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Methods on multiple configuration sensor is an important theme in this domain.

The specific methods on multiple watermarks were developed in [4] by introducing orthogonal watermarks into single watermarking algorithms.

The application of 3D methods on multiple conformer data sets normally reduces this weakness, but entails a significant computational overhead.

Results show that it outperforms the state-of-the-art cascaded regression-based methods on multiple in-the-wild face alignment databases.

Experimental results show that our deep network is able to extract useful features for the illumination estimation and our method outperforms all previous color constancy methods on multiple test datasets.

In the second experiment, we compared RSpredict with the other three methods on multiple sequence alignments of low and medium similarity.

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Next, we turn our attention to supervised tensor learning methods on multiple-biomarker tensors to classify strains into major lineages.

However, direct application of sequential Monte Carlo method on multiple object tracking is not feasible because it does not define an obvious way to identify individual hypotheses.

In this paper, we present an efficient algorithm for the assemblage and solution of interface equations which facilitates the solution of large-scale systems via the Schur complement method on multiple instruction multiple data (MIMD) distributed memory architectures.

We demonstrate this method on multiple cell populations from the rat pituitary and pancreatic islets of Langerhans, and the A. californica nervous system.

We tested this hypothesis by implementing and applying a modified permutation meta-analysis method on multiple microarray datasets and then validated the signature in independent datasets.

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