Category‐related attention domain adaptation for one‐stage cross‐domain object detection

Abstract Cross‐domain object detection aims to generalize the distribution of features extracted by an object detector from an annotated domain to an unknown and unlabelled domain. Although one‐stage cross‐domain object detectors have significant advantages in deployment than two‐stage ones, they suffer from two problems. First, neglect of category features and inaccurate alignment between multiple category features would lead to decreased domain adaptation efficiency. Second, one‐stage detectors are more sensitive to imbalance of samples and negative samples severely affect the alignment process of domain adaptation. To overcome these two problems, an innovative category‐related attention domain adaptive method that refines discrimination for each category's feature has been proposed in this paper. In the proposed method, a group of domain discriminators is assigned to each category to refine the fine‐grained features between categories. The discriminators are trained via an adversarial discriminant framework to align the fine‐grained distributions cross different domains. A category attention alignment (CAA) module is proposed to navigate more attention to the foreground regions in instance‐level, which effectively alleviates the negative migration problem caused by the positive and negative sample imbalance of the one‐stage detector. Specifically, two sub‐modules in the CAA module are developed: a local CAA module and a global CAA module. These modules aim to optimize the domain offsets in both the local and global dimensions. In addition, a progressive global alignment module is designed to align image‐level features, offering prior knowledge of migration for the CAA module. The progressive global alignment module and CAA module collaboratively engage in benign competition with the backbone network across various levels. Extensive transferring experiments are conducted among cityscapes, foggy cityscapes, SIM10K, and KITTI. Experimental results show that the proposed method has much superior performance than other one‐stage cross‐domain detectors

Intelligent Localization Sampling System Based on Deep Learning and Image Processing Technology

In this paper, deep learning and image processing technologies are combined, and an automatic sampling robot is proposed that can completely replace the manual method in the three-dimensional space when used for the autonomous location of sampling points. It can also achieve good localization accuracy, which solves the problems of the high labor intensity, low efficiency, and poor scientific accuracy of the manual sampling of mineral powder. To improve localization accuracy and eliminate non-linear image distortion due to wide-angle lenses, distortion correction was applied to the captured images. We solved the problem of low detection accuracy in some scenes of Single Shot MultiBox Detector (SSD) through data augmentation. A visual localization model has been established, and the image coordinates of the sampling point have been determined through color screening, image segmentation, and connected body feature screening, while coordinate conversion has been performed to complete the spatial localization of the sampling point, guiding the robot in performing accurate sampling. Field experiments were conducted to validate the intelligent sampling robot, which showed that the maximum visual positioning error of the robot is 36 mm in the x-direction and 24 mm in the y-direction, both of which meet the error range of less than or equal to 50 mm, and could meet the technical standards and requirements of industrial sampling localization accuracy

ABBV-744 induces autophagy in gastric cancer cells by regulating PI3K/AKT/mTOR/p70S6k and MAPK signaling pathways

The mortality rates of gastric cancer remain high due to limited therapeutic strategies. As a highly selective inhibitor of the BD2 domain of BET family proteins, ABBV-744 has potent chemotherapeutic activity against various human solid tumors. However, whether ABBV-744 has potential anti-tumor effects in gastric cancer remain largely unknown. In this study, we evaluated the effect of ABBV-744 on gastric cancer cells and explored the possible underlying mechanisms. We found that ABBV-744 inhibited the growth of gastric cancer cells and patient-derived tumor organoids in a dose-dependent manner. Cellular experiments revealed that ABBV-744 induced mitochondria damage, reactive oxygen species accumulation, cell cycle arrest and apoptotic cell death in gastric cancer cells. Transcriptomic analysis using RNA-sequencing data identified autophagy as a crucial pathway involved in the cell death caused by ABBV-744. Mechanically, further studies showed that ABBV-744 induced autophagy flux in gastric cancer cells by inactivating PI3K/AKT/mTOR/p70S6k and activating the MAPK signaling pathways. In vivo mouse xenograft studies demonstrated that ABBV-744 significantly suppressed the growth of gastric cancer cells via inducing autophagy. Taken together, our results suggest that ABBV-744 is a novel drug candidate for gastric cancer