White Noise Research Articles

The association between aberrant salience and psychotic experiences in general population twins, and genetic vulnerability as a modifier

BackgroundPrevious studies assessing the hypothesis that the construct of ‘aberrant salience’ is associated with psychosis and psychotic symptoms showed conflicting results. For this reason, the association between measures to index aberrant salience and subclinical psychotic symptoms in a general population sample was analysed. In addition, genetic vulnerability was added to the analysis as a modifier to test the hypothesis that modification by genetic vulnerability may explain variability in the results.MethodsThe TwinssCan project obtained data from general population twins (N = 887). CAPE (Community Assessment of Psychic Experience) scores were used to index psychotic experiences. Aberrant salience was assessed with white noise task and ambiguous situations task.ResultsMeasures of aberrant salience were not associated with psychotic experiences, nor was there evidence for an interaction with genetic predisposition in this association (Z = 1.08, p = 0.282).ConclusionsVarious studies including the present could not replicate the association between aberrant salience and psychotic experiences in general population samples. The conflicting findings might be explained by moderation by genetic vulnerability, but results are inconsistent. If there was evidence for a main effect or interaction, this was in the positive symptom scale only. On the other hand, the association was more robust in so-called ‘ultra-high risk’ patients and first episode psychosis patients. Thus, this association may represent a state-dependent association, present only at the more severe end of the psychosis spectrum.

Terahertz Non-Destructive Testing of Porosity in Multi-Layer Thermal Barrier Coatings Based on Small-Sample Data

Accurately characterizing the internal porosity rate of thermal barrier coatings (TBCs) was essential for prolonging their service life. This work concentrated on atmospheric plasma spray (APS)-prepared TBCs and proposed the utilization of terahertz non-destructive detection technology to evaluate their internal porosity rate. The internal porosity rates were ascertained through a metallographic analysis and scanning electron microscopy (SEM), followed by the reconstruction of the TBC model using a four-parameter method. Terahertz time-domain simulation data corresponding to various porosity rates were generated employing the time-domain finite difference method. In simulating actual test signals, white noise with a signal-to-noise ratio of 10 dB was introduced, and various wavelet transforms were utilized for denoising purposes. The effectiveness of different signal processing techniques in mitigating noise was compared to extract key features associated with porosity. To address dimensionality challenges and further enhance model performance, kernel principal component analysis (kPCA) was employed for data processing. To tackle issues related to limited sample sizes, this work proposed to use the Siamese neural network (SNN) and generative adversarial network (GAN) algorithms to solve this challenge in order to improve the generalization ability and detection accuracy of the model. The efficacy of the constructed model was assessed using multiple evaluation metrics; the results indicate that the novel hybrid WT-kPCA-GAN model achieves a prediction accuracy exceeding 0.9 while demonstrating lower error rates and superior predictive performance overall. Ultimately, this work presented an innovative, convenient, non-destructive online approach that was safe and highly precise for measuring the porosity rate of TBCs, particularly in scenarios involving small sample sizes facilitating assessments regarding their service life.

Methods in fluctuation (noise) spectroscopy and continuous analysis for high-throughput measurements

Abstract Fluctuation (noise) spectroscopy is widely used to investigate the low-frequency dynamics of charge carriers in condensed-matter systems, aiming to (i) improve the performance of micro- and nanoscale electronic devices and sensors, and (ii) use ‘noise as a signal’ in order to fundamentally investigate the microscopic motion and transitions of charge carriers coupled to the low-lying excitations in solids. Here, we focus on measurements of the ubiquitous 1 / f -type fluctuations, often composed by a superposition of (independent or correlated) two-level systems each giving rise to a Lorentzian spectrum, denoted random telegraph noise in the time domain. We briefly review the basic concepts of noise and fluctuations and give a comprehensive overview of state-of-the-art measuring techniques, using both commercially available signal analyzers and custom software in order to perform the spectral analysis of the recorded time signals, and critically evaluate their advantages and drawbacks. We describe how to use fast data acquisition devices in a cross-correlation setup providing additional quantitative information on the magnitude of uncorrelated instrument noise, and demonstrate the mathematical intricacies of extracting the noise spectra. We introduce a new method for high-throughput measurements by automated spectral analysis based on the concept of Continuous Analysis. We demonstrate the potential of this technique, working towards a FAIR data workflow, by measurements of magnetic flux noise within the hysteresis loop of ferromagnetic nanostructures.

GHZ protocols enhance frequency metrology despite spontaneous decay.

The use of correlated states and measurements promises improvements in the accuracy of frequency metrology and the stability of atomic clocks. However, developing strategies robust against dominant noise processes remains challenging. We address the issue of decoherence due to spontaneous decay and show that Greenberger-Horne-Zeilinger (GHZ) states, in conjunction with a correlated measurement and nonlinear estimation strategy, achieve gains of up to 2.25 decibel, comparable to fundamental bounds for up to about 80 atoms in the presence of decoherence. This result is unexpected because GHZ states do not provide any enhancement under dephasing due to white frequency noise compared to the standard quantum limit of uncorrelated states. The gain arises from a veto signal, which allows for the detection and mitigation of errors caused by spontaneous emission events. Through comprehensive Monte Carlo simulations of atomic clocks, we demonstrate the robustness of the GHZ protocol.

Heart sound classification algorithm based on bispectral feature extraction and convolutional neural networks

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.

Impacts of Data Preprocessing and Sampling Techniques on Solar Flare Prediction from Multivariate Time Series Data of Photospheric Magnetic Field Parameters

The accurate prediction of solar flares is crucial due to their risks to astronauts, space equipment, and satellite communication systems. Our research enhances solar flare prediction by employing sophisticated data preprocessing and sampling techniques for the Space Weather Analytics for Solar Flares (SWAN-SF) data set, a rich source of multivariate time series data of solar active regions. Our study adopts a multifaceted approach encompassing four key methodologies. Initially, we address over 10 million missing values in the SWAN-SF data set through our innovative imputation technique called fast Pearson correlation-based k-nearest neighbors imputation. Subsequently, we propose a precise normalization technique, called LSBZM normalization, tailored for time series data, merging various strategies (log, square root, Box–Cox, Z-score, and min–max) to uniformly scale the data set's 24 attributes (photospheric magnetic field parameters), addressing issues such as skewness. We also explore the “near decision boundary sample removal” technique to enhance the classification performance of the data set by effectively resolving the challenge of class overlap. Finally, a pivotal aspect of our research is a thorough evaluation of diverse oversampling and undersampling methods, including SMOTE, ADASYN, Gaussian noise injection, TimeGAN, Tomek links, and random undersampling, to counter the severe imbalance in the SWAN-SF data set, notably a 60:1 ratio of major (X and M) to minor (C, B, and FQ) flaring events in binary classification. To demonstrate the effectiveness of our methods, we use eight classification algorithms, including advanced deep-learning-based architectures. Our analysis shows significant true skill statistic scores, underscoring the importance of data preprocessing and sampling in time-series-based solar flare prediction.

Fringe photometric stereo

In fringe projection profilometry (FPP), a high spatial frequency of fringe typically indicates powerful error suppression capability; however, it complicates phase unwrapping, necessitating a greater number of patterns and thus compromising the real-time performance of scanning. In this letter, we report a fringe photometric stereo (FPS) to completely bypass phase unwrapping for recovering the continuous 3D surface. We reveal the linear mapping relationship between the phase gradient and depth gradient, thereby facilitating the computation of depth gradients from phase gradients. Thus, we can employ depth gradients to reconstruct high-quality 3D profiles. Experimental results demonstrate that our FPS surpasses traditional phase-shifting profilometry in mitigating Gaussian noise. Our approach enables high-quality and unambiguous 3D reconstruction using single-frequency fringe patterns, relying solely on a camera and a projector without the need for dual, multiple, or light field cameras, thereby paving a path to high-speed and precision 3D imaging.

Effects of white noise on straddle and soliton dynamics in birefringent fibers using the novel Kaup-Newell equation approach

Effects of white noise on straddle and soliton dynamics in birefringent fibers using the novel Kaup-Newell equation approach

Bayesian inference of initial conditions from non-linear cosmic structures using field-level emulators

ABSTRACT Analysing next-generation cosmological data requires balancing accurate modelling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full N-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from N-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95\,h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \,\, \mathrm{Mpc}^{-1}$. Posterior resimulations – using the inferred initial conditions for N-body simulations – show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}\,{\rm M}_{\odot }\,h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modelling, indicating the potential for extracting additional information at smaller scales.

Computer diffraction tomography: a comparative analysis of the use of controlled and wavelet filters for image processing

The paper provides digital processing of 2D X-ray projection images of a Coulomb-type point defect in a Si(111) crystal recorded by a detector against the background of statistical Gaussian noise. A managed filter and a wavelet filter with a 4th-order Daubechies function are used. The efficiency of filtering 2D images is determined by calculating the relative quadratic deviations of the intensities of filtered and reference (noiseless) 2D images averaged over all points. A comparison of the calculated values of the relative deviations of the intensities shows that the considered methods work quite well and both, in principle, can be effectively used in practice for noise processing of X-ray diffraction images used for 3D reconstruction of nanoscale defects of crystal structures.

Enhanced Sleep Stage Classification Using EEG and EOG: A Novel Approach for Feature Selection with Deep Learning and Gaussian Noise Data Augmentation

Enhanced Sleep Stage Classification Using EEG and EOG: A Novel Approach for Feature Selection with Deep Learning and Gaussian Noise Data Augmentation

Stochastic response analysis of a birhythmic oscillator under Poisson white noise excitation via path integration method

Stochastic response analysis of a birhythmic oscillator under Poisson white noise excitation via path integration method

The Effect of Thymoquinone on Acoustic Trauma-Induced Hearing Loss in Rats.

Background The aim of this study was to investigate the protective effect of thymoquinone, an antioxidant, on hearing loss induced by acoustic trauma in rats. Material and methods This study included 32 Wistar Albino rats divided into four groups: control, acoustic trauma, thymoquinone + acoustic trauma, and thymoquinone only, with eight rats per group. The control group received 0.5 mL of corn oil intraperitoneally for 10 days. The acoustic trauma group was exposed to 100 dB white noise at 4 kHz for 16 hours. The thymoquinone + acoustic trauma group received thymoquinone (10 mg/kg) intraperitoneally for two days before acoustic trauma and eight days after acoustic trauma. The thymoquinone only group received thymoquinone (10 mg/kg) for 10 days. Distortion product otoacoustic emissions (DPOAEs) were measured before and after treatments on days 1, 4, and 10. Results In the control group, DPOAE measurements showed no significant change over the study period. The acoustic trauma group exhibited a significant decrease in DPOAE on the first day after trauma, followed by some recovery. The thymoquinone + acoustic trauma group showed no significant decrease in DPOAE on the first day post-trauma, suggesting a protective effect. The thymoquinone only group also indicated no significant change in DPOAE measurements, suggesting that thymoquinone alone did not affect hearing function. Conclusion Thymoquinone demonstrated a protective effect against acoustic trauma-induced hearing loss in rats, as evidenced by stable DPOAE measurements post-trauma. These findings suggest that thymoquinone may help preserve hearing function by reducing oxidative stress in the cochlea. Further studies are needed to confirm these results in humans and optimize dosage and treatment protocols.

Firing patterns transitions and resonance effects of the extended Hindmarsh-Rose neural model with Gaussian noise and transcranial magneto-acousto-electrical stimulation

Considering the fact that the typical three-variable Hindmarsh-Rose(HR) neural model has limitations in describing the complex non-linear features and precise behavior patterns of neuron, the influences of transcranial magneto-acousto-electrical stimulation(TMAES) on firing patterns and resonance effects are analyzed based on an extended HR neural model in this paper. Obtained results show that TMAES can induce transitions in the firing patterns of extended HR neuron, such as spiking and multi-periodic bursting state, etc If appropriate parameters are selected, the multimodal discharge modes can also be observed. Coefficient of variation is calculated to further investigate the effect of TMAES and Gaussian white noise on the firing rhythm of extended HR neuron, and relevant results indicate that TMAES can induce coherent resonance phenomena in HR neuronal systems similar to the effects of Gaussian white noise, which reveals a new mechanism of coherent resonance induced by TMAES. Further more, TMAES can also regulate coefficient of variation to exhibit anti-coherent resonance and multiple anti-coherent resonance structures, exhibiting richer regulatory functions than Gaussian white noise in regulating neuronal firing rhythm. This study seeks to enhance the understanding of the processes that influence the firing patterns and coherence degree of neuron under TMAES in neuroses or psychoses.

Optical solitons for the concatenation model with differential group delay having multiplicative white noise by F–expansion approach

This study examines optical soliton solutions for the concatenation model, which includes differential group delay and white noise in both components. Through the F–expansion integration algorithm, an extensive spectrum of soliton solutions was found. It has been confirmed that white noise affects the phase of solitons along both components.

Investigating the effect of rTMS over the temporoparietal cortex on the Right Ear Advantage for perceived and imagined voices

A Right Ear Advantage (REA) is well-established in perceptual tasks but it has been found also during imagery. It is ascribed to the left temporoparietal activity for language, and it can be absent/reversed in some clinical conditions including auditory hallucinations. We applied 1-Hz repetitive TMS over TP3/TP4 (left/right language areas) identified through neuronavigation in 18 healthy participants, before administering a modified white noise (WN) speech illusion paradigm: a voice was presented at one ear, at the same or lower intensities with respect to binaural WN. In some trials the voice was not presented, but participants were anyway instructed to report in which ear they believed perceiving it in all trials. Results confirmed the REA both when the voice was present (perceptual REA) and when it was absent (imaginative REA). Interestingly, results suggested that the correct localization of the voice when the stimulus was ambiguous (presented at low intensity and “masked” by WN) was better when TMS was applied over the right/left hemisphere, in male participants with a low/high proneness to unusual experiences (e.g., auditory hallucinations), respectively. This interaction must be further explored to shed light on the relationship between hemispheric asymmetries and auditory hallucinations, in healthy and clinical samples.

Analyzing One- and Two-bit Data to Reduce Memory Requirements for F -statistic-based Gravitational Wave Searches

Searches for continuous-wave gravitational radiation in data collected by modern long-baseline interferometers, such as the Laser Interferometer Gravitational-wave Observatory (LIGO), the Virgo interferometer, and the Kamioka Gravitational Wave Detector, can be memory intensive. A digitization scheme is described that reduces the 64-bit interferometer output to a one- or two-bit data stream while minimizing distortion and achieving considerable reduction in storage and input/output cost. For the representative example of the coherent, maximum-likelihood matched filter known as the F -statistic, it is found using Monte Carlo simulations that the injected signal only needs to be ≈24% stronger (for one-bit data) and ≈6.4% stronger (for two-bit data with optimal thresholds) than a 64-bit signal in order to be detected with 90% probability in Gaussian noise. The foregoing percentages do not change significantly when the signal frequency decreases secularly, or when the noise statistics are not Gaussian, as verified with LIGO Science Run 6 data.

Punishment or reward? Environmental information disclosure in Chinese listed companies based on evolutionary game systems

AbstractIn February 2022, the Chinese government implemented a mandatory environmental information disclosure policy for publicly traded corporations. To assess the factors influencing environmental information disclosure, we developed a tripartite evolutionary game system model based on strategic interactions between the government, enterprises and the public. Afterwards, we conducted a simulation to examine the systemic interaction equilibrium among the parties involved. Additionally, we assessed critical factor changes in decision‐making for three stakeholders: initial strategy selection, related policy subsidies, regulatory penalties and public incentives. Incorporating Gaussian white noise into the evolutionary game model enhances the realism of simulations, allowing for a more accurate representation of how random disturbances affect decision‐making among the government, the public and enterprises. The study reveals that the active participation of the public in information disclosure activities has a certain degree of substitution effect on the government's choice to regulate high‐quality information disclosure by enterprises actively. The penalty mechanism is more effective than the subsidy mechanism. The public's decision‐making process is susceptible to the direct economic benefits that enterprises can gain from disclosing low‐quality environmental information.

Higher-order stochastic averaging for investigating a vehicle suspension system with nonlinear damping and stiffness

The paper deals with a quarter-car model with nonlinear damping and stiffness under white noise base excitation using the higher-order stochastic averaging method for analyzing approximate responses. Recently, a novel higher-order averaging procedure has been developed to find analytically the first-, second-, and third-order stationary joint probability density functions (PDF) of amplitude and full phase by solving the corresponding Fokker-Planck-Kolmogorov (FPK) equation, and it will be extended to the nonlinear quarter-car model. Accordingly, the mean-square responses such as the displacement, and velocity of the sprung mass can be obtained analytically. The influences of excitation intensity on the dynamic responses, as well as the variations of linear and nonlinear damping, are analyzed. A very satisfactory agreement is found between the accuracy of the solutions corresponding to higher-order stochastic averaging and that of Monte Carlo simulation.

Graph Regularized Sparse L2,1 Semi‐Nonnegative Matrix Factorization for Data Reduction

ABSTRACTNon‐negative Matrix Factorization (NMF) is an effective algorithm for multivariate data analysis, including applications to feature selection, pattern recognition, and computer vision. Its variant, Semi‐Nonnegative Matrix Factorization (SNF), extends the ability of NMF to render parts‐based data representations to include mixed‐sign data. Graph Regularized SNF builds upon this paradigm by adding a graph regularization term to preserve the local geometrical structure of the data space. Despite their successes, SNF‐related algorithms to date still suffer from instability caused by the Frobenius norm due to the effects of outliers and noise. In this paper, we present a new SNF algorithm that utilizes the noise‐insensitive norm. We provide monotonic convergence analysis of the SNF algorithm. In addition, we conduct numerical experiments on three benchmark mixed‐sign datasets as well as several randomized mixed‐sign matrices to demonstrate the performance superiority of SNF over conventional SNF algorithms under the influence of Gaussian noise at different levels.