinámica social, política, cultural, ambiental, económica y productiva en el agro colombiano, entre los cuales se incluyen los tratados comerciales y un eventual posconflicto. La nueva ruralidad, como enfoque para el desarrollo rural, plantea una visión distinta en torno a la temática: concibe lo rural como un espacio multisectorial y multidimensional, lo cual constituye el punto de partida desde el cual surgen los elementos de análisis que permiten adelantar un debate institucional amplio y participativo de cara a la transformación estructural de la realidad rural.

La institucionalidad rural en Colombia : Reflexiones para su análisis y fortalecimiento

La cuestión rural constituye uno de los grandes desafíos para la institucionalidad en Colombia. La discusión respecto a la eficiencia y eficacia institucional para el sector rural debe adelantarse fundamentada en aspectos coyunturales que a su vez median en la dinámica social, política, cultural, ambiental, económica y productiva en el agro colombiano, entre los cuales se incluyen los tratados comerciales y un eventual posconflicto. La nueva ruralidad, como enfoque para el desarrollo rural, plantea una visión distinta en torno a la temática: concibe lo rural como un espacio multisectorial y multidimensional, lo cual constituye el punto de partida desde el cual surgen los elementos de análisis que permiten adelantar un debate institucional amplio y participativo de cara a la transformación estructural de la realidad rural.

La institucionalidad rural en Colombia : Reflexiones para su análisis y fortalecimiento

La cuestión rural constituye uno de los grandes desafíos para la institucionalidad en Colombia. La discusión respecto a la eficiencia y eficacia institucional para el sector rural debe adelantarse fundamentada en aspectos coyunturales que a su vez median en la dinámica social, política, cultural, ambiental, económica y productiva en el agro colombiano, entre los cuales se incluyen los tratados comerciales y un eventual posconflicto. La nueva ruralidad, como enfoque para el desarrollo rural, plantea una visión distinta en torno a la temática: concibe lo rural como un espacio multisectorial y multidimensional, lo cual constituye el punto de partida desde el cual surgen los elementos de análisis que permiten adelantar un debate institucional amplio y participativo de cara a la transformación estructural de la realidad rural.

New Frontiers in Energy-Efficient Computing

Energy-efficient computing remains a critical challenge across the wide range of future data-processing engines — from ultra-low-power embedded systems to servers, mainframes, and supercomputers. In addition, the advent of cloud and mobile computing as well as the explosion of IoT technologies have created new research challenges in the already complex, multidimensional space of modern and future computer systems. These new research challenges led to the establishment of the IEEE Rebooting Computing Initiative, which specifically addresses novel low-power solutions and technologies as one of the main areas of concern.With this in mind, we thought it timely to survey the state of the art of energy-efficient computing.