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arXiv 2019年

作者： Zhang, Zhao Tang, Zemin Zhang, Zheng Wang, Yang Qin, Jie Wang, Meng School of Computer Science and Technology Soochow University China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer and Information Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology Shenzhen518055 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable;2) the traditional convolution operation optimizes slowly and the hierarchical features from different layers are not fully utilized. In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion operations for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recognition results, compared with other related deep models. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Adaptive structure-constrained robust latent low-rank coding for image recovery

arXiv

引用

arXiv 2019年

作者： Zhang, Zhao Wang, Lei Li, Sheng Wang, Yang Zhang, Zheng Zha, Zhengjun Wang, Meng School of Computer Science and Technology Soochow University Suzhou215006 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia 549 Boyd GSRC AthensGA30602 Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2,1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Recovery

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QUANTUM LOVÁSZ LOCAL LEMMA: SHEARER’S BOUND IS TIGHT

arXiv

引用

arXiv 2018年

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China University of Southern California United States

The Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all bad events under some weakly dependent conditions. In a seminal paper, Ambainis, Kempe, and Sattath (JACM 2012) introduced a quantum version LLL (QLLL) which shows the possibility of avoiding all "bad" Hamiltonians under some weakly dependent condition, and applied QLLL to the random k-QSAT problem. Sattath, Morampudi, Laumann, and moessner (PNAS 2015) extended Ambainis, Kempe, and Sattath’s result and showed that Shearer’s bound is a sufficient condition for QLLL, and conjectured that Shearer’s bound is indeed the tight condition for QLLL. In this paper, we affirm this conjecture. Precisely, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial. Our result implies the tightness of Gilyén and Sattath’s algorithm (FOCS 2017), and also implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits (Sattath, Morampudi, Laumann and moessner, PNAS 2015). The commuting LLL (CLLL), which focuses on commuting local Hamiltonians, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result indicates that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

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Learning personalized attribute preference via multi-task auc optimization

arXiv

引用

arXiv 2019年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute words are involved. In this paper, we develop a novel multi-task method to understand and predict personalized attribute annotations. Regarding the attribute preference learning for each annotator as a specific task, we first propose a multi-level task parameter decomposition to capture the evolution from a highly popular opinion of the mass to highly personalized choices that are special for each person. Meanwhile, for personalized learning methods, ranking prediction is much more important than accurate classification. This motivates us to employ an Area Under ROC Curve (AUC) based loss function to improve our model. On top of the AUC-based loss, we propose an efficient method to evaluate the loss and gradients. Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors. Furthermore, we also provide a generalization bound to guarantee a reasonable performance. Finally, empirical analysis consistently speaks to the efficacy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Τ-FPL: Tolerance-constrained learning in linear time 32

Τ-FPL: Tolerance-constrained learning in linear time

引用

32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

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Impact of Prior knowledge and data Correlation on Privacy Leakage: A Unified Analysis

arXiv

引用

arXiv 2019年

作者： Li, Yanan Ren, Xuebin Yang, Shusen Yang, Xinyu National Engineering Laboratory for Big Data Analytics [NEL-BDA Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security [MOE KLINNS Lab Xi’an Jiaotong University Xi’an Shaanxi710049 China

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

引用

IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

关键词：

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Ngram2vec: Learning improved word representations from ngram co-occurrence statistics

Ngram2vec: Learning improved word representations from ngram...

引用

2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017

作者： Zhao, Zhe Liu, Tao Li, Shen Li, Bofang Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Institute of Chinese Information Processing Beijing Normal University China UltraPower-BNU Joint Laboratory for Artificial Intelligence Beijing Normal University China

ISBN: (纸本)9781945626838

The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams. © 2017 Association for Computational Linguistics.

关键词： Natural language processing systems

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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

引用

ACM Transactions on knowledge Discovery from data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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High-quality high-order harmonic generation through preplasma truncation

引用

Physical Review E 2019年第5期100卷 053207-053207页

作者： B. Y. Li F. Liu M. Chen Z. Y. Chen X. H. Yuan S. M. Weng T. Jin S. G. Rykovanov J. W. Wang Z. M. Sheng J. Zhang Key Laboratory for Laser Plasmas (MoE) School of Physics and Astronomy Shanghai Jiao Tong University Shanghai 200240 China Collaborative Innovation Center of IFSA (CICIFSA) Shanghai Jiao Tong University Shanghai 200240 China National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid Physics China Academy of Engineering Physics Mianyang 621999 China Zhiyuan College Shanghai Jiao Tong University Shanghai 200240 China Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow 121205 Russia Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China SUPA Department of Physics University of Strathclyde Glasgow G4 0NG United Kingdom

By introducing preplasma truncation to cases with an initial preplasma scale length larger than 0.2λ, the efficiency of high-order harmonics generated from relativistic laser-solid interactions can be enhanced by more than one order of magnitude and the angular spread can be confined into near-diffraction-limited divergence. Numerical simulations show that density truncation results in more compact oscillation of the surface electron sheet and the curvature of the reflection surface for the driving laser is greatly reduced. This leads to an overall improvement in the harmonic beam quality. More importantly, density truncation makes the harmonic generation weakly dependent on the preplasma scale length, which provides a way to relax the extremely high requirement on the temporal contrast of the driving laser pulse. A feasible scheme to realize the required preplasma truncation is also proposed and demonstrated by numerical simulations.

关键词： High intensity laser-plasma interactions High-order harmonic generation X-ray generation in plasmas

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