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BioScape is a concurrent language motivated by the biological landscapes found at the interface of biology and biomaterials. It has been motivated by the need to model antibacterial surfaces, biofilm formation, and the effect of DNAse in treating and preventing biofilm infections. As its predecessor, SPiM, BioScape has a sequential semantics based on Gillespies algorithm, and its implementation does not scale beyond 1000 agents. However, in order to model larger and more realistic systems, a semantics that may take advantage of the new multi-core and GPU architectures is needed. This motivates the introduction of parallel semantics, which is the contribution of this paper: Parallel BioScape, an extension with fully parallel semantics.
We propose a new graph-based spatial temporal logic for knowledge representation and automated reasoning in this paper. The proposed logic achieves a balance between expressiveness and tractability in applications such as cognitive robots. The satisf
For many applications, we are unable to take full advantage of the potential massive parallelisation offered by supercomputers or cloud computing because it is too hard to work out how to divide up the computation task between processors in such a wa
A (fragment of a) process algebra satisfies unique parallel decomposition if the definable behaviours admit a unique decomposition into indecomposable parallel components. In this paper we prove that finite processes of the pi-calculus, i.e. processe
Stochastic equations play an important role in computational science, due to their ability to treat a wide variety of complex statistical problems. However, current algorithms are strongly limited by their sampling variance, which scales proportionat
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inferenc