O presente trabalho aborda a temática da modelagem de redes de transporte para terminais multialocados e capacitados de carga parcelada através de métodos quantitativos aplicados em Pesquisa Operacional, onde o objetivo principal é minimizar o custo total, além de determinar a configuração ótima da rede, levandos e em consideração os custos de transporte ponto a ponto, a capacidade de processamento de carga dos hubs e os custos de alocação dos mesmos. Uma rede Hub and Spoke é configurada de forma que as estações geradoras de cargas (spoke) se comunicam única e exclusivamente com os terminais consolidadores (hubs) que, por sua vez, direcionam as cargas para as estações destino. Esta operação de consolidação gera economia de escala e é o princípio utilizado em muitos modelos de localização de redes de transporte de carga. Os modelos matemáticos utilizados para a resolução pertencem à família dos problemas de localização com custos fixos e particularmente será utilizado o método CMAHLP (Capacitated Multiple Allocation Hub Location Problem), onde para a sua resolução será utilizado o software AIMMS (Advanced Integrated Multidimensional Modeling Software) como ferramenta de apoio. / This paper addresses the issue of modeling transportation networks for multi allocated and capacitated terminals for parcel cargos through quantitative methods applied in Operational Research, where the main objective is to minimize the total cost and to determine the optimal configuration of the network, taking into account the transportation costs, the loading capacity of the hubs and the cost of allocation thereof. A Hub and Spoke network is configured in a way that the generating stations (spokes) communicate solely with consolidators terminals (hubs) which dispatch the cargo to the destination stations. This consolidation operation generates economies of scale and it is the principle used in many models of location problems. The mathematical models used for the settlement belongs to the family of location problems with fixed costs and particularly the CMAHLP (Capacitated Multiple Allocation Hub Location Problem) method is used with the AIMMS (Advanced Integrated Multidimensional Modeling Software) software, which will be used will be used as a support tool.

Neste trabalho apresentamos uma nova abordagem para a exploração de fibras neurais a partir de imagens de tensores de difusão. A estratégia combina técnicas de visualização de informação e visualização científica para obter uma rápida e precisa interpretação das fibras. Para isto fazemos uma transformação das fibras para vetores, e utilizamos uma nova técnica de projeção multi-dimensional (P-LSP), para trabalhar com conjuntos grandes de dados. A exploração do espaço das fibras é feita através desta projeção. Além disso, é apresentada a extensão das técnicas LSP e P-LSP para criar projeções em 3D, assim como estratégias que permitem interagir com pontos em 3D. Outra contribuição deste trabalho é a modificação de um método apresentado para criar superfícies de densidade fechadas sobre pontos esparsos. Esta modificação torna possível criar superfícies sobre conjuntos de pontos maiores com uma qualidade aceitável, o que é utilizado para representar conjunto de fibras como uma superfície. Esta nova abordagem é comparada com trabalhos similares mostrando nossas vantagens em termos de tempo de processamento, qualidade e funcionalidades para analisar esta categoria de dados / This work presents a novel approach for the exploration of neural fibers extracted from Diffusion Tensor Images. The developed strategy combines techniques from information and scientific visualization in order to attain a fast and precise interpretation of fiber sets. The approach transforms fibers into vectors from which a new multidimensional projection technique (PLSP) capable of handling large data sets. The fiber space is explored through the projection. Additionally this work extends P-LSP and LSP projections to 3D, and defines strategies to interact with 3D sparse points. Another contribution of this work is the extension of a method to create close density surfaces over the sparse space generated by the projections. The visualization approach is compared with other similar work showing advantages in processing time, quality and exploration capability to analyze this type of data sets

Esta Tese apresenta um framework para análise exploratória de dados via técnicas de agrupamento. O objetivo é facilitar o trabalho dos especialistas no domínio dos dados. O ponto central do framework é um algoritmo de ensemble multi-objetivo, o algoritmo MOCLE, complementado por um método para a visualização integrada de um conjunto de partições. Pela aplicação conjunta das idéias de ensemble de agrupamentos e agrupamento multi-objetivo, o MOCLE efetua atomaticamente importantes passos da análise de agrupamento: executa vários algoritmos conceitualmente diferentes com várias configurações de parâmetros, combina as partições resultantes desses algoritmos e seleciona as partições com os melhores compromissos de diferentes medidas de validação. MOCLE é uma abordagem robusta para lidar com diferentes tipos de estrutura que podem estar presentes em um conjunto de dados. Ele resulta em um conjunto conciso e estável de estruturas alternativas de alta qualidade, sem a necessidade de conhecimento prévio sobre os dados e nem conhecimento profundo em análise de agrupamento. Além disso, para facilitar a descoberta de estruturas mais complexas, o MOCLE permite a integração automática de conhecimento prévio de uma estrutura simples por meio das suas funções objetivo. Finalmente, o método de visualização proposto permite a observação simultânea de um conjunto de partições. Isso ajuda na análise dos resultados do MOCLE. / This Thesis presents a framework for exploratory data analysis via clustering techniques. The goal is to facilitate the work of the experts in the data domain. The core of the framework is a multi-objective clustering ensemble algorithm, the MOCLE algorithm, complemented by a method for integrated visualization of a set of partitions. By applying together the ideas of clustering ensemble and multi-objective clustering, MOCLE automatically performs important steps of cluster analysis: run several conceptually different clustering algorithms with various parameter configuration, combine the partitions resulting from these algorithms, and select the partitions with the best trade-offs for different validation measures. MOCLE is a robust approach to deal with different types of structures that can be present in a dataset. It results in a concise and stable set of high quality alternative structures, without the need of previous knowledge about the data or deep knowledge on cluster analysis. Furthermore, in order to facilitate the discovery of more complex structures, MOCLE allows the automatic integration of previous knowledge of a simple structure via their objective functions. Finally, the visualization method proposed allows the simultaneous observation of a set of partitions. This helps in the analysis of MOCLE results.

A demanda por alto desempenho é um desafio enfrentado por diversas aplicações científicas. Nesse sentido, ambientes para processamento distribuído, tais como, clusters e grades computacionais, têm sido desenvolvidos para prover suporte ao uso de diversos recursos simultaneamente para uma mesma aplicação. Aplicações computacionalmente intensivas são organizadas em workflows e executadas com suporte de middlewares para abstrair a complexidade de uso de tais ambientes. Em ambientes de grade computacional, a execução de workflows contendo tarefas sequenciais e tarefas com paralelismo interno, obtendo bom desempenho, é um desafio, devido à heterogeneidade e comportamento dinâmico do ambiente. Nesse sentido, o escalonamento de workflows em ambientes de grade computacional é essencial. O problema de escalonamento de tarefas, em sua forma geral, é NPCompleto, dessa forma, o estudo do escalonamento de workflows em ambientes de grade computacional é fundamental para aprimorar a execução de aplicações computacionalmente intensivas. O objetivo dessa tese é propor estratégias de escalonamento de workflows, que exploram os seguintes aspectos: Avaliação da possibilidade de executar cada tarefa com paralelismo interno usando recursos de múltiplos clusters; Adaptação de planos de escalonamento no momento da submissão de novos workflows. Foram desenvolvidas duas estratégias: a primeira é uma estratégia para escalonamento estático de workflows, que considera o ambiente dedicado a execução de um workflow. A segunda foi desenvolvida para ser utilizada em conjunto com a primeira, para melhorar o tempo de resposta de múltiplos workflows que podem ser submetidos em diferentes momentos. As estratégias propostas foram avaliadas em um ambiente de simulação. / The demand for high performance is a common problem in many scientific applications. In this sense, distributed processing environments such as cluster, grid computing and multi-cluster environments have been developed to provide support for the use of several resources simultaneously for the same application. Computationally intensive applications are structured as workflows and executed with the support of middleware to abstract the complexity of using such environments. In grid computing environments the execution of workflows containing sequential and parallel tasks, with good performance is a challenge due to the heterogeneity and dynamic behavior of the environment. In this sense, the scheduling of workflows on grid computing environments is essential. The task scheduling problem in its general form is NP-Complete, in this sense, the study concerning workflow scheduling in grid computing environments is fundamental to improve the performance of computationally intensive applications. The aim of this thesis is to propose strategies for scheduling workflows that exploit the following aspects: Explore the possibility of performing single parallel tasks using multiple clusters; Adaptation plans escalation in accordance with the submission of new workflows. Two strategies were developed: the first one is a strategy for static scheduling of workflows, which considers a dedicated environment to the execution of a workflow. The second one was developed to use in conjunction with the first one, in order to improve the response time of multiple workflows that can be submitted at different times. The proposed strategies were evaluated in a simulation environment.

Neste trabalho apresentamos uma nova abordagem para a exploração de fibras neurais a partir de imagens de tensores de difusão. A estratégia combina técnicas de visualização de informação e visualização científica para obter uma rápida e precisa interpretação das fibras. Para isto fazemos uma transformação das fibras para vetores, e utilizamos uma nova técnica de projeção multi-dimensional (P-LSP), para trabalhar com conjuntos grandes de dados. A exploração do espaço das fibras é feita através desta projeção. Além disso, é apresentada a extensão das técnicas LSP e P-LSP para criar projeções em 3D, assim como estratégias que permitem interagir com pontos em 3D. Outra contribuição deste trabalho é a modificação de um método apresentado para criar superfícies de densidade fechadas sobre pontos esparsos. Esta modificação torna possível criar superfícies sobre conjuntos de pontos maiores com uma qualidade aceitável, o que é utilizado para representar conjunto de fibras como uma superfície. Esta nova abordagem é comparada com trabalhos similares mostrando nossas vantagens em termos de tempo de processamento, qualidade e funcionalidades para analisar esta categoria de dados / This work presents a novel approach for the exploration of neural fibers extracted from Diffusion Tensor Images. The developed strategy combines techniques from information and scientific visualization in order to attain a fast and precise interpretation of fiber sets. The approach transforms fibers into vectors from which a new multidimensional projection technique (PLSP) capable of handling large data sets. The fiber space is explored through the projection. Additionally this work extends P-LSP and LSP projections to 3D, and defines strategies to interact with 3D sparse points. Another contribution of this work is the extension of a method to create close density surfaces over the sparse space generated by the projections. The visualization approach is compared with other similar work showing advantages in processing time, quality and exploration capability to analyze this type of data sets

The majority of current multi-label classification research focuses on learning dependency structures among output labels. This paper provides a novel theoretical view on the purported assumption that effective multi-label classification models must exploit output dependencies. We submit that the flurry of recent dependency-exploiting, multi-label algorithms may stem from the deficiencies in existing datasets, rather than an inherent need to better model dependencies. We introduce a novel categorization of multi-label metrics, namely, evenly and unevenly weighted label metrics. We explore specific features that predispose datasets to improved classification by methods that model label dependence. Additionally, we provide an empirical analysis of 15 benchmark datasets, 1 real-life dataset, and a variety of synthetic datasets. We assert that binary relevance (BR) yields similar, if not better, results than dependency-exploiting models for metrics with evenly weighted label contributions. We qualify this claim with discussions on specific characteristics of datasets and models that render negligible the differences between BR and dependency-learning models.

An algorithm is presented that is capable of producing Pareto-optimal solutions for multi-objective infrastructure routing problems: the Multi-Objective Ant Colony Optimization (MOACO). This algorithm offers a constructive search technique to develop solutions to different types of infrastructure routing problems on an open grid framework. The algorithm proposes unique functions such as graph pruning and path straightening to enhance both speed and performance. It also possesses features to solve issues unique to infrastructure routing not found in existing MOACO algorithms, such as problems with multiple end points or multiple possible start points. A literature review covering existing MOACO algorithms and the Ant Colony algorithms they are derived from is presented. Two case studies are developed to demonstrate the performance of the algorithm under different infrastructure routing scenarios. In the first case study the algorithm is implemented into the Ice Road Planning module within the North Slope Decision Support System (NSDSS). Using this ice road planning module a case study is developed of the White Hills Ice road to test the performance of the algorithm versus an as-built road. In the second case study, the algorithm is applied to a raw water transmission routing problem in the Region C planning zone of Texas. For both case studies the algorithm produces a set of results which are similar to the preliminary designs. By successfully applying the algorithm to two separate case studies the suitability of the algorithm to different types of infrastructure routing problems is demonstrated.

We present versions of the Frank-Wolfe method for linearly constrained convex programs, in which consecutive search directions are made conjugate. Preliminary computational studies in a MATLAB environment applying pure Frank-Wolfe, Conjugate direction Frank-Wolfe (CFW), Bi-conjugate Frank-Wolfe (BFW) and ”PARTANized” Frank-Wolfe methods to some classical Traffic Assignment Problems show that CFW and BFW compare favorably to the other methods. This spurred a more detailed study, comparing our methods to Bar-Gera’s origin-based algorithm. This study indicates that our methods are competitive for accuracy requirements suggested by Boyce et al. We further show that CFW is globally convergent. We moreover point at independent studies by other researchers that show that our methods compare favourably with recent bush-based and gradient projection algorithms on computers with several cores. / <p>Updated from "E-publ" to published. QC 20130625</p>

This dissertation argues that a multi-site church that is not characterized by campus-wide gatherings does not have a sound biblical or theological basis for considering itself one church and is inconsistent with historic congregationalism. This argument is made in six steps. First, a framework for determining whether or not a church practice is biblical is put forward. Second, it is shown that the people of God as one assembly is a central theme in the Old Testament. Third, it is demonstrated that this theme of the people of God as one assembly is also a central theme of the New Testament. Fourth, it is argued that there is not precedent for multi-site church structure in the house churches of the New Testament. Fifth, it is shown that multi-site is not consistent with historic congregationalism as seen in Baptist ecclesiology. And sixth, it is argued that multi-site church structure is consistent with historic connectionalism as seen, for example, in Methodist ecclesiogy--a fact that only serves to heighten the problem with multi-site from a congregationalist point of view.

With the wireless mesh network in the embedded systems related applications booming in recent years, the demand of enhancing the overall end to end network traffic and ensuring a stable connection is growing. We proposed a Dynamic Multi-channel Multi-path Routing Protocol (DMMR Protocol) to decompose contending traffics over different channel, different time and different paths to enhance the end to end network traffic. Choosing channel dynamically according to the channel usage around node in the process of finding paths can avoid inter-flow and intra-flow channel competition. We tend to choose paths with less intersection nodes to reduce the probability of multiple paths are broken at same time when a single node cannot work. We can enhance end to end network traffic further by using multiple interfaces at one node. We use NS2 to test DMMR Protocol, and analyzing the overall end to end traffic when multiple connections are setting up in the network. If the network can accept a new connection, the increment of end to end traffic is same as the traffic of the new connection. In connection robust test, a single path broken will not affect other flows in same connection and the end to end traffic in the connection will recovery immediately when the broken path is repaired.