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background: prevalence of recurrent shoulder dislocation in patients taking tramadol has not been studiedyet so, this study aims to study the recurrent shoulder dislocation following tramadol induced seizure. methods: in this cross-sectional study, 205 patients with recurrent shoulder dislocation complaints (2 or more) referred to shafa orthopedic and iranmehr hospitals tehran, iran, from octob...

introduction: leakage of cerebrospinal fluid in the skull base may be accompanied with recurrent meningitis. the site of leakage may either be anterior (in the nose and paranasal sinuses) or posterior (in the temporal bone). various imaging techniques can be used to precisely locate the point of leakage but despite all the advances in imaging techniques there are still some rare cases in which ...

We report the results of our classification-based machine translation model, built upon the framework of a recurrent neural network using gated recurrent units. Unlike other RNN models that attempt to maximize the overall conditional log probability of sentences against sentences, our model focuses a classification approach of estimating the conditional probability of the next word given the in...

UNLABELLED Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter...

The Grey-White Decision Network is introduced as an application of an on-center, off-surround recurrent cooperative/competitive network for segmentation of magnetic resonance imaging (MRI) brain images. The three layer dynamical system relaxes into a solution where each pixel is labeled as either grey matter, white matter, or "other" matter by considering raw input intensity, edge information, ...

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS)...

where T(6), U(6), V(6) and W(6) are polynomial matrices in the indeterminate 6, which for recurrent equations can have the meaning of the one-step forward-shift operator. He showed that under the polynomial transformations on (1.1), (1.2) that he called strict system equivalence, if T(6) is square, detT(6) ^ 0 (and has a degree equal to the dimension of T(6)) and the input-output transfer matri...

In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language mo...

The dynamics of reservoir computing (RC), a compact recurrent neural network, is validated as higher-dimensional embedding the input nonlinear dynamics. Based on this rigorous validation, delayed RC established with significantly reduced network size and promoted memory capacity, which can achieve reconstruction even in single neuron.