BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout

Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomous driving systems with multi-view cameras as some long-tail scenarios can never be collected. Guided by the BEV segmentation layouts, the existing generative networks seem to synthesize photo-realistic street-view images when evaluated solely on scene-level metrics. However, once zoom-in, they usually fail to produce accurate foreground and background details such as heading. To this end, we propose a two-stage generative method, dubbed BEVControl, that can generate accurate foreground and background contents. In contrast to segmentation-like input, it also supports sketch style input, which is more flexible for humans to edit. In addition, we propose a comprehensive multi-level evaluation protocol to fairly compare the quality of the generated scene, foreground object, and background geometry. Our extensive experiments show that our BEVControl surpasses the state-of-the-art method, BEVGen, by a significant margin, from 5.89 to 26.80 on foreground segmentation mIoU. In addition, we show that using images generated by BEVControl to train the downstream perception model, it achieves on average 1.29 improvement in NDS score.Comment: 13 pages, 8 figure

ALIP: Adaptive Language-Image Pre-training with Synthetic Caption

Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image-text pairs collected from the web. However, the presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect the performance of representation learning. To address this issue, we first utilize the OFA model to generate synthetic captions that focus on the image content. The generated captions contain complementary information that is beneficial for pre-training. Then, we propose an Adaptive Language-Image Pre-training (ALIP), a bi-path model that integrates supervision from both raw text and synthetic caption. As the core components of ALIP, the Language Consistency Gate (LCG) and Description Consistency Gate (DCG) dynamically adjust the weights of samples and image-text/caption pairs during the training process. Meanwhile, the adaptive contrastive loss can effectively reduce the impact of noise data and enhances the efficiency of pre-training data. We validate ALIP with experiments on different scales of models and pre-training datasets. Experiments results show that ALIP achieves state-of-the-art performance on multiple downstream tasks including zero-shot image-text retrieval and linear probe. To facilitate future research, the code and pre-trained models are released at https://github.com/deepglint/ALIP.Comment: 15pages, 10figures, ICCV202

BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at {\url{https://github.com/ADLab-AutoDrive/BEVHeight}}.Comment: Accepted by CVPR 202

BEVHeight++: Toward Robust Visual Centric 3D Object Detection

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight++, to address this issue. In essence, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. By incorporating both height and depth encoding techniques, we achieve a more accurate and robust projection from 2D to BEV spaces. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. In terms of the ego-vehicle scenario, our BEVHeight++ possesses superior over depth-only methods. Specifically, it yields a notable improvement of +1.9% NDS and +1.1% mAP over BEVDepth when evaluated on the nuScenes validation set. Moreover, on the nuScenes test set, our method achieves substantial advancements, with an increase of +2.8% NDS and +1.7% mAP, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:2303.0849

A novel fusion protein consisting of anti-ANGPTL3 antibody and interleukin-22 ameliorates diabetic nephropathy in mice

IntroductionThe pathogenic mechanisms of diabetic nephropathy (DN) include podocyte injury, inflammatory responses and metabolic disorders. Although the antagonism of Angiopoietin-like protein 3 (ANGPTL3) can alleviate proteinuria symptoms by inhibiting the activation of integrin αvβ3 on the surface of podocytes, it can not impede other pathological processes, such as inflammatory responses and metabolic dysfunction of glucolipid. Interleukin-22 (IL-22) is considered to be a pivotal molecule involved in suppressing inflammatory responses, initiating regenerative repair, and regulating glucolipid metabolism.MethodsGenes encoding the mIL22IgG2aFc and two chains of anti-ANGPTL3 antibody and bifunctional protein were synthesized. Then, the DN mice were treated with intraperitoneal injection of normal saline, anti-ANGPTL3 (20 mg/kg), mIL22Fc (12 mg/kg) or anti-ANGPTL3 /IL22 (25.3 mg/kg) and irrigation of positive drug losartan (20mg/kg/d) twice a week for 8 weeks.ResultsIn this research, a novel bifunctional fusion protein (anti-ANGPTL3/IL22) formed by the fusion of IL-22 with the C-terminus of anti-ANGPTL3 antibody exhibited favorable stability and maintained the biological activity of anti-ANGPTL3 and IL-22, respectively. The fusion protein showed a more pronounced attenuation of proteinuria and improved dysfunction of glucolipid metabolism compared with mIL22Fc or anti-ANGPTL3. Our results also indicated that anti-ANGPTL3/IL22 intervention significantly alleviated renal fibrosis via inhibiting the expression of the inflammatory response-related protein nuclear factor kappa light-chain enhancer of activated B cells (NF-κB) p65 and NOD-like receptor family pyrin domain-containing protein 3 (NLRP3) inflammasome. Moreover, transcriptome analysis revealed the downregulation of signaling pathways associated with injury and dysfunction of the renal parenchymal cell indicating the possible protective mechanisms of anti-ANGPTL3/IL22 in DN.ConclusionCollectively, anti-ANGPTL3/IL22 bifunctional fusion protein can be a promising novel therapeutic strategy for DN by reducing podocyte injury, ameliorating inflammatory response, and enhancing renal tissue recovery

Expression Pattern and Value of Brain-Derived Neurotrophic Factor in Periodontitis

Background: Periodontitis is a common human disease with an increasing incidence. Brain-derived neurotrophic factor (BDNF) is known to play a crucial role in the regeneration of periodontal tissue; however, the expression, methylation level, molecular function, and clinical value of BDNF in periodontitis require further investigation. This study aimed to investigate the expression and potential functions of BDNF in periodontitis. Methods: RNA expression and methylation data were obtained from the Gene Expression Omnibus (GEO) database, and the expression and methylation levels of BDNF were compared between periodontitis and normal tissues. In addition, bioinformatics analysis was performed to investigate the downstream molecular functions of BDNF. Finally, Reverse transcription Quantitative real-time polymerase chain reaction was performed to determine the level of BDNF expression in periodontitis and normal tissues. Results: GEO database analysis revealed that BDNF was hypermethylated in periodontitis tissues and that its expression was downregulated. Reverse transcription Quantitative real-time polymerase chain reaction confirmed that BDNF expression was downregulated in periodontitis tissues. Several genes that interact with BDNF were determined using a protein–protein interaction network. Functional analysis of BDNF revealed that it was enriched in the Gene Ontology terms cytoplasmic dynein complex, glutathione transferase activity, and glycoside metabolic process. Kyoto Encyclopedia of Genes and Genomes analysis suggested that BDNF was associated with the mechanistic target of rapamycin signaling pathway, fatty acid metabolism, the Janus kinase-signal transducer and activator of transcription signaling pathway, glutathione metabolism, and others. Furthermore, the level of BDNF expression was correlated with the immune infiltration degree of B cells and CD4+ T cells. Conclusions: This study shown that BDNF was hypermethylated and downregulated in periodontitis tissues, which could be a biomarker and treatment target of periodontitis

Three-dimensional simulation of feeding and coal caving in fully mechanized caving face based on YADE

Based on the discrete element development environment YADE under Linux environment, this paper develops a three-dimensional simulation system for continuous feeding and coal caving in fully mechanized caving face. The system which sets the coal caving parameters based on the real geological environment of the coal mine can effectively simulate the swing of the tail beam, the pushing action of the hydraulic support, and the mixed dynamic change process of the coal and gangue on the top of the tail beam during the coal caving process. On this basis, the comparison experiments of single-wheel coal caving and double round coal caving were carried out. The experimental results show that the three-dimensional model of feeding and coal caving can truly simulate the top coal caving process. It also provides a new method for studying the top coal caving law of fully mechanized caving mining from three-dimensional perspective; under the condition of continuous feeding and coal caving in fully mechanized caving face, the effect of double-wheel sublevel interval caving is the best, the average top coal recovery rate is 86.64% and the gangue rate of 4.06%

Research on Developers’ Green Procurement Behavior Based on the Theory of Planned Behavior

In recent years, environmental problems in the construction industry have become increasingly prominent and need to be improved. The production and use of building materials remain one of the main causes of various eco-environmental problems in the construction industry. Compared with traditional building materials, green building materials are environmentally friendly and safer to the human body. Therefore, the promotion of green building materials in the construction industry can handle the eco-environmental problems of the industry to a great extent, and promote the green and low-carbon development of the industry. As the main buyer of building materials products, developers shoulder the responsibility of promoting the development of green building materials. The research on the green purchasing behavior of developers is conductive to winning a good reputation for developers and promoting the sustainable development of the construction industry. However, few scholars have focused on the in-depth research on the impact of developers’ green procurement behavior. Based on the theory of planned behavior, this study analyzes the problem of the developers’ green procurement using the structural equation model in the way of the questionnaire. The results showed that subjective norms and perceived behavior control factors exerted a significant influence on developers’ green procurement (GP) behavior attitude. The subjective norm, perceived behavior control, and other factors indirectly affected developers’ GP behavior through behavior intention. The influence mechanism of perceptual behavior control on developers’ GP behavior is more complicated, and the degree of influence is the greatest among the three kinds of factors. According to the research results, this paper puts forward some suggestions to promote developers to actively implement GP behavior. The results help to promote the GP behavior of developers and promote the planning development of green buildings. In addition, on the basis of a quantitative theoretical model, this paper finds out the key factors that influence the behavior of GP from the point of view of developers, enriches the theory and method of GP research, as well as complements the related literature of GP research. Future research will explore GP behavior from the perspective of other stakeholders and from the developers’ internal level