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 ...

bDepartment of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China cHunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and ...

Abstract: In vehicle trajectory prediction, traditional methods like Kalman filtering often rely heavily on user expertise and prior knowledge, while newer deep learning approaches, such as Long Short ...

BaNDyT (Bayesian Network analisis of molecular Dynamic simulation Trajectories): software package that implements the Bayesian Network Modeling specifically attuned to the MD simulation trajectories ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Atomistic molecular dynamics (MD) simulations have become an indispensable tool for ...

aThe Zena and Michael A Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA ...

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 ...

Climate change is the biggest challenge to global food security. Protected cultivation can protect crops from extreme weather conditions, reduce the incidence of pests and diseases, and ensure that ...

System identification learns models of dynamical systems from input–output measurements. Estimated models should generalize by predicting system’s output responses to new, previously unseen inputs.

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 ...