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It is demonstrated how prior knowledge about partial differential equations and their solution schemes can be used to provide deeper insight into the performance of two numerical implementations for this cellular model.
While hardware reported implementations for this class of algorithms have proven that a high throughput can be reached, those algorithms are inherently facing a tradeoff between speed and memory consumption.
Theoretical details and code implementations for this framework, that we call general noise SVRs with non-constant uncertainty intervals, are developed and made publicly available.
The algorithms and software implementations for this were perfected decades ago, and are routinely used by engineers to correctly simulate the behavior of circuits with enormous numbers of components.
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This level handles the discrete decisions of proposing circuit alternatives while the two lower levels determine the best possible implementation for this alternative.
We also present an efficient, pseudo-spectral implementation for this set of coupled equations which enables practical application of the model in periodic domains.
A methodology is presented to evaluate the design of a conveyor belt implementation for this acquisition scheme, investigating the trade-off between reconstruction quality and throughput.
In this paper, we first discuss a simplified and improved implementation for this procedure, which uses the relatively complicated ILW procedure only for the evaluation of the first order normal derivatives.
We present a user friendly implementation for this as part of the R package paramlink, originally designed by one of the authors (MDV) for pedigree manipulations and likelihood computations.
The reference implementation for this article is Jmol 14.6.1_2016.07.11.
We used a parallel implementation for this scheme in order to accelerate the computation.
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