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Unlike the TV-regularized methods, our model has the advantage that it performs well for very sparse data sets.

Different from other methods, our model can express the relationship between the node influence and data transmission and depict the dynamic change of node importance by our model in wireless sensor network.

However, as in all nonexperimental methods, our model is potentially subject to bias due to unobserved variables, but such variation would have to vary across time, students, and schools so as not to be captured by the student, school, and grade-by-year fixed effects.

Different from traditional sparse representation based tracking methods, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigen-subspace representation.

Compared with GeneMarkS, Metagene, Orphelia, and Heuristic Approach methods, our model achieved better prediction performance in short gene classification.

To overcome the limitation of conventional domain-prediction methods, our model framework regarded dc-pairs as the basic units of PPIs.

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In contrast with standard phylogenetic methods, our models include a central hypothesis or axiom that invokes an evolutionary search of conformational order in molecules which defines the general direction of the evolutionary path [22], [23], [25] [28].

These results show that, compared with the AP-MS scoring method, our model can increase the discrimination power between bait-prey interactions and prey-prey interactions.

Using this method, our models were fitted reasonably well to the various experimental measurements.

Using either method, our models employ consumption of food as a proxy for consumption of PBDEs in food, another source of exposure misclassification.

"Our coaching methods and our model of management has been taken on board by the club.

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