Regularization Method Research Articles

Radiation absorption, Hall and ion-slip current – driven MHD convection with spanwise cosinusoidally fluctuating temperature: a perturbative study

This study investigates the impact of radiation absorption, Hall and ion-slip current on magnetohydrodynamic free convective fluid flow past an infinite vertical porous plate with viscous dissipation and chemical reaction in the presence of Spanwise Cosinusoidally fluctuating temperature by time. Its significance extends across a wide array of applications, including advanced heat transfer systems for electronics, optimization of material processing kinetics, and fostering innovation for tailored material properties, with far-reaching implications spanning aerospace and renewable energy sectors. The governing equations, subject to stipulated boundary conditions are analyzed using the multiple regular perturbation method. The results are visually presented to evaluate the influence of various parameters on system dynamics. Graphical representations illustrate the physical significance of parameters including velocity, temperature, concentration, skin friction, mass transfer rate and heat transfer coefficients. The findings reveal that increased values of Hall and ion-slip current effects correlate with acceleration of velocity and skin friction. Moreover, higher radiation absorption parameter values lead to enhancements in velocity, temperature and Nusselt number. Conversely, different chemical reaction rates and Schmidt number values result in declines in velocity, concentration and Sherwood number. Additionally, incremental values of the Eckert number improve velocity and Nusselt number but have a reverse impact on temperature. The findings of this research align with those from prior examinations.

Advancing drinking water safety: Facile fabrication of nanofiltration membrane for enhanced antibiotics removal and efficient water softening

Long-term water consumption with contaminated antibiotics and high levels of hardness threatens the health of humans and animals. In this study, a novel positively charged nanofiltration membrane was designed for the dual purpose of efficiently removing antibiotics and softening hard water. The membrane was fabricated by casting P84 polymer solution onto non-woven fabrics, followed by immersion in the polyethyleneimine (PEI) solution for phase inversion and cross-linking, and finalized interfacial polymerization with trimesoyl chloride. Notably, the rapid immersion of the P84 substrate into PEI drastically reduces preparation time by 95 % compared to the regular method while maximizing PEI storage, resulting in uniform pore sizes and a positively charged surface. The optimal membrane showed a high water permeance of 11.3 L m−2 h−1 bar−1 and exceptional antibiotic rejection, i.e., 99.6 % for enrofloxacin, 99.2 % for ciprofloxacin, and 96.3 % for sulfamethoxazole, as well as a highly effective removal in water hardness (over 96 % of Ca2+ and Mg2+) at 4 bar. Simulated water separation tests showed that the optimal membrane effectively controls hard water salinity within optimum drinking water ranges (Ca2+ ≤ 80 mg/L; Mg2+ ≤ 30 mg/L), providing efficient water softening and antibiotic capture ability. Additionally, the optimal membrane demonstrated a superior anti-bacterial and long-term stability performance. This study has significant potential for improving drinking water supply safety and addressing pressing public health and environmental concerns.

Ward identities in a two-dimensional gravitational model: anomalous amplitude revisited using a completely regularization-independent mathematical strategy

We present a detailed investigation of the anomalous gravitational amplitude in a simple two-dimensional model with Weyl fermions. We employ a mathematical strategy that completely avoids any regularization prescription for handling divergent perturbative amplitudes. This strategy relies solely on the validity of the linearity of the integration operation and avoids modifying the amplitudes during intermediate calculations, unlike studies using regularization methods. Additionally, we adopt arbitrary routings for internal loop momenta, representing the most general analysis scenario. As expected, we show that surface terms play a crucial role in both preserving the symmetry properties of the amplitude and ensuring the mathematical consistency of the results. Notably, our final perturbative amplitude can be converted into the form obtained using any specific regularization prescription. We consider three common scenarios, one of which recovers the traditional results for gravitational anomalies. However, we demonstrate that this scenario inevitably breaks the linearity of integration, leading to an undesirable mathematical situation. This clean and transparent conclusion, enabled by the general nature of our strategy, would not be apparent in similar studies using regularization techniques.

Exploring the Feasibility and Educational Suggestions for Integrating OBE Educational Concepts into Higher Vocational English Courses

In response to the English curriculum standards of higher vocational colleges in both society and the nation, this study explores the feasibility of applying Outcome-Based Education (OBE) concepts into higher vocational English courses. A teaching experiment was conducted with 161 students selected from 4 parallel classes at a public higher vocational college in Guangdong Province. The intervention involved teachers in the experimental class utilizing the "Design Plan for the Application of OBE Educational Concepts in Hotel English Courses" provided by the researchers, while the control class proceeded with regular teaching methods. Following an 8-week intervention period, it was found that students in the experimental class demonstrated significantly higher academic performance compared to those in the control class, indicating the positive impact of integrating OBE educational concepts into higher vocational English courses. At the same time, researchers randomly selected 8 students for semi-structured interviews. The results of the interviews revealed that students in the experimental class responded positively and demonstrated recognition and acceptance of the OBE teaching method. Conclusion: The application of the OBE educational concept in higher vocational English courses is deemed feasible. As a result, the researchers have provided educational recommendations for English teachers in higher vocational schools, aiming to foster innovation and enhance educational quality in the field.

A modified iteratively regularized Landweber iteration method: Hölder stability and convergence rates

Abstract In this paper, we formulate the modified iteratively regularized Landweber iteration method in Banach spaces to solve the inverse problems for which the forward operator may be smooth or non-smooth. We study the convergence analysis of the modified method for both the perturbed as well as unperturbed data by utilizing the Hölder stability estimates. In the presence of perturbed data, we terminate the method via a discrepancy principle and show that it is in fact a convergence regularization method that terminates within a few iterations. In the presence of unperturbed data, we show that the iterates converge to the exact solution. Additionally, we deduce the convergence rates in the presence of perturbed as well as unperturbed data. Finally, we discuss two inverse problems on which the method is applicable.

CNN- based intrusion detection software for network operating system environment

Cybersecurity represents an important challenge specific to digital technology in the modern world, and is of vital importance for reducing or even preventing the impact of cybercrime. The Linux operating system is designed as open-source software that includes some features of software tools intended for network security and cybersecurity systems, such as intruder detection and penetration testing. With these tools in Linux, we need a special system to constantly detect intrusions into connected network devices. This research presents a method for detecting intrusion attacks based on analyzing the natural behavior of the system by building a special convolutional network to achieve this goal. The classification and detection results of the proposed convolutional neural network were compared with the regular machine learning method (SVM), with feature selection by correlation for both methods. Same datasets were used to train and test each of CNN and SVM. Some metrics were determined to evaluate the performance of classification and prediction models for a specific type of regular attacks, DoS and BOT attacks, where both SVM and CNN obtained an accuracy of 85.58% and 95.59%, respectively.

A single‐channel transmitarray antenna with abilities of super resolution and digital beamforming

AbstractA single‐channel transmitarray antenna with abilities of super resolution and digital beamforming (DBF) is proposed. The amplitude and phase distributions on the array aperture are reconstructed through beam switching with a single channel. By finding and solving relations between distributions and received signals of beams, aperture distributions can be reconstructed. The ill‐conditioned problem caused by limited scanning range and angular interval is solved using the regularization method. The obtained distributions can then be delivered to super‐resolution algorithm to obtain super resolution, or used to produce shaped beams by DBF. On the basis of the proposed theory, a bifocal beam‐reconfigurable transmitarray is designed and tested as a single‐channel DBF antenna without special and additional hardware design. It has the outstanding advantages of low cost, simple structure, low energy consumption, and high working frequency.

NeRF-Art: Text-Driven Neural Radiance Fields Stylization.

As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially in simulating a text-guided style with both the appearance and the geometry altered simultaneously. In this paper, we present NeRF-Art, a text-guided NeRF stylization approach that manipulates the style of a pre-trained NeRF model with a simple text prompt. Unlike previous approaches that either lack sufficient geometry deformations and texture details or require meshes to guide the stylization, our method can shift a 3D scene to the target style characterized by desired geometry and appearance variations without any mesh guidance. This is achieved by introducing a novel global-local contrastive learning strategy, combined with the directional constraint to simultaneously control both the trajectory and the strength of the target style. Moreover, we adopt a weight regularization method to effectively suppress cloudy artifacts and geometry noises which arise easily when the density field is transformed during geometry stylization. Through extensive experiments on various styles, we demonstrate that our method is effective and robust regarding both single-view stylization quality and cross-view consistency.

On the Identification of Control Failures by the Dynamic Regularization Method

On the Identification of Control Failures by the Dynamic Regularization Method

Clean affinity matrix induced hyper-Laplacian regularization for unsupervised multi-view feature selection

Most previous unsupervised multi-view feature selection (UMFS) methods have achieved appealing performance by exploring the consistency among multiple views. However, they have the following shortcomings: (1) They often fail to consider the potential inconsistency that might be caused by view-specific characteristics from the perspective of sparsity. (2) The previously learned hyper-graph might be affected by noise, thereby reducing the quality of the generated graph. To tackle these issues, this paper proposes a clean affinity matrix induced hyper-Laplacian regularization (CAHR) method for UMFS. Firstly, the initial affinity matrix is decomposed into the consistent and inconsistent parts, then a novel diversity penalty term is introduced to enforce the sparsity of the inconsistent part across views, thereby making the consistent part be cleaner. Secondly, a unified affinity matrix is generated by fusing the consistent factors of the initial affinity matrix in a self-weighted manner, thereby considering the consistency of multi-view data. Based on the unified affinity matrix, a hyper-Laplacian matrix is further constructed, which can maintain high-order manifold structure of data. Finally, a loss function is designed to find the best mapping for feature selection. Comprehensive experiments demonstrate that the proposed method significantly outperforms several state-of-the-art UMFS methods.

Identifying retinopathy in optical coherence tomography images with less labeled data via contrastive graph regularization.

Retinopathy detection using optical coherence tomography (OCT) images has greatly advanced with computer vision but traditionally requires extensive annotated data, which is time-consuming and expensive. To address this issue, we propose a novel contrastive graph regularization method for detecting retinopathies with less labeled OCT images. This method combines class prediction probabilities and embedded image representations for training, where the two representations interact and co-evolve within the same training framework. Specifically, we leverage memory smoothing constraints to improve pseudo-labels, which are aggregated by nearby samples in the embedding space, effectively reducing overfitting to incorrect pseudo-labels. Our method, using only 80 labeled OCT images, outperforms existing methods on two widely used OCT datasets, with classification accuracy exceeding 0.96 and an Area Under the Curve (AUC) value of 0.998. Additionally, compared to human experts, our method achieves expert-level performance with only 80 labeled images and surpasses most experts with just 160 labeled images.

Non-convex sparse regularization via convex optimization for blade tip timing

Blade Tip Timing (BTT), an emerging technology poised to replace strain gauges, enables contactless measurement of rotor blade vibration. However, the blade vibration signals measured by BTT systems often suffer from significant undersampling. Sparse reconstruction methods are instrumental in addressing the challenge of undersampled signal reconstruction. However, traditional approaches grounded in ℓ1 regularization tend to underestimate the amplitude of the true solution. This underestimation is particularly pronounced in the resonance state of the rotor blade, hindering effective prediction of the blade’s operational state. To overcome this limitation, this paper introduces a novel non-convex regularized BTT model, employing a non-convex penalty term with convex-preserving properties to achieve nearly unbiased reconstruction accuracy. Additionally, we propose a new threshold iteration algorithm designed for the swift solution of this model. The accuracy and robustness of the proposed method in identifying the multimodal vibration parameters of rotor blades are validated through simulations and experiments. Comparatively, the proposed method closely aligns with the Orthogonal Matching Pursuit (OMP) method in recognizing blade multimodal vibration amplitude, showcasing significant improvement over the ℓ1 regularization method. Furthermore, it demonstrates lower sensitivity to changes in BTT probe layout when compared to the OMP and ℓ1 regularization methods.

Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients.

We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC. Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME. Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively. Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.

New Trends in Applying LRM to Nonlinear Ill-Posed Equations

Tautenhahn (2002) studied the Lavrentiev regularization method (LRM) to approximate a stable solution for the ill-posed nonlinear equation κ(u)=v, where κ:D(κ)⊆X⟶X is a nonlinear monotone operator and X is a Hilbert space. The operator in the example used in Tautenhahn’s paper was not a monotone operator. So, the following question arises. Can we use LRM for ill-posed nonlinear equations when the involved operator is not monotone? This paper provides a sufficient condition to employ the Lavrentiev regularization technique to such equations whenever the operator involved is non-monotone. Under certain assumptions, the error analysis and adaptive parameter choice strategy for the method are discussed. Moreover, the developed theory is applied to two well-known ill-posed problems—inverse gravimetry and growth law problems.

A Tikhonov-type regularization method for Caputo fractional derivative

A Tikhonov-type regularization method for Caputo fractional derivative

Few-shot adaptation of GANs using self-supervised consistency regularization

Generative Adversarial Networks (GANs) demonstrate great success in content generation ranging from music to images. Classical GANs require an enormous amount of training data to match the distribution of real images, otherwise lead to overfitting and display implausible synthesis qualities. Previous methods adapt the pretrained model trained on large-scale data to the limited data but fail to handle the extreme few-shot cases. In this paper, we propose a self-supervised learning (SSL)-based regularization method to adapt the existing model to semantically related target domains. Our SSL-based regularization method constrains the relative distance between the source and adapting generator to transfer the diversity from the source domain to the target domain. To encourage a healthy competition with the generator, we also regularize the discriminator network to preserve the relative distances in the feature space. With extensive qualitative and quantitative evaluation on a variety of domains, our method demonstrates superior performance in terms of fidelity and diversity than existing state-of-the-art models. We achieve an FID score of 30.61 on the publicly available FFHQ Sunglasses dataset.

Development and validation of a patient-level model to predict dementia across a network of observational databases

BackgroundA prediction model can be a useful tool to quantify the risk of a patient developing dementia in the next years and take risk-factor-targeted intervention. Numerous dementia prediction models have been developed, but few have been externally validated, likely limiting their clinical uptake. In our previous work, we had limited success in externally validating some of these existing models due to inadequate reporting. As a result, we are compelled to develop and externally validate novel models to predict dementia in the general population across a network of observational databases. We assess regularization methods to obtain parsimonious models that are of lower complexity and easier to implement.MethodsLogistic regression models were developed across a network of five observational databases with electronic health records (EHRs) and claims data to predict 5-year dementia risk in persons aged 55–84. The regularization methods L1 and Broken Adaptive Ridge (BAR) as well as three candidate predictor sets to optimize prediction performance were assessed. The predictor sets include a baseline set using only age and sex, a full set including all available candidate predictors, and a phenotype set which includes a limited number of clinically relevant predictors.ResultsBAR can be used for variable selection, outperforming L1 when a parsimonious model is desired. Adding candidate predictors for disease diagnosis and drug exposure generally improves the performance of baseline models using only age and sex. While a model trained on German EHR data saw an increase in AUROC from 0.74 to 0.83 with additional predictors, a model trained on US EHR data showed only minimal improvement from 0.79 to 0.81 AUROC. Nevertheless, the latter model developed using BAR regularization on the clinically relevant predictor set was ultimately chosen as best performing model as it demonstrated more consistent external validation performance and improved calibration.ConclusionsWe developed and externally validated patient-level models to predict dementia. Our results show that although dementia prediction is highly driven by demographic age, adding predictors based on condition diagnoses and drug exposures further improves prediction performance. BAR regularization outperforms L1 regularization to yield the most parsimonious yet still well-performing prediction model for dementia.

TVNeRF: Improving few-view neural volume rendering with total variation maximization

Neural Radiance Fields (NeRF) has profoundly impacted few-shot novel view synthesis and 3D reconstruction with its innovative rendering techniques and high-quality output. However, synthesizing novel views from sparse inputs remains challenging for NeRF. With limited view inputs, NeRF tends to overfit the available views, leading to reconstruction discrepancies and artifacts. To address this issue, we propose TVNeRF, which introduces two complementary regularization methods. By maximizing the total variation of unseen rays, we control the density distribution of ray sampled points to prevent excessive sampling in non-critical areas, thereby reducing artifacts and floaters. Concurrently, minimizing the accumulated opacity of unseen rays allows for the effective selection of important rays and sampled points without additional computational overhead, while also mitigating potential over-regularization effects. These combined strategies not only prevent overfitting but also achieve effects similar to the Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Moreover, TVNeRF achieves these improvements without the need for pre-trained models or prior knowledge. By utilizing these straightforward yet effective regularization techniques, TVNeRF can deliver results comparable to state-of-the-art models while maintaining computational efficiency. This approach highlights that substantial improvements in NeRF performance can be realized through targeted regularization methods, effectively addressing the challenges posed by sparse view synthesis.

Martingale solution of the stochastic Camassa–Holm equation with pure jump noise

We study the stochastic Camassa–Holm equation with pure jump noise. We establish the existence of the global martingale solution by the regularization method, the tightness criterion, the generalization of the Skorokhod theorem for nonmetric spaces and the stochastic renormalized formulations.

HDG methods for the unilateral contact problem

This article presents the HDG approximation as a solution to the unilateral contact problem, leveraging the regularization method and an iterative procedure for resolution. In our study, u represents the potential (displacement of the elastic body) and q represents the flux (the force exerted on the body). Our analysis establishes that the utilization of polynomials of degree k(k≥1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$k (k \\ge 1)$\\end{document} leads to achieving an optimal convergence rate of order k+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$k+1$\\end{document} in L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$L^{2}$\\end{document}-norm for both u and q. Importantly, this optimal convergence is maintained irrespective of whether the domain is discretized through a structured or unstructured grid. The numerical results consistently align with the theoretical findings, underscoring the effectiveness and reliability of the proposed HDG approximation method for unilateral contact problems.