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Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories
Here we present a new data-driven method for working with diffusive trajectories.
Characterization of anomalous diffusion through convolutional transformers
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).
Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories
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.
Reversible Self-Replication of Spatio-Temporal Kerr Cavity Patterns
We uncover a novel and robust phenomenon that causes the gradual self-replication of spatiotemporal Kerr cavity patterns in cylindrical microresonators.
Optics
Zipf's and Benford's laws in Twitter hashtags
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.
