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The results show improvement in iiHadoop performance when the size of data increases.
Figures 6 and 7 show clearly SW ℓ2,1-SVD has a good performance when the size of the regularization parameter varies widely.
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This paper, however, shows that pores can cause improved or unique performance when the sizes, shapes, and orientation of pores as well as grains are controlled in porous silicon nitrides.
First, the BER performance degrades when the size of the constellation size increases, by comparing the average uncoded BERs listed in the last lines of the three tables.
We believe this extension could allow some flexibility to the Adaboost algorithm as well as a good performance especially when the size of a training data set is not large enough.
Although SHELXL has still the advantage of scaling better when using multiple cores, the performance gap disappears when the size of the least-squares problem decreases.
As shown in Fig. 15, the system gives the best performance when the block size K=4 and the SDR performance decreases with the increase of the K.
For purebred animals, a reference population of within-breed animals ensures a good performance of imputation, especially when the size of the reference population is large.
Merging uncorrelated queries does not increase the performance when the database size reaches terabyte scale.
The NW substrate interfaces therefore play a critical role in both mechanical reliability and electrical performance of these nanodevices, especially when the size of the NW is small [1, 2].
In order to convince TCP friendliness of SCTP- Rev +, we examine the cumulative average TCP throughput performance when the default rbuf sizes of 64 KB is used, when SCTP, SCTP-Rev, and SCTP- Rev + are used, respectively.
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