In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experiments relying on a dataset of 2000 audio recordings, which cover more than 300 years of music history. From the recordings, we extract different tonal features. We propose a method to visualize these features over the course of history using evolution curves. With the curves, we re-trace hypotheses concerning the evolution of chord transitions, intervals, and tonal complexity. Furthermore, we perform unsupervised clustering of recordings across composition years, individual pieces, and composers. In these studies, we found independent evidence of historical periods that broadly agrees with traditional views as well as recent data-driven experiments. This shows that computational experiments can provide novel insights into the evolution of styles.
We study a supersymmetric version of the type-III seesaw mechanism considering two variants of the model: a minimal version for explaining neutrino data with only two copies of 24 superfields and a model with three generations of 24-plets. The latter predicts, in general, rates for ! e inconsistent with experimental data. However, this bound can be evaded if certain special conditions within the neutrino sector are fulfilled. In the case of two 24-plets, lepton flavor violation constraints can be satisfied much more easily. After specifying the corresponding regions in the minimal supergravity parameter space, we show that under favorable conditions one can test the corresponding flavor structures in the leptonic sector at the LHC. For this we perform Monte Carlo studies for the signals, also taking into account the supersymmetry background. We find that it is only of minor importance for the scenarios studied here.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.