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Wavelet-Based Multiscale Intermittency Analysis: The Effect of Deformation.

ABSTRACT: Intermittency represents a certain form of heterogeneous behavior that has interest in diverse fields of application, particularly regarding the characterization of system dynamics and for risk assessment. Given its intrinsic location-scale-dependent nature, wavelets constitute a useful functional tool for technical analysis of intermittency. Deformation of the support may induce complex structural changes in a signal. In this paper, we study the effect of deformation on intermittency. Specifically, we analyze the interscale transfer of energy and its implications on different wavelet-based intermittency indicators, depending on whether the signal corresponds to a 'level'- or a 'flow'-type physical magnitude. Further, we evaluate the effect of deformation on the interscale distribution of energy in terms of generalized entropy and complexity measures. For illustration, various contrasting scenarios are considered based on simulation, as well as two segments corresponding to different regimes in a real seismic series before and after a significant earthquake.

SUBMITTER: Angulo JM

PROVIDER: S-EPMC10378465 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Wavelet-Based Multiscale Intermittency Analysis: The Effect of Deformation.

Angulo José M JM Madrid Ana E AE

Entropy (Basel, Switzerland) 20230718 7

Intermittency represents a certain form of heterogeneous behavior that has interest in diverse fields of application, particularly regarding the characterization of system dynamics and for risk assessment. Given its intrinsic location-scale-dependent nature, wavelets constitute a useful functional tool for technical analysis of intermittency. Deformation of the support may induce complex structural changes in a signal. In this paper, we study the effect of deformation on intermittency. Specifica ...[more]

PMID: 37510027

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Project description:BackgroundTreatment regimens for active tuberculosis (TB) that are intermittent, or use rifampin during only the initial phase, offer practical advantages, but their efficacy has been questioned. We conducted a systematic review of treatment regimens for active TB, to assess the effect of duration and intermittency of rifampin use on TB treatment outcomes.Methods and findingsPubMed, Embase, and the Cochrane CENTRAL database for clinical trials were searched for randomized controlled trials, published in English, French, or Spanish, between 1965 and June 2008. Selected studies utilized standardized treatment with rifampin-containing regimens. Studies reported bacteriologically confirmed failure and/or relapse in previously untreated patients with bacteriologically confirmed pulmonary TB. Pooled cumulative incidences of treatment outcomes and association with risk factors were computed with stratified random effects meta-analyses. Meta-regression was performed using a negative binomial regression model. A total of 57 trials with 312 arms and 21,472 participants were included in the analysis. Regimens utilizing rifampin only for the first 1-2 mo had significantly higher rates of failure, relapse, and acquired drug resistance, as compared to regimens that used rifampin for 6 mo. This was particularly evident when there was initial drug resistance to isoniazid, streptomycin, or both. On the other hand, there was little evidence of difference in failure or relapse with daily or intermittent schedules of treatment administration, although there was insufficient published evidence of the efficacy of twice-weekly rifampin administration throughout therapy.ConclusionsTB treatment outcomes were significantly worse with shorter duration of rifampin, or with initial drug resistance to isoniazid and/or streptomycin. Treatment outcomes were similar with all intermittent schedules evaluated, but there is insufficient evidence to support administration of treatment twice weekly throughout therapy.

Project description:Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.

Dataset Information

Wavelet-Based Multiscale Intermittency Analysis: The Effect of Deformation.

Publications

Wavelet-Based Multiscale Intermittency Analysis: The Effect of Deformation.

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