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Stochastic Processes

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A step towards intelligent EBSD microscopy: machine-learning prediction of twin activity in MgAZ31.

Journal of microscopy
UNLABELLED: Although microscopy is often treated as a quasi-static exercise for obtaining a snapshot of events and structure, it is clear that a more dynamic approach, involving real-time decision making for guiding the investigation process, may pro...

Pinning impulsive synchronization for stochastic reaction-diffusion dynamical networks with delay.

Neural networks : the official journal of the International Neural Network Society
This paper considers the problem of the asymptotic synchronization in mean square for stochastic reaction-diffusion complex dynamical networks with infinite delay driven by the Wiener processes in the infinite dimensional space under the pinning impu...

Fallback Variable History NNLMs: Efficient NNLMs by precomputation and stochastic training.

PloS one
This paper presents a new method to reduce the computational cost when using Neural Networks as Language Models, during recognition, in some particular scenarios. It is based on a Neural Network that considers input contexts of different length in or...

Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms.

Statistics in medicine
With the advancement in drug development, multiple treatments are available for a single disease. Patients can often benefit from taking multiple treatments simultaneously. For example, patients in Clinical Practice Research Datalink with chronic dis...

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...

Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks.

Cell systems
Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot ...

Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by...

Prediction of plant lncRNA by ensemble machine learning classifiers.

BMC genomics
BACKGROUND: In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA cl...

Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control.

Neural networks : the official journal of the International Neural Network Society
This paper focuses on stochastic exponential synchronization of delayed memristive neural networks (MNNs) by the aid of systems with interval parameters which are established by using the concept of Filippov solution. New intermittent controller and ...

Impulsive synchronization of stochastic reaction-diffusion neural networks with mixed time delays.

Neural networks : the official journal of the International Neural Network Society
This paper discusses impulsive synchronization of stochastic reaction-diffusion neural networks with Dirichlet boundary conditions and hybrid time delays. By virtue of inequality techniques, theories of stochastic analysis, linear matrix inequalities...