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arXiv 2019年

作者： Liu, Xuanwu Yu, Guoxian Domeniconi, Carlotta Wang, Jun Ren, Yazhou Guo, Maozu College of Computer and Information Sciences Southwest University Chongqing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Department of Computer Science George Mason University Fairfax United States SMILE Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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A novel experience-based exploration method for q-learning 1

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4th International Conference of Pioneer computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Yang, Bohong Lu, Hong Li, Baogen Zhang, Zheng Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Engineering Research Center for Video Technology and System School of Computer Science Fudan University Shanghai China School of Computer Science New York University Shanghai Shanghai China

ISBN: (数字)9789811322037

ISBN: (纸本)9789811322020

Reinforcement learning algorithms are used to deal with a lot of sequential problems, such as playing games, mechanical control, and so on. Q-Learning is a model-free reinforcement learning method. In traditional Q-learning algorithms, the agent stops immediately after it has reached the goal. We propose in this paper a new method—Experience-based Exploration method—in order to sample more efficient state-action pairs for Q-learning updating. In the Experience-based Exploration method, the agent does not stop and continues to search the states with high bellman-error inversely. In this setting, the agent will set the terminal state as a new start point, and generate pairs of action and state which could be useful. The efficacy of the method is proved analytically. And the experimental results verify the hypothesis on Gridworld. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Reinforcement learning

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PISTA-SENSE-ResNet for Parallel MRI Reconstruction

arXiv

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arXiv 2019年

作者： Lu, Tieyuan Zhang, Xinlin Huang, Yihui Yang, Yonggui Guo, Gang Bao, Lijun Huang, Feng Guo, Di Qu, Xiaobo Xiamen University Xiamen China Department of Radiology The Second Affiliated Hospital of Xiamen Medical College Xiamen China Neusoft Medical System Shanghai China Fujian Provincial University Key Laboratory of Internet of Things Application Technology School of Computer and Information Engineering Xiamen University of Technology Xiamen China

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Food recommendation: Framework, existing solutions and challenges

arXiv

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arXiv 2019年

作者： Min, Weiqing Jiang, Shuqiang Jain, Ramesh Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science University of California IrvineCA United States

A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field. Copyright © 2019, The Authors. All rights reserved.

关键词： Domain Knowledge

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The propositional normal default logic and the finite/infinite injury priority method

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Science China(information Sciences) 2017年第9期60卷 99-108页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang University Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences

In propositional normal default logic, given a default theory(?, D) and a well-defined ordering of D, there is a method to construct an extension of(?, D) without any injury. To construct a strong extension of(?, D) given a well-defined ordering of D, there may be finite injuries for a default δ∈ D. With approximation deduction ?s in propositional logic, we will show that to construct an extension of(?, D) under a given welldefined ordering of D, there may be infinite injuries for some default δ∈ D.

关键词： default extension strong extension finite/infinite injury priority method recursively enumerable sets

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Rotation invariant point cloud analysis: Where local geometry meets global topology

arXiv

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arXiv 2019年

作者： Zhao, Chen Yang, Jiaqi Xiong, Xin Zhu, Angfan Cao, Zhiguo Li, Xin National Key Laboratory of Science and Technology on Multi-spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown26506 United States

Point cloud analysis is a fundamental task in 3D computer vision. Most previous works have conducted experiments on synthetic datasets with well-aligned data;while real-world point clouds are often not pre-aligned. How to achieve rotation invariance remains an open problem in point cloud analysis. To meet this challenge, we propose an approach toward achieving rotation-invariant (RI) representations by combining local geometry with global topology. In our local-global-representation (LGR)-Net, we have designed a two-branch network where one stream encodes local geometric RI features and the other encodes global topology-preserving RI features. Motivated by the observation that local geometry and global topology have different yet complementary RI responses in varying regions, two-branch RI features are fused by an innovative multi-layer perceptron (MLP) based attention module. To the best of our knowledge, this work is the first principled approach toward adaptively combining global and local information under the context of RI point cloud analysis. Extensive experiments have demonstrated that our LGR-Net achieves the state-of-the-art performance on various rotation-augmented versions of ModelNet40, ShapeNet, ScanObjectNN, and S3DIS. Copyright © 2019, The Authors. All rights reserved.

关键词： Encoding (symbols)

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An improved Isomap method for manifold learning

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International Journal of Intelligent Computing and Cybernetics 2017年第1期10卷 30-40页

作者： Taiguo Qu Zixing Cai College of Computer Science and Technology Hengyang Normal UniversityHengyangChina Hunan Provincial Key Laboratory of Intelligent Information Processing and Application HengyangChina School of Information Science and Engineering Central South UniversityChangshaChina

Purpose-Isometric feature mapping(Isomap)is a very popular manifold learning method and is widely used in dimensionality reduction and data *** most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood *** classical Isomap(C-Isomap)is very slow,due to the use of Floyd’s algorithm to compute the shortest *** purpose of this paper is to speed up ***/methodology/approach-Through theoretical analysis,it is found that the neighbourhood graph in Isomap is *** this case,the Dijkstra’s algorithm with Fibonacci heap(Fib-Dij)is faster than Floyd’s *** this paper,an improved Isomap method based on Fib-Dij is *** using Fib-Dij to replace Floyd’s algorithm,an improved Isomap method is presented in this ***-Using the S-curve,the Swiss-roll,the Frey face database,the mixed national institute of standards and technology database of handwritten digits and a face image database,the performance of the proposed method is compared with C-Isomap,showing the consistency with C-Isomap and marked improvements in terms of the high *** also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N3)to O(N2lgN).Research limitations/implications-Due to the limitations of the computer,the sizes of the data sets in this paper are all smaller than 3,***,researchers are encouraged to test the proposed algorithm on larger data ***/value-The new method based on Fib-Dij can greatly improve the speed of Isomap.

关键词： Dijkstra’s algorithm Fibonacci heap Floyd’s algorithm Isomap Manifold learning Shortest path Paper type Research paper

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Position based catheterization and angiography simulation 6

Position based catheterization and angiography simulation

引用

6th IEEE International Conference on Serious Games and Applications for Health, SeGAH 2018

作者： Zuo, Zhihao Si, Weixin Qian, Weixin Yinling Liao, Xiangyun Heng, Pheng-Ann Department of Information Engineering Wuhan University of Technology Wuhan China Guangdong Provincial Key Laboratory of Machine Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781538662984

Effective and safe performance of cardiovascular interventions requires excellent catheter / guidewire manipulation skills. These skills are currently mainly gained through an apprenticeship on real patients, which may not be safe or cost-effective. computer simulation offers an alternative for core skills training. However, replicating the physical behaviour of real instruments navigated through blood vessels is a challenging task. In this work, we adopt the position based method to fast and robust model the dynamic behavior of catheter / guidewire and contrast medium propagation in vascular interventional radiology. Specifically, we simulate the elastic rods according to the cosserat theory and the contrast medium propagation is controlled by an advection-diffusion equation. Besides, we employ bounding volume hierarchy (BVH) to achieve fast and accurate collision detection between vessels and rods and compute the response with the Coulomb's law. Experimental results demonstrate the visual plausibility, robustness and efficiency of our method in interactive catheterization simulation. © 2018 IEEE.

关键词： Catheters

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On RNN Models for Solving Dynamic System of Linear Equations

On RNN Models for Solving Dynamic System of Linear Equations

引用

International Conference on Intelligent Control and information processing (ICICIP)

作者： Huiyan Lu Ruiqi Liu Xiujuan Du Haiqi Liu Mei Lin Long Jin Jiliang Zhang Special Environment Key Laboratory of Sichuan Province Southwest University of Science and Technology Mianyang China The Fifth Electronics Research Institute of MIIT Guangzhou China The Provincial key Laboratory of the Internet of Things Qinghai Normal University Xi'ning China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

Neural networks have a wide range of applications in dealing with various online computing problems. This paper mainly retrospects one of the latest recurrent neural network (RNN) models and supplies summarizes on it. Firstly, formulations on the RNN model for dealing with the dynamic underdetermined system of linear equations with double bound constraints on state variables and residual errors are presented. Secondly, simple structures of the RNN model, that is, the neuron-connection architecture of RNN model for handling with the perturbed dynamic underdetermined linear system, as well as the RNN model and the unfolding in time of the computation involved in its forward computation are analyzed. In addition, the whole flowchart on the presented method for establishing the RNN model is also given. Then, experiments on executing the tasks of the UR5 robot when the end-effector tracks a “four-leaf clover” path and a “tricuspid valve” path synthesized by the RNN model are conducted, which show the superiority and accuracy of the presented RNN model.

关键词： Mathematical model Computational modeling Analytical models computer architecture Linear systems Robots Task analysis

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Relay Node Selection Strategy for Wireless Sensor Network 4th

Relay Node Selection Strategy for Wireless Sensor Network

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4th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2017

作者： Kong, Lingping Pan, Jeng-Shyang Chu, Shu-Chuan Roddick, John F. Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Information Science and Engineering Fujian University of Technology Fujian China School of Computer Science Engineering and Mathematics Flinders University Adelaide Australia

ISBN: (纸本)9783319685267

In the past few years, people have experienced advanced interest in the potential use of wireless sensor network in applications such as environment surveillance, military field protection and medical treatment. Usually hundreds even thousands of sensors are scattered randomly in remote environment. In generally, for the scalability of sensor network, cluster techniques are often used to group nodes into several sets. And in this paper, we proposed a new method called GF_CENTER to select the positions of centers, which is also a kind of k-center problem. And a new fitness function is presented for optimize the resolution. We compare our method with two other algorithms. GF_CENTER method minimizes the number of centers in the network. From the experiments, GF_CENTER find smaller number of centers than the other methods, which lower the construction fee of network. © 2018, Springer International Publishing AG.

关键词： Genetic algorithms

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