Self-Supervised Learning of Accelerometer Data Provides New Insights for Sleep and Its Association with Mortality

Self-Supervised Learning of Accelerometer Data Provides New Insights for Sleep and Its Association with Mortality

Information Science, Artificial Intelligence, Medicine, Psychiatry, Neurology, Life Sciences, Biophysics,
self-supervised learning   accelerometer   sleep   mortality   deep learning   sleep stage classification   multicenter cohort study  

Insights into the Association Between Sleep and Mortality Revealed by Self-supervised Learning of Wrist-worn Accelerometer Data In modern society, sleep is an essential basic activity for life, and its importance is self-evident. Accurately measuring and classifying sleep/wake states and different sleep stages is crucial for diagnosing sleep disord...

Diffusion Model Optimization with Deep Learning

Diffusion Model Optimization with Deep Learning

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
deep learning   diffusion model   neural network   microstructural imaging   self-supervised learning  

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Self-Supervised Deep Learning-Based Denoising for Diffusion Tensor MRI

Medicine, Medical Imaging, Life Sciences, Bioengineering,
self-supervised learning   deep learning   convolutional neural network   diffusion tensor imaging   denoising  

Background Introduction Diffusion Tensor Magnetic Resonance Imaging (DTI) is a widely used neuroimaging technique for imaging the microstructure of brain tissues and white matter tracts. However, noise in Diffusion-Weighted Images (DWI) can reduce the accuracy of microstructural parameters derived from DTI data and also necessitate longer acquisiti...

Geometry-enhanced pretraining on interatomic potentials

Chemistry, Computational Chemistry, Information Science, Computer Science, Artificial Intelligence,
machine learning   molecular dynamics   graph neural networks   self-supervised learning   interatomic potentials  

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...