We investigated subset-based optimization methods for positron emission tomography (PET) image reconstruction incorporating a regularizing prior. PET reconstruction methods that use a prior, such as ...

Preparing for coding interviews can be a real challenge with developers often spending several weeks reviewing and learning new material. The truth is, that most developers never quite feel fully ...

In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal ...

This is the article I wish I had read when I started coding. I will dive deep into 20 problem-solving techniques that you must know to excel at your next interview. They have helped me at work too and ...

Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances.

Artificial neural networks are some of the most widely used tools in data science. Learning is, in principle, a hard problem in these systems, but in practice heuristic algorithms often find solutions ...

Untargeted metabolomics experiments usually rely on tandem MS (MS/MS) to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Recently, ...

Though the problems number from 1 to 99, there are some gaps and some additions marked with letters. There are actually only 88 problems. There is no nested list type in OCaml, so we need to define ...