Month: January 2020
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Sampling hyperspheres via extreme value theory: implications for measuring attractor dimensions
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Advancing computational power has encouraged the analysis of large, high-dimensional data sets with machine learning and data mining techniques, as well as the use of algorithms to compute dynamical indicators such as Lyapunov exponents or generalized dimensions.
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Forecasting the magnitude of the largest expected earthquake
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Geophysicists still lack a comprehensive understanding of the mechanisms and stochastic dynamics behind the earthquake generation process, and so also lack an ability to make reliable predictions of the likelihood of extreme events.
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Epidemic spreading with awareness and different timescales in multiplex networks
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Efforts to control epidemics rely on mathematical and computational models of how infectious agents spread. Such models help to find ways to deter transmission – through vaccination and quarantine, for example, or information campaigns to alter human behaviour.