检索结果-内蒙古大学图书馆

Corrigendum to “Discovering and learning sensational episodes of news events” [Inf. Syst. 78 (2018) 68–80]

information Systems 2020年 93卷

作者： Xiang Ao Ping Luo Chengkai Li Fuzhen Zhuang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Texas at Arlington USA

来源：评论

学校读者我要写书评

暂无评论

Mapping disulfide bonds from sub-micrograms of purified proteins or micrograms of complex protein mixtures

引用

Biophysics Reports 2018年第2期4卷 68-81页

作者： Shan Lu Yong Cao Sheng-Bo Fan Zhen-Lin Chen Run-Qian Fang Si-Min He Meng-Qiu Dong National Institute of Biological Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) University of CAS Institute of Computing Technology CAS University of Chinese Academy of Sciences

Disulfide bonds are vital for protein functions, but locating the linkage sites has been a challenge in protein chemistry, especially when the quantity of a sample is small or the complexity is high. In 2015,our laboratory developed a sensitive and efficient method for mapping protein disulfide bonds from simple or complex samples(Lu et al. in Nat Methods 12:329, 2015). This method is based on liquid chromatography–mass spectrometry(LC–MS) and a powerful data analysis software tool named p *** facilitate application of this method, we present step-by-step disulfide mapping protocols for three types of samples—purified proteins in solution, proteins in SDS-PAGE gels, and complex protein mixtures in solution. The minimum amount of protein required for this method can be as low as several hundred nanograms for purified proteins, or tens of micrograms for a mixture of hundreds of *** entire workflow—from sample preparation to LC–MS and data analysis—is described in great detail. We believe that this protocol can be easily implemented in any laboratory with access to a fastscanning, high-resolution, and accurate-mass LC–MS system.

关键词： Cross-linking Disulfide Identification of disulfide bonds Mass spectrometry PLink PLink-SS

来源：评论

学校读者我要写书评

暂无评论

Automatic Generation of Survey Paper Based on Template Tree

Automatic Generation of Survey Paper Based on Template Tree

引用

International Conference on Semantics, Knowledge and Grid (SKG)

作者： Xiaoping Sun Hai Zhuge Knowledge Grid Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China KLIIP Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences China Aston University Birmingham UK

ISBN: (数字)9781728158235

ISBN: (纸本)9781728158242

With rapid expansion of scientific papers, making a survey from a large collection of papers on a given research issue or domain becomes more and more important for researchers. This paper proposes a template-based framework for automatically generating survey paper. It allows users to compose a template tree as a syllabus for the required survey. Each tree node corresponds to a section to be composed in the survey therefore the whole tree defines the section structure of the survey. The template consists of two types of nodes, dimension node and topic node, which filter contents of papers. A recursive procedure along the survey generation template tree paths is conducted to process documents, rank sentences, and compose sections. We apply the approach to generating the survey of the reference papers of a survey paper and compare the result with the survey paper. Experiments show improvement over several baseline methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Edge-guided non-local fully convolutional network for salient object detection

arXiv

引用

arXiv 2019年

作者： Tu, Zhengzheng Ma, Yan Li, Chenglong Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Institute of Physical Science and Information Technology Anhui University Hefei230601 China

Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading to loss of spatial structure and blurred edges. To maintain the clear edge structure of salient objects, we propose a novel Edge-guided Non-local FCN (ENFNet) to perform edge-guided feature learning for accurate salient object detection. In a specific, we extract hierarchical global and local information in FCN to incorporate non-local features for effective feature representations. To preserve good boundaries of salient objects, we propose a guidance block to embed edge prior knowledge into hierarchical feature maps. The guidance block not only performs feature-wise manipulation but also spatial-wise transformation for effective edge embeddings. Our model is trained on the MSRA-B dataset and tested on five popular benchmark datasets. Comparing with the state-of-the-art methods, the proposed method achieves the best performance on all datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach

Identifying High Potential Talent: A Neural Network Based Dy...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Yuyang Ye Hengshu Zhu Tong Xu Fuzhen Zhuang Runlong Yu Hui Xiong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology University of Chinese Academy of Sciences

How to identify high-potential talent (HIPO) earlier in their career always has strategic importance for human resource management. While tremendous efforts have been made in this direction, most existing approaches are still based on the subjective selection of human resource experts. This could lead to unintentional bias and inconsistencies. To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks. A basic assumption is that HIPOs usually perform more actively and have higher competencies than their peers to accumulate their social capitals during their daily work practice. Along this line, we first propose to model the social profiles of employees with both Graph Convolutional Network (GCN) and social centrality analysis in a comprehensive way. Then, an adaptive Long Short Term Memory (LSTM) network with global attention mechanism is designed to capture the profile dynamics of employees in the organizational social networks during their early career. Finally, extensive experiments on real-world data clearly validate the effectiveness of our approach as well as the interpretability of our results.

关键词： Social networking (online) Companies Artificial neural networks Germanium Engineering profession

来源：评论

学校读者我要写书评

暂无评论

Fully Learnable Group Convolution for Acceleration of Deep Neural Networks

Fully Learnable Group Convolution for Acceleration of Deep N...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xijun Wang Meina Kan Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai China

ISBN: (纸本)9781728132945

Benefitted from its great success on many tasks, deep learning is increasingly used on low-computational-cost devices, e.g. smartphone, embedded devices, etc. To reduce the high computational and memory cost, in this work, we propose a fully learnable group convolution module (FLGC for short) which is quite efficient and can be embedded into any deep neural networks for acceleration. Specifically, our proposed method automatically learns the group structure in the training stage in a fully end-to-end manner, leading to a better structure than the existing pre-defined, two-steps, or iterative strategies. Moreover, our method can be further combined with depthwise separable convolution, resulting in 5 times acceleration than the vanilla Resnet50 on single CPU. An additional advantage is that in our FLGC the number of groups can be set as any value, but not necessarily 2^k as in most existing methods, meaning better tradeoff between accuracy and speed. As evaluated in our experiments, our method achieves better performance than existing learnable group convolution and standard group convolution when using the same number of groups.

关键词： acceleration Convolution numerical invariant Group Structure Neural network

来源：评论

学校读者我要写书评

暂无评论

Predicting compositional time series via autoregressive Dirichlet estimation

引用

Science China(information Sciences) 2018年第9期61卷 300-305页

作者： Ganbin ZHOU Ping LUO Qing HE Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

In recent years,compositional time series(CTS)prediction has become a widely applied data analysis method for modeling tactile sequence data[1],hydrological time series data using a four-

关键词： CTS Predicting compositional time series via autoregressive Dirichlet estimation

来源：评论

学校读者我要写书评

暂无评论

Direct speech-to-image translation

arXiv

引用

arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Robust unsupervised feature selection by nonnegative sparse subspace learning

引用

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

来源：评论

学校读者我要写书评

暂无评论

Research on SDN Multi Controller Deployment based on K-means++

引用

Journal of Physics: Conference Series 2020年第1期1606卷

作者： Li Yue Chen Junyan Liang Chuxin Lei Xiaochun School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi cloud computing and big data collaborative innovation center Guilin 541001 China Guangxi key laboratory of image and graphic intelligent processing Guilin 541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: the mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：