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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]