Dissecting the genome-wide evolution and function of R2R3-MYB transcription factor family in Rosa chinensis

Rosa chinensis, an important ancestor species of Rosa hybrida, the most popular ornamental plant species worldwide, produces flowers with diverse colors and fragrances. The R2R3-MYB transcription factor family controls a wide variety of plant-specific metabolic processes, especially phenylpropanoid metabolism. Despite their importance for the ornamental value of flowers, the evolution of R2R3-MYB genes in plants has not been comprehensively characterized. In this study, 121 predicted R2R3-MYB gene sequences were identified in the rose genome. Additionally, a phylogenomic synteny network (synnet) was applied for the R2R3-MYB gene families in 35 complete plant genomes. We also analyzed the R2R3-MYB genes regarding their genomic locations, Ka/Ks ratio, encoded conserved motifs, and spatiotemporal expression. Our results indicated that R2R3-MYBs have multiple synteny clusters. The RcMYB114a gene was included in the Rosaceae-specific Cluster 54, with independent evolutionary patterns. On the basis of these results and an analysis of RcMYB114a-overexpressing tobacco leaf samples, we predicted that RcMYB114a functions in the phenylpropanoid pathway. We clarified the relationship between R2R3-MYB gene evolution and function from a new perspective. Our study data may be relevant for elucidating the regulation of floral metabolism in roses at the transcript level

3D Numerical Simulation of Shield Tunnel Subjected to Swelling Effect Considering the Nonlinearity of Joint Bending Stiffness

In this paper, the authors developed a three dimensional shell-spring numerical model of a shield tunnel, in which the elastic shell elements were adopted to model the segments and the spring models were used for the simulation of the segmental joints. The highlight of this research is that the non-linearity of the joint bending stiffness was taken into consideration, which was first determined through the numerical simulation by using a refined 3D continuum model of the segment-joint structure. The automatic iteration of the joint bending stiffness was achieved through programming with the ANSYS ADPL software. Based on a specific engineering example, a 3D continuum-shell-spring model was established to analyze the internal forces of shield tunnel segmental linings subject to swelling soils. The developed numerical model and its application in the analysis of the internal forces of shield tunnel segmental linings in swelling ground will provide useful reference and guidance for the numerical calculation in similar engineering projects in future

SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins.Comment: Accepted as oral paper at AAAI202

Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration

We propose integrally pre-trained transformer pyramid network (iTPN), towards jointly optimizing the network backbone and the neck, so that transfer gap between representation models and downstream tasks is minimal. iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT). 2) Multi-stage supervision to the feature pyramid using masked feature modeling (MFM). iTPN is updated to Fast-iTPN, reducing computational memory overhead and accelerating inference through two flexible designs. 1) Token migration: dropping redundant tokens of the backbone while replenishing them in the feature pyramid without attention operations. 2) Token gathering: reducing computation cost caused by global attention by introducing few gathering tokens. The base/large-level Fast-iTPN achieve 88.75%/89.5% top-1 accuracy on ImageNet-1K. With 1x training schedule using DINO, the base/large-level Fast-iTPN achieves 58.4%/58.8% box AP on COCO object detection, and a 57.5%/58.7% mIoU on ADE20K semantic segmentation using MaskDINO. Fast-iTPN can accelerate the inference procedure by up to 70%, with negligible performance loss, demonstrating the potential to be a powerful backbone for downstream vision tasks. The code is available at: github.com/sunsmarterjie/iTPN.Comment: The tiny/small/base-level models report new records on ImageNet-1K. Code: github.com/sunsmarterjie/iTP

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.Comment: 12 pages, 5 figures,3 tables. This work is submitted to IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022). It's under review by IEEE Transactions on Systems, Man, and Cybernetics: Systems for no