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A Little Data Goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning

Seismic monitoring plays a fundamental role in mitigating hazards near volcanoes, where thousands of earthquakes can occur each day during periods of unrest. These events produce a diverse range of seismic signals, and the detection of the initial primary (P-) and secondary/shear (S-) wave arrivals forms the basis of most seismic processing tasks undertaken to […]

Big jump principle for heavy-tailed random walks with correlated increments

Since its first formulation in 1964, the so-called “big jump principle” (BJP) of extreme value statistics has played an important role in fields such as large deviation theory and financial mathematics. For heavy-tailed distributions, the principle considers N independent and identically distributed (IID) random variables, and links the statistics of the sum of these variables […]

On Generalized Langevin Dynamics and the Modelling of Global Mean Temperature

For more than half a century, researchers have employed mathematical models to better understand the response of the Earth’s climate to internal fluctuations as well as external perturbations, whether anthropogenic or solar. These models range from highly complex and multi-dimensional general circulation models to simplified energy balance models.

Root and community inference on latent network growth processes using noisy attachment models

Complex networks can be analysed using a variety of statistical models including classic Erdös–Rényi random graphs, latent space models, configuration graphs and others. These models generally specify some structure – arising from the existence of communities, for example – but the order in which the edges are added is of no importance.