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In terms of AI, I introduce the notion of human-robot fluency - the ability to accurately mesh the robot's activity with that of a human partner.
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In this paper, we present a novel framework for generating high quality triangular meshes that accurately approximate TDS.
Their gears mesh perfectly.
Did your styles mesh?
Their voices mesh.
Let's mesh.
However, to realize the process, it is necessary to accurately decompose the mesh and to extract a set of partial meshes, each of which corresponds to a single part, from the entire meshes of assemblies measured from the CT scans.
The major challenges to such a conversion include capturing the geometric features accurately when constructing the secondary (meshed) representation, apart from the efficiency of carrying out such a mesh generation step repeatedly as the geometric shape evolves.
In order to capture the flow solution around the shock waves accurately, the adaptive mesh refinement technique is coupled to the flow solution.
In case a query can be accurately represented in MeSH terms, improved retrieval performance was observed compared with a text-only representation.
We present a method for automatically and accurately extracting surface meshes of several tissues of the head from anatomical magnetic resonance (MR) images.
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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