August 5th, 2023

Deep learning criminal networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of […]

September 11th, 2022

Machine Learning Partners in Criminal Networks

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static […]

April 29th, 2022

Clustering free-falling paper motion with complexity and entropy

Many simple natural phenomena are characterized by complex motion that appears random at first glance, but that often displays underlying […]

November 15th, 2021

Determining liquid crystal properties with ordinal networks and machine learning

Machine learning methods are becoming increasingly important for the development of materials science. In spite of this, the use of […]

April 3rd, 2020

Learning physical properties of liquid crystals with deep convolutional neural networks

Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into […]

January 3rd, 2019

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via […]

December 12th, 2018

Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market

The efficient market hypothesis has far-reaching implications for financial trading and market stability. Whether or not cryptocurrencies are informationally efficient […]

March 27th, 2018

Crime prediction through urban metrics and statistical learning

Understanding the causes of crime is a longstanding issue in researcher’s agenda. While it is a hard task to extract […]