Agraphs: definição, implementação e suas ferramentas

Programs manipulate information. However, information is abstract in nature and needs to be represented, usually by data structures, making it possible to be manipulated. This work presents the AGraphs, a representation and exchange format of the data that uses typed directed graphs with a simulation of hyperedges and hierarchical graphs. Associated to the AGraphs format there is a manipulation library with a simple programming interface, tailored to the language being represented. The AGraphs format in ad-hoc manner was used as representation format in tools developed at UFRN, and, to make it more usable in other tools, an accurate description and the development of support tools was necessary. These accurate description and tools have been developed and are described in this work. This work compares the AGraphs format with other representation and exchange formats (e.g ATerms, GDL, GraphML, GraX, GXL and XML). The main objective this comparison is to capture important characteristics and where the AGraphs concepts can still evolve

Towards an intelligent graphical interface for linear programming modelling

The increase of computing power of the microcomputers has stimulated the building of direct manipulation interfaces that allow graphical representation of Linear Programming (LP) models. This work discusses the components of such a graphical interface as the basis for a system to assist users in the process of formulating LP problems. In essence, this work proposes a methodology which considers the modelling task as divided into three stages which are specification of the Data Model, the Conceptual Model and the LP Model. The necessity for using Artificial Intelligence techniques in the problem conceptualisation and to help the model formulation task is illustrated.

Aplicação de técnica de geoprocessamento em áreas degradadas de mata ciliar e sua correlação com qualidade da água numa subbacia hidrográfica

Studying the physical environment of a watershed is the basic condition for a successful planning of the riparian forest preservation, and for water production and conservation. The aims of the present study were to analyze and quantify the spatial and temporal evolution (1984 and 2010) using Landsat-5 satellite images of Cintra Stream sub-watershed, Botucatu, São Paulo State, Brazil, processed by the software IDRISI Andes, as well as to analyze the water quality through the parameters pH, EC, DO and BOD5 at 4 different sites in the years 1999, 2008 and 2009. Considering the 1076.48ha area of the sub-watershed, the pasture class of 1984 was reduced by 25.55% in 2010, resulting in an increase in the remaining classes. The most important class was native forest and reforestation since it had an increase of 5.08%, which indicates recovery of the riparian forest. Degraded areas were identified close to the inferior limit of the sub-watershed (P3 and P4), as well as local contamination (P1 and P2) with worsening of the water quality in the remaining sites in the periods 2008 and 2009. Recovery and management of the ecological succession of degraded areas and water quality monitoring at 1 and 2 sites will be necessary to reestablish the natural condition of the area studied.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

Efeito da interação genótipo ambiente, sob a forma de heterogeneidade de variâncias entre rebanhos

Foram simuladas estruturas de dados em modelos mistos representando o teste de 100 reprodutores, sendo cada reprodutor acasalado com 10 matrizes (total de 1000 matrizes), originando em cada acasalamento 2 proles, totalizando 2000 proles (vinte proles por reprodutor). De cada combinação reprodutor e matriz, dez proles tiveram seu fenótipo expresso no ambiente de baixa produção (Estrato 1) e, a outra metade, no ambiente de alta produção (Estrato 2). A simulação foi realizada de forma a representar diferentes situações de presença de heterogeneidade de variâncias, combinando-se as origens da heterogeneidade, de natureza genética e ambiental. Na presença de heterogeneidade residual, o valor estimado para o componente de variância residual, considerando homogeneidade de variâncias se aproximou do valor médio das variâncias entre os estratos. Houve superestimação, também, do componente de variância genético aditivo. Ao simular heterogeneidade de variância de origem genética, observou-se que a estimação desse componente situou-se em valor intermediário aos simulados. Nessa situação, o componente de variância residual estimado foi próximo do valor simulado, indicando que a heterogeneidade de variâncias quando proveniente de fatores genéticos, não interfere, substancialmente, sobre e estimação do componente de variância residual. Na simulação de dados com presença de heterogeneidade tanto de origem genética quanto ambiental (estrutura de dados 4), conduziu a estimação de componentes de variâncias intermediários aos valores simulados em cada estrato. Assim, observa-se que, mesmo quando os reprodutores apresentam proles bem distribuídas em ambos os estratos, a heterogeneidade de variância proveniente de fatores não genético provoca distorções sobre a estimação da variância genética aditiva. Mas por outro lado, quando a heterogeneidade de variância é decorrente de fatores genéticos, não há grande interferência sobre a estimativa da variância residual, tal comportamento pode ser explicado pela incorporação da matriz de parentesco na estimação do componente de variância genético aditivo, possibilitando discriminar melhor a origem da diferenças entre variâncias. Na estrutura onde a variância residual foi heterogênea a estimativa de herdabilidade foi menor em relação à estrutura de homogeneidade de variâncias. Por outro lado, quando somente a variância genética aditiva foi heterogênea, a estimativa de herdabilidade, considerando-se apenas o estrato de alta variabilidade genética, foi inflacionada pela superestimação da variância genética aditiva. No entanto, a estimativa de herdabilidade obtida, desconsiderando essa fonte de heterogeneidade de variância, foi próxima à situação de homogeneidade de variância, indicando que, quando os reprodutores possuem boa distribuição de proles em diferentes ambientes, as estimativas relacionadas ao efeito genético são ponderadas pelo desempenho dos animais em cada ambiente. As correlações de Spearman e de Pearson entre os valores genéticos preditos dos reprodutores, para todas as situações, foram maiores que 0,90. O resultado indica que, mesmo havendo presença de heterogeneidade de variância genética e/ou ambiental, se os reprodutores possuem proles bem distribuídas entre os ambientes (estratos heterogêneos) a classificação do mérito genético não se altera, o que era esperado, pois em análises unicarácter, quando ocorre uma fonte de viés na avaliação genética, ela é comum a todos os indivíduos. Na situação em que foi imposta a estrutura de dados à presença de heterogeneidade de variância residual com número de número desigual de proles por reprodutor nos estratos, provocou superestimação dos componentes de variância. Porém mesmo havendo alteração na magnitude dos valores genéticos preditos para os reprodutores, a heterogeneidade de variância não alterou a classificação entre os reprodutores todas as correlações de ordem foram próximas à unidade. O efeito da heterogeneidade de variância, oriunda de fatores ambientais, ocasiona em maiores distorções sobre a avaliação genética animal, em relação, quando a mesma é proveniente de causas genéticas. A conexidade genética entre diferentes ambientes, dilui o efeito da heterogeneidade de variância, tanto de origem genética, quanto ambiental, na predição de valores genéticos dos reprodutores.

Persistência da lactação e Influência da estrutura de dados sobre a estimação de parâmetros genéticos para a produção de leite de bovinos da raça Holandesa

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento de uma plataforma de software para a modelagem digital de terrenos baseada em TIN

Topographical surfaces can be represented with a good degree of accuracy by means of maps. However these are not always the best tools for the understanding of more complex reliefs. In this sense, the greatest contribution of this work is to specify and to implement the architecture of an opensource software system capable of representing TIN (Triangular Irregular Network) based digital terrain models. The system implementation follows the object oriented programming and generic paradigms enabling the integration of various opensource tools such as GDAL, OGR, OpenGL, OpenSceneGraph and Qt. Furthermore, the representation core of the system has the ability to work with multiple topological data structures from which can be extracted, in constant time, all the connectivity relations between the entities vertices, edges and faces existing in a planar triangulation what helps enormously the implementation for real time applications. This is an important capability, for example, in the use of laser survey data (Lidar, ALS, TLS), allowing for the generation of triangular mesh models in the order of millions of points.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Intercambio de información entre sistemas SAP R/3-Knapp para optimizar el proceso de expedición de pedidos

[ES] El objetivo de este Trabajo es el de parametrizar, implementar las estructuras de datos y programar las aplicaciones necesarias que posibilitan el intercambio de información entre dos entornos software, SAP R/3 y Knapp, líderes en sus campos de actuación. El resultado de aplicar tales cambios permitirá a la organización no sólo centralizar la información en el ERP, sino que mejorará sus procesos de negocio y agilizará la toma de decisiones por parte de los responsables. Se realiza un estudio de la situación actual y, tras un análisis detallado, se propone una solución que permita alcanzar los objetivos propuestos. Una vez diseñada, presentada y aprobada la propuesta, se procede a la parametrización de SAP R/3, a la definición de los segmentos y tipos de IDOC y a la codificación de funciones y programas que permitan tratar la información enviada por Knapp. Finalizadas estas tareas, se elaboran juegos de datos de los procesos comerciales y se ejecutan en un entorno de test, en colaboración con los usuarios claves, para comprobar la bondad de la solución implementada. Se analizan los resultados y se corrigen posibles deficiencias. Finalmente se transporta al sistema productivo todos los cambios realizados y se verifica la correcta ejecución de los procesos de negocio de la organización.

Modelli non lineari statici e dinamici su dati microeconomici: analisi delle condizioni finanziarie delle famiglie italiane

The thesis studies the economic and financial conditions of Italian households, by using microeconomic data of the Survey on Household Income and Wealth (SHIW) over the period 1998-2006. It develops along two lines of enquiry. First it studies the determinants of households holdings of assets and liabilities and estimates their correlation degree. After a review of the literature, it estimates two non-linear multivariate models on the interactions between assets and liabilities with repeated cross-sections. Second, it analyses households financial difficulties. It defines a quantitative measure of financial distress and tests, by means of non-linear dynamic probit models, whether the probability of experiencing financial difficulties is persistent over time. Chapter 1 provides a critical review of the theoretical and empirical literature on the estimation of assets and liabilities holdings, on their interactions and on households net wealth. The review stresses the fact that a large part of the literature explain households debt holdings as a function, among others, of net wealth, an assumption that runs into possible endogeneity problems. Chapter 2 defines two non-linear multivariate models to study the interactions between assets and liabilities held by Italian households. Estimation refers to a pooling of cross-sections of SHIW. The first model is a bivariate tobit that estimates factors affecting assets and liabilities and their degree of correlation with results coherent with theoretical expectations. To tackle the presence of non normality and heteroskedasticity in the error term, generating non consistent tobit estimators, semi-parametric estimates are provided that confirm the results of the tobit model. The second model is a quadrivariate probit on three different assets (safe, risky and real) and total liabilities; the results show the expected patterns of interdependence suggested by theoretical considerations. Chapter 3 reviews the methodologies for estimating non-linear dynamic panel data models, drawing attention to the problems to be dealt with to obtain consistent estimators. Specific attention is given to the initial condition problem raised by the inclusion of the lagged dependent variable in the set of explanatory variables. The advantage of using dynamic panel data models lies in the fact that they allow to simultaneously account for true state dependence, via the lagged variable, and unobserved heterogeneity via individual effects specification. Chapter 4 applies the models reviewed in Chapter 3 to analyse financial difficulties of Italian households, by using information on net wealth as provided in the panel component of the SHIW. The aim is to test whether households persistently experience financial difficulties over time. A thorough discussion is provided of the alternative approaches proposed by the literature (subjective/qualitative indicators versus quantitative indexes) to identify households in financial distress. Households in financial difficulties are identified as those holding amounts of net wealth lower than the value corresponding to the first quartile of net wealth distribution. Estimation is conducted via four different methods: the pooled probit model, the random effects probit model with exogenous initial conditions, the Heckman model and the recently developed Wooldridge model. Results obtained from all estimators accept the null hypothesis of true state dependence and show that, according with the literature, less sophisticated models, namely the pooled and exogenous models, over-estimate such persistence.

Modeling the spatial dynamics of economic models

The advances that have been characterizing spatial econometrics in recent years are mostly theoretical and have not found an extensive empirical application yet. In this work we aim at supplying a review of the main tools of spatial econometrics and to show an empirical application for one of the most recently introduced estimators. Despite the numerous alternatives that the econometric theory provides for the treatment of spatial (and spatiotemporal) data, empirical analyses are still limited by the lack of availability of the correspondent routines in statistical and econometric software. Spatiotemporal modeling represents one of the most recent developments in spatial econometric theory and the finite sample properties of the estimators that have been proposed are currently being tested in the literature. We provide a comparison between some estimators (a quasi-maximum likelihood, QML, estimator and some GMM-type estimators) for a fixed effects dynamic panel data model under certain conditions, by means of a Monte Carlo simulation analysis. We focus on different settings, which are characterized either by fully stable or quasi-unit root series. We also investigate the extent of the bias that is caused by a non-spatial estimation of a model when the data are characterized by different degrees of spatial dependence. Finally, we provide an empirical application of a QML estimator for a time-space dynamic model which includes a temporal, a spatial and a spatiotemporal lag of the dependent variable. This is done by choosing a relevant and prolific field of analysis, in which spatial econometrics has only found limited space so far, in order to explore the value-added of considering the spatial dimension of the data. In particular, we study the determinants of cropland value in Midwestern U.S.A. in the years 1971-2009, by taking the present value model (PVM) as the theoretical framework of analysis.

Permutation classes, sorting algorithms, and lattice paths

A permutation is said to avoid a pattern if it does not contain any subsequence which is order-isomorphic to it. Donald Knuth, in the first volume of his celebrated book "The art of Computer Programming", observed that the permutations that can be computed (or, equivalently, sorted) by some particular data structures can be characterized in terms of pattern avoidance. In more recent years, the topic was reopened several times, while often in terms of sortable permutations rather than computable ones. The idea to sort permutations by using one of Knuth’s devices suggests to look for a deterministic procedure that decides, in linear time, if there exists a sequence of operations which is able to convert a given permutation into the identical one. In this thesis we show that, for the stack and the restricted deques, there exists an unique way to implement such a procedure. Moreover, we use these sorting procedures to create new sorting algorithms, and we prove some unexpected commutation properties between these procedures and the base step of bubblesort. We also show that the permutations that can be sorted by a combination of the base steps of bubblesort and its dual can be expressed, once again, in terms of pattern avoidance. In the final chapter we give an alternative proof of some enumerative results, in particular for the classes of permutations that can be sorted by the two restricted deques. It is well-known that the permutations that can be sorted through a restricted deque are counted by the Schrӧder numbers. In the thesis, we show how the deterministic sorting procedures yield a bijection between sortable permutations and Schrӧder paths.

Multivariate modelling of responses to conditional items: New possibilities for latent class analysis

Questionnaire data may contain missing values because certain questions do not apply to all respondents. For instance, questions addressing particular attributes of a symptom, such as frequency, triggers or seasonality, are only applicable to those who have experienced the symptom, while for those who have not, responses to these items will be missing. This missing information does not fall into the category 'missing by design', rather the features of interest do not exist and cannot be measured regardless of survey design. Analysis of responses to such conditional items is therefore typically restricted to the subpopulation in which they apply. This article is concerned with joint multivariate modelling of responses to both unconditional and conditional items without restricting the analysis to this subpopulation. Such an approach is of interest when the distributions of both types of responses are thought to be determined by common parameters affecting the whole population. By integrating the conditional item structure into the model, inference can be based both on unconditional data from the entire population and on conditional data from subjects for whom they exist. This approach opens new possibilities for multivariate analysis of such data. We apply this approach to latent class modelling and provide an example using data on respiratory symptoms (wheeze and cough) in children. Conditional data structures such as that considered here are common in medical research settings and, although our focus is on latent class models, the approach can be applied to other multivariate models.

Collision Detection: Broad Phase Adaptation from Multi-Core to Multi-GPU Architecture

We present in this paper several contributions on the collision detection optimization centered on hardware performance. We focus on the broad phase which is the first step of the collision detection process and propose three new ways of parallelization of the well-known Sweep and Prune algorithm. We first developed a multi-core model takes into account the number of available cores. Multi-core architecture enables us to distribute geometric computations with use of multi-threading. Critical writing section and threads idling have been minimized by introducing new data structures for each thread. Programming with directives, like OpenMP, appears to be a good compromise for code portability. We then proposed a new GPU-based algorithm also based on the "Sweep and Prune" that has been adapted to multi-GPU architectures. Our technique is based on a spatial subdivision method used to distribute computations among GPUs. Results show that significant speed-up can be obtained by passing from 1 to 4 GPUs in a large-scale environment.

Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests

There is considerable evidence that biodiversity promotes multiple ecosystem functions (multifunctionality), thus ensuring the delivery of ecosystem services important for human well-being. However, the mechanisms underlying this relationship are poorly understood, especially in natural ecosystems. We develop a novel approach to partition biodiversity effects on multifunctionality into three mechanisms and apply this to European forest data. We show that throughout Europe, tree diversity is positively related with multifunctionality when moderate levels of functioning are required, but negatively when very high function levels are desired. For two well-known mechanisms, ‘complementarity’ and ‘selection’, we detect only minor effects on multifunctionality. Instead a third, so far overlooked mechanism, the ‘jack-of-all-trades’ effect, caused by the averaging of individual species effects on function, drives observed patterns. Simulations demonstrate that jack-of-all-trades effects occur whenever species effects on different functions are not perfectly correlated, meaning they may contribute to diversity–multifunctionality relationships in many of the world’s ecosystems.