dyebias.application.subset Return a subset of reporters that can be dye bias-corrected reasonably well Description Convenience function returning a subset of reporters that can be expected to be corrected reasonably well. Often, the logical AND of this set and that of maW(data.norm) == 1.0 is used. The resulting subset is passed as the application.subset-argument to dyebias.apply.correction. Ar...

The comparison of two objects is a usual task for many classification and learning methods. Comparisons are usually evaluated through a measure of comparison. The used measure is often a distance. But, more and more, a similarity or a dissimilarity measure is chosen. But the choice of an appropriate measure among all avalaible measures in literature is not an easy task. It is linked to the prob...

This paper reports the design and comparison of three visualizations to represent structure content within arguments. Arguments are artifacts reasoning widely used across domains such as education, policy making, science. An argument is made up sequences statements (premises) which can support or contradict each other, individually in groups through Boolean operators. Understanding resulting hi...

This note provides general transformations of lower bounds in Valiant’s parallel comparison decision tree model to lower bounds in the priority concurrent-read concurrent-write parallel-random-access-machine model. The proofs rely on standard Ramsey–theoretic arguments that simplify the structure of the computation by restricting the input domain. The transformation of comparison model lower bo...

This note provides general transformations of lower bounds in Valiant's parallel comparison decision tree model to lower bounds in the priority concurrent-read concurrent-write parallel-random-access-machine model. The proofs rely on standard Ramsey{theoretic arguments that simplify the structure of the computation by restricting the input domain. The transformation of comparison model lower bo...