AIMC Topic: Diffusion

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2D medical image synthesis using transformer-based denoising diffusion probabilistic model.

Physics in medicine and biology May 5, 2023
. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce...
Humans Artificial Intelligence COVID-19 Tomography, X-Ray Computed Image Processing, Computer-Assisted Models, Statistical Diffusion
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Securing Multimedia Using a Deep Learning Based Chaotic Logistic Map.

IEEE journal of biomedical and health informatics Mar 7, 2023
Telemedicine and online consultations with doctors has become very popular during the pandemic and involves the transmission of medical data through the internet. Thus this raises concern about the security of the medical data of the patient as the r...
Humans Algorithms Deep Learning Diffusion Entropy Multimedia
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Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions.

Medical image analysis Jan 13, 2023
Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of d...
Humans Brain Deep Learning Image Processing, Computer-Assisted Diffusion Diffusion Magnetic Resonance Imaging
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Synchronization of hybrid switching diffusions delayed networks via stochastic event-triggered control.

Neural networks : the official journal of the International Neural Network Society Dec 5, 2022
In this paper, the synchronization problem of stochastic complex networks with time delays and hybrid switching diffusions (SCNTH) is concerned based on event-triggered control. Therein, a new class of event-triggered function is proposed for the con...
Neural Networks, Computer Diffusion
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Topology, vorticity, and limit cycle in a stabilized Kuramoto-Sivashinsky equation.

Proceedings of the National Academy of Sciences of the United States of America Dec 2, 2022
A noisy stabilized Kuramoto-Sivashinsky equation is analyzed by stochastic decomposition. For values of the control parameter for which periodic stationary patterns exist, the dynamics can be decomposed into diffusive and transverse parts which act o...
Neural Networks, Computer Diffusion Motion
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Robust Composite H Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method.

IEEE transactions on cybernetics Nov 18, 2022
This article focuses on the composite H synchronization problem for jumping reaction-diffusion neural networks (NNs) with multiple kinds of disturbances. Due to the existence of disturbance effects, the performance of the aforementioned system would ...
Neural Networks, Computer Computer Simulation Feedback Diffusion
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Machine learning of pair-contact process with diffusion.

Scientific reports Nov 17, 2022
The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified...
Humans Machine Learning Principal Component Analysis Diffusion Phase Transition
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A class of doubly stochastic shift operators for random graph signals and their boundedness.

Neural networks : the official journal of the International Neural Network Society Nov 13, 2022
A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper L-boundedness for locally stationary random graph signals, (ii) L-isometry for i.i.d. random graph signals with the asymptotic increa...
Neural Networks, Computer Algorithms Diffusion
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Bayesian deep learning for error estimation in the analysis of anomalous diffusion.

Nature communications Nov 7, 2022
Modern single-particle-tracking techniques produce extensive time-series of diffusive motion in a wide variety of systems, from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in th...
Machine Learning Deep Learning Bayes Theorem Diffusion Motion
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Machine Learning Diffusion Monte Carlo Energies.

Journal of chemical theory and computation Nov 1, 2022
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small data sets (≈60 DMC calculations in total). The first uses voxel deep neural networks (VDNNs) to predict DMC energy densities ...
Neural Networks, Computer Machine Learning Diffusion Electrons Monte Carlo Method
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