Matching Problem Research Articles

A Generic Approach to Entity Resolution Mechanisms for Big Data on Real World Match Problems in the Global Oil and Gas Sector

Complex challenges are facing the global oil and gas industry. Oil prices are dropping due to OPEC production level, US oil boom, and other factors. Many experts believe that prices of oil will remain low for years at equilibrium of around $40-50 (Blumberg, 2018; Walls and Zheng 2018; Azar, 2019). Although 2019 oil price is expected to average at $65 with a further decline at $62 by 2020 (Amadeo, 2019; Kasim, 2019). Also, newly commercial resources are extremely expensive to develop, as massive capital investments are required. This research intends to develop a comprehensive entity resolution framework that has the ability to search across multiple databases with disparate forms, tame large amounts of data very quickly, efficiently resolving multiple entities into one, as well as finding hidden connections without human intervention. Putting in place a system to manage these entities will not only help to better assign resources, but to do so in a more expedient fashion. Although the necessary information is mostly already available within the oil and gas companies, it is spread around different company areas and application. Entity resolution will helps to aggregate these data, identify and exploit connection between entities and offer holistic all-in-one information that can helps to identify and deal with potential risk. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from Opec database are not sufficiently solved with conventional approaches based on the similarity of attribute values.

Towards Real-Time 3D Terrain Reconstruction from Aerial Imagery

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results.

Constrained many-to-many point matching in two dimensions

In the minimum-weight many-to-many point matching problem, we are given a set R of red points and a set B of blue points in the plane, of total size N, and we want to pair up each point in R to one or more points in B and vice versa so that the sum of distances between the paired points is minimized. This problem can be solved in O(N3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(N^3)$$\\end{document} time by using a reduction to the minimum-weight perfect matching problem, and thus, it is not fast enough to be used for on-line systems where a large number of tunes need to be compared. Motivated by similarity problems in music theory, in this paper we study several constrained minimum-weight many-to-many point matching problems in which the allowed pairings are given by geometric restrictions, i.e., a bichromatic pair can be matched if and only if the corresponding points satisfy a specific condition of closeness. We provide algorithms to solve these constrained versions in O(N) time when the sets R and B are given ordered by abscissa.

Multi-stage probabilistic linguistic matching method with the screening mechanism and individual preference relationship fusion

Considering the diversification of transaction information and complexity of matching environment, the original information with the language tendency plays an important role. Thus, under this circumstance, this paper gives a matching decision-making system under the probabilistic linguistic environment so as to improve the matching efficiency and quality. First, this paper demonstrates the multi-stage two-sided matching problem and illustrates the system frame. Then, for the multiple indicators, a probabilistic linguistic integrated cloud Bayesian network is constructed to present the dependency relationship, and determine the corresponding probability. It is known that the accuracy of agents’ preference information acts on the stability of final matching results, which may involve the strong ranking, agents’ psychological preferences and personal interests, etc. Thus, the improved ORESTE (organísation, rangement et Synthèse de données relarionnelles, in French) method is introduced to derive strong ranking and determine preference, indifference, and incomparability relation (PIR). Furthermore, this paper constructs the dynamic two-sided matching model considering the screening effect. Finally, a case study in second-hand house transaction is used to demonstrate the matching process. Simulation and comparison analysis validate its feasibility and effectiveness.

Popular Matchings with One-Sided Bias

Let G = (A ∪ B, E) be a bipartite graph where the set A consists of agents or main players and the set B consists of jobs or secondary players. Every vertex in A ∪ B has a strict ranking of its neighbors. A matching M is popular if for any matching N , the number of vertices that prefer M to N is at least the number that prefer N to M . Popular matchings always exist in G since every stable matching is popular. A matching M is A -popular if for any matching N , the number of agents (i.e., vertices in A ) that prefer M to N is at least the number of agents that prefer N to M . Unlike popular matchings, A -popular matchings need not exist in a given instance G and there is a simple linear time algorithm to decide if G admits an A -popular matching and compute one, if so. We consider the problem of deciding if G admits a matching that is both popular and A -popular and finding one, if so. We call such matchings fully popular . A fully popular matching is useful when A is the more important side—so along with overall popularity, we would like to maintain “popularity within the set A ”. A fully popular matching is not necessarily a min-size/max-size popular matching and all known polynomial-time algorithms for popular matching problems compute either min-size or max-size popular matchings. Here we show a linear time algorithm for the fully popular matching problem, thus our result shows a new tractable subclass of popular matchings.

Dense-YOLOv7: improved real-time insulator detection framework based on YOLOv7

Abstract Due to various mechanical and electrical stresses caused by changes in environmental and power load conditions, insulators may fail, resulting in serious economic losses. Manual cleaning of insulators is subject to weather and environmental constraints and poses significant safety risks. Traditional insulator detection methods have problems such as slow detection speed and poor robustness. Therefore, this paper proposes a real-time insulator detection algorithm based on the improved YOLOv7. First, in order to effectively streamline the number of parameters, Dense-YOLOv7 adopts the dense connection concept of DenseNet to design DenseBlock. Second, replacing the loss function with Focal Loss to solve the problem of unbalanced matching of foreground and background sample quantities has improved the detection accuracy of the model. Finally, to address the issue of PReLU and LeakyReLU activation functions being insensitive to spatial information, the activation function is replaced with FReLU to improve the robustness of the model. The experimental dataset used in this paper is a combination of the Chinese Power Transmission Line Insulator Dataset (CPTLID) and our own dataset. The experimental results show that the improved algorithm in this paper has only 44.23 M parameters and a detection speed of 44.87 FPS, which is 4.8% less than that of YOLOv7 and 8.14% quicker than that of YOLOv7. Experimental results show that Dense-YOLOv7 can significantly streamline the model parameter size while maintaining high accuracy and effectively improve the detection speed, which can meet the application requirements of real-time insulator detection.

Assisted Upscaling of Miscible CO2-Enhanced Oil Recovery Floods Using an Artificial Neural Network-Based Optimisation Algorithm

Due to the high computing cost of the fine-scale compositional simulations needed to effectively model miscible CO2 flooding, upscaling techniques are needed to approximate the behaviour of these fine-scale grids on more realistic coarse-scale models. The use of transport coefficients to better represent small-scale interactions, such as the time-dependent flux of the components within the hydrocarbon phases (molecular diffusion), and the pseudoisation of relative permeabilities to ensure the matching of large-scale effects, such as the volumetric fluxes of the phases, are two of these procedures. Most times, a mismatch between the phase fluxes of the integrated fine-scale and that of the coarse-scale is observed. By adjusting or calibrating some of the generated coarse-scale pseudo functions, such as the transport coefficients, absolute permeability, or relative permeability endpoints, the accuracy of the upscaling results can be improved. This procedure can be treated a reservoir history matching problem which is typically computationally expensive. In this study, we provide a framework for representing the dynamics of small-scale molecular diffusion and macro-scale heterogeneity-induced channelling related to miscible CO2 displacements on upscaled coarser grid reservoir models. The method used was based on the pseudoisation of relative permeability and transport coefficients and was applied to two benchmark reservoir models from the Society of Petroleum Engineers (SPE). Our results demonstrated that using effectively calibrated transport coefficients improved the upscaling results, so that the calculated pseudo-relative permeability functions can be ignored. We proposed a unique approach to upscaling miscible floods that utilised a genetic algorithm and a neural-network-based proxy model to minimise the associated computing cost. The data-driven approximation model considerably decreased the computing cost associated with the assisted tuning technique, and the optimisation algorithm was used to reduce the error between the predictions of the upscaled models. In conclusion, the methodology described in this study effectively captured the small- and large-scale behaviour related to the miscible displacements on upscaled coarse-scale reservoir models while reduced associated computational costs.

Person group detection with global trajectory extraction in a disjoint camera network

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people’s trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP.

EGLR: Two-staged Explanation Generation and Language Reasoning framework for commonsense question answering

In the field of natural language processing, strong commonsense reasoning abilities are often required for intelligent systems to excel in the task of commonsense question answering (QA). To enhance the interpretability of QA systems, a natural approach is to generate textual explanations in addition to the predicted answers, ensuring the understandability of the results. Recent efforts address this by using a prompted language model (LM) with frozen parameters to generate explanations in a few-shot manner. These explanations are then used as additional context to guide a finetuned LM in making the final decision. However, these methods still underutilize the semantic information embedded in the explanatory text. Consequently, the reasoning models tend to rely on word co-occurrence and the knowledge stored in the model itself rather than fully exploiting the explanations. Therefore, we propose a two-staged Explanation Generation and Language Reasoning Framework (EGLR), Our framework takes advantage of the in-context learning capability of LMs to generate explanations and reformulates the reasoning task based on explanations as a semantic matching problem. Through joint prompting and training, our model can select the most appropriate explanation by comparing multiple explanations. Experimental results on three public datasets demonstrate that our framework achieves superior performance on the full dataset while maintaining performance on out-of-domain scenarios.

An optimized system for sensor ontology meta‐matching using swarm intelligent algorithm

AbstractIt is beneficial to annotate sensor data with distinct sensor ontologies in order to facilitate interoperability among different sensor systems. However, for this interoperability to be possible, comparable sensor ontologies are required since it is essential to make meaningful links between relevant sensor data. Swarm Intelligent Algorithms (SIAs), namely the Beetle Swarm Optimisation Algorithm (BSO), present a possible answer to ontology matching problems. This research focuses on a method for optimizing ontology alignment that employs BSO. A novel method for effectively controlling memory use and striking a balance between algorithm exploration and exploitation is proposed: the Simulated Annealing‐based Beetle Swarm Optimisation Algorithm (SA‐BSO). Utilizing Gray code for solution encoding, two compact operators for exploitation and exploration, and Probability Vectors (PVs) for swarming choosing exploitation and exploration, SA‐BSO combines simulated annealing with the beetle search process. Through inter‐swarm communication in every generation, SA‐BSO improves search efficiency in addressing sensor ontology matching. Three pairs of real sensor ontologies and the Conference track were used in the study to assess SA‐BSO's efficacy. Statistics show that SA‐BSO‐based ontology matching successfully aligns sensor ontologies and other general ontologies, particularly in conference planning scenarios.

An errors-unidentified hand-eye calibration method via robot relocalization and iterative combinatorial refinement

The accuracy of hand-eye calibration is easily affected by robot kinematic error, measurement error, random error, etc., and can be to a certain extent enhanced by the complicated error identification. To address this problem, we propose a novel errors-unidentified hand-eye calibration method. A relocalization-based hand-eye calibration is implemented to overcome the challenge from robot kinematics at first owing to the high robot relocalization accuracy. The tool center point (TCP) coordinates are then obtained based on an iterative reweighted least squares (IRLS) spherical fitting algorithm. An iterative combinatorial refinement algorithm is finally presented to search the solutions in the form of permutation and combination when solving the rigid transformation matrix. Experiments on criterion sphere and blade demonstrate that by converting the hand-eye matrix solving problem into a point set matching problem, the accuracy of the proposed method is enhanced by 60% on average compared with three state-of-the-art hand-eye calibration methods.

Model matching problems for impulsive linear systems with polytopic uncertainties

This work deals with the problem of designing a feedback compensator that forces the output of a linear system with abrupt discontinuities in the state evolution and polytopic uncertainties to match that of a given model with the same features. First, the case in which the system and the model are initialized at zero and output matching is required to be exact is considered. Then, the case in which, for arbitrary initialization, output matching is required to be asymptotic for sufficiently slow sequences of the time instants wherein the state exhibits abrupt discontinuities is studied. In addition, on the assumption that the model is stable for sufficiently slow jump time sequences, also the further requirement that asymptotic output matching be achieved with stability of the compensated system is investigated. Constructive, directly checkable, solvability conditions for the problems addressed are derived by leveraging on appropriate structural notions and geometric tools. Algorithmic procedures for the synthesis of the compensators, when the solvability conditions are met, are devised. Some illustrative examples conclude the work.

Refined computational complexities of Hospitals/Residents problem with regional caps

The Hospitals/Residents problem (HR) is a many-to-one matching problem whose solution concept is stability. It is widely used in assignment systems such as assigning medical students (residents) to hospitals. To resolve imbalance in the number of residents assigned to hospitals, an extension called HR with regional caps (HRRC) was introduced. In this problem, a positive integer (called a regional cap) is associated with a subset of hospitals (called a region), and the total number of residents assigned to hospitals in a region must be at most its regional cap. Kamada and Kojima [1,2] defined strong stability for HRRC and demonstrated that a strongly stable matching does not necessarily exist. Recently, Aziz et al. [3] proved that the problem of determining if a strongly stable matching exists is NP-complete in general. In this paper, we refine Aziz et al.'s result by investigating the computational complexity of the problem in terms of the length of preference lists, the size of regions, and whether or not regions can overlap, and completely classify tractable and intractable cases.

Matching Market Design: Resource Matching Optimization in the Epidemic and Application of the Matching Theory

During the Covid-19 pandemic, the problem of inefficiency and one-sidedness of resources matching occurred, and economists proposed an innovative scheme-- reserve system. Different categories of patients, such as those with different symptoms, health care workers, and the elderly and young population, need to be matched with extremely limited medical resources (ventilator as study object in this research) in the epidemic. The upgrade from the current priority system to the reserve system which is capable of classified allocation is more in line with the fair and comprehensive ethical values, thus solving the complex matching problem better. Based on this original reserve system, this study further assists in explaining the concept connotation of its research on matching process, proposes possible improvements, and clarifies its effects and value. Moreover, based on this idea of matching, this study analyzes its role in the matching problem of "undergraduate-college".

Radiation-Variation Insensitive Coarse-to-Fine Image Registration for Infrared and Visible Remote Sensing Based on Zero-Shot Learning

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of radiation variation between heterogeneous sensors, which is caused by different imaging principles. In addition, the existence of sparse textures in infrared images as well as in some scenes and the small number of relevant trainable datasets also hinder the development of this field. Therefore, we combined data-driven and knowledge-driven methods to propose a Radiation-variation Insensitive, Zero-shot learning-based Registration (RIZER). First, RIZER, as a whole, adopts a detector-free coarse-to-fine registration framework, and the data-driven methods use a Transformer based on zero-shot learning. Next, the knowledge-driven methods are embodied in the coarse-level matches, where we adopt the strategy of seeking reliability by introducing the HNSW algorithm and employing a priori knowledge of local geometric soft constraints. Then, we simulate the matching strategy of the human eye to transform the matching problem into a model-fitting problem and employ a multi-constrained incremental matching approach. Finally, after fine-level coordinate fine tuning, we propose an outlier culling algorithm that only requires very few iterations. Meanwhile, we propose a multi-scene infrared and visible remote sensing image registration dataset. After testing, RIZER achieved a correct matching rate of 99.55% with an RMSE of 1.36 and had an advantage in the number of correct matches, as well as a good generalization ability for other multimodal images, achieving the best results when compared to some traditional and state-of-the-art multimodal registration algorithms.

Annealed quantitative estimates for the quadratic 2D-discrete random matching problem

We study a random matching problem on closed compact 2-dimensional Riemannian manifolds (with respect to the squared Riemannian distance), with samples of random points whose common law is absolutely continuous with respect to the volume measure with strictly positive and bounded density. We show that given two sequences of numbers n and m=m(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m=m(n)$$\\end{document} of points, asymptotically equivalent as n goes to infinity, the optimal transport plan between the two empirical measures μn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu ^n$$\\end{document} and νm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ u ^{m}$$\\end{document} is quantitatively well-approximated by (Id,exp(∇hn))#μn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\big (\ ext {Id},\\exp (\ abla h^{n})\\big )_\\#\\mu ^n$$\\end{document} where hn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$h^{n}$$\\end{document} solves a linear elliptic PDE obtained by a regularized first-order linearization of the Monge–Ampère equation. This is obtained in the case of samples of correlated random points for which a stretched exponential decay of the α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document}-mixing coefficient holds and for a class of discrete-time sub-geometrically ergodic Markov chains having a unique absolutely continuous invariant measure with respect to the volume measure.

SCGTracker: Spatio-temporal correlation and graph neural networks for multiple object tracking

Multiple Object Tracking is an important vitally important fundamental task in computer vision. Visual tracking becomes challenging when objects move in groups and are obscured from each other. There are two mainstream solution strategies for these group models. One is to transform the data association problem into a graph matching problem for solving, while the other is to apply the social power model as an advanced constraint for group tracking. In the former case, the solving difficulty geometric growth as the number of tracked objects increases, and the computing efficiency for real-time tracking demand cannot be met. The latter strategy tends to set up fixed-size groups or offline training rules, resulting in a lack of flexibility that limits their scenario generalization. According to the shortcomings of existing methods, this paper proposes a novel multiple object tracking method with spatio-temporal correlation and graph neural networks. Firstly, the relational features of the historical trajectories are extracted through the spatio-temporal relationship learning module, which models the spatio-temporal correlations of the objects and dynamically constructs the group structure online. Then, the graph neural network is combined with appearance and motion information, and the similarity between each detection and tracklet is used as a weight in node feature aggregation to make powerful distinctions between node features. Meanwhile, the spatio-temporal correlation method is also used to solve target loss issues caused by occlusion. Even collocated with linearly assigned data association method, good tracking results are still achieved, with a low computational complexity. Experiments on three challenging public datasets, namely MOT16, MOT17, and MOT20, validated the accuracy and efficiency of the proposed tracking method.

Investigation into the matching problem between insurance demands and insurance supply in Shanxi Province, China

Abstract With the development of the social economy and the improvement of people’s living standards, the role of insurance in society has become increasingly important. The insurance industry’s growth is not in line with social demands. Insufficient insurance capacity or a mismatch between insurance products and demands in specific regions or industries can lead to increased social risks and unprotected interests. Taking Shanxi province as an example, it is a substantial industrial and energy base in China with abundant resources such as coal and power. Shanxi province is facing challenges of transformation and upgrading due to the transformation of economic structure and the increasing environmental awareness. The matching problem between social demands and insurance capacity becomes particularly important in this process. This paper aims to examine the issue of insurance demand and supply matching, using Shanxi province as a case study. The demand for insurance protection is increasing as social risks increase with rapid economic development and improvements in people’s living standards. However, there is a gap between insurance capacity and social demands, which limits the development of the insurance industry and the effective management of social risks. The characteristics of insurance demands and the current situation of insurance supply in Shanxi province are analyzed through a literature review and empirical research methods in this paper. It discusses the problems and shortcomings of the insurance industry in meeting social demands and proposes a series of policy recommendations to promote the matching of insurance demands and supply.

On a many-sided matching problem with mixed preferences

Motivated by recent results on lexicographic and cyclic preferences, we present new sufficient conditions for the existence of stable matching in many-sided matching problems. Here, our focus shifted towards integrating the two-sided matching problem, characterized by reciprocal preferences, with the many-sided matching problem, which involves cyclic preferences. In particular, we show that one of the configurations presented recently by Zhang and Zhong for three-sided matching problems can be generalized to more dimensions. In our setting, the preferences are cyclic and, in the case of all but two pairs of consecutive sets of agents, also reciprocal, which generalizes the three-set setting of Zhang and Zhong. Our approach can be also applied to generalize the problems with any system of cyclic preferences for which the existence of a stable matching is guaranteed.

AMatFormer: Efficient Feature Matching via Anchor Matching Transformer

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have been exploited to address this issue. However, in many scenes, features are coming with large-scale, redundant and outliers contaminated. Previous self-/cross-attention models generally conduct message passing on all primal features which thus lead to redundant learning and high computational cost. To mitigate limitations, inspired by recent seed matching methods, in this paper, we propose a novel efficient Anchor Matching Transformer (AMatFormer) for the feature matching problem. AMatFormer has two main aspects: First, it mainly conducts self-/cross-attention on some anchor features and leverages these anchor features as message bottleneck to learn the representations for all primal features. Thus, it can be implemented efficiently and compactly. Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem. Experiments on several benchmarks demonstrate the effectiveness and efficiency of the proposed AMatFormer matching approach.