Variation Distance Research Articles

A hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment

Abstract Different protection states have different protection effects on bogie traction motor bearings under road transportation conditions, which directly affects the stability and safety of later vehicle operations. The motor bearing monitoring system usually adopts the time-frequency domain and other indicators to accurately describe its state, and the selection of specific indicators usually originates from the experience of domain experts. To explore the weak and effective features hidden in the data more deeply, this paper researches feature selection and fusion, and proposes a hybrid multi-measure and improved UMAP approach for train traction motor bearing protection state assessment. Firstly, a platform is built to collect multiple sensor signals of traction motor vibration during road transportation and the time-frequency domain feature set is extracted. Then, by comparing the feature extraction effects of 10 single-measure models, a hybrid multi-measure feature selection model and its evaluation indexes, which comprehensively consider the combined distance measure, correlation, and information measure, are constructed to optimize the best hybrid multimetric and the feature set corresponding to the optimal strategy. To perform multi-sensor feature fusion, the PSO-optimized UMAP algorithm is used for feature fusion of the feature set and compared with eight other algorithms. Finally, the results of feature selection and fusion are input into eight classifiers for performance comparison, and the method achieves more than 85% accuracy in distinguishing different protection states, obviously due to the original feature subset. The experimental results prove the effectiveness of the method proposed in this paper, and it provides a theoretical basis and technical reference for the protection scheme of the traction motor and the design and selection of bearings.

Attribute Feature Perturbation-Based Augmentation of SAR Target Data.

Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction, decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.

Hybrid recommendation system for movies using artificial neural network

RS are becoming more crucial in everyone’s life as the need for recommendations that reflect users’ interests grows every day. For new users, the movie RS based on a film’s ratings has become an exciting trait. However, scalability, cold start, and sparsity issues are the barriers for RS that must be overcome by proposed hybrid system. The MF Technique was utilizedin the modified SVD to minimizethe numberof features & spatial dimensions in the set of data. To detect the similarity between the movies the Content Driven KNN is proposed based on cosine similarity. To measure the closest neighbor, IKSOM is proposed by using EISEN cosine correlation distance which reduce the cluster overlapping. The K means ++ Clustering is proposed for categorize the data in the movies by silhouette method which calculate the optimal quantity of clusters. The overall hybrid method generates the scores also SVD-CF predicts movie ratings and provides the top recommendation for the movies. The results reveal the proposed hybrid system attains high accuracy, precision, Recall and F1-score also achieves the less error as RMSE and MAE when compared to the existing system.

A meta-ensemble machine learning strategy to assess groundwater holistic vulnerability in coastal aquifers

Assessing the vulnerability of groundwater in coastal aquifers is crucial for mitigating risks associated with seawater intrusion and anthropogenic impacts. This study introduces an innovative machine learning (ML)-enhanced methodology that synergizes the strengths of two established vulnerability assessment frameworks, DRASTIC and GALDIT. The hybrid approach overcomes the limitations of each framework—DRASTIC's inadequacies in coastal settings and GALDIT's limited consideration of agricultural and industrial impacts. Utilizing advanced decision tree-based ML algorithms—Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF)—this research was conducted in the Azarshahr Plain, NW Iran. Model efficacy was validated using Pearson's correlation coefficient (r) and distance correlation (DC), with nitrate (NO3−) and total dissolved solids (TDS) serving as proxies for evaluating the DRASTIC and GALDIT models, respectively. The original DRASTIC indices exhibited weak correlations with NO3− (r = 0.24, DC = 0.25), but ML-enhanced models, particularly AdaBoost, showed significant improvements (r = 0.78, DC = 0.79). Similar enhancements were observed with GALDIT, where correlations improved markedly with AdaBoost integration. A sophisticated second-level AdaBoost meta-ensemble was developed to integrate enhanced DRASTIC and GALDIT assessments, achieving superior correlation metrics (r = 0.80, DC = 0.84 for NO3−; r = 0.82, DC = 0.83 for TDS). These results underscore the effectiveness of an integrated ML-based approach in advancing beyond traditional vulnerability assessment methods, providing a more comprehensive, accurate, and robust evaluation of coastal aquifer vulnerability.

Circadian rhythms of macrophages are altered by the acidic pH of the tumor microenvironment.

Macrophages are prime therapeutic targets due to their pro-tumorigenic and immunosuppressive functions in tumors, but the varying efficacy of therapeutic approaches targeting macrophages highlights our incomplete understanding of how the tumor microenvironment (TME) can influence regulation of macrophages. The circadian clock is a key internal regulator of macrophage function, but how circadian rhythms of macrophages may be influenced by the tumor microenvironment remains unknown. We found that conditions associated with the TME such as polarizing stimuli, acidic pH, and elevated lactate concentrations can each alter circadian rhythms in macrophages. Circadian rhythms were enhanced in pro-resolution macrophages but suppressed in pro-inflammatory macrophages, and acidic pH had divergent effects on circadian rhythms depending on macrophage phenotype. While cyclic AMP (cAMP) has been reported to play a role in macrophage response to acidic pH, our results indicate that pH-driven changes in circadian rhythms are not mediated solely by the cAMP signaling pathway. Remarkably, clock correlation distance analysis of tumor-associated macrophages (TAMs) revealed evidence of circadian disorder in TAMs. This is the first report providing evidence that circadian rhythms of macrophages are altered within the TME. Our data further suggest that heterogeneity in circadian rhythms at the population level may underlie this circadian disorder. Finally, we sought to determine how circadian regulation of macrophages impacts tumorigenesis, and found that tumor growth was suppressed when macrophages had a functional circadian clock. Our work demonstrates a novel mechanism by which the tumor microenvironment can influence macrophage biology through altering circadian rhythms, and the contribution of circadian rhythms in macrophages to suppressing tumor growth.

Modal coupling characteristics of primary hunting in metro vehicles: Carbody suspension modes and hunting modes

Due to coupling with carbody suspension modes in railway vehicles, primary hunting can lead to significant low frequency swaying of the carbody, severely deteriorating ride quality. In order to address this issue, this paper conducts a detailed investigation into the coupling effect between suspension modes and hunting modes of metro vehicles. Initially, a simplified lateral dynamics model is established, and a continuous modal tracking method is adopted for track suspension modes and hunting modes. The results indicate that when the modal frequency of one hunting mode approaches that of corresponding suspension mode, the damping ratio of the two modes exhibits abnormal variation, and the modal frequency curve veers or is slightly disturbed. In exploring the coupling characteristics of hunting modes and suspension modes, revealing the variation trends and similarity of modal shapes, polar diagrams of modal shapes are presented, and the correlation distance is calculated. Equivalent conicity exerts a substantial influence on the effect of modal coupling, influencing minimum damping ratio and modal shape similarity within the modal coupling zone. To ensure sufficient stability margin for metro vehicles, it is necessary to avoid excessively low equivalent conicity. Finally, the phenomenon of vibration response deterioration within the modal coupling zone is confirmed through both a simplified model and a full DOF model.

Approximating the Stationary Distribution of the ASEP with Open Boundaries

We investigate the stationary distribution of asymmetric and weakly asymmetric simple exclusion processes with open boundaries. We project the stationary distribution onto a subinterval, whose size is allowed to grow with the length of the underlying segment. Depending on the boundary parameters of the exclusion process, we provide conditions such that the stationary distribution projected onto a subinterval is close in total variation distance to a product measure.

A fast dual-module hybrid high-dimensional feature selection algorithm

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and high computational cost in dealing with high-dimensional datasets. In this paper, we develop a dual-module hybrid feature selection algorithm based on correlation filtering and a multi-objective evolutionary algorithm based on dynamic variation distance (HMOFS-CFDVD), which aims to reduce the computational cost of high-dimensional features and search space. Firstly, a coarse-grained feature filtering method based on correlation is proposed, enabling the algorithm to quickly identify potentially better feature subsets in the later stages. After that, a fine-grained feature selection is further implemented based on the multi-objective evolutionary algorithm with sample variation distance to obtain a high-quality feature subset. This stage incorporates a novel population initialization method with self-regulation, an adaptive evolution strategy, an optimal sample selection mechanism based on dynamic sample distance, and an improved diversity mechanism to enhance the evolutionary performance. Moreover, the feature relevance metric is introduced as a third objective to improve the overall algorithm's performance. Experimental results on 12 high-dimensional feature datasets demonstrate that the proposed HMOFS-CFDVD achieves high accuracy and produces a smaller subset of features.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Incipient Near Surface Cracks Characterization and Crack Size Estimation based on Jensen–Shannon Divergence and Wasserstein Distance

Incipient Near Surface Cracks Characterization and Crack Size Estimation based on Jensen–Shannon Divergence and Wasserstein Distance

Efficiently improving the performance of noisy quantum computers

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named ``Noiseless Output Extrapolation" and ``Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to 86% improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

CLT for NESS of a reaction-diffusion model

We study the scaling properties of the non-equilibrium stationary states (NESS) of a reaction-diffusion model. Under a suitable smallness condition, we show that the density of particles satisfies a law of large numbers with respect to the NESS, with an explicit rate of convergence, and we also show that at mesoscopic scales the NESS is well approximated by a local equilibrium (product) measure, in the total variation distance. In addition, in dimensions d≤3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d \\le 3$$\\end{document} we show a central limit theorem for the density of particles under the NESS. The corresponding Gaussian limit can be represented as an independent sum of a white noise and a massive Gaussian free field, and in particular it presents macroscopic correlations.

Land use and spatial contiguity are key driven factors of antibiotic multimedia patterns in the megacity river network

The widespread spread of antibiotics in the environment poses a growing threat to human health. This study investigated the distribution and fate of antibiotics concerning land use characteristics, hydrological conditions, and spatial contiguity within a megacity river network. Temporally, the average concentrations of twenty antibiotics in water (354 ng/L), suspended particulate matter (SPM) (46 ng/L), and sediment (151 ng/g) during dry season were notably higher than that in the corresponding environment media (water: 127 ng/L, SPM: 2 ng/L, and sediment: 49 ng/g) during the wet season. Moreover, the inter-annual variation of antibiotics in water showed a decreasing trend. Spatially, substantial antibiotic contamination was observed in a human-intensive watershed, particularly in the upstream and central city sections. The macrolides in water were most affected by land use types and hydrological processes. Antibiotic contamination in water exhibited a stronger spatial autocorrelation compared to other media. Nevertheless, the interconnectedness of antibiotic contamination in sediments during the wet season warrants attention, and relevant authorities should enhance environmental monitoring in watersheds with pollution hotspots. Certain antibiotics, such as sulfamethoxazole, enrofloxacin, and florfenicol, were transported via urban rivers to the ocean, potentially posing environmental risks to coastal water quality. Local sources accounted for the predominant portion (>50 %) of most antibiotics in various media. The correlation distances of antibiotics in waters during the wet season could screen ecological risk prioritization in aquatic environments.

Path-dependent correlations in dynamically tuned Ising models and its short-time behavior: Application of Magnus expansion

We study both analytically and numerically the buildup of antiferromagnetic (AF) correlation in the dynamically tuned Ising models. In short-time scale, we apply Magnus expansion (ME) to derive the high-order analytic expression of the connected correlation functions and compare it with exactly numerical results for the different lattice geometries, e.g., 1D chain, 2×n lattice, and n×n lattice. It is shown that the high-order expansion is required to describe accurately the buildup of AF correlation in the quench dynamics. Moreover, through a 2D square lattice, we find that the magnitude of AF correlation for the same Manhattan distance is proportional to the number of the shortest paths in a sufficiently long time until long and distinct paths are involved significantly. Finally, we propose an applicable experimental setup to realize our findings.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.

SMART: Supervised multi-class image retargeting generative model based on a long-range sampling strategy

Content-aware image retargeting (CAIR) techniques are crucial in multimedia processing for displaying images on various devices while preserving visually salient contents with desirable visual effects. There are discrete and continuous algorithms. For the former, the artefacts happen when the foreground proportion is larger than the retargeting ratio; for the latter, the salient regions are prone to be squeezed. In this paper, we reformulate the retargeting process into sampling the salient signal and reconstruction under aesthetic supervision, the supervised multi-class image retargeting reconstruction (SMART) framework. The target images can be represented into complementary parts, the masked and unmasked ones, according to the saliency influences in the encoder phrase. The long-range sampling algorithm is proposed to calculate similarities through an 8-connected planar path while considering spatial distance and feature correlation. The sampled embeddings in latent space reconstruct the retargeted images under supervised signals for aesthetic quality. The semantic loss Lsem from the pretrained CLIP model can maintain consistency for both content and semantics. The supervised loss, Lir, is introduced to ensure the retargeted qualities are close to the preferred labels. Then, we release a new retargeting dataset comprising seven image classes (animal, building, car, flower, indoor, landscape and people) with supervised labels collected from designers for further aesthetic retargeting study. The ablation studies are conducted to confirm the effectiveness of the new dataset, and comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

Quantitative evaluation on the effects of the spatial variability in concrete materials on seismic damage of concrete gravity dams

The influencing mechanism of the spatial variability in concrete materials on the seismic damage of concrete gravity dams is still unclear, and existing methods for evaluating the seismic damage are insufficient. In this work, the effects of concrete’s spatial variability on the seismic damage distribution, energy dissipation, and deformation in concrete gravity dams are performed based on the damaged plastic model of concrete. Prior to the seismic damage analysis, the method for seismic inputting and the correlation function for realizing random fields of concrete materials are carefully determined. Based on the seismic damage analysis of the Koyna gravity dam, the tensile strength has the greatest influence on the seismic damage, followed by the elastic modulus and fracture energy. Aiming at the parameter of tensile strength, the decrease of correlation distance and the increase of the coefficient of variation increase the damage degree and complicate the damage distribution. A convenient and comprehensive damage profiling indicator is proposed to avoid the one-sidedness and evaluation error caused by using a single scalar damage value. The triangular area enclosed by the three individual damage indexes represents the comprehensive damage degree, and the shape change of the damage triangle indicates the change in the damage pattern of the dam. This damage profiling indicator is specifically designed to quantitatively distinguish and evaluate the damage degrees between a series of damage cases.

Performance Evaluation of Ultrasound Images Using Non-Local Means Algorithm with Adaptive Isotropic Search Window for Improved Detection of Salivary Gland Diseases: A Pilot Study.

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs.

Lancaster correlation: A new dependence measure linked to maximum correlation

AbstractWe suggest novel correlation coefficients which equal the maximum correlation for a class of bivariate Lancaster distributions while being only slightly smaller than maximum correlation for a variety of further bivariate distributions. In contrast to maximum correlation, however, our correlation coefficients allow for rank and moment‐based estimators which are simple to compute and have tractable asymptotic distributions. Confidence intervals resulting from these asymptotic approximations and the covariance bootstrap show good finite‐sample coverage. In a simulation, the power of asymptotic as well as permutation tests for independence based on our correlation measures compares favorably with competing methods based on distance correlation or rank coefficients for functional dependence, among others. Moreover, for the bivariate normal distribution, our correlation coefficients equal the absolute value of the Pearson correlation, an attractive feature for practitioners which is not shared by various competitors. We illustrate the practical usefulness of our methods in applications to two real data sets.

Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality

Abstract Distance covariance is a popular dependence measure for two random vectors $X$ and $Y$ of possibly different dimensions and types. Recent years have witnessed concentrated efforts in the literature to understand the distributional properties of the sample distance covariance in a high-dimensional setting, with an exclusive emphasis on the null case that $X$ and $Y$ are independent. This paper derives the first non-null central limit theorem for the sample distance covariance, and the more general sample (Hilbert–Schmidt) kernel distance covariance in high dimensions, in the distributional class of $(X,Y)$ with a separable covariance structure. The new non-null central limit theorem yields an asymptotically exact first-order power formula for the widely used generalized kernel distance correlation test of independence between $X$ and $Y$. The power formula in particular unveils an interesting universality phenomenon: the power of the generalized kernel distance correlation test is completely determined by $n\cdot \operatorname{dCor}^{2}(X,Y)/\sqrt{2}$ in the high-dimensional limit, regardless of a wide range of choices of the kernels and bandwidth parameters. Furthermore, this separation rate is also shown to be optimal in a minimax sense. The key step in the proof of the non-null central limit theorem is a precise expansion of the mean and variance of the sample distance covariance in high dimensions, which shows, among other things, that the non-null Gaussian approximation of the sample distance covariance involves a rather subtle interplay between the dimension-to-sample ratio and the dependence between $X$ and $Y$.