Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data....Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.展开更多
A series of pharmacy services of clinical pharmacists at the Wuhan Mental Health Center during the prevention and treatment of Coronavirus disease 2019(COVID-19),such as participation in the formulation of COVID-19 pr...A series of pharmacy services of clinical pharmacists at the Wuhan Mental Health Center during the prevention and treatment of Coronavirus disease 2019(COVID-19),such as participation in the formulation of COVID-19 prevention and treatment plans suitable for psychiatric departments,popular science of pharmacy,medical order review,real-time intervention,and medication education are summarized here.Due to the sudden public health incident,the service model of psychiatric clinical pharmacists should be addressed,as clinical pharmacists are an important part of the diagnosis and treatment of psychiatric diseases.Among the majors currently available in the clinical pharmacy training base curriculum,no psychiatry major has been set up in China yet;therefore,in this paper,we provide guidance in psychiatry pharmacy for those who wish to integrate clinical teams.展开更多
Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA me...Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA methods have acquired great success when labels in the source domain are pure.However,even the acquisition of scare clean labels in the source domain needs plenty of costs as well.In the presence of label noise in the source domain,the traditional UDA methods will be seriously degraded as they do not deal with the label noise.In this paper,we propose an approach named Robust Self-training with Label Refinement(RSLR)to address the above issue.RSLR adopts the self-training framework by maintaining a Labeling Network(LNet)on the source domain,which is used to provide confident pseudo-labels to target samples,and a Target-specific Network(TNet)trained by using the pseudo-labeled samples.To combat the effect of label noise,LNet progressively distinguishes and refines the mislabeled source samples.In combination with class rebalancing to combat the label distribution shift issue,RSLR achieves effective performance on extensive benchmark datasets.展开更多
Earth observations,especially satellite data,have produced a wealth of methods and results in meeting global challenges,often presented in unstructured texts such as papers or reports.Accurate extraction of satellite ...Earth observations,especially satellite data,have produced a wealth of methods and results in meeting global challenges,often presented in unstructured texts such as papers or reports.Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.The direct use of an existing dictionary to extract satellite and instrument entities suffers from the problem of poor matching,which leads to low recall.In this study,we present a named entity recognition model to automatically extract satellite and instrument entities from unstructured texts.Due to the lack of manually labeled data,we apply distant supervision to automatically generate labeled training data.Accordingly,we fine-tune the pre-trained language model with early stopping and a weighted cross-entropy loss function.We propose the dictionary-based self-training method to correct the incomplete annotations caused by the distant supervision method.Experiments demonstrate that our method achieves significant improvements in both precision and recall compared to dictionary matching or standard adaptation of pre-trained language models.展开更多
Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detec...Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.展开更多
基金supported by Guangdong Natural Science Foundation(2021B1515020085)Shenzhen Science and Technology Program(RCYX20210609103121030)+4 种基金National Natural Science Foundation of China(62322207,61872250,U2001206,U21B2023)Department of Education of Guangdong Province Innovation Team(2022KCXTD025)Shenzhen Science and Technology Innovation Program(JCYJ20210324120213036)the Natural Sciences and Engineering Research Council of Canada(NSERC)Guangdong Laboratory of Artificial Intelligence and Digital Economy(ShenZhen).
文摘Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.
基金Projects supported by National Natural Science Foundation of China(Project Number 81771445).
文摘A series of pharmacy services of clinical pharmacists at the Wuhan Mental Health Center during the prevention and treatment of Coronavirus disease 2019(COVID-19),such as participation in the formulation of COVID-19 prevention and treatment plans suitable for psychiatric departments,popular science of pharmacy,medical order review,real-time intervention,and medication education are summarized here.Due to the sudden public health incident,the service model of psychiatric clinical pharmacists should be addressed,as clinical pharmacists are an important part of the diagnosis and treatment of psychiatric diseases.Among the majors currently available in the clinical pharmacy training base curriculum,no psychiatry major has been set up in China yet;therefore,in this paper,we provide guidance in psychiatry pharmacy for those who wish to integrate clinical teams.
基金supported by the National Key R&D Program of China(2022ZD0114801)the National Natural Science Foundation of China(Grant No.61906089)the Jiangsu Province Basic Research Program(BK20190408).
文摘Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution changes.Previous UDA methods have acquired great success when labels in the source domain are pure.However,even the acquisition of scare clean labels in the source domain needs plenty of costs as well.In the presence of label noise in the source domain,the traditional UDA methods will be seriously degraded as they do not deal with the label noise.In this paper,we propose an approach named Robust Self-training with Label Refinement(RSLR)to address the above issue.RSLR adopts the self-training framework by maintaining a Labeling Network(LNet)on the source domain,which is used to provide confident pseudo-labels to target samples,and a Target-specific Network(TNet)trained by using the pseudo-labeled samples.To combat the effect of label noise,LNet progressively distinguishes and refines the mislabeled source samples.In combination with class rebalancing to combat the label distribution shift issue,RSLR achieves effective performance on extensive benchmark datasets.
基金supported by the National Key Research and Development Program of China:[grant number 2019YFE0126400].
文摘Earth observations,especially satellite data,have produced a wealth of methods and results in meeting global challenges,often presented in unstructured texts such as papers or reports.Accurate extraction of satellite and instrument entities from these unstructured texts can help to link and reuse Earth observation resources.The direct use of an existing dictionary to extract satellite and instrument entities suffers from the problem of poor matching,which leads to low recall.In this study,we present a named entity recognition model to automatically extract satellite and instrument entities from unstructured texts.Due to the lack of manually labeled data,we apply distant supervision to automatically generate labeled training data.Accordingly,we fine-tune the pre-trained language model with early stopping and a weighted cross-entropy loss function.We propose the dictionary-based self-training method to correct the incomplete annotations caused by the distant supervision method.Experiments demonstrate that our method achieves significant improvements in both precision and recall compared to dictionary matching or standard adaptation of pre-trained language models.
基金supported by the State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
文摘Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.
