Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have b...Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.展开更多
BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiolo...BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiologies of chronic liver disease.AIM To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.METHODS Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled.After excluding dual infection,alcoholism,autoimmune hepatitis,and others with incomplete data,this retrospective cohort were divided into hepatitis B(HBV,n=1064),hepatitis C(HCV,n=507),and non-HBV,non-HCV(NBNC,n=391)groups.The indexed cases were linked to cancer registration(1987-2020)and national mortality databases.The differences in morbidity and mortality among the groups were analyzed.RESULTS At the enrollment,the HBV group showed more males(77.5%),a higher prevalence of prediagnosed hepatocellular carcinoma(HCC),and a lower prevalence of comorbidities than the other groups(P<0.001).The HCV group was older and had a lower platelet count and higher ARFI score than the other groups(P<0.001).The NBNC group showed a higher body mass index and platelet count,a higher prevalence of pre-diagnosed non-HCC cancers(P<0.001),especially breast cancer,and a lower prevalence of cirrhosis.Male gender,ARFI score,and HBV were independent predictors of HCC.The 5-year risk of HCC was 5.9%and 9.8%for those ARFI-graded with severe fibrosis and cirrhosis.ARFI alone had an area under the receiver operating characteristic curve(AUROC)of 0.742 for prediction of HCC in 5 years.AUROC increased to 0.828 after adding etiology,gender,age,and platelet score.No difference was found in mortality rate among the groups.CONCLUSION The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups.Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.展开更多
Robustness and generalization are two challenging problems for learning point cloud representation.To tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengra...Robustness and generalization are two challenging problems for learning point cloud representation.To tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each other.We also introduce a large-scale point cloud dataset,PCNet184.It consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain generalization.Finally,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and S3DIS.Our method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised learning.Code and our dataset are available at https://github.com/MingyeXu/PCNet184.展开更多
In recent years,neoantigens have been recognized as ideal targets for tumor immunotherapy.With the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the...In recent years,neoantigens have been recognized as ideal targets for tumor immunotherapy.With the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies.We have built the tumor-specific neoantigen database(TSNAdb)previously,which has attracted much attention.In this study,we provide TSNAdb v2.0,an updated version of the TSNAdb.TSNAdb v2.0 offers several new features,including(1)adopting more stringent criteria for neoantigen identification,(2)providing predicted neoantigens derived from three types of somatic mutations,and(3)collecting experimentally validated neoantigens and dividing them according to the experimental level.展开更多
We propose a feature-fusion network for pose estimation directly from RGB images without any depth information in this study.First,we introduce a two-stream architecture consisting of segmentation and regression strea...We propose a feature-fusion network for pose estimation directly from RGB images without any depth information in this study.First,we introduce a two-stream architecture consisting of segmentation and regression streams.The segmentation stream processes the spatial embedding features and obtains the corresponding image crop.These features are further coupled with the image crop in the fusion network.Second,we use an efficient perspective-n-point(E-PnP)algorithm in the regression stream to extract robust spatial features between 3D and 2D keypoints.Finally,we perform iterative refinement with an end-to-end mechanism to improve the estimation performance.We conduct experiments on two public datasets of YCB-Video and the challenging Occluded-LineMOD.The results show that our method outperforms state-of-the-art approaches in both the speed and the accuracy.展开更多
基金the National Natural Science Foundation of China(62003298,62163036)the Major Project of Science and Technology of Yunnan Province(202202AD080005,202202AH080009)the Yunnan University Professional Degree Graduate Practice Innovation Fund Project(ZC-22222770)。
文摘Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
基金Supported by the Chang Gung Memorial Hospital and PAII Inc.(a United States subsidiary company of Ping An Insurance Group),No.SMRPG3I0011.
文摘BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiologies of chronic liver disease.AIM To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.METHODS Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled.After excluding dual infection,alcoholism,autoimmune hepatitis,and others with incomplete data,this retrospective cohort were divided into hepatitis B(HBV,n=1064),hepatitis C(HCV,n=507),and non-HBV,non-HCV(NBNC,n=391)groups.The indexed cases were linked to cancer registration(1987-2020)and national mortality databases.The differences in morbidity and mortality among the groups were analyzed.RESULTS At the enrollment,the HBV group showed more males(77.5%),a higher prevalence of prediagnosed hepatocellular carcinoma(HCC),and a lower prevalence of comorbidities than the other groups(P<0.001).The HCV group was older and had a lower platelet count and higher ARFI score than the other groups(P<0.001).The NBNC group showed a higher body mass index and platelet count,a higher prevalence of pre-diagnosed non-HCC cancers(P<0.001),especially breast cancer,and a lower prevalence of cirrhosis.Male gender,ARFI score,and HBV were independent predictors of HCC.The 5-year risk of HCC was 5.9%and 9.8%for those ARFI-graded with severe fibrosis and cirrhosis.ARFI alone had an area under the receiver operating characteristic curve(AUROC)of 0.742 for prediction of HCC in 5 years.AUROC increased to 0.828 after adding etiology,gender,age,and platelet score.No difference was found in mortality rate among the groups.CONCLUSION The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups.Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.
基金This work was partially supported by the National Natural Science Foundation of China(Grant Nos.61876176 and U1813218)the Joint Lab of CAS–HK,the Shenzhen Research Program(Grant No.RCJC20200714114557087)+1 种基金the Shanghai Committee of Science and Technology(Grant No.21DZ1100100)Shenzhen Institute of Artificial Intelligence and Robotics for Society.
文摘Robustness and generalization are two challenging problems for learning point cloud representation.To tackle these problems,we first design a novel geometry coding model,which can effectively use an invariant eigengraph to group points with similar geometric information,even when such points are far from each other.We also introduce a large-scale point cloud dataset,PCNet184.It consists of 184 categories and 51,915 synthetic objects,which brings new challenges for point cloud classification,and provides a new benchmark to assess point cloud cross-domain generalization.Finally,we perform extensive experiments on point cloud classification,using ModelNet40,ScanObjectNN,and our PCNet184,and segmentation,using ShapeNetPart and S3DIS.Our method achieves comparable performance to state-of-the-art methods on these datasets,for both supervised and unsupervised learning.Code and our dataset are available at https://github.com/MingyeXu/PCNet184.
基金supported by the National Natural Science Foundation of China(Grant Nos.31971371 and U20A20409)the Key R&D Program of Zhejiang Province,China(Grant No.2020C03010)+1 种基金the Huadong Medicine Joint Funds of the Zhejiang Provincial Natural Science Foundation of China(Grant No.LHDMZ22H300002)the AlibabaZhejiang University Joint Research Center of Future Digital Healthcare.
文摘In recent years,neoantigens have been recognized as ideal targets for tumor immunotherapy.With the development of neoantigen-based tumor immunotherapy,comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies.We have built the tumor-specific neoantigen database(TSNAdb)previously,which has attracted much attention.In this study,we provide TSNAdb v2.0,an updated version of the TSNAdb.TSNAdb v2.0 offers several new features,including(1)adopting more stringent criteria for neoantigen identification,(2)providing predicted neoantigens derived from three types of somatic mutations,and(3)collecting experimentally validated neoantigens and dividing them according to the experimental level.
基金the National Key Research and Development Program of China under Grant No.2021YFB1715900the National Natural Science Foundation of China under Grant Nos.12022117 and 61802406+2 种基金the Beijing Natural Science Foundation under Grant No.Z190004the Beijing Advanced Discipline Fund under Grant No.115200S001Alibaba Group through Alibaba Innovative Research Program.
文摘We propose a feature-fusion network for pose estimation directly from RGB images without any depth information in this study.First,we introduce a two-stream architecture consisting of segmentation and regression streams.The segmentation stream processes the spatial embedding features and obtains the corresponding image crop.These features are further coupled with the image crop in the fusion network.Second,we use an efficient perspective-n-point(E-PnP)algorithm in the regression stream to extract robust spatial features between 3D and 2D keypoints.Finally,we perform iterative refinement with an end-to-end mechanism to improve the estimation performance.We conduct experiments on two public datasets of YCB-Video and the challenging Occluded-LineMOD.The results show that our method outperforms state-of-the-art approaches in both the speed and the accuracy.
