CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available.Comment: Accepted by ICCV 201

Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Key Component Capture and Safety Intelligent Analysis of Beam String Structure Based on Digital Twins

In the construction process of beam string structures, the environmental effect and corresponding mechanical properties of the structure are complex. The problem of the misjudgment of structural safety performance caused by the uncertainty of a structural mechanical parameter analysis under various factors needs to be solved. In this study, a method for capturing key components and an intelligent safety analysis of beam string structures based on digital twins (DTs) was proposed. Combined with the characteristics of DTs mapping feedback, a component capture and security analysis framework was formed. Driven by twin framework, multi-source data for structural safety analysis were obtained and the parameter association mechanism established. Considering the space-time evolution and the interaction between the virtual and real elements of the construction process, a multidimensional model was established. Driven by the Dempster–Shafer (D–S) evidence theory, the fusion of structural mechanics parameters was carried out. The safety of the structure was analyzed intelligently by capturing key structural components, thereby providing a basis for the safety maintenance of the structure. The integration of DTs modeling and multi-source data improves the accuracy and intelligence of structural construction safety analysis. In the analysis process, capturing the key components of the structure is the core step. Taking the construction process of a string supported beam roof (symmetrical structure) in a convention and exhibition center as an example, the outlined research method was applied. Based on DTs and D–S evidence theory, the variation degree of mechanical parameters of various components under temperature was determined. By comprehensively investigating the changes of various mechanical parameters, the key components of the structure were captured. Thus, the intelligent analysis of structural safety was realized. The comparison of data verified that the intelligent method can effectively analyze the safety performance of the structure

Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network

In the construction process of a prestressed steel structure, it is a point of research interest to obtain the safety state of the structure according to the design parameters and working conditions of the structure. The intelligent prediction of structural construction safety provides the basis for safety control. This study proposes an intelligent prediction method of structural construction safety based on a back propagation (BP) neural network. Firstly, the correlation mechanism of structural construction safety performance parameters is established, which involves structural design parameters and mechanical parameters. According to the basic principle of a BP neural network, the relationship between design parameters and mechanical parameters is captured. The virtual model of a structure construction process is established based on digital twins (DTs). The DTs and BP neural network are combined to form a structural safety intelligent prediction framework and theoretical method, setting working conditions in a twin model to obtain mechanical parameters. Mechanical parameters are intelligently predicted by design parameters in neural networks. The safety performance of structure construction is evaluated according to mechanical parameters. Finally, the intelligent prediction method is applied to the construction process of string beam. Based on DTs and BP neural network, the intelligent analysis of structural construction safety is carried out. This provides a reliable basis for safety control. The feasibility of this research method is verified by comparing the predicted results of the theoretical method with the measured data on site

Intelligent Prediction of Prestressed Steel Structure Construction Safety Based on BP Neural Network

In the construction process of a prestressed steel structure, it is a point of research interest to obtain the safety state of the structure according to the design parameters and working conditions of the structure. The intelligent prediction of structural construction safety provides the basis for safety control. This study proposes an intelligent prediction method of structural construction safety based on a back propagation (BP) neural network. Firstly, the correlation mechanism of structural construction safety performance parameters is established, which involves structural design parameters and mechanical parameters. According to the basic principle of a BP neural network, the relationship between design parameters and mechanical parameters is captured. The virtual model of a structure construction process is established based on digital twins (DTs). The DTs and BP neural network are combined to form a structural safety intelligent prediction framework and theoretical method, setting working conditions in a twin model to obtain mechanical parameters. Mechanical parameters are intelligently predicted by design parameters in neural networks. The safety performance of structure construction is evaluated according to mechanical parameters. Finally, the intelligent prediction method is applied to the construction process of string beam. Based on DTs and BP neural network, the intelligent analysis of structural construction safety is carried out. This provides a reliable basis for safety control. The feasibility of this research method is verified by comparing the predicted results of the theoretical method with the measured data on site

Intelligent Analysis for Safety-Influencing Factors of Prestressed Steel Structures Based on Digital Twins and Random Forest

The structure of a prestressed steel structure is complex, which can result in insufficient control accuracy and the low efficiency of the structural safety. The traditional analysis method only obtains the mechanical parameters of the structure and it cannot obtain the key factors that affect the structural safety. In order to improve the intelligence level of the structural safety performance analysis, this study proposes an intelligent analysis for the safety-influencing factors of prestressed steel structures that is based on digital twins (DTs) and random forest (RF). Firstly, the high-precision twin modeling is carried out by the weighted average method. The design parameters and the mechanical parameters of the structure are extracted in real time in the twin model, and the parameters are classified by the RF. The fusion mechanism of the DTs and RF is formed, and the intelligent analysis model of the structural safety factors is established. Driven by the analysis model, the correlation mechanism between the design parameters and the mechanical parameters is formed. The safety state of the structure is judged by the mechanical parameters, and the key design parameters that affect the various mechanical parameters are analyzed. Through the integration of the design parameters and mechanical parameters, the intelligent analysis process of the safety-influencing factors of prestressed steel structures is formed. Finally, an intelligent analysis of the importance of the safety-influencing factors is carried out with the string-supported beam structure as the test object. Driven by the integration of DTs and RF, the key design parameters that affect the various mechanical parameters are accurately obtained, which provides a basis for the intelligent control of the structural safety

PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification

In person re-identification (ReID), very recent researches have validated pre-training the models on unlabelled person images is much better than on ImageNet. However, these researches directly apply the existing self-supervised learning (SSL) methods designed for image classification to ReID without any adaption in the framework. These SSL methods match the outputs of local views (e.g., red T-shirt, blue shorts) to those of the global views at the same time, losing lots of details. In this paper, we propose a ReID-specific pre-training method, Part-Aware Self-Supervised pre-training (PASS), which can generate part-level features to offer fine-grained information and is more suitable for ReID. PASS divides the images into several local areas, and the local views randomly cropped from each area are assigned with a specific learnable [PART] token. On the other hand, the [PART]s of all local areas are also appended to the global views. PASS learns to match the output of the local views and global views on the same [PART]. That is, the learned [PART] of the local views from a local area is only matched with the corresponding [PART] learned from the global views. As a result, each [PART] can focus on a specific local area of the image and extracts fine-grained information of this area. Experiments show PASS sets the new state-of-the-art performances on Market1501 and MSMT17 on various ReID tasks, e.g., vanilla ViT-S/16 pre-trained by PASS achieves 92.2\%/90.2\%/88.5\% mAP accuracy on Market1501 for supervised/UDA/USL ReID. Our codes are available at https://github.com/CASIA-IVA-Lab/PASS-reID.Comment: Accepted by ECCV2022. Codes are available at https://github.com/CASIA-IVA-Lab/PASS-reI