The development of high-performance materials for microelectronics,energy storage,and extreme environments depends on our ability to describe and direct property-defining microstructural order.Our present understandin...The development of high-performance materials for microelectronics,energy storage,and extreme environments depends on our ability to describe and direct property-defining microstructural order.Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data,which is difficult to scale,challenging to reproduce,and lacks the ability to reveal latent associations needed for mechanistic models.Here,we demonstrate a multi-modal machine learning(ML)approach to describe order from electron microscopy analysis of the complex oxide La_(1−x)Sr_(x)FeO_(3).We construct a hybrid pipeline based on fully and semi-supervised classification,allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble.We observe distinct differences in the performance of uni-and multi-modal models,from which we draw general lessons in describing crystal order using computer vision.展开更多
The right to data portability is an essential part of personal data protection in the booming era of big data,which is closely related to our work and lives,as it may play a crucial role in safeguarding self-determina...The right to data portability is an essential part of personal data protection in the booming era of big data,which is closely related to our work and lives,as it may play a crucial role in safeguarding self-determination right of the data subject and foster a favorable environment for the players in a fair competition market.However,the implementation of the right to data portability in China is still in its infancy,fraught with complexities and uncertainties.This paper studies the right to data portability in China based on the Personal Information Protection Law,and explores its development,current status and potential impact in China.Moreover,it conducts a comparative analysis of the EU and US experience,mostly from the legislative perspective,to better understand practices in the world.In addition,this paper puts forward some specific suggestions on implementing the right to data portability,hoping that the right to data portability can be fully guaranteed in our real life.展开更多
There are similarities between rheumatoid arthritis(RA)and systemic lupus erythematosus(SLE)in terms of clinical manifestations,immune responses,and therapeutic strategies,1 and thus a joint analysis of the two diseas...There are similarities between rheumatoid arthritis(RA)and systemic lupus erythematosus(SLE)in terms of clinical manifestations,immune responses,and therapeutic strategies,1 and thus a joint analysis of the two diseases could contribute to a deeper understanding of the shared pathogenesis of autoimmune diseases.The subtype analysis of RA and SLE is currently understudied,and the marker genes used for subtype definition in most studies are derived from bulk RNA sequencing data or microarray data,which are underrepresentative of individual immune cell status.2 Therefore,we aimed to identify cell type-specific expressed genes as biomarkers based on single-cell RNA sequencing data and to explore the commonalities and differences between RA and SLE by a combined subtype analysis based on microarray data.Both the representativeness of the markers in terms of immune characteristics and the reproducibility of the results are ensured by the sufficient sample size.Immune infiltration analysis revealed the subtype heterogeneity and significant differences in clinical characteristics between different subtypes of RA patients,which verified the heterogeneity between different subtypes.Finally,we constructed subtype prediction models by machine learning algorithms further validating the heterogeneity among subtypes.Detailed methodology and the overall flowchart(Fig.S1)are provided in the supplementary material.展开更多
Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavi...Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.展开更多
基金supported by the Laboratory Directed Research and Development (LDRD) program at Pacific Northwest National Laboratory (PNNL). PNNL is a multiprogram national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830Some sample preparation was performed at the Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the Department of Energy's Office of Biological and Environmental Research and located at PNNL. Ion irradiation work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Los Alamos National Laboratory (Contract 89233218CNA000001) and Sandia National Laboratories (Contract DE-NA-0003525)+1 种基金This work was authored in part by the National Renewable Energy Laboratory (NREL) for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. The views expressed in the presentation do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. R.P. and R.B.C. gratefully acknowledge funding support for film synthesis from the National Science Foundation under award DMR-1809847R.B.C. also acknowledges funding support for data science and machine learning efforts from the National Science Foundation under award DMR-2045993.
文摘The development of high-performance materials for microelectronics,energy storage,and extreme environments depends on our ability to describe and direct property-defining microstructural order.Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data,which is difficult to scale,challenging to reproduce,and lacks the ability to reveal latent associations needed for mechanistic models.Here,we demonstrate a multi-modal machine learning(ML)approach to describe order from electron microscopy analysis of the complex oxide La_(1−x)Sr_(x)FeO_(3).We construct a hybrid pipeline based on fully and semi-supervised classification,allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble.We observe distinct differences in the performance of uni-and multi-modal models,from which we draw general lessons in describing crystal order using computer vision.
文摘The right to data portability is an essential part of personal data protection in the booming era of big data,which is closely related to our work and lives,as it may play a crucial role in safeguarding self-determination right of the data subject and foster a favorable environment for the players in a fair competition market.However,the implementation of the right to data portability in China is still in its infancy,fraught with complexities and uncertainties.This paper studies the right to data portability in China based on the Personal Information Protection Law,and explores its development,current status and potential impact in China.Moreover,it conducts a comparative analysis of the EU and US experience,mostly from the legislative perspective,to better understand practices in the world.In addition,this paper puts forward some specific suggestions on implementing the right to data portability,hoping that the right to data portability can be fully guaranteed in our real life.
基金supported by the Fundamental Research Funds for the Provincial Universities in Heilongjiang Province,China(2024,to Wenhua Lv)College Student Innovation Training Project of Heilongjiang Province,China(S202410226008).
文摘There are similarities between rheumatoid arthritis(RA)and systemic lupus erythematosus(SLE)in terms of clinical manifestations,immune responses,and therapeutic strategies,1 and thus a joint analysis of the two diseases could contribute to a deeper understanding of the shared pathogenesis of autoimmune diseases.The subtype analysis of RA and SLE is currently understudied,and the marker genes used for subtype definition in most studies are derived from bulk RNA sequencing data or microarray data,which are underrepresentative of individual immune cell status.2 Therefore,we aimed to identify cell type-specific expressed genes as biomarkers based on single-cell RNA sequencing data and to explore the commonalities and differences between RA and SLE by a combined subtype analysis based on microarray data.Both the representativeness of the markers in terms of immune characteristics and the reproducibility of the results are ensured by the sufficient sample size.Immune infiltration analysis revealed the subtype heterogeneity and significant differences in clinical characteristics between different subtypes of RA patients,which verified the heterogeneity between different subtypes.Finally,we constructed subtype prediction models by machine learning algorithms further validating the heterogeneity among subtypes.Detailed methodology and the overall flowchart(Fig.S1)are provided in the supplementary material.
基金Key Program of Joint Funds of the National Natural Science Foundation of China,Grant/Award Number:U22B20118。
文摘Gas-insulated switchgear(GIS)plays a critical role in ensuring the reliability of power systems,but partial discharge(PD)is a primary cause of failures within GIS equipment.Traditional PD diagnostic methods rely heavily on laboratory data,which differ signifi-cantly from that under the complex conditions of field data,leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis.This study addresses the challenge by integrating field data into the training process,utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD.The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment.A deep residual network(ResNet50)was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions.The results show that the proposed model achieves a significantly higher recognition accuracy(93.7%)for field data compared to traditional methods(60%-70%).The integration of deep transfer learning ensures that both low-dimensional general features from labora-tory data and high-dimensional specific features from field data are effectively utilised.This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions,providing a robust method for defect detection in operational equipment.
