A successful polymer-type EOR project relies upon many factors, including an adequate characterization, description, and prediction of the polymer’s rheology. A high polymer viscosity can improve the mobility and sweep ...

Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the problem of learning from these noisy ...

Autonomous mobile robots have the potential to be extremely beneficial to society due to their ability to perform tasks that are difficult or dangerous for humans. These robots will necessarily interact with their environment ...

With better natural language semantic representations, computers can do more applications more efficiently as a result of better understanding of natural text. However, no single semantic representation at this time fulfills ...

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has ...

Extracting information and knowledge from large datasets often takes a significant amount of time in collecting, cleaning and processing the data. This process, from data curation to data interpretation can last from a ...

A physics-directed, statistically based, surrogate model of the small scale flow fea- tures that impact Chalk River unidentified deposit (crud) growth is presented in this work. The objective of the surrogate is to provide ...

Learning an unknown parameter from data is a problem of fundamental importance across many fields of engineering and science. Rapid development in information technology allows a large amount of data to be collected. The ...

The limited availability of ground truth relevance labels has been a major impediment to the application of supervised machine learning techniques to ad-hoc document retrieval and ranking. As a result, unsupervised scoring ...

The emergence of modern large-scale datasets has led to a huge interest in the problem of learning hidden complex structures. Not only can models from such structures fit the datasets, they also have good generalization ...