{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T05:48:49Z","timestamp":1790488129929,"version":"4.1.0"},"reference-count":90,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T00:00:00Z","timestamp":1532390400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Comm Pure Appl Math"],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n                    Various applications involve assigning discrete label values to a collection of objects based on some pairwise noisy data. Due to the discrete\u2014and hence nonconvex\u2014structure of the problem, computing the optimal assignment (e.g., maximum\u2010likelihood assignment) becomes intractable at first sight. This paper makes progress towards efficient computation by focusing on a concrete joint alignment problem; that is, the problem of recovering\n                    <jats:italic>n<\/jats:italic>\n                    discrete variables\n                    <jats:italic>x<\/jats:italic>\n                    <jats:sub>\n                      <jats:italic>i<\/jats:italic>\n                    <\/jats:sub>\n                    \u2009\u220a\u2009{1, \u2026,\n                    <jats:italic>m<\/jats:italic>\n                    }, 1\u2009\u2264\u2009\n                    <jats:italic>i<\/jats:italic>\n                    \u2009\u2264\u2009\n                    <jats:italic>n<\/jats:italic>\n                    , given noisy observations of their modulo differences {\n                    <jats:italic>\n                      x\n                      <jats:sub>i<\/jats:sub>\n                    <\/jats:italic>\n                    \u2014\n                    <jats:italic>\n                      x\n                      <jats:sub>j<\/jats:sub>\n                    <\/jats:italic>\n                    mod\n                    <jats:italic>m<\/jats:italic>\n                    }. We propose a low\u2010complexity and model\u2010free nonconvex procedure, which operates in a lifted space by representing distinct label values in orthogonal directions and attempts to optimize quadratic functions over hypercubes. Starting with a first guess computed via a spectral method, the algorithm successively refines the iterates via projected power iterations. We prove that for a broad class of statistical models, the proposed projected power method makes no error\u2014and hence converges to the maximum\u2010likelihood estimate\u2014in a suitable regime. Numerical experiments have been carried out on both synthetic and real data to demonstrate the practicality of our algorithm. We expect this algorithmic framework to be effective for a broad range of discrete assignment problems.\u00a9 2018 Wiley Periodicals, Inc.\n                  <\/jats:p>","DOI":"10.1002\/cpa.21760","type":"journal-article","created":{"date-parts":[[2018,7,24]],"date-time":"2018-07-24T22:04:09Z","timestamp":1532469849000},"page":"1648-1714","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["The Projected Power Method: An Efficient Algorithm for Joint Alignment from Pairwise Differences"],"prefix":"10.1002","volume":"71","author":[{"given":"Yuxin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering B316, Engineering Quad Princeton University Princeton NJ 08544 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel J.","family":"Cand\u00e8s","sequence":"additional","affiliation":[{"name":"Department of Statistics Stanford University 390 Serra Mall, Sequoia Hall, Stanford CA 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,7,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2490670"},{"key":"e_1_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Abbe E.;Sandon C.Community detection in general stochastic block models: fundamental limits and efficient algorithms for recovery.2015 IEEE 56th Annual Symposium on Foundations of Computer Science\u2014FOCS 2015 670\u2013688. IEEE Computer Society Los Alamitos Calif. 2015.","DOI":"10.1109\/FOCS.2015.47"},{"key":"e_1_2_1_3_1","unstructured":"Bandeira A.;Boumal N.;Voroninski V.On the low\u2010rank approach for semidefinite programs arising in synchronization and community detection. Preprint 2016. arxiv:1602.04426 [math.OC]"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Bandeira A. S.;Charikar M.;Singer A.;Zhu A.Multireference alignment using semidefinite programming.ITCS'14\u2014Proceedings of the 2014 Conference on Innovations in Theoretical Computer Science 459\u2013470. ACM New York 2014.","DOI":"10.1145\/2554797.2554839"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/15-AOP1025"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22679"},{"key":"e_1_2_1_7_1","unstructured":"Bhojanapalli S.;Neyshabur B.;Srebro N.Global optimality of local search for low rank matrix recovery. Preprint 2016. arxiv:1605.07221 [stat.ML]"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/8579.001.0001"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M105808X"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Brito G.;Dumitriu I.;Ganguly S.;Hoffman C.;Tran L. V.Recovery and rigidity in a regular stochastic block model.Proceedings of the Twenty\u2010Seventh Annual ACM\u2010SIAM Symposium on Discrete Algorithms 1589\u20131601. ACM New York 2016. doi:10.1137\/1.9781611974331.ch108","DOI":"10.1137\/1.9781611974331.ch108"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1443"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2399924"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/090761793"},{"key":"e_1_2_1_15_1","unstructured":"Chang A. X.;Funkhouser T.;Guibas L.;Hanrahan P.;Huang Q.;Li Z.;Savarese S.;Savva M.;Song S.;Su H.;Xiao J.;Yi L.;Yu F.ShapeNet: An information\u2010rich 3D model repository. Preprint 2015. arxiv:1512.03012 [cs.GR]"},{"key":"e_1_2_1_16_1","first-page":"35","article-title":"Spectral clustering of graphs with general degrees in the extended planted partition model","volume":"23","author":"Chaudhuri K.","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.21638"},{"issue":"2","key":"e_1_2_1_18_1","first-page":"100","article-title":"Near\u2010optimal joint object matching via convex relaxation","volume":"32","author":"Chen Y.","year":"2014","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2013.2249572"},{"key":"e_1_2_1_20_1","first-page":"2213","article-title":"Clustering partially observed graphs via convex optimization","volume":"15","author":"Chen Y.","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_21_1","unstructured":"Chen Y.;Kamath G.;Suh C.;Tse D.Community recovery in graphs with locality.Proceedings of the 33rd International Conference on Machine Learning 689\u2013698. Proceedings of Machine Learning Research 48. PMLR New York 2016. Available at:http:\/\/proceedings.mlr.press\/v48\/chena16.html"},{"key":"e_1_2_1_22_1","first-page":"2204","article-title":"Clustering sparse graphs","volume":"25","author":"Chen Y.","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2600566"},{"key":"e_1_2_1_24_1","unstructured":"Chen Y.;Wainwright M. J.Fast low\u2010rank estimation by projected gradient descent: General statistical and algorithmic guarantees. Preprint 2015. arxiv:1509.03025 [math.ST]"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2590468"},{"key":"e_1_2_1_26_1","unstructured":"Chin P.;Rao A.;Vu V.Stochastic block model and community detection in the sparse graphs: A spectral algorithm with optimal rate of recovery. Preprint 2015. arxiv:1501.05021 [cs.DS]"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0963548309990514"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-014-9215-y"},{"key":"e_1_2_1_29_1","first-page":"2717","article-title":"Cone\u2010constrained principal component analysis","volume":"27","author":"Deshpande Y.","year":"2014","journal-title":"Advances in Neural Information Processing System"},{"key":"e_1_2_1_30_1","unstructured":"Dragomir S.Upper and lower bounds for Csisz\u00e1r'sf\u2010divergence in terms of the Kullback\u2010Leibler distance and applications. Inequalities for the Csisz\u00e1r'sf\u2010Divergence in Information Theory 2000. Available at:http:\/\/rgmia.org\/papers\/Csiszar\/ULBCfDKLDApp.pdf"},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Duchi J.;Shalev\u2010Shwartz S.;Singer Y.;Chandra T.Efficient projections onto the \u21131\u2010ball for learning in high dimensions.Proceedings of the 25th International Conference on Machine Learning 272\u2013279. ACM New York 2008. doi:10.1145\/1390156.1390191","DOI":"10.1145\/1390156.1390191"},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Ganesh A.;Wright J.;Li X.;Candes E.;Ma Y.Dense error correction for low\u2010rank matrices via Principal Component Pursuit.2010 IEEE International Symposium on Information Theory 1513\u20131517. doi:10.1109\/ISIT.2010.5513538","DOI":"10.1109\/ISIT.2010.5513538"},{"key":"e_1_2_1_33_1","unstructured":"Gao T.;Brodzki J.;Mukherjee S.The geometry of synchronization problems and learning group actions. Preprint 2016. arxiv:1610.09051 [math.ST]"},{"key":"e_1_2_1_34_1","unstructured":"Ge R.;Lee J. D.;Ma T.Matrix completion has no spurious local minimum. Preprint 2016. arxiv:1605.07272 [cs.LG]"},{"key":"e_1_2_1_35_1","unstructured":"Giorgi D.;Biasotti S.;Paraboschi L.SHape REtrieval Contest 2007: Watertight models track. Available at:https:\/\/pdfs.semanticscholar.org\/2b5b\/b396160d11da2bc842b58045704cab70aa8c.pdf"},{"key":"e_1_2_1_36_1","unstructured":"Globerson A.;Roughgarden T.;Sontag D.;Yildirim C.How hard is inference for structured prediction?Proceedings of the 32nd International Conference on Machine Learning 2181\u20132190. Proceedings of Machine Learning Research 37. Available at:http:\/\/proceedings.mlr.press\/v37\/globerson15.html"},{"key":"e_1_2_1_37_1","volume-title":"Johns Hopkins Studies in the Mathematical Sciences","author":"Golub G. H.","year":"1996"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00440-015-0659-z"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2546280"},{"key":"e_1_2_1_40_1","first-page":"847","article-title":"An inverse power method for nonlinear eigenproblems with applications in 1\u2010spectral clustering and sparse PCA","volume":"23","author":"Hein M.","year":"2010","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22177"},{"key":"e_1_2_1_42_1","unstructured":"Huang Q.;Chen Y.;Guibas L.Scalable semidefinite relaxation for maximum a posterior estimation.Proceedings of the 31st International Conference on Machine Learning 64\u201372. Proceedings of Machine Learning Research 32. PMLR Beijing 2014. Available at:http:\/\/proceedings.mlr.press\/v32\/huang14.html"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12184"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508364"},{"key":"e_1_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Jain P.;Netrapalli P.;Sanghavi S.Low\u2010rank matrix completion using alternating minimization (extended abstract).STOC'13\u2014Proceedings of the 2013 ACM Symposium on Theory of Computing 665\u2013674. ACM New York 2013. doi:10.1145\/2488608.2488693","DOI":"10.1145\/2488608.2488693"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1523097113"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02278710"},{"key":"e_1_2_1_48_1","first-page":"517","article-title":"Generalized power method for sparse principal component analysis","volume":"11","author":"Journ\u00e9e M.","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.016107"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2046205"},{"key":"e_1_2_1_51_1","first-page":"2057","article-title":"Matrix completion from noisy entries","volume":"11","author":"Keshavan R. H.","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185550"},{"key":"e_1_2_1_53_1","unstructured":"Lee J. D.;Simchowitz M.;Jordan M. I.;Recht B.Gradient descent only converges to minimizers.29th Annual Conference on Learning Theory 1246\u20131257. Proceedings of Machine Learning Research 49. PMLR 2016. Available at:http:\/\/proceedings.mlr.press\/v49\/lee16.html"},{"key":"e_1_2_1_54_1","unstructured":"Li X.;Ling S.;Strohmer T.;Wei K.Rapid robust and reliable blind deconvolution via nonconvex optimization. Preprint 2016. arxiv:1606.04933 [cs.IT]"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M110109X"},{"key":"e_1_2_1_56_1","unstructured":"Ma C.;Wang K.;Chi Y.;Chen Y.Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution. Preprint 2017. arxiv:1711.10467 [cs.LG]"},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Massouli\u00e9 L.Community detection thresholds and the weak Ramanujan property.STOC'14\u2014Proceedings of the 2014 ACM Symposium on Theory of Computing 694\u2013703. ACM New York 2014.","DOI":"10.1145\/2591796.2591857"},{"key":"e_1_2_1_58_1","unstructured":"Mei S.;Bai Y.;Montanari A.The landscape of empirical risk for non\u2010convex losses. Preprint 2016. arxiv:1607.06534 [stat.ML]"},{"key":"e_1_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Merlev\u00e8de F.;Peligrad M.;Rio E.Bernstein inequality and moderate deviations under strong mixing conditions.High dimensional probability V: the Luminy volume 273\u2013292. Institute of Mathematical Statistics (IMS) Collections 5. Institute of Mathematical Statistics Beachwood Ohio 2009.","DOI":"10.1214\/09-IMSCOLL518"},{"key":"e_1_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Mossel E.;Neeman J.;Sly A.Consistency thresholds for binary symmetric block models. Preprint 2016. arxiv:1407.1591 [math.PR]","DOI":"10.1214\/16-EJP4185"},{"key":"e_1_2_1_61_1","first-page":"2796","article-title":"Phase retrieval using alternating minimization","volume":"26","author":"Netrapalli P.","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_62_1","unstructured":"Oymak S.;Hassibi B.Finding dense clusters via \u201clow rank\u2009+\u2009sparse\u201d decomposition. Preprint 2011. arxiv:1104.5186 [stat.ML]"},{"key":"e_1_2_1_63_1","first-page":"1860","article-title":"Solving the multi\u2010way matching problem by permutation synchronization","volume":"26","author":"Pachauri D.","year":"2013","journal-title":"Advanced in Neural Information Processing Systems"},{"key":"e_1_2_1_64_1","unstructured":"Park D.;Kyrillidis A.;Bhojanapalli S.;Caramanis C.;Sanghavi S.Provable non\u2010convex projected gradient descent for a class of constrained matrix optimization problems. Preprint 2016. arxiv:1606.01316 [stat.ML]"},{"key":"e_1_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Ravikumar P.;Lafferty J.Quadratic programming relaxations for metric labeling and Markov random field MAP estimation.Proceedings of the 23rd International Conference on Machine Learning 737\u2013744. ICML '06. ACM New York 2006. doi:10.1145\/1143844.1143937","DOI":"10.1145\/1143844.1143937"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2603151"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1017\/S096354830000420X"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2297687"},{"key":"e_1_2_1_69_1","first-page":"4925","article-title":"Normalized spectral map synchronization","volume":"29","author":"Shen Y.","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_70_1","doi-asserted-by":"crossref","unstructured":"Si H.;Vikalo H.;Vishwanath S.Haplotype assembly: An information theoretic view.2014 IEEE Information Theory Workshop (ITW 2014) 182\u2013186. IEEE 2014. doi:10.1109\/ITW.2014.6970817","DOI":"10.1109\/ITW.2014.6970817"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.02.001"},{"key":"e_1_2_1_72_1","unstructured":"Sun J.;Qu Q.;Wright J.Complete dictionary recovery using nonconvex optimization.Proceedings of the 32nd International Conference on Machine Learning37(2015) 2351\u20132360. Available at:http:\/\/proceedings.mlr.press\/v37\/sund15.html"},{"key":"e_1_2_1_73_1","unstructured":"Sun J.;Qu Q.;Wright J.When are nonconvex problems not scary?Preprint 2015. arxiv:1510.06096 [math.OC]"},{"key":"e_1_2_1_74_1","doi-asserted-by":"crossref","unstructured":"Sun J.;Qu Q.;Wright J.A geometric analysis of phase retrieval. Preprint 2016. arxiv:1602.06664 [cs.IT]","DOI":"10.1109\/ISIT.2016.7541725"},{"key":"e_1_2_1_75_1","doi-asserted-by":"crossref","unstructured":"Sun R.;Luo Z.\u2010Q.Guaranteed matrix completion via nonconvex factorization.2015 IEEE 56th Annual Symposium on Foundations of Computer Science\u2014FOCS 2015 270\u2013289. IEEE Computer Society Los Alamitos Calif. 2015.","DOI":"10.1109\/FOCS.2015.25"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02699376"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1090\/gsm\/132"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000048"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1007\/b13794"},{"key":"e_1_2_1_80_1","unstructured":"Tu S.;Boczar R.;Soltanolkotabi M.;Recht B.Low\u2010rank solutions of linear matrix equations via procrustes flow. Preprint 2015. arxiv:1507.03566 [math.OC]"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2017.2756858"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1093\/imaiai\/iat005"},{"key":"e_1_2_1_83_1","doi-asserted-by":"crossref","unstructured":"Wang Z.;Li W.;Sun Y.Automated network analysis by projected power method.International Conference on Information and Automation 626\u2013630. IEEE 2012. doi:10.1109\/ICInfA.2012.6246889","DOI":"10.1109\/ICInfA.2012.6246889"},{"key":"e_1_2_1_84_1","first-page":"4152","article-title":"Fast algorithms for robust PCA via gradient descent","volume":"29","author":"Yi X.","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_85_1","first-page":"899","article-title":"Truncated power method for sparse eigenvalue problems","volume":"14","author":"Yuan X.\u2010T.","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_2_1_86_1","unstructured":"Zhang H.;Chi Y.;Liang Y.Provable non\u2010convex phase retrieval with outliers: median truncated Wirtinger flow.Proceedings of the 33rd International Conference on Machine Learning 1022\u20131031. Proceedings of Machine Learning Research 48. Available at:http:\/\/proceedings.mlr.press\/v48\/zhange16.html"},{"key":"e_1_2_1_87_1","first-page":"2622","article-title":"Reshaped Wirtinger flow for solving quadratic systems of equations","volume":"29","author":"Zhang H.","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26287"},{"key":"e_1_2_1_89_1","first-page":"559","article-title":"A nonconvex optimization framework for low rank matrix estimation","volume":"28","author":"Zhao T.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_90_1","first-page":"109","article-title":"A convergent gradient descent algorithm for rank minimization and semidefinite programming from random linear measurements","volume":"28","author":"Zheng Q.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Communications on Pure and Applied Mathematics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fcpa.21760","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpa.21760","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/cpa.21760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T12:57:12Z","timestamp":1692968232000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/cpa.21760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,24]]},"references-count":90,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.1002\/cpa.21760"],"URL":"https:\/\/doi.org\/10.1002\/cpa.21760","archive":["Portico"],"relation":{},"ISSN":["0010-3640","1097-0312"],"issn-type":[{"value":"0010-3640","type":"print"},{"value":"1097-0312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,24]]},"assertion":[{"value":"2018-07-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}