开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标...开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标漏检等问题,提出了一种UBA-OWDT(UCSO,BiStrip and AFDF of open-world detection transformer)开放世界目标检测网络。针对未知类召回率偏低的问题,对未知类评分优化(unknown class scoring optimization,UCSO),将生成的浅层类激活图与聚合类激活图融合,获取细粒度特征信息,提高未知类的目标评分,进而提升未知类的召回率;针对小目标漏检问题,将双条状注意力(spatial attention based on strip pooling and strip convolution,BiStrip)应用于输入特征图,捕获长程依赖,保留目标精确的位置信息,增强感兴趣目标的表征,以检测小目标;针对密集目标漏检问题,采用自适应特征动态融合(adaptive feature dynamic fusion,AFDF),根据目标大小和形状,获得不同的感受野,动态分配注意力权重,关注目标的重要部分,融合不同层级的特征,以检测密集目标。在OWOD数据集的实验结果表明,未知类召回率增值范围在0.7~1.5个百分点,mAP增值范围在0.6~1.2个百分点,与现有的开放世界目标检测方法相比,在召回率偏低、密集目标与小目标漏检问题上具有一定的优势。展开更多
This paper aims to demonstrate whether students'self-evaluations influence their class performance.A questionnaire is designed and data is collected from 30 sophomore students in English class.The data analysis sh...This paper aims to demonstrate whether students'self-evaluations influence their class performance.A questionnaire is designed and data is collected from 30 sophomore students in English class.The data analysis shows that students who have positive self-evaluation perform better and get higher class performance scores than students who have negative self-evaluation.展开更多
考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方...考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。展开更多
The“six-and-twelve”(6&12)score is a new hepatocellular carcinoma(HCC)prognostic index designed for recommended transarterial chemoembolization(TACE)candidates.Quick and easy to use by the sum of tumor size(cm)an...The“six-and-twelve”(6&12)score is a new hepatocellular carcinoma(HCC)prognostic index designed for recommended transarterial chemoembolization(TACE)candidates.Quick and easy to use by the sum of tumor size(cm)and number,this model identifies three groups with different survival time(the sum is≤6;or>6 but≤12;or>12);a survival benefit with TACE can be expected for HCC patients with a score not exceeding twelve.Recently,Wang ZW et al showed that the“6&12”model was the best system correlated with radiological response after the first TACE.Thus,we wanted to assess its survival prediction ability as well as its prognostic value and compared it to other systems(Barcelona Clinic Liver Cancer,Hong Kong Liver Cancer(HKLC)staging,Albumin-Bilirubin grade,tumor nodularity,infiltrative nature of the tumor,alpha-fetoprotein,Child-Pugh class,and Performance Status score,Cancer of the Liver Italian Program,Model to Estimate Survival for HCC scores,up-to-seven criteria)different from Wang ZW et al study in a multicenter French cohort of HCC including only recommended TACE candidates retrospectively enrolled.As previously demonstrated,we show that the"6&12”score can classify survival within this French cohort,with a prognostic value comparable to that of other systems,except HKLC staging.More importantly,the“6&12”score simplicity and ability in patients’stratification outperform other systems for a routine clinical practice.展开更多
文摘开放世界目标检测(open world object detection,OWOD)的主要任务是检测已知类别和识别未知目标。此外,模型在下一个训练阶段中逐步学习已知新类。针对OW-DETR(open-world detection transformer)中未知类召回率偏低、密集目标与小目标漏检等问题,提出了一种UBA-OWDT(UCSO,BiStrip and AFDF of open-world detection transformer)开放世界目标检测网络。针对未知类召回率偏低的问题,对未知类评分优化(unknown class scoring optimization,UCSO),将生成的浅层类激活图与聚合类激活图融合,获取细粒度特征信息,提高未知类的目标评分,进而提升未知类的召回率;针对小目标漏检问题,将双条状注意力(spatial attention based on strip pooling and strip convolution,BiStrip)应用于输入特征图,捕获长程依赖,保留目标精确的位置信息,增强感兴趣目标的表征,以检测小目标;针对密集目标漏检问题,采用自适应特征动态融合(adaptive feature dynamic fusion,AFDF),根据目标大小和形状,获得不同的感受野,动态分配注意力权重,关注目标的重要部分,融合不同层级的特征,以检测密集目标。在OWOD数据集的实验结果表明,未知类召回率增值范围在0.7~1.5个百分点,mAP增值范围在0.6~1.2个百分点,与现有的开放世界目标检测方法相比,在召回率偏低、密集目标与小目标漏检问题上具有一定的优势。
文摘This paper aims to demonstrate whether students'self-evaluations influence their class performance.A questionnaire is designed and data is collected from 30 sophomore students in English class.The data analysis shows that students who have positive self-evaluation perform better and get higher class performance scores than students who have negative self-evaluation.
文摘考虑到传统物理分析方法无法解决导线舞动的预测问题,综合运用机器学习算法,对已有的舞动历史数据进行筛选和预处理,并挖掘有效信息,利用one class SVM算法解决舞动数据中负样本缺失问题,采用集成学习算法中Bagging算法建立分类器学习方法,实现了数据的随机抽样,分成不同组数据集进行相互独立的训练,避免对舞动数据过拟合,提升机器学习算法的抗噪声能力以及泛化能力,采用k折交叉验证算法进行模型的验证,并利用F1-score描述导线舞动预警模型的性能,验证了该方法在舞动预测方面的有效性。
文摘The“six-and-twelve”(6&12)score is a new hepatocellular carcinoma(HCC)prognostic index designed for recommended transarterial chemoembolization(TACE)candidates.Quick and easy to use by the sum of tumor size(cm)and number,this model identifies three groups with different survival time(the sum is≤6;or>6 but≤12;or>12);a survival benefit with TACE can be expected for HCC patients with a score not exceeding twelve.Recently,Wang ZW et al showed that the“6&12”model was the best system correlated with radiological response after the first TACE.Thus,we wanted to assess its survival prediction ability as well as its prognostic value and compared it to other systems(Barcelona Clinic Liver Cancer,Hong Kong Liver Cancer(HKLC)staging,Albumin-Bilirubin grade,tumor nodularity,infiltrative nature of the tumor,alpha-fetoprotein,Child-Pugh class,and Performance Status score,Cancer of the Liver Italian Program,Model to Estimate Survival for HCC scores,up-to-seven criteria)different from Wang ZW et al study in a multicenter French cohort of HCC including only recommended TACE candidates retrospectively enrolled.As previously demonstrated,we show that the"6&12”score can classify survival within this French cohort,with a prognostic value comparable to that of other systems,except HKLC staging.More importantly,the“6&12”score simplicity and ability in patients’stratification outperform other systems for a routine clinical practice.
