no code implementations • 2 Sep 2023 • Òscar Garibo-i-Orts, Nicolás Firbas, Laura Sebastiá, J. Alberto Conejero
Here we present a new data-driven method for working with diffusive trajectories.
no code implementations • 10 Oct 2022 • Nicolás Firbas, Òscar Garibo-i-Orts, Miguel Ángel Garcia-March, J. Alberto Conejero
The results of the Anomalous Diffusion Challenge (AnDi Challenge) have shown that machine learning methods can outperform classical statistical methodology at the characterization of anomalous diffusion in both the inference of the anomalous diffusion exponent alpha associated with each trajectory (Task 1), and the determination of the underlying diffusive regime which produced such trajectories (Task 2).
no code implementations • 5 Aug 2021 • Òscar Garibo i Orts, Miguel A. Garcia-March, J. Alberto Conejero
We present a data-driven method able to infer the anomalous exponent and to identify the type of anomalous diffusion process behind single, noisy and short trajectories, with good accuracy.
no code implementations • 14 Jan 2021 • Salim B. Ivars, Yaroslav V. Kartashov, Lluis Torner, J. Alberto Conejero, Carles Milián
We uncover a novel and robust phenomenon that causes the gradual self-replication of spatiotemporal Kerr cavity patterns in cylindrical microresonators.
Optics
no code implementations • EACL 2017 • Jos{\'e} Alberto P{\'e}rez Meli{\'a}n, J. Alberto Conejero, C{\`e}sar Ferri Ram{\'\i}rez
In particular, we study the similarity of frequency distribution of hashtag popularity with respect to Zipf{'}s law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution.