Random Noise Research Articles

Identification and estimation of panel semiparametric conditional heteroskedastic frontiers with dynamic inefficiency

We study a semiparametric panel stochastic frontier model with nonseparable unobserved heterogeneity, which allows for time-varying conditional heteroskedastic productivity components. It does not require distributional assumptions on random noise except conditional symmetry. We utilize conditional characteristic functions from Kotlarski’s Lemma to derive new moment conditions that yield the identification of the heteroskedastic variance parameters of inefficiency and random noise. Identification only requires a panel with three periods for serially correlated inefficiency. A nonparametric estimation procedure is also developed for the conditional variance of inefficiency, and its convergence rate is established. Monte Carlo simulation shows that the estimator is robust to misspecification of inefficiency distributions.

Enhancing Sea Level Rise Estimation and Uncertainty Assessment from Satellite Altimetry through Spatiotemporal Noise Modeling

The expected acceleration in sea level rise (SLR) throughout this century poses significant threats to coastal cities and low-lying regions. Since the early 1990s, high-precision multi-mission satellite altimetry (SA) has enabled the routine measurement of sea levels, providing a continuous 30-year record from which the mean sea level rise (global and regional) and its variability can be computed. The latest reprocessed product from CMEMS span the period from 1993 to 2020, and have enabled the acquisition of accurate sea level data within the coastal range of 0–20 km. In order to fully utilize this new dataset, we establish a global virtual network consisting of 184 virtual SA stations. We evaluate the impact of different stochastic noises on the estimation of the velocity of the sea surface height (SSH) time series using BIC_tp information criterion. In the second step, the principal component analysis (PCA) allows the common mode noise in the SSH time series to be mitigated. Finally, we analyzed the spatiotemporal characteristics and accuracy of sea level change derived from SA. Our results suggest that the stochasticity of the SSH time series is not well described by a combination of random, flicker, and white noise, but is best described by an ARFIM/ARMA/GGM process. After removing the common mode noise with PCA, about 96.7% of the times series’ RMS decreased, and most of the uncertainty associated with the computed SLR decreased. We confirm that the spatiotemporal correlations should be accounted for to yield trustworthy trends and reliable uncertainties. Our estimated SLR is 2.75 ± 0.89 mm/yr, which aligns closely with recent studies, emphasizing the robustness and consistency of our method using virtual SA stations. We additionally introduce open-source software (SA_Tool V1.0) to process the SA data and reduce noise in surface height time series to the community.

Audio-deepfake detection: Adversarial attacks and countermeasures

Audio has always been a powerful resource for biometric authentication: thus, numerous AI-based audio authentication systems (classifiers) have been proposed. While these classifiers are effective in identifying legitimate human-generated input their security, to the best of our knowledge, has not been explored thoroughly when confronted with advanced attacks that leverage AI-generated deepfake audio. This issue presents a serious concern regarding the security of these classifiers because, e.g., samples generated using adversarial attacks might fool such classifiers, resulting in incorrect classification. In this study, we prove the point: we demonstrate that state-of-the-art audio deepfake classifiers are vulnerable to adversarial attacks. In particular, we design two adversarial attacks on a state-of-the-art audio-deepfake classifier, i.e., the Deep4SNet classification model, which achieves 98.5% accuracy in detecting fake audio samples. The designed adversarial attacks11The code of the attacks will be released open-source in the camera ready. leverage a generative adversarial network architecture and reduce the detector’s accuracy to nearly 0%. In particular, under graybox attack scenarios, we demonstrate that when starting from random noise, we can reduce the accuracy of the state-of-the-art detector from 98.5% to only 0.08%. To mitigate the effect of adversarial attacks on audio-deepfake detectors, we propose a highly generalizable, lightweight, simple, and effective add-on defense mechanism that can be implemented in any audio-deepfake detector. Finally, we discuss promising research directions.

Enhancement of phonemic verbal fluency in multilingual young adults by transcranial random noise stimulation

Several studies have analyzed the effects of transcranial direct current stimulation on verbal fluency tasks in non-clinical populations. Nevertheless, the reported effects on verbal fluency are inconsistent. In addition, the effect of other techniques such as transcranial random noise stimulation (tRNS) on verbal fluency enhancement has yet to be studied in healthy multilingual populations. This study aims to explore the effects of tRNS on verbal fluency in healthy multilingual individuals. Fifty healthy multilingual (Spanish, English and Basque) adults were randomly assigned to a tRNS or sham group. Electrodes were placed on the left dorsolateral prefrontal cortex and left inferior frontal gyrus. All participants performed phonemic and semantic verbal fluency tasks before, during (online assessment) and immediately after (offline assessment) stimulation in three different languages. The results showed significantly better performance by participants who received tRNS in the phonemic verbal fluency tasks in Spanish (in the online and offline assessment) and English (in the offline assessment). No differences between conditions were found in Basque nor semantic verbal fluency. These findings suggests that tRNS on the left prefrontal cortex could help improve phonemic, yet not semantic, fluency in healthy multilingual adults.

Accurate Temperature Reconstruction in Radiofrequency Ablation for Atherosclerotic Plaques Based on Inverse Heat Transfer Analysis.

Radio frequency ablation has emerged as a widely accepted treatment for atherosclerotic plaques. However, monitoring the temperature field distribution in the blood vessel wall during this procedure presents challenges. This limitation increases the risk of endothelial cell damage and inflammatory responses, potentially leading to lumen restenosis. The aim of this study is to accurately reconstruct the transient temperature distribution by solving a stochastic heat transfer model with uncertain parameters using an inverse heat transfer algorithm and temperature measurement data. The nonlinear least squares optimization method, Levenberg-Marquardt (LM), was employed to solve the inverse heat transfer problem for parameter estimation. Then, to improve the convergence of the algorithm and reduce the computational resources, a method of parameter sensitivity analysis was proposed to select parameters mainly affecting the temperature field. Furthermore, the robustness and accuracy of the algorithm were verified by introducing random noise to the temperature measurements. Despite the high level of temperature measurement noise (ξ = 5%) and larger initial guess deviation, the parameter estimation results remained closely aligned with the actual values, with an overall ERMS consistently below 0.05. The absolute errors between the reconstruction temperature at the measurement points TC1, TC2, and TC3, and the actual temperature, remained within 0.33 °C, 2.4 °C, and 1.17 °C, respectively. The Levenberg-Marquardt algorithm employed in this study proficiently tackled the ill-posed issue of inversion process and obtained a strong consistency between the reconstructed temperature the actual temperature.

Investigation of the Influence of Machining Parameters and Surface Roughness on the Wettability of the Al6082 Surfaces Produced with WEDM.

Electrical Discharge Machining (EDM) is a non-conventional machining technique, capable of processing any kind of conductive material. Recently, it has been successfully utilized for producing hydrophobic characteristics in inherently hydrophilic metallic materials. In this work, Wire Electrical Discharge Machining (WEDM) was utilized for producing hydrophobic characteristics on the surface of the aluminum alloy 6082, and various parameters that can affect wettability were investigated. Adopting an orthogonal Taguchi approach, the effects of the process parameter values of peak current, pulse-on time, and gap voltage on the contact angles of the machined surfaces were investigated. After machining, all samples were observed to have obtained hydrophobic properties, reaching contact angles up to 132°. The peak current was identified as the most influential parameter regarding the contact angle, while the gap voltage was the less influential parameter. A contact angle variation of 30° was observed throughout different combinations of machining parameters. Each combination of the machining parameters resulted in a distinct surface morphology. The samples with moderate roughness values (3.4 μm > Sa > 5.7 μm) were found to be more hydrophobic than the samples with high or low values, where the contact angle was measured under 115°. In addition, the finite element modeling of the experimental setup, with parametric surfaces of uniform random and Perlin noise types of roughness, was implemented. Time dependent simulations coupling phase field and laminar flow for the modelingof the wetting of surfaces with different surface roughness characteristics showed that an increase in the Sa roughness and total wetted area can lead to an increase in the contact angle. The combination of experimental and computational results suggests that the complexity of the wettability outcomes of aluminum alloy surfaces processed with WEDM lies in the interplay between variations of the surface chemical composition, roughness, micro/nano morphology, and the surface capability of forming a composite air/water interface.

A Deterministic Chaos-Model-Based Gaussian Noise Generator

The abilities of quantitative description of noise are restricted due to its origin, and only statistical and spectral analysis methods can be applied, while an exact time evolution cannot be defined or predicted. This emphasizes the challenges faced in many applications, including communication systems, where noise can play, on the one hand, a vital role in impacting the signal-to-noise ratio, but possesses, on the other hand, unique properties such as an infinite entropy (infinite information capacity), an exponentially decaying correlation function, and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window, putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criterion on which the main idea of this study is based on is the central limit theorem, which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. This study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.), whose output signals are analyzed according to criteria that encompass the probability density functions of the chaotic signal itself, its envelope, and its phase and statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. Thus, a statistical-characteristic-based algorithm, which includes a series of tests, was developed for a Gaussian-like signal assessment. Following the algorithm, the analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates a Gaussian distribution. This allows for the generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Enhanced sensitivity with nonlinearity-induced exceptional points degeneracy lifting

Bifurcation of exceptional points (EPs), particularly higher-order EPs, can offer applications in metrology by amplifying sensitivity, but this method suffers from a tradeoff between sensitivity and robustness. To break this constraint, we experimentally introduce nonlinearity into the EP degeneracy lifting at the coupled electric resonators and observe a sixth-order nonlinear bifurcation which amplifies the sensitivity elevenfold compared to the conventional EP-based approach operating in the linear regime, while maintaining the degrees of freedom, thereby without cost in robustness. Moreover, we discover a chaotic dynamics near the EP due to the nonlinear contribution, which constitutes a distinct difference from the EP degeneracy lifting in the linear regime with random noise. Our study expands the scope of EP degeneracy lifting into nonlinearity, providing a paradigm to exploit the benefit of EPs.

Securing Social Media Imagery: GAN-Driven Encryption and CNN Analysis with DEA Protection

Generative adversarial networks (GANs) transform low-dimensional random noise into a photorealistic picture. The use of such misleading pictures laden with irrelevant information on social media platforms might lead to serious and challenging issues. The goal of this study is to create and identify the affected GAN pictures. Additionally, enhanced GAN detection accuracy by preprocessing and segmentation feature extraction. This study examines the effectiveness of several learning-based methods for detecting image-to-image translation. Generative adversarial networks use deep learning approaches like convolutional neural networks for generative modelling and the DEA (Data encryption standard) is used to produce the encryption key. To accurately identify fake images, an effective picture forgery detector is necessary. Recent advancements in generative adversarial networks (GANs) have been focused on producing photorealistic pictures quickly and successfully. However, GANs can complicate visual forensics and model attribution. Data from one source and pictures from another can have a wide range of uses, including in fields like computer vision, video, and language processing. The investigation of some photos reveals that both conventional and deep learning detectors may reach up to 95% detection accuracy. However, only deep learning maintains good accuracy on compressed data. An article that explains how to spot GAN-generated false images on social media also provides context on GANs and the theoretical concepts underlying them.

Three-channel robust optical encryption via engineering coherence Stokes vector of partially coherent light

Optical encryption strategies utilizing fully coherent light have been widely explored but often face challenges such as speckle noise and beam instabilities. In this work, we introduce a novel protocol for multi-channel optical information encoding and encryption using vectorial spatial coherence engineering of a partially coherent light beam. By characterizing the beam’s spatial coherence structure with a 2×2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2 \ imes 2$$\\end{document} coherence matrix, we demonstrate independent control over the three components of the coherence Stokes vector. This allows for three-channel optical information encoding and encryption, with applications in color image representation. Unlike existing methods based on fully coherent light modulations, our approach utilizes a two-point dependent coherence Stokes vector, proving resilient to random noise in experimental scenarios. Our findings provide a robust foundation for higher-dimensional optical encoding and encryption, addressing limitations associated with partially coherent light in complex environments.

Hyperspectral imaging combined with GA-SVM for maize variety identification.

The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification.

Using Monte-Carlo simulation to test predictions about the time-course of semantic and lexical access in reading.

One of the main theoretical distinctions between reading models is how and when they predict semantic processing occurs. Some models assume semantic activation occurs after word-form is retrieved. Other models assume there is no-word form, and that what people think of as word-form is actually just semantics. These models thus predict semantic effects should occur early in reading. Results showing words with inconsistent spelling-sound correspondences are faster to read aloud if they are imageable/concrete compared to if they are abstract have been used as evidence supporting this prediction, although null-effects have also been reported. To investigate this, I used Monte-Carlo simulation to create a large set of simulated experiments from RTs taken from different databases. The results showed significant main effects of concreteness and spelling-sound consistency, as well as age-of-acquisition, a variable that can potentially confound the results. Alternatively, simulations showing a significant interaction between spelling-sound consistency and concreteness did not occur above chance, even without controlling for age-of-acquisition. These results support models that use lexical form. In addition, they suggest significant interactions from previous experiments may have occurred due to idiosyncratic items affecting the results and random noise causing the occasional statistical error.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

Fusion of a priori information and energy distribution for the centroiding method of the star sensor

The star sensor is the most accurate measurement instrument in the spacecraft attitude measurement system, and the accurate centroid of the star point is the basis for ensuring the performance of the star sensor. Currently, the centroid of the gray method is the most widely used centroid extraction method in practice. Systematic errors caused by the centroid of the gray method and random noise in the detector imaging process are the main factors contributing to the deviation of the star centroiding coordinates. Considering the relationship between the point spread function and the pixel gray value, this paper proposes a centroiding method to reduce the star point centroiding error by fusing a priori information and energy distribution. The star charts are first preprocessed using a curvature filter and Gaussian blur to reduce the random noise. Then the complexity of the point spread function is considered, and the pixel gray values are corrected based on a priori information and gray value fuzzy processing. Finally, the symmetry of the one-dimensional energy distribution is used to quickly determine the sub-pixel deviation to get the star centroid coordinates. Through simulation and physical simulation experiments, the method was verified to be effective, and the extraction accuracy met the requirements of high-precision star sensors. The night sky observation test results demonstrate that the method in this paper can improve the measurement accuracy of the star sensors.

An Adaptive Black-Box Defense Against Trojan Attacks (TrojDef).

Trojan backdoor is a poisoning attack against neural network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans into model parameters for backdoor breaches through a poisoned training process. To misclassify an input to a target class, the attacker activates the backdoor by augmenting the input with a predefined trigger that is only known to her/him. Most of the proposed defenses against Trojan attacks assume a white-box setup, in which the defender either has access to the inner state of NN or is able to run backpropagation through it. In this work, we propose a more practical black-box defense, dubbed TrojDef. In a black-box setup, the defender can only run forward-pass of the NN. TrojDef is motivated by the Trojan poisoned training, in which the model is trained on both benign and Trojan inputs. TrojDef tries to identify and filter out Trojan inputs (i.e., inputs augmented with the Trojan trigger) by monitoring the changes in the prediction confidence when the input is repeatedly perturbed by random noise. We derive a function based on the prediction outputs which is called the prediction confidence bound to decide whether the input example is Trojan or not. The intuition is that Trojan inputs are more stable as the misclassification only depends on the trigger, while benign inputs will suffer when augmented with noise due to the perturbation of the classification features. Through mathematical analysis, we show that if the attacker is perfect in injecting the backdoor, the Trojan infected model will be trained to learn the appropriate prediction confidence bound, which is used to distinguish Trojan and benign inputs under arbitrary perturbations. However, because the attacker might not be perfect in injecting the backdoor, we introduce a nonlinear transform to the prediction confidence bound to improve the detection accuracy in practical settings. Extensive empirical evaluations show that TrojDef significantly outperforms the-state-of-the-art defenses and is highly stable under different settings, even when the classifier architecture, the training process, or the hyperparameters change.

DiffSeqMol: A Non-Autoregressive Diffusion-Based Approach for Molecular Sequence Generation and Optimization

Background: The application of deep generative models for molecular discovery has witnessed a significant surge in recent years. Currently, the field of molecular generation and molecular optimization is predominantly governed by autoregressive models regardless of how molecular data is represented. However, an emerging paradigm in the generation domain is diffusion models, which treat data non-autoregressively and have achieved significant breakthroughs in areas such as image generation. Methods: The potential and capability of diffusion models in molecular generation and optimization tasks remain largely unexplored. In order to investigate the potential applicability of diffusion models in the domain of molecular exploration, we proposed DiffSeqMol, a molecular sequence generation model, underpinned by diffusion process. Results & Discussion: DiffSeqMol distinguishes itself from traditional autoregressive methods by its capacity to draw samples from random noise and direct generating the entire molecule. Through experiment evaluations, we demonstrated that DiffSeqMol can achieve, even surpass, the performance of established state-of-the-art models on unconditional generation tasks and molecular optimization tasks. Conclusion: Taken together, our results show that DiffSeqMol can be considered a promising molecular generation method. It opens new pathways to traverse the expansive chemical space and to discover novel molecules.

The Drain Bias Modulation Effect of Random Telegraph Noise in Gate-All-Around FETs for Cryogenic Applications

The Drain Bias Modulation Effect of Random Telegraph Noise in Gate-All-Around FETs for Cryogenic Applications

Elimination of Random Mixed Noise in ECG using Convolutional Denoising Autoencoder with Transformer Encoder.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

Enhancing Privacy-Preserving Localization by Integrating Random Noise With Blockchain in Internet of Things

Enhancing Privacy-Preserving Localization by Integrating Random Noise With Blockchain in Internet of Things

Water‐Wave‐Induced OBS Noise: Theories, Observations, and Potential Applications

AbstractThe horizontal records of ocean‐bottom seismometers (OBS) are usually highly noisy, generally due to ocean‐bottom currents tilting the instrument, which greatly limits their practical usage in ocean‐bottom seismology. In shallow water, water waves with energy concentration around 0.07 Hz induce additional noise on OBSs. Such noise is not well understood. In this article, we propose a noise model to explain the horizontal noise around 0.07 Hz. The noise model consists of three types of noise, that is, water‐wave‐induced noise, current noise with a relatively constant orientation, and background random noise. The wave‐induced horizontal acceleration is theoretically shown to be proportional to the time derivative of ocean‐bottom pressure. We validate the noise model and related theories using realistic observations. Results are potentially applicable to determine the propagation direction of water waves nearshore, and also provide constraints on the underlying Earth structure. The results can also be applied to the removal of wave‐induced noise, achieving a typical maximum improvement in the signal‐to‐noise ratio of 10–20 dB for time periods with strong wave noise.