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We consider the most important characteristics of modular architectures of neural networks and their advantages under traditional monolithic neural networks.
First, in-depth understanding of the modular architectures of upstream regulatory regions for each annotated gene locus (i.e., open-reading frame) in the reference genome could nucleate direct comparison of orthologs and paralogs by phylogenetic footprinting across a meaningful germplasm diversity panel.
Therefore, a visualization of all those different types of modules would help to compare the modular architectures of proteins at different levels of resolution, including the level between the large conserved domains and the sequence itself, which this work is particularly concerned with.
The modular architectures of MaSp1 and MaSp2 likely reflect concerted evolution within a single gene, as has been implicated in maintaining similarity among Flag (∼440 aa) ensemble repeats and the long repeats of TuSp1 (∼200 aa) and AcSp1 (aciniform silk; 200 aa [14]).
Modular architectures of forward and inverse models were initially proposed for motor control as the MOSAIC architecture [ 19, 45].
In simulations, complex hierarchical modular architectures of cortical networks have been shown to support critical dynamics (Wang et al., 2011).
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We will examine the modular architecture of DDB1 CUL4 in complex with DDB2, CSA and CDT2 focusing on shared architectural, targeting and regulatory principles.
A modular architecture of the Hypervisor identifies the following three components: Adapter, Node Manager, Abstraction, and Virtualization Unit.
The Modules language expresses the modular architecture of a software system.
We use a modular architecture of the cortex divided into sub-regions.
This cooperativity is explained in terms of the modular architecture of binding sites.
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