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Analytical wave families and stability dynamics in a modified complex Ginzburg-Landau model via the modified extended direct algebraic method. [PDF]
Sci RepRateb AE +4 more
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Neuromorphic robust framework for integrated estimation and control in dynamical systems using spiking neural networks. [PDF]
Sci RepAhmadvand R, Sharif SS, Banad YM.
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Allgemeines Statistisches Archiv, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Breitung, Jörg, Eickmeier, Sandra
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Current Eye Research, 2007
Quantitative understanding of tear dynamics may help in developing better ophthalmic drug delivery vehicles and dry eye treatments. This paper attempts to develop a comprehensive model that can predict the effect of physiological parameters on various issues related to tear dynamics.The model is based on mass balances of water and solutes such as ...
Heng, Zhu, Anuj, Chauhan
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Quantitative understanding of tear dynamics may help in developing better ophthalmic drug delivery vehicles and dry eye treatments. This paper attempts to develop a comprehensive model that can predict the effect of physiological parameters on various issues related to tear dynamics.The model is based on mass balances of water and solutes such as ...
Heng, Zhu, Anuj, Chauhan
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Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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WIREs Systems Biology and Medicine, 2010
AbstractActin monomers assemble into filaments that structurally support cells as well as drive membrane protrusion for cell movement. Within cells, some actin structures are very dynamic and turn over rapidly, while others are very stable. Even purified actin filament dynamics are complex, and researchers have often turned to mathematical models in ...
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AbstractActin monomers assemble into filaments that structurally support cells as well as drive membrane protrusion for cell movement. Within cells, some actin structures are very dynamic and turn over rapidly, while others are very stable. Even purified actin filament dynamics are complex, and researchers have often turned to mathematical models in ...
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Econometric Reviews, 2001
This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated ...
Christian Gourieroux, Joanna Jasiak
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This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated ...
Christian Gourieroux, Joanna Jasiak
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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020
Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies.
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Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies.
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Multivariate Behavioral Research, 2017
Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series.
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Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series.
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