Results 131 to 140 of about 7,083,147 (335)
White Matter Microstructural Abnormalities in Neonatal Onset Genetic Epilepsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Recent evidence indicates that epilepsy is associated with abnormal white matter. If seizures alter white matter, then the impact upon network function, epileptogenesis, and cognition could be pronounced in neonates undergoing rapid developmental myelination. Neonates with epilepsy due to nonstructural genetic causes provide a unique
Amanda G. Sandoval Karamian +8 more
wiley +1 more source
Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase
CT Lilun yu yingyong yanjiuIdentifying primary phases of seismic waveforms is a routine task in seismic data processing. Owing to the low efficiency of manual identification and the influence of human subjective factors, many methods for the automatic identification of the primary
Tianzhe WANG +3 more
doaj +1 more source
Contextual Recurrent Neural Networks
CoRR, 2019 There is an implicit assumption that by unfolding recurrent neural networks (RNN) in finite time, the misspecification of choosing a zero value for the initial hidden state is mitigated by later time steps. This assumption has been shown to work in practice and alternative initialization may be suggested but often overlooked.
Sam Wenke, Jim Fleming
openaire +2 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Focal cortical dysplasia (FCD) is the most common etiology of drug‐resistant epilepsy in children. Focal to bilateral tonic–clonic seizures (FBTCS) mark a high risk of drug‐resistant epilepsy and involve thalamocortical circuitry in their generation and propagation.
Hua Xie +8 more
wiley +1 more source
Pixel Recurrent Neural Networks
CoRR, 2016 Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel
Aäron van den Oord +2 more
openaire +4 more sources
Natural Frequencies of Levodopa‐Induced Dyskinesia in Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Abnormal involuntary movements, known as dyskinesias, are common complications of levodopa treatment in patients with Parkinson's disease and can significantly impair quality of life. The underlying pathophysiology remains unclear, and current therapeutic options are limited.
Ioannis U. Isaias +3 more
wiley +1 more source
ArrayThe ability to predict seiches can help prevent the damage and mitigate the risks associated with these natural phenomena. This paper presents a novel approach for seiche prediction in Marsaxlokk, Malta, using Long Short-Term Memory (LSTM) neural network
Nicole Borg +4 more
doaj +1 more source
Informatics in Medicine Unlocked, 2018 Myocardial infarction (MI) is a medical emergency for which the early detection of symptoms is desirable. The prevalence of portable electrocardiogram (ECG) devices makes frequent screening for MI possible. In this study, we develop an MI classifier that
Hin Wai Lui, King Lau Chow
doaj +1 more source
Sliced Recurrent Neural Networks
CoRR, 2018 12 pages (including references), 2 figures, 3 tables, conference: The 27th International Conference on Computational Linguistics (COLING 2018)
Zeping Yu, Gongshen Liu
openaire +3 more sources
Recurrent high order neural network modeling for wastewater treatment process
, 2011 Due to the multi-variable, nonlinear, large time delay and strong coupling features of the wastewater treatment process, a recurrent high-order neural network is used to model the key water quality parameters(Chemical Oxygen Demand, Biological Oxygen ...
Yang WW(杨维维) +2 more
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