Mixture Model Research Articles

Mixtures of Probit Regression Models with Overlapping Clusters

Studies with binary outcomes on a heterogeneous population are quite common. Typically, the heterogeneity is modelled through varying effect coefficients within some binary regression setting combined with a clustering procedure. Most of the existing methods assign statistical units to distinct and non-overlapping clusters. However, there are scenarios where units exhibit a more complex organization and the clusters can be thought as partially overlapping. In this case, the standard approach does not work. In this paper, we define a mixture of regression models that allows overlapping clusters. This approach involves an overlap function that maps the regression coefficients, either at the unit or response level, of the parent clusters into the coefficients of the multiple allocation clusters. In order to deal with this intrinsic heterogeneity, regression analyses have to be stratified for different groups of observations or clusters. We present a computationally efficient Monte Carlo Markov Chain (MCMC) scheme for the case of a mixture of probit regressions. A simulation study shows the overall performance of the method. We conclude with two illustrative examples of modelling voting behavior, involving United States (US) Supreme Court justices over a number of topics and members of the United Kingdom (UK) parliament over divisions related to Brexit. These applications provide insights on the usefulness of the method in real applications. The method described can be extended to the case of a generic mixture of multivariate generalized linear models under overlapping clusters.

A nonparametric mixed-effects mixture model for patterns of clinical measurements associated with COVID-19

A nonparametric mixed-effects mixture model for patterns of clinical measurements associated with COVID-19

Problematic smartphone use and adolescent’s depression trajectory patterns

ABSTRACT This research aimed to examine the trajectory patterns of depression among South Korean adolescents and the individual and social predictors of these trajectories. For this purpose, a growth mixture model was used, employing 4 years of longitudinal data from 2,607 adolescents (50.4% female, M age ranged from 10 to 13 years, SD = .096–.097). The analysis revealed three subtypes: moderate-maintained depression, high-decreasing depression, and low-increasing depression. Problematic smartphone use emerged as a critical factor influencing the depression trajectories, which has not been extensively explored in previous research. Depression was linked to being female, low self-esteem, parental rejection and inconsistency, and low peer support.

Numerical Study of Cavitation Phenomenon in a Venturi Tube

This research aims to understand and numerically analyze the cavitation phenomenon that occurs in Venturi tubes with variations in throat length and pressure changes. This research uses Ansys Fluent 2023 R2 numerical simulation with venturi tube geometries of 25 mm, 30 mm, and 35 mm and pressures of 300,000, 600,000, and 900,000 Pa. A multiphase flow model with water liquid and water vapor is applied to predict cavitation using a mixture model. RANS steady state conditions with the k-ε turbulence model are used to solve the continuity, momentum, energy and volume fraction equations. The Schnerr-Sauer cavitation model calculates the phase transition between water-liquid and water vapor. Geometry varies by reference journal with different converging and diverging angles, outlined in tables and figures. 2D simulations are carried out using a pressure based solver with specified boundary conditions, using the Presto! for pressure solutions, and upwind and Quick schemes for discretization. The results of this research show that 1) Length throat 25 mm has the most stable distribution compared to 30 and 35 mm geometries at a pressure of 600,000 Pa. 2) The cavitation phenomenon is influenced by changes in geometry where at 35 mm geometry greater cavitation occurs in the area near the wall inlet convergent. 3) At a pressure of 900,000 Pa, the cavitation area that forms becomes larger and becomes a critical point in this journal.

CoFix: Advancing Semi-Supervised Learning with Noisy Label Mitigation Through Sample Selection and Consistent Regularization

Deep Neural Networks (DNNs) have revolutionized various fields through their ability to model complex patterns, yet their performance critically hinges on the availability of large-scale, accurately labeled datasets. The costly and time-consuming process of creating such datasets has motivated research into Learning with Noisy Labels (LNL), which aims to reduce reliance on perfect labels. This paper introduces CoFix, an innovative LNL framework that integrates sample selection with semi-supervised learning techniques to address the challenge of noisy labels in training. CoFix employs a Gaussian Mixture Model (GMM) to dynamically segment the training data into clean and noisy subsets, leveraging semi-supervised learning approaches on both. Inspired by the FixMatch algorithm, CoFix refines consistent regularization and pseudo-labeling strategies, enhancing augmentation strategies and temperature sharpening techniques. Additionally, CoFix explores label smoothing to augment the loss function, further refining model performance. Our experiments demonstrate CoFix’s superiority over state-of-the-art methods, achieving significant improvements in fewer training epochs, particularly in lower noise scenarios. The robustness and versatility of CoFix are evident through its consistent performance across various benchmark datasets and noise levels. The contributions of this paper include a novel LNL method with enhanced generalization capability, an investigation into the impact of label smoothing on the loss function, and extensive testing that confirms CoFix’s efficiency and adaptability to different noise levels.

An Unsupervised Abnormal Power Consumption Detection Method Combining Multi-Cluster Feature Selection and the Gaussian Mixture Model

An Unsupervised Abnormal Power Consumption Detection Method Combining Multi-Cluster Feature Selection and the Gaussian Mixture Model

Distinct clinical trajectories of gastrointestinal progression among patients with systemic sclerosis.

Systemic sclerosis (SSc) is heterogeneous in its clinical presentation. Common manifestations cluster together, defining unique subgroups. This investigation aims to characterize gastrointestinal (GI) phenotypes and determine whether they can be distinguished by temporal progression. We examine a well-established SSc patient cohort with a modified Medsger GI severity score measured over time to determine heterogeneity in disease progression. Growth mixture models estimate each patient's phenotype and disease severity trajectory over time. We compare the characteristics of estimated phenotypes using non-parametric statistics and linear and logistic regression to compare patient characteristics between phenotypes while adjusting for disease duration. We examined 2696 SSc patients with at least two Medsger GI scores, identifying four unique phenotypes. The most common phenotype (n = 2325) ("Stable") had an average score of 1 that was consistent over time. Two phenotypes were progressive ["Early Progressive" (n = 142) and "Late Progressive" (n = 115)] with an initial average score of 1. The Early Progressive group increased initially and stabilized, and the Late Progressive group worsened slowly over time. A fourth phenotype ["Early Severe GI"; (n = 114)] had an initial average Medsger GI score just below 3 with high mortality and improving GI severity over time. Clinically distinct GI phenotypes exist among patients with SSc. These phenotypes are not only distinguished by GI and extra-intestinal SSc clinical complications, but they are also temporally distinct. Distinct autoantibody profiles are associated strongly with more severe GI disease.

Changes in moral neutralization leading to recidivism in low-to-moderate-risk justice-involved youth: replication and extension

In the present study, a growth mixture modeling (GMM) analysis performed on 7117 low-to-moderate-risk justice-involved youth disclosed the presence of four latent classes of neutralization scores measured over four non-overlapping six-month periods. These four patterns were subsequently labeled low accelerating, moderate accelerating, high accelerating and decelerating. The four classes displayed differing levels of recidivism, and so a series of binary logistic regression analyses were computed in which each individual class was compared to every other class. The results of these binary logistic regression analyses revealed that recidivism occurred significantly more often in the high accelerating group than in the low and moderate accelerating groups and significantly less often in the decelerating group than in the low, moderate and high accelerating groups, even after Time 1 moral neutralization scores were controlled. These results corroborate prior research denoting that declining moral neutralization scores portend decreased odds of future recidivism.

GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation

GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation

Motor Imagery Recognition Based on GMM-JCSFE Model.

Features from EEG microstate models, such as time-domain statistical features and state transition probabilities, are typically manually selected based on experience. However, traditional microstate models assume abrupt transitions between states, and the classification features can vary among individuals due to personal differences. To date, both empirical and theoretical classification results of EEG microstate features have not been entirely satisfactory. Here, we introduce an enhanced feature extraction method that combines Joint label-Common and label-Specific Feature Exploration (JCSFE) with Gaussian Mixture Models (GMM) to explore microstate features. First, GMMs are employed to represent the smooth transitions of EEG spatiotemporal features within microstate models. Second, category-common and category-specific features are identified by applying regularization constraints to linear classifiers. Third, a graph regularizer is used to extract subject-invariant microstate features. Experimental results on publicly available datasets demonstrate that the proposed model effectively encodes microstate features and improves the accuracy of motor imagery recognition across subjects. The primary code is accessible for download from the website: https://github.com/liaoliao3450/GMM-JCSFE.

Claim Prediction and Premium Pricing for Telematics Auto Insurance Data Using Poisson Regression with Lasso Regularisation

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

Mechanical durability and degradation characteristics of long kenaf-reinforced PLA composites fabricated using an eco-friendly method

The research has focused on evaluating the mechanical strength of long unidirectional kenaf-PLA composite and the degradation effect after accelerated weathering exposure. The composite was fabricated using a green traditional method of hot-pressing method with different compositions of non-chemically treated kenaf fiber to PLA with the ratio of 30:70, 40:60, 50:50, and 60:40. The finding of this work demonstrated fiber weight-matrix ratio of 50:50 gain a remarkable optimum strength with 224 % and 239 % enhancement for tensile and flexural strength respectively. The applied enhanced Rule of Mixture (E-ROM) model used in this study was reliable in predicting the experimental results whereby the composites reached an optimum value. Despite the successful enhancement of fiber–matrix compatibility and composite strength, the presence of fiber misalignment, structural imperfections, and voids observed in scanning electron microscopy (SEM) images still has some detrimental effects on its degradability. Nevertheless, a noteworthy strength reduction of PLA (80 %) as compared to the PLA-kenaf composite demonstrated better resistivity of weathering for the composite after 508 h. Hence, this study suggests deeper insights into the viability of eco-friendly combed methods as fiber treatments for kenaf as PLA reinforcement. It is proven that even without any chemical treatment, the composite demonstrated a remarkable mechanical resilience suitable for indoor non-structural applications.

Analysis of cyclone separator solutions depending on spray ejector condenser conditions

The core design strategy for minimizing CO2 emissions in gas power plant entails combining a spray ejector condenser (SEC) and separator to accomplish steam condensation and CO2 purification. This innovative process involves direct-contact condensation of steam with CO2, facilitated by interaction with a subcooled water spray, along with a cyclone separator mechanism intended for generating pure CO2. The investigation of the SEC section, both experimentally and analytically, provides crucial insights into its operational dynamics. Given the susceptibility of cyclone efficiency to fluctuations in SEC conditions, this research endeavors to examine the impacts of CO2 volumetric flow rate and droplet break-up within the SEC on the separation efficacy of the cyclone separator. Additionally, the impact of cone size on the performance of the cyclone has been investigated. Here, a three-dimensional, transient, and turbulent cyclone separator is numerically simulated using Ansys Fluent 2021 R1. The Reynolds Stress Model is employed to simulate turbulent flow, while a mixture model is utilized to replicate swirl two-phase flow within the separators. The findings revealed that reductions in steam and CO2 flow rates were associated with a decrease in outlet temperature but an increase in SEC inlet temperature, leading to a rise in temperature difference and heat transfer rate. Furthermore, an augmentation in cyclone cone size (from 0.2 to 0.5 m) resulted in enhanced separation efficiency (from 77.30 % to 80.98 %) alongside an elevation in pressure drop (from 6.08 Pa to 10.91 Pa), suggesting a compromise between CO2 purification and energy consumption. Additionally, elevated CO2 flow rates induced a rise in pressure drop and separation efficiency, ultimately achieving maximum efficiency at a rate of 24 gs. Moreover, the exploration into droplet breakup manifesting in a boost in separation efficiency from 50.98 % to 100 % across droplet diameters ranging from 1 to 20 μm.

Trajectory modeling and response prediction in transcranial magnetic stimulation for depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) therapy could be improved by more accurate and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modeling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models are useful in predicting clinically meaningful change in symptom severity, i.e. categorical (non)response as opposed to continuous scores. MethodsWe compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression, across multiple coils and protocols. We then compared the predictive power of those models. ResultsLCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC=0.70, 95 % CI=[0.52–0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC=0.76, 95 % CI=[0.58–0.94], but likewise, not before. ConclusionsIn showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.

Cardiovascular disease detection from cardiac arrhythmia ECG signals using artificial intelligence models with hyperparameters tuning methodologies

Cardiovascular disease (CVD) is connected with irregular cardiac electrical activity, which can be seen in ECG alterations. Due to its convenience and non-invasive aspect, the ECG is routinely exploited to identify different arrhythmias and automatic ECG recognition is needed immediately. In this paper, enhancement for the detection of CVDs such as Ventricular Tachycardia (VT), Premature Ventricular Contraction (PVC) and ST Change (ST) arrhythmia using different dimensionality reduction techniques and multiple classifiers are presented. Three-dimensionality reduction methods, such as Local Linear Embedding (LLE), Diffusion Maps (DM), and Laplacian Eigen (LE), are employed. The dimensionally reduced ECG samples are further feature selected with Cuckoo Search (CS) and Harmonic Search Optimization (HSO) algorithms. A publicly available MIT-BIH (Physionet) - VT database, PVC database, ST Change database and NSR database were used in this work. The cardiac vascular disturbances are classified by using seven classifiers such as Gaussian Mixture Model (GMM), Expectation Maximization (EM), Non-linear Regression (NLR), Logistic Regression (LR), Bayesian Linear Discriminant Analysis (BDLC), Detrended Fluctuation Analysis (Detrended FA), and Firefly. For different classes, the average overall accuracy of the classification techniques is 55.65 % when without CS and HSO feature selection, 64.36 % when CS feature selection is used, and 75.39 % when HSO feature selection is used. Also, to improve the performance of classifiers, the hyperparameters of four classifiers (GMM, EM, BDLC and Firefly) are tuned with the Adam and Grid Search Optimization (GSO) approaches. The average accuracy of classification for the CS feature-based classifiers that used GSO and Adam hyperparameter tuning was 79.92 % and 85.78 %, respectively. The average accuracy of classification for the HSO feature-based classifiers that used GSO and Adam hyperparameter tuning was 86.87 % and 93.77 %, respectively. The performance of the classifier is analyzed based on the accuracy parameter for both with and without feature selection methods and with hyperparameter tuning techniques. In the case of ST vs. NSR, a higher accuracy of 98.92 % is achieved for the LLE dimensionality reduction with HSO feature selection for the GMM classifier with Adam's hyperparameter tuning approach. The GMM classifier with the Adam hyperparameter tuning approach with 98.92 % accuracy in detecting ST vs. NSR cardiac disease is outperforming all other classifiers and methodologies.

A study of fine-scale low-temperature cracking in geopolymer grouted porous asphalt mixtures based on real aggregate profile modeling

At a micro-scale level, grouted porous asphalt mixtures are multiphase composites consisting of aggregates, asphalt mortar, filler, asphalt mortar-aggregate interfacial transition zone, and asphalt mortar-filler interfacial transition zone. It exhibits different mechanical behavior under different temperatures and loading conditions. Compared with aggregate, asphalt mortar, and filler, the interfacial transition zone (ITZ) is more prone to damage at low temperatures, with both the fine structure and the nature of the contact interface having a significant effect on the overall strength and crack resistance of the material. To evaluate the influence of ITZ and filler material properties on the cracking resistance of the material, a grouted porous asphalt mixture model based on the real aggregate shape was established using finite element modeling. A cohesive zone model (CZM) was employed to characterize the internal mechanical behavior of the material, and a mesoscale damage simulation was performed for the grouted asphalt mixture. The study revealed the influence of different interfacial and filler material properties on the low temperature cracking resistance and proposed a design approach for anti-cracking grouted asphalt mixtures. The findings indicated that interfacial transition zone (ITZ) and filler properties play a crucial role on determining the damage characteristics. Increasing the ITZ and filler properties significantly enhanced the low temperature crack resistance of the grouted asphalt mixture.

Carrier phase error overbounding using Gaussian mixture model for high-precision air navigation integrity monitoring

Carrier phase measurements play a pivotal role in enabling high-precision positioning within future air traffic systems and autonomous vehicles. Current aviation navigation integrity monitoring, primarily using pseudorange measurements, cannot meet system positioning accuracy and integrity requirements, leading to limitations and false alarm risks, especially in complex urban canyons with signal reflections. This work presents a carrier phase-based positioning integrity monitoring algorithm, utilizing the expectation–maximization algorithm to compute parameters for the Gaussian mixture model (GMM) and nonparametric bootstrapping to construct an overbounding model of carrier phase residuals. It establishes a precise protection level (PL) calculation method, which is validated with static and flight data, demonstrating accurate overbounding of carrier phase residuals using GMM. Protection levels during testing remain below the alarm limit and exceed system positioning error, ensuring normal system operation without false or missed alarms, confirming the effectiveness of the established carrier phase error overbounding model.

A general method for mode decomposition on additive mixture: Generalized Variational Mode Decomposition and its sequentialization

Variational Mode Decomposition(VMD) method was proposed to separate non-stationary signal mixture by solving a optimization problem. This method is powerful and can reconstruct the signal components precisely when they are orthogonal(or quasi-orthogonal) in frequency domain. The crucial problem for VMD is that it requires the information of modal number before the decomposition. Also its applications are limited in 1D and 2D signal processing fields, of narrow scope.In this paper, by inheriting and developing the core idea of VMD, we build a general form for this method and extend it to the modal decomposition for common additive mixture, not only limited in signal processing. To overcome the obstacle of modal number, we sequentialize the generalized VMD method, such that the modes can be extracted one by one, without knowing the modal number a priori. After the generalization and sequentialization for the VMD, we apply them in different fields of additive case, such as texture segmentation, Gaussian Mixture Model(GMM), clustering, etc. From the experiments, we conclude that the generalized and sequentialized VMD methods can solve variety classical problems from the view of modal decomposition, which implies that our methods have higher generality and wider applicability. A raw Matlab code for this algorithm is shown in https://github.com/changwangke/SGVMD_additive_Clustering/blob/main/SGVMD_clustering.m.

Construction of a full color gamut color mixture model and a color prediction neural network model for rotor spinning melange yarn

This research is devoted to solving the bottleneck problem of spinning and color prediction of rotor spun melange yarn within the full color gamut range. Firstly, a full color gamut grid color mixture model with hue regulation range of 0–360°, saturation regulation range of 0-1 and lightness regulation range of 0-1 was constructed based on the preferred seven primary-color fibers through grid color mixing. Secondly, in the full color gamut grid color mixture model, 55 representative grid points were planned and corresponding melange yarns and fabrics were prepared based on grid point parameters, which served as the training set, testing set, and validation set for constructing neural network prediction models. Thirdly, based on the demand for color prediction of melange yarns, a color prediction model and a blending ratio prediction model with six sub-models are constructed by taking the reflectivity of the melange yarn fabric and the blending ratio of the primary-color fibers as input and output values. Finally, six prediction samples were produced to verify the predictive performance of the neural network prediction model. The results show that the average color difference between the predicted and measured colors is 1.06, and the average root mean square error value between the predicted and actual blending ratios is 0.48%. It indicates that the neural network prediction model based on the grid mixing of seven primary-color fibers can adapt to the prediction of the mixing color and blending ratio of primary-color fibers in the full color gamut. Due to the construction of six sub-models based on local hue regions, the prediction accuracy of the neural network prediction model has been greatly improved.

Short term hydropower scheduling considering cumulative forecasting deviation of wind and photovoltaic power

The cumulative forecasting errors of wind and photovoltaic (PV) power pose serious challenges to the short-term scheduling of hydropower stations that connected to the same regional power grid. The variable and intermittent nature of wind and PV power necessitate a flexible power source to achieve power balance. Consequently, hydropower, with its flexible adjustment capability, plays a crucial role in supporting the consumption of wind and PV power stations in short-term scheduling. Meanwhile, to address the grid's peak shaving requirement, the scheduling of cascaded hydropower system becomes increasingly complex. Given the limitations of current wind and PV power output prediction technologies, achieving precise forecast remains challenging. In practice, the actual outputs of wind and PV power stations often diverge considerably from predicted values over multiple consecutive periods. This variability may result in the underutilization of wind and PV power if the reserve capacity of hydropower stations is insufficient. Therefore, it is essential to account for the cumulative deviation in electricity from wind and PV sources. By computing probability distribution and establishing boundary condition of the cumulative deviation, the operation of the cascaded hydropower system can be controlled more effectively, thus enhancing the stability of a multi-energy complementary system. This study employs Gaussian Mixture Model (GMM) to characterize the probability distributions of cumulative electricity deviation across multiple periods of wind and PV power and takes it as restriction to regulate the operation of hydropower stations. In order to balance the deviation electricity, the downward electricity reserve, upward electricity reserve and the storage energy of the cascaded reservoirs are introduced as restrictive conditions. The proposed Hydro-wind-PV peak shaving model is then applied to the short-term optimal scheduling of the Lancang River cascaded hydropower system to validate the feasibility and efficacy.