Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Omic data analysis is steadily growing as a driver of basic and applied molecular biology
research. Core to the interpretation of complex and heterogeneous biological phenotypes …

Most of the currently used techniques for linear system identification are based on classical
estimation paradigms coming from mathematical statistics. In particular, maximum likelihood …

During the past few years, research in applying machine learning (ML) to design efficient,
effective, and robust metaheuristics has become increasingly popular. Many of those …

One of the core challenges in digital marketing is that the business conditions continuously
change, which impacts the reception of campaigns. A winning campaign strategy can …

We provide a new formulation for the problem of learning the optimal classification tree of a
given depth as a binary linear program. A limitation of previously proposed Mathematical …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

Replacing part of ordinary Portland cement with sludge can reduce the use of cement while
recycling sludge and achieve low CO 2 emissions, which is an environment-friendly method …