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Depending on the differences in template structure, template method can be divided into two groups called hard and soft template methods.
Additionally, this soft template method can be extended to the fabrication of other binary or even ternary metal oxides hollow micro-/nanostructure materials.
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The proposed template-based method can be applied to PET data acquired in several CNS disorders in which serotonin neurons in the brainstem might be affected.
Therefore, in combination with specific templates, this method can be applied to multiplex miRNA detection by simply using the same MB.
The template-stripping method can yield smooth patterned films without surface contamination.
We believe that iMatrix and our template-based method can be useful for the binding mechanisms of TCR-pMHC complexes and peptide vaccine designs.
Template protection methods can be categorized into two groups: (i) biometric cryptosystems [5] (i.e., fuzzy commitment [6], fuzzy vault [8]) and (ii) transformation-based methods/salting [9] (i.e., biohashing [10]).
A question naturally arises is how would the proposed template-matching methods interact with an underlying acoustic model from which the GMMs are derived and the phone or word lattices are generated, and of particular interest is that as a baseline HMM system improves, whether the performance gain we have observed by the proposed template matching methods can still hold.
Although in the current work we used the basic acoustic modeling techniques to train our HMM baselines, the proposed template matching methods can take advantage of and add value to more advanced GMM/HMM systems, and as such they are promising for further improving the state-of-the-art speech recognition.
For a small image block which is the same size as the used template size in a halftone image, if the difference between the predicted value and the original real gray value using Gaussian filtering method is larger than the difference using LUT method with a specified template, it means that LUT method can obtain a better result than Gaussian filtering on the image block.
Later we will show that when either the sequence or template has NEFF ≤6, our method can generate much better alignments than HHpred.
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