Framed within a discussion of populism, this thesis provides a critical analysis of the campaign and short tenure in office of Ecuadorian President Lucio Gutiérrez Borbúa. It outlines a multi-dimensional approach to populism that is characterized by five components: (1) personalistic leadership, (2) a heterogeneous coalition of support, (3) top-down political mobilization, (4) an ambiguous ideological discourse, and (5) a redistributive and clientelistic economic approach. Applied to the Gutiérrez case, the multi-dimensional approach highlights the viability and volatility of populism.<p> This thesis argues that Gutiérrez ascended to the presidency through the successful application of a populist strategy, which generated significant expectations among the public and his political allies. Yet, once in office, Gutiérrez populist strategy was unable to sustain the support he enjoyed during the campaign. The expectations he generated went unmet as he engaged in clear reversals of the populist imaginary created by his candidacy. His twenty-eight months in office were characterized by neoliberalism, corruption, and status quo political machinations that had sunk his predecessors. Tracking Gutiérrez transition from populist champion to political pariah using the multi-dimensional approach indicates that although populism can be an effective electoral strategy, it can also impose significant limitations on a government. Ultimately, the Gutiérrez case reinforces the important role played by the populist imaginary in determining the success or failure of populist leaders.

Framed within a discussion of populism, this thesis provides a critical analysis of the campaign and short tenure in office of Ecuadorian President Lucio Gutiérrez Borbúa. It outlines a multi-dimensional approach to populism that is characterized by five components: (1) personalistic leadership, (2) a heterogeneous coalition of support, (3) top-down political mobilization, (4) an ambiguous ideological discourse, and (5) a redistributive and clientelistic economic approach. Applied to the Gutiérrez case, the multi-dimensional approach highlights the viability and volatility of populism.<p> This thesis argues that Gutiérrez ascended to the presidency through the successful application of a populist strategy, which generated significant expectations among the public and his political allies. Yet, once in office, Gutiérrez populist strategy was unable to sustain the support he enjoyed during the campaign. The expectations he generated went unmet as he engaged in clear reversals of the populist imaginary created by his candidacy. His twenty-eight months in office were characterized by neoliberalism, corruption, and status quo political machinations that had sunk his predecessors. Tracking Gutiérrez transition from populist champion to political pariah using the multi-dimensional approach indicates that although populism can be an effective electoral strategy, it can also impose significant limitations on a government. Ultimately, the Gutiérrez case reinforces the important role played by the populist imaginary in determining the success or failure of populist leaders.

A finite-volume procedure, comprising a gradient-reconstruction technique and a multidimensional limiter, has been proposed for upwind algorithms on unstructured grids. The high-resolution strategy, with its inherent dependence on a wide computational stencil, does not suffer from a catastrophic loss of accuracy on a grid with poor connectivity as reported recently is the case with many unstructured-grid limiting procedures. The continuously-differentiable limiter is shown to be effective for strong discontinuities, even on a grid which is composed of highly-distorted triangles, without adversely affecting convergence to steady state. Numerical experiments involving transient computations of two-dimensional scalar convection to steady-state solutions of Euler and Navier-Stokes equations demonstrate the capabilities of the new procedure.

在現今資訊潮流中，企業的龐大資料庫可藉由統計及人工智慧的科學技術尋找出有價值的隱藏事件。利用資料做深入分析，找出其中的知識，並根據企業的問題，建立不同的模型，進而提供企業進行決策時的參考依據。資料挖掘的工作是近年來資料庫應用領域中相當熱門的議題。它雖是個神奇又時髦的技術，卻不是一門創新的學問。美國政府在第二次世界大戰前，就於人口普查以及軍事方面使用資料挖掘的分析方法。隨著資訊科技的進展，新工具的出現，以及網路通訊技術的發展，常常能超越歸納範圍的關係來執行資料挖掘，而由資料堆中挖掘寶藏，使資料挖掘成為企業智慧的一部份。在本篇論文當中，將資料挖掘技術中的關聯法則 ( Association Rule ) 運用至房地產的價格分析，進而提供有效的關聯法則，對於複雜之房價與週邊環境因素作一整合探討。購屋者將有一適當依循的投資計畫，房產業者亦可針對適當的族群做出適當的銷售企畫。 / At this technological stream of time, it is able to extract the value of corporations’ large data sets by applying the knowledge of statistics and the scientific techniques from artificial intelligence. Through the use of these algorithms, the database will be analyzed and its knowledge will be generated. In addition to these, data models will be sorted by different corporation issues resulting in the reference for any strategic decision processes. More advantages are the predictions of future events and how much public is willing to contribute and feedback to new products or promotions. The probability of outcomes will be helpful as references since this information is referable to ensure companies providing quality services at the right time. In another words, companies will have clues in attempts to understand and familiarize their customers’ needs, wants and behaviors, as a result of delivering best services for customers’ satisfactions. Data mining is such a new knowledge that is commonly discussed in the field of database applications. Although it is a relatively new term, the technology is not exactly due to the analysis methods used. Before World War II, the analysis techniques were used in particular to the statistics in census or cases related to military affairs by the US government. Knowledge discovery has been one part of business intelligence in current corporations because these new techniques are inherently geared towards explicit information, rather than just simple analysis. By applying association rules from knowledge discovery technology, this dissertation will provide a discussion of price estimation in real estates. This discussion is involved in investigations into diverse housing prices resulting from the factors of surrounding environment. By referring to this association rule, buyers will acquire information about investment plans while housing agents will gain knowledge for their plans or projects in particular to their target markets.

We present a theory for constructing linear, black box approximations to face recognition algorithms and empirically demonstrate that a surprisingly diverse set of face recognition approaches can be approximated well using a linear model. The construction of the linear model to a face recognition algorithm involves embedding of a training set of face images constrained by the distances between them, as computed by the face recognition algorithm being approximated. We accomplish this embedding by iterative majorization, initialized by classical multi-dimensional scaling (MDS). We empirically demonstrate the adequacy of the linear model using six face recognition algorithms, spanning both template based and feature based approaches on standard face recognition benchmarks such as the Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. The experimental results show that the average Error in Modeling for six algorithms is 6.3% at 0.001 False Acceptance Rate (FAR), for FERET fafb probe set which contains maximum number of subjects among all the probe sets. We demonstrate the usefulness of the linear model for algorithm dependent indexing of face databases and find that it results in more than 20 times reduction in face comparisons for Bayesian Intra/Extra-class person classifier (BAY), Elastic Bunch Graph Matching algorithm (EBGM), and the commercial face recognition algorithms. We also propose a novel paradigm to reconstruct face templates from match scores using the linear model and use the reconstructed templates to explore the security breach in a face recognition system. We evaluate the proposed template reconstruction scheme using three, fundamentally different, face recognition algorithms: Principal Component Analysis (PCA), Bayesian Intra/Extra-class person classifier (BAY), and a feature based commercial algorithm. With an operational point set at 1% False Acceptance Rate (FAR) and 99% True Acceptance Rate (TAR) for 1196 enrollments (FERET gallery), we show that at most 600 attempts (score computations) are required to achieve 73%, 72% and 100% chance of breaking in as a randomly chosen target subject for the commercial, BAY and PCA based face recognition system, respectively. We also show that the proposed reconstruction scheme has 47% more probability of breaking in as a randomly chosen target subject for the commercial system as compared to a hill climbing approach with the same number of attempts.

Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

The aim of this thesis was to choose some types of investments and set the expected return using time series; furthermore to compare these possibilities using multi-dimensional assessment. I chose four types of investments, namely: mutual funds, life insurance, additional pension savings and building savings. I expected a monthly deposit of 1000 CZK for a period of 20 years in my thesis. In case the investor would keep this money cash or on a bank account, where he would not have to pay account fees and have a zero interest rate, he would save 240 000 CZK during this period. Based on published historical returns I modeled the expected return of the investment by means of methods of time series. For modeling the return I used the model of linear trend. The highest revaluation is expected with the additional pension savings, where the dynamic strategy brings the overall return of 414 214 CZK, the balanced strategy 379 874 CZK and the conservative strategy 333 209 CZK. The investment into mutual funds using conservative strategy brings 317 894 CZK, using balanced strategy brings the return of 314 986 CZK. When choosing the conservative strategy of life insurance the overall return is 296 071 CZK and when choosing the balanced strategy it is 292 614 CZK. The expected return of the building saving is 286 139 CZK. However, it is not recommended to opt for an investment only based on the expected return. We have to take into consideration as well the risk of the investment, input one-time fee and the overall fee (monthly or annual fees). For this reason I determined the category of the above mentioned criteria, which I set using scoring method. For the determination of the order I chose the TOPSIS method and the scoring method. Based on the carried out above methods it is certain that the best investment is investing in additional pension savings.