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Fast stochastic ordinal embedding with variance reduction and adaptive step size

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

New algorithm of target classification in polarimetric SAR

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Journal of Systems Engineering and Electronics 2008年第2期000卷

作者： Wang Yang Lu Jiaguo Wu Xianliang Wang Yang (Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China) Lu Jiaguo (The 38th Research Inst. China Electronic Technology Corporation Hefei 230031 P. R. China) Wu Xianliang (Key Lab of Intelligent Computing & Signal Processing of Ministry of Education Anhui Univ. Heifei 230039 P. R. China)

The different approaches used for target decomposition (TD) theory in radar polarimetry are reviewed and three main types of theorems are introduced: those based on Mueller matrix, those using an eigenvector analysis of the coherency matrix, and those employing coherent decomposition of the scattering matrix. Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated success in many fields. A new algorithm of target classification, by combining target decomposition and the support vector machine, is *** conduct the experiment, the polarimetric synthetic aperture radar (SAR) data are used. Experimental results show that it is feasible and efficient to target classification by applying target decomposition to extract scattering mechanisms, and the effects of kernel function and its parameters on the classification efficiency are significant.

关键词： polarimetric synthetic aperture radar target decomposition support vector machine target classifi-cation kernel function.

Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization

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Computational biology and chemistry 2015年 57卷 21-8页

作者： Zhi-Qin Zhao Guo-Sheng Han Zu-Guo Yu Jinyan Li Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China. Hunan Key Laboratory for Computation and Simulation in Science and Engineering and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. Electronic address: yuzg1970@***. Advanced Analytics Institute & Centre for Health Technologies University of Technology Sydney Broadway NSW 2007 Australia. Electronic address: jinyan.li@uts.edu.au.

Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene-phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene-phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request.

关键词： Disease genes and phenotypes Heterogeneous network Laplacian normalization Leave-one-out cross-validation Random walk with restart Shannon information entropy

Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

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Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

Deterministic realization of collective measurements via photonic quantum walks

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arXiv 2017年

作者： Hou, Zhibo Tang, Jun-Feng Shang, Jiangwei Zhu, Huangjun Li, Jian Yuan, Yuan Wu, Kang-Da Xiang, Guo-Yong Li, Chuan-Feng Guo, Guang-Can Key Laboratory of Quantum Information University of Science and Technology of China CAS Hefei230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Naturwissenschaftlich-Technische Fakultät Universität Siegen Siegen57068 Germany Beijing Key Laboratory of Nanophotonics Ultrafine Optoelectronic Systems School of Physics Beijing Institute of Technology Beijing100081 China Institute for Theoretical Physics University of Cologne Cologne50937 Germany Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Fudan University Shanghai200433 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China Institute of Signal Processing Transmission Nanjing University of Posts and Telecommunications Nanjing210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing210003 China

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information processing and for exploring the intriguing physics behind this power. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

Kinetic-molecular theory optimization algorithm based on Kent chaotic mapping

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AIP Conference Proceedings 2017年第1期1864卷

作者： Huguang Gong Chaodong Fan Bo Ouyang 1Key Laboratory for Intelligent Computing & Information Processing of the Ministry of Education (Xiangtan University) Xiangtan Hunan 411105 China 2Hunan 2011 Collaborative Innovation Center of the Change of Wind Power and Electrical Energy Xiangtan Hunan 411105 China

Aiming at the shortage that Kinetic-molecular theory optimization algorithm (KMTOA) is more likely to show premature convergence and the accuracy of searching for the optimum needs to be improved, a Kinetic-molecular theory optimization algorithm (KCKMTOA) based on Kent chaotic mapping was proposed. The algorithm utilizes the characteristics of chaotic algorithm, such as ergodicity, non-periodicity, randomness etc. When algorithm falls into the local optimum, Kent chaotic mapping is used to perform chaotic search around the local optimum to replace partial particles of original particle swarm in order to jump out from the local optimum to find the global optimum. As the algorithm is carried out, the radius of chaotic search decreases linearly, so as to ensure the precision of searching and speed of the algorithm. 20 classical test functions are taken into consideration in simulation experiments. After making a comprehensive comparison for the performance of searching for the optimum of DE, GA, QPSO, KMTOA and KCKMTOA in 20 classical test functions, the results of experiments show that this algorithm has obvious advantages in precision, speed and robustness optimization etc.

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Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

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IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Sequential Manipulation Against Rank Aggregation: Theory and Algorithm

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arXiv 2024年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Liu, Wei Cao, Xiaochun Sun, Yingfei Huang, Qingming The School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China The Tencent Data Platform Shenzhen518054 China The School of Cyber Science and Technology Shenzhen Campus of Sun Yat-Sen University Shenzhen518107 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to manipulate the ranking list. However, the ideal attack opportunity and the excessive adversarial capability cause the existing methods to be impractical. To fully explore the potential risks, we leverage an online attack on the vulnerable data collection process. Since it is independent of rank aggregation and lacks effective protection mechanisms, we disrupt the data collection process by fabricating pairwise comparisons without knowledge of the future data or the true distribution. From the game-theoretic perspective, the confrontation scenario between the online manipulator and the ranker who takes control of the original data source is formulated as a distributionally robust game that deals with the uncertainty of knowledge. Then we demonstrate that the equilibrium in the above game is potentially favorable to the adversary by analyzing the vulnerability of the sampling algorithms such as Bernoulli and reservoir methods. According to the above theoretical analysis, different sequential manipulation policies are proposed under a Bayesian decision framework and a large class of parametric pairwise comparison models. For attackers with complete knowledge, we establish the asymptotic optimality of the proposed policies. To increase the success rate of the sequential manipulation with incomplete knowledge, a distributionally robust estimator, which replaces the maximum likelihood estimation in a saddle point problem, provides a conservative data generation solution. Finally, the corroborating empirical evidence shows that the proposed method manipulates the results of rank aggregation methods in a sequential manner. © 2024, CC BY.

关键词： Maximum likelihood estimation