HUE VARIANCE PREDICTION - An Empirical Estimate of the Variance within the Hue of an Image

2007 ◽  
Keyword(s):  
Empirical Estimate

scholarly journals Ocean-driven thinning enhances iceberg calving and retreat of Antarctic ice shelves

2015 ◽  
Vol 112 (11) ◽  
pp. 3263-3268 ◽  
Author(s):  
Yan Liu ◽  
John C. Moore ◽  
Xiao Cheng ◽  
Rupert M. Gladstone ◽  
Jeremy N. Bassis ◽  
...  
Keyword(s):  
Mass Loss ◽  
Mass Balance ◽  
Climate Forcing ◽  
Empirical Estimate ◽  
Positive Mass ◽  
Ice Shelves ◽  
Total Mass Loss ◽  
Rapid Changes ◽  
Iceberg Calving ◽  
Ice Sheet Mass Balance

Iceberg calving from all Antarctic ice shelves has never been directly measured, despite playing a crucial role in ice sheet mass balance. Rapid changes to iceberg calving naturally arise from the sporadic detachment of large tabular bergs but can also be triggered by climate forcing. Here we provide a direct empirical estimate of mass loss due to iceberg calving and melting from Antarctic ice shelves. We find that between 2005 and 2011, the total mass loss due to iceberg calving of 755 ± 24 gigatonnes per year (Gt/y) is only half the total loss due to basal melt of 1516 ± 106 Gt/y. However, we observe widespread retreat of ice shelves that are currently thinning. Net mass loss due to iceberg calving for these ice shelves (302 ± 27 Gt/y) is comparable in magnitude to net mass loss due to basal melt (312 ± 14 Gt/y). Moreover, we find that iceberg calving from these decaying ice shelves is dominated by frequent calving events, which are distinct from the less frequent detachment of isolated tabular icebergs associated with ice shelves in neutral or positive mass balance regimes. Our results suggest that thinning associated with ocean-driven increased basal melt can trigger increased iceberg calving, implying that iceberg calving may play an overlooked role in the demise of shrinking ice shelves, and is more sensitive to ocean forcing than expected from steady state calving estimates.

scholarly journals A lower bound for the spectral gap of the conjugate Kac process with 3 interacting particles

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Luís Simão Ferreira
Keyword(s):  
Monte Carlo ◽  
Lower Bound ◽  
Monte Carlo Simulations ◽  
Entropy Production ◽  
Quantitative Estimate ◽  
Final Section ◽  
Spectral Gap ◽  
Empirical Estimate ◽  
Interacting Particles

<p style='text-indent:20px;'>In this paper, we proceed as suggested in the final section of [<xref ref-type="bibr" rid="b2">2</xref>] and prove a lower bound for the spectral gap of the conjugate Kac process with 3 interacting particles. This bound turns out to be around <inline-formula><tex-math id="M1">\begin{document}$ 0.02 $\end{document}</tex-math></inline-formula>, which is already physically meaningful, and we perform Monte Carlo simulations to provide a better empirical estimate for this value via entropy production inequalities. This finishes a complete quantitative estimate of the spectral gap of the Kac process.</p>

scholarly journals Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Krzysztof M. Graczyk ◽  
Maciej Matyka
Keyword(s):  
Neural Networks ◽  
Porous Medium ◽  
Porous Media ◽  
Deep Learning ◽  
Lattice Boltzmann ◽  
Good Accuracy ◽  
Two Dimensional ◽  
Empirical Estimate ◽  
Flow Through ◽  
Boltzmann Method

AbstractConvolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$ φ ∈ ( 0.37 , 0.99 ) which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$ k ∈ ( 0.78 , 2.1 × 10 5 ) and tortuosity $$T \in (1.03,2.74)$$ T ∈ ( 1.03 , 2.74 ) . It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$ φ has been obtained and compared with the empirical estimate.

Flexible docking using tabu search and an empirical estimate of binding affinity

1998 ◽  
Vol 33 (3) ◽  
pp. 367-382 ◽  
Author(s):  
Carol A. Baxter ◽  
Christopher W. Murray ◽  
David E. Clark ◽  
David R. Westhead ◽  
Matthew D. Eldridge
Keyword(s):  
Tabu Search ◽  
Binding Affinity ◽  
Empirical Estimate ◽  
Flexible Docking
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