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To speed up our method, we take advantage of the embarrassingly parallel feature of boundary integral formulation, and parallelize the schemes with the message passing interface (MPI) implementation.
To speed up our method, we take advantage of the parallel nature of the boundary integral formulation and parallelize the schemes within CUDA shared memory architecture on GPU.
By the proposed method, we take the strengths of both machine learning and the variational level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation.
Here, utilizing transfer matrix method, we take the Si/TiO2 heterojunction as a prototype to shed light on the rational design of the semiconductor heterojunction photocatalyst in terms of its intrinsic characteristics, such as TiO2 thickness and the dopant concentration.
In our method, we take advantage of these patterns in the representation and selection of patches.
To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example.
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In order to assess the accuracy of citation indices computed from the estimate PCRs generated by our method, we took our method's PCR estimates and computed h- and e-indices for each one.
To validate our flow cytometry based method, we took replicate samples from macrophage infections with the same GFP-expressing strain and compared proliferation quantification by flow cytometry, CFU and haemocytometer count.
In the first method, we took one hemispherical photograph at a position just beneath the canopy layer.
For each method, we took the top- k triplets, and the precision is defined as the proportion of true interactions in all interactions in top- k triplets.
To demonstrate the biological applicability of our method, we took the literature-proven functional TF-gene binding pair (Leu3p, BAP2) as an example.
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