Topology of Networks in Generalized Musical Spaces

The abstraction of musical structures (notes, melodies, chords, harmonic or rhythmic progressions, etc.) as mathematical objects in a geometrical space is one of the great accomplishments of contemporary music theory. Building on this foundation, I generalize the concept of musical spaces as networks and derive functional principles of compositional design by the direct analysis of the network topology. This approach provides a novel framework for the analysis and quantification of similarity of musical objects and structures, and suggests a way to relate such measures to the human perception of different musical entities. Finally, the analysis of a single work or a corpus of compositions as complex networks provides alternative ways of interpreting the compositional process of a composer by quantifying emergent behaviors with well-established statistical mechanics techniques. Interpreting the latter as probabilistic randomness in the network, I develop novel compositional design frameworks that are central to my own artistic research

Topology of Networks in Generalized Musical Spaces

Leonardo Music Journal, Massachusetts Institute of Technology Press (MIT Press): Arts & Humanities Titles etc, In press. Publication date: December 2020The abstraction of musical structures (notes, melodies, chords, harmonic or rhythmic progressions, etc.) as mathematical objects in a geometrical space is one of the great accomplishments of contemporary music theory. Building on this foundation, I generalize the concept of musical spaces as networks and derive functional principles of compositional design 15 by the direct analysis of the network topology. This approach provides a novel framework for the analysis and quantification of similarity of musical objects and structures, and suggests a way to relate such measures to the human perception of different musical entities. Finally, the analysis of a single work or a corpus of compositions as complex networks provides alternative ways of interpreting the compositional process of a composer by quantifying emergent behaviors with 20 well-established statistical mechanics techniques. Interpreting the latter as probabilistic randomness in the network, I develop novel compositional design frameworks that are central to my own artistic research. One Sentence Summary: Network theory is an innovative tool for the classification of generalized musical spaces and provides a framework for the discovery or generation of functional 25 principles of compositional design

MUSICNTWRK: data tools for music theory, analysis and composition

International audienceWe present the API for MUSICNTWRK, a python library for pitch class set and rhythmic sequences classification and manipulation, the generation of networks in generalized music and sound spaces, deep learning algorithms for timbre recognition, and the sonification of arbitrary data. The software is freely available under GPL 3.0 and can be downloaded at www.musicntwrk.com

Ab-initio transport properties of nanostructures from maximally-localized Wannier functions

We present a comprehensive first-principles study of the ballistic transport properties of low dimensional nanostructures such as linear chains of atoms (Al, C) and carbon nanotubes in presence of defects. A novel approach is introduced where quantum conductance is computed from the combination of accurate plane-wave electronic structure calculations, the evaluation of the corresponding maximally-localized Wannier functions, and the calculation of transport properties by a real-space Green's function method based on the Landauer formalism. This approach is computationally very efficient, can be straightforwardly implemented as a post-processing step in a standard electronic-structure calculation, and allows to directly link the electronic transport properties of a device to the nature of the chemical bonds, providing insight onto the mechanisms that govern electron flow at the nanoscale.Comment: to be published in Phys. Rev. B (2003

Methods for the Reconstruction of Vertical Profiles from Surface Data: Multivariate Analyses, Residual GEM, and Variable Temporal Signals in the North Pacific Ocean

AbstractDifferent methods for the extrapolation of vertical profiles from sea surface measurements have been tested on 14 yr of conductivity–temperature–depth (CTD) data collected within the Hawaii Ocean Time-series (HOT) program at A Long-Term Oligotrophic Habitat Assessment (ALOHA) station in the North Pacific Ocean. A new technique, called multivariate EOF reconstruction (mEOF-R), has been proposed. The mEOF-R technique is similar to the previously developed coupled pattern reconstruction (CPR) technique and relies on the availability of surface measurements and historical profiles of salinity, temperature, and steric heights. The method is based on the multivariate EOF analysis of the vertical profiles of the three parameters and on the assumption that only a few modes are needed to explain most of the variance/covariance of the fields. The performances of CPR, single EOF reconstruction (sEOF-R), and mEOF-R have been compared with the results of residual GEM techniques and with ad hoc climatologies, stressing the potential of each method in relation to the length of the time series used to train the models and to the accuracy expected from planned satellite missions for the measurement of surface salinity, sea level, and temperature. The mEOF-R method generally produces the most reliable estimates (in the worst cases comparable to the climatologies) and seems to be slightly less susceptible to errors in the surface input. Multivariate EOF analysis of HOT data also gave by itself interesting results, being able to discriminate the three major signals driving the temporal variability in the area

Electronic transport in extended systems: Application to carbon nanotubes

Article on electronic transport in extended systems and the application to carbon nanotubes