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We prove a version of Hardy's type inequality in a domain Ω⊂Rn which involves the distance to the boundary and the volume of Ω.
In the kernel metric learning (kML) approach (Vert and Yamanishi, 2005), a feature f1 is identified by optimizing a function that involves the distance between known interacting objects.
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Note that the Stansfield estimator involves the distances R j between sensors and target; however, as discussed in [16] and confirmed by simulation results (see section 5.2), the dependency of the estimator on R is quite weak.
Another aspect of your speed training involves the distances you should be doing your training over.
Under this assumption, we prove the Hardy inequality with optimal constant involving the distance to the boundary.
The criteria for the stated energy mechanisms involve the distance between the donor and acceptor chromophore, and the orientation of the chromophores.
If not involved, the distance of the melanoma from the closest uninvolved margin must be recorded, and the location(s) of the closest uninvolved margins should be recorded, if possible.
Epitaxies are governed by a one-dimensional match which involves the interturn distance, i.e. the distance between successive outer helical paths.
Distance measures involve the euclidean distance in kilometers to the closest variable of interest (e.g., shop, bus stop, train station, recreational facility) from each resident's home.
Moscot's move involves the shortest of distances.
The method involves computing the distance from and footpoints in both the Euclidean motion group and the configuration space of surface-surface contact.
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CEO of Professional Science Editing for Scientists @ prosciediting.com