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Figure 8 shows the F1 scores obtained on different sizes of vector sets.
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We note that the input compression ratio defined as the ratio between the sizes of vectors (varvec{f}) and (varvec{y}) in Eq. 1 is not expected to match the sparsity of the output (varvec{s}) of the StOMP algorithm.
The sizes of vectors containing inserts were about 200 bp and 100 bp by electrophoresis, as the NotI recognition sequence was incorporated into the primers.
For feature fusion, a new technique is implemented which concentrate on the size of vector dimension.
Further, we observe that the ER performance of our considered system depends on code book size of vector quantized parameters.
The quantization problem of the whole vector is decomposed into ρ sub-quantization problems where each sub-quantization deals with a smaller size of vector.
The main advantage of the method proposed in [28] is that it reduces the size of vector sets and computational complexity of the searching and matching processes.
The size of vector c is smaller than the number of unknown weights (Delta w), thus many solutions exist for (8).
This way the time-domain data are represented in a small size of vector or matrix and further used for feature extraction.
It is noticed that there is an upper bound for this parameter: the number of iterations cannot exceed the size of vector b because the matrix that is involved in the linear system given by Equation 46 then becomes singular.
Feature whose value is a set of ordered discrete numbers (a vector): suppose that (a^k_i = (x_1,ldotsdots x_n)), (a^k_j = (y_1,y_2,ldots y_n)) (n is the size of vector) are two vector values on the features (a^k), of two agents (i) and (j), respectively.
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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