Michele Loreti
Abstract
“Statistical abstraction for multi-scale spatio-temporal systems” proposes a methodology that supports analysis of large-scaled spatio-temporal systems. These are represented via a set of agents whose behaviour depends on a perceived field. The proposed approach is based on a novel simulation strategy based on a statistical abstraction of the agents. The abstraction makes use of Gaussian Processes, a powerful class of non-parametric regression techniques from Bayesian Machine Learning, to estimate the agent’s behaviour given the environmental input. The authors use two biological case studies to show how the proposed technique can be used to speed up simulations and provide further insights into model behaviour. This replicated computation results report focuses on the scripts used in the paper to perform such analysis. The required software was straightforward to install and use. All the experimental results from the paper have been reproduced.
- Jane Hillston, Michalis Michaelides, and Guido Sanguinetti. 2019. Statistical abstraction for multi-scale spatio-temporal systems. Trans, Mod, Comput, Simul, (2019). To appear.Google Scholar
Index Terms
- Replicated Computations Results (RCR) Report for “Statistical Abstraction for Multi-scale Spatio-temporal Systems”
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Reviews
Judy Marian Myerson
This brief paper takes an unusual approach to presenting the experimental results of simulating two case studies from [1]: a model of Escherichia coli chemotaxis and a model of Dictyostelium discoideum aggregation. It introduces readers to the proposed methodology of statistical analysis, and then presents its use for replicating the results in a Git repository. Pictures, graphs, and tables are absent from the paper; however, they are made available after the reader implements the scripts and tools in a Git repository. Detailed installation instructions are available online (http://bit.ly/2WMoi2E). It is unclear whether a repository hosting service or a version control system was used. Readers will find that they can get the results for the first case study faster using a standard personal computer (PC) equipped with at least a 3.5GHz Intel Core i7 processor and 16GB of random-access memory (RAM). The second case study uses a 64-core server. In both cases, readers can get live results faster with a top-of-the-line PC/server, rather than reading the detailed results on paper. Human eyes move much more slowly than the speed of processors. Seven required tools for the E. coli and D. discoideum codebases are listed, including Python 3.5 (or later), NumPy, SciPy, and Matplotlib. NumPY is used to compute multidimensional matrices. SciPy is short for scientific computing in Python. Matplotlib speaks for itself; it contains a library of plotting algorithms in Python. The E. coli codebase depends on pyGPs for Guassian processes and StochPy for stochastic modeling algorithms. The D. discoideum codebase needs GPflow (https://www.gpflow.org/) to build flows in Gaussian process models. Loreti kept "this replicated computations results report" brief, but should have included a graph or two to give readers a better idea of what [1] is all about. Those interested in the challenges of replicating the results for two biological case studies should read the paper and use the Git repository tools and scripts to replicate results.
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