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We see that the build time for the fused analysis is directly related to the size of the trace set.
On a first analysis, Marchal's algorithm time grows linearly with the size of the trace, with a quadratic component for local search of Euclidean distance.
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Finally, we evaluate the impact of the size of the traces on the classification accuracy.
An issue here is the typical size of the traces – an average of 1219 bp.
In particular, we reduce the size of the observed traces in the CenceMe dataset to 20%, 40 %, 60 and 80%and80%e ofiginal size.
We show how the results vary as we reduce the size of the observed traces in the CenceMe dataset to 20% (blue), 40% (red), 60% (green) and 80% (magenta) of the original size.
In contrast both the spatio-temporal analysis and evaluation of logic properties only depend on the size of the simulation traces and are expected to scale well (polynomially) with respect to the size of the system.
The size of a trace T is the number of solutions which it represents.
The total number of "leftovers", i.e. executions of syntax trees not assigned to any pattern is not larger than 5%% of the total size of workload trace.
Figure 8 shows how the average classification accuracy varies as we increase the number of the sampled points, for different sizes of the observed traces.
Computation times show that the time needed for the computation depends mostly on the size of scenario since tracing of the rays is the most time-consuming part of the algorithm.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com