Multichannel dynamic modeling of non-Gaussian mixtures

[EN] This paper presents a novel method that combines coupled hidden Markov models (HMM) and non Gaussian mixture models based on independent component analyzer mixture models (ICAMM). The proposed method models the joint behavior of a number of synchronized sequential independent component analyzer mixture models (SICAMM), thus we have named it generalized SICAMM (G-SICAMM). The generalization allows for flexible estimation of complex data densities, subspace classification, blind source separation, and accurate modeling of both local and global dynamic interactions. In this work, the structured result obtained by G-SICAMM was used in two ways: classification and interpretation. Classification performance was tested on an extensive number of simulations and a set of real electroencephalograms (EEG) from epileptic patients performing neuropsychological tests. G-SICAMM outperformed the following competitive methods: Gaussian mixture models, HMM, Coupled HMM, ICAMM, SICAMM, and a long short-term memory (LSTM) recurrent neural network. As for interpretation, the structured result returned by G-SICAMM on EEGs was mapped back onto the scalp, providing a set of brain activations. These activations were consistent with the physiological areas activated during the tests, thus proving the ability of the method to deal with different kind of data densities and changing non-stationary and non-linear brain dynamics. (C) 2019 Elsevier Ltd. All rights reserved.This work was supported by Spanish Administration (Ministerio de Economia y Competitividad) and European Union (FEDER) under grants TEC2014-58438-R and TEC2017-84743-P.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Gomez, E.; Villanueva, V. (2019). Multichannel dynamic modeling of non-Gaussian mixtures. Pattern Recognition. 93:312-323. https://doi.org/10.1016/j.patcog.2019.04.022S3123239

Understanding pollen tube growth dynamics using the Unscented Kalman Filter

In the process of pollination, a pollen tube grows from a pollen grain that has fallen on the stigma of a flower. This tube grows towards the ovary of the flower where it will deliver male reproductive material. Knowledge of the dynamics of pollen tube growth will provide a basis for understanding more complex cells that exhibit similar growth behavior. Current pollen tube growth models are a collection of differential equations that represent the level of understanding that biologists have concerning apical growth. Due to their complex nature, these models are not used to verify observed behavior in living cells as seen under a microscope. We present a model that can be used to describe the behavior of growing pollen tube cells in actual experiments. We propose biologically relevant functions based on knowledge of the growth process to explain the dynamics of model parameters. Our model uses an affine transformation to propagate the tip of the cell and statistical parameter estimation to measure necessary parameters during growth. Using experimental videos of pollen tube growth, we show that our model can adapt to various growth scenarios while extracting growth parameters from the videos