Online Stochastic DCA With Applications to Principal Component Analysis

Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular,...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 35; no. 5; pp. 7035 - 7047
Main Authors:	Le Thi, Hoai An, Luu, Hoang Phuc Hau, Dinh, Tao Pham
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Big Data Computer Science Convex functions DC~algorithm (DCA) Difference of Convex functions (DC) programming Divers Learning algorithms Machine learning Machine learning algorithms Martingales Mathematics nonconvex optimization online stochastic DCA (osDCA) Optimization Principal component analysis principal component analysis (PCA) Principal components analysis Programming Stochastic processes Stochasticity Difference of Convex functions (DC) programming Online stochastic DCA (osDCA) DC algorithm (DCA) Nonconvex optimization Principal component analysis (PCA)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular, machine learning. We propose new online stochastic algorithms based on the state-of-the-art DC Algorithm (DCA)-a powerful approach in nonconvex programming framework, in the online context of streaming data continuously generated by some (unknown) source distribution. The new schemes use the stochastic approximations (SAs) principle: deterministic quantities of the standard DCA are replaced by their noisy estimators constructed using newly arriving samples. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in principal component analysis (PCA).
AbstractList	Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular, machine learning. We propose new online stochastic algorithms based on the state-of-the-art DC Algorithm (DCA)-a powerful approach in nonconvex programming framework, in the online context of streaming data continuously generated by some (unknown) source distribution. The new schemes use the stochastic approximations (SAs) principle: deterministic quantities of the standard DCA are replaced by their noisy estimators constructed using newly arriving samples. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in principal component analysis (PCA).Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular, machine learning. We propose new online stochastic algorithms based on the state-of-the-art DC Algorithm (DCA)-a powerful approach in nonconvex programming framework, in the online context of streaming data continuously generated by some (unknown) source distribution. The new schemes use the stochastic approximations (SAs) principle: deterministic quantities of the standard DCA are replaced by their noisy estimators constructed using newly arriving samples. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in principal component analysis (PCA). Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex functions (DC) programs-the major class of nonconvex stochastic optimization, which have a variety of applications in divers domains, in particular, machine learning. We propose new online stochastic algorithms based on the state-of-the-art DC Algorithm (DCA)-a powerful approach in nonconvex programming framework, in the online context of streaming data continuously generated by some (unknown) source distribution. The new schemes use the stochastic approximations (SAs) principle: deterministic quantities of the standard DCA are replaced by their noisy estimators constructed using newly arriving samples. The convergence analysis of the proposed algorithms is studied intensively with the help of tools from modern convex analysis and martingale theory. Finally, we study several aspects of the proposed algorithms on an important problem in machine learning: the expected problem in principal component analysis (PCA).
Author	Dinh, Tao Pham Le Thi, Hoai An Luu, Hoang Phuc Hau
Author_xml	– sequence: 1 givenname: Hoai An orcidid: 0000-0002-2239-2100 surname: Le Thi fullname: Le Thi, Hoai An email: hoai-an.le-thi@univ-lorraine.fr organization: Université de Lorraine, Laboratoire de Génie Informatique, de Production et de Maintenance (LGIPM), Metz, France – sequence: 2 givenname: Hoang Phuc Hau orcidid: 0000-0003-0908-9817 surname: Luu fullname: Luu, Hoang Phuc Hau email: hoang-phuc-hau.luu@univ-lorraine.fr organization: Université de Lorraine, Laboratoire de Génie Informatique, de Production et de Maintenance (LGIPM), Metz, France – sequence: 3 givenname: Tao Pham surname: Dinh fullname: Dinh, Tao Pham email: pham@insa-rouen.fr organization: Laboratory of Mathematics, National Institute for Applied Sciences (INSA)-Rouen, University of Normandie, Saint-Étienne-du-Rouvray, France
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36315540$$D View this record in MEDLINE/PubMed https://hal.univ-lorraine.fr/hal-04003755$$DView record in HAL
BookMark	eNpdkU1rGzEQhkVJadI0fyCBsNBLe7A70uhj97i4-SiYpJCU5iZk7RgrrKXtal3Iv886dn2oLiPE8868mvcjO4opEmPnHKacQ_Xt8e5u_jAVIMQUBUelynfsRHAtJgLL8uhwN0_H7CznZxiPBqVl9YEdo0aulIQTdnMf2xCpeBiSX7k8BF98n9XF7zCsirrr2uDdEFLMxZCKn32IPnSuLWZp3Y124lDU0bUvOeRP7P3StZnO9vWU_bq-epzdTub3Nz9m9XziEfUwUY6bRVPKpqLRMwcjqXTScw0SOWnpSrPQUBIZL3XTlA4bqRdL4kajdpXEU_Z113flWtv1Ye36F5tcsLf13G7fQAKgUeovH9kvO7br058N5cGuQ_bUti5S2mQrDIKWqOW27ef_0Oe06ce_ZYugAAErrkbqck9tFmtqDvP_rXMExA7wfcq5p-UB4WC3sdm32Ow2NruPbRRd7ESBiA6CqkI0yPEVlgyPVw
CODEN	ITNNAL
Cites_doi	10.1080/01621459.2016.1148611 10.1007/978-3-7908-2604-3_16 10.1002/cjs.10105 10.1051/ps:2005018 10.1016/j.amc.2020.125904 10.1137/s1052623494274313 10.1515/9783110845563 10.1137/19M1276819 10.1007/s10479-004-5022-1 10.1007/978-0-387-70914-7 10.1016/j.ejor.2014.11.031 10.1137/16M1080173 10.1145/1961189.1961200 10.1109/TIT.2015.2457942 10.1137/20M1385706 10.1137/S1052623497331063 10.1109/LWC.2019.2963032 10.1109/GlobalSIP45357.2019.8969411 10.1137/18M1178244 10.1137/18M117337X 10.1007/978-3-642-54455-2_1 10.1016/j.neucom.2014.11.051 10.1145/1143844.1143870 10.1016/j.knosys.2014.08.003 10.1007/s10107-016-1021-7 10.1137/120863277 10.1007/s10107-018-1235-y 10.1109/TSP.2016.2531627 10.1016/j.neunet.2020.08.024 10.1214/aoms/1177729586 10.1137/120880811 10.1007/s10994-021-05996-7
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 licence_http://creativecommons.org/publicdomain/zero
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 – notice: licence_http://creativecommons.org/publicdomain/zero
DBID	97E RIA RIE AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 1XC
DOI	10.1109/TNNLS.2022.3213558
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef PubMed Aluminium Industry Abstracts Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biotechnology and BioEngineering Abstracts MEDLINE - Academic Hyper Article en Ligne (HAL)
DatabaseTitle	CrossRef PubMed Materials Research Database Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Materials Business File Aerospace Database Engineered Materials Abstracts Biotechnology Research Abstracts Chemoreception Abstracts Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Civil Engineering Abstracts Aluminium Industry Abstracts Electronics & Communications Abstracts Ceramic Abstracts Neurosciences Abstracts METADEX Biotechnology and BioEngineering Abstracts Computer and Information Systems Abstracts Professional Solid State and Superconductivity Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Corrosion Abstracts MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed Materials Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science Mathematics
EISSN	2162-2388
EndPage	7047
ExternalDocumentID	oai:HAL:hal-04003755v1 36315540 10_1109_TNNLS_2022_3213558 9933731
Genre	orig-research Journal Article
GroupedDBID	0R~ 4.4 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD IFIPE IPLJI JAVBF M43 MS~ O9- OCL PQQKQ RIA RIE RNS AAYXX CITATION NPM 7QF 7QO 7QP 7QQ 7QR 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 8BQ 8FD F28 FR3 H8D JG9 JQ2 KR7 L7M L~C L~D P64 7X8 1XC
ID	FETCH-LOGICAL-c336t-5a17bd84d9e1351074e8a4c160431e64a87b608ee7c46dd8a3d46bfe17636a943
IEDL.DBID	RIE
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001214608800094&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2162-237X 2162-2388
IngestDate	Wed Nov 05 07:32:18 EST 2025 Sat Sep 27 21:21:35 EDT 2025 Sun Nov 30 04:37:24 EST 2025 Mon Jul 21 06:07:44 EDT 2025 Sat Nov 29 01:40:23 EST 2025 Wed Aug 27 02:06:32 EDT 2025
IsPeerReviewed	false
IsScholarly	true
Issue	5
Keywords	Difference of Convex functions (DC) programming Online stochastic DCA (osDCA) DC algorithm (DCA) Nonconvex optimization Principal component analysis (PCA)
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 licence_http://creativecommons.org/publicdomain/zero/: http://creativecommons.org/publicdomain/zero
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c336t-5a17bd84d9e1351074e8a4c160431e64a87b608ee7c46dd8a3d46bfe17636a943
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-0908-9817 0000-0002-2239-2100
PMID	36315540
PQID	3050303915
PQPubID	85436
PageCount	13
ParticipantIDs	crossref_primary_10_1109_TNNLS_2022_3213558 proquest_journals_3050303915 pubmed_primary_36315540 ieee_primary_9933731 hal_primary_oai_HAL_hal_04003755v1 proquest_miscellaneous_2730643644
PublicationCentury	2000
PublicationDate	2024-05-01
PublicationDateYYYYMMDD	2024-05-01
PublicationDate_xml	– month: 05 year: 2024 text: 2024-05-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Piscataway
PublicationTitle	IEEE transaction on neural networks and learning systems
PublicationTitleAbbrev	TNNLS
PublicationTitleAlternate	IEEE Trans Neural Netw Learn Syst
PublicationYear	2024
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref12 ref34 ref15 Pham Dinh (ref16) 1997; 22 ref37 ref14 ref36 ref30 ref11 ref10 ref32 Mairal (ref24); 26 ref2 Nitanda (ref31) ref1 ref17 ref39 ref38 ref19 ref18 Xu (ref13) Le Thi (ref28) Metel (ref33) ref23 ref26 ref25 ref20 ref22 ref21 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 Henriksen (ref35) 2019
References_xml	– ident: ref4 doi: 10.1080/01621459.2016.1148611 – ident: ref21 doi: 10.1007/978-3-7908-2604-3_16 – ident: ref3 doi: 10.1002/cjs.10105 – ident: ref37 doi: 10.1051/ps:2005018 – start-page: 6942 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref13 article-title: Stochastic optimization for DC functions and non-smooth non-convex regularizers with non-asymptotic convergence – ident: ref12 doi: 10.1016/j.amc.2020.125904 – ident: ref17 doi: 10.1137/s1052623494274313 – ident: ref38 doi: 10.1515/9783110845563 – ident: ref30 doi: 10.1137/19M1276819 – ident: ref15 doi: 10.1007/s10479-004-5022-1 – ident: ref39 doi: 10.1007/978-0-387-70914-7 – ident: ref2 doi: 10.1016/j.ejor.2014.11.031 – ident: ref20 doi: 10.1137/16M1080173 – volume: 26 start-page: 2283 volume-title: Proc. Adv. Neural Inf. Process. Syst. ident: ref24 article-title: Stochastic majorization-minimization algorithms for large-scale optimization – ident: ref9 doi: 10.1145/1961189.1961200 – start-page: 4537 volume-title: Proc. 36th Int. Conf. Mach. Learn. ident: ref33 article-title: Simple stochastic gradient methods for non-smooth non-convex regularized optimization – start-page: 470 volume-title: Proc. 20th Int. Conf. Artif. Intell. Stat. ident: ref31 article-title: Stochastic difference of convex algorithm and its application to training deep Boltzmann machines – ident: ref11 doi: 10.1109/TIT.2015.2457942 – volume: 22 start-page: 289 issue: 1 year: 1997 ident: ref16 article-title: Convex analysis approach to DC programming: Theory, algorithms and applications publication-title: Acta Math. Vietnamica – ident: ref32 doi: 10.1137/20M1385706 – year: 2019 ident: ref35 article-title: AdaOja: Adaptive learning rates for streaming PCA publication-title: arXiv:1905.12115 – ident: ref23 doi: 10.1137/S1052623497331063 – ident: ref26 doi: 10.1109/LWC.2019.2963032 – ident: ref36 doi: 10.1109/GlobalSIP45357.2019.8969411 – ident: ref8 doi: 10.1137/18M1178244 – ident: ref10 doi: 10.1137/18M117337X – ident: ref18 doi: 10.1007/978-3-642-54455-2_1 – ident: ref14 doi: 10.1016/j.neucom.2014.11.051 – ident: ref6 doi: 10.1145/1143844.1143870 – start-page: 3394 volume-title: Proc. 34th Int. Conf. Mach. Learn. ident: ref28 article-title: Stochastic DCA for the large-sum of non-convex functions problem and its application to group variable selection in classification – ident: ref7 doi: 10.1016/j.knosys.2014.08.003 – ident: ref25 doi: 10.1007/s10107-016-1021-7 – ident: ref34 doi: 10.1137/120863277 – ident: ref1 doi: 10.1007/s10107-018-1235-y – ident: ref27 doi: 10.1109/TSP.2016.2531627 – ident: ref29 doi: 10.1016/j.neunet.2020.08.024 – ident: ref19 doi: 10.1214/aoms/1177729586 – ident: ref22 doi: 10.1137/120880811 – ident: ref5 doi: 10.1007/s10994-021-05996-7
SSID	ssj0000605649
Score	2.474203
Snippet	Stochastic algorithms are well-known for their performance in the era of big data. In this article, we study nonsmooth stochastic Difference-of-Convex...
SourceID	hal proquest pubmed crossref ieee
SourceType	Open Access Repository Aggregation Database Index Database Publisher
StartPage	7035
SubjectTerms	Algorithms Big Data Computer Science Convex functions DC~algorithm (DCA) Difference of Convex functions (DC) programming Divers Learning algorithms Machine learning Machine learning algorithms Martingales Mathematics nonconvex optimization online stochastic DCA (osDCA) Optimization Principal component analysis principal component analysis (PCA) Principal components analysis Programming Stochastic processes Stochasticity
Title	Online Stochastic DCA With Applications to Principal Component Analysis
URI	https://ieeexplore.ieee.org/document/9933731 https://www.ncbi.nlm.nih.gov/pubmed/36315540 https://www.proquest.com/docview/3050303915 https://www.proquest.com/docview/2730643644 https://hal.univ-lorraine.fr/hal-04003755
Volume	35
WOSCitedRecordID	wos001214608800094&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE/IET Electronic Library customDbUrl: eissn: 2162-2388 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000605649 issn: 2162-237X databaseCode: RIE dateStart: 20120101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4BqiouPEpbAhS5Fbc2kMSO7RxXFMoBrZCg6t4sJ55ouWwqNsvvx-M8BBIcuEWJNY5mxp7Pj_kG4EQTB4moytjVaR2LmmdxYRMbS7TIM5WroqxDsQk1nerZrLhZg19jLgwihstneEqP4SzfNdWKtsrOfCzlipKm15WSXa7WuJ-SeFwuA9rNUpnFGVezIUcmKc7uptPrW78azLJTnqVEKb4JH7nkFE2TFyFpfU4XIkOllbdBZwg-l9vv--0d2OpBJpt0XrELa7j4BNtDAQfWj-c9-NMRjbLbtqnmlhib2e_zCft3387Z5NnJNmsbdtPtynuxJKdZ-C7ZwGjyGf5eXtydX8V9ZYW44ly2cW5TVTotXIFUoc_DCNRWVKkkqh2UwmpVykQjqkpI57TlTsiyxtTPRtIWgn-BjYXvaR8YrScr7dclmFphE2cdahResk7ynCuM4OegXPO_I9AwYeGRFCZYxZBVTG-VCH54_Y8Nifv6anJt6B3NNlzl-WMawR4peWzV6zeCo8Fcph-IS8OJ7yaw4EfwffzshxCdi9gFNqul8QiOgJlHhhF87cw8yh585OD1Pg9h0_--6G5AHsFG-7DCb_Chemzvlw_H3k9n-jj46RNWUd82
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgqAXCpRHoIBB3CBtEju2c1wVyiKWqFIXsTfLiSfaXjaom-3vx-M8BBIcuEWJNU5m7Mw3tucbgHeaOEhEXcWuSZtYNDyLC5vYWKJFnqlcFVUTik2ostSrVXGxBx-mXBhEDIfP8IQuw16-a-sdLZWdel_KFSVN36bKWUO21rSiknhkLgPezVKZxRlXqzFLJilOl2W5uPTxYJad8CwlUvEDuMslJ3-a_OGUbq3pSGSotfJv2Bncz_nh_734A7g_wEw268fFQ9jDzSM4HEs4sGFGH8HnnmqUXXZtvbbE2cw-ns3Yj6tuzWa_7W2zrmUX_bq8F0ty2o3vko2cJo_h-_mn5dk8HmorxDXnsotzm6rKaeEKpBp9HkigtqJOJZHtoBRWq0omGlHVQjqnLXdCVg2m_n8kbSH4E9jf-J6eAaOIstY-MsHUCps461Cj8JJ1kudcYQTvR-Wanz2FhgmhR1KYYBVDVjGDVSJ46_U_NST26_lsYege_W-4yvObNIIjUvLUatBvBMejucwwFbeGE-NN4MGP4M302E8i2hmxG2x3W-MxHEEzjw0jeNqbeZI9jpHnf-_zNdybL78tzOJL-fUFHPhPEf15yGPY7653-BLu1Dfd1fb6VRitvwDfqeGX
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Online+Stochastic+DCA+With+Applications+to+Principal+Component+Analysis&rft.jtitle=IEEE+transaction+on+neural+networks+and+learning+systems&rft.au=Thi%2C+Hoai+An+Le&rft.au=Luu%2C+Hoang+Phuc+Hau&rft.au=Dinh%2C+Tao+Pham&rft.date=2024-05-01&rft.eissn=2162-2388&rft.volume=PP&rft_id=info:doi/10.1109%2FTNNLS.2022.3213558&rft_id=info%3Apmid%2F36315540&rft.externalDocID=36315540
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2162-237X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2162-237X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2162-237X&client=summon