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Empirical studies investigating the complexity of data decomposition and communication are lacking.
In this paper, a novel data decomposition method based on polygon complexity (DMPC) is proposed.
In parallel rasterization, it is difficult to design an effective data decomposition method.
The approach (briefed as AD-PDSA) relies on adaptive algorithms for data decomposition.
In accordance with TG data, decomposition process is completed in dynamic conditions at 420°C.
This chapter provides an overview of the main multiway methods used for data decomposition, calibration and pattern recognition.
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Using this technology, distinct simulation codes with disparate distributed data decompositions can work together to achieve greater scientific discoveries.
The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions.
This section is devoted to provide a through description of the wavelet transform as applied to the number of the death data decompositions, together with the multivariate GARCH model as used in our proper analysis.
Besides supplying the required geometric flexibility, the multi-domain formulation also provides a very natural load-balanced data-decomposition, suitable for parallel environments.
The data suggest decomposition of a carbonate impurity, as organic matter would have suffered substantial decomposition at 600°C.
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