The tool permits the early detection of errors at any point during the modelling process, not just on completion, as is the case now
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.
A new study, led by a theoretical physicist at Berkeley Lab, suggests that never-before-observed particles called axions may be the source of unexplained, high-energy X-ray emissions surrounding a group of neutron stars.
Astronomers have catalogued 126 years of changes to HS Hydra, a rare evolving eclipsing binary star system. Analyzing observations from astro-photographic plates in the late 1800s to TESS observations in 2019, they show that the two stars in HS Hydrae began to eclipse each other around a century ago, peaking in the 1960s. The degree of eclipsing then plummeted over the course of just a half century, and will cease around February 2021.
Computational materials science experts at the US Department of Energy's Ames Laboratory enhanced an algorithm that borrows its approach from the nesting habits of cuckoo birds, reducing the search time for new high-tech alloys from weeks to mere seconds.
The software was developed by the University of Trento. It is a new and revolutionary method to manage the accommodation of guests in hotel. RoomTetris finds the best solution, the ideal combination between demand and supply, optimizing room occupancy. A tile-matching game that no human mind, no matter how experienced and skilled, could do better, with the seriousness and scientific rigor of a mathematical demonstration
Researchers from the University of Cambridge, the University of Milan and Google Research have used machine learning techniques to predict how proteins, particularly those implicated in neurological diseases, completely change their shapes in a matter of microseconds.
Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture.
A major roadblock to computational design of high-entropy alloys has been removed, according to scientists at Iowa State University and Lehigh University. Engineers from the Ames Lab and Lehigh University's Department of Mechanical Engineering and Mechanics have developed a process that reduces search time used for predictive design 13,000-fold.
By combining large amounts of low-fidelity data with smaller quantities of high-fidelity data, nanoengineers from the Materials Virtual Lab at UC San Diego have developed a new machine learning method to predict the properties of materials with more accuracy than existing models. Crucially, their approach is also the first to predict the properties of disordered materials--those with atomic sites that can be occupied by more than one element, or can be vacant.