Results 11 to 20 of about 71,871 (269)
CoRR, 2022 In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy. However, these methods are usually tested at low-resolution settings, and it remains to be seen whether they can scale
Meire Fortunato +4 more
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CoRR, 2021 Future societal systems will be characterized by heterogeneous human behaviors and also collective action. The interaction between local systems and global systems will be complex. Humandemics will propagate because of the pathways that connect the different systems and several invariant behaviors and patterns that have emerged globally.
David Pastor-Escuredo +1 more
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, 2021 In the seminal paper E. Tadmor, S. Nezzar and L. Vese, A multiscale image representation using hierarchical (BV, L 2) decompositions, Multiscale Model. Simul., 2(4), 554-579, (2004), the authors introduce a multiscale image decomposition model providing a hierarchical decomposition of a given image into the sum of scale-varying components. In line with
Debroux, Noémie +2 more
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The multiscale classifier [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996 In this paper we propose a rule-based inductive learning algorithm called Multiscale Classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by successively splitting the feature space in half.
Brian C. Lovell, Andrew P. Bradley
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Image Processing On Line, 2014 While the retinex theory aimed at explaining human color perception, its derivations have led to efficient algorithms enhancing local image contrast, thus permitting among other features, to "see in the shadows". Among these derived algorithms, Multiscale Retinex is probably the most successful center-surround image filter.
Ana Belén Petro +2 more
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Multiscale influenza forecasting [PDF]
Nature Communications, 2021 AbstractInfluenza forecasting in the United States (US) is complex and challenging due to spatial and temporal variability, nested geographic scales of interest, and heterogeneous surveillance participation. Here we present Dante, a multiscale influenza forecasting model that learns rather than prescribes spatial, temporal, and surveillance data ...
Dave Osthus, Kelly R. Moran
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Applied Intelligence, 2022 Machine learning methods have greatly changed science, engineering, finance, business, and other fields. Despite the tremendous accomplishments of machine learning and deep learning methods, many challenges still remain. In particular, the performance of machine learning methods is often severely affected in case of diverse data, usually associated ...
Ekaterina Merkurjev +2 more
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Entropy, 2021 Multiscale thermodynamics is a theory of the relations among the levels of investigation of complex systems. It includes the classical equilibrium thermodynamics as a special case, but it is applicable to both static and time evolving processes in externally and internally driven macroscopic systems that are far from equilibrium and are investigated at
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Proceedings of the National Academy of Sciences, 2001 Multiscale computing (MSC) involves the computation, manipulation, and analysis of information at different resolution levels. Widespread use of MSC algorithms and the discovery of important relationships between different approaches to implementation were catalyzed, in part, by the recent interest in wavelets.
M, Kobayashi, T, Irino, W, Sweldens
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A Multiscale Deformation Representation
SIAM Journal on Imaging Sciences, 2023 Motivated by Tadmor et al.'s work ([31]) dedicated to multiscale image representation using hierarchical (BV, L 2) decompositions, we propose transposing their approach to the case of registration, task which consists in determining a smooth deformation aligning the salient constituents visible in an image into their counterpart in another.
Debroux, Noémie +2 more
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