旨在明确贵州农田优势杂草马唐和反枝苋对草甘膦的抗药性水平,为杂草的抗性治理提供理论依据。以32个马唐和30个反枝苋的不同种群为研究试材,采用整株盆栽法,施药2周后测定草甘膦对杂草地上部分的鲜重抑制率。结果表明:毕节七星关区六...旨在明确贵州农田优势杂草马唐和反枝苋对草甘膦的抗药性水平,为杂草的抗性治理提供理论依据。以32个马唐和30个反枝苋的不同种群为研究试材,采用整株盆栽法,施药2周后测定草甘膦对杂草地上部分的鲜重抑制率。结果表明:毕节七星关区六合村马唐种群敏感性最低,ED_(50)值为16.23 g a.i/hm^(2),其他种群相对抗性倍数在1.0~5.3之间,马唐有12个种群处于敏感阶段,17个种群处于低水平抗性,1个种群处于中等抗性水平;赫章县双坪乡反枝苋种群ED_(50)值最低,为15.38g a.i/hm^(2),其他种群相对抗性倍数在1.0~9.0之间,反枝苋12个种群处于敏感阶段,14个种群处于低水平抗性,4个种群处于中等抗性水平。从结果来看,贵州省范围内不同马唐和反枝苋种群对草甘膦都产生了不同程度的抗性,但大多数马唐和反枝苋种群均处于敏感阶段或低抗药性水平。展开更多
Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved i...Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.展开更多
文摘旨在明确贵州农田优势杂草马唐和反枝苋对草甘膦的抗药性水平,为杂草的抗性治理提供理论依据。以32个马唐和30个反枝苋的不同种群为研究试材,采用整株盆栽法,施药2周后测定草甘膦对杂草地上部分的鲜重抑制率。结果表明:毕节七星关区六合村马唐种群敏感性最低,ED_(50)值为16.23 g a.i/hm^(2),其他种群相对抗性倍数在1.0~5.3之间,马唐有12个种群处于敏感阶段,17个种群处于低水平抗性,1个种群处于中等抗性水平;赫章县双坪乡反枝苋种群ED_(50)值最低,为15.38g a.i/hm^(2),其他种群相对抗性倍数在1.0~9.0之间,反枝苋12个种群处于敏感阶段,14个种群处于低水平抗性,4个种群处于中等抗性水平。从结果来看,贵州省范围内不同马唐和反枝苋种群对草甘膦都产生了不同程度的抗性,但大多数马唐和反枝苋种群均处于敏感阶段或低抗药性水平。
基金supported by the National Natural Science Foundation of China(61172173,61303114,61271256,61272544,U1304615,U1404618)the National High Technology Research and Development Program of China(863 Program)No.2013AA014602
文摘Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.
