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We also introduce a data caching mechanism at cloudlets to further improve the overall MCC performance.
Here we introduce a data driven, Bayesian framework to quantify DEM predictions.
In this paper, we introduce a data mining method into the cache size allocation.
Second, we introduce a data model for traceability and a set of suitable patterns to encode generic traceability semantics.
The authors introduce a data mining technique, entropy-based classification, to improve preliminary base-isolated design procedures.
To deal with the inadequacy of training samples during learning, we introduce a data augmentation scheme which is very efficient due to its origin at cropping across videos.
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In this work, we introduce a data-driven region descriptor.
We introduce a data-driven approach for the estimation of these coarse scale basis functions.
To keep the analysis simple, we introduce a data-dependent variable α to represent the size of the k-means neighborhood (including the vector itself).
We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs).
In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs).
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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