ﻻ يوجد ملخص باللغة العربية
The Gini coefficient, a non-parametric measure of galaxy morphology, has recently taken up an important role in the automated identification of galaxy mergers. I present a critical assessment of its stability, based on a comparison of HST/ACS imaging data from the GOODS and UDF surveys. Below a certain signal-to-noise level, the Gini coefficient depends strongly on the signal-to-noise ratio, and thus becomes useless for distinguishing different galaxy morphologies. Moreover, at all signal-to-noise levels the Gini coefficient shows a strong dependence on the choice of aperture within which it is measured. Consequently, quantitative selection criteria involving the Gini coefficient, such as a selection of merger candidates, cannot always be straightforwardly applied to different datasets. I discuss whether these effects could have affected previous studies that were based on the Gini coefficient, and establish signal-to-noise limits above which measured Gini values can be considered reliable.
We systematically analyze the flavor color spin structure of the pentaquark $q^4bar{Q}$ system in a constituent quark model based on the chromomagnetic interaction in both the SU(3) flavor symmetric and SU(3) flavor broken case with and without charm
We propose an extended version of Gini index defined on the set of infinite utility streams, $X=Y^mathbb{N}$ where $Ysubset mathbb{R}$. For $Y$ containing at most finitely many elements, the index satisfies the generalized Pigou-Dalton transfer principles in addition to the anonymity axiom.
We solve the oscillation stability problem for the Urysohn sphere, an analog of the distortion problem for the Hilbert space in the context of the Urysohn universal metric space. This is achieved by solving a purely combinatorial problem involving a
Peaceful citizens and hard-working taxpayers are under government surveillance. Confidential communication of journalists is intercepted. Civilians are killed by drones, without a chance to prove their innocence. How could it come that far? And what are the alternatives?
Gini-type correlation coefficients have become increasingly important in a variety of research areas, including economics, insurance and finance, where modelling with heavy-tailed distributions is of pivotal importance. In such situations, naturally,