Regular Model Research Articles

Maser polarization through anisotropic pumping

Context. Polarized emission from masers is an excellent tool to study magnetic fields in maser sources. The linear polarization of the majority of masers is understood as an interplay of maser saturation and anisotropic pumping. However, for the latter mechanism, no quantitative modeling has been presented yet. Aims. We aim to construct a comprehensive model of maser polarization, including quantitative modeling of both anisotropic pumping and the effects of maser saturation on the polarization of masers. Methods. We extended regular (isotropic) maser excitation modeling with a dimension that describes the molecular population alignments, as well as including the linear polarization dimension to the radiative transfer. The results of the excitation analysis yielded the anisotropic pumping and decay parameters, which were subsequently used in one-dimensional proper maser polarization radiative transfer modeling. Results. We present the anisotropic pumping parameters for a variety of transitions from class I CH3OH masers, H2O masers, and SiO masers. SiO masers are highly anisotropically pumped due to them occurring in the vicinity of a late-type star, which irradiates the maser region with a strong directional radiation field. Class I CH3OH masers and H2O masers occur in association with shocks, and they are modestly anisotropically pumped due to the anisotropy of the excitation region. Conclusions. Our modeling constitutes the first quantitative constraints on the anisotropic pumping of masers. We find that anisotropic pumping can explain the high polarization yields of SiO masers, as well as the modest polarization of unsaturated class I CH3OH masers. The common 22 GHz H2O maser has a relatively weak anisotropic pumping; in contrast, we predict that the 183 GHz H2O maser is strongly anisotropically pumped. Finally, we outline a mechanism through which non-Zeeman circular polarization is produced, when the magnetic field changes direction along the propagation through an anisotropically pumped maser.

Mixed convection of a viscoplastic fluid with a variable yield stress in a lid-driven cavity

This study is a numerical investigation on heat and momentum transfer in viscoplastic fluids that exhibit a variable yield stress. Viscoplastic fluids are recognized for transitioning from solid to liquid under flow-induced shear-rate. However, these materials exhibit intricate rheological behaviors beyond this fundamental characteristic, often linked to thixotropy. Thixotropy delineates reversible, time-dependent alterations in a fluid's viscosity at a specific shear-rate. The temporal changes in viscosity stem from variations in the fluid's microstructure, responsive to the induced shear-rate. When subjected to shear, the fluid's microstructure breaks down into smaller units, countered by Brownian motion, resulting in a rearrangement of the microstructure due to attractive forces between microconstituents. These microstructural variations are thus reversible. Notably, these changes affect not only viscosity but also the yield stress of the fluid, categorizing it as a non-ideal yield-stress fluid with yield-stress variations linked to microstructure, termed isotropic hardening. This study aims to explore how variations in yield-stress fluid microstructure impact heat and momentum transfer. As a starting point, this study considers the lid-driven cavity flow with differentially heated walls in the presence of an external magnetic field. Addressing the yield-stress fluid microstructure variations involves utilizing the Houska–Papanastasiou model, a regularized model capturing thixotropy and isotropic hardening. The resulting governing equations are made dimensionless and numerically solved through the finite-element method. The findings indicate that a more pronounced breakdown of the fluid's microstructure correlates with a higher Nusselt number at the hot wall. Additionally, variations in fluid microstructure influence both the size and location of unyielded zones.

머신러닝과 통계 모형을 이용한 미술품 가격 예측

The art market is comprised of complex cultural, economic, and social characteristics and has recently received a lot of attention. Art transactions are carried out in a variety of ways, of which art auctions account for a large portion. We collected auction data on Post-War and Contemporary Art artists born after 1920 from Artnet and implemented an art price prediction model. In addition to the hedonic price model commonly used in existing studies, price prediction performance was compared using various machine learning models such as regression model, random effects model, regularization model, tree-based model, and SVM. To compare various machine learning models, RMSE and MAE were compared using two cross-validation methods. As a result, the nonlinear random effects model using author information showed the good performance. The impact of explanatory variables on price was analyzed using the optimal model. In addition, we were able to obtain price curves of various patterns according to the passage of time for each author. Among machine learning models that do not use author information, Bagging showed the best performance. As a result of measuring the importance of variables through Bagging, it was found that the size of the work had the most influence in predicting the price, followed by the artist's year of birth, the method of painting, the material with which the painting was painted, and the year of production.

Multi-hazard stochastic response analysis and reliability evaluation of a 5 MW monopile offshore wind turbine based on extended platform FAST-S

This study investigates the random dynamic response of a 5 MW monopile offshore wind turbine (MOWT) subjected to wind, wave and earthquake loads. Based on the aero-hydro-servo-elastic code FAST, a multi-field coupled simulation module considering earthquake, FAST-S, is developed for the fully coupled multi-hazard analysis of OWTs. Compared to the original FAST code, a seismic analysis module (SeismoDyn) and a soil-structure interaction (SSI) module (SoilDyn) have been supplemented in FAST-S. SeismoDyn is developed by modifying the Kane’s kinetic equation in the FAST, and the SoilDyn is developed through Winkler foundation model in order to consider the nonlinear SSI. Moreover, FAST-S has been validated by other recognized codes. Based on FAST-S, a fully coupled numerical analysis model of the NREL 5 MW MOWT is established, the random responses and reliability evaluation of the offshore wind turbine under wind, wave and earthquake loads are analyzed by probability density evolution method. The results show that the aerodynamic and hydrodynamic actions increase the damping of the wind turbine system, which has a certain effect on inhibiting the tower’s vibration caused by the earthquake. Therefore, compared with the coupled wind-wave-earthquake case, the sole earthquake case may be more unfavorable, and should be paid more attention to in high risk earthquake regions. The results show that the probability density functions of offshore wind turbine response under strong earthquakes obviously deviates from the typical regular distribution models. Based on the theory of equivalent extreme value event, the dynamic reliability of the offshore wind turbine is calculated. Seismic actions maybe the major factor in the design of the support structure. Nevertheless, owing to the aerodynamic and hydrodynamic damping effects of wind and wave loadings, the structural responses under wind-wave-earthquake are more moderate than ones subjected to solely earthquake. The strong seismic loads would cause significant complex variations of time-dependent probability density function of the OWT response.

Strong posterior contraction rates via Wasserstein dynamics

In Bayesian statistics, posterior contraction rates (PCRs) quantify the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of a true model, in a suitable way, as the sample size goes to infinity. In this paper, we develop a new approach to PCRs, with respect to strong norm distances on parameter spaces of functions. Critical to our approach is the combination of a local Lipschitz-continuity for the posterior distribution with a dynamic formulation of the Wasserstein distance, which allows to set forth an interesting connection between PCRs and some classical problems arising in mathematical analysis, probability and statistics, e.g., Laplace methods for approximating integrals, Sanov’s large deviation principles in the Wasserstein distance, rates of convergence of mean Glivenko–Cantelli theorems, and estimates of weighted Poincaré–Wirtinger constants. We first present a theorem on PCRs for a model in the regular infinite-dimensional exponential family, which exploits sufficient statistics of the model, and then extend such a theorem to a general dominated model. These results rely on the development of novel techniques to evaluate Laplace integrals and weighted Poincaré–Wirtinger constants in infinite-dimension, which are of independent interest. The proposed approach is applied to the regular parametric model, the multinomial model, the finite-dimensional and the infinite-dimensional logistic-Gaussian model and the infinite-dimensional linear regression. In general, our approach leads to optimal PCRs in finite-dimensional models, whereas for infinite-dimensional models it is shown explicitly how the prior distribution affect PCRs.

Empirical Bayes factors for common hypothesis tests.

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P-value is available, an approximate empirical Bayes factor is 10p. I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.

Near-real-time 3D Reconstruction of the Solar Coronal Parameters Based on the Magnetohydrodynamic Algorithm outside a Sphere Using Deep Learning

For the first time, we generate solar coronal parameters (density, magnetic field, radial velocity, and temperature) on a near-real-time basis by deep learning. For this, we apply the Pix2PixCC deep-learning model to three-dimensional (3D) distributions of these parameters: synoptic maps of the photospheric magnetic field as an input and the magnetohydrodynamic algorithm outside a sphere (MAS) results as an output. To generate the 3D structure of the solar coronal parameters from 1 to 30 solar radii, we train and evaluate 152 distinct deep-learning models. For each parameter, we consider the data of 169 Carrington rotations from 2010 June to 2023 February: 132 for training and 37 for testing. The key findings of our study are as follows: First, our deep-learning models successfully reconstruct the 3D distributions of coronal parameters from 1 to 30 solar radii with an average correlation coefficient of 0.98. Second, during the solar active and quiet periods, the AI-generated data exhibits consistency with the target MAS simulation data. Third, our deep-learning models for each parameter took a remarkably short time (about 16 s for each parameter) to generate the results with an NVIDIA Titan XP GPU. As the MAS simulation is a regularization model, we may significantly reduce the simulation time by using our results as an initial configuration to obtain an equilibrium condition. We hope that the generated 3D solar coronal parameters can be used for the near-real-time forecasting of heliospheric propagation of solar eruptions.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis.

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

Changing dividend payout behavior and predicting dividend policy in emerging markets: Evidence from India

Dividends have become increasingly important for capital market participants to achieve financial goals in the rapidly changing Indian economy. This study aims to simplify the evolving Indian dividend puzzle by analyzing the dividend trends, examining the evolving nature of firm and macroeconomic determinants of dividends, and developing a dividend policy prediction model. Dividend trends of 3,162 non-financial listed Indian firms from 2006–2022 are studied to gain insights about the Indian dividend puzzle. Regularization and logit models are used to explore the nature of impact of important dividend determinants. Data-mining methods are employed to build a robust model for dividend policy prediction. Trend analysis reveals a decline in the quantum of dividends and proportion of dividend-paying firms with approximately 90% of the dividend-payers belonging to the manufacturing and service sector. Further findings suggest that size, age, maturity, profitability, past dividends, earnings, and bank monitoring of firms had a favorable impact on the likelihood of dividend payments. Macroeconomic indicators such as GDP growth rate, repo rate, percentage change in equity issues, listings, gross fixed assets formation also had a positive impact. The annual percentage change in debt issues and new project announcements at the macro level with investment prospects at firm level negatively impacted dividends. Dividend prediction model based on the random forest technique achieved the highest prediction accuracy of 90.77% and 77.31% under binomial and multi-class situations. These findings are expected to help corporate executives, portfolio managers and investors proactively design optimal dividend policies and formulate their investment strategies.

Regularity model based offspring generation in surrogate-assisted evolutionary algorithms for expensive multi-objective optimization

Evolutionary algorithms face significant challenges when it comes to solving expensive multi-objective optimization problems, which require costly evaluations. One of the most popular approaches to addressing this issue is to use surrogate models, which can replace the expensive real function evaluations with cheaper approximations. However, in many surrogate-assisted evolutionary algorithms (SAEAs), the process of offspring generation has not received sufficient attention. In this paper, we propose a novel framework for expensive multi-objective optimization called RM-SAEA, which utilizes a regularity model (RM) operator to generate offspring more effectively. The regularity model operator is combined with a general genetic algorithm operator to create a heterogeneous offspring generation module that can better approximate the Pareto front. Moreover, to overcome the data deficiency issue in expensive optimization scenarios, we employ a data augmentation strategy while training the regularity model. Finally, we embed three representative SAEAs into the proposed RM-SAEA to demonstrate its efficacy. Experimental results on several benchmark test suites with up to 10 objectives and real-world applications show that RM-SAEA achieves superior overall performance compared to eight state-of-the-art algorithms. By focusing on more effective offspring generation and addressing data deficiencies, our proposed framework is able to generate better approximations of the Pareto front and improve the optimization process in expensive multi-objective optimization scenarios.

Pseudo-L0-Norm Fast Iterative Shrinkage Algorithm Network: Agile Synthetic Aperture Radar Imaging via Deep Unfolding Network

A novel compressive sensing (CS) synthetic-aperture radar (SAR) called AgileSAR has been proposed to increase swath width for sparse scenes while preserving azimuthal resolution. AgileSAR overcomes the limitation of the Nyquist sampling theorem so that it has a small amount of data and low system complexity. However, traditional CS optimization-based algorithms suffer from manual tuning and pre-definition of optimization parameters, and they generally involve high time and computational complexity for AgileSAR imaging. To address these issues, a pseudo-L0-norm fast iterative shrinkage algorithm network (pseudo-L0-norm FISTA-net) is proposed for AgileSAR imaging via the deep unfolding network in this paper. Firstly, a pseudo-L0-norm regularization model is built by taking an approximately fair penalization rule based on Bayesian estimation. Then, we unfold the operation process of FISTA into a data-driven deep network to solve the pseudo-L0-norm regularization model. The network’s parameters are automatically learned, and the learned network significantly increases imaging speed, so that it can improve the accuracy and efficiency of AgileSAR imaging. In addition, the nonlinearly sparsifying transform can learn more target details than the traditional sparsifying transform. Finally, the simulated and data experiments demonstrate the superiority and efficiency of the pseudo-L0-norm FISTA-net for AgileSAR imaging.

Efficient structural damage detection via the lω regularization and randomized extended Kaczmarz algorithm

Structural damage detection (SDD) is an important aspect of structural health monitoring. This study aimed to explore a new method IT ω -REK θ (ω = 1, 1/2; θ = 1,2,3) for SDD based on the lω sparse regularization model and the randomized extended Kaczmarz (REK) type algorithms. When ω = 1/2, the l1/2 sparse regularization model was applied to enhance the ill-posedness of the damage identification problem and ensure the sparsity of the solution. The REK, θ = 1, partially randomized extended Kaczmarz (θ = 2), and fast maximum-distance extended Kaczmarz (θ = 3) algorithms with different threshold operators were used to solve the lω regularization model. These algorithms could obtain optimal identification results and significantly improve computational efficiency by randomly and partially selecting the data of the sensitivity equations. Numerical and experimental studies on different structures showed that the proposed method could fast locate structural damage and accurately identify the damage extents, which was robust to the SDD problem with noise.

Luminosity calibration by means of van-der-Meer scan for Q-Gaussian beams

Luminosity is the key quantity characterizing the performance of charged particle colliders. Precise luminosity determination is an important task in collider physics. Part of this task is the proper calibration of detectors dedicated for luminosity measurements. The wide-used experimental method of calibration is the van-der-Meer scan, which is the beam separation scan performed at specifically optimized beam conditions. This work is devoted to modeling this scan with the q-Gaussian distribution of particles in colliding beams. Because of its properties, the q-Gaussian distribution is believed to describe the density closer to reality than regular Gaussian-based models. In this work, the q-Gaussian model is applied for van-der-Meer scan modeling, and the benefits of this model for luminosity calibration task are demonstrated.

Using a centered general linear model for detection of interactions among biomarkers.

The dummy variable based general linear model (gLM) is commonly used to model categorical factors and their interactions. However, the main factors and their interactions in a general linear model are often correlated even when the factors are independently distributed. Alternatively, the classical two-way factorial analysis of variance (ANOVA) model can avoid the correlation between the main factors and their interactions when the main factors are independent. But the ANOVA model is hardly applicable to a regular linear regression model especially in the presence of other covariates due to constraints on its model parameters. In this study, a centered general linear model (cgLM) is proposed for modeling interactions between categorical factors based on their centered dummy variables. We show that the cgLM can avoid the correlation between the main factors and their interactions as the ANOVA model when the main factors are independent. Meanwhile, similar to gLM, it can be used in regular regression and fitted conveniently using the standard least square approach by choosing appropriate baselines to avoid constraints on its model parameters. The potential advantage of cgLM over gLM for detection of interactions in model building procedures is also illustrated and compared via a simulation study. Finally, the cgLM is applied to a postmortem brain gene expression data set.

A Phase-Field Regularized Cohesion Model for Hydrogen-Assisted Cracking

Hydrogen-assisted cracking (HAC) usually causes premature mechanical failure of the material and results in structural damage in hydrogen environments. A phase-field regularized cohesion model (PF-CZM) was proposed to address hydrogen-assisted cracking. It incorporated the hydrogen-enhanced decohesion mechanism to decrease the critical energy release rate to address damage initiation and progression in a chemo-mechanical coupled environment. This model is based on coupled mechanical and hydrogen diffusion responses, driven by chemical potential gradients, and the introduction of hydrogen-related fracture energy degradation laws. The coupling problem is solved by an implicit time integral, in which hydrogen concentration, displacement and phase-field order parameters are the main variables. Three commonly used loading regimes (tension, shear, and three-point bending) were provided for comparing crack growth. Specifically, (i) hydrogen-dependent fracture energy degradation, (ii) mechanical–chemical coupling, and (iii) the diffusion coefficient D is influenced by both the phase field and the chemical field. By considering these factors, the PF-CZM model provided a variational framework by coupling mechanical loading with concentration diffusion for studying the complex interplay between a chemo-mechanical coupled environment and material damage, thereby enhancing our understanding of hydrogen-assisted cracking phenomena.

Quaternion Regularization of Differential Equations of Perturbed Central Motion and Regular Models of Orbital (Trajectory) Motion: Review and Analysis of Models, Their Applications

Quaternion Regularization of Differential Equations of Perturbed Central Motion and Regular Models of Orbital (Trajectory) Motion: Review and Analysis of Models, Their Applications

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically. Detection of multiple objects, heavy occlusions, and similar appearances in congested places are some causes of computer vision model inaccuracies. This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects. Due to the nature of the reported problem caused by many misses and mismatches, the power of quantum computing with the alternating direction method of multipliers (ADMM) optimizer was leveraged. A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking (MOT) algorithms. This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value. Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy (MOTA) by 16% more than the regular YOLOv5-DeepSORT model when using a quantum optimizer. Also, a 6% multiple object tracking precision (MOTP) increases and a 6% identification metrics (F1) score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4. This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

Detecting phishing websites through improving convolutional neural networks with Self-Attention mechanism

Emerging technologies have made internet connection a vital activity facilitating access to many services. However, internet connection raises many security concerns, such as illegally acquiring private information, passwords, and identifiers. Phishing websites are the first choice for attackers that try to have users' private space. Social engineering attacks are performed by designing fake websites similar to real ones and inviting the victim to access those websites to collect their sensitive information and then redirect them to the actual site. Due to the importance of detecting phishing websites, building a robust detector that filters them and blocks their activity on the Internet is necessary. In this paper, we proposed a phishing website detector based on improving the convolutional neural network (CNN) with a self-attention mechanism. The proposed detector collects phishing Uniform Resource Locators (URLs) by treating them as strings. CNN models have proved their efficiency when dealing with text strings compared to Long Short-Term Memory (LSTM) which focuses on temporal features. Using CNN allows learning comprehensive features of the URLs and facilitates the detection of phishing ones. The self-attention mechanism was added to enhance the model's focus and detection accuracy. Besides, the training dataset was balanced by generating phishing URLs using a Generative Adversarial Network (GAN). A set of experiments has proved the robustness of the proposed detector by achieving high detection accuracy on the test set. Besides, the proposed detector was tested using unknown URLs and achieved excellent results. The improved CNN's detection precision of 99.7 is higher than the regular CNN model by 2.74%. The reported results show that using the self-attention mechanism has improved the detection accuracy and made the CNN model more efficient for detecting phishing websites.

Comparison of CNN-based methods for yoga pose classification

Yoga is an exercise developed in ancient India. People perform yoga in order to have mental, physical, and spiritual benefits. While yoga helps build strength in the mind and body, incorrect postures might result in serious injuries. Therefore, yoga exercisers need either an expert or a platform to receive feedback on their performance. Since access to experts is not an option for everyone, a system to provide feedback on the yoga poses is required. To this end, commercial products such as smart yoga mats and smart pants are produced; Kinect cameras, sensors, and wearable devices are used. However, these solutions are either uncomfortable to wear or not affordable for everyone. Nonetheless, a system that employs computer vision techniques is a requirement. In this paper, we propose a deep-learning model for yoga pose classification, which is the first step of a quality assessment and personalized feedback system. We introduce a wavelet-based model that first takes wavelet transform of input images. The acquired subbands, i.e., approximation, horizontal, vertical, and diagonal coefficients of the wavelet transform are then fed into separate convolutional neural networks (CNN). The obtained probability results for each group are fused to predict the final yoga class. A publicly available dataset with 5 yoga poses is used. Since the number of images in the dataset is not enough for a deep learning model, we also perform data augmentation to increase the number of images. We compare our results to a CNN model and the three models that employ the subbands separately. Results obtained using the proposed model outperforms the accuracy output achieved with the compared models. While the regular CNN model has 61% and 50% accuracy for the training and test data, the proposed model achieves 91% and 80%, respectively.

Sparse optimization guided pruning for neural networks

Neural network pruning is a critical field aimed at reducing the infrastructure costs of neural networks by removing parameters. Traditional methods follow a fixed paradigm including pretraining, pruning, and fine-tuning. Despite the close relationship among these three stages, most pruning methods treat them as independent processes. In this paper, we propose a novel two-stage pruning method, which includes pretraining a network that is instructive for subsequent pruning, and a unified optimization model that integrates pruning and fine-tuning. Specifically, in the first stage, we design a group sparse regularized model for pretraining. This model not only safeguards the network from irreversible damage but also offers valuable insights for the pruning process. In the second stage, we introduce an element-wise sparse regularization into pruning model. This model enables us to pinpoint sparse weights more precisely than pretrained network. It automatically derives effective pruning criteria, and omits the step of fine-tuning. To implement the two-stage process in practice, we utilize stochastic gradient algorithm for the pretraining and design a threshold algorithm for pruning stage. Extensive experiments confirm the competitive performance of our proposed method in terms of both accuracy and memory cost when compared to various benchmarks. Furthermore, ablation experiments validate the effectiveness of the proposed pretraining model’s guidance for the pruning process.