The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing general...The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.展开更多
Annotating named entity recognition （NER） training corpora is a costly but necessary process for supervised NER approaches. This paper presents a general framework to generate large-scale NER training data from para...Annotating named entity recognition （NER） training corpora is a costly but necessary process for supervised NER approaches. This paper presents a general framework to generate large-scale NER training data from parallel corpora. In our method, we first employ a high performance NER system on one side of a bilingual corpus. Then, we project the named entity （NE） labels to the other side according to the word level alignments. Finally, we propose several strategies to select high-quality auto-labeled NER training data. We apply our approach to Chinese NER using an English-Chinese parallel corpus. Experimental results show that our approach can collect high-quality labeled data and can help improve Chinese NER.展开更多
Predicting comfort levels in cities is challenging due to the many metric assessment.To overcome these challenges,much research is being done in the computing community to develop methods capable of generating outdoor...Predicting comfort levels in cities is challenging due to the many metric assessment.To overcome these challenges,much research is being done in the computing community to develop methods capable of generating outdoor comfort data.Machine Learning(ML)provides many opportunities to discover patterns in large datasets such as urban data.This paper proposes a data-driven approach to build a predictive and data-generative model to assess outdoor thermal comfort.The model benefits from the results of a study,which analyses Computational Fluid Dynamics(CFD)urban simulation to determine the thermal and wind comfort in Tallinn,Estonia.The ML model was built based on classification,and it uses an opaque ML model.The results were evaluated by applying different metrics and show us that the approach allows the implementation of a data-generative ML model to generate reliable data on outdoor comfort that can be used by urban stakeholders,planners,and researchers.展开更多
The AI revolution is altering the innovation ecosystem at an unprecedented pace.Breakthroughs in generative AI,big data analytics,autonomous driving,and other fields have rendered conventional IP frameworks increasing...The AI revolution is altering the innovation ecosystem at an unprecedented pace.Breakthroughs in generative AI,big data analytics,autonomous driving,and other fields have rendered conventional IP frameworks increasingly inadequate.A central challenge confronting global IP systems is how to safeguard innovators’rights while fostering technological progress.展开更多
This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include...This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.展开更多
文摘The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years. As a result, many linear methods and nonlinear methods have been developed. But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed. A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 61133012, 61273321） and the National 863 Leading Technology Research Project （2012AA011102）. Special thanks to Wanxiang Che, Yanyan Zhao, Wei He, Fikadu Gemechu, Yuhang Guo, Zhenghua Li, Meishan Zhang and the anonymous reviewers for insightful comments and suggestions.
文摘Annotating named entity recognition （NER） training corpora is a costly but necessary process for supervised NER approaches. This paper presents a general framework to generate large-scale NER training data from parallel corpora. In our method, we first employ a high performance NER system on one side of a bilingual corpus. Then, we project the named entity （NE） labels to the other side according to the word level alignments. Finally, we propose several strategies to select high-quality auto-labeled NER training data. We apply our approach to Chinese NER using an English-Chinese parallel corpus. Experimental results show that our approach can collect high-quality labeled data and can help improve Chinese NER.
基金This work has been supported by the European Commission through the H2020 project Finest Twins(grant No.856602).
文摘Predicting comfort levels in cities is challenging due to the many metric assessment.To overcome these challenges,much research is being done in the computing community to develop methods capable of generating outdoor comfort data.Machine Learning(ML)provides many opportunities to discover patterns in large datasets such as urban data.This paper proposes a data-driven approach to build a predictive and data-generative model to assess outdoor thermal comfort.The model benefits from the results of a study,which analyses Computational Fluid Dynamics(CFD)urban simulation to determine the thermal and wind comfort in Tallinn,Estonia.The ML model was built based on classification,and it uses an opaque ML model.The results were evaluated by applying different metrics and show us that the approach allows the implementation of a data-generative ML model to generate reliable data on outdoor comfort that can be used by urban stakeholders,planners,and researchers.
文摘The AI revolution is altering the innovation ecosystem at an unprecedented pace.Breakthroughs in generative AI,big data analytics,autonomous driving,and other fields have rendered conventional IP frameworks increasingly inadequate.A central challenge confronting global IP systems is how to safeguard innovators’rights while fostering technological progress.
文摘This paper presents an investigation into the spatio-temporal dynamics of Severe Acute Respiratory Syndrome(SARS)across the diverse health regions of Brazil from 2016 to 2024.Leveraging extensive datasets that include SARS cases,climate data,hospitalization records,and COVID-19 vaccination information,our study employs a Bayesian spatio-temporal generalized linear model to capture the intricate dependencies inherent in the dataset.The analysis reveals significant variations in the incidence of SARS cases over time,particularly during and between the distinct eras of pre-COVID-19,during,and post-COVID-19.Our modeling approach accommodates explanatory variables such as humidity,temperature,and COVID-19 vaccine doses,providing a comprehensive understanding of the factors influencing SARS dynamics.Our modeling revealed unique temporal trends in SARS cases for each region,resembling neighborhood patterns.Low temperature and high humidity were linked to decreased cases,while in the COVID-19 era,temperature and vaccination coverage played significant roles.The findings contribute valuable insights into the spatial and temporal patterns of SARS in Brazil,offering a foundation for targeted public health interventions and preparedness strategies.
