TY  - JOUR
A1  - Wiesner, Karoline
A1  - Ladyman, James
T1  - Complex systems are always correlated but rarely information processing
JF  - Journal of physics. Complexity
N2  - '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.
KW  - correlations
KW  - information theory
KW  - complex systems
KW  - information
KW  - processing
KW  - self-organisation
Y1  - 2021
U6  - https://doi.org/10.1088/2632-072X/ac371c
SN  - 2632-072X
VL  - 2
IS  - 4
PB  - IOP Publ. Ltd.
CY  - Bristol
ER  - 
TY  - JOUR
A1  - Pikovskij, Arkadij
T1  - Chimeras on a social-type network
JF  - Mathematical modelling of natural phenomena : MMNP
N2  - 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.
KW  - Network
KW  - Chimera
KW  - correlations
KW  - Lyapunov exponent
Y1  - 2021
U6  - https://doi.org/10.1051/mmnp/2021012
SN  - 0973-5348
SN  - 1760-6101
VL  - 16
PB  - EDP Sciences
CY  - Les Ulis
ER  - 
TY  - JOUR
A1  - Cesca, Simone
A1  - Sen, Ali Tolga
A1  - Dahm, Torsten
T1  - Seismicity monitoring by cluster analysis of moment tensors
JF  - Geophysical journal international
N2  - 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.
KW  - Persistence
KW  - memory
KW  - correlations
KW  - clustering
KW  - Earthquake source observations
Y1  - 2014
U6  - https://doi.org/10.1093/gji/ggt492
SN  - 0956-540X
SN  - 1365-246X
VL  - 196
IS  - 3
SP  - 1813
EP  - 1826
PB  - Oxford Univ. Press
CY  - Oxford
ER  -