In the field of computer vision, object detection is a prominent and challenging task. Despite the favorable performance of deep learning-based object detection techniques on clear images, it fails in inclement weather conditions like snow because of image degradation. Recent efforts have explored using image restoration methods to enhance degraded images before object detection. However, direct restoration can sometimes cause new disturbances, impeding detection performance improvements. To address this issue, we propose a joint framework that connects the iterative desnow module and detection module in an end-to-end manner. Specially, we design an Advantage Union structure for multi-feature fusion, which effectively combines original, intermediate, and restored features, reducing potential information loss from restoration. Experimental results show that our method achieves higher accuracy compared to the recent state-of-the-art methods in both synthetic dataset and real-to-world snowy images.
Abstract Controlled and precise synthesis of materials has been a central pursuit in academia and industry. With the rise of twistronics research, there is growing demand for synthesizing orientation‐controlled 2D materials with atomic precision. Previous theories for 2D materials can predict graphene (Gr) growth on low‐index metal substrates but fail to explain the discrete orientations on most high‐index substrates, inconsistent with experimental data. Using density functional theory (DFT) and ab‐initio molecular dynamics (AIMD), this study explores graphene growth on high‐index substrates, showing that atomic steps do not dominate in trapping C atoms or driving preferential graphene nucleation at high temperatures. Thus, the possibility of intrinsic atomic steps in inducing graphene orientation on high‐index substrates is ruled out. Interfacial coupling strength between graphene and substrates is quantified using a close contact index (CCI), linking atomic structure and electronic states. The coupling between graphene and high‐index Cu surfaces is generally weak, reducing substrate anchoring and increasing graphene orientation dispersion. The extension of this theory to bilayer graphene (BLG) reveals competition between Gr/Cu interfacial coupling and Gr/Gr interlayer coupling, offering insights for controlling twist angles. This theory explains the discrete orientations on high‐index substrates, providing a theoretical basis for synthesizing orientation‐controlled graphene.
The method toward azepinoindoles has been demonstrated via 1,5-hydride transfer/cyclization reaction, using the 4-amino-substituted isatin and diethyl aminomalonate as strating material. The redox-neutral protocol provide a atom- and step-economic method to access the azepinoindoles.
