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This method implements an anytime search algorithm that generates partial plans to feed the negotiation process.
Since this signal has unknown and time-varying characteristics, the proposed estimation method implements an adaptive control scheme based on the internal model principle and on the use of Youla Kučera (YK) parameterization technique (also known as Q-parameterization).
The method implements an optimization procedure designed to identify the best hydrogen bonding network based on a generic scoring function.
This method implements an expectation-maximization (EM) algorithm for gene expression mixture models, which compares the patterns of differential expression across multiple conditions and provides a posterior probability.
Our method implements an empirical Bayes algorithm, 'Shrunken Average (SHAVE)', using regression toward the mean for every individual as a function of their number of visits and the correlation between visits.
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Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network.
The advanced method implements a mechanical model to represent the composite node in unbraced frames, which allows considering the behavior of each component of the composite connection individually.
The developed method implements a complete and irreducible geometric model for serial manipulator, which takes into account different sources of errors (link lengths, joint offsets, etc).
The proposed method implements a nonuniform spacing search that reduces certain instabilities in the fitted curve.
Each method implements a shared, adaptive time-step criterion according to equation (1), with a time-step parameter (eta= 2^{-7}).
The method implements a Newton Krylov solver with GMRES linear solver, the eigensolver is based on the Arnoldi algorithm.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com