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10,285 results on '"Meng Fan"'

170. TCR-GAN: Predicting tropical cyclone passive microwave rainfall using infrared imagery via generative adversarial networks

Author

Meng, Fan, Song, Tao, and Xu, Danya

Subjects
Computer Science - Machine Learning
Abstract

Tropical cyclones (TC) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to the low temporal resolution of microwave sensors. This study attempts to solve this problem by directly forecasting PMR from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to convert IR images into PMR, and establish the mapping relationship between TC cloud-top bright temperature and PMR, the algorithm is named TCR-GAN. Meanwhile, a new dataset that is available as a benchmark, Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP) was established, which is expected to advance the development of artificial intelligence in this direction. Experimental results show that the algorithm can effectively extract key features from IR. The end-to-end deep learning approach shows potential as a technique that can be applied globally and provides a new perspective tropical cyclone precipitation prediction via satellite, which is expected to provide important insights for real-time visualization of TC rainfall globally in operations.

Published
2022

171. The bHLH subgroup IIId factors negatively regulate jasmonate-mediated plant defense and development.

Author

Susheng Song, Tiancong Qi, Meng Fan, Xing Zhang, Hua Gao, Huang Huang, Dewei Wu, Hongwei Guo, and Daoxin Xie

Subjects
Genetics, QH426-470
Abstract

Plants have evolved sophisticated systems for adaptation to their natural habitat. In response to developmental and environmental cues, plants produce and perceive jasmonate (JA) signals, which induce degradation of JASMONATE-ZIM-Domain (JAZ) proteins and derepress the JAZ-repressed transcription factors to regulate diverse aspects of defense responses and developmental processes. Here, we identified the bHLH subgroup IIId transcription factors (bHLH3, bHLH13, bHLH14 and bHLH17) as novel targets of JAZs. These bHLH subgroup IIId transcription factors act as transcription repressors and function redundantly to negatively regulate JA responses. The quadruple mutant bhlh3 bhlh13 bhlh14 bhlh17 showed severe sensitivity to JA-inhibited root growth and JA-induced anthocyanin accumulation, and exhibited obvious increase in JA-regulated plant defense against pathogen infection and insect attack. Transgenic plants overexpressing bHLH13 or bHLH17 displayed reduced JA responses. Furthermore, these bHLH factors functioned as transcription repressors to antagonize the transcription activators, such as MYC2 and the WD-repeat/bHLH/MYB complex, through binding to their target sequences. Coordinated regulation of JA responses by transcription activators and repressors would benefit plants by allowing fine regulation of defense and development, and survival in their frequently changing environment.

Published
2013
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175. A Bayes Theory-Based Modeling Algorithm to End-to-end Network Traffic

Author

Zhao Hong-hao, Meng Fan-bo, Zhao Si-wen, Zhao Si-hang, and Lu Yi

Subjects
Information technology, T58.5-58.64
Abstract

Recently, network traffic has exponentially increasing due to all kind of applications, such as mobile Internet, smart cities, smart transportations, Internet of things, and so on. the end-to-end network traffic becomes more important for traffic engineering. Usually end-to-end traffic estimation is highly difficult. This paper proposes a Bayes theory-based method to model the end-to-end network traffic. Firstly, the end-to-end network traffic is described as a independent identically distributed normal process. Then the Bases theory is used to characterize the end-to-end network traffic. By calculating the parameters, the model is determined correctly. Simulation results show that our approach is feasible and effective.

Published
2016
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176. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

Author

Meng Fan-Bo, Zhao Hong-Hao, Zhao Si-Hang, Zhao Si-Wen, and Lin Zhong-Qiu

Subjects
Information technology, T58.5-58.64
Abstract

Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

Published
2016
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185. Cross-domain neurobiology data integration and exploration

Author

Song Jean, Mirel Barbara, Buckner Josh, Dai Manhong, Xuan Weijian, Athey Brian, Watson Stanley J, and Meng Fan

Subjects
Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Understanding the biomedical implications of data from high throughput experiments requires solutions for effective cross-scale and cross-domain data exploration. However, existing solutions do not provide sufficient support for linking molecular level data to neuroanatomical structures, which is critical for understanding high level neurobiological functions. Results Our work integrates molecular level data with high level biological functions and we present results using anatomical structure as a scaffold. Our solution also allows the sharing of intermediate data exploration results with other web applications, greatly increasing the power of cross-domain data exploration and mining. Conclusions The Flex-based PubAnatomy web application we developed enables highly interactive visual exploration of literature and experimental data for understanding the relationships between molecular level changes, pathways, brain circuits and pathophysiological processes. The prototype of PubAnatomy is freely accessible at:[http://brainarray.mbni.med.umich.edu/Brainarray/prototype/PubAnatomy]

Published
2010
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