Global Matching Research Articles

In Silico Investigation of the Interactions Between Cotton Leaf Curl Multan Virus Proteins and the Transcriptional Gene Silencing Factors of Gossypium hirsutum L.

The highly dynamic nature of the Cotton leaf curl virus (CLCuV) complex (causing Cotton leaf curl disease, a significant global threat to cotton) presents a formidable challenge in unraveling precise molecular mechanisms governing viral-host interactions. To address this challenge, the present study investigated the molecular interactions of 6 viral proteins (Rep, TrAP, C4, C5, V2, and βC1) with 18 cotton Transcriptional Gene Silencing (TGS) proteins. Protein-protein dockings conducted for different viral-host protein pairs using Clustered Protein Docking (ClusPro) and Global RAnge Molecular Matching (GRAMM) (216 docking runs), revealed variable binding energies. The interacting pairs with the highest binding affinities were further scrutinized using bioCOmplexes COntact MAPS (COCOMAPS) server, which revealed robust binding of three viral proteins- TrAP, C4, and C5 with 14 TGS proteins, identifying several novel interactions (not reported yet by earlier studies), such as TrAP targeting DCL3, HDA6, and SUVH6; C4 targeting RAV2, CMT2, and DMT1; and C5 targeting CLSY1, RDR1, RDR2, AGO4, SAMS, and SAHH. Visualizing these interactions in PyMol provided a detailed insight into interacting regions. Further assessment of the impact of 18 variants of the C4 protein on interaction with CMT2 revealed no correlation between sequence variation and docking energies. However, conserved residues in the C4 binding regions emerged as potential targets for disrupting viral integrity. Hence, this study provides valuable insights into the viral-host interplay, advancing our understanding of Cotton leaf curl Multan virus pathogenicity and opening novel avenues for devising various antiviral strategies by targeting the host-viral interacting regions after experimental validation.

The Multiple Dimensions of Familiarity: From Representations to Phenomenology.

This article focuses on familiarity, the form of memory allowing humans to recognize stimuli that have been encountered before. We aim to emphasize its complex nature which includes representational and phenomenological dimensions. The former implies that its neural correlates depend on the type and complexity of the cue stimulus, as different classes of stimuli are represented in distributed ventral visual and medial temporal regions. The second dimension relates to the subjective feeling of familiarity, which results from a fluency signal that is attributed to past encounters with the stimulus. We review mnemonic and non-mnemonic sources of fluency that can induce a sense of familiarity, as well as cases where fluency is not attributed to memory, among which the phenomenological experience of déjà-vu. Across these two dimensions, we highlight key questions to be answered by future studies to improve our understanding of the underpinnings of this form of memory and contribute to building an integrative neurocognitive model of familiarity. Essential to this aim is the clarification of the computational, cognitive, and neural mechanisms involved, namely global matching, fluency attribution, and sharpening. Furthermore, future research is needed to unravel the relationships between these mechanisms. We argue that to achieve these goals, researchers must use appropriate behavioral paradigms and clearly define which dimension of familiarity they investigate.

Spcolor: Semantic prior guided exemplar-based image colorization

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods directly search for correspondence over the entire reference image, and this type of global matching is prone to mismatch. Intuitively, a reasonable correspondence should be established between objects which are semantically similar. Motivated by this, we introduce the idea of semantic prior and propose SPColor, a semantic prior guided exemplar-based image colorization framework. Several novel components are systematically designed in SPColor, including a semantic prior guided correspondence network (SPC), a category reduction algorithm (CRA), and a similarity masked perceptual loss (SMP loss). Different from previous methods, SPColor establishes the correspondence between the pixels in the same semantic class locally. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. In addition, SPColor supports region-level class assignments before SPC in the pipeline. With this feature, a category manipulation process (CMP) is proposed as an interactive interface to control colorization, which can also produce more varied colorization results and improve the flexibility of reference selection. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset. Our code is available at https://github.com/viector/spcolor.

GMDIC: a digital image correlation measurement method based on global matching for large deformation displacement fields

The digital image correlation method is a non-contact optical measurement method, which has the advantages of full-field measurement, simple operation, and high measurement accuracy. The traditional DIC method can accurately measure displacement and strain fields, but there are still many limitations. (i) In the measurement of large displacement deformations, the calculation accuracy of the displacement field and strain field needs to be improved due to the unreasonable setting of parameters such as subset size and step size. (ii) It is difficult to avoid under-matching or over-matching when reconstructing smooth displacement or strain fields. (iii) When processing large-scale image data, the computational complexity will be very high, resulting in slow processing speeds. In recent years, deep-learning-based DIC has shown promising capabilities in addressing the aforementioned issues. We propose a new, to the best of our knowledge, DIC method based on deep learning, which is designed for measuring displacement fields of speckle images in complex large deformations. The network combines the multi-head attention Swin-Transformer and the high-efficient channel attention module ECA and adds positional information to the features to enhance feature representation capabilities. To train the model, we constructed a displacement field dataset that conformed to the real situation and contained various types of speckle images and complex deformations. The measurement results indicate that our model achieves consistent displacement prediction accuracy with traditional DIC methods in practical experiments. Moreover, our model outperforms traditional DIC methods in cases of large displacement scenarios.

The power of one: A single flanker produces compatibility effects in the episodic flanker task

AbstractThe episodic flanker task is an episodic version of the Eriksen and Eriksen (Perception & Psychophysics, 16 (1), 143–149, 1974) perceptual flanker task, showing the same compatibility and distance effects. Subjects are presented with a list followed by a probe display in which one item is cued. The task, to indicate whether the probed letter appeared in the same position in the memory list, requires focusing attention on a single item in memory. The probe display contains flanking items to be ignored. They are same as the memory list or different. Same flankers are compatible with “yes” responses and incompatible with “no” responses. Different flankers are incompatible with “yes” responses and compatible with “no” responses. Previously, we presented multiple flankers in the probe, allowing a global matching strategy. Here, we report two episodic flanker experiments with just one flanker in the probe to encourage focusing sharply on the target. We found flanker compatibility effects in both experiments when a single flanker appeared immediately adjacent to the target. Experiment 2 varied the distance between the flanker and the target in the probe and the memory list and found the compatibility effect in response time only when the flanker was immediately adjacent to the target in both the probe and the memory list. The effect in accuracy also appeared when the flanker was two positions away in both the probe and the memory list. These results show that attention is focused sharply on elements of a memory structure during retrieval, suggesting that memory retrieval is perceptual attention turned inward.

BAMRE: Joint extraction model of Chinese medical entities and relations based on Biaffine transformation with relation attention

Electronic Health Records (EHRs) contain various valuable medical entities and their relationships. Although the extraction of biomedical relationships has achieved good results in the mining of electronic health records and the construction of biomedical knowledge bases, there are still some problems. There may be implied complex associations between entities and relationships in overlapping triplets, and ignoring these interactions may lead to a decrease in the accuracy of entity extraction. To address this issue, a joint extraction model for medical entity relations based on a relation attention mechanism is proposed. The relation extraction module identifies candidate relationships within a sentence. The attention mechanism based on these relationships assigns weights to contextual words in the sentence that are associated with different relationships. Additionally, it extracts the subject and object entities. Under a specific relationship, entity vector representations are utilized to construct a global entity matching matrix based on Biaffine transformations. This matrix is designed to enhance the semantic dependencies and relational representations between entities, enabling triplet extraction. This allows the two subtasks of named entity recognition and relation extraction to be interrelated, fully utilizing contextual information within the sentence, and effectively addresses the issue of overlapping triplets.Experimental observations from the CMeIE Chinese medical relation extraction dataset and the Baidu2019 Chinese dataset confirm that our approach yields the superior F1 score across all cutting-edge baselines. Moreover, it offers substantial performance improvements in intricate situations involving diverse overlapping patterns, multitudes of triplets, and cross-sentence triplets.

Associative asymmetry of the recognition without cued-recall effect in thematic relations

ABSTRACT In the present study, two experiments were conducted to examine whether thematic relation can produce recognition without cued-recall effect and whether the direction of the association has an influence on this effect. The participants provided higher familiarity ratings for studied items than for unstudied items during target retrieval failure. Additionally, the thematic relation-elicited recognition without cued-recall effect was larger in the forward association than in the backward association. Collectively, these results indicated that thematic relations can elicit the recognition without cued-recall effect, and this effect is asymmetrical. The current findings support the features overlap hypothesis described in the global match model.

Macroscale brain states support the control of semantic cognition

A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty – global association and feature matching judgments – which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain’s dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

Factors Contributing to Slab Locations and Geometries in Reconstructions of Past Mantle Flow

AbstractIndividual sinking slabs present markedly different geometries between 410 and 660 km depths, from vertical slabs penetrating the lower mantle to slabs stagnating above the lower mantle. The proposed factors determining these contrasted geometries include mantle viscosity and the magnitude and evolution of trench retreat. Here, we assess the success of paleo‐geographically driven global mantle flow models in matching slabs in tomographic models between 400 km and 1,000 km depth. We quantify the spatial match between predicted present‐day mantle temperature anomalies and vote maps of tomographic models. We investigate the sensitivity of the spatial match to input parameters of the mantle flow model: imposed tectonic reconstruction, model start age, and viscosity contrast between the upper and lower mantle. We evaluate the visual match between model slabs and tomographic vote maps for three circum‐Pacific regions with contrasted slab dip angles between 400 km and 1,000 km depth. Predicted model slabs better match slabs inferred from tomography when there is an increase in viscosity at 660 km depth. The temporal evolution of the models and the global match at present day suggest that the subduction history could be refined in the global tectonic reconstructions that we considered. For example, we suggest that the subduction to the east of Japan should be offset by approximately 100 km to the west at ∼80 Ma to match the anchoring of a continuous slab into the lower mantle suggested by tomography.

A Self-Distillation Contrastive Learning Architecture for Global and Local Underwater Terrain Feature Extraction and Matching

A Self-Distillation Contrastive Learning Architecture for Global and Local Underwater Terrain Feature Extraction and Matching

Development and Evaluation of a Two-Staged 3D Keypoint Based Workflow for the Co-Registration of Unstructured Multi-Temporal and Multi-Modal 3D Point Clouds

Abstract. Robust and automated point cloud registration methods are required in many geoscience applications using multi-temporal and multi-modal 3D point clouds. Therefore, a 3D keypoint-based coarse registration workflow has been implemented, utilizing the ISS keypoint detector and 3DSmoothNet descriptor. This paper contributes to keypoint-based registration research through variations of the standard workflow proposed in the literature, applying a two-staged strategy of global and local keypoint matching as well as prototypical keypoint projection and fine registration based on ICP. Further, by testing the utilized detector and descriptor on unstructured, multi-temporal and multi-source point clouds with variations in point cloud density, generalization ability is tested outside benchmark data. Therefore, data of the Bøverbreen glacier in Jotunheimen, Norway has been acquired in 2022 and 2023, deploying UAV-based image matching and terrestrial laser scanning. The results show good performance of the implemented robust matching algorithm PROSAC, requiring fewer iterations than the well-known RANSAC approach, but solving the rigid body transformation with TEASER++ is faster and more robust to outliers without demanding pre-knowledge of the data. Further, the results identify the keypoint detection as most limiting factor in speed and accuracy. Summarizing, keypoint-based coarse registration on low density point clouds, applying a global and local matching strategy and transformation estimation using TEASER++ is recommended. Keypoint projection shows potential, increasing number and precision in low density clouds, but has to be more robust. Further research needs to be carried out, focusing on identifying a fast and robust keypoint detector.

Reference-based super-resolution reconstruction of remote sensing images based on a coarse-to-fine feature matching transformer

Remote sensing image super-resolution reconstruction technology mines the deep details of remote sensing image, which has been widely used in the fields of intelligent and precise agriculture, intelligent transportation, earth surface object recognition, and so on. To obtain more detailed information,we design a reference-based super-resolution reconstruction network. Firstly, the coarse-to-fine feature matching strategy is adopted for the features of the input image. Global coarse matching is performed on the center patch of each block, and then pixel-level local fine matching is performed on the edge patches of the block. This both reduces the amount of computation and improves the matching accuracy. A threshold is set to determine whether the feature matching results meet the criteria for feature transfer. Finally, different scale features are fused through several convolutional layers and sampling operations, obtaining the reconstruction features after a fourfold increase in resolution. The ultimate super-resolution image is generated through a decoder. We have performed training and testing on remote sensing datasets. Compared to the current state-of-the-art methods, our proposed method is visually more details and outperforms other methods in terms of objective evaluation metrics.

Improvement methods for feature matching in Basalt SLAM

Abstract Visual simultaneous localization and mapping (SLAM) is widely used to provide localization information for mobile robots, but most visual SLAM algorithms lack effective data association methods, resulting in unstable position tracking results. In this paper, relevant improvement schemes for global feature matching in the Basalt SLAM algorithm are proposed to enhance the effectiveness of its data association method. Firstly, the more efficient box average difference (BAD) descriptor is used instead of the ORB descriptor. Secondly, secondary matching is performed by narrowing the matching range with the epipolar geometric constraint to expand matching pairs. Finally, grid-based motion statistics (GMS) and histogram-based rotation consistency constraints are introduced to eliminate false matches. Experiments on the EuRoC dataset demonstrate that the proposed methods significantly enhance the effectiveness and stability of visual data association and improve the localization accuracy of the Basalt SLAM system.

Person re‐identification via deep compound eye network and pose repair module

AbstractPerson re‐identification is aimed at searching for specific target pedestrians from non‐intersecting cameras. However, in real complex scenes, pedestrians are easily obscured, which makes the target pedestrian search task time‐consuming and challenging. To address the problem of pedestrians' susceptibility to occlusion, a person re‐identification via deep compound eye network (CEN) and pose repair module is proposed, which includes (1) A deep CEN based on multi‐camera logical topology is proposed, which adopts graph convolution and a Gated Recurrent Unit to capture the temporal and spatial information of pedestrian walking and finally carries out pedestrian global matching through the Siamese network; (2) An integrated spatial‐temporal information aggregation network is designed to facilitate pose repair. The target pedestrian features under the multi‐level logic topology camera are utilised as auxiliary information to repair the occluded target pedestrian image, so as to reduce the impact of pedestrian mismatch due to pose changes; (3) A joint optimisation mechanism of CEN and pose repair network is introduced, where multi‐camera logical topology inference provides auxiliary information and retrieval order for the pose repair network. The authors conducted experiments on multiple datasets, including Occluded‐DukeMTMC, CUHK‐SYSU, PRW, SLP, and UJS‐reID. The results indicate that the authors’ method achieved significant performance across these datasets. Specifically, on the CUHK‐SYSU dataset, the authors’ model achieved a top‐1 accuracy of 89.1% and a mean Average Precision accuracy of 83.1% in the recognition of occluded individuals.

Deep Convolutional Dictionary Learning Denoising Method Based on Distributed Image Patches

To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ=50, it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM.

A non-contact method for weighting of aggregate pile based on binocular vision

Concrete is indispensable in contemporary industry, and the weighing of aggregates directly determines the quality of concrete preparation. In response to the difficulty of traditional methods in meeting the industrial requirements for accuracy, efficiency, and cost of measurement, this paper proposes a non-contact bone pile weight measurement method based on binocular vision. Firstly, the on-site image is obtained through a binocular camera, and the camera parameters are used to improve the image edge information and preprocess the image. Then, the improved semi global matching (SGM) algorithm is used to improve computational efficiency, obtaining three-dimensional information of the aggregate pile for volume calculation. Based on the watershed algorithm and T-S inference, the aggregate clearance rate is calculated, and the initial volume is corrected. Finally, a linear relationship is established between the density of the aggregate pile and the clearance rate, and the weight of the aggregate pile can be obtained by the density formula. The experimental results on different sizes of aggregate piles shows that the average error of the experiment is less than 4%. Efficiency of improved SGM matching algorithm has increased several times compared to the original algorithm, providing a low-cost and efficient measurement method for industrial aggregate weighing.

Understanding the careers of freelancers on digital labor platforms: The case of IT work

AbstractOnline freelancing, an alternative form of work where independent workers offer services on digital labor platforms, gains increasing importance in IS research. While the general understanding of this form of work is growing, research lacks understanding careers on digital labor platforms. However, these differ from careers in offline labor markets due to volatility, global matching and platform mediation, the digital and temporary nature of work, and algorithmic management as particular platform working conditions. Therefore, to understand how working conditions on digital labor platforms influence the dynamic career paths of freelancers, we conduct an exploratory analysis using 35 interviews with freelancers and clients on digital labor platforms. We thus contribute to the body of knowledge on alternative forms of work on digital labor platforms by developing a long‐term freelancing career model and outlining the dynamics of advancement, decline, and exit within platform careers. We also illustrate mechanisms between career phases in terms of platform lock‐in effects, which arise from the career advancement dynamics and career exit dynamics.

QUIZ: An arbitrary volumetric point matching method for medical image registration

Rigid pre-registration involving local–global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead to varying tissue structures, or where image quality is poor, these methods tend to exhibit instability and inaccuracies. In this study, we propose a novel method for medical image registration based on arbitrary voxel point of interest matching, called query point quizzer (QUIZ). QUIZ focuses on the correspondence between local–global matching points, specifically employing CNN for feature extraction and utilizing the Transformer architecture for global point matching queries, followed by applying average displacement for local image rigid transformation.We have validated this approach on a large deformation dataset of cervical cancer patients, with results indicating substantially smaller deviations compared to state-of-the-art methods. Remarkably, even for cross-modality subjects, it achieves results surpassing the current state-of-the-art.

SFEMM: A cotton binocular matching method based on YOLOv7x.

The cotton-picking robot needs to locate the target object in space in the process of picking in the field and other outdoor strong light complex environments. The difficulty of this process was binocular matching. Therefore, this paper proposes an accurate and fast binocular matching method. This method used the deep learning model to obtain the position and shape of the target object, and then used the matching equation proposed in this paper to match the target object. Matching precision of this method for cotton matching was much higher than that of similar algorithms. It was 54.11, 45.37, 6.15, and 12.21% higher than block matching (BM), semi global block matching (SGBM), pyramid stereo matching network (PSMNet), and geometry and context for deep stereo regression (GC-net) respectively, and its speed was also the fastest. Using this new matching method, the cotton was matched and located in space. Experimental results show the effectiveness and feasibility of the algorithm.

Discriminative Domain Adaption Network for Simultaneously Removing Batch Effects and Annotating Cell Types in Single-Cell RNA-Seq.

Machine learning techniques have become increasingly important in analyzing single-cell RNA and identifying cell types, providing valuable insights into cellular development and disease mechanisms. However, the presence of batch effects poses major challenges in scRNA-seq analysis due to data distribution variation across batches. Although several batch effect mitigation algorithms have been proposed, most of them focus only on the correlation of local structure embeddings, ignoring global distribution matching and discriminative feature representation in batch correction. In this paper, we proposed the discriminative domain adaption network (D2AN) for joint batch effects correction and type annotation with single-cell RNA-seq. Specifically, we first captured the global low-dimensional embeddings of samples from the source and target domains by adversarial domain adaption strategy. Second, a contrastive loss is developed to preliminarily align the source domain samples. Moreover, the semantic alignment of class centroids in the source and target domains is achieved for further local alignment. Finally, a self-paced learning mechanism based on inter-domain loss is adopted to gradually select samples with high similarity to the target domain for training, which is used to improve the robustness of the model. Experimental results demonstrated that the proposed method on multiple real datasets outperforms several state-of-the-art methods.