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 ...

Abstract: Seismic data denoising is essential for subsequent inversion and interpretation tasks. However, most existing methods rely on loss functions, which assume that seismic noise follows an ...

Abstract: In this letter, we propose a Gaussian mixture model (GMM)-based channel estimator which is learned on imperfect training data, i.e., the training data are solely comprised of noisy and ...

Engineers affiliated with the institute behind China’s sixth-generation stealth fighter program are publishing peer-reviewed research on automated carrier landing systems, structural loads for ...

Nous Research has released Hermes Mixture of Agents 2.0 (MoA 2.0), an update to its open-source Hermes Agent framework that ...

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...

A hybrid artificial intelligence model that combines two well-established deep learning techniques has improved the accuracy ...

A team of researchers at the University of Warwick and Monash University has solved a puzzle that has stumped drug developers ...

RNA has emerged as one of the most promising molecules in modern medicine, enabling advances from mRNA vaccines and gene ...

Forensic science plays a vital role in identifying, characterizing, and quantifying physical and biological traces recovered from crime scenes — a task that ...

Some say being Latino is a source of advantage or connection but others say it is tied to barriers and discrimination. Fresh data delivered Saturday mornings Thank you for subscribing! Americans’ ...