Networks are becoming a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a gener...
Dependency networks are a compelling alternative to Bayesian networks for learning joint probability distributions from data and using them to compute probabilities. A dependency ...
Encouraging the release of network data is central to promoting sound network research practices, though the publication of this data can leak sensitive information about the publ...
Scott E. Coull, Charles V. Wright, Fabian Monrose,...
Abstract. Diffusion Tensor Imaging (DTI) provides estimates of local directional information regarding paths of white matter tracts in the human brain. An important problem in DTI ...
Maxwell D. Collins, Vikas Singh, Andrew L. Alexand...
We presented a novel procedure to extract ground road networks from airborne LiDAR data. First point clouds were separated into ground and non-ground parts, and ground roads were ...