Bernstein Theorems for Space-like Graphs with Parallel Mean Curvature and Controlled Growth

In this paper, we obtain an Ecker-Huisken type result for entire graphs with parallel mean curvature.Comment: 12 page

EVA-CLIP: Improved Training Techniques for CLIP at Scale

Contrastive language-image pre-training, CLIP for short, has gained increasing attention for its potential in various scenarios. In this paper, we propose EVA-CLIP, a series of models that significantly improve the efficiency and effectiveness of CLIP training. Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs. Notably, our largest 5.0B-parameter EVA-02-CLIP-E/14+ with only 9 billion seen samples achieves 82.0 zero-shot top-1 accuracy on ImageNet-1K val. A smaller EVA-02-CLIP-L/14+ with only 430 million parameters and 6 billion seen samples achieves 80.4 zero-shot top-1 accuracy on ImageNet-1K val. To facilitate open access and open research, we release the complete suite of EVA-CLIP to the community at https://github.com/baaivision/EVA/tree/master/EVA-CLIP.Comment: To Rei and the moon. Code & Models: https://github.com/baaivision/EVA/tree/master/EVA-CLI

Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202

Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance

Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.publishedVersio

De novo assembly and transcriptome characterization: novel insights into the natural resistance mechanisms of Microtus fortis against Schistosoma japonicum

BACKGROUND: Microtus fortis is a non-permissive host of Schistosoma japonicum. It has natural resistance against schistosomes, although the precise resistance mechanisms remain unclear. The paucity of genetic information for M. fortis limits the use of available immunological methods. Thus, studies based on high-throughput sequencing technologies are required to obtain information about resistance mechanisms against S. japonicum. RESULTS: Using Illumina single-end technology, a de novo assembly of the M. fortis transcriptome produced 67,751 unigenes with an average length of 868 nucleotides. Comparisons were made between M. fortis before and after infection with S. japonicum using RNA-seq quantification analysis. The highest number of differentially expressed genes (DEGs) occurred two weeks after infection, and the highest number of down-regulated DEGs occurred three weeks after infection. Simultaneously, the strongest pathological changes in the liver were observed at week two. Gene ontology terms and pathways related to the DEGs revealed that up-regulated transcripts were involved in metabolism, immunity and inflammatory responses. Quantitative real-time PCR analysis showed that patterns of gene expression were consistent with RNA-seq results. CONCLUSIONS: After infection with S. japonicum, a defensive reaction in M. fortis commenced rapidly, increasing dramatically in the second week, and gradually decreasing three weeks after infection. The obtained M. fortis transcriptome and DEGs profile data demonstrated that natural and adaptive immune responses, play an important role in M. fortis immunity to S. japonicum. These findings provide a better understanding of the natural resistance mechanisms of M. fortis against schistosomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi: 10.1186/1471-2164-15-417) contains supplementary material, which is available to authorized users