47 resultados para Legacy datasets

em University of Queensland eSpace - Australia


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The Anglo cluster comprises Australia, Canada, England, Ireland, New Zealand, South Africa (White sample), and the United States of America. These countries are all developed nations, predominantly English speaking, and were all once British colonies. Today, they are amongst the wealthiest countries in the world. The GLOBE results show that the Anglo cluster is characterized by an individualistic performance orientation. Further, although they value gender equality, the Anglo cluster countries tend to be male-dominated in practice. Effective leadership in the Anglo cultures is affected by a combination of charismatic inspiration and a articipative style.

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The debate about the dynamics and potential policy responses to asset inflation has intensified in recent years. Some analysts, notably Borio and Lowe, have called for 'subtle' changes to existing monetary targeting frameworks to try to deal with the problems of asset inflation and have attempted to developed indicators of financial vulnerability to aid this process. In contrast, this paper argues that the uncertainties involved in understanding financial market developments and their potential impact on the real economy are likely to remain too high to embolden policy makers. The political and institutional risks associated with policy errors are also significant. The fundamental premise that a liberalised financial system is based on 'efficient' market allocation cannot be overlooked. The corollary is that any serious attempt to stabilize financial market outcomes must involve at least a partial reversal of deregulation.

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Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

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The problem of asset price bubbles, and more generally of instability in the financial system, has been a matter of concern since the 1980s but has only recently moved to the center of the macroeconomic policy debate. The main concern with bubbles arises when they burst, imposing losses on investors holding the bubble assets and potentially on the financial institutions that have extended credit to them. Asset price volatility is an inevitable consequence of financial market liberalization and, in extreme cases, generates asset price bubbles, the bursting of which can impose substantial economic and social costs. Policy responses within the existing liberalized financial system face daunting levels of uncertainty and risk. Given the pattern of increasing asset market volatility over recent decades and the policy issues highlighted in this paper, the future looks uncertain. Another significant cycle of asset price movements, especially in one of the major economies, could see a fundamental revision of thinking about the costs and benefits of liberalized financial systems.

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Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.