Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Maiorino, Enrico"'
Many complex flows such as those arising from ocean plastics in geophysics or moving cells in biology are characterized by sparse and noisy trajectory datasets. We introduce techniques for identifying Lagrangian Coherent Structures (LCSs) of hyperbol
Externí odkaz:
http://arxiv.org/abs/2110.10884
Akademický článek
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Autor:
Bianchi, Filippo Maria, Maiorino, Enrico, Kampffmeyer, Michael C., Rizzi, Antonello, Jenssen, Robert
The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effectiv
Externí odkaz:
http://arxiv.org/abs/1705.04378
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are tr
Externí odkaz:
http://arxiv.org/abs/1701.05159
Akademický článek
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Autor:
Spagnolo, Nicolò, Maiorino, Enrico, Vitelli, Chiara, Bentivegna, Marco, Crespi, Andrea, Ramponi, Roberta, Mataloni, Paolo, Osellame, Roberto, Sciarrino, Fabio
Publikováno v:
Scientific Reports 7, 14316 (2017)
Recent developments in integrated photonics technology are opening the way to the fabrication of complex linear optical interferometers. The application of this platform is ubiquitous in quantum information science, from quantum simulation to quantum
Externí odkaz:
http://arxiv.org/abs/1610.03291
Autor:
Maiorino, Enrico, Bianchi, Filippo Maria, Livi, Lorenzo, Rizzi, Antonello, Sadeghian, Alireza
In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evol
Externí odkaz:
http://arxiv.org/abs/1510.07146
In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected by considerin
Externí odkaz:
http://arxiv.org/abs/1503.03332
In this paper we present a generative model for protein contact networks. The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysi
Externí odkaz:
http://arxiv.org/abs/1503.02336
The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic
Externí odkaz:
http://arxiv.org/abs/1410.0890