We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulatio...
Predicting subject-specific responses to exoskeleton assistance may aid in maximizing functional gait outcomes, such as achieving full knee-extension at foot contact in individuals with crouch gait from cerebral palsy (CP). The purpose of this study ...
IEEE transactions on neural networks and learning systems
Mar 6, 2019
In complex-valued Hopfield neural networks (CHNNs), the neuron states are complex numbers whose amplitudes are: 1) they can also be described in special orthogonal matrices of order and 2) here, we propose a new Hopfield model, the O(2) -valued Hopfi...
Computational intelligence and neuroscience
Feb 26, 2019
In recent years, convolutional neural network (CNN) has attracted considerable attention since its impressive performance in various applications, such as Arabic sentence classification. However, building a powerful CNN for Arabic sentiment classific...
This paper describes a new implementation for calculating Jacobian and its time derivative for robot manipulators in real-time. The estimation of Jacobian is the key in the real-time implementation of kinematics and dynamics of complex planar or spat...
Computational intelligence and neuroscience
Feb 25, 2019
An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi-Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The s...
The British journal of mathematical and statistical psychology
Feb 22, 2019
Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fu...
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this remains e...
The aim of the present work was to develop a computational model of how children acquire inflectional morphology for marking person and number; one of the central challenges in language development. First, in order to establish which putative learnin...
Journal of computational neuroscience
Feb 16, 2019
Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Jo...
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