Software

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ESports are becoming something really big with professional video gaming constantly growing. Professional organizations mean greater competitions with media coverage that can go beyond the tournaments’ live streaming and more money. There are arguments about eSports being real sports but the International Olympic Committee already started looking into their possible recognition. Don’t expect to watch eSports at the next Olympic Games but you can be sure that you’ll hear about them more and more. If you’re into gaming and you’re motivated enough, you might consider them as a possible line of work.

Machine learning used to create new polymers

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An article published in the journal “npj Computational Materials” reports the results of the use of a machine learning algorithm in the search for new materials. A team of researchers used an algorithm specialized in molecular design trained to recognize candidates with certain properties. In this way they designed polymers with high thermal conductivity, very useful to use for example in mobile technologies connected to 5G.

Screenshot from an IllustrisTNG simulation (Image courtesy IllustrisTNG project)

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An article published in the journal “Computational Astrophysics and Cosmology” presents the complete public release of the simulations coded as TNG100 and TNG300 of the IllustrisTNG project, a highly sophisticated simulation of the universe that has been improved over years of work. A team of researchers kept on improving its details and functionalities also by developing new interaction and exploration 2D and 3D tools to simulate two cubes of space of 100 and 300 million parsec side length.

Deep learning used to map storms on the planet Saturn

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An article published in the journal “Nature Astronomy” reports the application of a deep learning algorithm to recognize storms on the planet Saturn creating a map of their components and characteristics. Ingo Waldmann of the University College London, England, and Caitlin Griffith of the University of Arizona, USA, developed the algorithm called PlanetNet based on the TensorFlow “engine” to analyze data collected by the Cassini space probe with an efficiency higher than traditional techniques thanks to the recognition of recurrent characteristics across various multiple data sets. PlanetNet could be adapted to analyze observations of other planets.

Neural networks to predict the mass of planets in formation

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An article published in the journal “Astronomy and Astrophysics” shows an example of the use of a neural network and a deep learning algorithm to reduce the time to create simulations of star systems formation obtaining even better results. Yann Alibert of NCCR PlanetS and of the Swiss University of Bern and Julia Venturini of the International Space Science Institute (ISSI) of Bern and a PlanetS collaborator developed this new system that predicts the mass of a planet starting from the conditions in which it formed with a excellent accuracy and a much higher speed than models based on differential equations.