@article{WiesnerLadyman2021,
  author    = {Wiesner, Karoline and Ladyman, James},
  title     = {Complex systems are always correlated but rarely information processing},
  series = {Journal of physics. Complexity},
  volume    = {2},
  journal   = {Journal of physics. Complexity},
  number    = {4},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {2632-072X},
  doi       = {10.1088/2632-072X/ac371c},
  pages     = {4},
  year      = {2021},
  abstract  = {'Complex systems are information processors' is a statement that is frequently made. Here we argue for the distinction between information processing-in the sense of encoding and transmitting a symbolic representation-and the formation of correlations (pattern formation/self-organisation). The study of both uses tools from information theory, but the purpose is very different in each case: explaining the mechanisms and understanding the purpose or function in the first case, versus data analysis and correlation extraction in the latter. We give examples of both and discuss some open questions. The distinction helps focus research efforts on the relevant questions in each case.},
  language  = {en}
}
@article{Pikovskij2021,
  author    = {Pikovskij, Arkadij},
  title     = {Chimeras on a social-type network},
  series = {Mathematical modelling of natural phenomena : MMNP},
  volume    = {16},
  journal   = {Mathematical modelling of natural phenomena : MMNP},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  issn      = {0973-5348},
  doi       = {10.1051/mmnp/2021012},
  pages     = {9},
  year      = {2021},
  abstract  = {We consider a social-type network of coupled phase oscillators. Such a network consists of an active core of mutually interacting elements, and of a flock of passive units, which follow the driving from the active elements, but otherwise are not interacting. We consider a ring geometry with a long-range coupling, where active oscillators form a fluctuating chimera pattern. We show that the passive elements are strongly correlated. This is explained by negative transversal Lyapunov exponents.},
  language  = {en}
}
@article{CescaSenDahm2014,
  author    = {Cesca, Simone and Sen, Ali Tolga and Dahm, Torsten},
  title     = {Seismicity monitoring by cluster analysis of moment tensors},
  series = {Geophysical journal international},
  volume    = {196},
  journal   = {Geophysical journal international},
  number    = {3},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0956-540X},
  doi       = {10.1093/gji/ggt492},
  pages     = {1813 -- 1826},
  year      = {2014},
  abstract  = {We suggest a new clustering approach to classify focal mechanisms from large moment tensor catalogues, with the purpose of automatically identify families of earthquakes with similar source geometry, recognize the orientation of most active faults, and detect temporal variations of the rupture processes. The approach differs in comparison to waveform similarity methods since clusters are detected even if they occur in large spatial distances. This approach is particularly helpful to analyse large moment tensor catalogues, as in microseismicity applications, where a manual analysis and classification is not feasible. A flexible algorithm is here proposed: it can handle different metrics, norms, and focal mechanism representations. In particular, the method can handle full moment tensor or constrained source model catalogues, for which different metrics are suggested. The method can account for variable uncertainties of different moment tensor components. We verify the method with synthetic catalogues. An application to real data from mining induced seismicity illustrates possible applications of the method and demonstrate the cluster detection and event classification performance with different moment tensor catalogues. Results proof that main earthquake source types occur on spatially separated faults, and that temporal changes in the number and characterization of focal mechanism clusters are detected. We suggest that moment tensor clustering can help assessing time dependent hazard in mines.},
  language  = {en}
}