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METHOD: We utilized cross-sectional data from the 2002 National Survey on Drug Use and Health (n = 36,370).
So as to assess the performance of the method, we utilized LISA-Q Front FOV Dataset [35] which contains three different annotated video sequences.
The method we utilized for the analysis is the sequential technique and regularization, and the existence of positive solutions is obtained by the fixed point theorem.
In stationary signal cases involving the IAA method, we utilized the batch processing brute force version in[47], and subsequently in nonstationary signal cases, we made use of the adaptive brute force version in[53].
The body proportion-sensitive method we utilized was outlined by Cross et al. [9].
A similar technique, known as the case-crossover,[19] can also control for fixed confounders, however this method requires the assumption that exposure distribution in successive time periods is exchangeable.[20] The self-controlled case-series method we utilized does not require such an assumption.
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To further the practicability of this method, we utilize a dynamic grey prediction, which shows high accuracy for decision making.
In the proposed watermarking method, we utilize a reversible data-embedding technique called prediction-error expansion on integers to achieve reversibility.
Secondly, to improve the efficiency of the non-parametric identification method, we utilize the multitone excitation to drive the MIMO nonlinear system, and propose a searching algorithm of the frequency base of multitone excitation.
In the proposed method, we utilize some of these guard subcarriers for PAPR reduction.
For Asymmetric Gaussian filtering method, we utilize strong smoothing parameters to eliminate the disoccluded areas caused by large camera baseline.
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