The Experts, Algo, the Subscribers, Wilfred and Stanley - come join us and play along Illustrations: Alice Devine; Image: Dan Goldfarb for The Athletic Welcome to The Athletic’s daily World Cup ...

For decades, the message out of Silicon Valley was a relentless optimism to the point of being unsettling. But there’s a new ...

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: This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is ...

Creating a highly accurate geological model at a large scale presents a considerable challenge, primarily due to constraints imposed by sparse data availability. A promising strategy to mitigate these ...

y_i -- the (n_i x 1) vector of responses for cluster i. These are given at at training. X_i -- the (n_i x p) fixed effects covariates that are associated with the y_i. These are given at training. Z_i ...

In this study, we present a novel and robust methodology for the automatic detection of influenza A virus ribonucleoproteins (RNPs) in single-particle cryo-electron microscopy (cryo-EM) images.

ABSTRACT: This paper is concerned about studying modeling-based methods in cluster analysis to classify data elements into clusters and thus dealing with time series in view of this classification to ...

Abstract: This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient ...

ABSTRACT: Accurate segmentation is an important and challenging task in any computer vision system. It also plays a vital role in computerized analysis of skin lesion images. This paper presents a new ...