Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolut...Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolution on March 1 and 8, 1994 (spray-B) on the soil with 0.28 mg kg--1’ rapidly available B. Comparedwith no borate treatment (CK), B concentrations of leaves, short branches and flowers were higher and thepercentage of flower and fruit drop was lower in the treatments of soil-B and spray-B. B fertilizer increased Bconcentrations in flowers, leaves and short branches, promoted pollen germination, reduced the percentage offall of flowers and fruits of prunes, increased the percentage of fertile fruits, and thus increased yields of prunesby 46% and 34.3% in the treatments of soil-B and spray-B, respectively. It could be inferred preliminarilythat if B concentration of leaves was lower than 35 mg kg--1, the prunes should be fertilized with B. Themeasured leaves should be picked from branches (3-10 cm in length) germinating from the central sectionof a tree crown during the last ten days of May to the early days of June.展开更多
The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study ai...Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study aims to highlight the challenges and peculiarities in the management of PBS in a resource-poor setting. Materials and Methods: This is a ten-year retrospective study conducted at the University of Port Harcourt Teaching Hospital. Ethical approval for the study was sought and gotten from the hospital’s ethical committee. The information gotten included history, duration of symptoms, examination findings, age of the patient, category of disease, and intraoperative findings. The data from the folders were collected and evaluated. Frequencies, percentages, the mean and standard deviation were used to summarize the data as appropriate. Results: Fifteen patients were included in the study. The hospital incidence of PBS was 112/100,000, twelve males and three females. The age range was from 1 day to 15 years, mean age was 14 months ± 2.3 months. Most patients presented between 3 months and 2 years and 11 months. Twelve patients had category three PBS and five patients had associated anomalies. Eleven male patients died after 5 years of follow-up from progressive renal deterioration. The female patient fared better than the males. Conclusion: PBS is rare, most patients with the condition present late. The most common cause of mortality was progressive renal deterioration.展开更多
Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to ...Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from s...Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.展开更多
Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the ...Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the storage pressure from existing full nodes to stockpile new data. Basic pruning and other techniques have been explored to address these concerns but have been insufficient to reduce node synchronization delay and effectively suppress the growth of synchronized data. In this study, we propose SnapshotPrune, a novel pruning and synchronization protocol that achieves fast node bootstrapping in the Bitcoin blockchain. Real Bitcoin historical data are leveraged to measure the synchronization time and monitor the network traffic during node bootstrapping. The protocol requires data downloads that are 99.70% less than Bitcoin Core, 81% less than CoinPrune, and 60% less than SnapshotSave, thereby saving 97.23% of download time. Findings show that the proposed design enhances the storage efficiency and reduces the node synchronization delay compared with existing techniques. We hypothesize that the efficiency of this protocol increases with the block height.展开更多
针对户外导盲场景中道路目标检测存在的复杂背景干扰及关键语义信息需求,当前目标检测算法在道路目标检测中表现出较低的准确性以及容易出现漏检的问题,为此提出一种基于YOLOv8n的道路目标检测算法OD-YOLO。基于FasterNet和SPPF构建主...针对户外导盲场景中道路目标检测存在的复杂背景干扰及关键语义信息需求,当前目标检测算法在道路目标检测中表现出较低的准确性以及容易出现漏检的问题,为此提出一种基于YOLOv8n的道路目标检测算法OD-YOLO。基于FasterNet和SPPF构建主干网络;使用FasterNet以增强特征提取能力,在SPPF模块中引入可分离大核注意力机制(large separable kernel attention,LSKA)以提高算法对道路目标整体的感知能力。提出一种新的C2f模块GAC2f,在减小模型计算量的同时提高其特征捕获能力,同时通过使用多样分支模块(diverse branch block,DBB)中结构重参数化思想优化GAC2f,在不损失模型性能的前提下,融合多种特征信息以显著提高模型精度,另一方面使用卷积门控线性单元(convolutional gated linear unit,Convolutional GLU)改进LarK中的大核卷积以优化GAC2f,使模型能够捕获更多上下文信息。提出一种轻量级非对称检测头PADH,在提高模型性能的同时减少参数量,并使用PIoUv2改进原有的损失函数,通过基于层自适应稀疏度的量级剪枝(layer-adaptive sparsity for the magnitude-based pruning,LAMP)操作进一步优化算法模型。实验结果表明,在公共人行道路目标数据集WOTR上,OD-YOLO与YOLOv8n相比,经过剪枝后模型参数量同为3×10^(6),但mAP@0.5、mAP@0.5:0.95分别提升3.4和4.1个百分点,证明算法OD-YOLO在面向户外导盲场景的道路目标检测中可以达到预期的效果。展开更多
文摘Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolution on March 1 and 8, 1994 (spray-B) on the soil with 0.28 mg kg--1’ rapidly available B. Comparedwith no borate treatment (CK), B concentrations of leaves, short branches and flowers were higher and thepercentage of flower and fruit drop was lower in the treatments of soil-B and spray-B. B fertilizer increased Bconcentrations in flowers, leaves and short branches, promoted pollen germination, reduced the percentage offall of flowers and fruits of prunes, increased the percentage of fertile fruits, and thus increased yields of prunesby 46% and 34.3% in the treatments of soil-B and spray-B, respectively. It could be inferred preliminarilythat if B concentration of leaves was lower than 35 mg kg--1, the prunes should be fertilized with B. Themeasured leaves should be picked from branches (3-10 cm in length) germinating from the central sectionof a tree crown during the last ten days of May to the early days of June.
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
文摘Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study aims to highlight the challenges and peculiarities in the management of PBS in a resource-poor setting. Materials and Methods: This is a ten-year retrospective study conducted at the University of Port Harcourt Teaching Hospital. Ethical approval for the study was sought and gotten from the hospital’s ethical committee. The information gotten included history, duration of symptoms, examination findings, age of the patient, category of disease, and intraoperative findings. The data from the folders were collected and evaluated. Frequencies, percentages, the mean and standard deviation were used to summarize the data as appropriate. Results: Fifteen patients were included in the study. The hospital incidence of PBS was 112/100,000, twelve males and three females. The age range was from 1 day to 15 years, mean age was 14 months ± 2.3 months. Most patients presented between 3 months and 2 years and 11 months. Twelve patients had category three PBS and five patients had associated anomalies. Eleven male patients died after 5 years of follow-up from progressive renal deterioration. The female patient fared better than the males. Conclusion: PBS is rare, most patients with the condition present late. The most common cause of mortality was progressive renal deterioration.
基金supported in part by National Natural Science Foundation of China (61101114, 61671324) the Program for New Century Excellent Talents in University (NCET-12-0401)
文摘Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
基金supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20222003the National Natural Science Foundation of China under Grant Nos.61972196,61832008,and 61832005the Collaborative Innovation Center of Novel Software Technology and Industrialization,and the Sino-German Institutes of Social Computing.
文摘Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.
基金supported by the National Key Project of China(No.2020YFB1005700)the Natural Science Foundation of Shandong Province(No.ZR2021MF086)+3 种基金the National Key Research and Development Program of China(No.2021YFA1000600)the National Natural Science Foundation of China(Nos.62132018 and 62172117)the National Key Research and Development Program,the Young Scientist Scheme(No.2022YFB3102400)the National Key Research and Development Program of Guangdong Province(No.2020B0101090002).
文摘Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the storage pressure from existing full nodes to stockpile new data. Basic pruning and other techniques have been explored to address these concerns but have been insufficient to reduce node synchronization delay and effectively suppress the growth of synchronized data. In this study, we propose SnapshotPrune, a novel pruning and synchronization protocol that achieves fast node bootstrapping in the Bitcoin blockchain. Real Bitcoin historical data are leveraged to measure the synchronization time and monitor the network traffic during node bootstrapping. The protocol requires data downloads that are 99.70% less than Bitcoin Core, 81% less than CoinPrune, and 60% less than SnapshotSave, thereby saving 97.23% of download time. Findings show that the proposed design enhances the storage efficiency and reduces the node synchronization delay compared with existing techniques. We hypothesize that the efficiency of this protocol increases with the block height.
文摘针对户外导盲场景中道路目标检测存在的复杂背景干扰及关键语义信息需求,当前目标检测算法在道路目标检测中表现出较低的准确性以及容易出现漏检的问题,为此提出一种基于YOLOv8n的道路目标检测算法OD-YOLO。基于FasterNet和SPPF构建主干网络;使用FasterNet以增强特征提取能力,在SPPF模块中引入可分离大核注意力机制(large separable kernel attention,LSKA)以提高算法对道路目标整体的感知能力。提出一种新的C2f模块GAC2f,在减小模型计算量的同时提高其特征捕获能力,同时通过使用多样分支模块(diverse branch block,DBB)中结构重参数化思想优化GAC2f,在不损失模型性能的前提下,融合多种特征信息以显著提高模型精度,另一方面使用卷积门控线性单元(convolutional gated linear unit,Convolutional GLU)改进LarK中的大核卷积以优化GAC2f,使模型能够捕获更多上下文信息。提出一种轻量级非对称检测头PADH,在提高模型性能的同时减少参数量,并使用PIoUv2改进原有的损失函数,通过基于层自适应稀疏度的量级剪枝(layer-adaptive sparsity for the magnitude-based pruning,LAMP)操作进一步优化算法模型。实验结果表明,在公共人行道路目标数据集WOTR上,OD-YOLO与YOLOv8n相比,经过剪枝后模型参数量同为3×10^(6),但mAP@0.5、mAP@0.5:0.95分别提升3.4和4.1个百分点,证明算法OD-YOLO在面向户外导盲场景的道路目标检测中可以达到预期的效果。
