In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Scagliarini:2022:10.1103/PhysRevResearch.4.013184,
author = {Scagliarini, T and Marinazzo, D and Guo, Y and Stramaglia, S and Rosas, FE},
doi = {10.1103/PhysRevResearch.4.013184},
journal = {Physical Review Research},
title = {Quantifying high-order interdependencies on individual patterns via the local O-information: Theory and applications to music analysis},
url = {http://dx.doi.org/10.1103/PhysRevResearch.4.013184},
volume = {4},
year = {2022}
}
TY - JOUR
AB - High-order, beyond-pairwise interdependencies are at the core of biological, economic, and social complex systems, and their adequate analysis is paramount to understand, engineer, and control such systems. This paper presents a framework to measure high-order interdependence that disentangles their effect on each individual pattern exhibited by a multivariate system. The approach is centered on the local O-information, a new measure that assesses the balance between synergistic and redundant interdependencies at each pattern. To illustrate the potential of this framework, we present a detailed analysis of music scores from J. S. Bach, which reveals how high-order interdependence is deeply connected with highly nontrivial aspects of the musical discourse. Our results place the local O-information as a promising tool of wide applicability, which opens other perspectives for analyzing high-order relationships in the patterns exhibited by complex systems.
AU - Scagliarini,T
AU - Marinazzo,D
AU - Guo,Y
AU - Stramaglia,S
AU - Rosas,FE
DO - 10.1103/PhysRevResearch.4.013184
PY - 2022///
SN - 2643-1564
TI - Quantifying high-order interdependencies on individual patterns via the local O-information: Theory and applications to music analysis
T2 - Physical Review Research
UR - http://dx.doi.org/10.1103/PhysRevResearch.4.013184
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000768378400002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.013184
UR - http://hdl.handle.net/10044/1/96508
VL - 4
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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