Abstract: Gaussian process regression (GPR) models are becoming increasingly tightly integrated into robotic systems, particularly in the context of robot model predictive control (MPC) operating in ...

The published wheels are currently not built with LAMMPS. Thus, running multiscale simulations with molecular dynamics is not possible with this quick installation. For the full functionality it is ...

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

Abstract: With the rapid development of Internet technologies, the network traffic continues to grow exponentially, which leads to serious congestion and makes the service quality difficult to ...

This uses a Gaussian Processes regression, implemented in R, using the kernlab package. The software takes raw T1-weighted MRI scans, then uses SPM12 for segmentation and normalisation. A slightly ...

Spontaneous regression of cancer is an uncommon event observed most often in neuroblastoma, leukemia, lung cancer, and melanoma. The underlying mechanisms are not fully understood; however, infections ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...

Most companies want to scale efficiently, but few truly understand the mechanics of how work gets done. That’s where business process mapping comes in. Business process mapping visually outlines how ...

Firms are constantly wanting to cut costs and become more efficient, yet I find that most are still bogged down with traditional manual processes. Even when manual workflows appear innocuous or even ...