Objectives Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room ...

Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...

Abstract: Bayesian Optimization (BO) is an efficient method for finding optimal cloud configurations for several types of applications. On the other hand, Machine Learning (ML) can provide helpful ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Abstract: Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...