Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Researchers from the University of Hong Kong studying the structure of 2D materials have predicted new types of phase transitions that have yet to be seen in experiments ...

The selective and efficient capture of phosphopeptides is critical for comprehensive and in-depth phosphoproteome analysis. Here we report a new switchable two-dimensional (2D) supramolecular polymer ...

In a groundbreaking achievement, US researchers have created a computer using two-dimensional materials, potentially replacing silicon in future electronics. This pioneering CMOS computer, built at ...

What if electricity and magnetism, usually considered as separate or even competing forces in materials, could actually work ...

From four-dimensional hexagons to the mind-bending amplituhedron, geometrical shapes are wilder than we learn at school - and ...

A better understanding of how the human brain represents objects that exist in nature, such as rocks, plants, animals, and so ...

A condition long considered to be unfavorable to electrical conduction in semiconductor materials may actually be beneficial ...

Recently, the development of nanozymes with high catalytic performance is gaining more and more attention due to the ever-growing demands for their practical applications. The elaborate design of its ...

An algorithm for computing the Euclidean distance between a pair of convex sets in R/sup m/ is described. Extensive numerical experience with a broad family of polytopes in R/sup 3/ shows that the ...

Deep convolutional neural network (DCNN) object detection is a powerful solution in visual perception, but it requires huge computation and communication costs. We proposed a fast and low-power always ...