Tidal marshes are an important habitat and nursery area for fi sh.In the past few decades,rapid economic development in the coastal areas of China has led to the interruption and destruction of an increasing number of...Tidal marshes are an important habitat and nursery area for fi sh.In the past few decades,rapid economic development in the coastal areas of China has led to the interruption and destruction of an increasing number of tidal marshes.The growing interest in tidal marsh restoration has increased the need to understand the relationship between geomorphological features and fi sh assemblages in the design of marsh restoration projects.We studied temporal variations in,and the effects of creek geomorphological features on,the estuarine tidal creek fi sh community.Using modifi ed channel nets,we sampled fi sh monthly from March 2007 to February 2008 from seven tidal creeks along an intertidal channel system in Chongming Dongtan National Nature Reserve.Fourteen creek geomorphological variables were measured or derived to characterize intertidal creek geomorphological features.The Gobiidae,with 10 species,was the most speciesrich family.The most abundant fi sh species were Liza affi nis,Chelon haematocheilus,and Lateolabrax maculatus.The fi sh community was dominated by juvenile marine transients,which comprised about 80% of the total catch.The highest abundance of fi sh occurred in June and July,and the highest biomass occurred in December.Canonical redundancy analyses demonstrated that depth,steepness,cross-sectional area,and volume signifi cantly affected the fi sh species assemblage.L.affi nis favored small creeks with high elevations.Synechogobius ommaturus,Acanthogobius luridus,and Carassius auratus preferred deep,steep creeks with a large cross-sectional area and volume.These fi ndings indicate that the geomorphological features of tidal creeks should be considered in the conservation and sustainable management of fi sh species and in the restoration of salt marshes.展开更多
A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images,...A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.展开更多
Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the pr...Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO展开更多
基金Supported by the National Basic Research Program of China(973 Program)(No.2013CB430404)the National Science and Technology Ministry(No.2010BAK69B14)the Science and Technology Department of Shanghai(No.10dz1200700)
文摘Tidal marshes are an important habitat and nursery area for fi sh.In the past few decades,rapid economic development in the coastal areas of China has led to the interruption and destruction of an increasing number of tidal marshes.The growing interest in tidal marsh restoration has increased the need to understand the relationship between geomorphological features and fi sh assemblages in the design of marsh restoration projects.We studied temporal variations in,and the effects of creek geomorphological features on,the estuarine tidal creek fi sh community.Using modifi ed channel nets,we sampled fi sh monthly from March 2007 to February 2008 from seven tidal creeks along an intertidal channel system in Chongming Dongtan National Nature Reserve.Fourteen creek geomorphological variables were measured or derived to characterize intertidal creek geomorphological features.The Gobiidae,with 10 species,was the most speciesrich family.The most abundant fi sh species were Liza affi nis,Chelon haematocheilus,and Lateolabrax maculatus.The fi sh community was dominated by juvenile marine transients,which comprised about 80% of the total catch.The highest abundance of fi sh occurred in June and July,and the highest biomass occurred in December.Canonical redundancy analyses demonstrated that depth,steepness,cross-sectional area,and volume signifi cantly affected the fi sh species assemblage.L.affi nis favored small creeks with high elevations.Synechogobius ommaturus,Acanthogobius luridus,and Carassius auratus preferred deep,steep creeks with a large cross-sectional area and volume.These fi ndings indicate that the geomorphological features of tidal creeks should be considered in the conservation and sustainable management of fi sh species and in the restoration of salt marshes.
基金partially supported by the National Natural Science Foundation of China under Grants No. 61071146, No. 61171165the Natural Science Foundation of Jiangsu Province under Grant No. BK2010488+1 种基金sponsored by Qing Lan Project, Project 333 "The Six Top Talents" of Jiangsu Province
文摘A Single Image Super-Resolution (SISR) reconstruction method that uses clustered sparse representation and adaptive patch aggregation is proposed. First, we randomly extract image patch pairs from the training images, and divide these patch pairs into different groups by K-means clustering. Then, we learn an over-complete sub-dictionary pair offline from corresponding group patch pairs. For a given low-resolution patch, we adaptively select one sub-dictionary to reconstruct the high resolution patch online. In addition, non-local self-similarity and steering kernel regression constraints are integrated into patch aggregation to improve the quality of the recovered images. Experiments show that the proposed method is able to realize state-of-the-art performance in terms of both objective evaluation and visual perception.
基金This project is supported in part by National Natural Science Foundation of China （60673060）, Science Foundation of Jiangsu Province （BK2005047）.
文摘Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO
