Alessia Gentili

TL;DR: The Anomalous Diffusion Challenge (AnDi) as mentioned in this paper was an open competition for the characterization of anomalous diffusion from the measurement of an individual trajectory, which traditionally relies on calculating the trajectory mean squared displacement.

TL;DR: This paper presents a meta-anatomy of the response of the immune system to chemotherapy, a model derived from the model developed by Carl Friedrich Gauss in 1916.

TL;DR: In this paper, a novel method (CONDOR) combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy and infer its exponent with a small mean absolute error in single 1D, 2D and 3D trajectories corrupted by localization noise.

TL;DR: In this article, a novel method which combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy (up to 91%) and infer its exponent with a small mean absolute error (down to 0.12).

TL;DR: In this paper, a novel method (CONDOR) combines feature engineering based on classical statistics with supervised deep learning to efficiently identify the underlying anomalous diffusion model with high accuracy and infer its exponent with a small mean absolute error in single 1D, 2D and 3D trajectories corrupted by localization noise.