A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary.

A nonnegative matrix factorization (NMF) can be computed efficiently under the , which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these are based on nonnegative sparse regression and self-dictionaries, and require the solution of large-scale convex optimization problems. In this paper, we study a particular nonnegative sparse regression model with self-dictionary.

Using Separable Nonnegative Matrix Factorization Techniques for the Analysis of Time-Resolved Raman Spectra.

The key challenge of time-resolved Raman spectroscopy is the identification of the constituent species and the analysis of the kinetics of the underlying reaction network. In this work we present an integral approach that allows for determining both the component spectra and the rate constants simultaneously from a series of vibrational spectra. It is based on an algorithm for nonnegative matrix factorization that is applied to the experimental data set following a few pre-processing steps.

Outbreak of staphylococcal food poisoning among children and staff at a Swiss boarding school due to soft cheese made from raw milk.

On October 1, 2014, children and staff members at a Swiss boarding school consumed Tomme, a soft cheese produced from raw cow milk. Within the following 7h, all 14 persons who ingested the cheese fell ill, including 10 children and 4 staff members. Symptoms included abdominal pain and violent vomiting, followed by severe diarrhea and fever.