Watching the World Cup, one data point at a time

Retirado do blog de Marc Pickren do dia 13 jun. 2014.

Watching the World Cup, one data point at a time

Sound the vuvuzelas, the World Cup is officially here. The biggest sporting event in the world is set to break all kinds of viewing records. Sporting in the digital world is just as much about stats as it is about the game itself. Enter Brandwatch. The social media analytics company has taken it upon itself to track social media statistics for the entire run of the World Cup with their new real-time data visualization tool.

Aquisição, processamento e análise de dados de sísmica de alta resolução na plataforma continental norte do Rio Grande do Norte: Vale inciso do rio açu

On the modern Continental Shelf to the north of Rio Grande do Norte state (NE Brazil) is located a paleo-valley, submerged during the last glacial sea-level lowstand, that marks continuation of the most important river of this area (Açu River). Despite the high level of exploration activity of oil industry, there is few information about shallow stratigraphy. Aiming to fill this gap, situated on the Neogene, was worked a marine seismic investigation, the development of a processing flow for high resolution data seismic, and the recognition of the main feature morphology of the study area: the incised valley of the River Açu. The acquisition of shallow seismic data was undertaken in conjunction with the laboratory of Marine Geology/Geophysics and Environmental Monitoring - GGEMMA of Federal University of Rio Grande do Norte UFRN, in SISPLAT project, where the geomorphological structure of the Rio paleovale Açu was the target of the investigation survey. The acquisition of geophysical data has been over the longitudinal and transverse sections, which were subsequently submitted to the processing, hitherto little-used and / or few addressed in the literature, which provided a much higher quality result with the raw data. Once proposed for the flow data was developed and applied to the data of X-Star (acoustic sensor), using available resources of the program ReflexW 4.5 A surface fluvial architecture has been constructed from the bathymetric data and remote sensing image fused and draped over Digital Elevation Models to create three-dimensional (3D) perspective views that are used to analyze the 3D geometry geological features and provide the mapping morphologically defined. The results are expressed in the analysis of seismic sections that extend over the region of the continental shelf and upper slope from mouth of the Açu River to the shelf edge, providing the identification / quantification of geometrical features such as depth, thickness, horizons and units seismic stratigraphyc area, with emphasis has been placed on the palaeoenvironmental interpretation of discordance limit and fill sediment of the incised valley, control by structural elements, and marked by the influence of changes in the sea level. The interpretation of the evolution of this river is worth can bring information to enable more precise descriptions and interpretations, which describes the palaeoenvironmental controls influencing incised valley evolution and preservation to provide a better comprehensive understanding of this reservoir analog system

3D geovisualisation techniques applied in spatial data mining

The increase in the number of spatial data collected has motivated the development of geovisualisation techniques, aiming to provide an important resource to support the extraction of knowledge and decision making. One of these techniques are 3D graphs, which provides a dynamic and flexible increase of the results analysis obtained by the spatial data mining algorithms, principally when there are incidences of georeferenced objects in a same local. This work presented as an original contribution the potentialisation of visual resources in a computational environment of spatial data mining and, afterwards, the efficiency of these techniques is demonstrated with the use of a real database. The application has shown to be very interesting in interpreting obtained results, such as patterns that occurred in a same locality and to provide support for activities which could be done as from the visualisation of results. © 2013 Springer-Verlag.

Avaliação clínica da estabilidade dimensional de próteses totais submetidas à desinfecção por microondas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Análise da cintilação ionosférica no Brasil empregando GNSS e técnicas de mineração e visualização de dados

Pós-graduação em Ciências Cartográficas - FCT

Engine LES with fuel-spray modeling

Pós-graduação em Engenharia Mecânica - FEG

Learning kernels for support vector machines with polynomial powers of sigmoid

In the pattern recognition research field, Support Vector Machines (SVM) have been an effectiveness tool for classification purposes, being successively employed in many applications. The SVM input data is transformed into a high dimensional space using some kernel functions where linear separation is more likely. However, there are some computational drawbacks associated to SVM. One of them is the computational burden required to find out the more adequate parameters for the kernel mapping considering each non-linearly separable input data space, which reflects the performance of SVM. This paper introduces the Polynomial Powers of Sigmoid for SVM kernel mapping, and it shows their advantages over well-known kernel functions using real and synthetic datasets.

Aprendizado de máquina baseado em tensores e suas aplicacções para floresta de caminhos ótimos

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

Forecasting and visualization of wildfires in a 3D geographical information system

[EN]This paper describes a wildfi re forecasting application based on a 3D virtual environment and a fi re simulation engine. A novel open source framework is presented for the development of 3D graphics applications over large geographic areas, off ering high performance 3D visualization and powerful interaction tools for the Geographic Information Systems (GIS) community. The application includes a remote module that allows simultaneous connection of several users for monitoring a real wildfi re event.

NMR, Metabonomics and Molecular Profiles: Applications to the Quality Assessment of Foodstuffs

Nuclear Magnetic Resonance (NMR) is a branch of spectroscopy that is based on the fact that many atomic nuclei may be oriented by a strong magnetic field and will absorb radiofrequency radiation at characteristic frequencies. The parameters that can be measured on the resulting spectral lines (line positions, intensities, line widths, multiplicities and transients in time-dependent experi-ments) can be interpreted in terms of molecular structure, conformation, molecular motion and other rate processes. In this way, high resolution (HR) NMR allows performing qualitative and quantitative analysis of samples in solution, in order to determine the structure of molecules in solution and not only. In the past, high-field NMR spectroscopy has mainly concerned with the elucidation of chemical structure in solution, but today is emerging as a powerful exploratory tool for probing biochemical and physical processes. It represents a versatile tool for the analysis of foods. In literature many NMR studies have been reported on different type of food such as wine, olive oil, coffee, fruit juices, milk, meat, egg, starch granules, flour, etc using different NMR techniques. Traditionally, univariate analytical methods have been used to ex-plore spectroscopic data. This method is useful to measure or to se-lect a single descriptive variable from the whole spectrum and , at the end, only this variable is analyzed. This univariate methods ap-proach, applied to HR-NMR data, lead to different problems due especially to the complexity of an NMR spectrum. In fact, the lat-ter is composed of different signals belonging to different mole-cules, but it is also true that the same molecules can be represented by different signals, generally strongly correlated. The univariate methods, in this case, takes in account only one or a few variables, causing a loss of information. Thus, when dealing with complex samples like foodstuff, univariate analysis of spectra data results not enough powerful. Spectra need to be considered in their wholeness and, for analysing them, it must be taken in consideration the whole data matrix: chemometric methods are designed to treat such multivariate data. Multivariate data analysis is used for a number of distinct, differ-ent purposes and the aims can be divided into three main groups: • data description (explorative data structure modelling of any ge-neric n-dimensional data matrix, PCA for example); • regression and prediction (PLS); • classification and prediction of class belongings for new samples (LDA and PLS-DA and ECVA). The aim of this PhD thesis was to verify the possibility of identify-ing and classifying plants or foodstuffs, in different classes, based on the concerted variation in metabolite levels, detected by NMR spectra and using the multivariate data analysis as a tool to inter-pret NMR information. It is important to underline that the results obtained are useful to point out the metabolic consequences of a specific modification on foodstuffs, avoiding the use of a targeted analysis for the different metabolites. The data analysis is performed by applying chemomet-ric multivariate techniques to the NMR dataset of spectra acquired. The research work presented in this thesis is the result of a three years PhD study. This thesis reports the main results obtained from these two main activities: A1) Evaluation of a data pre-processing system in order to mini-mize unwanted sources of variations, due to different instrumental set up, manual spectra processing and to sample preparations arte-facts; A2) Application of multivariate chemiometric models in data analy-sis.

Slope stability analysis by multi-temporal DEMs and 3D modelling: The 2002 and 2007 Stromboli landslide events

Natural hazard related to the volcanic activity represents a potential risk factor, particularly in the vicinity of human settlements. Besides to the risk related to the explosive and effusive activity, the instability of volcanic edifices may develop into large landslides often catastrophically destructive, as shown by the collapse of the northern flank of Mount St. Helens in 1980. A combined approach was applied to analyse slope failures that occurred at Stromboli volcano. SdF slope stability was evaluated by using high-resolution multi-temporal DTMMs and performing limit equilibrium stability analyses. High-resolution topographical data collected with remote sensing techniques and three-dimensional slope stability analysis play a key role in understanding instability mechanism and the related risks. Analyses carried out on the 2002–2003 and 2007 Stromboli eruptions, starting from high-resolution data acquired through airborne remote sensing surveys, permitted the estimation of the lava volumes emplaced on the SdF slope and contributed to the investigation of the link between magma emission and slope instabilities. Limit Equilibrium analyses were performed on the 2001 and 2007 3D models, in order to simulate the slope behavior before 2002-2003 landslide event and after the 2007 eruption. Stability analyses were conducted to understand the mechanisms that controlled the slope deformations which occurred shortly after the 2007 eruption onset, involving the upper part of slope. Limit equilibrium analyses applied to both cases yielded results which are congruent with observations and monitoring data. The results presented in this work undoubtedly indicate that hazard assessment for the island of Stromboli should take into account the fact that a new magma intrusion could lead to further destabilisation of the slope, which may be more significant than the one recently observed because it will affect an already disarranged deposit and fractured and loosened crater area. The two-pronged approach based on the analysis of 3D multi-temporal mapping datasets and on the application of LE methods contributed to better understanding volcano flank behaviour and to be prepared to undertake actions aimed at risk mitigation.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Airborne in situ measurements of ice particles in the tropical tropopause layer

Ice clouds have a strong effect on the Earth-atmosphere radiative energy balance, on the distribution of condensable gases in the atmosphere, as well as on the chemical composition of the air. The ice particles in these clouds can take on a variety of shapes which makes the description of the cloud microphysical properties more difficult. In the tropical upper troposphere/lower stratosphere (UTLS), a region where ice cloud abundance is relatively high, different types of ice clouds can be observed. However, in situ measurements are rare due to the high altitude of these clouds and the few available research aircraft, only three worldwide, that can fly at such altitudes.rnThis work focuses on in situ measurements of the tropical UTLS clouds performedrnwith a Cloud Imaging Probe (CIP) and a Forward Scattering Spectrometer Probern(FSSP-100), whereof the CIP is the key instrument of this thesis. The CIP is anrnairborne in situ instrument that obtains two-dimensional shadow images of cloud particles. Several cloud microphysical parameters can be derived from these measurements, e.g. number concentrations and size distributions. In order to obtain a high quality data set, a careful image analysis and several corrections need to be applied to the CIP observations. These methods are described in detail.rnMeasurements within the tropical UTLS have been performed during two campaigns:rnSCOUT-O3, 2005 in Northern Australia and SCOUT-AMMA, 2006 inWest Africa. Thernobtained data set includes first observations of subvisible cirrus clouds over a continental area and observations of the anvils of deep convective clouds. The latter can be further divided into clouds in mesoscale convective system outflows of different ages and clouds in overshooting cloud turrets that even penetrated the stratosphere. The microphysical properties of these three cloud types are discussed in detail. Furthermore, the vertical structure of the ice clouds in the UTLS is investigated. The values of the microphysical parameters were found to decrease with increasing altitude in the upper troposphere. Particle numbers and maximum sizes were also decreasing with increasing age of the outflow clouds. Further differences between the deep convective clouds and subvisible cirrus were found in the particle morphology as well as in the ratio of the observed aerosol particles to cloud particles which indicates that the different freezing processes (deposition, contact, immersion freezing) play different roles in the formation of the respective clouds. For the achievementrnof a better microphysical characterisation and description numerical fits have been adjusted onto the cloud particle size distributions of the subvisible cirrus as well as on the size distributions of the clouds at different altitudes in the UTLS.