目的了解经皮冠状动脉介入(PCI)术后患者在新型冠状病毒肺炎(COVID-19)疫情期间的焦虑和抑郁状况,为制订针对性的干预措施提供依据。方法选取广州市某三级甲等综合医院2019年3月至12月实施了PCI术患者68例,于2020年2月4日至11日采用自...目的了解经皮冠状动脉介入(PCI)术后患者在新型冠状病毒肺炎(COVID-19)疫情期间的焦虑和抑郁状况,为制订针对性的干预措施提供依据。方法选取广州市某三级甲等综合医院2019年3月至12月实施了PCI术患者68例,于2020年2月4日至11日采用自制调查问卷、焦虑自评量表(SAS)和抑郁自评量表(SDS)进行调查和测评。采用描述性、单因素、二元Logistics回归分析和Pearson相关性分析分析影响因素及其相关性。结果共回收问卷62份。PCI术后患者的SAS和SDS评分分别为(49.24±12.01)分和(54.48±11.72)分,显著高于全国常模的(29.78±0.46)分和(41.88±10.57)分(P <0.001)。51.61%(32/62)和50.00%(31/62)的患者分别出现了焦虑和抑郁状况,且两者呈正相关(r=0.725, P <0.001)。二元Logistics回归分析显示,药物是否充足(OR=10.297,P=0.001)和最近1周血压、血糖较疫情暴发前的状况(OR=0.198, P=0.001),是影响PCI术后患者焦虑的主要因素;而居住状况(OR=0.021, P=0.003)和最近1周血压、血糖较疫情暴发前的状况(OR=0.054, P <0.001),是影响PCI术后患者抑郁的主要因素。结论 COVID-19流行期间,PCI术后患者存在明显的焦虑和抑郁情绪,应加强该类人群心理健康的关注,采取有针对性的措施减轻疫情导致的心理问题。展开更多
Convolutional Neural Networks(CNN)have achieved great success in many computer vision tasks.However,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models ...Convolutional Neural Networks(CNN)have achieved great success in many computer vision tasks.However,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models are computationally expensive.Therefore,CNN model compression and acceleration have become a hot research topic in the deep learning area.Typical schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing,low-rank factorization,compact convolutional filters and knowledge distillation.In this study,we propose a general acceleration scheme that replaces the floating-point multiplication with integer addition.To this end,we propose a general accelerate scheme,where the floating point multiplication is replaced by integer addition.The motivation is based on the fact that every floating point can be replaced by the summation of an exponential series.Therefore,the multiplication between two floating points can be converted to the addition among exponentials.In the experiment section,we directly apply the proposed scheme to AlexNet,VGG,ResNet for image classification,and Faster-RCNN for object detection.The results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance,even with only one item of exponential.Moreover,we analyzed the eciency of our method on mainstream FPGAs.The experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss.展开更多
文摘目的了解经皮冠状动脉介入(PCI)术后患者在新型冠状病毒肺炎(COVID-19)疫情期间的焦虑和抑郁状况,为制订针对性的干预措施提供依据。方法选取广州市某三级甲等综合医院2019年3月至12月实施了PCI术患者68例,于2020年2月4日至11日采用自制调查问卷、焦虑自评量表(SAS)和抑郁自评量表(SDS)进行调查和测评。采用描述性、单因素、二元Logistics回归分析和Pearson相关性分析分析影响因素及其相关性。结果共回收问卷62份。PCI术后患者的SAS和SDS评分分别为(49.24±12.01)分和(54.48±11.72)分,显著高于全国常模的(29.78±0.46)分和(41.88±10.57)分(P <0.001)。51.61%(32/62)和50.00%(31/62)的患者分别出现了焦虑和抑郁状况,且两者呈正相关(r=0.725, P <0.001)。二元Logistics回归分析显示,药物是否充足(OR=10.297,P=0.001)和最近1周血压、血糖较疫情暴发前的状况(OR=0.198, P=0.001),是影响PCI术后患者焦虑的主要因素;而居住状况(OR=0.021, P=0.003)和最近1周血压、血糖较疫情暴发前的状况(OR=0.054, P <0.001),是影响PCI术后患者抑郁的主要因素。结论 COVID-19流行期间,PCI术后患者存在明显的焦虑和抑郁情绪,应加强该类人群心理健康的关注,采取有针对性的措施减轻疫情导致的心理问题。
基金the National Natural Science Foundation of China(Grant Nos.41971424,61701191)the Key Technical Project of Xiamen Ocean Bureau(Grant No.18CZB033HJ11)+2 种基金the Natural Science Foundation of Fujian Province(Grant Nos.2019J01712,2020J01701)the Key Technical Project of Xiamen Science and Technology Bureau(Grant Nos.3502Z20191018,3502Z20201007,3502Z20191022,3502Z20203057)the Science and Technology Project of Education Department of Fujian Province(Grant Nos.JAT190321,JAT190318,JAT190315)。
文摘Convolutional Neural Networks(CNN)have achieved great success in many computer vision tasks.However,it is difficult to deploy CNN models on low-cost devices with limited power budgets,because most existing CNN models are computationally expensive.Therefore,CNN model compression and acceleration have become a hot research topic in the deep learning area.Typical schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing,low-rank factorization,compact convolutional filters and knowledge distillation.In this study,we propose a general acceleration scheme that replaces the floating-point multiplication with integer addition.To this end,we propose a general accelerate scheme,where the floating point multiplication is replaced by integer addition.The motivation is based on the fact that every floating point can be replaced by the summation of an exponential series.Therefore,the multiplication between two floating points can be converted to the addition among exponentials.In the experiment section,we directly apply the proposed scheme to AlexNet,VGG,ResNet for image classification,and Faster-RCNN for object detection.The results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance,even with only one item of exponential.Moreover,we analyzed the eciency of our method on mainstream FPGAs.The experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss.
