Cyber-Physical Systems(CPS)tightly integrate cyber and physical components and transcend traditional control systems and embedded system.Such systems are often mission-critical;therefore,they must be high-assurance.Hi...Cyber-Physical Systems(CPS)tightly integrate cyber and physical components and transcend traditional control systems and embedded system.Such systems are often mission-critical;therefore,they must be high-assurance.Highassurance CPS require co-verification which takes a comprehensive view of the whole system to verify the correctness of a cyber and physical components together.Lack of strict multiple semantic definition for interaction between the two domains has been considered as an obstacle to the CPS co-verification.A Cyber/Physical interface model for hierarchical a verification of CPS is proposed.First,we studied the interaction mechanism between computation and physical processes.We further classify the interaction mechanism into two levels:logic interaction level and physical interaction level.We define different types of interface model according to combinatorial relationships of the A/D(Analog to Digital)and D/A(Digital to Analog)conversion periodical instants.This interface model has formal semantics,and is efficient for simulation and formal verification.The experiment results show that our approach has major potential in verifying system level properties of complex CPS,therefore improving the high-assurance of CPS.展开更多
Background Im paired sesitwity of he soin Mlush response bo miacin is one of the most rpicated findngs in patents with schizoprenia Howewer.prior studies have usaly focused on postonset psychusis,and ll is knowm about...Background Im paired sesitwity of he soin Mlush response bo miacin is one of the most rpicated findngs in patents with schizoprenia Howewer.prior studies have usaly focused on postonset psychusis,and ll is knowm about the dinical high-risk(CHR)phase of niacin senstity in psychosis Aims To proftle and compare the miacin flush responsge among CHR individuals(converters and non-coverters)patients with frstepso schinophrenia(FES)and healty controls(HCs).Methods Sensivily 1o ftour concentralions (0.1-0001M)of aqueous methylnicotinate was tested in 105 CHR individuals,57 patients with FES and 52 HCs.CHR individuals were further grouped as converters and non converters according to the 2-year follow-up outcomes.Skin flush response scores were rated on a 4-point scale.Results Of the 105 CHR individuals,21 individuals were lost during the study,leaving 84 CHR individuals;16(19.0%)converted to full psychosis at 2 years of fllow-up.Flush response scores identifed in the CHR samples were characterised as modest degree levels,intermediate between those of HC individuals and patients with FES.The flush responses in the CHR group mimicked the responses observed in the FES group at higher concentrations(0.01 M,0.1 M)and longer time points(15 min,20min);however,these became comparable vith the responses in the HC group at the shorter time points and at lower concentr ations.The converters exhibited lower mean flush response scores than the non-converters.Conclusions Attenuated niacin-induced flushing emerged during the early phase of psychosis.New devices should be developed and verified for objective quantification of skin responses in the CHR population.展开更多
Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) ...Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.展开更多
Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicat...Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.展开更多
Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first cen...Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first century has experienced an unprecedented era of technological development and demographic changes:exploding population growth,increased airline flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement restrictions.In this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks.This article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne diseases.The review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and Influenza.Despite significant research into the relationship between infectious diseases and human mobility,four knowledge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread.By tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.展开更多
The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Curren...The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.展开更多
A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time.Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patter...A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time.Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure.Little work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire structure.In this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network.The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data.In particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular matrices.Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures.We evaluate our technique via a case study and a user study.The evaluation results verify the effectiveness of our system.展开更多
With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have ...With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry.In this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual Analytics.AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively.The code is available at https://github.com/antvis/AVA.展开更多
Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time.They are common in multimedia applications.Anomaly detection is one of the essential tasks in anal...Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time.They are common in multimedia applications.Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed.In this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks.We conclude features of rare categories and two types of anomalies of rare categories.Then we present a novel rare category detection method,called DIRAD,to detect rare category candidates with anomalies.We develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes.Evaluations,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.展开更多
基金This research received financial support from Natural Science Foundation of Hainan province(Grant Nos.617062,2018CXTD333,617048)the National Natural Science Foundation of China(Grant Nos.61462022,61762033,61662019)+1 种基金Major Science and Technology Project of Hainan province(Grant No.ZDKJ2016015)Scientific Research Staring Foundation of Hainan University(Grant No.kyqd1610).
文摘Cyber-Physical Systems(CPS)tightly integrate cyber and physical components and transcend traditional control systems and embedded system.Such systems are often mission-critical;therefore,they must be high-assurance.Highassurance CPS require co-verification which takes a comprehensive view of the whole system to verify the correctness of a cyber and physical components together.Lack of strict multiple semantic definition for interaction between the two domains has been considered as an obstacle to the CPS co-verification.A Cyber/Physical interface model for hierarchical a verification of CPS is proposed.First,we studied the interaction mechanism between computation and physical processes.We further classify the interaction mechanism into two levels:logic interaction level and physical interaction level.We define different types of interface model according to combinatorial relationships of the A/D(Analog to Digital)and D/A(Digital to Analog)conversion periodical instants.This interface model has formal semantics,and is efficient for simulation and formal verification.The experiment results show that our approach has major potential in verifying system level properties of complex CPS,therefore improving the high-assurance of CPS.
基金This study was supported by the National Natural Science Foundation of China(82171544,81971251,81671329,82001406 and 81871050)Science and Technology Comission of Shanghai Municipality(19441907800,16ZR1430500,19ZR1445200,19ZR1445100,17411953100,21S31903100,2018SHZDZX01,19410710800,19411969100,19411950800)+5 种基金Shanghai Clinical Research Center for Mental Health(19MC1911100)The Clinical Research Center at Shanghai Mental Health Center(CRC2018Z001,CRC2018ZD04)Project of the Key Discipline Construction,Shanghai 3-Year Public Health Action Plan(GWV-10.1-XK18)Clinical Research Center at Shanghai Jiao Tong University School of Medicine(DLY201817,20190102)Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01,2018SHZDZX05)ZJLab.Foundation of Shanghai Mental Health Center(2020-FX-02).
文摘Background Im paired sesitwity of he soin Mlush response bo miacin is one of the most rpicated findngs in patents with schizoprenia Howewer.prior studies have usaly focused on postonset psychusis,and ll is knowm about the dinical high-risk(CHR)phase of niacin senstity in psychosis Aims To proftle and compare the miacin flush responsge among CHR individuals(converters and non-coverters)patients with frstepso schinophrenia(FES)and healty controls(HCs).Methods Sensivily 1o ftour concentralions (0.1-0001M)of aqueous methylnicotinate was tested in 105 CHR individuals,57 patients with FES and 52 HCs.CHR individuals were further grouped as converters and non converters according to the 2-year follow-up outcomes.Skin flush response scores were rated on a 4-point scale.Results Of the 105 CHR individuals,21 individuals were lost during the study,leaving 84 CHR individuals;16(19.0%)converted to full psychosis at 2 years of fllow-up.Flush response scores identifed in the CHR samples were characterised as modest degree levels,intermediate between those of HC individuals and patients with FES.The flush responses in the CHR group mimicked the responses observed in the FES group at higher concentrations(0.01 M,0.1 M)and longer time points(15 min,20min);however,these became comparable vith the responses in the HC group at the shorter time points and at lower concentr ations.The converters exhibited lower mean flush response scores than the non-converters.Conclusions Attenuated niacin-induced flushing emerged during the early phase of psychosis.New devices should be developed and verified for objective quantification of skin responses in the CHR population.
文摘Infectious diseases have posed a global threat recently, progressing from endemic to pandemic. Early detection and finding a better cure are methods for curbing the disease and its transmission. Machine learning (ML) has demonstrated to be an ideal approach for early disease diagnosis. This review highlights the use of ML algorithms for monkeypox (MP). Various models, such as CNN, DL, NLP, Naïve Bayes, GRA-TLA, HMD, ARIMA, SEL, Regression analysis, and Twitter posts were built to extract useful information from the dataset. These findings show that detection, classification, forecasting, and sentiment analysis are primarily analyzed. Furthermore, this review will assist researchers in understanding the latest implementations of ML in MP and further progress in the field to discover potent therapeutics.
基金This study was supported by National Natural Science Foundation of China(82171544,81971251,81671329,and 81871050),Science and Technology Commission of Shanghai Municipality(19441907800,16ZR1430500,19ZR1445200,17411953100,21S31903100,2018SHZDZX01,19410710800,19411969100,19411950800)。
文摘Schizophrenia is a devastating mental disorder affecting 20 million people worldwide.Early diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the disease.Based on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.
基金supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number[3R01AI127203-04S1]National Science Foundation under Award Number[2028791].
文摘Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the globe.At the same time,the twenty-first century has experienced an unprecedented era of technological development and demographic changes:exploding population growth,increased airline flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement restrictions.In this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease outbreaks.This article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne diseases.The review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and Influenza.Despite significant research into the relationship between infectious diseases and human mobility,four knowledge gaps were identified based on reviewed literature in this study:1)although some studies have used big data in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardization in the methodology for measuring the impacts of human mobility on infectious disease spread.By tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate interplay between human mobility and infectious diseases.
文摘The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.
基金Project supported by the National Natural Science Foundation of China(Nos.U1866602,61772456,U1736109,and 61972122)。
文摘A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time.Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure.Little work focuses on detecting anomalous changing patterns in the dynamic network,the rare occurrence of which could damage the development of the entire structure.In this study,we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network.The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data.In particular,a novel visualization is introduced,which represents the snapshots of a dynamic network in a series of connected triangular matrices.Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures.We evaluate our technique via a case study and a user study.The evaluation results verify the effectiveness of our system.
基金National Natural Science Foundation of China(62132017)Zhejiang Provincial Natural Science Foundation of China(LD24F020011).
文摘With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large datasets.Though several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry.In this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual Analytics.AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively.The code is available at https://github.com/antvis/AVA.
基金This work was supported by National Key Research and Development Program(2018YFB0904503)the National Natural Science Foundation of China(Grant Nos.61772456,U1866602,61761136020,U1736109).
文摘Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along time.They are common in multimedia applications.Anomaly detection is one of the essential tasks in analyzing these networks though it is not well addressed.In this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic networks.We conclude features of rare categories and two types of anomalies of rare categories.Then we present a novel rare category detection method,called DIRAD,to detect rare category candidates with anomalies.We develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex attributes.Evaluations,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.
