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Modified cuckoo search ased neural networks for forest types classification 2

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Modified cuckoo search ased neural networks for forest types...

2nd International Conference on information Technology and Intelligent Transportation Systems, ITITS 2017

作者： Chatterjee, Sankhadeep Dey, Nilanjan Sen, Soumya Ashour, Amira S. Fong, Simon James Shi, Fuqian Department of Computer Science and Engineering University of Calcutta Kolkata India Department of Information Technology Techno India College of Technology Kolkata India A. K. Choudhury School of Information Technology University of Calcutta Kolkata West Bengal India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Egypt Department of Computer and Information Science Data Analytics and Collaborative Computing Laboratory University of Macau Taipa China College of Information and Engineering Wenzhou Medical University Wenzhou China

ISBN: (纸本)9781614997849

Pixel classification in land scape images is a challenging process especially in forest images due to the similar spectral features of pixels situated close to each other. Previously, meta-heuristic coupled artificial neural network (ANN) models have been used to classify the two-different species, namely Japanese Cedar, Japanese Cypress and one mixed forest class. Previous attempts have shown reasonable improvement in the classification process using genetic algorithm (GA) supported neural network over other traditional approaches. Consequently, in the current work, a modified Cuckoo Search (CS) supported Neural Network (NN-MCS) classifier is proposed. The lévy flight associated with cuckoo search has been modified using McCulloch's method of generating stable random numbers. The proposed approach is compared with GA-NN using single objective function and CS-NN (ANN trained with CS) classifiers in terms of confusion matrix based performance metrics. The results depicted the dominance of the suggested NN-MCS model compared to the CS-NN model to a greater extent. © 2017 The authors and IOS Press. All rights reserved.

关键词： Neural networks

TensorFlow quantum: A software framework for quantum machine learning

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arXiv 2020年

作者： Broughton, Michael Verdon, Guillaume McCourt, Trevor Martinez, Antonio J. Yoo, Jae Hyeon Isakov, Sergei V. Massey, Philip Halavati, Ramin Niu, Murphy Yuezhen Zlokapa, Alexander Peters, Evan Lockwood, Owen Skolik, Andrea Jerbi, Sofiene Dunjko, Vedran Leib, Martin Streif, Michael Von Dollen, David Chen, Hongxiang Cao, Shuxiang Wiersema, Roeland Huang, Hsin-Yuan McClean, Jarrod R. Babbush, Ryan Boixo, Sergio Bacon, Dave Ho, Alan K. Neven, Hartmut Mohseni, Masoud Google Quantum Ai Mountain ViewCA United States Sandbox Alphabet Mountain ViewCA United States Google Mountain ViewCA United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Division of Physics Mathematics and Astronomy Caltech PasadenaCA91125 United States Fermi National Accelerator Laboratory P.O. Box 500 BataviaIL605010 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Data:Lab Volkswagen Group Ungererstr. 69 München80805 Germany Leiden University Niels Bohrweg 1 Leiden2333 CA Netherlands Institute for Theoretical Physics University of Innsbruck Technikerstr. 21a InnsbruckA-6020 Austria Institute of Theoretical Physics Staudtstr. 7 Erlangen91058 Germany Volkswagen Group Advanced Technologies San FranciscoCA94108 United States Rahko Ltd. Finsbury Park LondonN4 3JP United Kingdom Department of Computer Science University College London WC1E 6BT United Kingdom Clarendon Laboratory Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Vector Institute MaRS Centre TorontoONM5G 1M1 Canada Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada Institute for Quantum Information and Matter Caltech PasadenaCA United States Department of Computing and Mathematical Sciences Caltech PasadenaCA United States

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

An Open Source Map Optimization Platform for Efficient Navigation

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An Open Source Map Optimization Platform for Efficient Navig...

IEEE International Conference on Computational science and engineering, CSE

作者： Liangming Cai Chunqiang Chen Xiaoling Wang Shurui Lin Kai Cheng Jiefeng Huang Jinping Jiang Minchen Zhu Min Du Rituparna Datta Yurong Li Jingshan Huang Zhicheng College College of Physics and Information Engineering Fuzhou University Fuzhou China University of South Alabama HaiXi Institutes Chinese Academy of Science Fuzhou China Zhicheng College Zhicheng College Fuzhou University Fuzhou China College of Allied Health Professions University of South Alabama Mobile USA Department of Respiratory Medicine Fujian Medical University Fuzhou China College of Electrical Engineering & Automation Fuzhou University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China Provincial Key Laboratory of Eco-industrial Green Technology Wuyi College Wuyishan China Fuzhou University School of Computing University of South Alabama Mobile USA Fuzhou University Fuzhou China

Aiming at the problem that the map operation time occupies more resources in the embedded system. By studying the digital map, then the corresponding map is drawn and the map is optimized. Using the GPS/BEIDOU module to locate and use the Dijkstra algorithm to achieve the shortest path of the navigation module design, the use of the map database to remove the corresponding area of the data mapping map information, in a translation to a certain distance when the scene clear the data, draw a map. In this way, there will be no data left in the view. Since the Dijkstra algorithm is based on the graph, it is necessary to link the roads in the area to create an indirect graph to find the shortest path to the end point. If the point of the line as an indirect node, the composition of the indirect map will be very large. Moreover, the use of the line as an indirect node, we can reduce the number of nodes without the map, to improve the efficiency of Dijkstra algorithm traversal. Based on the open system platform and data standard, this paper designs and develops a secure, stable and low cost embedded GPS/BEIDOU navigation and navigation system. The experimental results are optimized. The results show that the proposed method is efficient for navigation.

关键词： Conferences Scientific computing Ubiquitous computing

A Multilayer Framework for Online Metric Learning

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arXiv 2018年

作者： Li, Wenbin Liu, Yanfang Huo, Jing Shi, Yinghuan Gao, Yang Wang, Lei Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The College of Mathematics and Information Engineering Longyan University 364012 China The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science University of Rochester RochesterNY14611 United States

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML. Copyright © 2018, The Authors. All rights reserved.

关键词： Multilayers

An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

Automatic vector-based road structure mapping using multi-beam lidar

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arXiv 2018年

作者： He, Xudong Zhao, Junqiao Sun, Lu Huang, Yewei Zhang, Xinglian Li, Jun Ye, Chen Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

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arXiv 2018年

作者： Yang, Peng Zhang, Ning Zhang, Shan Yu, Li Zhang, Junshan Shen, Xuemin School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan Hubei430074 China Department of Computing Sciences Texas A and M University-Corpus Christi Corpus ChristiTX78412 United States Beijing Key Laboratory of Computer Networks School of Computer Science and Engineering Beihang University Beijing100191 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an H1 filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy. Copyright © 2018, The Authors. All rights reserved.

关键词： Mobile edge computing

Separability-entanglement classifier via machine learning

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Physical Review A 2018年第1期98卷 012315-012315页

作者： Sirui Lu Shilin Huang Keren Li Jun Li Jianxin Chen Dawei Lu Zhengfeng Ji Yi Shen Duanlu Zhou Bei Zeng Department of Physics Tsinghua University Beijing 100084 China Institute for Quantum Computing University of Waterloo Waterloo Ontario Canada N2L 3G1 Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China Department of Mathematics & Statistics University of Guelph Guelph Ontario Canada N1G 2W1 Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Centre for Quantum Software and Information School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo Waterloo Ontario Canada N2L 3G1 Institute of Physics Chinese Academy of Sciences Beijing 100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing. Despite the many methods—such as the positive partial transpose criterion and the k-symmetric extendibility criterion—to tackle this problem, none of them enables a general, practical solution due to the problem's NP-hard complexity. Explicitly, separable states form a high-dimensional convex set of vastly complicated structures. In this work, we build a different separability-entanglement classifier underpinned by machine learning techniques. We use standard tools from machine learning to learn the entanglement feature of arbitrary given quantum states. We perform substantial numerical tests on two-qubit and two-qutrit systems, and the results indicate that our method can outperform the existing methods in generic cases in terms of both speed and accuracy. This opens up avenues to explore quantum entanglement via the machine learning approach.

关键词： Machine learning Quantum entanglement

TiEV: The tongji intelligent electric vehicle in the intelligent vehicle future challenge of China

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arXiv 2018年

作者： Zhao, Junqiao Ye, Chen Wu, Yan Guan, Linting Cai, Lewen Sun, Lu Yang, Tao He, Xudong Li, Jun Ding, Yongchao Zhang, Xinglian Wang, Xinchen Huang, Jinlin Zhang, Enwei Huang, Yewei Jiang, Wei Zhang, Shaoming Xiong, Lu Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of electrics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

TiEV is an autonomous driving platform implemented by the Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, large-scale mapping and updating, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016, and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and discuss the future trends. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles