Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
In recent years,with the development of blockchain,electronic bidding auction has received more and more attention.Aiming at the possible problems of privacy leakage in the current electronic bidding and auction,this ...In recent years,with the development of blockchain,electronic bidding auction has received more and more attention.Aiming at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party.The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography.It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary attacks.The security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is analyzed.The efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.展开更多
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金supported by Inner Mongolia Natural Science Foundation(2021MS06006)2023 Inner Mongolia Young Science and Technology Talents Support Project(NJYT23106)+10 种基金2022 Basic Scientific Research Project of Direct Universities of Inner Mongolia(2022-101)2022 Fund Project of Central Government Guiding Local Science and Technology Development(2022ZY0024)2022 Chinese Academy of Sciences“Western Light”Talent Training Program“Western Young Scholars”Project(22040601)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2023-1-08)Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Open Project Fund(IMDBD202020)Baotou Kundulun District Science and Technology Plan Project(YF2020013)the 14th Five Year Plan of Education and Science of Inner Mongolia(NGJGH2021167)Inner Mongolia Science and Technology Major Project(2019ZD025)2022 Inner Mongolia Postgraduate Education and Teaching Reform Project(JGSZ2022037)Inner Mongolia Postgraduate Scientific Research Innovation Project(S20231164Z)Research and Application Project of Big Data Privacy Security Computing System(2023)。
文摘In recent years,with the development of blockchain,electronic bidding auction has received more and more attention.Aiming at the possible problems of privacy leakage in the current electronic bidding and auction,this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries,which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party.The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography.It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants,which can solve the problem of resisting malicious adversary attacks.The security of the protocol is proved by the real/ideal model paradigm,and the efficiency of the protocol is analyzed.The efficiency of the protocol is verified by simulating experiments,and the protocol has practical value.
