Joint research led by Sosuke Ito of the University of Tokyo has shown that nonequilibrium thermodynamics, a branch of physics that deals with constantly changing systems, explains why optimal transport theory, a mathematical framework for the optimal change of distribution to reduce the cost, makes generative models optimal. As nonequilibrium thermodynamics has yet to be fully leveraged in designing generative models, the discovery offers a novel thermodynamic approach to machine learning research. The findings were published in the journal Physical Review X.

A research team at the Politecnico di Milano has developed an innovative single-atom catalyst capable of selectively adapting its chemical activity. This is a crucial step forward in sustainable chemistry and the design of more efficient and programmable industrial processes.

The study was published in the Journal of the American Chemical Society, one of the world’s most authoritative scientific journals in chemistry.

Even a toddler knows that plants need water. It’s perhaps the first thing we learn about these green lifeforms. But how plants budget this resource varies considerably. The kapok trees of the Amazon have adopted vastly different strategies than the switchgrass of the American plains. Unfortunately, it’s hard to directly measure which ones prevail in different ecosystem types and how they shift under changing conditions.