To analysis the efficiency of the PCA estimator quantitatively, we obtain an estimated watermark signal wpca from the set of gray-scale watermarked images using the PCA estimator, and calculate correlations between wpca and a set of watermarks {wi}, i = 1, 2, · · · , 100 generated by 100 different seeds, including the specific watermark signal used for embedding (i = 50).
Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
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Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
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Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
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Sample text:```The methods working directly on the count data can be broadly divided into parametric (baySeq [14], EBSeq [24], ShrinkSeq [27], edgeR [23], DESeq [7], NBPSeq [15] and TSPM [13]) and non-parametric methods (NOISeq [25] and SAMseq [26]).```
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Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
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Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
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Sample text:```The next communication [2] considered the most general characteristics of the parameter of spa tial EEG synchronization (the average in each of the fre quency bands of the EEG’s coherence value), such as multiplicity, directedness, and reproducibility of varia tions of this parameter owing to the change of state of the subjects.```
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Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
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Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
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Sample text:```For seeking the optimal value of Wh of the sub-problems which corresponds to the h-th level, we can solve the problem by the smoothing proximal gradient (SPG) method [6] fortunately.```
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Sample text:```We used the stepwise multiple linear regression that accounts for repeated measurements in the same dog (4) first to test whether there was a difference among values for resistances and for elastances between control and emphysema at each mean Paw, considering the entire range of f and VT of forcing.```
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Sample text:```Our heritability result is generally in agreement with the studies of Kien et al. (2008) in Eucalyptus urophylla and Zhao et al. (2014a) in poplar.```
Sample intent: result



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
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Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
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Sample text:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
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