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This method achieved relative high performance [ 21].
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Overall, both methods show more robustness at low and moderate expression levels when compared to existing parametric static methods indicating that our methods achieve relative improvements in test of identification of temporal genes and AR(1) model shows more sensitive TDE calls than SETI resampling procedure in two real data applications.
For RWCP database, the SS-based dereverberation method achieved a relative word recognition error reduction rate of 19.2% relative to CMN with delay-and-sum beamforming.
The proposed method achieved a relative error reduction of 83.7% compared with the baseline (method 1) and 28.4% compared with reverberant speech models (method 3).
As a result, the proposed efficient combination method achieved a relative error reduction of 87.5% compared with the baseline, and 44.8% compared with reverberant speech models, for almost the same computational cost.
The design has achieved relative negative permeability, relative negative permittivity and relative negative refractive index.
Similarly, 10% replacement also achieved relative high modulus of elasticity.
A speech recognition experiment showed that the GSS-based dereverberation method achieved an average relative word error reduction rate of 32.6% compared with conventional CMN with beamforming [22].
In both development and test sets, RDE-based method achieved over 20% relative improvement on F1 and AUC performance against classical supervised learning methods, e.g. support vector machine and logistic regression.
The first method achieves a relative error below 2.4% in the obtained segmentation, while the second method has an error below 4.8%.
The light yield characterized by using a relative measurement method achieved as high as 75.85% of the anthracene crystal.
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