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Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification [article]

Philip Sellars and Angelica Aviles-Rivero and Nicolas Papadakis and David Coomes and Anita Faul and Carola-Bibane Schönlieb
<span title="2019-05-14">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.  ...  We then construct a superpixel graph, based on carefully considered feature vectors, before performing classification.  ...  We present a novel graph base framework for HSI classification, which we call hyperspectral superpixel graph classification (HSGC).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.04240v4">arXiv:1901.04240v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ddwlkdsuyvdsjg4wvgjy7fsf54">fatcat:ddwlkdsuyvdsjg4wvgjy7fsf54</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200828124136/https://arxiv.org/pdf/1901.04240v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4b/f7/4bf7ec15b4096f08d88675cac8dd7c48e326d58b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.04240v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Semi-Supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification

Philip Sellars, Angelica I. Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, Carola-Bibiane Schonlieb
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6i67v2zuujhfvocqaapmnsoifm" style="color: black;">IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium</a> </i> &nbsp;
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification.  ...  We then construct a superpixel graph, based on carefully considered feature vectors, before performing classification.  ...  We present a novel graph base framework for HSI classification, which we call hyperspectral superpixel graph classification (HSGC).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2019.8898189">doi:10.1109/igarss.2019.8898189</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/igarss/SellarsAPCFS19.html">dblp:conf/igarss/SellarsAPCFS19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rwwmcbyhkjc3bndn2lpugpxrzq">fatcat:rwwmcbyhkjc3bndn2lpugpxrzq</a> </span>
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Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification [article]

Philip Sellars, Angelica Aviles-Rivero, Carola-Bibiane Schönlieb
<span title="2019-03-19">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our graph is then fed into a graph-based semi-supervised classifier which gives the final classification.  ...  Our approach utilises a novel superpixel method, specifically designed for hyperspectral data, to define meaningful local regions in an image, which with high probability share the same classification  ...  CONCLUSION In this paper, we have developed a novel semi-semisupervised graph-based approach, SGL, for the classification of hyperspectral images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.06548v3">arXiv:1903.06548v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/he4nhhkcw5bwhmocc6j2qrxz6e">fatcat:he4nhhkcw5bwhmocc6j2qrxz6e</a> </span>
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Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Yifei Zhao, Fenzhen Su, Fengqin Yan
<span title="2020-05-11">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
To address this problem, in this work, a novel,semi-supervised,superpixel-levelclassification method foran HSIwas proposed based on a graph and discrete potential (SSC-GDP).  ...  Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data.However,it is a non-trivial task to classify HSIaccurately and rapidly due to  ...  Conclusions This paper suggests anovel, semi-supervised,superpixel-level classification method for HSI based on a graph and discrete potential method.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12091528">doi:10.3390/rs12091528</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2isfi6s6e5bjfcvwkiyuxheegu">fatcat:2isfi6s6e5bjfcvwkiyuxheegu</a> </span>
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Hyperspectral Image Classification Based on Sparse Superpixel Graph

Yifei Zhao, Fengqin Yan
<span title="2021-09-09">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG).  ...  Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing.  ...  Acknowledgments: We would like to thank the authors of GCN, EPF, IFRF, SCMK, SSC-SL, MDGCN and SPCNN for sharing the source code.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs13183592">doi:10.3390/rs13183592</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6hkvuw4eqrdwzhuyv4fsos3sea">fatcat:6hkvuw4eqrdwzhuyv4fsos3sea</a> </span>
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SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification

Yunsong Li, Bobo Xi, Jiaojiao Li, Rui Song, Yuchao Xiao, Jocelyn Chanussot
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;
Recently, the semi-supervised graph convolutional network (SSGCN) has been verified effective for hyperspectral image (HSI) classification.  ...  To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm.  ...  They appreciate the GRSS Image Analysis and Data Fusion Technical Committee for releasing the standard training-test sets of the benchmark data set for developing new algorithms. 2 The authors would also  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2021.3135548">doi:10.1109/jstars.2021.3135548</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wpz3z3wdmzbkbitapzfdrgb6si">fatcat:wpz3z3wdmzbkbitapzfdrgb6si</a> </span>
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Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism

Haoyu Jing, Yuanyuan Wang, Zhenhong Du, Feng Zhang
<span title="2022-06-01">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Convolutional neural network (CNN) has achieved excellent performance in the classification of hyperspectral images (HSI) due to its ability to extract spectral and spatial feature information.  ...  The recently proposed graph convolutional network (GCN) has been successfully applied to the analysis of non-Euclidean data and is suitable for irregular image regions.  ...  The poor performance of GCN is due to the fact that the performance of the semi-supervised learning model will be limited when the labeled samples are sparse.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs14112653">doi:10.3390/rs14112653</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tj6cs3huljgoxktzy66xoxdgh4">fatcat:tj6cs3huljgoxktzy66xoxdgh4</a> </span>
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Superpixel-Based Minimum Noise Fraction Feature Extraction for Classification of Hyperspectral Images

Behnam Asghari Beirami, Mehdi Mokhtarzade
<span title="2020-11-25">2020</span> <i title="International Information and Engineering Technology Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wsuaubw4tnbbhjln4ji2em2dmi" style="color: black;">Traitement du signal</a> </i> &nbsp;
The basic idea behind the SuperMNF is that each superpixel contains its specific signal and noise covariance matrices which are different from the adjacent superpixels.  ...  Experiments that are conducted on two real hyperspectral images, named Indian Pines and Pavia University, demonstrate the efficiency of SuperMNF since it yielded more promising results than some other  ...  INTRODUCTION The high dimensionality of hyperspectral data and the limited size of training samples make the supervised classification of hyperspectral images challenging.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/ts.370514">doi:10.18280/ts.370514</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jphiutjrkfcfdm2zat64rfjdtm">fatcat:jphiutjrkfcfdm2zat64rfjdtm</a> </span>
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An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
<span title="2022-01-07">2022</span> <i title="IM Publications Open LLP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vd3srshgk5cszja7pj642mcc2y" style="color: black;">Journal of Spectral Imaging</a> </i> &nbsp;
As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification.  ...  Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction.  ...  To create a semi-supervised HSI classification approach, Cui et al. 52 analysed Rolling Guidance Filter (RGF) and Extended Label Propagation (ELP) for graph-based learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1255/jsi.2022.a1">doi:10.1255/jsi.2022.a1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rue5klkmlfcrzftepc6lzfcbfe">fatcat:rue5klkmlfcrzftepc6lzfcbfe</a> </span>
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Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
<span title="2019-02-10">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature.  ...  Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis.  ...  A semi-supervised classification method that utilized MRF with semi-supervised graph priors on parameters was introduced in [180] . Li et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.08701v2">arXiv:1802.08701v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bfi6qkpx2bf6bowhyloj2duugu">fatcat:bfi6qkpx2bf6bowhyloj2duugu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200929194828/https://arxiv.org/pdf/1802.08701v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/12/a4/12a48795a520bbc0d359df470741e50bcba01e6b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.08701v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Scalable Machine Learning Approaches for Neighborhood Classification Using Very High Resolution Remote Sensing Imagery

Manu Sethi, Yupeng Yan, Anand Rangarajan, Ranga Raju Vatsavai, Sanjay Ranka
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD &#39;15</a> </i> &nbsp;
A semi-supervised learning approach for identifying neighborhoods is presented which employs superpixel tessellation representations of VHR imagery.  ...  The semi-supervised learning approach uses a support vector machine (SVM) to obtain a preliminary classification which is then subsequently refined using graph Laplacian propagation.  ...  This approach combines a superpixel image tessellation representation with semi-supervised label propagation (SVM followed by graph Laplacian-based propagation).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2783258.2788625">doi:10.1145/2783258.2788625</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/SethiYRVR15.html">dblp:conf/kdd/SethiYRVR15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dwign5oy4vbpvmfrganilyoxk4">fatcat:dwign5oy4vbpvmfrganilyoxk4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829085924/https://www.cise.ufl.edu/~anand/pdf/KDD15_cameraready.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d1/de/d1de49b4b6ca73e3e6b522a2c256c910eb95697e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2783258.2788625"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Subhashree Subudhi, Ram Narayan Patro, Pradyut Kumar Biswal, Fabio Dell'Acqua
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;
Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution.  ...  The challenges and future research directions for the implementation of superpixel algorithms are also discussed. Index Terms-Hyperspectral Image, Superpixel Segmentation, Evaluation.  ...  A new approach combining AL and semi-supervised learning (SSL) for HSI classification is proposed in [96] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2021.3076005">doi:10.1109/jstars.2021.3076005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/smfb6jeox5eldbv6ys7ioeoko4">fatcat:smfb6jeox5eldbv6ys7ioeoko4</a> </span>
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Spectral-Spatial Active Learning with Structure Density for Hyperspectral Classification

Qianming Li, Bohong Zheng, Yusheng Yang
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
learning (SSL) [22] , active learning (AL) [23] , [24] , semi-supervised active learning (SSAL) [25] - [28] , and spectral-spatial classification [29] - [32] .  ...  In order to overcome the problem, many scholars and researchers in hyperspectral domain are willing to devote oneself to study advanced machine learning and classification methods, such as semi-supervised  ...  CLASSIFICATION RESULTS OF ACTIVE LEARNING METHODS Here, the SD sampling criterion based LORSAL-ERW-AL classification method is compared with other widely used AL-based spectral-spatial classification methods  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3074405">doi:10.1109/access.2021.3074405</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p7c4h6qjanfmjdvx2tiejgxrp4">fatcat:p7c4h6qjanfmjdvx2tiejgxrp4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210422024658/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09409072.pdf?tp=&amp;arnumber=9409072&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ba/c3/bac39437181fb8ade1f60b7f3d6354b00ced5c4a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3074405"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Ground-based image analysis: A tutorial on machine-learning techniques and applications

Soumyabrata Dev, Bihan Wen, Yee Hui Lee, Stefan Winkler
<span title="">2016</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xwpybjcqyffjdipbdviyet75c4" style="color: black;">IEEE Geoscience and Remote Sensing Magazine</a> </i> &nbsp;
We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applicationssegmentation, classification, and denoising.  ...  However, powerful machine learning techniques have become available to aid with the image analysis.  ...  In addition to supervised and unsupervised methods, Semi-Supervised Learning (SSL) methods are also widely used in remote sensing [73] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mgrs.2015.2510448">doi:10.1109/mgrs.2015.2510448</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/57pkeukq2jclxkz3q23fn5kn6e">fatcat:57pkeukq2jclxkz3q23fn5kn6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220106153840/https://researchrepository.ucd.ie/bitstream/10197/12703/2/grsm2016.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/36/3a365d77dd1c0eb8dadd165bc9ed362d783d16f2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mgrs.2015.2510448"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Machine Learning Techniques and Applications For Ground-based Image Analysis [article]

Soumyabrata Dev, Bihan Wen, Yee Hui Lee, Stefan Winkler
<span title="2016-06-09">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We demonstrate the advantages of using machine learning techniques in ground-based image analysis via three primary applications -- segmentation, classification, and denoising.  ...  However, powerful machine learning techniques have become available to aid with the image analysis.  ...  In addition to supervised and unsupervised methods, Semi-Supervised Learning (SSL) methods are widely used in remote sensing [76] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.02811v1">arXiv:1606.02811v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k5n7u7lynzd4tciedcw54wdv2m">fatcat:k5n7u7lynzd4tciedcw54wdv2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200930162503/https://arxiv.org/pdf/1606.02811v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/26/5a26ee31eba2d1a4de783c2b8456a29006a6c1ca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1606.02811v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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