This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the origi...This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.展开更多
The performance of deep learning on many tasks has been impressive.However,recent studies have shown that deep learning systems are vulnerable to small specifically crafted perturbations imperceptible to humans.Images...The performance of deep learning on many tasks has been impressive.However,recent studies have shown that deep learning systems are vulnerable to small specifically crafted perturbations imperceptible to humans.Images with such perturbations are called adversarial examples.They have been proven to be an indisputable threat to deep neural networks(DNNs)based applications,but DNNs have yet to be fully elucidated,consequently preventing the development of efficient defenses against adversarial examples.This study proposes a two-stream architecture to protect convolutional neural networks(CNNs)from attacks by adversarial examples.Our model applies the idea of“two-stream”used in the security field.Thus,it successfully defends different kinds of attack methods because of differences in“high-resolution”and“low-resolution”networks in feature extraction.This study experimentally demonstrates that our two-stream architecture is difficult to be defeated with state-of-the-art attacks.Our two-stream architecture is also robust to adversarial examples built by currently known attacking algorithms.展开更多
In celebration of the 30th anniversary of China-ivory Coast diplomatic ties, "Entering West Africa:an Exhibition on Contemporary Chinese Painting Masterpieces"was held in La Rotonde des Arts in Abidjan,capit...In celebration of the 30th anniversary of China-ivory Coast diplomatic ties, "Entering West Africa:an Exhibition on Contemporary Chinese Painting Masterpieces"was held in La Rotonde des Arts in Abidjan,capital of Ivory Coast from May 21 to June 1,2013.Jointly organized by China Artists Association,Chinese Embassy to Ivory Coast and the Ministry of Culture and Affairs of French-speaking Countries of Ivory Coast,the exhibition displayed展开更多
Scattering experiments become increasingly popular in modern scientific research,including the areas of materials,biology,chemistry,physics,etc.Besides,various types of scattering facilities have been developed recent...Scattering experiments become increasingly popular in modern scientific research,including the areas of materials,biology,chemistry,physics,etc.Besides,various types of scattering facilities have been developed recently,such as labbased x-ray scattering equipment,national synchrotron facilities and large neutron facilities.These above-mentioned trends bring up fast-increasing data amounts of scattering data,as well as different scattering types(x-ray,neutron,laser and even microwaves).To help researchers process and analyze scattering data more efficiently,we developed a general and model-free scattering data analysis software based on matrix operation,which has the unique advantage of high throughput scattering data processing,analysis and visualization.To maximize generality and efficiency,data processing is performed based on a three-dimensional matrix,where scattering curves are saved as matrices or vectors,rather than the traditional definition of paired values.It can not only realize image batch processing,background subtraction and correction,but also analyze data according to scattering theory and model,such as radius of gyration,fractal dimension and other physical quantities.In the aspect of visualization,the software allows the modify the color maps of two-dimensional scattering images and the gradual color variation of one-dimensional curves to suit efficient data communications.In all,this new software can work as a stand-alone platform for researchers to process,analyze and visualize scattering data from different research facilities without considering different file types or formats.All codes in this manuscript are open-sourced and can be easily implemented in matrix-based software,such as MATLAB,Python and Igor.展开更多
目的:研究黄柏(phellodendri chinrnsis cortex,PCC)治疗骨性关节炎(osteoarthritis,OA)的作用机制。方法:运用中药系统药理学分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)从化...目的:研究黄柏(phellodendri chinrnsis cortex,PCC)治疗骨性关节炎(osteoarthritis,OA)的作用机制。方法:运用中药系统药理学分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)从化合物口服利用度以及类药性两方面对黄柏的活性成分进行筛选和收集,通过人类基因数据库(GeneCards)和在线人类孟德尔遗传数据库(OMIM)筛选OA的作用靶标。结合STRING数据库构建靶蛋白相互作用网络,筛选连接度排名前5位的靶蛋白,并利用分子对接服务器预测其与PCC活性成分的结合活性。借助网络拓扑属性分析软件Cytoscape 3.7.1构建PCC活性成分-靶点-OA疾病网络。使用Cytoscape 3.7.1软件中的ClueGO插件对靶点基因本体(gene ontology,GO)生物过程和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)中代谢通路进行富集分析。结果:获得生物活性成分37个,相对应的作用靶标78个,得到OA相关的基因靶点共2470个;获取PCC治疗OA的关键靶蛋白45个,筛选出连接度排名前5位的靶蛋白;GO分析与KEGG分析共富集到抗炎、抗软骨氧化、正向调控软骨细胞分化等35个生物过程条目(P<0.001),以及炎症、细胞凋亡、糖尿病相关等47个信号通路条目(P<0.001)。结论:黄柏治疗骨性关节炎的机制具有多成分-多靶点-多层次-多通路的特点,其治疗机制可能通过多个生物过程与通路对骨性关节炎引发的炎症反应进行抑制、对关节软骨进行保护以防治疾病。展开更多
基金the National Key Research and Development Program of China under Grant No.2018AAA0103203.
文摘This paper presents an effective image classification algorithm based on superpixels and feature fusion.Differing from classical image classification algorithms that extract feature descriptors directly from the original image,the proposed method first segments the input image into superpixels and,then,several different types of features are calculated according to these superpixels.To increase classification accuracy,the dimensions of these features are reduced using the principal component analysis(PCA)algorithm followed by a weighted serial feature fusion strategy.After constructing a coding dictionary using the nonnegative matrix factorization(NMF)algorithm,the input image is recognized by a support vector machine(SVM)model.The effectiveness of the proposed method was tested on the public Scene-15,Caltech-101,and Caltech-256 datasets,and the experimental results demonstrate that the proposed method can effectively improve image classification accuracy.
基金supported by the Ph.D.Programs Foundation of Ministry of Education of China under Grant No.20130185130001.
文摘The performance of deep learning on many tasks has been impressive.However,recent studies have shown that deep learning systems are vulnerable to small specifically crafted perturbations imperceptible to humans.Images with such perturbations are called adversarial examples.They have been proven to be an indisputable threat to deep neural networks(DNNs)based applications,but DNNs have yet to be fully elucidated,consequently preventing the development of efficient defenses against adversarial examples.This study proposes a two-stream architecture to protect convolutional neural networks(CNNs)from attacks by adversarial examples.Our model applies the idea of“two-stream”used in the security field.Thus,it successfully defends different kinds of attack methods because of differences in“high-resolution”and“low-resolution”networks in feature extraction.This study experimentally demonstrates that our two-stream architecture is difficult to be defeated with state-of-the-art attacks.Our two-stream architecture is also robust to adversarial examples built by currently known attacking algorithms.
文摘In celebration of the 30th anniversary of China-ivory Coast diplomatic ties, "Entering West Africa:an Exhibition on Contemporary Chinese Painting Masterpieces"was held in La Rotonde des Arts in Abidjan,capital of Ivory Coast from May 21 to June 1,2013.Jointly organized by China Artists Association,Chinese Embassy to Ivory Coast and the Ministry of Culture and Affairs of French-speaking Countries of Ivory Coast,the exhibition displayed
基金Project supported by School Project Cultivation Fund(Grant No.WK2310000101)。
文摘Scattering experiments become increasingly popular in modern scientific research,including the areas of materials,biology,chemistry,physics,etc.Besides,various types of scattering facilities have been developed recently,such as labbased x-ray scattering equipment,national synchrotron facilities and large neutron facilities.These above-mentioned trends bring up fast-increasing data amounts of scattering data,as well as different scattering types(x-ray,neutron,laser and even microwaves).To help researchers process and analyze scattering data more efficiently,we developed a general and model-free scattering data analysis software based on matrix operation,which has the unique advantage of high throughput scattering data processing,analysis and visualization.To maximize generality and efficiency,data processing is performed based on a three-dimensional matrix,where scattering curves are saved as matrices or vectors,rather than the traditional definition of paired values.It can not only realize image batch processing,background subtraction and correction,but also analyze data according to scattering theory and model,such as radius of gyration,fractal dimension and other physical quantities.In the aspect of visualization,the software allows the modify the color maps of two-dimensional scattering images and the gradual color variation of one-dimensional curves to suit efficient data communications.In all,this new software can work as a stand-alone platform for researchers to process,analyze and visualize scattering data from different research facilities without considering different file types or formats.All codes in this manuscript are open-sourced and can be easily implemented in matrix-based software,such as MATLAB,Python and Igor.
文摘目的:研究黄柏(phellodendri chinrnsis cortex,PCC)治疗骨性关节炎(osteoarthritis,OA)的作用机制。方法:运用中药系统药理学分析平台(traditional Chinese medicine systems pharmacology database and analysis platform,TCMSP)从化合物口服利用度以及类药性两方面对黄柏的活性成分进行筛选和收集,通过人类基因数据库(GeneCards)和在线人类孟德尔遗传数据库(OMIM)筛选OA的作用靶标。结合STRING数据库构建靶蛋白相互作用网络,筛选连接度排名前5位的靶蛋白,并利用分子对接服务器预测其与PCC活性成分的结合活性。借助网络拓扑属性分析软件Cytoscape 3.7.1构建PCC活性成分-靶点-OA疾病网络。使用Cytoscape 3.7.1软件中的ClueGO插件对靶点基因本体(gene ontology,GO)生物过程和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)中代谢通路进行富集分析。结果:获得生物活性成分37个,相对应的作用靶标78个,得到OA相关的基因靶点共2470个;获取PCC治疗OA的关键靶蛋白45个,筛选出连接度排名前5位的靶蛋白;GO分析与KEGG分析共富集到抗炎、抗软骨氧化、正向调控软骨细胞分化等35个生物过程条目(P<0.001),以及炎症、细胞凋亡、糖尿病相关等47个信号通路条目(P<0.001)。结论:黄柏治疗骨性关节炎的机制具有多成分-多靶点-多层次-多通路的特点,其治疗机制可能通过多个生物过程与通路对骨性关节炎引发的炎症反应进行抑制、对关节软骨进行保护以防治疾病。
