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Frequently in biomedical literature, measurements are considered "not statistically different" if a statistical test fails to achieve a P value that is < or = 0.05. This conclusion may be misleading because the size of each group is too small or the variability is large, and a type II error (false negative) is committed. In this study, we examined the(More)
We consider stochastic transition matrices from large social and information networks. For these matrices, we describe and evaluate three fast methods to estimate one column of the matrix exponential. The methods are designed to exploit the properties inherent in social networks, such as a power-law degree distribution. Using only this property, we prove(More)
We consider random-walk transition matrices from large social and information networks. For these matrices, we describe and evaluate a fast method to estimate one column of the matrix exponential. Our method runs in sublinear time on networks where the maximum degree grows doubly logarithmic with respect to the number of nodes. For collaboration networks(More)
Personalized PageRank vectors used for many community detection and graph diffusion problems have a subtle dependence on a parameter epsilon that controls their accuracy. This parameter governs the sparsity of the solution and can be interpreted as a regularization parameter. We study algorithms to estimate the solution path as a function of the sparsity(More)
BACKGROUND AND OBJECTIVE We are currently working with a novel class of photoactivated 4-amino substituted 1,8-naphthalimide compounds for tissue bonding. With promising results in other tissues, we are pursuing potential vascular applications. This study focused on determining the appropriate compound formulation(s), concentration, and exposure times to(More)
The Flow Decomposition problem, which asks for the smallest set of weighted paths that “covers” a flow on a DAG, has recently been used as an important computational step in genetic assembly problems. We prove the problem is in FPT when parameterized by the number of paths, and we give a practical linear fpt algorithm. Combining this approach with algorithm(More)
Identifying communities plays a central role in understanding the structure of large networks. As practitioners analyze progressively larger networks, it becomes increasingly important to understand the computational complexity of candidate algorithms. We examine the complexity of the link clustering algorithm (Ahn et al., 2010) for overlapping community(More)