Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Mos...Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere interactions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9%and a false alarm rate of 2.2%for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.展开更多
Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is...Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U2142211,42075141&42341202)the National Key Research and Development Program of China(Grant No.2020YFA0608000)+1 种基金the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100)the Fundamental Research Funds for the Central Universities。
文摘Tropical cloud clusters(TCCs)can potentially develop into tropical cyclones(TCs),leading to significant casualties and economic losses.Accurate prediction of tropical cyclogenesis(TCG)is crucial for early warnings.Most traditional deep learning methods applied to TCG prediction rely on predictors from a single time point,neglect the ocean-atmosphere interactions,and exhibit low model interpretability.This study proposes the Tropical Cyclogenesis Prediction-Net(TCGP-Net)based on the Swin Transformer,which leverages convolutional operations and attention mechanisms to encode spatiotemporal features and capture the temporal evolution of predictors.This model incorporates the coupled ocean-atmosphere interactions,including multiple variables such as sea surface temperature.Additionally,causal inference and integrated gradients are employed to validate the effectiveness of the predictors and provide an interpretability analysis of the model's decision-making process.The model is trained using GridSat satellite data and ERA5 reanalysis datasets.Experimental results demonstrate that TCGP-Net achieves high accuracy and stability,with a detection rate of 97.9%and a false alarm rate of 2.2%for predicting TCG 24 hours in advance,significantly outperforming existing models.This indicates that TCGP-Net is a reliable tool for tropical cyclogenesis prediction.
基金supported by the National Basic Research Program of China(973)(2012CB316402)The National Natural Science Foundation of China(Grant Nos.61332005,61725205)+3 种基金The Research Project of the North Minzu University(2019XYZJK02,2019xYZJK05,2017KJ24,2017KJ25,2019MS002)Ningxia first-classdisciplinc and scientific research projects(electronic science and technology,NXYLXK2017A07)NingXia Provincial Key Discipline Project-Computer ApplicationThe Provincial Natural Science Foundation ofNingXia(NZ17111,2020AAC03219).
文摘Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.
