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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu 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 Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

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22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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Final results on the decay half-life limit of Mo from the CUPID-Mo experiment

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The European Physical Journal C 2022年第11期82卷 1-20页

作者： Augier, C. Barabash, A. S. Bellini, F. Benato, G. Beretta, M. Bergé, L. Billard, J. Borovlev, Yu. A. Cardani, L. Casali, N. Cazes, A. Chapellier, M. Chiesa, D. Dafinei, I. Danevich, F. A. De Jesus, M. de Marcillac, P. Dixon, T. Dumoulin, L. Eitel, K. Ferri, F. Fujikawa, B. K. Gascon, J. Gironi, L. Giuliani, A. Grigorieva, V. D. Gros, M. Helis, D. L. Huang, H. Z. Huang, R. Imbert, L. Johnston, J. Juillard, A. Khalife, H. Kleifges, M. Kobychev, V. V. Kolomensky, Yu. G. Konovalov, S. I. Loaiza, P. Ma, L. Makarov, E. P. Mariam, R. Marini, L. Marnieros, S. Navick, X.-F. Nones, C. Norman, E. B. Olivieri, E. Ouellet, J. L. Pagnanini, L. Pattavina, L. Paul, B. Pavan, M. Peng, H. Pessina, G. Pirro, S. Poda, D. V. Polischuk, O. G. Pozzi, S. Previtali, E. Redon, Th. Rojas, A. Rozov, S. Sanglard, V. Scarpaci, J. A. Schmidt, B. Shen, Y. Shlegel, V. N. Singh, V. Tomei, C. Tretyak, V. I. Umatov, V. I. Vagneron, L. Velázquez, M. Welliver, B. Winslow, L. Xue, M. Yakushev, E. Zarytskyy, M. Zolotarova, A. S. Univ Lyon Université Lyon 1 CNRS/IN2P3 IP2I-Lyon Villeurbanne France National Research Centre Kurchatov Institute Institute of Theoretical and Experimental Physics Moscow Russia Dipartimento di Fisica Sapienza Università di Roma Rome Italy INFN Sezione di Roma Rome Italy IRFU CEA Université Paris-Saclay Gif-sur-Yvette France INFN Laboratori Nazionali del Gran Sasso Assergi Italy Department of Physics University of California Berkeley USA Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Nikolaev Institute of Inorganic Chemistry Novosibirsk Russia Dipartimento di Fisica Università di Milano-Bicocca Milan Italy INFN Sezione di Milano-Bicocca Milan Italy Institute for Nuclear Research Kyiv Ukraine Institute for Astroparticle Physics Karlsruhe Institute of Technology Karlsruhe Germany Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley USA Gran Sasso Science Institute L’Aquila Italy Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) Fudan University Shanghai People’s Republic of China Massachusetts Institute of Technology Cambridge USA Institute for Data Processing and Electronics Karlsruhe Institute of Technology Karlsruhe Germany Department of Nuclear Engineering University of California Berkeley USA Physik Department Technische Universität München Garching Germany Department of Modern Physics University of Science and Technology of China Hefei People’s Republic of China LSM Laboratoire Souterrain de Modane Modane France Laboratory of Nuclear Problems JINR Dubna Russia Department of Physics and Astronomy Northwestern University Evanston USA Université Grenoble Alpes CNRS Grenoble INP SIMAP Saint Martin d’Héres France

The CUPID-Mo experiment to search for 0 $$\nu \beta \beta $$ decay in $$^{100}$$ Mo has been recently completed after about 1.5 years of operation at Laboratoire Souterrain de Modane (France). It served as a demonstrator for CUPID, a next generation 0 $$\nu \beta \beta $$ decay experiment. CUPID-Mo was comprised of 20 enriched $$\hbox {Li}_{{2}}$$ $$^{100}$$ $$\hbox {MoO}_4$$ scintillating calorimeters, each with a mass of $$\sim 0.2$$ kg, operated at $$\sim 20$$ mK. We present here the final analysis with the full exposure of CUPID-Mo ( $$^{100}$$ Mo exposure of 1.47 $$\hbox {kg} \times \hbox {year}$$ ) used to search for lepton number violation via 0 $$\nu \beta \beta $$ decay. We report on various analysis improvements since the previous result on a subset of data, reprocessing all data with these new techniques. We observe zero events in the region of interest and set a new limit on the $$^{100}$$ Mo 0 $$\nu \beta \beta $$ decay half-life of $$T_{1/2}^{0\nu }$$ $$> {1.8}\times 10^{24}$$ year (stat. + syst.) at 90% CI. Under the light Majorana neutrino exchange mechanism this corresponds to an effective Majorana neutrino mass of $$\left$$ $$<~{(0.28{-}0.49)} $$ eV, dependent upon the nuclear matrix element utilized.

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Fully-convolutional intensive feature flow neural network for text recognition

arXiv

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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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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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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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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.

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