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Exact(9)

Further visual comparison of these class maps is left to the reader.For convenience of visual comparison with the class maps, we also show here a ground truth image (Figure 6) that will be described, and referred to later, in Section 4.4.

In addition to comparing voxel-wise maps, we also examined the median rLOCAL values across all voxels in a network (Fig. 3B).

In order to enlarge coverage of the interaction maps, we also collected physical interactions for Caenorhabditis elegans (worm) and Saccharomyces cerevisiae (yeast).

To improve the visualization of white matter difference in mean diffusivity maps we also created 'attenuation coefficient maps' [attenuation coefficient = exp −b × mean diffusivity)].

From interaction maps, we also found that genes in pathways involved in calcium, mTOR, Erbb signaling, and Alzheimer's disease were significantly enriched (Table  2).

To determine the effects multiple residences may have had on maps, we also performed analyses that included for each individual only the residence of longest duration that met latency assumptions.

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Similar(51)

By introducing the nonlinear mapping function, we also derive the kernel version of OMMPS, namely orthogonal kernel maximum margin projection subspace (OKMMPS).

As T i is asymptotically quasi-ϕ-nonexpansive mapping, we also have (3.6).

However, instead of a simply syntactical mapping, we also propose the concept of a class model to capture associations and global constraints.

By Theorem 2.2, T λ n f 1 is a firmly nonexpansive mapping; we also know that the identity mapping I is a firmly nonexpansive mapping.

Since (T Cto C) is a hemicontinuous and pseudocontractive mapping, we also know that F is a hemicontinuous and monotone operator.

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