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With increasing filter length, the performance of sidelobe suppression is improved at the cost of increasing running time, so that it is important to select a balanced filter length.
However, many of these approaches require the repeated application of a learning process upon the presentation of unseen data input vectors, or else involve the use of large numbers of parameters and hyper-parameters, which must be chosen through cross-validation, thus increasing running time dramatically.
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The plots in Figure 3 show a trend of increasing running times for larger BCCs, but the graphs are not very smooth, i.e. there are some sudden decreases and increases in the running times observed.
Instead the electropherogram contains zones of fully bound and free aptamer that define an intermediate region reflecting dissociation with increasing run time.
The priors did not significantly increase running time.
A Cartesian grid substantially increases running time and additionally yields poor predictions.
Utilizing PCA method or skipping filtering (FILTERLESS case) increases running time subsequently.
Additionally, other procedures such as model calibration, which may require thousands of model iterations, can increase running time and thus further reduce rapid modeling and analysis.
The main reason for using FW is that even a large number of gateways does not increase running time of the algorithm.
Mice receiving GW501516 also exhibited increased running time (2.5-fold) and distance (2.7-fold) to failure relative to those receiving vehicle; however, these differences were not statistically significant.
On average, aging 1 year increased running time 0.2%, or 9 s (P < 0.001).
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