Our knowledge of physics and chemistry is relatively well defined.Results from that knowledge are predictable as, largely, are those of their technical offspring such as electrical, chemical, mechanical and civil engineering.By contrast, biology is relatively unconstrained and unpredictable.A factor common to all areas is the trade-off, which provi
Double parton distribution functions (dPDF) represent a tool to explore the 3D proton structure.They can be measured in lycogel high energy proton-proton and proton nucleus collisions and encode information on how partons inside a proton are correlated among each other.dPFDs are studied here in the valence quark region, by means of a constituent qu
Feature extraction-based subspace learning methods normally learn a projection that can convert the high-dimensional data to the low-dimensional representation.However, they may not be suitable for better classification since features obtained by these methods ignore discriminability of the data-pixel itself.Given this, we propose a novel approach