10 resultados para TOPOLOGICAL INDEXES

em Deakin Research Online - Australia


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A number of governments and public policy institutes have developed ldquoQuality of Life Indexesrdquo – statistics that attempt to measure the quality of life for entire states or regions. We develop 14 criteria for determining the validity and usefulness of such QOL indexes to public policy. We then review 22 of the most-used QOL indexes from around the world. We conclude that many of the indexes are successful in that they are reliable, have established time series measures, and can be disaggregated to study subpopulations. However, many fall short in four areas: (1) indexes vary greatly in their coverage and definitions of domains of QOL, (2) none of the indexes distinguish among the concepts of input, throughput, and output that are used by public policy analysts, (3) they fail to show how QOL outputs are sensitive to public policy inputs, and (4) none have examined convergent validity against each other. We conclude that many of these indexes are potentially very useful for public policy and recommend research to further improve them.

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This paper presents a topological sorting based algorithm for logit network loading problem to exclude all cycles by removing certain links from loops. The new algorithm calculates the link weights and flows according to topological order. It produces the theoretical results for networks without loops. Numerical examples show that the new algorithm can reduce errors introduced by the strict definition of 'reasonable route' in Dial's algorithm.

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We examine the forecast quality of Chicago Board Options Exchange (CBOE) implied volatility indexes based on the Nasdaq 100 and Standard and Poor's 100 and 500 stock indexes. We find that the forecast quality of CBOE implied volatilities for the S&P 100 (VXO) and S&P 500 (VIX) has improved since 1995. Implied volatilities for the Nasdaq 100 (VXN) appear to provide even higher quality forecasts of future volatility. We further find that attenuation biases induced by the econometric problem of errors in variables appear to have largely disappeared from CBOE volatility index data since 1995.

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In this study, we develop some deterministic metamodels to quickly and precisely predict the future of a technically complex system. The underlying system is essentially a stochastic, discrete event simulation model of a big baggage handling system. The highly detailed simulation model of this is used for conducting some experiments and logging data which are then used for training artificial neural network metamodels. Demonstrated results show that the developed metamodels are well able to predict different performance measures related to the travel time of bags within this system. In contrast to the simulation models which are computationally expensive and expertise extensive to be developed, run, and maintained, the artificial neural network metamodels could serve as real time decision aiding tools which are considerably fast, precise, simple to use, and reliable.

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Computer simulations were used to test the effect of increasing phylogenetic topological inaccuracy on the results obtained from correlation tests of independent contrasts. Predictably, increasing the number of disruptions in the tree increases the likelihood of significant error in the r values produced and in the statistical conclusions drawn from the analysis. However, the position of the disruption in the tree is important: Disruptions closer to the tips of the tree have a greater effect than do disruptions that are close to the root of the tree. Independent contrasts derived from inaccurate topologies are more likely to lead to erroneous conclusions when there is a true significant relationship between the variables being tested (i.e., they tend to be conservative). The results also suggest that random phylogenies perform no better than nonphylogenetic analyses and, under certain conditions, may perform even worse than analyses using raw species data. Therefore, the use of random phylogenies is not beneficial in the absence of knowledge of the true phylogeny.

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A common view for the preferable positions of thwarting worm propagation is at the highly connected nodes. However, in certain conditions, such as when some popular users (highly connected nodes in the network) have more vigilance on the malicious codes, this may not always be the truth. In this letter, we propose a measure of betweenness and closeness to locate the most suitable positions for slowing down the worm propagation. This work provides practical values to the defense of topological worms.

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The self organising map is a well established unsupervised
learning technique which is able to form sophisticated representations of an input data set. However, conventional Self Organising Map (SOM) algorithms are limited to the production of topological maps — that is, maps where distance between points on the map have a direct relationship to the Euclidean distance between the training vectors corresponding to those points.

It would be desirable to be able to create maps which form clusters on primitive attributes other than Euclidean distance; for example, clusters based upon orientation or shape. Such maps could provide a novel approach to pattern recognition tasks by providing a new method to associate groups of data.

In this paper, it is shown that the type of map produced by SOM algorithms is a direct consequence of the lateral connection strategy employed. Given this knowledge, a technique is required to establish the feasability of using an alternative lateral connection strategy. Such a technique is presented. Using this technique, it is possible to rule out lateral connection strategies that will not produce output states useful to the organisation process. This technique is demonstrated using conventional Laplacian interconnection as well as a number of novel interconnection strategies.