139,643 research outputs found
Dissimilarity metric based on local neighboring information and genetic programming for data dissemination in vehicular ad hoc networks (VANETs)
This paper presents a novel dissimilarity metric based on local neighboring information
and a genetic programming approach for efficient data dissemination in Vehicular Ad Hoc Networks
(VANETs). The primary aim of the dissimilarity metric is to replace the Euclidean distance in
probabilistic data dissemination schemes, which use the relative Euclidean distance among vehicles
to determine the retransmission probability. The novel dissimilarity metric is obtained by applying a
metaheuristic genetic programming approach, which provides a formula that maximizes the Pearson
Correlation Coefficient between the novel dissimilarity metric and the Euclidean metric in several
representative VANET scenarios. Findings show that the obtained dissimilarity metric correlates with
the Euclidean distance up to 8.9% better than classical dissimilarity metrics. Moreover, the obtained
dissimilarity metric is evaluated when used in well-known data dissemination schemes, such as
p-persistence, polynomial and irresponsible algorithm. The obtained dissimilarity metric achieves
significant improvements in terms of reachability in comparison with the classical dissimilarity
metrics and the Euclidean metric-based schemes in the studied VANET urban scenarios
Dissimilarity is used as evidence of category membership in multidimensional perceptual categorization: a test of the similarity-dissimilarity generalized context model
In exemplar models of categorization, the similarity between an exemplar and category members constitutes evidence that the exemplar belongs to the category. We test the possibility that the dissimilarity to members of competing categories also contributes to this evidence. Data were collected from two 2-dimensional perceptual categorization experiments, one with lines varying in orientation and length and the other with coloured patches varying in saturation and brightness. Model fits of the similarity-dissimilarity generalized context model were used to compare a model where only similarity was used with a model where both similarity and dissimilarity were used. For the majority of participants the similarity-dissimilarity model provided both a significantly better fit and better generalization, suggesting that people do also use dissimilarity as evidence
Can Dissimilarity Indexes Resolve the Issue of When to Chain Price Indexes?
Chaining is used in index number construction to update weights and link new items into an index. However, chained indexes can suffer from, sometimes substantial, drift. The Consumer Price Index Manual (ILO, 2004) recommends the use of dissimilarity indexes to determine when chaining is appropriate. This study provides the first empirical application of dissimilarity indexes in this context. We find that dissimilarity indexes do not appear to be sufficient to resolve the issue of when to chain.Index numbers; price indexes; chain drift; dissimilarity
Estimation of Distribution Overlap of Urn Models
A classical problem in statistics is estimating the expected coverage of a
sample, which has had applications in gene expression, microbial ecology,
optimization, and even numismatics. Here we consider a related extension of
this problem to random samples of two discrete distributions. Specifically, we
estimate what we call the dissimilarity probability of a sample, i.e., the
probability of a draw from one distribution not being observed in k draws from
another distribution. We show our estimator of dissimilarity to be a
U-statistic and a uniformly minimum variance unbiased estimator of
dissimilarity over the largest appropriate range of k. Furthermore, despite the
non-Markovian nature of our estimator when applied sequentially over k, we show
it converges uniformly in probability to the dissimilarity parameter, and we
present criteria when it is approximately normally distributed and admits a
consistent jackknife estimator of its variance. As proof of concept, we analyze
V35 16S rRNA data to discern between various microbial environments. Other
potential applications concern any situation where dissimilarity of two
discrete distributions may be of interest. For instance, in SELEX experiments,
each urn could represent a random RNA pool and each draw a possible solution to
a particular binding site problem over that pool. The dissimilarity of these
pools is then related to the probability of finding binding site solutions in
one pool that are absent in the other.Comment: 27 pages, 4 figure
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