We propose a new method to transform a pixel image to the corresponding quantum-pixel using a qubit per pixel to represent each pixels classical weight in a quantum image matrix *** qubits are linear superposition,cha...
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We propose a new method to transform a pixel image to the corresponding quantum-pixel using a qubit per pixel to represent each pixels classical weight in a quantum image matrix *** qubits are linear superposition,changing the coefficients level by level to the entire longitude of the gray scale with respect to the base states of the ***,these states are just bytes represented in a binary matrix,having code combinations of 1 or 0 at all pixel *** method introduces a qubit-pixel image representation of images captured by classical optoelectronic methods.
Whereas a rich literature exists for estimating population genetic divergence, metrics of phenotypic trait divergence are lacking, particularly for comparing multiple traits among three or more populations. Here, we r...
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Whereas a rich literature exists for estimating population genetic divergence, metrics of phenotypic trait divergence are lacking, particularly for comparing multiple traits among three or more populations. Here, we review and analyze via simula- tion Hedges' g, a widely used parametric estimate of effect size. Our analyses indicate that g is sensitive to a combination of unequal trait variances and unequal sample sizes among populations and to changes in the scale of measurement. We then go on to derive and explain a new, non-parametric distance measure, 'Aft', which is caiculated based upon a joint cumulative distribution function (CDF) from all populations under study. More precisely, distances are measured in terms of the percentiles in this CDF at which each population's median lies. Ap combines many desirable features of other distance metrics into a single metric; namely, compared to other metrics, p is relatively insensitive to unequal variances and sample sizes among the populations sam- pied. Furthermore, a key feature of Ap--and our main motivation for developing it--is that it easily accommodates simultaneous comparisons of any number of traits across any number of populations. To exemplify its utility, we employ Ap to address a ques- tion related to the role of sexual selection in speciation: are sexual signals more divergent than ecological traits in closely related taxa? Using traits of known function in closely related populations, we show that traits predictive of reproductive performance are indeed, more divergent and more sexually dimorphic than traits related to ecological adaptation [Current Zoology 58 (3): 426-439 2012].
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