A tool to detect higher-order phenomena in real-world data
Jan 2023, phys.org
Researchers' multivariate time series method was able to detect oscillations between chaotic and synchronized neural interactions occurring in a brain at rest, between periods of financial stability and crisis. In the epidemiological example, or interactions between the spread of different diseases, like flu and pertussis."We are able to use ancient mathematics in new ways thanks to modern computing power, and access to big data. We are creating a new mathematics."
via Ecole Polytechnique Federale de Lausanne, Neuro-X Institute, Austria's Central European University and Italy's CENTAI Institute: Andrea Santoro et al, Higher-order organization of multivariate time series, Nature Physics (2023). DOI: 10.1038/s41567-022-01852-0
Image credit: Anatoly Fomenko
Harnessing incoherence to make sense of real-world networks
Mar 2023, phys.org
Mapping the hierarchies and also the incoherence within a system will enable us to predict the system's strong and weak points.Most real-world systems are neither perfectly coherent nor completely incoherent, but lie somewhere in between. In a food web, for instance, this might occur because of omnivorous animals that will eat both plants and other animals.It was possible to use this trophic incoherence to estimate the point at which a network becomes strongly connected. They demonstrated that the method works for any type of network, including those of neurons, people, species, metabolites, genes and words..."This modeling approach could be used to disrupt networks as well, because the points at which connectivity becomes strong can be targeted. Neurologists, for example, might find new ways to treat epilepsy by pinpointing specific connections responsible for maintaining seizures."
via University of Birmingham: Niall Rodgers et al, Strong connectivity in real directed networks, Proceedings of the National Academy of Sciences (2023). DOI: 10.1073/pnas.2215752120
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