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

In Situ Probing the Crystallization Kinetics in Gas‐Quenching‐Assisted Coating of Perovskite Films

The pursuit of commercializing perovskite photovoltaics is driving the development of various scalable perovskite crystallization techniques. Among them, gas quenching is a promising crystallization approach for high‐throughput deposition of perovskite films. However, the perovskite films prepared by gas‐quenching assisted blade coating are susceptible to the formation of pinholes and frequently show inferior crystallinity if the interplay between film coating, film drying, and crystallization kinetics is not fully optimized. That arguably requires a thorough understanding of how single processing steps influence the crystallization kinetics of printed perovskite films. Here, in situ optical spectroscopies are integrated into a doctor‐blading setup that allows to real‐time monitor film formation during the gas‐quenching process. It is found that the essential role of gas quenching treatment is in achieving a smooth and compact perovskite film by controlling the nucleation rate. Moreover, with the assistance of phase‐field simulations, the role of excessive methylammonium iodide is revealed to increase grain size by accelerating the crystal growth rate. These results show a tailored control of crystal growth rate is critical to achieving optimal film quality, leading to fully printed solar cells with a champion power conversion efficiency of 19.50% and mini solar modules with 15.28% efficiency are achieved.Utilizing in situ monitoring techniques to optimize the crystallization kinetics of the perovskite films in the gas‐quenching‐assisted blade coating process, a champion power conversion efficiency of 19.50% for a fully printed carbon‐electrode perovskite solar cell is achieved through the tailored control of crystal growth rates. image Bavarian State GovernmentNational Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809Guangzhou Basic and Applied Basic Research FoundationBavarian Ministry of Science and ArtsDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/50110000165

Anatomy of an AI-powered malicious social botnet

Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to generate human-like content. Through heuristics, we identify 1,140 accounts and validate them via manual annotation. These accounts form a dense cluster of fake personas that exhibit similar behaviors, including posting machine-generated content and stolen images, and engage with each other through replies and retweets. ChatGPT-generated content promotes suspicious websites and spreads harmful comments. While the accounts in the AI botnet can be detected through their coordination patterns, current state-of-the-art LLM content classifiers fail to discriminate between them and human accounts in the wild. These findings highlight the threats posed by AI-enabled social bots

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