{"id":7060,"date":"2018-08-13T08:18:59","date_gmt":"2018-08-13T06:18:59","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/"},"modified":"2018-08-13T08:18:59","modified_gmt":"2018-08-13T06:18:59","slug":"research-seminar-on-data-selective-adaptive-filtering","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/","title":{"rendered":"Research Seminar – On Data-Selective Adaptive Filtering"},"content":{"rendered":"

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.<\/p>

Paulo S. R. Diniz<\/strong> was born in Niteroi, Brazil. He received the Electronics Eng. degree\u00a0(Cum Laude) from the Federal University of Rio de Janeiro (UFRJ) in 1978, the M.Sc. degree\u00a0from COPPE\/UFRJ in 1981, and the Ph.D. from Concordia University, Montreal, P.Q., Canada,\u00a0in 1984, all in electrical engineering.<\/p>

Since 1979 he has been with the Department of Electronics and Computer Engineering UFRJ.\u00a0He has also been with the Program of Electrical Engineering (the graduate studies dept.), COPPE\/UFRJ,\u00a0since 1984, where he is presently a Professor. He served as Undergraduate Course Coordinator and\u00a0as Chairman of the Graduate Department.<\/p>

His teaching and research interests are in analog and\u00a0digital signal processing, adaptive signal processing, digital communications, wireless communications, multi-rate systems, stochastic processes, and electronic circuits.<\/p>

From January 1991 to July 1992, he was a Visiting Research Associate in the Department of\u00a0Electrical and Computer Engineering of University of Victoria, Victoria, B.C., Canada. He also held a Docent position at Helsinki University of Technology (now Aalto University). From January\u00a02002 to June 2002, he was a Melchor Chair Professor in the Department of Electrical Engineering\u00a0of University of Notre Dame, Notre Dame, IN, USA.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"

The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.<\/p>\n","protected":false},"author":0,"featured_media":7061,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2018-08-30 14:00:00","event_end_date":"2018-08-30 15:00:00","event_speaker_name":"Prof. Paulo S.R. Diniz (Universidade Federal do Rio de Janeiro)","event_speaker_link":"","event_is_online":false,"event_location":"Room E004, JFK Building","event_street":"29, Avenue John F. Kennedy","event_location_link":"","event_zip_code":"L-1855","event_city":"Kirchberg","event_country":"LU"},"events-topic":[],"events-type":[],"organisation":[184,226],"authorship":[],"acf":[],"yoast_head":"\nResearch Seminar - On Data-Selective Adaptive Filtering - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Seminar - On Data-Selective Adaptive Filtering\" \/>\n<meta property=\"og:description\" content=\"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/\" \/>\n<meta property=\"og:site_name\" content=\"UNI FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/uni.lu\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/\",\"url\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/\",\"name\":\"Research Seminar - On Data-Selective Adaptive Filtering - Universit\u00e9 du Luxembourg\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg\",\"datePublished\":\"2018-08-13T06:18:59+00:00\",\"dateModified\":\"2018-08-13T06:18:59+00:00\",\"description\":\"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg\",\"width\":800,\"height\":600},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.uni.lu\/fr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\/\/www.uni.lu\/fr\/events\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Research Seminar - On Data-Selective Adaptive Filtering\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#website\",\"url\":\"https:\/\/www.uni.lu\/fr\/\",\"name\":\"Uni.lu\",\"description\":\"Universit\u00e9 du Luxembourg\",\"publisher\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#organization\"},\"alternateName\":\"Universit\u00e9 du Luxembourg\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.uni.lu\/fr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#organization\",\"name\":\"Universit\u00e9 du Luxembourg\",\"alternateName\":\"Uni.lu\",\"url\":\"https:\/\/www.uni.lu\/fr\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\",\"contentUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg\",\"width\":2560,\"height\":2560,\"caption\":\"Universit\u00e9 du Luxembourg\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/uni.lu\",\"https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/\",\"https:\/\/www.instagram.com\/uni.lu\",\"https:\/\/www.youtube.com\/@uni_lu\",\"https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Research Seminar - On Data-Selective Adaptive Filtering - Universit\u00e9 du Luxembourg","description":"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/","og_locale":"fr_FR","og_type":"article","og_title":"Research Seminar - On Data-Selective Adaptive Filtering","og_description":"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.","og_url":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/","og_site_name":"UNI FR","article_publisher":"https:\/\/www.facebook.com\/uni.lu","og_image":[{"width":2560,"height":2560,"url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","type":"image\/jpeg"}],"twitter_card":"summary_large_image","twitter_misc":{"Dur\u00e9e de lecture estim\u00e9e":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/","url":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/","name":"Research Seminar - On Data-Selective Adaptive Filtering - Universit\u00e9 du Luxembourg","isPartOf":{"@id":"https:\/\/www.uni.lu\/fr\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage"},"thumbnailUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg","datePublished":"2018-08-13T06:18:59+00:00","dateModified":"2018-08-13T06:18:59+00:00","description":"The current trend of acquiring data pervasively calls for some data-selection strategy, particularly in the case a subset of the data does not bring enough innovation. As a byproduct, in addition to reducing power consumption and some computation, the discarding of data results in more accurate parameter estimation. In many practical situations, it is possible to verify if the acquired set of data qualifi es to improve the related statistical inference or if it consists of an outlier or a noninnovative entry. In this presentation, we discuss some extensions of the existing adaptive fi ltering algorithms enabling data selection which also address the censorship of outliers measured through unexpected high estimation errors. The resulting algorithms allow the prescription of how often the acquired data is expected to be incorporated in the learning process based on some a priori assumptions regarding the environment data. A detailed derivation of how to implement the data selection in a computationally efficient way is provided along with the proper choice of the parameters inherent to the data-selective affine projection (DS-AP) algorithms. Similar discussions lead to the proposal of the data-selective least-mean square (DS-LMS) and data-selective recursive least squares (DS-RLS) algorithms. Simulation results show the effectiveness of the proposed algorithms for selecting the innovative data without sacri ficing the estimation accuracy, while reducing the computational cost.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2018\/08\/research_seminar_on_data_selective_adaptive_filtering.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fr\/events\/research-seminar-on-data-selective-adaptive-filtering\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr\/"},{"@type":"ListItem","position":2,"name":"Events","item":"https:\/\/www.uni.lu\/fr\/events\/"},{"@type":"ListItem","position":3,"name":"Research Seminar - On Data-Selective Adaptive Filtering"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fr\/#website","url":"https:\/\/www.uni.lu\/fr\/","name":"Uni.lu","description":"Universit\u00e9 du Luxembourg","publisher":{"@id":"https:\/\/www.uni.lu\/fr\/#organization"},"alternateName":"Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fr\/#organization","name":"Universit\u00e9 du Luxembourg","alternateName":"Uni.lu","url":"https:\/\/www.uni.lu\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"Universit\u00e9 du Luxembourg"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/uni.lu","https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/","https:\/\/www.instagram.com\/uni.lu","https:\/\/www.youtube.com\/@uni_lu","https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/7060"}],"collection":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/comments?post=7060"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/7060\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media\/7061"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media?parent=7060"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-topic?post=7060"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-type?post=7060"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/organisation?post=7060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}