In a dissipative quantum system, we report the dynamic mode decomposition (DMD) analysis of damped oscillation signals. We used a reflection-type pump-probe method to observe time-domain signals, including the coupled modes of long-lived longitudinal optical phonons and quickly damped plasmons (LOPC) at various pump powers. The Fourier transformed spectra of the observed damped oscillation signals show broad and asymmetric modes, making it difficult to evaluate their frequencies and damping rates.
View Article and Find Full Text PDFDeep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can eliminate the noise contained in an image by repeating the mapping between latent and data space.
View Article and Find Full Text PDFBackground: Previous studies have examined when activities of daily living (ADL) recovery more than six months after surgery can be predicted, and how much accuracy the predictors have.
Objective: The purpose of this study was to determine the predictors of ADL decline and evaluate their accuracies one year post-operation for hip-fracture patients.
Methods: We studied patients who underwent hip fracture surgery and were able to walk independently pre-operatively.
Sci Technol Adv Mater
January 2020
Measurements of relaxation processes are essential in many fields, including nonlinear optics. Relaxation processes provide many insights into atomic/molecular structures and the kinetics and mechanisms of chemical reactions. For the analysis of these processes, the extraction of modes that are specific to the phenomenon of interest (normal modes) is unavoidable.
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