Search results for "Neural Networks"
showing 10 items of 599 documents
Effect of physical aging on the low-frequency vibrational density of states of a glassy polymer
2003
The effects of the physical aging on the vibrational density of states (VDOS) of a polymeric glass is studied. The VDOS of a poly(methyl methacrylate) glass at low-energy (<15 meV), was determined from inelastic neutron scattering at low-temperature for two different physical thermodynamical states. One sample was annealed during a long time at temperature lower than Tg, and another was quenched from a temperature higher than Tg. It was found that the VDOS around the boson peak, relatively to the one at higher energy, decreases with the annealing at lower temperature than Tg, i.e., with the physical aging.
Re-entrant glass transition in a colloid-polymer mixture with depletion attractions.
2002
Performing light scattering experiments we show that introducing short-ranged attraction to a colloidal suspension of nearly hard spheres by addition of free polymer produces new glass transition phenomena. We observe a dramatic acceleration of the density fluctuations amounting to the melting of a colloidal glass. Increasing the strength of the attractions the system freezes into another nonergodic state sharing some qualitative features with gel states occurring at lower colloid packing fractions. This reentrant glass transition is in qualitative agreement with recent theoretical predictions.
Monte Carlo and molecular dynamics simulation of the glass transition of polymers
1998
Two coarse-grained models for polymer chains in dense glass-forming polymer melts are studied by computer simulation: the bond-fluctuation model on a simple cubic lattice, where a bond-length potential favors long bonds, is treated by dynamic Monte Carlo methods, and a bead-spring model in the continuum with a Lennard-Jones potential between the beads is treated by Molecular Dynamics. While the dynamics of both models differ for short length scales and associated time scales, on mesoscopic spatial and temporal scales both models behave similarly. In particular, the mode coupling theory of the glass transition can be used to interpret the slowing down of the undercooled polymer melt. For the…
Interfacial properties of glassy polymer melts: A Monte Carlo study
1996
The properties of the interface between a polymer melt and a solid wall are studied over a wide range of temperatures by dynamic Monte Carlo simulations. It is shown that in the supercooled state near the glass transition of the melt an “interphase” forms, the structure of which is influenced by the wall. The thickness of this interphase is determined from the monomer density profile near the surface and is strongly temperature dependent. At low glass-like temperatures it is larger than the bulk radius of gyration of the chains.
Optical Hole Burning and Thermal Irreversibility of Non-Ergodic Systems: Polymers, Proteins, Glasses
1989
A spectral hole is used to probe configurational dynamics of non-ergodic systems far below the glass transition temperature
From orientational glasses to structural glasses: What computer simulations have contributed to understand experiments
2002
Abstract Orientational glasses, produced by random dilution of molecular crystals, exhibit a freezing transition of the quadrupole moments. Monte Carlo simulations of lattice models (generalization of the Edwards–Anderson spin glass model) have been used to elucidate this behavior. While short range models exhibit a static glass transition at zero temperature only, the infinite range Potts glass exhibits a transition where a glass order parameter appears discontinuously. At higher temperature, a dynamical transition occurs, described by mode-coupling theory (MCT). MCT has also been tested by Monte Carlo and molecular dynamics simulations of coarse-grained models of glass-forming polymers. W…
Can Physiological and Psychological Factors Predict Dropout from Intense 10-Day Winter Military Survival Training?
2020
Background: In the military context, high levels of physiological and psychological stress together can compromise individual&rsquo
LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.
2021
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost at various aspects, such as computation power, storage, network bandwidth, power consumption, and hardware complexity. For further practical applications (e.g., wearable sleep monitoring devices), there is a need for simple and compact models. In this paper, we propose a lightweight model, namely LightSleepNet, for rapid sleep stage classification based on spectrograms. Our model is assembled by a much fewer number of model parameters compared to…
One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals
2021
Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…
Computational Modeling of Human Visual Function using Psychophysics, Deep Neural Networks, and Information Theory
2023
Visual perception is a key to unlocking the secrets of brain functions because most of the information is processed through the early visual system and then transmitted to the high-level cognitive perception brain regions. The brain functions as a self-organizing, bio-dynamic, and chaotic system that receives outside information and then decomposes it into pieces of information that can be processed efficiently and independently. The work connects natural image statistics, psychophysics, deep neural networks, and information theory to perceptual vision systems to explore how vision processes information from the outside world and how the information coupled drives functional connectivity be…