Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian linear model (beta distribution) of the proportion of prey deliveries given by the female throughout the night (top) and throughout the nestling period (bottom). Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian linear mixed effects model (gaussian distribution) of adult female (top) and male (bottom) mass (g) throughout the nestling period. Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Ecology and Evolution

Article Title: Year‐round weather alters nest‐provisioning rates in a migratory owl

doi: 10.1002/ece3.10333

Figure Lengend Snippet: Bayesian changepoint model with random effects (gaussian distribution) of owlet mass (g) throughout the nestling period. Solid and dashed lines represent the fitted value computed using the mean of posteriors, and shaded ribbons represent the 95% credible interval of posteriors. Wet = green/solid, dry = yellow/dashed, warm = red/dashed, cold = blue/solid.

Article Snippet: To assess the effects of precipitation and temperature on owlet growth, we used a Bayesian linear mixed effects model (Gaussian distribution) with a single changepoint, with nestling age as the fixed effect and mass as the response variable.

Techniques:

Journal: Scientific Reports

Article Title: Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

doi: 10.1038/s41598-024-72982-z

Figure Lengend Snippet: AnDePeD Pro additional notations.

Article Snippet: We compare our new methods, AnDePeD and AnDePeD Pro, with state-of-the-art anomaly detection algorithms (Bayesian Changepoint , Windowed Gaussian , Relative Entropy , KNN CAD , Earthgecko Skyline , the Alter- \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Re}^2$$\end{document} algorithm and AREP ) on traditional metrics (Precision, Recall, F-score and adjusted MCC).

Techniques:

Journal: Scientific Reports

Article Title: Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

doi: 10.1038/s41598-024-72982-z

Figure Lengend Snippet: Parameter settings for AnDePeD and AnDePeD Pro.

Article Snippet: We compare our new methods, AnDePeD and AnDePeD Pro, with state-of-the-art anomaly detection algorithms (Bayesian Changepoint , Windowed Gaussian , Relative Entropy , KNN CAD , Earthgecko Skyline , the Alter- \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Re}^2$$\end{document} algorithm and AREP ) on traditional metrics (Precision, Recall, F-score and adjusted MCC).

Techniques:

Journal: Scientific Reports

Article Title: Machine learning-based real-time anomaly detection using data pre-processing in the telemetry of server farms

doi: 10.1038/s41598-024-72982-z

Figure Lengend Snippet: Computational complexity of algorithms tested by the number of past data points - \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n_\text {p}$$\end{document} .

Article Snippet: We compare our new methods, AnDePeD and AnDePeD Pro, with state-of-the-art anomaly detection algorithms (Bayesian Changepoint , Windowed Gaussian , Relative Entropy , KNN CAD , Earthgecko Skyline , the Alter- \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {Re}^2$$\end{document} algorithm and AREP ) on traditional metrics (Precision, Recall, F-score and adjusted MCC).

Techniques: Targeted Proteomics