Anomalous spin-related quantum phase in mesoscopic hole rings

We have obtained numerically exact results for the spin-related geometric quantum phases that arise in p-type semiconductor ring structures. The interplay between gate-controllable (Rashba) spin splitting and quantum-confinement-induced mixing between hole-spin states causes a much higher sensitivity of magnetoconductance oscillations to external parameters than previously expected. Our results imply a much-enhanced functionality of hole-ring spin-interference devices and shed new light on recent experimental findings.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.