Volcano Early Warning Systems (VEWS) have become a research topic in order to preserve human lives and material losses. In this setting, event detection criteria based on classification using machine learning techniques have proven useful, and a number of systems have been proposed in the literature.
We model the luminosity-dependent projected and redshift-space two-point correlation functions (2PCFs) of the Sloan Digital Sky Survey (SDSS) Data Release 7 Main galaxy sample, using the halo occupation distribution (HOD) model and the subhalo abundance matching (SHAM) model and its extension.
We propose a realistic scheme to quantum simulate the so-far experimentally unobserved topological Mott insulator phase—an interaction-driven topological insulator—using cold atoms in an optical Lieb lattice.
We have simulated the formation of a massive galaxy cluster (Mcrit200M200crit = 1.1 × 1015h−1 M⊙) in a Λ cold dark matter universe using 10 different codes (RAMSES, 2 incarnations of AREPO and 7 of GADGET), modelling hydrodynamics with full radiative subgrid physics.
Determining the precise value of the tangential component of the velocity of M31 is a non trivial astrophysical issue, that relies on complicated modeling. This has recently lead to con- flicting estimates, obtained by several groups that used different methodologies and assump- tions.
This paper presents a method for accelerating the evaluation of individuals in Grammatical Evolution. The method is applied for identification and modeling problems, where, in order to obtain the fitness value of one individual, we need to compute a mathematical expression for different time events.