TOWARDS AN ENVIRONMENTALLY SUSTAINABLE AGRICULTURE: MATHEMATICS AND DEEP LEARNING IN THE FIELD

Alessandro Benfenati, Paola Causin, Roberto Oberti Università degli Studi di Milano Crop protection from diseases through applications of plant protection products is crucial to secure

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GENEO and Explainable Machine Learning applied to protein pocket detection

This post deals with some new geometrical techniques for explainable machine learning, called GENEOs [1], that we are applying in a research group working at

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€21million Centre for Research Training in Applied Mathematics, Statistics, and Machine Learning Announced

€21million Centre for Research Training in Foundations of Data Science Announced In partnership with University College Dublin and Maynooth University, University of Limerick is to

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Stochastic Modelling of Cold Spray Coatings Microstructure

Modern materials manufacturing has evolved toward a better control and optimization at the microscopic scale. Hence, microstructures commonly exhibit complex morphologies mixing various materials including

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Numerical models and homogenization to study the sensitivity of energetic materials: effect of damage and microstructure

Shock sensitivity is often studied in order to improve the safety of energetic materials (i.e., explosives). Numerous studies have shown that the former is sensitive

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Inferring the Structure of Galaxies with Deep Convolutional Neural Networks

The popular and beautiful galaxy images provided from telescopes like the NASA’s Hubble Space Telescope or the Sloan Digital Sky Survey, enabled astronomers to a

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ECMI SIG Shape and size in Medicine, Biotechnology and Materials Science

Shape analysis deals with the geometrical information on objects that is left after location, scale and rotation effects are removed. If scale effects are not

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