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.

Analyzing randomness effects on the reliability of exploratory landscape analysis

ACEMS researchers examine how accurate the methods are for measuring landscape characteristics on optimisation black-box problems. Their work also provided a methodology and curated data that can be used by other researchers to identify robust measuring methods.

Inference for a complex world

What is ABC? In the world of statistics, it stands for Approximate Bayesian Computation. ACEMS' research assistant Abhishek Vargese recently completed an AMSI Vacation Research Experience Scheme (VRES) and wrote this blog about what he learned about ABC.

Robust Approximate Bayesian Inference with Synthetic Likelihood

ACEMS researchers develop a novel statistical approach to the parameter estimation of complex models.

Revisiting where are the hard knapsack problems? via Instance Space Analysis

ACEMS Chief Investigator Professor Kate Smith-Miles talks about her just-published work on the 'knapsack problem' and instance space analysis. It was a paper eight years in the making.

Identifiability analysis for stochastic differential equation models in systems biology

ACEMS researchers bring together existing tools that are designed for one type of model and show how they can be used for paramater identifiability in stochastic models of biological processes.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Using Bayesian statistics and mathematical models to assess the global response to COVID-19. This analysis uses data from 158-countries.

Advanced Bayesian approaches for state-space models with a case study on soil carbon sequestration

The methods presented in this paper are useful for farmers and landowners in estimating carbon stocks in agricultural production systems.

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.

Multivariate Bayesian meta‑analysis: joint modelling of multiple cancer types using summary statistics

This research provides a tool so that people can use readily available data from online cancer atlases, and understand patterns for multiple cancer types together.

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