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In order to improve the accuracy and detection effect of fabric defect detection, a fabric defect detection method based on local similarity comparison is proposed in this paper. This method first takes each pixel in the image as the central pixel, selects a specific window as the region size, and then uses the similarity between the central region and the surrounding neighborhood to find the neighborhood most similar to the central region to complete the estimation of the central pixel. Finally, the target image is obtained by the principle of background difference, so as to detect fabric defects. The results show that this method is superior to the traditional detection method, which can not only detect the defect image under the complex background, but also have good detection results for the fabric defect image under the influence of different organization and lighting factors. The detection accuracy rate under factory conditions can reach 98.45%, which has a high applicability and detection rate, and also demonstrates certain anti-interference performance. [ABSTRACT FROM AUTHOR]

Image registration techniques are used for mapping two images of the same scene or image objects to one another. There are several image registration techniques available in the literature for registering rigid body as well as non-rigid body transformations. A very important image transformation is zooming in or out which also called scaling. Very few research articles address this particular problem except a number of feature-based approaches. This paper proposes a method to register two images of the same image object where one is a zoomed-in version of the other. In the proposed intensity-based method, we consider a circular neighborhood around an image pixel of the zoomed-in image, and search for the pixel in the reference image whose circular neighborhood is most similar to that of the neighborhood in the zoomed-in image with respect to various similarity measures. We perform this procedure for all pixels in the zoomed-in image. On images where the features are small in number, our proposed method works better than the state-of-the-art feature-based methods. We provide several numerical examples as well as a mathematical justification in this paper which support our statement that this method performs reasonably well in many situations. [ABSTRACT FROM AUTHOR]

The Chepaizi Exploration Area, Junggar Basin (NW China) holds substantial importance for seismic exploration endeavors, yet it poses notable challenges due to the intricate nature of its subsurface and near-surface conditions. To address these challenges, we introduce a novel and comprehensive workflow tailored to evaluate and optimize seismic acquisition geometries while considering the impacts of near-surface viscosity. By integrating geological knowledge, historical seismic data, and subsurface modeling, we conduct simulations employing the visco-acoustic wave equation and reverse-time migration to produce detailed subsurface images. The quality of these images is quantitatively evaluated using a local similarity metric, a pivotal tool for evaluating the accuracy of seismic imaging. The culmination of this workflow results in an automated optimization strategy for acquisition geometries that enhances subsurface exploration. Our proposed methodology underscores the importance of incorporating near-surface viscosity effects in seismic imaging, offering a robust framework for improving the accuracy of subsurface imaging. Herein, we aim to contribute to the advancement of seismic imaging methodologies by providing valuable insights for achieving high-quality seismic exploration outcomes in regions characterized by complex subsurface and near-surface conditions. [ABSTRACT FROM AUTHOR]

In this paper, non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) are used to express the local features of an object as basis vector (or basis matrix). To recognize an object through its partial appearance, it should be expressed as a combination of basis vectors (or basis matrices). We applied the part-based image representation to the vehicle recognition for autonomous driving, where occlusion is a problem. As a result, the recognition rate was about 90% which shows that the part-based representation is effective. We also propose an local similarity based on the relative ratio between vectors and showed that it is more robust against occlusion than conventional similarities such as Euclidean distance and cosine similarity. At the 60% of occlusion, the conventional similarity showed a recognition rate of 33.3%, while the local similarity showed a recognition rate of 66.7%, which means that the local similarity is superior and less sensitive to the loss of feature information due to occlusion. [ABSTRACT FROM AUTHOR]

Aeolian barchan dunes on Earth and other planets have been widely investigated. Much of the understanding of barchan dune morphodynamics comes from field observations, numerical simulations, and downsized water‐tunnel experiments as well. Many of the evolution of barchan dunes in water‐tunnel experiments are similar to those of aeolian cases, although they have notable differences in scale, sand particle motion and hydrodynamic characteristics. Here, we first review the literature on the local similarities between aeolian and downsized subaqueous barchan dunes, focusing on (a) dune formation, (b) dune morphology, (c) particle‐scale characteristics, and (d) sand/dune emission at horns. A comprehensive description of double‐dune interaction modes is then presented to illustrate the local similarity of barchan dune morphodynamics. Specifically, as the interaction mode undergoes a process of "merging‐splitting‐chasing," the similarity between the interaction modes of aeolian and downsized subaqueous dunes continuously decreases. Furthermore, we summarize the significance and limitations of downsized water‐tunnel experiments for barchan dunes, and highlight the focus for future investigation. Plain Language Summary: There is a possibility to use the rapid evolution of downsized dunes under water to derive the morphodynamics of aeolian (wind‐formed) dunes formed over long periods. Here, we discuss the morphodynamics of aeolian and downsized subaqueous (underwater) barchan dunes and explain their local similarities in dune formation, dune morphology, and sand particle motion. We then study, for the first time, their local similarity through a conceptual chain of double‐dune interaction modes, indicating that the similarity between the morphodynamics of the two types of dunes continuously decreases as the interaction mode changes from "merging" to "splitting" and then to "chasing." We conclude that such local similarity cannot be completely denied simply due to the difference in the modes of sand particle motion (i.e., wind vs. water); instead, the coupling effects of sand particle motion and dune body migration in both environments should be considered and compared, which help achieve the steady‐state barchan dune evolution in a macroscopically similar manner. Furthermore, the morphological, physical and dynamic parameters are expected to be integrated to establish a general scaling law describing local similarities between the morphodynamics of aeolian and downsized subaqueous barchan dunes. Key Points: Local similarities between aeolian barchan dunes and their downsized subaqueous counterparts are reviewedThe two are compared based on the characteristics of individual dunes and on the modes of dune interactionsThe significance, limitations and possible future work of the downsized dune experiments are summarized [ABSTRACT FROM AUTHOR]

Gas recognition has been widely used in many fields such as air quality monitoring in dangerous areas. However, existing recognition methods suffer from two limitations: first, the recognition accuracy is not high. Due to the stochastic nature of air turbulence, gas features are not steady. The global features are sensitive to feature variations. Existing methods are based on global similarity, ignoring local similarity. Samples may be dissimilar in respect of global similarity, but are similar in terms of local similarity; Second, most existing recognition methods are based on the closed-set assumption that the gases categories in the train and test set are same. However, in real world applications, the test set may have non-overlapping gas category with the train set. To address above limitations, biclustering is used to extract local similarity. However, original biclustering method is not suitable for extraction. Since original biclustering method is used to find all kinds of biclusters, here we just want to find column nearly constant bicluster Therefore, a modified biclustering method is proposed. The local similarity can be used to construct classifier to recognize gas with adaboost. However, original adaboost cannot be used for open-set recognition. Thus a modified adaboost that uses two thresholds is proposed to recognize the unknown gases. To assess the efficacy of the proposed method, it is tested on public dataset. Experiment results demonstrate that the proposed method outperforms several state-of-the-art methods in respect of several evaluation measures on both closed-set and open-set cases.

In order to improve the accuracy and detection effect of fabric defect detection, a fabric defect detection method based on local similarity comparison is proposed in this paper. This method first takes each pixel in the image as the central pixel, selects a specific window as the region size, and then uses the similarity between the central region and the surrounding neighborhood to find the neighborhood most similar to the central region to complete the estimation of the central pixel. Finally, the target image is obtained by the principle of background difference, so as to detect fabric defects. The results show that this method is superior to the traditional detection method, which can not only detect the defect image under the complex background, but also have good detection results for the fabric defect image under the influence of different organization and lighting factors. The detection accuracy rate under factory conditions can reach 98.45%, which has a high applicability and detection rate, and also demonstrates certain anti-interference performance.

Accelerating the deep integration of industry-university-research has become a key link in school-enterprise collaborative education, which is an important initiative to deepen collaborative innovation among enterprises, universities, and research institutes and to promote the high-quality cultivation of talents. The article establishes an all-around collaborative education model of government, university, and enterprise association, and constructs a framework of university-enterprise collaborative case base with the technical support of AI platform and the Internet. To enhance the application feasibility of the university-enterprise collaborative case database, the nearest neighbor method is adopted for case retrieval, and the case feature attributes are obtained through local similarity, combined with global similarity for matching and retrieval of similar cases. Based on the collaborative case library between universities and enterprises, a teaching experiment was designed. The construction of the case library and its teaching effect were verified using the social network analysis method. The time consumed in case retrieval through Manhattan similarity is only about 1.5s. The network density of the online learning community based on a collaborative case between a university and an enterprise is 0.274. The average distance and cohesion index between learners are 2.003 and 0.576, respectively. The collaborative case base between universities and enterprises can improve students’ learning attitudes, enhance students’ learning interests, and provide a new avenue for cultivating high-quality talents that meet the needs of enterprises.

Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising networks require a training set of paired images, which is expensive to obtain and cannot be perfectly simulated. Unsupervised learning utilizes unpaired data and is highly desirable for LDCT denoising. As an example, an artifact disentanglement network (ADN) relies on unpaired images and obviates the need for supervision but the results of artifact reduction are not as good as those through supervised learning. An important observation is that there is often hidden similarity among unpaired data that can be utilized. This paper introduces a new learning mode, called quasi-supervised learning, to empower ADN for LDCT image denoising. For every LDCT image, the best matched image is first found from an unpaired normal-dose CT (NDCT) dataset. Then, the matched pairs and the corresponding matching degree as prior information are used to construct and train our ADN-type network for LDCT denoising. The proposed method is different from (but compatible with) supervised and semi-supervised learning modes and can be easily implemented by modifying existing networks. The experimental results show that the method is competitive with state-of-the-art methods in terms of noise suppression and contextual fidelity. The code and working dataset are publicly available at https://github.com/ruanyuhui/ADN-QSDL.git. [ABSTRACT FROM AUTHOR]

In recent years, with the diversification of data acquisition, multi-view learning has become more and more important. Most multi-view clustering methods obtain the similarity between samples through self-representation or local structures. However, these methods do not consider the influence of noise and the clustering structure of the overall sample. This way may lead to a large error in the clustering study. To address this issue, this paper proposes a Multiview Latent subspace Clustering with Cluster structure and Local similarity（MLC2L）, which combines shared information on different views and suppresses the presence of possible noise through latent representations. Besides, it simultaneously explores the clustering structure and local similarity in the latent space, so the samples can be promoted to achieve the purpose of homogeneous aggregation and heterogeneous separation. Further, this paper introduces an alternate direction iterative optimization algorithm to quickly solve the objective function. The experimental results in six real datasets show that the proposed method is optimal for five evaluation metrics on MSRC-v1, UCI, and 100Leaves, and four out of five metrics on 3Sources, WebKB, and Prokaryotic datasets. Extensive experimental results demonstrate the effectiveness of the MLC2L, which combines local structure and overall clustering structure, in multiview clustering tasks. [ABSTRACT FROM AUTHOR]

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Aiming at the problem of instability caused by the randomness of node starting order and updating labels in the label propagation algorithm, this paper proposed a new label propagation algorithm for detection of complex network community (Density Peaks and Node Similarity, DPNS-LPA）. It includes the determination of community center and label propagation of peripheral nodes. Firstly, the method defined the local similarity index of nodes which was characterized by the hub depressed index, Jaccard index and the structural characteristics of nodes with degree 1,and this index was used to measure the distance between nodes and solve the random selection of the same maximum label. Then introduced the improved density peak clustering algorithm to find community centers and determined the number of communities. Finally, obtained the final community division result based on the label propagation of the community center and peripheral nodes. Experimental results on artificial networks and real networks show that the standardized mutual information, modularity and D-Score index values are superior to the comparison algorithms. The proposed algorithm can effectively discover community structure in complex networks, and has higher robustness. [ABSTRACT FROM AUTHOR]

We present a method for automatically generating polygonal shapes from an example using a graph grammar. Most procedural modeling techniques use grammars with manually created rules, but our method can create them automatically from an example. Our graph grammars generate graphs that are locally similar to a given example. We disassemble the input into small pieces called primitives and then reassemble the primitives into new graphs. We organize all possible locally similar graphs into a hierarchy and find matching graphs within the hierarchy. These matches are used to create a graph grammar that can construct every locally similar graph. Our method generates graphs using the grammar and then converts them into a planar graph drawing to produce the final shape. [ABSTRACT FROM AUTHOR]

The turbulent standard deviations and the turbulent third-order and fourth-order moments are the key turbulence dispersion parameters in Lagrangian dispersion models. However, the characteristics of these parameters under heavy haze conditions in urban areas have not been fully investigated, and the commonly used similarity relations of these parameters in models were based on observations in highly flat and sparsely populated areas. In this paper, the vertical profiles of these parameters and their local similarity relations under heavy haze conditions in the wintertime of Beijing have been analyzed by using data collected at a 325-m meteorological tower. The heavy haze process has been divided into three stages: transport stage (TS), cumulative stage (CS), and dispersion stage (DS). Results show that the turbulent dispersion parameters behave differently during three stages. In the TS and DS, the maxima appear in the profiles of the turbulent standard deviations above the urban canopy; in the CS, the turbulent standard deviation are almost constant with height. The analysis of the third and fourth order moments shows that the wind velocities above the urban canopy in the TS deviate from the Gaussian distribution more significantly than those in the CS and DS. The local similarity relations of the turbulent dispersion parameters in the TS, especially for the longitudinal wind components, are normally different from those in the CS and DS. Thus, different from the common assumptions in Lagrangian models, the turbulence dispersion in horizontal directions is anisotropic and should be parameterized by multiple similarity relations under heavy haze conditions. [Display omitted] [ABSTRACT FROM AUTHOR]

We find that local similarity is an essential factor for data augmentation in deep learning tasks concerning time series data, the applications of which are prevalent in various domains such as smart healthcare, intelligent transportation, smart finance, etc. With empirical and theoretical analysis, we find deep learning models achieve excellent performance only when the data augmentation method performs with appropriate intensity of local similarity—during the data augmentation process, too large/small intra-class local similarity will decrease the performance of deep learning models. With this discovery, we propose a time series augmentation method based on intra-class Sim ilarity Mix ing (SimMix), which accurately controls the intensity by quantifying and adjusting the similarity between augmented samples and original samples. With a PAC (i.e., Probably Approximately Correct) theoretical foundation, we design a cutmix strategy for non-equal length segments to eliminate semantic information loss and noise introduction defects in traditional methods. Through extensive validation on 10 real-world datasets, we demonstrate that the proposed method can outperform the state-of-the-art by a large margin. • Controlled augmentation intensity dramatically improves model performance. • Local similarity epitomizes pioneering advancements in time series augmentation. • Precise modulation of local similarity ensures optimal performance during augmentation. • PAC theory drives innovation in data augmentation, offering theoretical insights. • Cutmix strategy improves flexibility by mitigating issues in segments of varying lengths. [ABSTRACT FROM AUTHOR]

Partial Label Learning (PLL) is a weakly supervised learning problem that induces a multi-class classifier from data with candidate labels, among which only one is the ground-truth label. The crucial challenge in PLL is to disambiguate the false-positive labels from the candidate labels. However, most existing PLL methods fail to simultaneously consider both the instance-level similarity and the class-level information during label disambiguation. In this paper, we propose a novel two-stage method based on weighted centroid clustering, which efficiently utilizes both the local similarity among instances and the per-class global information. In the first stage, we initialize the center of each class using label propagation based on the instance-level similarity, and obtain the disambiguated instances via weighted centroid clustering derived from the per-class global information. In the second stage, the disambiguated instances are used to train a multi-class classifier. Extensive experiments on both controlled UCI datasets and real-world datasets show the superiority of the proposed method in classification accuracy. [ABSTRACT FROM AUTHOR]

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have broad application prospects in various industries, and node localization technology is the foundation of WSN applications. Recently, many range-free node localization algorithms have been proposed, but most of them suffer from low accuracy. In order to improve the localization accuracy, in this paper we proposed the node localization algorithm based on multi-hop distance vector and error correction (MDV-EC). In terms of distance estimation, firstly the MDV-EC algorithm calculates the neighbor distance according to node neighbor relationship, then estimates the distance between unknown node and anchor node in multi-hop manner, and finally calibrates the distance refer to distance correction coefficient. In view of similarity of localization errors of nodes in similar regions, an error correction scheme is also investigated, which corrects the node initial estimated locations of nodes refer to the localization error vector of nearby anchor node. Simulation results show that our proposed MDV-EC has better performance than the other two algorithms in terms of node localization accuracy, and the error correction scheme can effectively reduce the localization errors. [ABSTRACT FROM AUTHOR]

Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher's knowledge, useful for the depth completion, we introduce local similarity-preserving knowledge distillation (LSPKD), which allows similarities between local neighbors to be transferred during the distillation. With our LSPKD, a lightweight student network is precisely guided by a heavy teacher network, regardless of the density of the ground-truth data. Experimental results demonstrate that our method is effective to reduce computational costs during both training and inference stages while achieving superior performance over other lightweight networks. [ABSTRACT FROM AUTHOR]

One of the popular categories of community detection methods are label propagation-based algorithms. Label propagation-based algorithms use local criteria and have a near-linear time complexity. However, these algorithms have problems such as low accuracy, instability, and high computational time in comparison with other local methods. This article presents a fast and simple label diffusion method (FSLD), using local criteria to discover communities accurately in large-scale networks. In FSLD method, community formation is initially started from a low-degree periphery node and then it diffuses its label from outer to inner side of community in a multi-level way. In next step, using a label updating step, all nodes from high-degree to low-degree have the potential to update and finalize their label to obtain initial communities. The experimental results reveal the higher accuracy and performance of the proposed FSLD algorithm in comparison to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The detection and removal methods of impulse noise often need to estimate the noise level of the damaged image in advance to obtain a better detection rate. An effective method of random-value impulse noise level estimation based on local similarity is proposed in this paper. Firstly, quantify the similarity between the center pixel x and any pixel y in the neighborhood of x based on their Euclidean distance and gray difference, then the similarity of pixel x and each pixel in its neighborhood is accumulated and summed to obtain the local similarity (LS) of pixel x. The value of LS represents the local consistency of a given pixel with respect to its neighboring pixels in which can also determine if a pixel is an impulse noise or a clean pixel. Hence, the LS value of a pixel could be regarded as an effective index to measure whether it is a clean pixel. Then the noise pixels in multiple flat regions of the noise image are detected to obtain the noise level of each region, and the noise level of these flat regions are processed with average operation to estimate the impulse noise level of the entire image finally. Extensive experiments were conducted to verify the effectiveness of the method and the experimental results show that the method is effective in scenarios with various noise levels, and the estimation error of the noise level of most images is within 1%. By comparing the RMSE and Std of different noise level estimation algorithms, it can be found that the algorithm proposed in this paper has higher robustness and accuracy, which can be well applied to practical applications with impulse noise level as the key parameter. [ABSTRACT FROM AUTHOR]

The accurate image of underground medium is determined by the quality of the seismic data, which can be improved by random noise attenuation and structural continuity enhancement. We proposed an adaptive nonlocal median filter that can protect geological structure while attenuating random noise. We combine the nonlocal idea with the weighted median filter and design the appropriate weights of the nonlocal median filter based on seismic data characteristics. The local structure is represented by the neighborhood around the center point. The directional difference of spatial vectors in the neighborhood is considered when computing the similarity. According to the local dip attribute of seismic data, the anisotropic Gaussian window is adaptively adjusted to increase the constraint along the structural direction. The proposed method can search more precisely for points with similar local structure to the filtered points and effectively attenuate seismic random noise. The continuity of events is enhanced while the goal of protecting fault information is achieved. The experimental results of the theoretical model and field data show that the adaptive nonlocal median filter can strike a balance between preserving structure information and attenuating seismic random noise. [ABSTRACT FROM AUTHOR]

Neighborhood-based algorithms are some of the most promising memory-based collaborative filtering approaches for recommender systems. Many of these algorithms rely on a global similarity measure to select the most similar neighbors for rating prediction. However, these approaches may fail in capturing some meaningful relationships among users. In the real world, although users can show interest in a wide range of objects, they can express more interest in objects contained in a specific topic, which typically comprises a bulk of closely related objects. In this paper, we propose a local similarity method that has the ability to exploit multiple correlation structures between users who express their preferences for objects that are likely to have similar properties. For this, we use a clustering method to find groups of similar objects. Then we create a user-based similarity model for each cluster, which we named Cluster-based Local Similarity (CBLS) model. Each similarity model relies on rating normalization and resource allocation techniques that are sensitive to the ratings assigned to objects contained in the cluster. We performed experiments using two clustering algorithms (affinity propagation and K-Means) and compared the results with other neighborhood-based collaborative filtering approaches. Our numerical results on three benchmark datasets (MovieLens 100k, MovieLens 1M, and Netflix) demonstrate that the proposed method is competitive and outperforms traditional and state-of-the-art collaborative filtering-based similarity models in terms of accuracy metrics like mean absolute error (MAE) and root-mean-square error (RMSE).

A two-stage denoising algorithm based on local similarity is proposed to process lowly and moderate corrupted images with random-valued impulse noise in this paper. In the noise detection stage, the pixel to be detected is centered and the local similarity between the pixel and each pixel in its neighborhood is calculated, which can be used as the probability that the pixel is noise. By obtaining the local similarity of each pixel in the image and setting an appropriate threshold, the noise pixels and clean pixels in the damaged image can be detected. In the image restoration stage, an improved bilateral filter based on local similarity and geometric distance is designed. The pixel detected as noise in the first stage is filtered and the new intensity value is the weighted average of all pixel intensities in its neighborhood. A large number of experiments have been conducted on different test images and the results show that compared with the mainstream denoising algorithms, the proposed method can detect and filter out the random-value impulse noise in the image more effectively and faster, while better retaining the edges and other details of the image.

Considering the 3D propagation characteristics of seismic waves, theoretically, 3D surface-related multiples elimination (3D SRME) can suppress multiples with high accuracy. However, 3D SRME has strict requirements for acquisition geometry, which makes it difficult to be implemented in practice. In the process of 3D SRME, the multiple contribution gather (MCG) is a collection of wavefields with different propagation paths. The accuracy of the multiple propagation paths in the MCGs can be directly characterized by the inclination of the wavefields, which can achieve the weighted superposition of the wavefields. The direct summation of the sparse MCGs in the crossline direction produces serious spatial aliasing, which can easily cause the contamination of primaries. Based on the kinematic characteristics of multiple propagation, MCGs can be considered as a set of hyperbolas with temporal and spatial characteristics. Then, the direct summation of the sparse MCGs can be transformed into a process of superposition along the hyperbolic integration paths. However, as the stable phase points of the events, the apexes of the hyperbola have different spatial distributions in complex geological structures. Such hyperbolic stacking paths are difficult to be controlled by conventional Radon transform or constrained inversion. In this paper, we modify the apex-shifted hyperbolic Radon transform (ASHRT) to implement the summation of crossline MCGs with variable stable phase points along the hyperbolic integration paths. Improved ASHRT uses local similarity to locate the position of stable phase points, which can improve the stability of the algorithm and the efficiency of the computation. The proposed method is demonstrated on a 3D synthetic data set, as well as on a 3D marine data set, effectively avoiding the spatial aliasing caused by sparse crossline MCGs and improving the accuracy of multiple suppression. [ABSTRACT FROM AUTHOR]

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model.

Community structure is one of the most important topological properties of complex networks, which can help us to understand the functions and guide the development of networks. In this article, a community detection algorithm is proposed based on local similarity and hierarchical clustering. Local similarity is used to measure link similarities instead of node similarities in order to form a similarity metric. Hierarchical clustering is used to gather all the links to form a hierarchical tree, and then cut the tree with the optimization value of modularity to get the community structure. Experiments on real-world and generated benchmark networks show the significant performance of the algorithm both in accuracy and efficiency.

Community structures are integral and independent parts in a network. Community detection plays an important role in social networks for understanding the structure and predicting user behaviors. Many algorithms have been devised for accurate and efficient community detecting, but there are few community detection algorithms using node similarity. In most real-world networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties. The higher the clustering coefficient of a node, the more aggregative the neighboring nodes are. In this paper, we propose an adjacent node similarity optimization combination connectivity algorithm (ASOCCA) for accurate community detection. ASOCCA utilizes the local similarity measure based on clustering coefficient to identify the closest neighbors of each node, then obtains several sets of connected components by combining different pairs of nodes, and finally forms initial communities. In addition, the community merging strategy is applied to further optimize the community structure. To evaluate the performance of the proposed algorithm, six real-world networks and two LFR networks with diverse network size are used to compare ASOCCA with five state-of-the-art community detection algorithms. The experimental results show that ASOCCA achieves better detection accuracy than several existing algorithms.

Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher’s knowledge, useful for the depth completion, we introduce local similarity-preserving knowledge distillation (LSPKD), which allows similarities between local neighbors to be transferred during the distillation. With our LSPKD, a lightweight student network is precisely guided by a heavy teacher network, regardless of the density of the ground-truth data. Experimental results demonstrate that our method is effective to reduce computational costs during both training and inference stages while achieving superior performance over other lightweight networks.

Passive seismic source imaging can be utilized to recover geophysical information from subsurface ambient noise. Compared with conventional active seismic exploration, passive seismic source imaging is cost-effective and environmentally friendly. However, passive data acquisition cannot easily satisfy the theoretical condition, leading to noised virtual-shot gathers. Furthermore, coherent noise limits the application of passive source data. Although image quality improvement techniques for passive source data have recently attracted considerable interest, the denoising problem for virtual-shot gathers is seldom considered. In this study, we propose an iterative denoising approach for passive seismic data. The criterion used to extract useful signals is the difference between the wavefield similarity of useful events and the coherent noise in various gathers, i.e., the common shot gather and common receiver gather. We adopted local similarity to measure the similarity level and extract major useful events. However, the close local similarity between weak events and coherent noise may cause signal leakages and singular noise residuals. We incorporated an iterative two-dimensional model shrinkage algorithm into the denoising process to suppress the singular noise residual and highlight useful events. The proposed approach can overcome the limits of strong coherent noise in virtual-shot gathers, which can extend the choice range for data processing. Synthetic and field examples demonstrate a promising coherent noise attenuation performance, illustrating the effectiveness and feasibility of the proposed method. The denoised migrated section exhibits a smaller depth error and higher quality. [ABSTRACT FROM AUTHOR]

Abstract Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix. The bidimensional nature of the matrix cannot well support the multidimensional analysis of spatiotemporal field data. Here, we introduce an improved tensor‐based feature analysis method for spatiotemporal field data with heterogeneous variation, by utilizing the similarity measurement in multidimensional space and feature capture of tensor decomposition. In this method, the heterogeneous spatiotemporal field data are reorganized first according to the similarity and difference within the data. The feature analysis by integrating the spatiotemporal coupling is then obtained by tensor decomposition. Since the reorganized data have a more consistent internal structure than original data, the feature analysis bias caused by heterogeneous variation in tensor decomposition can be effectively avoided. We demonstrate our method based on the climatic reanalysis field data released by the National Oceanic and Atmospheric Administration. The comparison with conventional tensor decomposition showed that the proposed method can approximate the original data more accurately both in global and local regions. Especially in the area influenced by the complex modal aliasing and in the period time of the climatic anomaly events, the approximation accuracy can be significantly improved. The proposed method can also reveal the zonal variation of temperature gradient and abnormal variations of air temperature ignored in the conventional tensor method.

A pansharpening method based on local geometrical similarity is proposed in this paper. According to the observation model, the relationships among low spatial resolution multispectral (LRMS), panchromatic (Pan) and high spatial resolution multispectral (HRMS) images are formulated. In this paper, in order to reduce the color distortion and enhance the spatial information of fused images, we propose a Pan-Sharpening method via Local Geometrical Similarity (PLGS). First, the structure similarity prior within a local region in the Pan image is employed to regularize the solution space to obtain a more accurate solution. Then, the prior is embedded into the LRMS image to enhance the spatial resolution. In order to capture better geometrical structure information, such as orientation information and geometric textures, the steerable kernel is used to calculate the similarity coefficients in a local window. Some experiments are considered on different datasets and the results show that the proposed method can improve the visual effect and the quantitative values.

Passive seismic source imaging can extract geophysical information from underground noise and has been widely utilized in geophysical research. Compared with conventional active seismic exploration, it is low-cost and eco-friendly; however, the application of passive seismic data is limited by coherent noise in the virtual-shot gathers. An approach involving direct denoising in the virtual-shot gathers has not previously been discussed; therefore, we present an iterative denoising strategy for passive seismic data. The reflection-preserving characteristic of focal transformation is adopted in the virtual-shot gathers to eliminate the coherent noise, and L1-norm sparse inversion is utilized to obtain a more accurate solution during focal transformation. A key aspect of this strategy is clean focal operator building at high noise levels. We apply local similarity as the criterion for extracting the majority of reflection energy for the focal operator. Because of strong coherent noise, a clean focal operator cannot be obtained in one iteration. We therefore obtain both denoised passive seismic data and a clean focal operator by denoising using a cleaner focal operator and operator building using updated denoising results. The presented approach can overcome the limits of coherent noise in virtual-shot gathers, which is significant for subsequent data processing and wider application. Synthetic examples achieve excellent performance in coherent noise attenuation and reflection energy reconstruction, especially in far-offset sections. [ABSTRACT FROM AUTHOR]

Few-shot learning is one of the most challenging problems in computer vision due to the difficulty of sample collection in many real-world applications. It aims at classifying a sample when the number of training samples for each identity is limited. Most of the existing few-shot learning models learn a distance metric with pairwise or triplet constraints. In this paper, we make initial attempts on learning local and global similarities simultaneously to improve the few-shot classification performance in terms of accuracy. In particular, our system differs in two aspects. Firstly, we develop a neural network to learn the pairwise local relationship between each pair of samples in the union set that is composed of support set and query set, which fully utilize the supervision. Secondly, we design a global similarity function from the manifold perspective. The latent assumption is that if the neighbors of one sample are similar to those of another sample, the global similarity between them will be high. Otherwise, the global similarity of the two samples will become very low even if the local similarity between them is high. Meanwhile, we propose a new loss according to the pairwise local loss and task-specific global loss, encouraging the model toward better generalization. Extensive experiments on three popular benchmarks (Omniglot, miniImageNet and tieredImageNet) demonstrate that our simple, yet effective approach can achieve competitive accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the rise of the network society, as the mapping Internet space, the public opinion has become the most active way of expressing social public opinion. It gradually gets deeply involved in the development and change of various social phenomena, social problems and social events, and evolves into the real politics and public management. In this context, it is of great practical significance to explore the evolution process and laws of online public opinions and systematically analyze the influence mechanism in the evolution process of online public opinions. This paper comprehensively uses the modeling simulation, empirical analysis, fuzzy systems and other research methods, adopts the reasonable abstraction of the main behavior characteristics, behavior motives and network relations of network users, and then constructs the evolution model of network public opinion in the complex social network. Besides, from the new research perspective of network members and network relations of the dynamic interaction between the government, media and netizen, this paper makes an in-depth study on the influence mechanism of the dynamic evolution of online public opinion. [ABSTRACT FROM AUTHOR]

Heterogeneity is an essential characteristic of the geographic phenomenon. However, most existing researches concerning heterogeneity are based on the matrix. The bidimensional nature of the matrix cannot well support the multidimensional analysis of spatiotemporal field data. Here, we introduce an improved tensor‐based feature analysis method for spatiotemporal field data with heterogeneous variation, by utilizing the similarity measurement in multidimensional space and feature capture of tensor decomposition. In this method, the heterogeneous spatiotemporal field data are reorganized first according to the similarity and difference within the data. The feature analysis by integrating the spatiotemporal coupling is then obtained by tensor decomposition. Since the reorganized data have a more consistent internal structure than original data, the feature analysis bias caused by heterogeneous variation in tensor decomposition can be effectively avoided. We demonstrate our method based on the climatic reanalysis field data released by the National Oceanic and Atmospheric Administration. The comparison with conventional tensor decomposition showed that the proposed method can approximate the original data more accurately both in global and local regions. Especially in the area influenced by the complex modal aliasing and in the period time of the climatic anomaly events, the approximation accuracy can be significantly improved. The proposed method can also reveal the zonal variation of temperature gradient and abnormal variations of air temperature ignored in the conventional tensor method.Plain Language Summary: The heterogeneity and the multidimensionality are essential characteristics of spatiotemporal data. However, few existing works incorporate both characteristics simultaneously in the process of feature analysis. In this paper, an improved tensor‐based method for the multidimensional analysis of spatiotemporal field data with heterogeneous variation was introduced. Specially, the local consistency of data and multidimensional feature captured by tensor decomposition are considered. The experiments verify the correctness and the advantages of our idea. We hope that our approach will provide you with an alternative method that deserves further study.Key Points: Integrating the heterogeneity to tensor decomposition can reduce the feature analysis bias caused by heterogeneous variation within the dataThe proposed method can capture features more accurately and extracts more fine structure as compared to conventional tensor methodThe proposed method can efficiently improve the performance of conventional tensor method especially for the complex variation structure [ABSTRACT FROM AUTHOR]

In this paper, we proposed methods to accurately predict pixel values by effectively using local similarity, curved surface characteristics, and edge characteristics present in an image. Furthermore, to hide more confidential data in a cover image using the prediction image composed of precisely predicted pixel values, we proposed an effective data hiding technique that applied the prediction image to the conventional reversible data hiding technique. Precise prediction of pixel values greatly increases the frequency at the peak point in the histogram of the difference sequence generated using the cover and prediction images. This considerably increases the amount of confidential data that can be hidden in the cover image. The proposed reversible data hiding algorithm (ARDHA) can hide up to 24.5% more confidential data than the existing algorithm. Moreover, it is not possible to determine the presence of hidden confidential data in stego-images, as they possess excellent visual quality. The confidential data can be extracted from the stego-image without loss, and the original cover image can be restored from the stego-image without distortion. Therefore, the proposed algorithm can be effectively used in digital image watermarking, military, and medical applications. [ABSTRACT FROM AUTHOR]

Abstract Background In modern structural bioinformatics, comparison of molecular structures aimed to identify and assess similarities and differences between them is one of the most commonly performed procedures. It gives the basis for evaluation of in silico predicted models. It constitutes the preliminary step in searching for structural motifs. In particular, it supports tracing the molecular evolution. Faced with an ever-increasing amount of available structural data, researchers need a range of methods enabling comparative analysis of the structures from either global or local perspective. Results Herein, we present a new, superposition-independent method which processes pairs of RNA 3D structures to identify their local similarities. The similarity is considered in the context of structure bending and bonds’ rotation which are described by torsion angles. In the analyzed RNA structures, the method finds the longest continuous segments that show similar torsion within a user-defined threshold. The length of the segment is provided as local similarity measure. The method has been implemented as LCS-TA algorithm (Longest Continuous Segments in Torsion Angle space) and is incorporated into our MCQ4Structures application, freely available for download from http://www.cs.put.poznan.pl/tzok/mcq/ . Conclusions The presented approach ties torsion-angle-based method of structure analysis with the idea of local similarity identification by handling continuous 3D structure segments. The first method, implemented in MCQ4Structures, has been successfully utilized in RNA-Puzzles initiative. The second one, originally applied in Euclidean space, is a component of LGA (Local-Global Alignment) algorithm commonly used in assessing protein models submitted to CASP. This unique combination of concepts implemented in LCS-TA provides a new perspective on structure quality assessment in local and quantitative aspect. A series of computational experiments show the first results of applying our method to comparison of RNA 3D models. LCS-TA can be used for identifying strengths and weaknesses in the prediction of RNA tertiary structures.

Non-invasive reconstruction of electrophysiological activity in the heart is of great significance for clinical disease prevention and surgical treatment. The distribution of transmembrane potential (TMP) in three-dimensional myocardium can help us diagnose heart diseases such as myocardial ischemia and ectopic pacing. However, the problem of solving TMP is ill-posed, and appropriate constraints need to be added. The existing state-of-art method total variation minimisation only takes advantage of the local similarity in space, which has the problem of over-smoothing, and fails to take into account the relationship among frames in the dynamic TMP sequence. In this work, the authors introduce a novel regularisation method called graph-based total variation to make up for the above shortcomings. The graph structure takes the TMP value of a time sequence on each heart node as the criterion to establish the similarity relationship among the heart. Two sets of phantom experiments were set to verify the superiority of the proposed method over the traditional constraints: infarct scar reconstruction and activation wavefront reconstruction. In addition, experiments with ten real premature ventricular contractions patient data were used to demonstrate the accuracy of the authors’ method in clinical applications.

Face sketch synthesis plays an important role in public security and digital entertainment. In this paper, we present a novel face sketch synthesis method via local similarity and nonlocal similarity regularization terms. The local similarity can overcome the technological bottlenecks of the patch representation scheme in traditional learning-based methods. It improves the quality of synthesized sketches by penalizing the dissimilar training patches (thus have very small weights or are discarded). In addition, taking the redundancy of image patches into account, a global nonlocal similarity regularization is employed to restrain the generation of the noise and maintain primitive facial features during the synthesized process. More robust synthesized results can be obtained. Extensive experiments on the public databases validate the generality, effectiveness, and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Singular value decomposition (SVD) is a useful method for random noise suppression in seismic data processing. A structure-oriented SVD (SOSVD) approach which incorporates structure prediction to the SVD filter is effcient in attenuating noise except distorting seismic events at faults and crossing points. A modified SOSVD approach using a weighted stack, called structure-oriented weighted SVD (SOWSVD), is proposed. In this approach, the SVD filter is used to attenuate noise for prediction traces of a primitive trace which are produced via the plane-wave prediction. A weighting function related to local similarity and distance between each prediction trace and the primitive trace is applied to the denoised prediction traces stacking. Both synthetic and field data examples suggest the SOWSVD performs better than the SOSVD in both suppressing random noise and preserving the information of the discontinuities for seismic data with crossing events and faults. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities. [ABSTRACT FROM AUTHOR]

Online learning techniques have been widely used in many fields where instances come one by one. However, in early stage of a data stream, online learning models cannot exhibit good classification accuracy for it cannot collect sufficient instances to learn. For example, a well-known online learning algorithm named as very fast decision tree (VFDT) needs to wait for Hoeffding bound satisfied to split, which leads to poor classification accuracy at the beginning of data stream. Thus, VFDT may not be appropriate for some real applications which demand us a fast and accurate online detection. This situation will become more serious in the scenario of data stream classification with concept drift. This paper attempts to take transfer learning algorithm to make up this shortcoming of VFDT. To achieve this goal, a new decision tree method named as VFDT-D is first proposed to cache instances in its leaf nodes to handle numerical attributes and adapt to a framework of online transfer learning (OTL), and then a measure which considers tree path, classification accuracy and classification confidence is proposed to evaluate the local similarity between source and target domain classifiers. At last, a multiple-source online transfer learning algorithm named as DMOTL is proposed to take VFDT-D as base classifier and use the proposed measure of local similarity to select the optimal source domain classifier to help transfer learning. The extensive experiments on several synthetic and real-world datasets demonstrate the advantage of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract The bottom boundary layer of a fire whirl over a fuel pool is coupled to the outer rotating flow, the vaporization process, and the subsequent diffusional burning in the gas. As such, it plays an essential role in the dynamics of the fire whirl, particularly the resulting burning rate and flame height. In this paper, the bottom boundary-layer structure of a typical laminar fire whirl, generated by a rotating screen, is investigated both numerically and theoretically. The associated local similarity solutions of the momentum and coupling functions are derived, leading to the determination of the flame configuration as well as the surface gasification rate. It is demonstrated that the bottom boundary layer becomes thinner and the flame base is closer to the pool surface as the swirl intensity increases. As a result, the applied circulation increases the burning rate and thereby the flame height of the fire whirl by enhancing the convective heat transfer and the evaporation of the fuel, especially in the outer region of the fuel pool. The theoretical results agree well with the simulation results over a wide range of the Ekman number Ek and the Grashof number Gr , both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In order to alleviate the problem o£ data sparsity in recommendation systems，based on the advantage of convolutional neural networks (CNNs) in capturing local features，we propose a local similarity prediction recommendation model based on improved CNN (LSPCNN) by adding an adjustment layer. The new model iteratively adjusts the initial user-item scoring matrix to localize user7 s interest preference. And then CNNs are integrated to predict the missing score and achieve personalized recommendation. Experimental results show that the MAE value of the LSPCNN model under different degrees o£ data sparsity decreases by an average of 4% compared with the traditional recommendation methods，and it effectively alleviates the data sparsity problem and improves the performance of recommendation systems. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is an important technology for hyperspectral data analysis, which has the challenge of high dimensionality and limited training samples. Moreover, due to the spatial continuity of material distribution, it is reasonable to improve the performance of HSI classification from the local similarity viewpoint, i.e., pixels in the same local region have a high probability to share similar features. Therefore, in this paper, a novel method based on local similarity and kernel low-rank representation is proposed to improve the classification accuracy for HSI. First, taking into consideration that pixels in the same homogeneous region share similar spectral features (termed local similarity), the HSI is segmented by the superpixel segmentation algorithm to obtain the homogeneous regions. Then, a model of kernel low-rank representation based on local similarity (KLRRLS) is proposed for HSI classification. In the model of KLRRLS, the nonlinear structure characteristics of HSI are extracted by employing the kernel trick, and the low-rank constraint is used as a prior to restrict the recovery coefficient matrix in each segmentation region, thus efficiently solving the linear non-separable problem and exploiting the contextual information of HSI. Finally, the classification results are generated by fusing the result of each segmented map via the maximum voting mechanism. Experimental results on three real HSI datasets demonstrate that the proposed method can effectively improve the classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract While the presence of an impermeable leading segment is normally inevitable for a boundary layer diffusion flame established over a fuel surface, it also offers an opportunity to control and stabilize the flame by design. In this study, the classical Emmons' problem with an impermeable leading section is investigated numerically and theoretically. The local similarity structure of the boundary layer is discussed and an explicit solution of the skin friction, local gasification rate, and flame characteristics is derived. It is shown that the existence of an impermeable leading section of length L reduces the skin friction and the gasification rate, while increases the flame height in the fuel plate section. This history effect of the flow on the boundary layer development is reduced as the streamwise distance, x , increases, and is negligible for x / L > ∼ 10. The theoretical results agree well with the numerical results, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global and local similarity information from data. In this paper, we propose a novel local similarity learning approach in the convex NMF framework, which encourages inter-class separability that is desired for clustering. Thus, the new model is capable of enhancing intra-class similarity and inter-class separability with simultaneous global and local learning. Moreover, the model learns the factor matrices in an augmented kernel space, which is a convex combination of pre-defined kernels with auto-learned weights. Thus, the learnings of cluster structure, representation factor matrix, and the optimal kernel mutually enhance each other in a seamlessly integrated model, which leads to informative representation. Multiplicative updating rules are developed with theoretical convergence guarantee. Extensive experimental results have confirmed the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we proposed methods to accurately predict pixel values by effectively using local similarity, curved surface characteristics, and edge characteristics present in an image. Furthermore, to hide more confidential data in a cover image using the prediction image composed of precisely predicted pixel values, we proposed an effective data hiding technique that applied the prediction image to the conventional reversible data hiding technique. Precise prediction of pixel values greatly increases the frequency at the peak point in the histogram of the difference sequence generated using the cover and prediction images. This considerably increases the amount of confidential data that can be hidden in the cover image. The proposed reversible data hiding algorithm (ARDHA) can hide up to 24.5% more confidential data than the existing algorithm. Moreover, it is not possible to determine the presence of hidden confidential data in stego-images, as they possess excellent visual quality. The confidential data can be extracted from the stego-image without loss, and the original cover image can be restored from the stego-image without distortion. Therefore, the proposed algorithm can be effectively used in digital image watermarking, military, and medical applications.