Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Quantum computing news usually picks up near the end of the year, as companies try to provide evidence that they are hitting benchmarks on time. However, there have been interesting announcements as ...

(1) Organic Revenue, Adjusted Gross Margin, Adjusted Net Income, Adjusted EBITDA and Adjusted EBITDA Margin are non-GAAP financial measures. See tables below under “Use of Non-GAAP Financial Measures” ...

Abstract: Wideband source localization using acoustic sensor networks has been drawing a lot of research interest recently. The maximum-likelihood is the predominant objective which leads to a variety ...

In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function ...

This paper shows that the Expectation-Maximization (EM) algorithm for regime-switching dynamic factor models provides satisfactory performance relative to other estimation methods and delivers a good ...

Abstract: The convergence of expectation-maximization (EM)-based algorithms typically requires continuity of the likelihood function with respect to all the unknown parameters (optimization variables) ...

ABSTRACT: Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%.

ABSTRACT: The purpose of this study was to analyze the lesion brightness (image contrast) in multiple MRI sequences in patients with relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), ...