Gaussian Distribution Research Articles

EFFECT OF COLOR RANDOMIZATION ON pT BROADENING OF FAST PARTONS IN TURBULENT QUARK-GLUON PLASMA

We analyze the effect of the parton color randomization on pT broadening in the quark-gluon plasma with turbulent color fields. We calculate the transport coefficient for a simplified model of fluctuating color fields in the form of alternating sequential transverse layers with homogenous transverse chromomagnetic fields with random orientation in the SU(3) group and gaussian distribution in the magnitude. Our numerical results show that the color randomization can lead to a sizable reduction of the turbulent contribution to ˆq. The magnitude of the effect grows with increasing ratio of the electric and magnetic screening masses.

Moment varieties from inverse Gaussian and gamma distributions

Moment varieties from inverse Gaussian and gamma distributions

Blind Super-Resolution via Meta-Learning and Markov Chain Monte Carlo Simulation.

Learning based approaches have witnessed great successes in blind single image super-resolution (SISR) tasks, however, handcrafted kernel priors and learning based kernel priors are typically required. In this paper, we propose a meta-learning and Markov Chain Monte Carlo (MCMC) based SISR approach to learn kernel priors from organized randomness. In concrete, a lightweight network is adopted as kernel generator, and is optimized via learning from the MCMC simulation on random Gaussian distributions. This procedure provides an approximation for the rational blur kernel, and introduces a network-level Langevin dynamics into SISR optimization processes, which contributes to preventing bad local optimal solutions for kernel estimation. Meanwhile, a meta-learning based alternating optimization procedure is proposed to optimize the kernel generator and image restorer, respectively. In contrast to the conventional alternating minimization strategy, a meta-learning based framework is applied to learn an adaptive optimization strategy, which is less-greedy and results in better convergence performance. These two procedures are iteratively processed in a plug-and-play fashion, for the first time, realizing a learning-based but plug-and-play blind SISR solution in unsupervised inference. Extensive simulations demonstrate the superior performance and generalization ability of the proposed approach when compared with the Start-of-the-Art solutions on synthesis and real-world datasets.

A Novel Improved Method for Prediction of Heart Disease using ECG Hybrid 0F PTB-ECG and MIT-BIH Signal Dataset

Heart disease is the leading cause of death worldwide, making early detection critical. Various diagnostic methods, including clinical tests, CT, MRI, ECG, and impedance cardiography, are commonly used to detect heart disease. However, traditional coronary artery disease (CAD) detection methods using ECG data face challenges due to the time-series nature of ECG signals, which complicates handling multiple classes. To address this, the study proposes a deep learning-based approach that enhances CAD detection accuracy by integrating two models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with a hybrid dataset combining PTB-ECG and MIT-BIH data. This hybrid dataset consists of two target classes: normal (0) and abnormal (1), created by merging all MIT-BIH classes with the PTB-ECG normal class as “0” and abnormal samples from PTB-ECG as “1”. Pre-processing was performed using Gaussian distribution for normalization, standardization, and outlier removal. The study applied four classification approaches: CNN, CNN+LSTM, CNN with SMOTE-balanced data, and CNN+LSTM with SMOTE-balanced data. Results indicate that CNN with SMOTE-balanced data achieved the best performance, with training metrics of 0.9998 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score for both classes. Testing results using CNN+SMOTE reached 0.9991 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. The model surpasses state-of-the-art studies, which achieved 0.992 accuracy and F1-score of 0.986 on PTB-ECG and MIT-BIH datasets, respectively. This study demonstrates that combining CNN with SMOTE on a hybrid dataset can significantly improve CAD detection accuracy.

Clustering Analysis of Food Security, Waste and Loss: Malaysia Agricultural Insights

As the world population grows rapidly nowadays, the demand for food has come to rise. The escalating demand for food has caused substantial wastage and loss, which not only hampers food security efforts but also aggravates greenhouse gas (GHG) emissions, intensifying the environmental crisis. Among numerous countries, Malaysia, with its diverse agricultural profile, emerges as a good fit for our case study. This study chooses the clustering technique to examine food sector data in Malaysia and investigate the link between the clustering results on food data and the data on GHG emissions. This case study aims to find crops depending on their production efficiency, underline those that match major waste, and estimate their contribution to greenhouse gas emissions. Three clustering techniques, Gaussian Mixture Modelling (GMM), Birch, and Density Peak clustering, are applied in the Production and Supply Utilisation Accounts (SUA) datasets, help to identify and cluster crops based on their similar traits to acquire uncovered patterns between the food sector and environmental issues. Using cutting-edge clustering algorithms and visualization tools, this study investigated in-depth the complex interactions among food production, waste, and greenhouse gas emissions in Malaysia. By addressing food production efficiency and waste reduction, the outcome will be a cascade of benefits that not only improve food security but also help to lessen negative environmental effects. This study illuminates the multifaceted dynamics of food production, waste, and environmental impact, offering valuable insights and pathways toward a more sustainable future for Malaysia and potentially other nations.

Parameter estimation for stable distributions and their mixture

In this paper, we consider estimating the parameters of univariate α-stable distributions and their mixtures. First, using a Gaussian kernel density distribution estimator, we propose an estimation method based on the characteristic function. The optimal bandwidth parameter was selected using a plug-in method. We highlight another estimation procedure for the Maximum Likelihood framework based on the False position algorithm to find a numerical root of the log-likelihood through the score functions. For mixtures of α-stable distributions, the EM algorithm and the Bayesian estimation method have been modified to propose an efficient and valuable tool for parameter estimation. The proposed methods can be generalised to multiple mixtures, although we have limited the mixture study to two components. A simulation study is carried out to evaluate the performance of our methods, which are then applied to real data. Our results appear to accurately estimate mixtures of α-stable distributions. Applications concern the estimation of the number of replicates in the Mayotte COVID-19 dataset and the distribution of the N-acetyltransferase activity of the Bechtel et al. data for a urinary caffeine metabolite implicated in carcinogens. We compare the proposed methods, together with a detailed discussion. We conclude with the limitations of this study, together with other forthcoming work and a future implementation of an R package or Python library for the proposed methods in data modelling.

The invasion pore induced by Toxoplasma gondii.

Obligate intracellular parasites invade host cells to survive. Following host cell contact, the apicomplexan Toxoplasma gondii injects proteins required for invasion into the host cell. Here, electrophysiological recordings of host cells acquired at sub-200 ms resolution allowed detection and analysis of a transient increase in host membrane conductance following exposure to Toxoplasma gondii. Transients always preceded invasion but parasites depleted of the moving junction protein RON2 generated transients without invading, ruling out a direct structural role for RON2 in generating the conductance pathway or restricting the diffusion of its components. Time-series analysis developed for transients and applied to the entire transient dataset (910,000 data points) revealed multiple quantal conductance changes in the parasite-induced transient, consistent with a rapid insertion, then slower removal, blocking, or inactivation of pore-like conductance steps. Quantal steps for RH had a principal mode with Gaussian mean of 0.26 nS, similar in step size to the apicomplexan protein translocon EXP2. Without RON2 the quantal mean was significantly different (0.19 nS). Because no invasion occurs without poration, the term 'invasion pore' is proposed.

High-fidelity digital-analog hybrid RoF fronthaul link enabled by nonlinear radio signal shaping.

We propose a simple and effective method to enhance the fidelity of radio signals in a digital-analog radio-over-fiber (DA-RoF) system by introducing nonlinearity into the transmitted signal. This nonlinearity is introduced via an arctangent function for radio signal shaping (RSS), while the corresponding tangent function is used for signal recovery. Nonlinear shaping spreads near-zero amplitude signals and compresses far-from-zero ones. After nonlinear recovery, noise in near-zero signals is suppressed, while noise in far-from-zero signals is amplified. Since the radio signal follows a Gaussian distribution, with most amplitudes near zero, the overall SNR is improved. In our experiments, we demonstrate the proposed radio signal shaping technology using a DA-RoF system based on intensity modulation with a direct detection communication system, achieving a 1.8-dB SNR gain. We achieve an SNR of 34.17 dB for QAM-1024 signals at a DA-RoF symbol rate of 104 Gbaud over 1 km of fiber. This corresponds to a CPRI-equivalent rate of 1560 Gbps and an equivalent fronthaul channel capacity of 294.85 Gbps. Our proposed scheme offers a promising solution for high-fidelity, high-capacity RoF links in Beyond 5G (B5G) and 6G networks.

Single Trace Analysis of Visible vs. Invisible Leakage for Comparison-Operation-Based CDT Sampling

The emergence of quantum computers poses a significant threat to the security of conventional public-key cryptosystems, driving the demand for quantum-resistant cryptographic solutions. In response, the National Institute of Standards and Technology (NIST) conducted a multi-year competition, ultimately selecting four ciphers. Among these, Falcon employs cumulative distribution table (CDT) sampling, which produces arrays of random values derived from a discrete Gaussian distribution during the signature generation phase. This array is then used with secret key information, forming the core of Falcon. Enhanced variants of Falcon, such as Mitaka, SOLMAE, and Antrag, implemented CDT sampling using comparison operations. Previous research by Choi et al. proposed a single trace analysis and countermeasure for CDT sampling, which exploited a non-constant-time vulnerability in 8-bit AVR microcontrollers. However, this vulnerability is specific to certain environments, and a potential vulnerability in comparison-operation-based constant-time CDT sampling remains unstudied. This paper is an extension of that study. This paper investigates the constant-time operation of comparison-operation-based CDT sampling on Arm Cortex-M4-based chips and proposes a deep learning-based side-channel analysis to recover the sampling values using a novel vulnerability. The proposed model achieves an F1 score of 1.0 and a recovery success rate of 99.97%.

A Bivariate Extension of Type-II Generalized Crack Distribution for Modeling Heavy-Tailed Losses

As an extension of the (univariate) Birnbaum–Saunders distribution, the Type-II generalized crack (GCR2) distribution, built on an appropriate base density, provides a sufficient level of flexibility to fit various distributional shapes, including heavy-tailed ones. In this paper, we develop a bivariate extension of the Type-II generalized crack distribution and study its dependency structure. For practical applications, three specific distributions, GCR2-Generalized Gaussian, GCR2-Student’s t, and GCR2-Logistic, are considered for marginals. The expectation-maximization algorithm is implemented to estimate the parameters in the bivariate GCR2 models. The model fitting results on a catastrophic loss dataset show that the bivariate GCR2 distribution based on the generalized Gaussian density fits the data significantly better than other alternative models, such as the bivariate lognormal distribution and some Archimedean copula models with lognormal or Pareto marginals.

Modeling Product Degradation with Heterogeneity: A General Random-Effects Wiener Process Approach

Degradation of many products in practical applications is often subject to unit-to-unit heterogeneity. Such heterogeneity can be attributed to heterogeneous quality of the raw materials and the fluctuation of the manufacturing process, as well as the heterogeneous usage conditions and environments. The heterogeneity leads to the scattering of the degradation rates and diffusion intensities in the population. To model this phenomenon, this study proposes a general random-effects Wiener process model that accounts for the unit-to-unit heterogeneity in the degradation drift and the volatility simultaneously. In particular, the drift of the Wiener process is characterized by a normal distribution and the diffusion parameter is characterized by an independent inverse Gaussian distribution. The proposed model is flexible for characterization of heterogeneous degradation, and permits an analytically tractable model inference. An EM algorithm incorporating the variational Bayesian method is developed to estimate the model parameters, and a parametric bootstrap approach is proposed to construct confidence intervals. The performance of the proposed model and the estimation algorithm is validated by Monte Carlo simulations. The degradation data of an infrared LED device and the wearing data of the magnetic head of a hard disk driver are studied based on the proposed model. With comprehensive comparative studies, the good performance of the proposed model in fitting the real degradation data is validated.

BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical imaging tasks with limited data. Deep learning models are highly effective at linearizing features, enabling the alteration of feature semantics through the shifting of latent space representations—an approach known as semantic data augmentation (SDA). The paradigm of SDA involves shifting features in a specified direction. Current SDA methods typically sample the amount of shifting from a Gaussian distribution or the sample variance. However, excessive shifting can lead to changes in data labels, which may negatively impact model performance. To address this issue, we propose a computationally efficient method called Bayesian Random Semantic Data Augmentation (BSDA). BSDA can be seamlessly integrated as a plug-and-play component into any neural network. Our experiments demonstrate that BSDA outperforms competitive methods and is suitable for both 2D and 3D medical image datasets, as well as most medical imaging modalities. Additionally, BSDA is compatible with mainstream neural network models and enhances baseline performance. The code is available online.

Multiple-Input Multiple-Output Synthetic Aperture Radar Waveform and Filter Design in the Presence of Uncertain Interference Environment

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance.

Inference of evidence reasoning rule with Gaussian distribution reliability and its application in safety assessment

Inference of evidence reasoning rule with Gaussian distribution reliability and its application in safety assessment

Fish-Finder: A robust small target detection method for aquaculture fish in low-quality underwater images.

Underwater fish object detection serves as a pivotal research direction in marine biology, aquaculture management, and computer vision, yet it poses substantial challenges due to the complexity of underwater environments, occultations, and the small-sized and frequently moving fish in aquaculture. Addressing these challenges, we propose a novel underwater fish object detection algorithm named Fish-Finder. First, we engendered a structure titled "C2fBF," utilizing the dual-path routing attention protocol of BiFormer. The primary objective of this structure is to alleviate the perturbations induced by underwater intricacies during the phase of downsampling in the backbone network, thereby discerning and conserving finer contextual features. Subsequently, we co-opted the RepGFPN method within our neck network-a distinctive approach that adeptly merges high-level semantic constructs with low-level spatial specifics, thus fortifying its multi-scale detection prowess. Then, in an endeavor to diminish the sensitivity toward positional aberrations during the detection of diminutive aquatic creatures, we incorporated a novel bounding box regression loss function, the Wasserstein loss, to the existing CIoU. This innovative function gauges the congruity between the predicted bounding box Gaussian distribution and the reference bounding box Gaussian distribution. Finally, in regard to the dataset, we independently assembled a specific dataset termed "SmallFish." This unique dataset, meticulously designed for the detection of small-scale fish within intricate underwater settings, includes 5000 annotated images of small fish. Experimental results demonstrate that, compared to the state-of-the-art detection methods, our proposed method improves the accuracy by and , and mean average precision (mAP) increases and in public dataset Kaggle-Fish and our SmallFish dataset, respectively.

High-Precision Permeability Evaluation of Complex Carbonate Reservoirs in Marine Environments: Integration of Gaussian Distribution and Thomeer Model Using NMR Logging Data

High-Precision Permeability Evaluation of Complex Carbonate Reservoirs in Marine Environments: Integration of Gaussian Distribution and Thomeer Model Using NMR Logging Data

A study of stable wormhole solution with non-commutative geometry in the framework of linear f(R,Lm,T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(R,{\\mathcal {L}}_m, T)$$\\end{document} gravity

This research delves into the potential existence of traversable wormholes (WHs) within the framework of modified, curvature based gravity. The modification includes linear perturbations of the matter Lagrangian and the trace of the energy-momentum tensor with specific coupling strengths α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} and β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} and can thus be viewed as a special case of linear f(R, T)-gravity with a variable matter coupling or as the simplest additively separable f(R,Lm,T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(R,{\\mathcal {L}}_m,T)$$\\end{document}-model. A thorough examination of static WH solutions is undertaken using a constant redshift function; therefore, our work can be regarded as the first-order approximation of WH theories in f(R,Lm,T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$f(R,{\\mathcal {L}}_m,T)$$\\end{document}. The analysis involves deriving WH shape functions based on non-commutative geometry, with a particular focus on Gaussian and Lorentzian matter distributions ρ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho $$\\end{document}. Constraints on the coupling parameters are developed so that the shape function satisfies both the flaring-out and asymptotic flatness conditions. Moreover, for positive coupling parameters, violating the null energy condition (NEC) at the WH throat r0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r_0$$\\end{document} demands the presence of exotic matter. For negative couplings, however, we find that exotic matter can be avoided by establishing the upper bound β+α/2<-1ρr02-8π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta +\\alpha /2<-\\frac{1}{\\rho r_0^2}-8\\pi $$\\end{document}. Additionally, the effects of gravitational lensing are explored, revealing the repulsive force of our modified gravity for large negative couplings. Lastly, the stability of the derived WH solutions is verified using the Tolman–Oppenheimer–Volkoff (TOV) formalism.

A simplified approach for simulating pollutant transport in small rivers with dead zones using convolution

Abstract In the paper an alternative method to solve the one-dimensional advective-diffusive equation describing the pollutants transport in river with dead zones is presented. Because very often transport in a small river can be treated as a 1D issue, then instead of numerical solution of the advection-diffusion equation an equivalent approach based on the convolution technique can be used. Consequently, for a given impulse response function the numerical calculations are required to compute a convolution only. The impulse response function is obtained as an analytical solution of the linear advection-diffusion equation for the Dirac delta function imposed as the boundary condition at the upstream end. Therefore, it represents the Gauss distribution and consequently, this approach is unreliable when the dead zones occur. To reproduce an asymmetric distribution of concentration along the channel axis an approximation of analytical impulse response function using the asymmetric Gumbel distribution is proposed. This approach valid for solution of the transport equation with constant coefficients is extended for piecewise constant coefficients. Convolution approach does not produce any numerical dissipation and dispersion errors typically generated by the methods based on the finite difference technique. Validation of the method using the results of field measurements confirmed its effectiveness.

Dimensionality Reduction for Data Analysis With Quantum Feature Learning

ABSTRACTTo improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we investigate several qDR techniques on a variety of datasets such as quantum Gaussian distribution adaptation (qGDA), quantum principal component analysis (qPCA), quantum linear discriminant analysis (qLDA), and quantum t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, and Iris are among the datasets used in this investigation. Through comparison evaluations against well‐established classical approaches, such as classical PCA (cPCA), classical LDA (cLDA), and classical GDA (cGDA), and using well‐established metrics like loss, fidelity, and processing time, the effectiveness of these techniques is assessed. The findings show that cPCA produced positive results with the lowest loss and highest fidelity when used on the Iris dataset. On the other hand, quantum uniform manifold approximation and projection (qUMAP) performs well and shows strong fidelity when tested against the Wine dataset, but ct‐SNE shows mediocre performance against the Digits dataset. Isomap and locally linear embedding (LLE) function differently depending on the dataset. Notably, LLE showed the largest loss and lowest fidelity on the Olivetti Faces dataset. The hypothesis testing findings showed that the qDR strategies did not significantly outperform the classical techniques in terms of maintaining pertinent information from quantum datasets. More specifically, the outcomes of paired t‐tests show that when it comes to the ability to capture complex patterns, there are no statistically significant differences between the cPCA and qPCA, the cLDA and qLDA, and the cGDA and qGDA. According to the findings of the assessments of mutual information (MI) and clustering accuracy, qPCA may be able to recognize patterns more clearly than standardized cPCA. Nevertheless, there is no discernible improvement between the qLDA and qGDA approaches and their classical counterparts.

Simulation of displacement damage in Si & SiO2 caused by protons

Nowadays Si & SiO2 have been the most widely used materials in semiconductor devices as CPU and other various integrated circuit chips, utilized in aerospace electronic systems, and at the same time protons are important components of cosmic rays that can cause displacement damage in Si & SiO2. Therefore, it is essential to study the displacement damage of Si & SiO2 caused by protons. The software of Geant4 is adopted in this paper, to simulate transportation process of incident protons with different energy in Si & SiO2. The simulation results indicates that primary knock-on atom (PKA) generated by incident proton in Si & SiO2 is predominant in lower energy range, its spatial distribution increases gradually along the direction of incident proton, and the scattering angle of the PKA is about 90°, following a Gaussian distribution approximately. And in lower energy range, 28Si and 16O generated by elastic scattering are a primary source of radiation damage in Si & SiO2. But as the proton energy increases, the contribution of nuclear inelastic scattering becomes more and more important, but the overall level of induced damage diminishes gradually. Meanwhile, the simulation results indicate that with increasing depth of the material, the non-ionizing energy loss (NIEL) increases gradually, and NIEL caused by elastic scattering is higher near the surface layer of the materials, and as for NIEL caused by nuclear inelastic scattering is also higher near the surface layer of the materials. The simulation results in this paper can provide useful data and theoretical guidance for study on Si & SiO2 displacement damage caused by proton irradiation.