Creating a 'virtual seismologist'

Understanding earthquakes is a challenging problem—not only because they are potentially dangerous but also because they are complicated phenomena that are difficult to study. Interpreting the massive, often convoluted ...

Deep learning for electron microscopy

Finding defects in electron microscopy images takes months. Now, there's a faster way. It's called MENNDL, the Multinode Evolutionary Neural Networks for Deep Learning. It creates artificial neural networks—computational ...

Understanding metabolic processes through machine learning

Bioinformatics researchers at Heinrich Heine University Düsseldorf (HHU) and the University of California at San Diego (UCSD) are using machine learning techniques to better understand enzyme kinetics and thus also complex ...

The promise of the "learn to code" movement

This week, educators, students and the public around the world are participating in Computer Science Education Week by organizing and leading one-hour coding tutorials.

Smarter AI—machine learning without negative data

A research team from the RIKEN Center for Advanced Intelligence Project (AIP) has successfully developed a new method for machine learning that allows an AI to make classifications without what is known as "negative data," ...

Computing solutions for biological problems

Producing research outputs that have computational novelty and contributions, as well as biological importance and impacts, is a key motivator for computer scientist Xin Gao. His Group at KAUST has experienced a recent explosion ...

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