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arXiv 2018年

作者： Huang, Dong Wang, Chang-Dong Peng, Hongxing Lai, Jianhuang Kwoh, Chee-Keong College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Computational Science Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Ensemble clustering has been a popular research topic in data mining and machine learning. Despite its significant progress in recent years, there are still two challenging issues in the current ensemble clustering research. First, most of the existing algorithms tend to investigate the ensemble information at the object-level, yet often lack the ability to explore the rich information at higher levels of granularity. Second, they mostly focus on the direct connections (e.g., direct intersection or pairwise co-occurrence) in the multiple base clusterings, but generally neglect the multi-scale indirect relationship hidden in them. To address these two issues, this paper presents a novel ensemble clustering approach based on fast propagation of cluster-wise similarities via random walks. We first construct a cluster similarity graph with the base clusters treated as graph nodes and the cluster-wise Jaccard coefficient exploited to compute the initial edge weights. Upon the constructed graph, a transition probability matrix is defined, based on which the random walk process is conducted to propagate the graph structural information. Specifically, by investigating the propagating trajectories starting from different nodes, a new cluster-wise similarity matrix can be derived by considering the trajectory relationship. Then, the newly obtained cluster-wise similarity matrix is mapped from the cluster-level to the object-level to achieve an enhanced co-association (ECA) matrix, which is able to simultaneously capture the object-wise co-occurrence relationship as well as the multi-scale cluster-wise relationship in ensembles. Finally, two novel consensus functions are proposed to obtain the consensus clustering result. Extensive experiments on a variety of real-world datasets have demonstrated the effectiveness and efficiency of our approach. Copyright © 2018, The Authors. All rights reserved.

关键词： Cluster analysis

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A local algorithm and its percolation analysis of bipartite z-matching problem

arXiv

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arXiv 2018年

作者： Zhao, Jin-Hua School of Data Science and Engineering South China Normal University Shanwei516600 China Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China

A z-matching on a bipartite graph is a set of edges, among which each vertex of two types of the graph is adjacent to at most 1 and at most z (≥ 1) edges, respectively. The z-matching problem concerns finding z-matchings with the maximum size. Our approach to this combinatorial optimization problem is twofold. From an algorithmic perspective, we adopt a local algorithm as a linear approximate solver to find z-matchings on any graph instance, whose basic component is a generalized greedy leaf removal procedure in graph theory. From a theoretical perspective, on uncorrelated random bipartite graphs, we develop a mean-field theory for percolation phenomenon underlying the local algorithm, leading to an analytical estimation of z-matching sizes on random graphs. Our analytical theory corrects the prediction by belief propagation algorithm at zero-temperature limit in (Kreačić and Bianconi 2019 EPL 126 028001). Besides, our theoretical framework extends a core percolation analysis of k-XORSAT problems to a general context of uncorrelated random hypergraphs with arbitrary degree distributions of factor and variable nodes. Copyright © 2018, The Authors. All rights reserved.

关键词： Graph theory

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Fast 3D Map Reconstruction Using Dense Visual Simultaneous Localization and Mapping Based on Unmanned Aerial Vehicle

Fast 3D Map Reconstruction Using Dense Visual Simultaneous L...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Siyuan Peng Fang Huang Jian Tao Bo Tie Jun Lu Xiaodong Zhang School of Resources & Environment University of Electronic Science and Technology of China Chengdu P.R. China Institute of Remote Sensing Big Data University of Electronic Science and Technology of China Chengdu P.R. China Texas A&M Engineering Experiment Station and High Performance Research Computing Texas A&M University College Station TX USA

Traditional 3D map reconstruction methods based on unmanned aerial (UA) always relies on multi-camera optical equipment or additional space positioning equipment. All of these restrict the UA's application scenarios to some extent. Visual simultaneous localization and mapping (VSLAM), using the camera as the only external sensor, can construct a 3D map of its spatial environment while recording its own localization. This paper proposes a technique of fast 3D map reconstruction based on UAs by using VSLAM, with parallel computing based on CUDA, to achieve fast dense reconstruction based on a UA platform. This research belongs to one of the emerging but very exciting application areas using UAs. It helps to expand the application scope of UAs to some extent. From the experiments, it is demonstrated that the methodology presented in this paper works well, and could be applied to real world applications.

关键词： Unmanned aerial Visual SLAM 3D map High performance computing Dense reconstruction

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Ti interstitial flows giving rutile TiO2 reoxidation process enhanced in (001) surface

arXiv

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arXiv 2019年

作者： Ichibha, Tom Benali, Anouar Hongo, Kenta Maezono, Ryo School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Computational Science Division Argonne National Laboratory 9700 Cass Avenue LemontIL60439 United States Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

We revisited ab initio evaluations of the energy barriers along the possible diffusion paths of the defects in rutile TiO2. By using a method carefully considering the cancellation of the self-interaction, Ti interstitials hopping along c-axis are identified as the major diffusion directing to [001] surface. The conclusion is contradicting to any of previous theoretical works, and the discrepancy is explained by the overestimation of the radius of defects due to the poor cancellations in the previous works. The updated prediction here can explain the superior photocatalysis activity in [001] surface to [110]. Copyright © 2019, The Authors. All rights reserved.

关键词： Titanium dioxide

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Making the most of data: Quantum monte carlo post-analysis revisited

arXiv

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arXiv 2019年

作者： Ichibha, Tom Hongo, Kenta Maezono, Ryo Thom, Alex J.W. School of Information Science JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods, Straatsma, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness, for the energy time-series given by Diffusion Monte Carlo, Full Configuration Interaction Quantum Monte Carlo and Coupled Cluster Monte Carlo methods. From these analyses we describe a hybrid analysis method which provides reliable error estimates for series of all lengths. Equally important is the estimation of the appropriate start point of the equilibrated phase, and two heuristic schemes are tested, establishing that MSER (mean squared error rule) gives reasonable and constant estimations independent of the length of time-series. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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Corrigendum to “Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS”

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Security and Communication Networks 2018年第1期2018卷

作者： Weizhong Qiang Shizhen Wang Hai Jin Jiangying Zhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 ***

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What is the optimal network deployment for a fixed density of antennas?

arXiv

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arXiv 2017年

作者： Yao, Xuefeng Ding, Ming Pérez, David López Lin, Zihuai Mao, Guoqiang School of Electrical and Information Engineering University of Sydney Australia Data61 CSIRO Australia Nokia Bell Labs Ireland School of Computing and Communication University of Technology Sydney Australia

In this paper, we answer a fundamental question: when the total number of antennas per square kilometer is fixed, what is the optimal network deployment? A denser network with a less number of antennas per base station (BS) or the opposite case. To evaluate network performance, we consider a practical network scenario with a fixed antennas density and multi-user multiple-input-multiple-output (MU-MIMO) operations for single-antenna users. The number of antennas in each BS is calculated by dividing the antenna density by the BS density. With the consideration of several practical network models, i.e., pilot contamination, a limited user equipment (UE) density and probabilistic line-of-sight (LoS)/non-line-of-sight (NLoS) path loss model, we evaluate the area spectral efficiency (ASE) performance. From our simulation results, we conclude that there exists an optimal BS density for a certain UE density to maximize the ASE performance when the antenna density is fixed. The intuition is that (i) by densifying the network with more BSs, we can achieve a receive power gain due to the smaller distance between the typical UE and its serving BS;(ii) by installing more antennas in each BS, we can achieve a beamforming gain for UEs using MU-MIMO, although such beamforming gain is degraded by pilot contamination;(iii) thus, a trade-off exists between the receive power gain and the beamforming gain, if we fix the antenna density in the network. Copyright © 2017, The Authors. All rights reserved.

关键词： Beamforming

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Ultra-dense networks: Is there a limit to spatial spectrum reuse?

arXiv

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arXiv 2017年

作者： Ding, Ming López-Pérez, David Mao, Guoqiang Lin, Zihuai Data61 CSIRO Australia Nokia Bell Labs Ireland School of Computing and Communication University of Technology Sydney Australia School of Electrical and Information Engineering University of Sydney Australia

The aggressive spatial spectrum reuse (SSR) by network densification using smaller cells has successfully driven the wireless communication industry onward in the past decades. In our future journey toward ultra-dense networks (UDNs), a fundamental question needs to be answered. Is there a limit to SSR? In other words, when we deploy thousands or millions of small cell base stations (BSs) per square kilometer, is activating all BSs on the same time/frequency resource the best strategy? In this paper, we present theoretical analyses to answer such question. In particular, we find that both the signal and interference powers become bounded in practical UDNs with a non-zero BS-to-UE antenna height difference and a finite UE density, which leads to a constant capacity scaling law. As a result, there exists an optimal SSR density that can maximize the network capacity. Hence, the limit to SSR should be considered in the operation of future UDNs. Copyright © 2017, The Authors. All rights reserved.

关键词： Antennas

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Radio galaxy zoo: Unsupervised clustering of convolutionally auto-encoded radio-astronomical images

arXiv

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arXiv 2019年

作者： Ralph, Nicholas O. Norris, Ray P. Fang, Gu Park, Laurence A.F. Galvin, Timothy J. Alger, Matthew J. Andernach, Heinz Lintott, Chris Rudnick, Lawrence Shabala, Stanislav Wong, O. Ivy Western Sydney University School of Computing Engineering and Mathematics Locked Bag 1797 PenrithNSW2751 Australia CSIRO Astronomy and Space Science Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia CSIRO Astronomy and Space Science KensingtonWA6151 Australia Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Data61 CSIRO CanberraACT2601 Australia Departamento de Astronomía DCNE Universidad de Guanajuato Apdo. Postal 144 Guanajuato Gto.CP 36000 Mexico Department of Physics University of Oxford Denys Wilkinson Building Keble Road OxfordOX1 3RH United Kingdom Minnesota Institute for Astrophysics University of Minnesota University of Tasmania School of Natural Sciences Private Bag 37 HobartTAS7001 Australia M468 University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand for machine learning methods as solutions to classification and outlier detection. Major astronomical discoveries are unplanned and found in the unexpected, making unsupervised machine learning highly desirable by operating without assumptions and labelled training data. Our approach shows SOM training time is drastically reduced and high-level features can be clustered by training on auto-encoded feature vectors instead of raw images. Our results demonstrate this method is capable of accurately separating outliers on a SOM with neighborhood similarity and K-means clustering of radio-astronomical features complexity. We present this method as a powerful new approach to data exploration by providing a detailed understanding of the morphology and relationships of Radio Galaxy Zoo (RGZ) dataset image features which can be applied to new radio survey data. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network for Distributed Motion Planning of Multiple Robot Arms

Nonlinearly-Activated Noise-Tolerant Zeroing Neural Network ...

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International Joint Conference on Neural Networks

作者： Long Jin Shuai Li Xin Luo Ming-sheng Shang School of Information Science and Engineering Lanzhou University Department of Computing The Hong Kong Polytechnic University Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences

ISBN: (纸本)9781509061839

This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

关键词： robotic arm motion planning Distribute Noise Neural network Automatic incident detection Simulation results

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