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For differential network recovery, the naïve baseline is almost identical to purely data-driven results because the prior knowledge seldom includes a differential edge in a large network with sparse changes.
We constructed a Gaussian Markov random field with p = 100 nodes and 150 samples following the approach used in [ 23], and then randomly modified 10% of the edges to create two condition‐specific networks with sparse changes.
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This represents a sharp, sparse change in the network.
However, our results also indicate that some of the patients with PMA exhibit sparse morphological changes in the UMN system, even at death.
By the 1850s, Indiana had undergone major changes: what was once a frontier with sparse population had become a developing state with several cities.
Selenium deficiency in an 18-month-old male led to dry skin, erythematous changes on cheeks, hips and thighs along with sparse, short, thin, light-colored hair [15].
The problem of simultaneously learning network structures and their changes under two conditions is formulated as a regularized linear regression problem with sparse constraints and solved by convex optimization.
Changes to the blowout lobe surrounding the feature were also determined for areas with sparse enough vegetation cover to allow laser returns from the sand surface.
A hybrid pixel/object-based change detection approach was developed to effectively collect training samples for historical images with sparse reference data.
A small, thin man, with sparse hair and dark skin.
Calamites had long, jointed stems with sparse foliage.
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