The present work introduces a method by means of which direct measurement of grain boundary mobility vs. misorientation dependence is made possible.
Both Maimonides and Ibn Rushd, when faced with cases in which there was a clash between the claims of the rational Aristotelian philosophy or science and the accepted beliefs of their traditional religions, had open to them a method by means of which they could find for themselves a fairly easy solution.
They must address the issue of what the proper methods are by means of which categories are to be distinguished, how many categories there are and what they are, whether or not there is a single summum genus subsuming all other categories, and whether we should distinguish a single system of categories or multiple dimensions of categories issues on which there has been little agreement.
If we consider his overall strategy, it seems that Pomponazzi was drawing on the distinction between methods of science and methods of faith, by means of which 13th-century secular masters of philosophy at the University of Paris such as Boethius of Dacia had claimed the freedom to pursue their philosophical enquiries without theological constraints.
A typical approach in this context is to resort to the asymptotic theory of Prediction Error Methods for system identification, by means of which ellipsoidal uncertainty regions can be constructed for the uncertain parameters.
He also discovered a method for obtaining one curve from another, by means of which planar regions of finite dimensions can be found that are equal in area to the regions between certain curves and their asymptotes (lines that the curves approach but never intersect).
In what follows we will discuss some methods based in probabilistic inference and information theory by means of which researchers may be able to infer and assess complex network structure from the probability distributions of some empirical quantitative features.
Yet none of these thinkers developed anything like an explicit philosophical theory of understanding, let alone a method or a set of normatively binding rules by means of which the process of interpretation should proceed.
The authors have demonstrated the efficacy of the two methods by means of bar graphs (figures 3 and 4), which represent the coverage percentage of each of the approaches of MQ and MPMQ.
Our method seeks to combine image characterization and learning methods by means of a meta-learning approach responsible for assessing which methods contribute more towards the solution of a given problem.
