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In this system, a canonical model is trained using normal contextual factors and then a set of transforms is built by weak contextual factors.
To compute such a vast set of transforms, the proposed architecture adopts, as its base structure, the multi-transform core that was presented in [7].
There, cluster adaptive training [55] is employed such that an average model is built and then this general model is adapted using a set of transforms.
The adaptation is done by finding a set of transforms for the model parameters in order to maximize the likelihood that the adapted models have produced the adaptation data.
In fact, to the best of the authors' knowledge, the MST architecture herein proposed is one of the first structures that is able to compute the complete set of transforms adopted in the AVC, AVS, VC-1 and HEVC standards.
Nevertheless, it should be noted that while the architecture presented in [7] is capable of realizing the complete set of transforms defined in the standard, the one proposed in [27] is able to compute only the HEVC inverse transforms.
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We give a construction of a set of transforming matrices which produce a maximum number of such copies whenever q is a prime power.
A new set of transformed composition variables is introduced to simplify the design equations for double-feed, multicomponent reactive distillation columns.
A new set of transformed composition variables is introduced to simplify the design equations for single-feed, multicomponent reactive distillation columns.
A new set of transformed composition variables is proposed for mixtures including one or more components that are inert under the process conditions.
The formulation includes a complete set of transformed stress potential and displacement potential relations, within the framework of Fourier expansions and Hankel integral transforms, that is useful in a variety of elastodynamic as well as elastostatic problems.
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Justyna Jupowicz-Kozak
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