ACEMS Research Briefs

These are short summary descriptions of our latest, just-published research.

ACEMS Research Briefs Projects

The R-Matrix of the Quantum Toroidal Algebra \(U_{q,t}(\overset{..}{gl}_{1})\) in the Fock Module

The mathematics of quantum toroidal algebras is remarkably rich, and so are their applications to physics. These algebras and their representation theory arise in the mathematics of high-energy physics, string theory and supersymmetric gauge theories. They also play an important role in the analysis of central models in condensed matter physics, stochastic interacting particle systems and integrable probability. ACEMS researchers contribute to quantum toroidal algebras, which are quantum versions of toroidal Lie algebras. 

Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours

What if farmers could tell if one of their livestock was sick, without even being around the animal? Farmers are now using inertial motion sensors to study grazing behaviour. But the challenge is interpreting all the data those sensors provide. New research led by ACEMS PhD candidate Shuwen Hu reveals which machine learning methods are best suited to handle the problem.