Machine learning and data mining are quickly becoming essential techniques in the field of (astro)physics. Such powerful tools provide precious insights into the laws governing natural processes and ...

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

From time to time I receive emails from people trying to extract tabular data from PDFs. I'm fine with that and I'm glad to help. However, some people think that pdftabextract is some kind of magic ...

Abstract: Data-driven Quality of Experience (QoE) modeling using Machine Learning (ML) is a key enabler for future communication networks as it allows accelerated and unbiased QoE modeling while ...

Accelerating catalyst discovery and development is of paramount importance in addressing the global energy, sustainability and healthcare demands. The past decade has witnessed significant momentum in ...

Euny Hong is the former supervising editor at Investopedia.com. She is also the author of two critically-acclaimed, published books. Dr. JeFreda R. Brown is a financial consultant, Certified Financial ...

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its ...

The table below shows my favorite go-to R packages for data import, wrangling, visualization and analysis — plus a few miscellaneous tasks tossed in. The package names in the table are clickable if ...

The use of data has become an integral part of investigative journalism. Increasingly, reporters need to know how to obtain, clean and analyze the growing archive of digitized information. For our ...