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Abstract [Objective] WRKY is one of the plant specific transcription factor families, involved in various plant life activities. However, currently there are few reports related to the WRKY70 gene in strawberry. Analyzing the role of WRKY70 homologous genes in strawberry in response to stress will help accelerate the application of molecular breeding technology and cultivate new strawberry germplasm resources. [Methods] The FaWRKY70 gene was cloned from ‘Benihoppe’ strawberry fruits using homologous cloning method. Its conserved domain, physicochemical properties, protein structure, and evolutionary relationship were analyzed using bioinformatics. Expression pattern analysis was performed using qRT-PCR. [Results] The FaWRKY70 gene had a length of 1 020 bp and encodes 339 amino acids. Homologous gene alignment revealed that FaWRKY70 had a high degree of amino acid sequence similarity with species in the same family such as apple and peony. The homologous genes were mostly related to plant responses to biotic and abiotic stress, suggesting that FaWRKY70 may be involved in resistance to stress. FaWRKY70 was expressed in different organs of strawberry, with significant differences. The expression level was highest in flowers and lowest in fruits. Under salicylic acid treatment, the FaWRKY70 gene was quickly responded, reaching its highest expression level after 3 hours, and gradually decreased thereafter. Under the treatment with methyl jasmonate, the FaWRKY70 gene showed a slight degree of induction and an overall downward trend. [Conclusion] FaWRKY70 is involved in growth and hormone signal transduction through different response modes in strawberry.

After the completion of the three steps of "circling back", the lunar exploration has changed from the short-term single point exploration to the long-term exploration of lunar scientific research stations with the con-struction of lunar surface infrastructure, the higher requirements for the planning and demonstration, overall de-sign, system development and in-orbit exploration of lunar exploration missions are put forward. The thought of model based system engineering (MBSE) is adopted, a model-based forward process for the system analysis of the lunar scientific research station proposal is proposed, and the system model is taken as a carrier to analyze the mis-sion overall, mission requirements and task application scenario. By carrying out the model-based mission analysis of the lunar scientific research station, the process of the mission of the lunar scientific research station has been pre-liminarily realized.

Domain adaption has attracted quite a lot of interest in object detection. It is hoped that domain adaption can mitigate the discrepancy between the source domain with label-rich and the target domain with no label or label-scarce. In this paper, we address the domain transfer issue from three aspects: (1) We design a synthetic image generator with automatically labelled based on 3d scenes. (2) A structured domain adaptation framework was designed and two domain adapters on global-local levels, respectively, were constructed to mitigate the cross-domain gap. (3) By the virtue of synthetic data, different strategies are adopted to deal with sample transferability in the global and local levels, respectively, to eliminate the performance bottleneck. Recently, adversarial-based methods were used to make alignment with distributions for source and target objects that have been proven effective when adapting object classifiers. We reinforce the two domain adapters to learn a domain classifier with synthetic data and a consistency regularisation is applied to boost the performance. We conduct extensive experiments to demonstrate that the proposed model achieves large performance margins, which show significant advances over the other state-of-the-art models, performing a promotion of 10% −15% mAP on various domain adaptation scenarios. [ABSTRACT FROM AUTHOR]

Shadow removal is a challenging task because the variety of shadows is influenced by surface texture and lighting. This paper proposes a dynamic alignment and illumination‐aware convolution (DAIC), which consists of a Feature Alignment Module (FAM) and a Dynamic Weight Module (DWM). FAM aligns the downsampled deep features with the original features and helps to extract the optimal local information to ensure that the object texture features are not corrupted. DWM generates weights according to different lighting variations for a better shadow removal result. The shadow removal approach is based on an image decomposition algorithm using a multi‐exposure image fusion model. Here, the shadow removal network and refinement network use U‐Net framework, and the transposed convolution operations are replaced with DAIC in the decoder part of U‐Net to improve the performance of the network. The experiments are conducted on two large shadow removal datasets, ISTD+ and SRD. Compared to state‐of‐the‐art methods, this model achieves optimal performance in terms of Root Mean Square Error (RMSE) for the non‐shadow region. It also achieves performance comparable to the state‐of‐the‐art method in terms of RMSE for the shadow region and structural similarity index measurement for the entire image. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a rapid advance in training and testing synthetic data through deep learning networks for the annotation of synthetic data that can be automatically marked. However, a domain discrepancy still exists between synthetic data and real data. In this paper, we address the domain discrepancy issue from three aspects: 1) We design a synthetic image generator with automatically labeled based on 3D scenes. 2) A novel adversarial domain adaptation model is proposed to learn robust intermediate representation free of distractors to improve the transfer performance. 3) We construct a distractor-invariant network and adopt the sample transferability strategy on global-local levels, respectively, to mitigate the cross-domain gap. Additional exploratory experiments demonstrate that the proposed model achieves large performance margins, which show significant advance over the other state-of-the-art models, performing a promotion of 10%–15% mAP on various domain adaptation scenarios. [ABSTRACT FROM AUTHOR]

The traditional feature selection method only considers the linear correlation between variables and ignores the nonlinear correlation, so it is difficult to select effective feature subsets to build the effective model to predict the number of faults in software modules. Considering the linear and nonlinear relationship, a feature selection method based on maximum information coefficient (MIC) was proposed. The proposed method separated the redundancy analysis and correlation analysis into two phases. In the previous phase, the cluster algorithm, which was based on the correlation between features, was used to divide the redundant features into the same cluster. In the later phase, the features in each cluster were sorted in descending order according to the correlation between features and the number of software defects, and then the top features were selected to form the feature subset. The experimental results show that the proposed method can improve the prediction performance of software defect number prediction model by effectively removing redundant and irrelevant features. [ABSTRACT FROM AUTHOR]