{"id":2635,"date":"2023-05-26T10:05:28","date_gmt":"2023-05-26T13:05:28","guid":{"rendered":"http:\/\/127.0.0.1\/?page_id=2635"},"modified":"2023-09-27T17:00:40","modified_gmt":"2023-09-27T20:00:40","slug":"palestras","status":"publish","type":"page","link":"http:\/\/127.0.0.1\/home\/palestras\/","title":{"rendered":"Palestras"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"2635\" class=\"elementor elementor-2635\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f015616 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f015616\" data-element_type=\"section\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1050b90\" data-id=\"1050b90\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-889c706 elementor-widget elementor-widget-heading\" data-id=\"889c706\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 01-11-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">palestras<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-25f2331 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"25f2331\" data-element_type=\"section\" id=\"alejandro\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-0c1c610\" data-id=\"0c1c610\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5374036 elementor-widget elementor-widget-heading\" data-id=\"5374036\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Introduction to Signal Analysis with Ordinal Patterns\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dd030ed elementor-widget elementor-widget-heading\" data-id=\"dd030ed\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Palestrante\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8272be8 elementor-widget elementor-widget-ha-member happy-addon ha-member\" data-id=\"8272be8\" data-element_type=\"widget\" data-widget_type=\"ha-member.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\n\t\t\t\t\t<figure class=\"ha-member-figure\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"315\" height=\"315\" src=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Alejandro-Frery-ID-1.jpg\" class=\"elementor-animation-disable-animation attachment-full size-full wp-image-2617\" alt=\"\" srcset=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Alejandro-Frery-ID-1.jpg 315w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Alejandro-Frery-ID-1-300x300.jpg 300w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Alejandro-Frery-ID-1-150x150.jpg 150w\" sizes=\"(max-width: 315px) 100vw, 315px\" \/>\t\t\t<\/figure>\n\t\t\n\t\t<div class=\"ha-member-body\">\n\t\t\t<h2 class=\"ha-member-name\">Alejandro C. Frery<\/h2>\n\t\t\t\t\t\t\t<div class=\"ha-member-position\">Professor of Statistics and Data Science School of Mathematics and Statistics, Victoria University of Wellington, New Zealand<\/div>\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b275097 elementor-widget elementor-widget-text-editor\" data-id=\"b275097\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.17.0 - 01-11-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>Abstract:<\/b> Ordinal\u00a0Patterns\u00a0are a non-parametric transformation of subsequences. They were proposed by Bandt &amp; Pompe in 2002 as a means to unveil the underlying dynamics that govern signals.\u00a0Ordinal\u00a0Patterns\u00a0have several interesting properties, among them being resistant to outliers and invariant to strictly increasing transformations. With more than 2600 citations, they are a powerful tool for analysing time series, images, and networks. This\u00a0talk\u00a0is an overview of\u00a0Ordinal\u00a0Patterns, ways of analysing them, applications, and future research directions.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-2c60a2d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2c60a2d\" data-element_type=\"section\" id=\"ali\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-ad2f042\" data-id=\"ad2f042\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-694e68c elementor-widget elementor-widget-heading\" data-id=\"694e68c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Social Machine Learning\n\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b868a75 elementor-widget elementor-widget-heading\" data-id=\"b868a75\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Palestrante\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73c7cc3 elementor-widget elementor-widget-ha-member happy-addon ha-member\" data-id=\"73c7cc3\" data-element_type=\"widget\" data-widget_type=\"ha-member.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\n\t\t\t\t\t<figure class=\"ha-member-figure\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1153\" height=\"1153\" src=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1.jpg\" class=\"elementor-animation-disable-animation attachment-full size-full wp-image-2637\" alt=\"\" srcset=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1.jpg 1153w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1-300x300.jpg 300w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1-1024x1024.jpg 1024w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1-150x150.jpg 150w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/05\/Ali-Sayed-1-768x768.jpg 768w\" sizes=\"(max-width: 1153px) 100vw, 1153px\" \/>\t\t\t<\/figure>\n\t\t\n\t\t<div class=\"ha-member-body\">\n\t\t\t<h2 class=\"ha-member-name\">Ali H. Sayed<\/h2>\n\t\t\t\t\t\t\t<div class=\"ha-member-position\">Dean of Engineering, \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (Swiss Federal Institute of Technology Lausanne), Lausanne, Switzerland<\/div>\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6783d83 elementor-widget elementor-widget-text-editor\" data-id=\"6783d83\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Abstract:<\/b> Modern society is witnessing the emergence of complex networked systems driven by\u00a0exchanges of information among their elements, such as robotic swarms, autonomous\u00a0systems, social networks, and Internet-of-Things (IoT) architectures. In these\u00a0applications, data is often collected from heterogeneous sources and from dispersed\u00a0locations. It becomes imperative to design learning machines and decision-making algorithms that are better suited to the reality of networked units. The methodologies will need to account for coupling among intelligent agents in a manner that enables multi-tasking and is robust to interference.<\/p><p>This talk introduces a framework for\u00a0decentralized\u00a0learning from networked data, which we refer to as Social Machine Learning. This approach handles heterogeneity in data more gracefully than existing methods, learns with performance guarantees, is more resilient to adversarial attacks, and promotes\u00a0explainable and fair learning. By relying on social interactions among agents, the architecture is also infused with a higher level of robustness to manipulation. This is because it is more difficult to fool a group of agents than an individual agent.<\/p><p><br \/><br \/><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-1561b70 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1561b70\" data-element_type=\"section\" id=\"daniel\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-900f73a\" data-id=\"900f73a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f952846 elementor-widget elementor-widget-heading\" data-id=\"f952846\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Path Beyond 5G: Challenges and A Glimpse from a Multiple Access Perspective<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9dc024f elementor-widget elementor-widget-heading\" data-id=\"9dc024f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Palestrante\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2c09e85 elementor-widget elementor-widget-ha-member happy-addon ha-member\" data-id=\"2c09e85\" data-element_type=\"widget\" data-widget_type=\"ha-member.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\n\t\t\t\t\t<figure class=\"ha-member-figure\">\n\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1888\" height=\"1889\" src=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides.jpg\" class=\"elementor-animation-disable-animation attachment-full size-full wp-image-2479\" alt=\"\" srcset=\"http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides.jpg 1888w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides-300x300.jpg 300w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides-1024x1024.jpg 1024w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides-150x150.jpg 150w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides-768x768.jpg 768w, http:\/\/127.0.0.1\/wp-content\/uploads\/2023\/04\/Daniel-Benevides-1536x1536.jpg 1536w\" sizes=\"(max-width: 1888px) 100vw, 1888px\" \/>\t\t\t<\/figure>\n\t\t\n\t\t<div class=\"ha-member-body\">\n\t\t\t<h2 class=\"ha-member-name\">Daniel Benevides da Costa<\/h2>\n\t\t\t\t\t\t\t<div class=\"ha-member-position\">Principal Researcher, Technology Innovation Institute (TII), Abu Dhabi, United Arab Emirates<br><br><\/div>\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11d53c5 elementor-widget elementor-widget-text-editor\" data-id=\"11d53c5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 []\"><strong>Abstract:<\/strong> Is 10-year still a feasible gap between G\u2019s? Did 5G fulfill what was promised? What are the 5G lessons learned? While 5G is still under implementation in many countries of the world, the scientific community (plus International Telecommunication Union) started to work on beyond 5G standardization even though there are some unconnected dots which still need to be connected. One of the unaddressed issues is related to massive connectivity (one of the pillars of 5G), especially on how to deal with multi-user interference. Since the fundamental limits of multi-user multi-antenna communications with imperfect channel state information (CSI) are still an open problem, 5G design relied on a kind of \u201crobustification\u201d instead of being designed from scratch to be truly robust to imperfect CSI.<\/p><p>This talk will first discuss the journey and challenges beyond 5G. After a brief summary on the road ahead, the need of a flexible and robust multiple access framework to tackle multi-user interference in a general system setup will be emphasized, in which rate-splitting multiple access (RSMA) will be proposed as a potential candidate to cope with that. Three dual-polarized downlink transmission approaches for a massive multiple-input multiple-output (MIMO) RSMA network under the effects of polarization interference and residual errors of imperfect successive interference cancellation (SIC). In addition, the potential synergy between intelligent reflecting surface (IRS) and RSMA will be discussed, in which it will be exploited the advanced capabilities of dual-polarized IRS for unleashing an enhanced RSMA polarization multiplexing. Finally, it will be presented a general idea on how to implement a space-efficient method for realizing enhanced and adaptive integrated communication and powering. In a nutshell, the talk aims to provide a big picture on the path beyond 5G, focusing on some unaddressed points and showing a glimpse from a multiple access perspective.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>palestras Introduction to Signal Analysis with Ordinal Patterns Palestrante Alejandro C. Frery Professor of Statistics and Data Science School of Mathematics and Statistics, Victoria University of Wellington, New Zealand Abstract: Ordinal\u00a0Patterns\u00a0are a non-parametric transformation of subsequences. They were proposed by Bandt &amp; Pompe in 2002 as a means to unveil the underlying dynamics that govern [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":1617,"parent":1631,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/pages\/2635"}],"collection":[{"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/comments?post=2635"}],"version-history":[{"count":10,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/pages\/2635\/revisions"}],"predecessor-version":[{"id":3177,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/pages\/2635\/revisions\/3177"}],"up":[{"embeddable":true,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/pages\/1631"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/media\/1617"}],"wp:attachment":[{"href":"http:\/\/127.0.0.1\/wp-json\/wp\/v2\/media?parent=2635"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}