Global Matching Research Articles

Regulations for carbon capture, utilization and storage: Comparative analysis of development in Europe, China and the Middle East

The development of the carbon capture, utilization and storage (CCUS) supply chain will require the establishment of a regulatory and legal regime, both at the international and domestic level, to manage risks at various parts of the supply chain and to support technology investment. Within this regulatory and legal framework, the regional and domestic regime has been identified as a crucial factor that could facilitate or inhibit the creation of a CCUS supply chain at a larger scale. According to the relevant literature, policy backing for CCUS has been supported in several countries and jurisdictions which represent both the source and sink of carbon dioxide. To evaluate the legal and regulatory developments which underpin the global matching of CO2 sources and sinks, this article reviews and critically analyzes the legal and regulatory systems to address various challenges facing the CCUS development in Europe, China and the Middle East, because these jurisdictions have good representations, as sinks and sources of CO2, in developing the CCUS supply chain. These challenges include ensuring regulatory clarity to support CCUS development, integration of CCUS in the portfolio of climate change mitigation to create an enabling environment to improve the financial viability of CCUS, and ensuring consistency with international law. The findings of this article suggest that the regulatory systems in the three regions face various challenges and this article proposes some regulatory changes that are necessary for facilitating the CCUS supply chain at the domestic and regional level.

Correlation and Calibration of 4D Seismic Vintages for Effective Postproduction Intervention

Poor or low data quality usually has an adverse effect on the quantitative usage of (4D) seismic data for accurate analysis. Repeatability of 4D Seismic or time-lapse survey is considered as a vital tool for effective, potent, and impressive monitoring of productivity of reservoirs. Inconsistencies and disagreement of ‘time-lapse’ data will greatly affect the accuracy and outcome of research when comparing two or more seismic surveys having low repeatability. Correlation is a statistic procedure that measures the linear relation between all points of two variables. Error due to acquisition and processing must be checked for before interpretation in order to minimize exploration failure and the number of dry holes drilled. The seismic data available for this study comprises of 779 crosslines and 494 inlines. The 4D seismic data consisting of the base Seismic shot in 1998 before production and the monitor Seismic shot in 2010 at different stages of hydrocarbon production were cross correlated to ascertain repeatability between the two vintages. A global average matching process was applied while phase and time shift were estimated using the Russell-Liang technique. Two pass full shaping filters were applied for the phase matching. Maximum and minimum ‘cross-correlation’ are 0.85 (85%) and 0.60 (60%) respectively. Statistics of the ‘cross-correlation’ shift show standard deviation (0.3), variance (0.12), and root mean square (0.78). For high percentage repeatability and maximum correlations, the requested correlation threshold is 0.7 but 1 and 0.99 were obtained for the first and the second matching respectively. Conclusively, the overall results show that there is high repeatability between the 4D seismic data used and the data can be employed conveniently for accurate ‘time-lapse’ (future) production monitoring and investigation on the field.

In-game Residential Home Planning via Visual Context-aware Global Relation Learning

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. Given a construction layout, we propose a visual context-aware graph generation network that learns the implicit global relations among the scene components and infers the location of a new building unit. The proposed network takes as input the scene graph and the corresponding top-view depth image. It provides the location recommendations for a newly added building units by learning an auto-regressive edge distribution conditioned on existing scenes. We also introduce a global graph-image matching loss to enhance the awareness of essential geometry semantics of the site. Qualitative and quantitative experiments demonstrate that the recommended location well reflects the implicit spatial rules of components in the residential estates, and it is instructive and practical to locate the building units in the 3D scene of the complex construction.

Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation

Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods is unsatisfactory on real datasets where the new target domain comprises of heterogeneous sub-domains (e.g. diverse weather characteristics). We point out that carefully reasoning about the multiple modalities in the target domain can improve the robustness of adaptation models. To this end, we propose a condition-guided adaptation framework that is empowered by a special attentive progressive adversarial training (APAT) mechanism and a novel self-training policy. The APAT strategy progressively performs condition-specific alignment and attentive global feature matching. The new self-training scheme exploits the adversarial ambivalences of easy and hard adaptation regions and the correlations among target sub-domains effectively. We evaluate our method (DCAA) on various adaptation scenarios where the target images vary in weather conditions. The comparisons against baselines and the state-of-the-art approaches demonstrate the superiority of DCAA over the competitors.

Proxy Graph Matching with Proximal Matching Networks

Estimating feature point correspondence is a common technique in computer vision. A line of recent data-driven approaches utilizing the graph neural networks improved the matching accuracy by a large margin. However, these learning-based methods require a lot of labeled training data, which are expensive to collect. Moreover, we find most methods are sensitive to global transforms, for example, a random rotation. On the contrary, classical geometric approaches are immune to rotational transformation though their performance is generally inferior. To tackle these issues, we propose a new learning-based matching framework, which is designed to be rotationally invariant. The model only takes geometric information as input. It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization. To justify our approach, we provide a convergence guarantee for the proximal method for graph matching. The overall performance is validated by numerical experiments. In particular, our approach is trained on the synthetic random graphs and then applied to several real-world datasets. The experimental results demonstrate that our method is robust to rotational transform and highlights its strong performance of matching accuracy.

IA-GM: A Deep Bidirectional Learning Method for Graph Matching

Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementary benefit from the optimization layer to the learning component. In this paper, we overcome the above limitation under a deep bidirectional learning framework.Our method circulates the output of the GM optimization layer to fuse with the input for affinity learning. Such direct feedback enhances the input by a feature enrichment and fusion technique, which exploits andintegrates the global matching patterns from the deviation of the similarity permuted by the current matching estimate. As a result, the circulation enables the learning component to benefit from the optimization process, taking advantage of both global feature and the embedding result which is calculated by local propagationthrough node-neighbors. Moreover, circulation consistency induces an unsupervised loss that can be implemented individually or jointly to regularize the supervised loss. Experiments on challenging datasets demonstrate the effectiveness of our methods for both supervised learning and unsupervised learning.

Global Pandemic and Plastic Surgery Residency Match: Can Social Media Fill the Void?

BackgroundThe type of content that influences plastic and reconstructive surgery (PRS) residency program selection and attracts applicants is everchanging and not clearly understood. Further, the COVID-19 pandemic had a major impact on residency selection that is undetermined.ObjectivesThe purpose of this study was to determine to what degree and the type of PRS SM content that drives prospective applicants’ interest of a residency program, especially in the context of COVID-19.MethodsProspective PRS residency applicants were surveyed anonymously.ResultsAn average of 60% of respondents reported that PRS social media (SM) content influenced their perception of a program. Fifty-eight percent reported that resident lifestyle content made them gain interest in a program. Separately, 32% reported that resident lifestyle content influenced them to rank a program higher. Seventy-two percent of respondents claimed SM content did not make them lose interest in a program. Rarely posting, outdated content, and lack of engagement were cited as factors for loss of interest in a program. A majority of respondents (53%) reported wanting to see more resident life and culture content on SM. Of the existing PRS SM content, respondents were most interested in resident lifestyle, followed by clinical and program-specific content.ConclusionsThe COVID-19 pandemic amplified the importance of SM PRS residency selection. Resident lifestyle content was consistently indicated as more likely to make respondents gain interest in a program, rank a program higher, and as the most desired content. PRS programs will benefit from highlighting resident camaraderie, quality of life, hobbies, and lifestyle to attract applicants.

Robust Frame Duplication Detection for Degraded Videos

To detect frame duplication in degraded videos, we proposed a coarse-to-fine approach based on locality-sensitive hashing and image registration. The proposed method consists of a coarse matching stage and a duplication verification step. In the coarse matching stage, visually similar frame sequences are preclustered by locality-sensitive hashing and considered as potential duplication candidates. These candidates are further checked by a duplication verification step. Being different from the existing methods, our duplication verification does not rely on a fixed distance (or correlation) threshold to judge whether two frames are identical. We resorted to image registration, which is intrinsically a global optimal matching process, to determine whether two frames coincide with each other. We integrated the stability information into the registration objective function to make the registration process more robust for degraded videos. To test the performance of the proposed method, we created a dataset, which consists of 3 subsets of different kinds of degradation and 117 forged videos in total. The experimental results show that our method outperforms state-of-the-art methods for most cases in our dataset and exhibits outstanding robustness under different conditions. Thanks to the coarse-to-fine strategy, the running time of the proposed method is also quite competitive.

Two-Stage User Identification Based on User Topology Dynamic Community Clustering

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering (UIUTDC) algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.

The optimization of parameters and matching point pairs in the 3D reconstruction of coronary artery

In coronary angiography, the accuracy of the geometric transformation matrix and the matching of corresponding points between two angiographic images with different angles is the key to achieve 3D reconstruction of coronary. However, due to the movement of heart and hospital bed which commonly occurs during clinical practice, the geometric transformation matrix obtained directly from the imaging system can not well reflect the spatial geometric relationship between different imaging coordinate systems. Therefore, according to the principle of minimizing the error between the actual vessel and projection of the 3D reconstructed vessel, this paper proposes a parameter-adjusting Levenberg-Marquarelt(PALM) algorithm combined with a trust region method to optimize the geometric transformation matrix. As for the matching of corresponding points, the traditional method is achieved by epipolar constraint, which easily leads to wrong matching point pairs. Therefore, this paper further adopts the error matrix defined by the epipolar matching error to achieve global optimal matching point pairs of vascular through dynamic programming while considering the constraints of the vascular smoothness. The experiments show that the 3D reconstruction accuracy has been dramatically improved in terms of mean back projection errors, which confirms the validity and applicability of the algorithm put forward in this paper.

An Efficient Multi-stage Object-Based Classification to Extract Urban Building Footprints from HR Satellite Images

Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

Spatial patterns of long-term trends in thunderstorms in India

Long-term trends in the frequency of thunderstorm days are investigated using fifty years of data recorded by weather observatories throughout India. An attempt has been made to identify whether the trends are spatially cohesive and collocated with regions with maximum of thunderstorm days. This analysis of trends is significant, since most of the weather over India is of convective origin and has devastating impact on life and property. There are three thunderstorm maximum regions (a) northwest Indian Himalayas and adjoining Pakistan, (b) east India and (c) extreme southern parts of peninsular India. The results of the study revealed on a monthly scale, the frequency of thunderstorm days has a consistent spatially cohesive decreasing trend over east central and northeast India, collocated with the intra-annually shifting maximum of thunderstorm activity. The trend is more diffused for the thunderstorm maxima over north and south peninsular India. Comparison with rainfall trends from previous studies indicates that this decreasing trend of thunderstorm days is in phase with decreasing trends in rainfall activity over east central and northeast Indian regions. Detailed analysis indicates that frequency of thunderstorm days over these regions decreased during the latter half of the twentieth century followed by gradually increasing trend. This corresponds to a global warming hiatus and matches similar findings around the world. It seems likely that the decreasing moisture inflow from the surrounding oceans into the Indian subcontinent throughout the year due to weakening of the land-sea thermal gradient is causing a decreasing trend in the convection during this period. Over north and south peninsular India, where circum-global planetary waves play an important role in triggering convection, the strengthening of these waves offsets the effect of the generally decreasing moisture.

Movement Patterns and Match Statistics in the National Rugby League Women's (NRLW) Premiership.

As women's rugby league grows, the need for understanding the movement patterns of the sport is essential for coaches and sports scientists. The aims of the present study were to quantify the position-specific demographics, technical match statistics, and movement patterns of the National Rugby League Women's (NRLW) Premiership and to identify whether there was a change in the intensity of play as a function of game time played. A retrospective observational study was conducted utilizing global positioning system, demographic, and match statistics collected from 117 players from all NRLW clubs across the full 2018 and 2019 seasons and were compared between the ten positions using generalized linear mixed models. The GPS data were separated into absolute (i.e., total distance, high-speed running distance, and acceleration load) and relative movement patterns (i.e., mean speed, mean high speed (> 12 km·h−1), and mean acceleration). For absolute external outputs, fullbacks covered the greatest distance (5,504 m), greatest high-speed distance (1,081 m), and most ball-carry meters (97 m), while five-eighths recorded the greatest acceleration load (1,697 m·s−2). For relative external outputs, there were no significant differences in mean speed and mean high speed between positions, while mean acceleration only significantly differed between wingers and interchanges. Only interchange players significantly decreased in mean speed as their number of minutes played increased. By understanding the load of NRLW matches, coaches, high-performance staff, and players can better prepare as the NRLW Premiership expands. These movement patterns and match statistics of NRLW matches can lay the foundation for future research as women's rugby league expands. Similarly, coaches, high-performance staff, and players can also refine conditioning practices with a greater understanding of the external output of NRLW players.

RCIVMM: A Route Choice-Based Interactive Voting Map Matching Approach for Complex Urban Road Networks

With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This article aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.

Spread, Scale-up, and Sustainability of Video Consulting in Health Care: Systematic Review and Synthesis Guided by the NASSS Framework.

BackgroundCOVID-19 has thrust video consulting into the limelight, as health care practitioners worldwide shift to delivering care remotely. Evidence suggests that video consulting is acceptable, safe, and effective in selected conditions and settings. However, research to date has mostly focused on initial adoption, with limited consideration of how video consulting can be mainstreamed and sustained.ObjectiveThis study sought to do the following: (1) review and synthesize reported opportunities, challenges, and lessons learned in the scale-up, spread, and sustainability of video consultations, and (2) identify transferable insights that can inform policy and practice.MethodsWe identified papers through systematic searches in PubMed, CINAHL, and Web of Science. Included articles reported on synchronous, video-based consultations that had spread to more than one setting beyond an initial pilot or feasibility stage, and were published since 2010. We used the Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) framework to synthesize findings relating to 7 domains: an understanding of the health condition(s) for which video consultations were being used, the material properties of the technological platform and relevant peripherals, the value proposition for patients and developers, the role of the adopter system, organizational factors, wider macro-level considerations, and emergence over time.ResultsWe identified 13 papers describing 10 different video consultation services in 6 regions, covering the following: (1) video-to-home services, connecting providers directly to the patient; (2) hub-and-spoke models, connecting a provider at a central hub to a patient at a rural center; and (3) large-scale top-down evaluations scaled up or spread across a national health administration. Services covered rehabilitation, geriatrics, cancer surgery, diabetes, and mental health, as well as general specialist care and primary care. Potential enablers of spread and scale-up included embedded leadership and the presence of a telehealth champion, appropriate reimbursement mechanisms, user-friendly technology, pre-existing staff relationships, and adaptation (of technology and services) over time. Challenges tended to be related to service development, such as the absence of a long-term strategic plan, resistance to change, cost and reimbursement issues, and the technical experience of staff. There was limited articulation of the challenges to scale-up and spread of video consultations. This was combined with a lack of theorization, with papers tending to view spread and scale-up as the sum of multiple technical implementations, rather than theorizing the distinct processes required to achieve widespread adoption.ConclusionsThere remains a significant lack of evidence that can support the spread and scale-up of video consulting. Given the recent pace of change due to COVID-19, a more definitive evidence base is urgently needed to support global efforts and match enthusiasm for extending use.

Global matching in music familiarity: How musical features combine across memory traces to increase familiarity with the whole in which they are embedded

Across three experiments, the present study tested specific hypotheses regarding how the encoding of different isolated musical feature-types (rhythm and pitch) affects perceived familiarity with later piano song clips in which those features are embedded. The results are broadly consistent with global matching approaches to the computation of the familiarity signal for a musical piece. Further, the results suggest that during the global matching process, the feature-match assessments of separately encoded instances of isolated rhythm and isolated pitch information combine additively across memory traces to increase the familiarity of the test song clip in which they are embedded. As we show through simulations, this additive combination of feature-match levels across memory traces adheres to an assumption present in the MINERVA 2 model of familiarity signal computation.

Two-Stage vSLAM Loop Closure Detection Based on Sequence Node Matching and Semi-Semantic Autoencoder

Visual scene understanding and place recognition are the most challenging problems that mobile robots must solve for to achieve autonomous navigation. To reduce the high computational complexity of many global optimal search strategies, a new two-stage loop closure detection (LCD) strategy is developed in this paper. The front-end sequence node level matching (FSNLM) algorithm is based on the local continuity constraint of the motion process, which avoids the blind search for the global optimal match, and matches the image nodes via a sliding window to accurately find the local optimal matching candidate nodesets. In addition, the back-end image level matching (BILM) algorithm combined with an improved semantic model, DeepLab_AE, uses a convolutional neural network (CNN) as a feature detector to extract visual descriptors. It replaces traditional feature detectors that are manually designed by researchers in the computer vision field and cannot be applied to all environments. Finally, the performance of the two-stage LCD algorithm is evaluated on five public datasets, and is compared with the performance of other state-of-the-art algorithms. The evaluation results prove that the proposed method compares favorably with other algorithms.

Projection aided digital shearography scanning detection technology

Digital shearography has been widely used in non-destructive testing of composite materials. But the conventional detection area is limited and the defect detection of large-size composite materials is difficult to achieve. A digital shearography assisted with projection scanning detection for large-size composite materials was introduced. Based on digital shearography and the auxiliary projection which could project additional surface features, global matching and coordinate unification of the sub-view original images and the speckle interferograms were calculated by the homography matrix of the corresponding projection images to realize the multi-field scanning and matching stitching of projection, origin, and interferogram images. At the same time, a 4f optical path and an ultra-wide-angle lens were imported for the one detection to expand the detection area. Experimental results show that the scanning detection system can achieve more accurate measurement of the defect position and size. The root mean square error of location positioning is 7.0 mm, and the root mean square value of dimension measurement error is 4.9 mm. At a working distance of 1.2 m, the single detection area can reach 600 mm×500 mm and the global scanning detection area can reach 3.5 m×4.0 m. This method has the advantages of strong resistance to rigid body displacement interference and high sensitivity of defect detection, which is suitable for non-destructive testing of large-size and high performance composite materials.

Research on the Application of Intelligent Choreography for Musical Theater Based on Mixture Density Network Algorithm.

Musical choreography is usually completed by professional choreographers, which is very professional and time-consuming. In order to realize the intelligent choreography of musical, based on the mixed density network (MDN), this paper generates the dance matching with the target music through three steps: motion generation, motion screening, and feature matching. The choreography results in this paper have a high degree of matching with music, which makes it possible for the development of motion capture technology and artificial intelligence and computer automatic choreography based on music. In the process of motion generation, the average value of Gaussian model output by MDN is used as the bone position and the consistency of motion is measured according to the change rate of joint velocity in adjacent frames in the process of motion selection. Compared with the existing studies, the dance generated in this paper has improved in motion coherence and realism. In this paper, a multilevel music and action feature matching algorithm combining global feature matching and local feature matching is proposed. The algorithm improves the unity and coherence of music and action. The algorithm proposed in this paper improves the consistency and novelty of movement, the compatibility with music, and the controllability of dance characteristics. Therefore, the algorithm in this paper technically changes the way of artistic creation and provides the possibility for the development of motion capture technology and artificial intelligence.

Regulations for carbon capture, utilization and storage: Comparative analysis of development in Europe, China and the Middle East

The development of the carbon capture, utilization and storage (CCUS) supply chain will require the establishment of a regulatory and legal regime, both at the international and domestic level, to manage risks at various parts of the supply chain and to support technology investment. Within this regulatory and legal framework, the regional and domestic regime has been identified as a crucial factor that could facilitate or inhibit the creation of a CCUS supply chain at a larger scale. According to the relevant literature, policy backing for CCUS has been supported in several countries and jurisdictions which represent both the source and sink of carbon dioxide. To evaluate the legal and regulatory developments which underpin the global matching of CO2 sources and sinks, this article reviews and critically analyzes the legal and regulatory systems to address various challenges facing the CCUS development in Europe, China and the Middle East, because these jurisdictions have good representations, as sinks and sources of CO2, in developing the CCUS supply chain. These challenges include ensuring regulatory clarity to support CCUS development, integration of CCUS in the portfolio of climate change mitigation to create an enabling environment to improve the financial viability of CCUS, and ensuring consistency with international law. The findings of this article suggest that the regulatory systems in the three regions face various challenges and this article proposes some regulatory changes that are necessary for facilitating the CCUS supply chain at the domestic and regional level.