Abstract: Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. Sparse probe data represent the vast majority of the data ...
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
BaNDyT (Bayesian Network analisis of molecular Dynamic simulation Trajectories): software package that implements the Bayesian Network Modeling specifically attuned to the MD simulation trajectories ...
Abstract: This study introduces a novel approach that integrates dynamic Bayesian network with attention based spatio-temporal graph convolutional network to forecast railway train delays, capturing ...
Implementation of BANSAC, a new guided sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In order to create artificial enzymatic networks capable of increasingly complex ...
This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2018. Cells are the basic units of life, yet their architecture and ...
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