Euclidean Distance Matrix Analysis (EDMA) of form is a coordinate free approach to the analysis o f form using landmark data. In this paper, the problem of estimation of mean form, variancecovariance matrix, and mean form difference under the Gaussian perturbation model is considered using EDMA. The suggested estimators are based on the method of moments. They are shown to be consistent, that i...

Today the Chaumian Mix idea is not only an academic approach for anonymous communication, it is also a practical system with many variants used in real-world scenarios. It is therefore important to find an appropriate measure for the anonymity provided by these approaches. Many measurement approaches have been proposed that consider only the static state of the system without accounting for pas...

Phase synchrony and model-based magnitude squared (MS) coherence analyses of the EEG are investigated for their usefulness in the discrimination between non-ADHD and ADHD children. We observed that phase synchrony is higher – on average – for non-ADHD participants than for the ADHD group. Based on the relatively small sample available to us, we find that a Euclidean distance-based classifier ma...

We propose a novel type filter for multicolor imaging to improve on the photometric redshift estimation of galaxies. An extra filter—specific to a certain photometric system—may be utilized with high efficiency. We present a case study of the Hubble Space Telescope’s Advanced Camera for Surveys and show that one extra exposure could cut down the mean square error on photometric redshifts by 34%...

Muons are produced in the upper atmosphere as a result of the collisions of incoming highenergy particles from outer space with atmospheric particles. These muons then travel through the atmosphere at high energies and are thus measured to exist for a much longer time than their mean lifetime at rest. This experiment measures both the mean lifetime of muons at rest as well as the mean speed thr...

Introducing flexible covariance functions is critical for interpolating spatial data since the properties of interpolated surfaces depend on the covariance function used for Kriging. An extensive literature is devoted to covariance functions on Euclidean spaces, where the Matérn covariance family is a valid and flexible parametric family capable of controlling the smoothness of corresponding st...

We consider a graph, where the nodes have a pre-described degree distribution F , and where nodes are randomly connected in accordance to their degree. Based on a recent result [22], we improve the approximation of the mean distance between two randomly chosen nodes given by Newman et al. [32]. Our new expression for the mean distance involves the expectation of the logarithm of the limit of a ...