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left: auto; right: auto; top: 0; margin-top: 0; } .ui-icon.ui-icon-triangle-1-e, .ui-accordion .ui-accordion-header.ui-state-default:hover .ui-icon.ui-icon-triangle-1-e { width: 24px; height: 24px; float: right; background: url(https://staging6.odsc.com/wp-content/themes/oshin/images/rounded_arrow.png) no-repeat center center transparent; } .svg_container.big.handshake img { max-width: none; width: 56px; } p.date { color: #15c2e0; font-weight: bold; text-transform: uppercase; margin-bottom: 0; } .svg_container.big.handshake { padding: 25px 30px 20px 15px; } .svg_container.blue, .blue .svg_container { background: rgba(0, 188, 221, 0.32); } .svg_container.big { padding: 20px; width: 40px; height: 40px; } .svg_container { width: 28px; height: 28px; float: left; padding: 14px; margin-top: 0; margin-right: 14px; border-radius: 50%; }</style>   <style type="text/css"> /* Add your CSS code here. For example: .example { color: red; } For brushing up on your CSS knowledge, check out http://www.w3schools.com/css/css_syntax.asp End of comment */ #ptsTableInitEditHtmlDlg{width:100%!important} #odsc-tickets .groups__group { } @media only screen and (max-width: 960px){ body.sticky-header #header #header-inner-wrap.no-transparent { position: fixed!important; left: 0!important; right: 0 !important; top: 0px !important; }} .kolumnspec select { width: 100%!important; font: 400 18px "Source Sans Pro","Open Sans","Arial",sans-serif!important; border:none!important; height:41px!important; } .newslee input[type="email"]{ width: 100%!important;} .kolumnspec ul { list-style: none!important; } #odsc-tickets .groups__group:hover { background: #f7f5f5; } /* Timetable */ @media only screen and (min-width: 992px){ .tcode-event-schedule .scheduled-event .artist-image { height: 92px; width: 92px; } } .artist-image .img-responsive{border-radius: 50%;} @media only screen and (max-width: 767px){ .klasicc .tatsu-eq-cols .tatsu-column { min-height: 270px!important; } } /*register page*/ @media only screen and (max-width: 460px) { .fixe span{line-height:55px;} } .mobmenu{display:none!important;} .mobmenu-push-wrap, body.mob-menu-slideout-over { padding-top: 0px!important; } @media only screen and (max-width: 964px) { .page-id-161 #header-inner-wrap{ /* display:none!important;*/ } .page-id-161 .mobmenu{display:block!important;} .page-id-161 .mobmenu-push-wrap, .page-id-161 body.mob-menu-slideout-over { padding-top: 40px!important; } } .mptt-shortcode-wrapper .mptt-shortcode-table{ color: white; } .mptt-shortcode-wrapper .mptt-shortcode-table tr.mptt-shortcode-row th{ background-color: transparent; color: white; padding: 1rem; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody tr:nth-child(2n+2) { background-color: transparent; } .mptt-shortcode-wrapper .mptt-shortcode-table.mptt-theme-mode tbody td.event{ background-color: transparent; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody div[data-color="#48AFDE"] .event-subtitle, .mptt-shortcode-wrapper .mptt-shortcode-table tbody div[data-color="#48AFDE"] .timeslot{ color: white !important; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody .mptt-shortcode-event.mptt-event-vertical-top[colspan="4"] .mptt-event-container{ background-color: transparent !important; justify-content: center; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody .mptt-event-container .event-title{ font-weight: bold; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody [colspan="4"] .event-title{ font-size: 24px; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody .mptt-event-container:hover .event-title{ text-decoration: none !important; } .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17418"] .mptt-event-container, .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17423"] .mptt-event-container{ background-color: rgba(105, 217, 226, 0.15); } .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17418"] .mptt-event-container:hover, .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17423"] .mptt-event-container:hover{ background-color: rgba(105, 217, 226, .25); } .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17424"] .mptt-event-container, .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17425"] .mptt-event-container{ background-color: rgba(226, 152, 178, 0.15); } .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17424"] .mptt-event-container:hover, .mptt-shortcode-wrapper .mptt-shortcode-event.mptt-event-vertical-top[data-column-id="17425"] .mptt-event-container:hover{ background-color: rgba(226, 152, 178, .25); } .mptt-shortcode-list { color: white; } [data-event-id="17452"] .timeslot{ display: none; } .mptt-shortcode-wrapper .mptt-shortcode-table tbody .mptt-event-container[data-event-id="17452"] .event-subtitle { font-size: .9em; } .mfp-content { position: relative; background-color: #FFF; padding: 40px 20px; width: 90%; max-width: 700px; margin: 20px auto; } .mfp-content h4{ text-align: center; } .mfp-content .timeslots-title,.mfp-content .timeslot{ display: none; } .mfp-preloader{ color: black; } .mfp-content{ position: relative; background-color: #FFF!important; padding: 40px 20px; width: 90%; max-width: 700px; margin: 20px auto;} .mptt-event-container.id-56 a{ font-weight:bold; color: rgb(0, 188, 221)!important; } .mptt-event-container.id-56 a::before{ } .mptt-event-container.id-63,.mptt-event-container.id-64 { background-image: url(https://staging6.odsc.com/wp-content/uploads/2018/05/DinnerDS.jpg);} .mptt-event-container.id-121,.mptt-event-container.id-64 { background-image: url(https://staging6.odsc.com/wp-content/uploads/2017/09/Data-Robot-Networking-Party1.jpg);} .linkic{width: 100%; height: 100%; position: absolute;} .overl{ position: absolute; top: 35%; background: url(https://staging6.odsc.com/wp-content/uploads/2018/05/play-white.svg); width: 20%; background-repeat: no-repeat; left: 40%; height: 100%;} #hu-header-mobile .hu-header-logo { max-height: 75px; margin: 41px auto 0; display: none!important; } .agendica tbody tr:nth-child(2n+2) { background-color: transparent!important; } .agendica tr.mptt-shortcode-row th { background-color: transparent!important; } .agendica tbody td.event { background-color: transparent!important; } .agendica .mptt-shortcode-hours{color:white!important} .agendica th{color:white!important} .agendica .timeslot{color:white!important} .agendica .event-subtitle{color:white!important} .agendica .event-title{color:white!important} .price__table__column .column__header { height: 250px!important; } .price__table__column .column__header .column__desc { font-size: 15px!important; line-height: 20px!important; } .faq_categories .attending { background: url(images/attendee.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .attending.activated { background: url(images/attendee_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .hiring { background: url(images/hiring.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .hiring.activated { background: url(images/hiring_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .videos { background: url(images/videos.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .videos.activated { background: url(images/videos_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .speakers { background: url(images/speaker.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .speakers.activated { background: url(images/speaker_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .volunteers { background: url(images/volunteer.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .volunteers.activated { background: url(images/volunteer_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .sponsor { background: url(images/sponsor.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .sponsor.activated { background: url(images/sponsor_light.svg) no-repeat center 15px #fff; background-size: 37px; } .faq_categories .group-tickets { background: url(images/group.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .ticket-type { background: url(images/ticket.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .schedule, .faq_categories .odsc { background: url(images/schedule.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .refund-policy { background: url(images/refund.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .trainings { background: url(images/training.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .app-info { background: url(images/mobile.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .scholarships { background: url(images/scholarship.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .promotions-pricing { background: url(images/pricing.svg) no-repeat center 12px #fff; background-size: 27px; } .faq_categories .odsc-europe-2017, .faq_categories .odsc-west-2017, .faq_categories .odsc-east-2017 { background: url(images/logo.png) no-repeat center 10px #fff; background-size: 37px; padding-top: 35px; } .faq_categories li a { font-size: 15px; line-height: 20px; display: inline-block; width: 120px; height: 60px; padding-top: 60px; border-radius: 50%; -moz-border-radius: 50%; -webkit-border-radius: 50%; text-align: center; /* padding: 10px 15px 10px 50px; */ background: #fff; -webkit-box-shadow: 0px 3px 3px rgba(0,0,0,0.25); -moz-box-shadow: 0px 3px 3px rgba(0,0,0,0.25); box-shadow: 0px 3px 3px rgba(0,0,0,0.25); } @media only screen and (min-width: 992px){ .tcode-event-schedule .col-md-2 { width: 8.33333333%; } .tcode-event-schedule .col-md-9 { width: 83.33333333%; } .tcode-event-schedule .col-md-8{ width: 83.33333333%; } .tcode-event-schedule .col-md-offset-2 { margin-left: 8.33333333%; } } </style>   <script class="hsq-set-content-id" data-content-id="listing-page"> var _hsq = _hsq || []; _hsq.push(["setContentType", "listing-page"]); </script>  <noscript><style type="text/css">.mptt-shortcode-wrapper .mptt-shortcode-table:first-of-type{display:table!important}.mptt-shortcode-wrapper .mptt-shortcode-table .mptt-event-container:hover{height:auto!important;min-height:100%!important}body.mprm_ie .mptt-shortcode-wrapper .mptt-event-container{height:auto!important}@media (max-width:767px){.mptt-shortcode-wrapper .mptt-shortcode-table:first-of-type{display:none!important}}</style></noscript><meta name="facebook-domain-verification" content="am2vglo5kisnxesykxjg911bcapxcr" /> <script type="text/javascript"> var ajaxurl = 'https://odsc.com/wp-admin/admin-ajax.php'; </script> <script type='text/javascript'> var video_popup_unprm_general_settings = { 'unprm_r_border': '' }; </script> <style type="text/css" media="all" id="wcs_styles"></style><noscript><style>.wp-grid-builder .wpgb-card.wpgb-card-hidden .wpgb-card-wrapper{opacity:1!important;visibility:visible!important;transform:none!important}.wpgb-facet {opacity:1!important;pointer-events:auto!important}.wpgb-facet *:not(.wpgb-pagination-facet){display:none}</style></noscript>   <script data-cfasync="false" data-pagespeed-no-defer> var dataLayer_content = {"pagePostType":"post","pagePostType2":"category-post","pageCategory":["machine-learning"]}; dataLayer.push( dataLayer_content ); </script> <script data-cfasync="false"> (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= '//www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-M62L34N'); </script> <style id = "be-dynamic-css" type="text/css"> body { background-color: rgb(255,255,255);background-color: rgba(255,255,255,1);} .layout-box #header-inner-wrap, #header-inner-wrap, body.header-transparent #header #header-inner-wrap.no-transparent, .left-header .sb-slidebar.sb-left, .left-header .sb-slidebar.sb-left #slidebar-menu a::before { background-color: rgb(50,56,64);background-color: rgba(50,56,64,1);} #mobile-menu, #mobile-menu ul { background-color: rgb(50,56,64);background-color: rgba(50,56,64,1);} #mobile-menu li{ border-bottom-color: #efefef ; } body.header-transparent #header-inner-wrap{ background: transparent; } .be-gdpr-modal-item input:checked + .slider{ background-color: #00bcdd; } .be-gdpr-modal-iteminput:focus + .slider { box-shadow: 0 0 1px #00bcdd; } .be-gdpr-modal-item .slider:before { background-color:#ffffff; } .be-gdpr-cookie-notice-bar .be-gdpr-cookie-notice-button{ background: #00bcdd; color: #ffffff; } #header .header-border{ border-bottom: 0px none #00bcdd; } #header-top-bar{ background-color: rgb(21,22,26);background-color: rgba(21,22,26,1); border-bottom: 0px none #ffffff; color: #ffffff; } #header-top-bar #topbar-menu li a{ color: #ffffff; } #header-bottom-bar{ background-color: rgb(255,255,255);background-color: rgba(255,255,255,1); border-top: none #323232; border-bottom: none #323232; } /*Adjusted the timings for the new effects*/ body.header-transparent #header #header-inner-wrap { -webkit-transition: background .25s ease, box-shadow .25s ease, opacity 700ms cubic-bezier(0.645, 0.045, 0.355, 1), transform 700ms cubic-bezier(0.645, 0.045, 0.355, 1); -moz-transition: background .25s ease, box-shadow .25s ease, opacity 700ms cubic-bezier(0.645, 0.045, 0.355, 1), transform 700ms cubic-bezier(0.645, 0.045, 0.355, 1); 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} .layout-box-top, .layout-box-bottom, .layout-box-right, .layout-box-left, .layout-border-header-top #header-inner-wrap, .layout-border-header-top.layout-box #header-inner-wrap, body.header-transparent .layout-border-header-top #header #header-inner-wrap.no-transparent { background-color: rgb(211,211,211);background-color: rgba(211,211,211,1);} .left-header.left-sliding.left-overlay-menu .sb-slidebar{ background-color: rgb(8,8,8);background-color: rgba(8,8,8,0.90); } .top-header.top-overlay-menu .sb-slidebar{ background-color: rgb(26,26,26);background-color: rgba(26,26,26,1);} .search-box-wrapper{ background-color: rgb(255,255,255);background-color: rgba(255,255,255,0.85);} .search-box-wrapper.style1-header-search-widget input[type="text"]{ background-color: transparent !important; color: #000000; border: 1px solid #000000; } .search-box-wrapper.style2-header-search-widget input[type="text"]{ background-color: transparent !important; color: #000000; border: none !important; box-shadow: none !important; } .search-box-wrapper .searchform .search-icon{ color: #000000; } #header-top-bar-right .search-box-wrapper.style1-header-search-widget input[type="text"]{ border: none; } .post-title , .post-date-wrap { margin-bottom: 12px; } /* ====================== Dynamic Border Styling ====================== */ .layout-box-top, .layout-box-bottom { height: 30px; } .layout-box-right, .layout-box-left { width: 30px; } #main.layout-border, #main.layout-border.layout-border-header-top{ padding: 30px; } .left-header #main.layout-border { padding-left: 0px; } #main.layout-border.layout-border-header-top { padding-top: 0px; } .be-themes-layout-layout-border #logo-sidebar, .be-themes-layout-layout-border-header-top #logo-sidebar{ margin-top: 70px; } /*Left Static Menu*/ .left-header.left-static.be-themes-layout-layout-border #main-wrapper{ margin-left: 310px; } .left-header.left-static.be-themes-layout-layout-border .sb-slidebar.sb-left { left: 30px; } /*Right Slidebar*/ body.be-themes-layout-layout-border-header-top .sb-slidebar.sb-right, body.be-themes-layout-layout-border .sb-slidebar.sb-right { right: -250px; } .be-themes-layout-layout-border-header-top .sb-slidebar.sb-right.opened, .be-themes-layout-layout-border .sb-slidebar.sb-right.opened { right: 30px; } /* Top-overlay menu on opening, header moves sideways bug. Fixed on the next line code */ /*body.be-themes-layout-layout-border-header-top.top-header.slider-bar-opened #main #header #header-inner-wrap.no-transparent.top-animate, body.be-themes-layout-layout-border.top-header.slider-bar-opened #main #header #header-inner-wrap.no-transparent.top-animate { right: 310px; }*/ body.be-themes-layout-layout-border-header-top.top-header:not(.top-overlay-menu).slider-bar-opened #main #header #header-inner-wrap.no-transparent.top-animate, body.be-themes-layout-layout-border.top-header:not(.top-overlay-menu).slider-bar-opened #main #header #header-inner-wrap.no-transparent.top-animate { right: 310px; } /* Now not needed mostly, as the hero section image is coming properly */ /*Single Page Version*/ body.be-themes-layout-layout-border-header-top.single-page-version .single-page-nav-wrap, body.be-themes-layout-layout-border.single-page-version .single-page-nav-wrap { right: 50px; } /*Split Screen Page Template*/ .top-header .layout-border #content.page-split-screen-left { margin-left: calc(50% + 15px); } .top-header.page-template-page-splitscreen-left .layout-border .header-hero-section { width: calc(50% - 15px); } .top-header .layout-border #content.page-split-screen-right { width: calc(50% - 15px); } .top-header.page-template-page-splitscreen-right .layout-border .header-hero-section { left: calc(50% - 15px); } @media only screen and (max-width: 960px) { body.be-themes-layout-layout-border-header-top.single-page-version .single-page-nav-wrap, body.be-themes-layout-layout-border.single-page-version .single-page-nav-wrap { right: 35px; } body.be-themes-layout-layout-border-header-top .sb-slidebar.sb-right, body.be-themes-layout-layout-border .sb-slidebar.sb-right { right: -280px; } #main.layout-border, #main.layout-border.layout-border-header-top { padding: 0px !important; } .top-header .layout-border #content.page-split-screen-left, .top-header .layout-border #content.page-split-screen-right { margin-left: 0px; width:100%; } .top-header.page-template-page-splitscreen-right .layout-border .header-hero-section, .top-header.page-template-page-splitscreen-left .layout-border .header-hero-section { width:100%; } } body, .special-heading-wrap .caption-wrap .body-font, .woocommerce .woocommerce-ordering select.orderby, .woocommerce-page .woocommerce-ordering select.orderby { font: 400 18px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #5f6263; line-height: 26px; letter-spacing: 0px; text-transform: none; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } h1 { font: 700 55px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 70px; letter-spacing: 0px; text-transform: none;} h2 { font: 700 42px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 63px; letter-spacing: 0px; text-transform: none;} h3 { font: 700 35px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 52px; letter-spacing: 1px; text-transform: none;} h4, .woocommerce-order-received .woocommerce h2, .woocommerce-order-received .woocommerce h3, .woocommerce-view-order .woocommerce h2, .woocommerce-view-order .woocommerce h3{ font: 400 26px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 42px; letter-spacing: 0px; text-transform: none;} h5, #reply-title { font: 400 20px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 36px; letter-spacing: 0px; text-transform: none; } h6, .testimonial-author-role.h6-font, .menu-card-title, .menu-card-item-price, .slider-counts, .woocommerce-MyAccount-navigation ul li { font: 400 15px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 32px; letter-spacing: 0px; text-transform: none;} h6.gallery-side-heading { font-size: 18px; } .special-subtitle , .style1.thumb-title-wrap .portfolio-item-cats { font-style: ; font-size: 15px; font-weight: ; font-family: Crimson Text; text-transform: none; letter-spacing: 0px; } .gallery-side-heading { font-size: 18px; } .attachment-details-custom-slider { background-color: rgb(0,0,0);background-color: rgba(0,0,0,1); font: 15px "Crimson Text","Open Sans","Arial",sans-serif; color: ; line-height: 15px; letter-spacing: 0px; text-transform: none;} .single-portfolio-slider .carousel_bar_wrap { background-color: rgb(255,255,255);background-color: rgba(255,255,255,0.5);} .top-right-sliding-menu .sb-right ul#slidebar-menu li, .overlay-menu-close, .be-overlay-menu-close { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 50px; letter-spacing: 1px; text-transform: uppercase;} .top-right-sliding-menu .sb-right ul#slidebar-menu li a { color: #ffffff !important; } .top-right-sliding-menu .sb-right #slidebar-menu ul.sub-menu li { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 25px; letter-spacing: ; text-transform: none;} .top-right-sliding-menu .sb-right ul#slidebar-menu li a { color: #ffffff !important; } .sb-right #slidebar-menu .mega .sub-menu .highlight .sf-with-ul { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 50px; letter-spacing: 1px; text-transform: uppercase; color: #ffffff !important; } .post-meta.post-top-meta-typo, .style8-blog .post-meta.post-category a, .hero-section-blog-categories-wrap a { font: 12px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #757575; line-height: 24px; letter-spacing: 0px; text-transform: uppercase;; } #portfolio-title-nav-bottom-wrap h6, #portfolio-title-nav-bottom-wrap .slider-counts { font: 400 15px "Montserrat","Open Sans","Arial",sans-serif; color: ; line-height: ; letter-spacing: 0px; text-transform: none;; line-height: 40px; } .filters .filter_item { font: 400 12px "Montserrat","Open Sans","Arial",sans-serif; color: #222222; line-height: 32px; letter-spacing: 1px; text-transform: uppercase;; } ul#mobile-menu a { font: 400 12px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 40px; letter-spacing: 1px; text-transform: uppercase;} ul#mobile-menu ul.sub-menu a { font: 400 13px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #bbbbbb; line-height: 27px; letter-spacing: 0px; text-transform: none; } ul#mobile-menu li.mega ul.sub-menu li.highlight > :first-child { font: 400 12px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 40px; letter-spacing: 1px; text-transform: uppercase;} #navigation, .style2 #navigation, .style13 #navigation, #navigation-left-side, #navigation-right-side, .sb-left #slidebar-menu, .header-widgets, .header-code-widgets, body #header-inner-wrap.top-animate.style2 #navigation, .top-overlay-menu .sb-right #slidebar-menu, #navigation .mega .sub-menu .highlight .sf-with-ul, .special-header-menu .menu-container { font: 600 16px "Montserrat","Open Sans","Arial",sans-serif; color: #00bcdd; line-height: 51px; letter-spacing: 1px; text-transform: uppercase;} #navigation .sub-menu, #navigation .children, #navigation-left-side .sub-menu, #navigation-left-side .children, #navigation-right-side .sub-menu, #navigation-right-side .children, .sb-left #slidebar-menu .sub-menu, .top-overlay-menu .sb-right #slidebar-menu .sub-menu, .special-header-menu .menu-container .sub-menu, .special-header-menu .sub-menu { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #bbbbbb; line-height: 28px; letter-spacing: 0px; text-transform: none;} .thumb-title-wrap .thumb-title { font: 400 14px "Montserrat","Open Sans","Arial",sans-serif; color: ; line-height: 30px; letter-spacing: 0px; text-transform: uppercase;} .thumb-title-wrap .portfolio-item-cats { font-size: 12px; line-height: 17px; text-transform: none; letter-spacing: 0px; } .full-screen-portfolio-overlay-title { font: 400 14px "Montserrat","Open Sans","Arial",sans-serif; color: ; line-height: 30px; letter-spacing: 0px; text-transform: uppercase;} #footer { font: 400 14px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 14px; letter-spacing: 0px; text-transform: none;} #bottom-widgets h6 { font: 400 18px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #474747; line-height: 22px; letter-spacing: 1px; text-transform: uppercase; margin-bottom:20px; } #bottom-widgets { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #757575; line-height: 24px; letter-spacing: 0px; text-transform: none;} .sidebar-widgets h6 { font: 400 18px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #333333; line-height: 22px; letter-spacing: 1px; text-transform: uppercase; margin-bottom:20px; } .sidebar-widgets { ?php be_themes_print_typography('sidebar_widget_text'); ?> } .sb-slidebar .widget { font: 400 14px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #a2a2a2; line-height: 25px; letter-spacing: 0px; text-transform: none;} .sb-slidebar .widget h6 { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #ffffff; line-height: 22px; letter-spacing: 1px; text-transform: none;} .woocommerce ul.products li.product .product-meta-data h3, .woocommerce-page ul.products li.product .product-meta-data h3, .woocommerce ul.products li.product h3, .woocommerce-page ul.products li.product h3 { font: 400 13px "Montserrat","Open Sans","Arial",sans-serif; color: #222222; line-height: 27px; letter-spacing: 1px; text-transform: uppercase;} .woocommerce ul.products li.product .product-meta-data .woocommerce-loop-product__title, .woocommerce-page ul.products li.product .product-meta-data .woocommerce-loop-product__title, .woocommerce ul.products li.product .woocommerce-loop-product__title, .woocommerce-page ul.products li.product .woocommerce-loop-product__title, .woocommerce ul.products li.product-category .woocommerce-loop-category__title, .woocommerce-page ul.products li.product-category .woocommerce-loop-category__title { font: 400 13px "Montserrat","Open Sans","Arial",sans-serif; color: #222222; line-height: 27px; letter-spacing: 1px; text-transform: uppercase; margin-bottom:5px; text-align: center; } .woocommerce-page.single.single-product #content div.product h1.product_title.entry-title { font: 400 25px "Montserrat","Open Sans","Arial",sans-serif; color: #222222; line-height: 27px; letter-spacing: 0px; text-transform: none;} .contact_form_module input[type="text"], .contact_form_module textarea { font: 400 13px "Montserrat","Open Sans","Arial",sans-serif; color: #222222; line-height: 26px; letter-spacing: 0px; text-transform: none;} .page-title-module-custom .page-title-custom, h6.portfolio-title-nav{ font: 400 18px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 36px; letter-spacing: 3px; text-transform: uppercase;} .tatsu-button, .be-button, .woocommerce a.button, .woocommerce-page a.button, .woocommerce button.button, .woocommerce-page button.button, .woocommerce input.button, .woocommerce-page input.button, .woocommerce #respond input#submit, .woocommerce-page #respond input#submit, .woocommerce #content input.button, .woocommerce-page #content input.button, input[type="submit"], .more-link.style1-button, .more-link.style2-button, .more-link.style3-button, input[type="button"], input[type="submit"], input[type="reset"], button, input[type="file"]::-webkit-file-upload-button { font-family: Source Sans Pro; font-weight: ; } .post-title , .post-date-wrap { font: 400 20px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #000000; line-height: 40px; letter-spacing: 0px; text-transform: none; margin-bottom: 12px; } .style3-blog .post-title, .style8-blog .post-title { font: 400 16px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #363c3b; line-height: 28px; letter-spacing: 0px; text-transform: none;} .post-nav li, .style8-blog .post-meta.post-date, .style8-blog .post-bottom-meta-wrap, .hero-section-blog-bottom-meta-wrap { font: 12px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #757575; line-height: 24px; letter-spacing: 0px; text-transform: uppercase;} .ui-tabs-anchor, .accordion .accordion-head, .skill-wrap .skill_name, .chart-wrap span, .animate-number-wrap h6 span, .woocommerce-tabs .tabs li a, .be-countdown { font-family: Source Sans Pro; letter-spacing: 0px; font-style: ; font-weight: 600; } .ui-tabs-anchor { font-size: 13px; line-height: 17px; text-transform: uppercase; } .accordion .accordion-head { font-size: 18px; line-height: 17px; text-transform: uppercase; } .skill-wrap .skill_name { font-size: 20px; line-height: 17px; text-transform: none; } .countdown-section { font-size: 15px; line-height: 30px; text-transform: uppercase; } .countdown-amount { font-size: 55px; line-height: 95px; text-transform: uppercase; } .tweet-slides .tweet-content { font-family: Source Sans Pro; letter-spacing: 0px; font-style: ; font-weight: ; text-transform: none; } .testimonial_slide .testimonial-content { font-family: Crimson Text; letter-spacing: 0px; font-style: ; font-weight: ; text-transform: none; } .oshine-animated-link, .view-project-link.style4-button { font-family: Montserrat; font-weight: ; letter-spacing: ; font-style: ; text-transform: none; } a.navigation-previous-post-link, a.navigation-next-post-link { font: 700 13px "Montserrat","Open Sans","Arial",sans-serif; color: ; line-height: 20px; letter-spacing: 0px; text-transform: none;; } @media only screen and (max-width : 767px ) { #hero-section h1 , .full-screen-section-wrap h1, .tatsu-fullscreen-wrap h1 { font-size: 30px; line-height: 40px; } #hero-section h2, .full-screen-section-wrap h2, .tatsu-fullscreen-wrap h2 { font-size: 25px; line-height: 35px; } #hero-section h4, .full-screen-section-wrap h4, .tatsu-fullscreen-wrap h3 { font-size: 16px; line-height: 30px; } #hero-section h5, .full-screen-section-wrap h5, .tatsu-fullscreen-wrap h5 { font-size: 16px; line-height: 30px; } } /* RELATED TO TYPOGRAPHY */ #header-controls-right, #header-controls-left { color: #00bcdd} #be-left-strip .be-mobile-menu-icon span { background-color: #323232} ul#mobile-menu .mobile-sub-menu-controller { line-height : 40px ; } ul#mobile-menu ul.sub-menu .mobile-sub-menu-controller{ line-height : 27px ; } .breadcrumbs { color: #515151; } .search-box-wrapper.style2-header-search-widget input[type="text"]{ font-style: ; font-weight: ; font-family: Crimson Text; } .portfolio-share a.custom-share-button, .portfolio-share a.custom-share-button:active, .portfolio-share a.custom-share-button:hover, .portfolio-share a.custom-share-button:visited { color: #515151; } .more-link.style2-button { color: #000000 !important; border-color: #000000 !important; } .style8-blog .post-bottom-meta-wrap .be-share-stack a.custom-share-button, .style8-blog .post-bottom-meta-wrap .be-share-stack a.custom-share-button:active, .style8-blog .post-bottom-meta-wrap .be-share-stack a.custom-share-button:hover, .style8-blog .post-bottom-meta-wrap .be-share-stack a.custom-share-button:visited { color: #757575; } .hero-section-blog-categories-wrap a, .hero-section-blog-categories-wrap a:visited, .hero-section-blog-categories-wrap a:hover, .hero-section-blog-bottom-meta-wrap .hero-section-blog-bottom-meta-wrap a, .hero-section-blog-bottom-meta-wrap a:visited, .hero-section-blog-bottom-meta-wrap a:hover { color : #000000; } #navigation .mega .sub-menu .highlight .sf-with-ul { color: #bbbbbb !important; line-height:1.5; } .view-project-link.style4-button { color : #515151; } .pricing-table .pricing-feature{ font-size: 17px; } /* Woocommerce */ .related.products h2, .upsells.products h2, .cart-collaterals .cross-sells h2, .cart_totals h2, .shipping_calculator h2, .woocommerce-billing-fields h3, .woocommerce-shipping-fields h3, .shipping_calculator h2, #order_review_heading, .woocommerce .page-title { font-family: Montserrat; font-weight: 400; } .woocommerce form .form-row label, .woocommerce-page form .form-row label { color: #515151; } .woocommerce-tabs .tabs li a { color: #515151 !important; } /* BB Press Plugin */ a.bbp-forum-title, #bbpress-forums fieldset.bbp-form label, .bbp-topic-title a.bbp-topic-permalink { font: 400 15px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 32px; letter-spacing: 0px; text-transform: none;} #bbpress-forums ul.forum-titles li, #bbpress-forums ul.bbp-replies li.bbp-header { font: 400 15px "Source Sans Pro","Open Sans","Arial",sans-serif; color: #515151; line-height: 32px; letter-spacing: 0px; text-transform: none; line-height: inherit; letter-spacing: inherit; text-transform: uppercase; font-size: inherit; } #bbpress-forums .topic .bbp-topic-meta a, .bbp-forum-freshness a, .bbp-topic-freshness a, .bbp-header .bbp-reply-content a, .bbp-topic-tags a, .bbp-breadcrumb a, .bbp-forums-list a { color: #515151; } /*Event On Plugin*/ .ajde_evcal_calendar .calendar_header p, .eventon_events_list .eventon_list_event .evcal_cblock { font-family: Source Sans Pro !important; } .eventon_events_list .eventon_list_event .evcal_desc span.evcal_desc2, .evo_pop_body .evcal_desc span.evcal_desc2 { font-family: Source Sans Pro !important; font-size: 14px !important; text-transform: none; } .eventon_events_list .eventon_list_event .evcal_desc span.evcal_event_subtitle, .evo_pop_body .evcal_desc span.evcal_event_subtitle, .evcal_evdata_row .evcal_evdata_cell p, #evcal_list .eventon_list_event p.no_events { text-transform: none !important; font-family: Source Sans Pro !important; font-size: inherit !important; } /* END RELATED TO TYPOGRAPHY */ .filters.single_border .filter_item{ border-color: #00bcdd; } .filters.rounded .current_choice{ border-radius: 50px; background-color: #00bcdd; color: #ffffff; } .filters.single_border .current_choice, .filters.border .current_choice{ color: #00bcdd; } .exclusive-mobile-bg .menu-controls{ background-color: background-color: rgb(255,255,255);background-color: rgba(255,255,255,0);; } #header .be-mobile-menu-icon span { background-color : #323232; } #header-controls-right, #header-controls-left, .overlay-menu-close, .be-overlay-menu-close { color : #323232; } #header .exclusive-mobile-bg .be-mobile-menu-icon, #header .exclusive-mobile-bg .be-mobile-menu-icon span, #header-inner-wrap.background--light.transparent.exclusive-mobile-bg .be-mobile-menu-icon, #header-inner-wrap.background--light.transparent.exclusive-mobile-bg .be-mobile-menu-icon span, #header-inner-wrap.background--dark.transparent.exclusive-mobile-bg .be-mobile-menu-icon, #header-inner-wrap.background--dark.transparent.exclusive-mobile-bg .be-mobile-menu-icon span { background-color: #323232} .be-mobile-menu-icon{ width: 18px; height: 2px; } .be-mobile-menu-icon .hamburger-line-1{ top: -5px; } .be-mobile-menu-icon .hamburger-line-3{ top: 5px; } .thumb-title-wrap { color: #ffffff; } #bottom-widgets .widget ul li a, #bottom-widgets a { color: inherit; } #bottom-widgets .tagcloud a:hover { color: #ffffff; } a, a:visited, a:hover, #bottom-widgets .widget ul li a:hover, #bottom-widgets a:hover{ color: #00bcdd; } #header-top-menu a:hover, #navigation .current_page_item a, #navigation .current_page_item a:hover, #navigation a:hover, #navigation-left-side .current_page_item a, #navigation-left-side .current_page_item a:hover, #navigation-left-side a:hover, #navigation-right-side .current_page_item a, #navigation-right-side .current_page_item a:hover, #navigation-right-side a:hover, #menu li.current-menu-ancestor > a, #navigation .current-menu-item > a, #navigation .sub-menu .current-menu-item > a, #navigation .sub-menu a:hover, #navigation .children .current-menu-item > a, #navigation .children a:hover, #slidebar-menu .current-menu-item > a, .special-header-menu a:hover + .mobile-sub-menu-controller i, .special-header-menu #slidebar-menu a:hover, .special-header-menu .sub-menu a:hover, .single-page-version #navigation a:hover, .single-page-version #navigation-left-side a:hover, .single-page-version #navigation-right-side a:hover, .single-page-version #navigation .current-section.current_page_item a, .single-page-version #navigation-left-side .current-section.current_page_item a, .single-page-version #navigation-right-side .current-section.current_page_item a, .single-page-version #slidebar-menu .current-section.current_page_item a, .single-page-version #navigation .current_page_item a:hover, .single-page-version #navigation-left-side .current_page_item a:hover, .single-page-version #navigation-right-side .current_page_item a:hover, .single-page-version #slidebar-menu .current_page_item a:hover, .be-sticky-sections #navigation a:hover, .be-sticky-sections #navigation-left-side a:hover, .be-sticky-sections #navigation-right-side a:hover, .be-sticky-sections #navigation .current-section.current_page_item a, .be-sticky-sections #navigation-left-side .current-section.current_page_item a, .be-sticky-sections #navigation-right-side .current-section.current_page_item a, .be-sticky-sections #navigation .current_page_item a:hover, .be-sticky-sections #navigation-left-side .current_page_item a:hover, .be-sticky-sections #navigation-right-side .current_page_item a:hover, #navigation .current-menu-ancestor > a, #navigation-left-side .current-menu-ancestor > a, #navigation-right-side .current-menu-ancestor > a, #slidebar-menu .current-menu-ancestor > a, .special-header-menu .current-menu-item > a, .sb-left #slidebar-menu a:hover { color: #ffffff; } #navigation .current_page_item ul li a, #navigation-left-side .current_page_item ul li a, #navigation-right-side .current_page_item ul li a, .single-page-version #navigation .current_page_item a, .single-page-version #navigation-left-side .current_page_item a, .single-page-version #navigation-right-side .current_page_item a, .single-page-version #slidebar-menu .current_page_item a, .single-page-version #navigation .sub-menu .current-menu-item > a, .single-page-version #navigation .children .current-menu-item > a .be-sticky-sections #navigation .current_page_item a, .be-sticky-sections #navigation-left-side .current_page_item a, .be-sticky-sections #navigation-right-side .current_page_item a, .be-sticky-sections #navigation .sub-menu .current-menu-item > a, .be-sticky-sections #navigation .children .current-menu-item > a { color: inherit; } .be-nav-link-effect-1 a::after, .be-nav-link-effect-2 a::after, .be-nav-link-effect-3 a::after{ background-color: rgb(255,255,255);background-color: rgba(255,255,255,1);} #portfolio-title-nav-wrap .portfolio-nav a { color: #00bcdd; } #portfolio-title-nav-wrap .portfolio-nav a .home-grid-icon span{ background-color: #00bcdd; } #portfolio-title-nav-wrap .portfolio-nav a:hover { color: #323840; } #portfolio-title-nav-wrap .portfolio-nav a:hover .home-grid-icon span{ background-color: #323840; } .page-title-module-custom .header-breadcrumb { line-height: 36px; } #portfolio-title-nav-bottom-wrap h6, #portfolio-title-nav-bottom-wrap ul li a, .single_portfolio_info_close, #portfolio-title-nav-bottom-wrap .slider-counts{ background-color: rgb(255,255,255);background-color: rgba(255,255,255,0);} .more-link.style2-button:hover { border-color: #00bcdd !important; background: #00bcdd !important; color: #ffffff !important; } .woocommerce a.button, .woocommerce-page a.button, .woocommerce button.button, .woocommerce-page button.button, .woocommerce input.button, .woocommerce-page input.button, .woocommerce #respond input#submit, .woocommerce-page #respond input#submit, .woocommerce #content input.button, .woocommerce-page #content input.button { background: transparent !important; color: #000 !important; border-color: #000 !important; border-style: solid !important; border-width: 2px !important; background: transparent !important; color: #000000 !important; border-width: 2px !important; border-color: #000000 !important; line-height: 41px; text-transform: uppercase; } .woocommerce a.button:hover, .woocommerce-page a.button:hover, .woocommerce button.button:hover, .woocommerce-page button.button:hover, .woocommerce input.button:hover, .woocommerce-page input.button:hover, .woocommerce #respond input#submit:hover, .woocommerce-page #respond input#submit:hover, .woocommerce #content input.button:hover, .woocommerce-page #content input.button:hover { background: #e0a240 !important; color: #fff !important; border-color: #e0a240 !important; border-width: 2px !important; background: #e0a240 !important; color: #ffffff !important; border-color: #e0a240 !important; } .woocommerce a.button.alt, .woocommerce-page a.button.alt, .woocommerce .button.alt, .woocommerce-page .button.alt, .woocommerce input.button.alt, .woocommerce-page input.button.alt, .woocommerce input[type="submit"].alt, .woocommerce-page input[type="submit"].alt, .woocommerce #respond input#submit.alt, .woocommerce-page #respond input#submit.alt, .woocommerce #content input.button.alt, .woocommerce-page #content input.button.alt { background: #e0a240 !important; color: #fff !important; border-color: #e0a240 !important; border-style: solid !important; border-width: 2px !important; background: #e0a240 !important; color: #ffffff !important; border-width: 2px !important; border-color: #e0a240 !important; line-height: 41px; text-transform: uppercase; } .woocommerce a.button.alt:hover, .woocommerce-page a.button.alt:hover, .woocommerce .button.alt:hover, .woocommerce-page .button.alt:hover, .woocommerce input[type="submit"].alt:hover, .woocommerce-page input[type="submit"].alt:hover, .woocommerce input.button.alt:hover, .woocommerce-page input.button.alt:hover, .woocommerce #respond input#submit.alt:hover, .woocommerce-page #respond input#submit.alt:hover, .woocommerce #content input.button.alt:hover, .woocommerce-page #content input.button.alt:hover { background: transparent !important; color: #000 !important; border-color: #000 !important; border-style: solid !important; border-width: 2px !important; background: transparent !important; color: #000000 !important; border-color: #000000 !important; } .woocommerce .woocommerce-message a.button, .woocommerce-page .woocommerce-message a.button, .woocommerce .woocommerce-message a.button:hover, .woocommerce-page .woocommerce-message a.button:hover { border: none !important; color: #fff !important; background: none !important; } .woocommerce .woocommerce-ordering select.orderby, .woocommerce-page .woocommerce-ordering select.orderby { border-color: #eeeeee; } .style7-blog .post-title{ margin-bottom: 9px; } .style8-blog .post-comment-wrap a:hover{ color : #00bcdd; } .style8-blog .element:not(.be-image-post) .post-details-wrap{ background-color: #ffffff ; } .accordion .accordion-head.with-bg.ui-accordion-header-active{ background-color: #00bcdd; color: #ffffff !important; } #portfolio-title-nav-wrap{ padding-top: 15px; padding-bottom: 15px; border-bottom: 1px solid #cecece; } #portfolio-title-nav-bottom-wrap h6, #portfolio-title-nav-bottom-wrap ul, .single_portfolio_info_close .font-icon, .slider-counts{ color: #2b2b2b ; } #portfolio-title-nav-bottom-wrap .home-grid-icon span{ background-color: #2b2b2b ; } #portfolio-title-nav-bottom-wrap h6:hover, #portfolio-title-nav-bottom-wrap ul a:hover, #portfolio-title-nav-bottom-wrap .slider-counts:hover, .single_portfolio_info_close:hover { background-color: rgb(235,73,73);background-color: rgba(235,73,73,0.85);} #portfolio-title-nav-bottom-wrap h6:hover, #portfolio-title-nav-bottom-wrap ul a:hover, #portfolio-title-nav-bottom-wrap .slider-counts:hover, .single_portfolio_info_close:hover .font-icon{ color: #ffffff ; } #portfolio-title-nav-bottom-wrap ul a:hover .home-grid-icon span{ background-color: #ffffff ; } /* ====================== Layout ====================== */ body #header-inner-wrap.top-animate #navigation, body #header-inner-wrap.top-animate .header-controls, body #header-inner-wrap.stuck #navigation, body #header-inner-wrap.stuck .header-controls { -webkit-transition: line-height 0.5s ease; -moz-transition: line-height 0.5s ease; -ms-transition: line-height 0.5s ease; -o-transition: line-height 0.5s ease; transition: line-height 0.5s ease; } .header-cart-controls .cart-contents span{ background: #646464; } .header-cart-controls .cart-contents span{ color: #f5f5f5; } .left-sidebar-page, .right-sidebar-page, .no-sidebar-page .be-section-pad:first-child, .page-template-page-940-php #content , .no-sidebar-page #content-wrap, .portfolio-archives.no-sidebar-page #content-wrap { padding-top: 80px; padding-bottom: 80px; } .no-sidebar-page #content-wrap.page-builder{ padding-top: 0px; padding-bottom: 0px; } .left-sidebar-page .be-section:first-child, .right-sidebar-page .be-section:first-child, .dual-sidebar-page .be-section:first-child { padding-top: 0 !important; } .style1 .logo, .style4 .logo, #left-header-mobile .logo, .style3 .logo, .style7 .logo, .style10 .logo{ padding-top: 15px; padding-bottom: 15px; } .style5 .logo, .style6 .logo{ margin-top: 15px; margin-bottom: 15px; } #footer-wrap { padding-top: 25px; padding-bottom: 25px; } /* ====================== Colors ====================== */ .sec-bg, .gallery_content, .fixed-sidebar-page .fixed-sidebar, .style3-blog .blog-post.element .element-inner, .style4-blog .blog-post, .blog-post.format-link .element-inner, .blog-post.format-quote .element-inner, .woocommerce ul.products li.product, .woocommerce-page ul.products li.product, .chosen-container.chosen-container-single .chosen-drop, .chosen-container.chosen-container-single .chosen-single, .chosen-container.chosen-container-active.chosen-with-drop .chosen-single { background: #fafbfd; } .sec-color, .post-meta a, .pagination a, .pagination a:visited, .pagination span, .pages_list a, input[type="text"], input[type="email"], input[type="password"], textarea, .gallery_content, .fixed-sidebar-page .fixed-sidebar, .style3-blog .blog-post.element .element-inner, .style4-blog .blog-post, .blog-post.format-link .element-inner, .blog-post.format-quote .element-inner, .woocommerce ul.products li.product, .woocommerce-page ul.products li.product, .chosen-container.chosen-container-single .chosen-drop, .chosen-container.chosen-container-single .chosen-single, .chosen-container.chosen-container-active.chosen-with-drop .chosen-single { color: #7a7a7a; } .woocommerce .quantity .plus, .woocommerce .quantity .minus, .woocommerce #content .quantity .plus, .woocommerce #content .quantity .minus, .woocommerce-page .quantity .plus, .woocommerce-page .quantity .minus, .woocommerce-page #content .quantity .plus, .woocommerce-page #content .quantity .minus, .woocommerce .quantity input.qty, .woocommerce #content .quantity input.qty, .woocommerce-page .quantity input.qty, .woocommerce-page #content .quantity input.qty { background: #fafbfd; color: #7a7a7a; border-color: #eeeeee; } .woocommerce div.product .woocommerce-tabs ul.tabs li, .woocommerce #content div.product .woocommerce-tabs ul.tabs li, .woocommerce-page div.product .woocommerce-tabs ul.tabs li, .woocommerce-page #content div.product .woocommerce-tabs ul.tabs li { color: #7a7a7a!important; } .chosen-container .chosen-drop, nav.woocommerce-pagination, .summary.entry-summary .price, .portfolio-details.style2 .gallery-side-heading-wrap, #single-author-info, .single-page-atts, article.comment { border-color: #eeeeee !important; } .fixed-sidebar-page #page-content{ background: #ffffff; } .sec-border, input[type="text"], input[type="email"], input[type="tel"], input[type="password"], textarea { border: 2px solid #eeeeee; } .chosen-container.chosen-container-single .chosen-single, .chosen-container.chosen-container-active.chosen-with-drop .chosen-single { border: 2px solid #eeeeee; } .woocommerce table.shop_attributes th, .woocommerce-page table.shop_attributes th, .woocommerce table.shop_attributes td, .woocommerce-page table.shop_attributes td { border: none; border-bottom: 1px solid #eeeeee; padding-bottom: 5px; } .woocommerce .widget_price_filter .price_slider_wrapper .ui-widget-content, .woocommerce-page .widget_price_filter .price_slider_wrapper .ui-widget-content{ border: 1px solid #eeeeee; } .pricing-table .pricing-title, .chosen-container .chosen-results li { border-bottom: 1px solid #eeeeee; } .separator { border:0; height:1px; color: #eeeeee; background-color: #eeeeee; } .alt-color, li.ui-tabs-active h6 a, a, a:visited, .social_media_icons a:hover, .post-title a:hover, .fn a:hover, a.team_icons:hover, .recent-post-title a:hover, .widget_nav_menu ul li.current-menu-item a, .widget_nav_menu ul li.current-menu-item:before, .woocommerce ul.cart_list li a:hover, .woocommerce ul.product_list_widget li a:hover, .woocommerce-page ul.cart_list li a:hover, .woocommerce-page ul.product_list_widget li a:hover, .woocommerce-page .product-categories li a:hover, .woocommerce ul.products li.product .product-meta-data h3:hover, .woocommerce table.cart a.remove:hover, .woocommerce #content table.cart a.remove:hover, .woocommerce-page table.cart a.remove:hover, .woocommerce-page #content table.cart a.remove:hover, td.product-name a:hover, .woocommerce-page #content .quantity .plus:hover, .woocommerce-page #content .quantity .minus:hover, .post-category a:hover, a.custom-like-button.liked, .menu-card-item-stared { color: #00bcdd; } .content-slide-wrap .flex-control-paging li a.flex-active, .content-slide-wrap .flex-control-paging li.flex-active a:before { background: #00bcdd !important; border-color: #00bcdd !important; } #navigation .menu > ul > li.mega > ul > li { border-color: #3d3d3d; } .sb-slidebar.sb-right .menu{ border-top: 1px solid #2d2d2d; border-bottom: 1px solid #2d2d2d; } .post-title a:hover { color: #00bcdd !important; } .alt-bg, input[type="submit"], .tagcloud a:hover, .pagination a:hover, .widget_tag_cloud a:hover, .pagination .current, .trigger_load_more .be-button, .trigger_load_more .be-button:hover { background-color: #00bcdd; transition: 0.2s linear all; } .mejs-controls .mejs-time-rail .mejs-time-current , .mejs-controls .mejs-horizontal-volume-slider .mejs-horizontal-volume-current, .woocommerce span.onsale, .woocommerce-page span.onsale, .woocommerce a.add_to_cart_button.button.product_type_simple.added, .woocommerce-page .widget_shopping_cart_content .buttons a.button:hover, .woocommerce nav.woocommerce-pagination ul li span.current, .woocommerce nav.woocommerce-pagination ul li a:hover, .woocommerce nav.woocommerce-pagination ul li a:focus, .testimonial-flex-slider .flex-control-paging li a.flex-active, #back-to-top, .be-carousel-nav, .portfolio-carousel .owl-controls .owl-prev:hover, .portfolio-carousel .owl-controls .owl-next:hover, .owl-theme .owl-controls .owl-dot.active span, .owl-theme .owl-controls .owl-dot:hover span, .more-link.style3-button, .view-project-link.style3-button{ background: #00bcdd !important; } .single-page-nav-link.current-section-nav-link { background: #ffffff !important; } .view-project-link.style2-button, .single-page-nav-link.current-section-nav-link { border-color: #00bcdd !important; } .view-project-link.style2-button:hover { background: #00bcdd !important; color: #ffffff !important; } .tagcloud a:hover, .testimonial-flex-slider .flex-control-paging li a.flex-active, .testimonial-flex-slider .flex-control-paging li a { border-color: #00bcdd; } a.be-button.view-project-link, .more-link { border-color: #00bcdd; } .portfolio-container .thumb-bg { background-color: rgba(0,188,221,0.85); } .photostream_overlay, .be-button, .more-link.style3-button, .view-project-link.style3-button, button, input[type="button"], input[type="submit"], input[type="reset"] { background-color: #00bcdd; } input[type="file"]::-webkit-file-upload-button{ background-color: #00bcdd; } .alt-bg-text-color, input[type="submit"], .tagcloud a:hover, .pagination a:hover, .widget_tag_cloud a:hover, .pagination .current, .woocommerce nav.woocommerce-pagination ul li span.current, .woocommerce nav.woocommerce-pagination ul li a:hover, .woocommerce nav.woocommerce-pagination ul li a:focus, #back-to-top, .be-carousel-nav, .single_portfolio_close .font-icon, .single_portfolio_back .font-icon, .more-link.style3-button, .view-project-link.style3-button, .trigger_load_more a.be-button, .trigger_load_more a.be-button:hover, .portfolio-carousel .owl-controls .owl-prev:hover .font-icon, .portfolio-carousel .owl-controls .owl-next:hover .font-icon{ color: #ffffff; transition: 0.2s linear all; } .woocommerce .button.alt.disabled { background: #efefef !important; color: #a2a2a2 !important; border: none !important; cursor: not-allowed; } .be-button, input[type="button"], input[type="submit"], input[type="reset"], button { color: #ffffff; transition: 0.2s linear all; } input[type="file"]::-webkit-file-upload-button { color: #ffffff; transition: 0.2s linear all; } .button-shape-rounded #submit, .button-shape-rounded .style2-button.view-project-link, .button-shape-rounded .style3-button.view-project-link, .button-shape-rounded .style2-button.more-link, .button-shape-rounded .style3-button.more-link, .button-shape-rounded .contact_submit { border-radius: 3px; } .button-shape-circular .style2-button.view-project-link, .button-shape-circular .style3-button.view-project-link{ border-radius: 50px; padding: 17px 30px !important; } .button-shape-circular .style2-button.more-link, .button-shape-circular .style3-button.more-link{ border-radius: 50px; padding: 7px 30px !important; } .button-shape-circular .contact_submit, .button-shape-circular #submit{ border-radius: 50px; padding-left: 30px; padding-right: 30px; } .view-project-link.style4-button:hover::after{ border-color : #00bcdd; } .mfp-arrow{ color: #ffffff; transition: 0.2s linear all; -moz-transition: 0.2s linear all; -o-transition: 0.2s linear all; transition: 0.2s linear all; } .portfolio-title a { color: inherit; } .arrow-block .arrow_prev, .arrow-block .arrow_next, .arrow-block .flickity-prev-next-button { background-color: rgb(0,0,0);background-color: rgba(0,0,0,1);} .arrow-border .arrow_prev, .arrow-border .arrow_next, .arrow-border .flickity-prev-next-button { border: 1px solid #000000; } .gallery-info-box-wrap .arrow_prev .font-icon, .gallery-info-box-wrap .arrow_next .font-icon{ color: #ffffff; } .flickity-prev-next-button .arrow{ fill: #ffffff; } .arrow-block .arrow_prev:hover, .arrow-block .arrow_next:hover, .arrow-block .flickity-prev-next-button:hover { background-color: rgb(0,0,0);background-color: rgba(0,0,0,1);} .arrow-border .arrow_prev:hover, .arrow-border .arrow_next:hover, .arrow-border .flickity-prev-next-button:hover { border: 1px solid #000000; } .gallery-info-box-wrap .arrow_prev:hover .font-icon, .gallery-info-box-wrap .arrow_next:hover .font-icon{ color: #ffffff; } .flickity-prev-next-button:hover .arrow{ fill: #ffffff; } #back-to-top.layout-border, #back-to-top.layout-border-header-top { right: 50px; bottom: 50px; } .layout-border .fixed-sidebar-page #right-sidebar.active-fixed { right: 30px; } body.header-transparent.admin-bar .layout-border #header #header-inner-wrap.no-transparent.top-animate, body.sticky-header.admin-bar .layout-border #header #header-inner-wrap.no-transparent.top-animate { top: 62px; } body.header-transparent .layout-border #header #header-inner-wrap.no-transparent.top-animate, body.sticky-header .layout-border #header #header-inner-wrap.no-transparent.top-animate { top: 30px; } body.header-transparent.admin-bar .layout-border.layout-border-header-top #header #header-inner-wrap.no-transparent.top-animate, body.sticky-header.admin-bar .layout-border.layout-border-header-top #header #header-inner-wrap.no-transparent.top-animate { top: 32px; z-index: 15; } body.header-transparent .layout-border.layout-border-header-top #header #header-inner-wrap.no-transparent.top-animate, body.sticky-header .layout-border.layout-border-header-top #header #header-inner-wrap.no-transparent.top-animate { top: 0px; z-index: 15; } body.header-transparent .layout-border #header #header-inner-wrap.no-transparent #header-wrap, body.sticky-header .layout-border #header #header-inner-wrap.no-transparent #header-wrap { margin: 0px 30px; -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; position: relative; } .mfp-content.layout-border img { padding: 70px 0px 70px 0px; } body.admin-bar .mfp-content.layout-border img { padding: 102px 0px 70px 0px; } .mfp-content.layout-border .mfp-bottom-bar { margin-top: -60px; } body .mfp-content.layout-border .mfp-close { top: 30px; } body.admin-bar .mfp-content.layout-border .mfp-close { top: 62px; } pre { background-image: -webkit-repeating-linear-gradient(top, #FFFFFF 0px, #FFFFFF 30px, #fafbfd 24px, #fafbfd 56px); background-image: -moz-repeating-linear-gradient(top, #FFFFFF 0px, #FFFFFF 30px, #fafbfd 24px, #fafbfd 56px); background-image: -ms-repeating-linear-gradient(top, #FFFFFF 0px, #FFFFFF 30px, #fafbfd 24px, #fafbfd 56px); background-image: -o-repeating-linear-gradient(top, #FFFFFF 0px, #FFFFFF 30px, #fafbfd 24px, #fafbfd 56px); background-image: repeating-linear-gradient(top, #FFFFFF 0px, #FFFFFF 30px, #fafbfd 24px, #fafbfd 56px); display: block; line-height: 28px; margin-bottom: 50px; overflow: auto; padding: 0px 10px; border:1px solid #eeeeee; } .post-title a{ color: inherit; } /*Animated link Typography*/ .be-sidemenu, .special-header-menu a::before{ background-color: rgb(26,26,26);background-color: rgba(26,26,26,1);} /*For normal styles add the padding in top and bottom*/ .be-themes-layout-layout-border .be-sidemenu, .be-themes-layout-layout-border .be-sidemenu, .be-themes-layout-layout-border-header-top .be-sidemenu, .be-themes-layout-layout-border-header-top .be-sidemenu{ padding: 30px 0px; box-sizing: border-box; } /*For center-align and left-align overlay, add padding to all sides*/ .be-themes-layout-layout-border.overlay-left-align-menu .be-sidemenu, .be-themes-layout-layout-border.overlay-center-align-menu .be-sidemenu, .be-themes-layout-layout-border-header-top.overlay-left-align-menu .be-sidemenu, .be-themes-layout-layout-border-header-top.overlay-center-align-menu .be-sidemenu{ padding: 30px; box-sizing: border-box; } .be-themes-layout-layout-border-header-top .be-sidemenu{ padding-top: 0px; } body.perspective-left.perspectiveview, body.perspective-right.perspectiveview{ background-color: rgb(26,26,26);background-color: rgba(26,26,26,1);} body.left-header.perspective-right.perspectiveview{ background-color: rgb(8,8,8);background-color: rgba(8,8,8,0.90);} body.perspective-left .be-sidemenu, body.perspective-right .be-sidemenu{ background-color : transparent; } /*Portfolio navigation*/ .loader-style1-double-bounce1, .loader-style1-double-bounce2, .loader-style2-wrap, .loader-style3-wrap > div, .loader-style5-wrap .dot1, .loader-style5-wrap .dot2, #nprogress .bar { background: #00bcdd !important; } .loader-style4-wrap { border-top: 7px solid rgba(0, 188, 221 , 0.3); border-right: 7px solid rgba(0, 188, 221 , 0.3); border-bottom: 7px solid rgba(0, 188, 221 , 0.3); border-left-color: #00bcdd; } #nprogress .spinner-icon { border-top-color: #00bcdd !important; border-left-color: #00bcdd !important; } #nprogress .peg { box-shadow: 0 0 10px #00bcdd, 0 0 5px #00bcdd !important; } .style1 #navigation, .style3 #navigation, .style4 #navigation, .style5 #navigation, #header-controls-left, #header-controls-right, #header-wrap, .mobile-nav-controller-wrap, #left-header-mobile .header-cart-controls, .style6 #navigation-left-side, .style6 #navigation-right-side, .style7 #navigation{ line-height: 66px; } body.header-transparent #header-wrap #navigation, body.header-transparent #header-wrap #navigation-left-side, body.header-transparent #header-wrap #navigation-right-side, body.header-transparent #header-inner-wrap .header-controls, body.header-transparent #header-inner-wrap #header-controls-left, body.header-transparent #header-inner-wrap #header-controls-right, body.header-transparent #header-inner-wrap #header-wrap, body.header-transparent #header-inner-wrap .mobile-nav-controller-wrap { line-height: 66px; } body #header-inner-wrap.top-animate #navigation, body #header-inner-wrap.top-animate #navigation-left-side, body #header-inner-wrap.top-animate #navigation-right-side, body #header-inner-wrap.top-animate .header-controls, body #header-inner-wrap.top-animate #header-wrap, body #header-inner-wrap.top-animate #header-controls-right, body #header-inner-wrap.top-animate #header-controls-left { line-height: 66px; } .header-transparent #content.page-split-screen-left, .header-transparent #content.page-split-screen-right{ } #navigation-left-side { padding-right: 90.5px; } #navigation-right-side { padding-left: 90.5px; } #bbpress-forums li.bbp-body ul.forum, #bbpress-forums li.bbp-body ul.topic { border-top: 1px solid #eeeeee; } #bbpress-forums ul.bbp-lead-topic, #bbpress-forums ul.bbp-topics, #bbpress-forums ul.bbp-forums, #bbpress-forums ul.bbp-replies, #bbpress-forums ul.bbp-search-results { border: 1px solid #eeeeee; } #bbpress-forums li.bbp-header, #bbpress-forums li.bbp-footer, .menu-card-item.highlight-menu-item { background: #fafbfd; } #bbpress-forums .topic .bbp-topic-meta a:hover, .bbp-forum-freshness a:hover, .bbp-topic-freshness a:hover, .bbp-header .bbp-reply-content a:hover, .bbp-topic-tags a:hover, .bbp-breadcrumb a:hover, .bbp-forums-list a:hover { color: #00bcdd; } div.bbp-reply-header, .bar-style-related-posts-list, .menu-card-item { border-color: #eeeeee; } #evcal_list .eventon_list_event .evcal_desc span.evcal_event_title, .eventon_events_list .evcal_event_subtitle { padding-bottom: 10px !important; } .eventon_events_list .eventon_list_event .evcal_desc, .evo_pop_body .evcal_desc, #page-content p.evcal_desc { padding-left: 100px !important; } .evcal_evdata_row { background: #fafbfd !important; } .eventon_events_list .eventon_list_event .event_description { background: #fafbfd !important; border-color: #eeeeee !important; } .bordr, #evcal_list .bordb { border-color: #eeeeee !important; } .evcal_evdata_row .evcal_evdata_cell h3 { margin-bottom: 10px !important; } /**** Be single portfolio - overflow images ****/ /* Optiopn Panel Css */ /********************************************** *** FORMS **********************************************/ .mc4wp-form input {height: 50px;} .wpcf7-form textarea { height: 100px; } .wpcf7-submit { height: 50px; } input[type="submit"] { border-radius: 2px; cursor: pointer; } input.wpcf7-form-control.wpcf7-submit.be-button { border: 2px solid; } .hbspt-form label { color: #fff; } .hbspt-form .input { padding-bottom: 15px; } #hsForm_1692b5bd-5639-41c0-9e31-af463bc55211 .actions input[type="submit"], #hsForm_4b36f79d-3c51-4938-bd29-6b08acce3c48 .actions input[type="submit"]{ background-color: #00bcdd; } .actions input[type="submit"] { background-color: #323840; } /********************************************** *** TEAM **********************************************/ .team-description { font-weight: bold; } .speaker-details-circle img { border-radius: 50%; } .speaker-social-icons { margin-bottom: 15px; } /********************************************** *** GENERAL **********************************************/ // .odsc__box-shadow { // box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.5); // } .flip-wrap { box-shadow: 0 2px 4px 0 rgba(0, 0, 0, 0.5); } .pricing-table li.pricing-button .be-button { border-radius: 2px; } .color-white { color: #fff; } span.top-bar-widgets { font-size: 105%; font-weight: bold; } .be-nav-link-effect-1 a::after { top: 140%; } .myTable { background-color:#fff;border-collapse:collapse; } .myTable th { background-color:#fff;color:white;width:50%; } .myTable td, .myTable th { padding:5px;border:1px solid #fff; } .menu-card-item-price { line-height: 12px !important; top: 50%; transform: translateY(-50%); width: 40%; } a#back-to-top { border-radius: 4px; border: solid #323840 2px; } @media (min-width: 60rem) { .ai-max-width { max-width: 50rem; margin: 0 auto !important; } } .icon-quote { transform: 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url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TjASc2CsTKlA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TjASc5CsTKlA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TjASc1CsTKlA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TjASc0CsTKlA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TjASc6CsQ.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xFIzIFKw.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xMIzIFKw.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xEIzIFKw.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xLIzIFKw.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xHIzIFKw.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xGIzIFKw.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1Mu51xIIzI.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, 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font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51S7ACc5CsTKlA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51S7ACc1CsTKlA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51S7ACc0CsTKlA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51S7ACc6CsQ.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic3CsTKlA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic-CsTKlA.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic2CsTKlA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic5CsTKlA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic1CsTKlA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic0CsTKlA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TzBic6CsQ.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc3CsTKlA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc-CsTKlA.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc2CsTKlA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc5CsTKlA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc1CsTKlA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc0CsTKlA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: italic; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOjCnqEu92Fr1Mu51TLBCc6CsQ.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxFIzIFKw.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxMIzIFKw.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxEIzIFKw.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxLIzIFKw.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxHIzIFKw.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxGIzIFKw.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 100; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOkCnqEu92Fr1MmgVxIIzI.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fCRc4EsA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fABc4EsA.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fCBc4EsA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fBxc4EsA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fCxc4EsA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fChc4EsA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 300; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmSU5fBBc4.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu72xKOzY.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu5mxKOzY.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu7mxKOzY.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu4WxKOzY.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu7WxKOzY.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu7GxKOzY.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 400; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOmCnqEu92Fr1Mu4mxK.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fCRc4EsA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fABc4EsA.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fCBc4EsA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fBxc4EsA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fCxc4EsA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fChc4EsA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 500; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmEU9fBBc4.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfCRc4EsA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F; } /* cyrillic */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfABc4EsA.woff2) format('woff2'); unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116; } /* greek-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfCBc4EsA.woff2) format('woff2'); unicode-range: U+1F00-1FFF; } /* greek */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfBxc4EsA.woff2) format('woff2'); unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF; } /* vietnamese */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfCxc4EsA.woff2) format('woff2'); unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB; } /* latin-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfChc4EsA.woff2) format('woff2'); unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF; } /* latin */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 700; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmWUlfBBc4.woff2) format('woff2'); unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; } /* cyrillic-ext */ @font-face { font-family: 'Roboto'; font-style: normal; font-weight: 900; font-display: swap; src: url(https://fonts.gstatic.com/s/roboto/v32/KFOlCnqEu92Fr1MmYUtfCRc4EsA.woff2) format('woff2'); unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, 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style=""><div class="post-content-wrap"><header class="post-header clearfix"><div class="post-thumb"><div class=""> <a href="https://odsc.com/blog/harnessing-machine-learning-on-big-data-with-pyspark-on-aws/" class=" thumb-wrap"><img width="640" height="300" src="https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs.jpg" class="attachment-blog-image size-blog-image wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs.jpg 640w, https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs-300x141.jpg 300w, https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs-107x50.jpg 107w, https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs-50x23.jpg 50w, https://odsc.com/wp-content/uploads/2023/08/Apache-Spark-for-big-data-needs-100x47.jpg 100w" sizes="(max-width: 640px) 100vw, 640px" /><div class="thumb-overlay"><div class="thumb-bg"><div class="thumb-title fadeIn animated"> <i class="portfolio-ovelay-icon"></i></div></div></div> </a></div></div></header><div class="article-details"><div class="clearfix post-title-section-wrap"><div class="left post-date-wrap"><div>Aug</div><div>02</div></div><div class="left post-title-section"><h2 class="post-title"> <a href="https://odsc.com/blog/harnessing-machine-learning-on-big-data-with-pyspark-on-aws/"> Harnessing Machine Learning on Big Data with PySpark on AWS </a></h2><nav class="post-nav meta-font secondary_text"><ul class="clearfix"><li class="post-meta post-author">Posted By : ODSC Team<span class="post-meta-sep">/</span></li><li class="post-meta post-comments"> <a href="https://odsc.com/blog/harnessing-machine-learning-on-big-data-with-pyspark-on-aws/#respond">0 comments</a> <span class="post-meta-sep">/</span></li><li class="post-meta post-category">Under :<a href="https://odsc.com/blog/category/machine-learning/" style= "color: ;" data-background-color = "" title="View all posts in Machine Learning"> Machine Learning</a></li></ul></nav></div></div><div class="post-details clearfix"><div class="post-content clearfix"><div class="tatsu-Hy-vqHJdin tatsu-section tatsu-bg-overlay tatsu-clearfix" data-title="" data-headerscheme="background--dark"><div class='tatsu-section-pad clearfix' data-padding='{"d":"0px 0px 0px 0px"}' data-padding-top='0px'><div class="tatsu-row-wrap tatsu-wrap tatsu-row-one-col tatsu-row-has-one-cols tatsu-medium-gutter tatsu-reg-cols tatsu-clearfix tatsu-rklD9r1_o3" ><div class="tatsu-row " ><div class="tatsu-column tatsu-bg-overlay tatsu-one-col tatsu-column-image-none tatsu-column-effect-none tatsu-rJPcSJuo3" data-parallax-speed="0" style=""><div class="tatsu-column-inner " ><div class="tatsu-column-pad-wrap"><div class="tatsu-column-pad" ><div class="tatsu-module tatsu-text-block-wrap tatsu-S1PjByOjn "><div class="tatsu-text-inner tatsu-align-center clearfix" ><style>.tatsu-S1PjByOjn.tatsu-text-block-wrap .tatsu-text-inner{width: 100%;text-align: left;}</style><p><em>Editor’s note: Suman Debnath is a speaker for ODSC APAC this August 22-23. Be sure to check out his talk, “<a href="https://odsc.com/speakers/build-classification-and-regression-models-with-spark-on-aws/?__hstc=39712252.e5f1e7f84a3336c7d408388648d039e0.1690232954495.1690982543269.1690985194304.20&__hssc=39712252.4.1690985194304&__hsfp=2180945085">Build Classification and Regression Models with Spark on AWS</a>,” there!</em></p><p>In the unceasingly dynamic arena of data science, discerning and applying the right instruments can significantly shape the outcomes of your machine learning initiatives. A cordial greeting to all data science enthusiasts! I consider myself fortunate to have the opportunity to speak at the upcoming <a href="https://opendatascience.com/tag/apac-2023/" target="_blank" rel="noopener">ODSC APAC</a> conference slated for the 22nd of August 2023. My presentation will focus on the development of classification and regression models using PySpark on AWS.</p><h2><b>Understanding the Session</b></h2><p>In this engaging and interactive session, we will delve into PySpark MLlib, an invaluable resource in the field of machine learning, and explore how various classification algorithms can be implemented using AWS Glue/EMR as our platform.</p><p>Our focus will be hands-on, with an emphasis on the practical application and understanding of essential machine learning concepts. Attendees will be introduced to a variety of machine learning algorithms, placing a spotlight on logistic regression, a potent supervised learning technique for solving binary classification problems.</p><p>But this session goes beyond just concepts and algorithms. We will also navigate through critical data preprocessing techniques, essential for creating effective machine learning models. By the session’s conclusion, participants will acquire skills to handle missing values, modify column data types, and divide their data into training and testing datasets. This hands-on experience will all take place within the versatile AWS Glue/EMR environment.</p><h2><b>What Will You Gain?</b></h2><p>This session is designed to help participants gain an in-depth understanding of:</p><ul><li aria-level="1">PySpark MLlib</li><li aria-level="1">Unsupervised learning techniques</li><li aria-level="1">Various types of classification algorithms</li><li aria-level="1">Implementation of logistic regression classifiers</li><li aria-level="1">Data preprocessing using PySpark on AWS using AWS Glue and Amazon EMR</li><li aria-level="1">Model building with PySpark on AWS</li></ul><p>If you’re a data engineer, data scientist, or machine learning enthusiast looking to get started with Machine Learning with Apache Spark on AWS, this session is perfect for you.</p><p>Now, let’s give you a taste of what’s in store (the GitHub code repository can be found<a href="https://github.com/debnsuma/data-engineering-spark-aws/blob/main/MachineLearning/ML_with_Spark.ipynb"> here</a>).</p><p>We selected a dataset comprising 20,057 dish names, each detailed with 680 columns that characterize the ingredient list, the nutritional content, and the dish’s category. Our collective objective here is to predict whether a dish is a dessert. This is a straightforward and mostly clear-cut question – most of us can likely classify a dish as a dessert or not simply by reading its name, which makes it an excellent candidate for a simple ML model.</p><p><b>Step 1: Importing the Necessary Libraries</b></p><p>The first step involves importing the necessary libraries, including PySpark SQL functions and types</p><pre>import pyspark.sql.functions as F import pyspark.sql.types as T from pyspark.ml.feature import Imputer, MinMaxScaler, VectorAssembler</pre><p><b>Step 2: Data Preprocessing and EDA (Exploratory Data Analysis)</b></p><p>We load the CSV dataset of food recipes using Spark’s read.csv function. The inferSchema parameter is set to True to infer the data types of the columns, and header is set to True to use the first row as headers.</p><pre># Loading the data dataset = 's3://fcc-spark-example/dataset/2023/recipes_dataset/epi_r.csv' food = ( spark .read .csv(dataset, inferSchema=True, header=True) ) # Sanitizing the column names def sanitize_column_name(name): answer = name for i, j in ((" ", "_"), ("-", "_"), ("/", "_"), ("&", "and")): answer = answer.replace(i, j) return "".join( [ char for char in answer if char.isalpha() or char.isdigit() or char == "_" ] ) food = food.toDF(*[sanitize_column_name(name) for name in food.columns])</pre><p>This part of the script sanitises the column names by replacing spaces, dashes, slashes, and ampersands with underscores. It also removes non-alphanumeric characters.</p><pre># Filtering the data food = food.where( ( F.col("cakeweek").isin([0.0, 1.0]) | F.col("cakeweek").isNull() ) & ( F.col("wasteless").isin([0.0, 1.0]) | F.col("wasteless").isNull() ) )</pre><p>Here we filter the data to keep only the rows where the cakeweek and wasteless columns have values of 0.0 or 1.0, or are null.</p><pre># Defining identifier, continuous, target, and binary columns IDENTIFIERS = ["title"] CONTINUOUS_COLUMNS = [ "rating", "calories", "protein", "fat", "sodium", ] TARGET_COLUMN = ["dessert"] BINARY_COLUMNS = [ x for x in food.columns if x not in CONTINUOUS_COLUMNS and x not in TARGET_COLUMN and x not in IDENTIFIERS ]</pre><p>In this section, we define which columns are identifiers, continuous variables, target variables, and binary variables.</p><pre># Handling missing values food = food.dropna( how="all", subset=[x for x in food.columns if x not in IDENTIFIERS], ) food = food.dropna(subset=TARGET_COLUMN) food = food.fillna(0.0, subset=BINARY_COLUMNS)</pre><p>We handle missing values by dropping rows that have all nulls (excluding identifier columns), dropping rows with nulls in the target column, and filling nulls in binary columns with 0.0.</p><pre># Converting string numbers to float and capping continuous variables from typing import Optional @F.udf(T.BooleanType()) def is_a_number(value: Optional[str]) -> bool: if not value: return True try: _ = float(value) except ValueError: return False return True for column in ["rating", "calories"]: food = food.where(is_a_number(F.col(column))) food = food.withColumn(column, F.col(column).cast(T.DoubleType())) maximum = { "calories": 3203.0, "protein": 173.0, "fat": 207.0, "sodium": 5661.0, } for k, v in maximum.items(): food = food.withColumn( k, F.when(F.isnull(F.col(k)), F.col(k)).otherwise( F.least(F.col(k), F.lit(v)) ), )</pre><p>In this part, we create a user-defined function is_a_number to check if a string can be converted to a float. We use this function to filter out non-numeric values in the “rating” and “calories” columns and then cast them to double type.</p><p>We then cap the values of continuous variables “calories”, “protein”, “fat”, and “sodium” at specified maximums to handle possible outliers.</p><pre># Calculating the sum of each binary column inst_sum_of_binary_columns = [ F.sum(F.col(x)).alias(x) for x in BINARY_COLUMNS ] # Selecting the sums of binary columns and converting the result to a dictionary sum_of_binary_columns = ( food.select(*inst_sum_of_binary_columns).head().asDict() ) # Counting the total number of rows num_rows = food.count() # Identifying the rare features too_rare_features = [ k for k, v in sum_of_binary_columns.items() if v < 10 or v > (num_rows - 10) ] # Excluding the rare features from the binary columns BINARY_COLUMNS = list(set(BINARY_COLUMNS) - set(too_rare_features))</pre><p>Next, we calculate the sum of each binary column and convert the result to a dictionary. Then, we identify the “rare” features—those that are true less than 10 times or true in all but less than 10 instances—and remove them from our binary columns.</p><pre># Creating new features food = food.withColumn( "protein_ratio", F.col("protein") * 4 / F.col("calories") ).withColumn( "fat_ratio", F.col("fat") * 9 / F.col("calories") ) # Handling missing values in the new features food = food.fillna(0.0, subset=["protein_ratio", "fat_ratio"]) # Adding new features to the continuous columns CONTINUOUS_COLUMNS += ["protein_ratio", "fat_ratio"]</pre><p>Here, we create new features “protein_ratio” and “fat_ratio” that represent the ratio of protein and fat to calories, respectively. We fill missing values in these new features with 0.0 and add them to our continuous columns.</p><pre># Imputing missing values in the continuous columns OLD_COLS = ["calories", "protein", "fat", "sodium"] NEW_COLS = ["calories_i", "protein_i", "fat_i", "sodium_i"] imputer = Imputer( strategy="mean", inputCols=OLD_COLS, outputCols=NEW_COLS, ) imputer_model = imputer.fit(food) # Updating the continuous columns CONTINUOUS_COLUMNS = ( list(set(CONTINUOUS_COLUMNS) - set(OLD_COLS)) + NEW_COLS ) # Applying the imputer model to the data food = imputer_model.transform(food)</pre><p>In this section, we impute missing values in the “calories”, “protein”, “fat”, and “sodium” columns with their mean values using Spark’s Imputer. We then update our list of continuous columns to include the imputed ones.</p><pre># Assembling continuous features into a single vector CONTINUOUS_NB = [x for x in CONTINUOUS_COLUMNS if "ratio" not in x] continuous_assembler = VectorAssembler( inputCols=CONTINUOUS_NB, outputCol="continuous" ) food_features = continuous_assembler.transform(food)</pre><p>Next, we use the VectorAssembler to assemble our continuous features into a single vector column “continuous.”</p><pre># Scaling the continuous features continuous_scaler = MinMaxScaler( inputCol="continuous", outputCol="continuous_scaled", ) food_features = continuous_scaler.fit(food_features).transform( food_features )</pre><p>Finally, we scale the continuous features to the range [0, 1] using the MinMaxScaler, fitting it to our data and transforming our data. At this point, our dataset is ready for machine learning tasks!</p><p>Now we are ready to perform the Machine Learning training job.</p><p><b>Step 3: Train, Test, and Evaluate Model</b></p><p>Once the data is processed and transformed, we can split it into a training set and a testing set. After training the model, we can then evaluate its performance using various metrics. In this section, we build an ML pipeline with the estimators we used for our dessert prediction feature preparation program and add the modeling step in the mix.</p><pre>from pyspark.ml import Pipeline import pyspark.ml.feature as MF imputer = MF.Imputer( strategy="mean", inputCols=["calories", "protein", "fat", "sodium"], outputCols=["calories_i", "protein_i", "fat_i", "sodium_i"], ) continuous_assembler = MF.VectorAssembler( inputCols=["rating", "calories_i", "protein_i", "fat_i", "sodium_i"], outputCol="continuous", ) continuous_scaler = MF.MinMaxScaler( inputCol="continuous", outputCol="continuous_scaled", ) food_pipeline = Pipeline( stages=[imputer, continuous_assembler, continuous_scaler] )</pre><p>We can assemble the final dataset with the vector column type.</p><pre>preml_assembler = MF.VectorAssembler( inputCols=BINARY_COLUMNS + ["continuous_scaled"] + ["protein_ratio", "fat_ratio"], outputCol="features", ) food_pipeline.setStages( [imputer, continuous_assembler, continuous_scaler, preml_assembler] ) food_pipeline_model = food_pipeline.fit(food) food_features = food_pipeline_model.transform(food)</pre><p>Our data frame is ready for machine learning! We have a number of records, each with</p><ul><li aria-level="1">A target (or label ) column, dessert, containing a binary input (1.0 if the recipe is a dessert, 0.0 otherwise)</li><li aria-level="1">A vector of features, called features, containing all the information we want to train our machine learning model with</li></ul><p>We can display the predicted outcomes:</p><pre>food_features.select("title", "dessert", "features").show(30, truncate=30)</pre><p>Let’s now train an ML model using a LogisticRegression classifier:</p><pre>from pyspark.ml.classification import LogisticRegression lr = LogisticRegression( featuresCol="features", labelCol="dessert", predictionCol="prediction" ) food_pipeline.setStages( [ imputer, continuous_assembler, continuous_scaler, preml_assembler, lr, ] ) # Splitting our data frame for training and testing train, test = food.randomSplit([0.7, 0.3], 13) train.cache() food_pipeline_model = food_pipeline.fit(train) results = food_pipeline_model.transform(test) Let us now evaluate the model, and look at the confusion matrix results.select("prediction", "rawPrediction", "probability").show(3, False) # Creating a confusion matrix for our model using pivot() results.groupby("dessert").pivot("prediction").count().show() Finally, we can calculate the precision and recall of our model: lr_model = food_pipeline_model.stages[-1] metrics = lr_model.evaluate(results.select("title", "dessert", "features")) print(f"Model precision: {metrics.precisionByLabel[1]}") print(f"Model recall: {metrics.recallByLabel[1]}")</pre><p>Please note that the full script has been simplified for the purpose of this tutorial. For a comprehensive understanding of the practical applications, including a detailed code walkthrough from data preparation to model deployment, please join us at the <a href="https://odsc.com/apac/?__hstc=39712252.e5f1e7f84a3336c7d408388648d039e0.1690232954495.1690982543269.1690985194304.20&__hssc=39712252.4.1690985194304&__hsfp=2180945085">ODSC APAC conference 2023</a>.</p><p>This brief tutorial has given you a glimpse of what’s to be covered in the ODSC session. By attending the session, you’ll get to explore these topics more profoundly and understand the intricacies of PySpark MLlib. The primary objective is to empower data science enthusiasts and professionals to harness the full potential of Spark MLlib in their machine learning projects.</p><p>Remember, the key to mastering any skill lies in consistent learning and practical implementation. So, buckle up and get ready to <a href="https://odsc.com/speakers/build-classification-and-regression-models-with-spark-on-aws/?__hstc=39712252.e5f1e7f84a3336c7d408388648d039e0.1690232954495.1690982543269.1690985194304.20&__hssc=39712252.4.1690985194304&__hsfp=2180945085">dive deeper into the fascinating world of machine learning with Spark on AWS</a> at the ODSC conference. Looking forward to seeing you there!</p><p><strong>About the Author:</strong></p><p><img decoding="async" class="alignleft lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://odsc.com/wp-content/uploads/2023/07/Suman-Debnath-Cropped.jpg" /><noscript><img decoding="async" class="alignleft lazyload" src="https://odsc.com/wp-content/uploads/2023/07/Suman-Debnath-Cropped.jpg" /></noscript><a href="https://www.linkedin.com/in/suman-d/">Suman Debnath</a> is a Principal Developer Advocate(Data Engineering) at Amazon Web Services, primarily focusing on Data Engineering, Data Analysis and Machine Learning. He is passionate about large scale distributed systems and is a vivid fan of Python. His background is in storage performance and tool development, where he has developed various performance benchmarking and monitoring tools.</p></div></div></div></div><div class = "tatsu-column-bg-image-wrap"><div class = "tatsu-column-bg-image" ></div></div><div class="tatsu-overlay tatsu-column-overlay tatsu-animate-none" ></div></div><style>.tatsu-row > .tatsu-rJPcSJuo3.tatsu-column{width: 100%;}.tatsu-rJPcSJuo3.tatsu-column > .tatsu-column-inner > .tatsu-column-overlay{mix-blend-mode: normal;}.tatsu-rJPcSJuo3 > .tatsu-column-inner > .tatsu-top-divider{z-index: 9999;}.tatsu-rJPcSJuo3 > .tatsu-column-inner > .tatsu-bottom-divider{z-index: 9999;}.tatsu-rJPcSJuo3 > .tatsu-column-inner > .tatsu-left-divider{z-index: 9999;}.tatsu-rJPcSJuo3 > .tatsu-column-inner > .tatsu-right-divider{z-index: 9999;}@media only screen and (max-width:1377px) {.tatsu-row > .tatsu-rJPcSJuo3.tatsu-column{width: 100%;}}@media only screen and (min-width:768px) and (max-width: 1024px) {.tatsu-row > .tatsu-rJPcSJuo3.tatsu-column{width: 100%;}}@media only screen and (max-width: 767px) {.tatsu-row > .tatsu-rJPcSJuo3.tatsu-column{width: 100%;}}</style></div></div></div></div><div class="tatsu-section-background-wrap"><div class = "tatsu-section-background" ></div></div><div class="tatsu-overlay tatsu-section-overlay"></div><style>.tatsu-Hy-vqHJdin .tatsu-section-offset-wrap{transform: translateY(-0px);}.tatsu-Hy-vqHJdin > .tatsu-bottom-divider{z-index: 9999;}.tatsu-Hy-vqHJdin > .tatsu-top-divider{z-index: 9999;}.tatsu-Hy-vqHJdin .tatsu-section-overlay{mix-blend-mode: normal;}</style></div></div></div></div><div class="clearfix"></div></div></div><div class="blog-separator clearfix"><hr class="separator" /></div></article><article id="post-94443" class="element not-wide blog-post clearfix post-94443 post type-post status-publish format-standard has-post-thumbnail hentry category-machine-learning tag-europe-2023"><div class="element-inner" style=""><div class="post-content-wrap"><header class="post-header clearfix"><div class="post-thumb"><div class=""> <a href="https://odsc.com/blog/getting-up-to-speed-on-real-time-machine-learning-with-spark-and-sbert/" class=" thumb-wrap"><img width="640" height="300" src="https://odsc.com/wp-content/uploads/2023/06/DBTop.png" class="attachment-blog-image size-blog-image wp-post-image" alt="" decoding="async" srcset="https://odsc.com/wp-content/uploads/2023/06/DBTop.png 640w, https://odsc.com/wp-content/uploads/2023/06/DBTop-300x141.png 300w, https://odsc.com/wp-content/uploads/2023/06/DBTop-107x50.png 107w, https://odsc.com/wp-content/uploads/2023/06/DBTop-50x23.png 50w, https://odsc.com/wp-content/uploads/2023/06/DBTop-100x47.png 100w" sizes="(max-width: 640px) 100vw, 640px" /><div class="thumb-overlay"><div class="thumb-bg"><div class="thumb-title fadeIn animated"> <i class="portfolio-ovelay-icon"></i></div></div></div> </a></div></div></header><div class="article-details"><div class="clearfix post-title-section-wrap"><div class="left post-date-wrap"><div>Jun</div><div>02</div></div><div class="left post-title-section"><h2 class="post-title"> <a href="https://odsc.com/blog/getting-up-to-speed-on-real-time-machine-learning-with-spark-and-sbert/"> Getting Up to Speed on Real-Time Machine Learning with Spark and SBERT </a></h2><nav class="post-nav meta-font secondary_text"><ul class="clearfix"><li class="post-meta post-author">Posted By : ODSC Team<span class="post-meta-sep">/</span></li><li class="post-meta post-comments"> <a href="https://odsc.com/blog/getting-up-to-speed-on-real-time-machine-learning-with-spark-and-sbert/#respond">0 comments</a> <span class="post-meta-sep">/</span></li><li class="post-meta post-category">Under :<a href="https://odsc.com/blog/category/machine-learning/" style= "color: ;" data-background-color = "" title="View all posts in Machine Learning"> Machine Learning</a></li></ul></nav></div></div><div class="post-details clearfix"><div class="post-content clearfix"><div class="tatsu-rJ-MA2RD8n tatsu-section tatsu-bg-overlay tatsu-clearfix" data-title="" data-headerscheme="background--dark"><div class='tatsu-section-pad clearfix' data-padding='{"d":"0px 0px 90px 0px"}' data-padding-top='0px'><div class="tatsu-row-wrap tatsu-wrap tatsu-row-one-col tatsu-row-has-one-cols tatsu-medium-gutter tatsu-reg-cols tatsu-clearfix tatsu-H1gzC3ADL2" ><div class="tatsu-row " ><div class="tatsu-column tatsu-bg-overlay tatsu-one-col tatsu-column-image-none tatsu-column-effect-none tatsu-r1fR3RDL3" data-parallax-speed="0" style=""><div class="tatsu-column-inner " ><div class="tatsu-column-pad-wrap"><div class="tatsu-column-pad" ><div class="tatsu-module tatsu-text-block-wrap tatsu-Byj0nCwU3 "><div class="tatsu-text-inner tatsu-align-center clearfix" ><style>.tatsu-Byj0nCwU3.tatsu-text-block-wrap .tatsu-text-inner{width: 100%;text-align: left;}</style><p><em>Editor’s note: Dillon Bostwick and Avinash Sooriyarachchi are speakers for ODSC Europe 2023 this June 14th-15th. Be sure to check out their talk, “<a href="https://odsc.com/speakers/getting-up-to-speed-on-real-time-machine-learning/?__hstc=39712252.e5f1e7f84a3336c7d408388648d039e0.1684179919339.1685728351965.1685736572997.54&__hssc=39712252.13.1685736572997&__hsfp=1136147948">Getting Up to Speed on Real-Time Machine Learning</a>,” there!</em></p><p>The benefits of real-time machine learning are becoming increasingly apparent. Digital native companies have long proven that use cases like fraud detection, recommendation systems, and dynamic pricing benefit from lower processing latencies. In a recent <a href="https://dl.acm.org/doi/10.1145/3292500.3330744">KDD paper</a>, Booking.com found that even a 30% improvement in model serving latency caused a .5% decrease in user conversion, a significant cost to their business.</p><p>While real-time machine learning presents many opportunities, few teams successfully serve production-grade machine learning models in real-time, and most struggle to deliver the feature freshness needed for low-latency inference. This is due to a deep disconnect between data engineering and data science practices. Historically, our space has perceived streaming as a complex technology reserved for experienced data engineers with a deep understanding of incremental event processing. But now, modern streaming platforms make it much easier for anyone to build reliable streaming pipelines, regardless of their streaming background.</p><p>Anomaly detection, including fraud detection and network intrusion monitoring, particularly exemplifies the challenges of real-time machine learning. In these use cases, the ability to detect various drifts in the data has an immediate impact on business risk. Models must be continuously retrained or fine-tuned to adjust to various forms of drift very quickly, and predictions generally must be made at low latency to provide an optimal customer experience. In adversarial scenarios like e-commerce fraud, failure to adjust the model to new attack patterns can result in incorrectly predicting the <a href="https://www.verifi.com/wp-content/uploads/2022/04/Verifi_Global_Fraud_and_Payments_report_2022.pdf">3% of transactions that are fraudulent</a>.</p><h2>Using Embeddings to Detect Anomalies</h2><p><a href="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png"><img decoding="async" class="aligncenter size-full wp-image-42909 lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png" sizes="(max-width: 791px) 100vw, 791px" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png 791w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-300x88.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-768x226.png 768w" alt="" width="791" height="233" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png 791w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-300x88.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-768x226.png 768w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-42909 lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png" sizes="(max-width: 791px) 100vw, 791px" srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png 791w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-300x88.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-768x226.png 768w" alt="" width="791" height="233" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB1.png 791w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-300x88.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB1-768x226.png 768w" /></noscript></a></p><p><i>Figure 1: Using a trained deep neural network, it is possible to convert unstructured data to numeric representations, i.e., embeddings</i></p><p><a href="https://jalammar.github.io/illustrated-word2vec/">Embeddings</a> are numerical representations generated from unstructured data like images, text, and audio, and greatly influence machine learning approaches for handling such data. While embeddings have become a popular way to represent unstructured data, they can also be generated for categorical and numeric variables in tabular datasets. This allows multi-modal unstructured and structured data to be embedded in a common embedding space, where similar observations are located close to each other and dissimilar ones are far apart.</p><p><a href="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png"><img decoding="async" class="aligncenter size-full wp-image-42910 lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png" sizes="(max-width: 796px) 100vw, 796px" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png 796w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-300x128.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-768x327.png 768w" alt="" width="796" height="339" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png 796w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-300x128.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-768x327.png 768w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-42910 lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png" sizes="(max-width: 796px) 100vw, 796px" srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png 796w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-300x128.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-768x327.png 768w" alt="" width="796" height="339" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB2.png 796w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-300x128.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB2-768x327.png 768w" /></noscript></a></p><p><i>Figure 2: In an embedding space, embeddings corresponding to similar items are located closer and dissimilar ones are far apart. [</i><a href="https://www.databricks.com/blog/2022/03/01/building-a-similarity-based-image-recommendation-system-for-e-commerce.html"><i>source</i></a><i>]</i></p><p>Multi-modal embeddings, such as those produced by<a href="https://imagebind.metademolab.com/"> Meta’s ImageBind model</a>, are an emerging trend in which neural network architectures map data from diverse modalities into a shared embedding space. This approach offers a fresh perspective on anomaly detection problems. For instance, outliers can be detected within an embedding space involving image, text, and tabular data.</p><p>With this in mind, we can use a real-time event streaming framework to monitor <i>embedding drift</i> across features to determine underlying concept drift or detect anomalous events. To demonstrate the power of combining streaming with embeddings, we will present an example using a combination of <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html">Apache Spark Structured Streaming</a> and <a href="https://huggingface.co/docs/transformers/model_doc/bert">BERT transformers</a> to detect real-time embedding drift and find hidden anomalies faster and more accurately. This is an exciting combination of two powerful technologies traditionally used by disparate personas in an organization. This can be further applied to multi-modal embeddings to find novel patterns throughout an organization’s data that previously would have gone unnoticed.</p><h2>Using Spark Structured Streaming to Process Embeddings in Real-Time</h2><p>We will rely heavily on Apache Spark Structured Streaming’s high-level semantics for stateful processing. Spark supports 3 different kinds of <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#window-operations-on-event-time">window operations over event time</a>:</p><p><a href="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png"><img decoding="async" class="aligncenter size-full wp-image-42911 lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png" sizes="(max-width: 789px) 100vw, 789px" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png 789w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-300x178.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-768x457.png 768w" alt="" width="789" height="469" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png 789w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-300x178.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-768x457.png 768w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-42911 lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png" sizes="(max-width: 789px) 100vw, 789px" srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png 789w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-300x178.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-768x457.png 768w" alt="" width="789" height="469" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB3.png 789w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-300x178.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB3-768x457.png 768w" /></noscript></a><br /> We can combine one of these event-time window functions with <a href="https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.GroupedData.applyInPandas.html"><b>applyInPandas()</b></a>, a newer Spark feature added in version 3.2.1 (October 2022). This function makes it easy to define custom aggregation functions in Python. When combined with event-time windows, analyzing the embeddings in real-time becomes much more feasible.</p><p>When combining these capabilities, we can see how anomalous an event is relative to other events that happened shortly before or even shortly <i>after</i> the event occurred due to Structured Steraming’s stateful processing capabilities. This means we no longer need a static ground truth to determine anomalous behavior via unsupervised models with no training phase. Spark Structured Streaming provides the semantics and guarantees needed to do this, and the net result is a more accurate, more robust fraud detection framework that can reduce losses by millions of dollars.</p><h2>Anomaly Detection with Real-Time Embeddings</h2><p>Imagine we have some text, images, or tabular data arriving at a <a href="https://delta.io/">Delta Lake</a> table every few seconds. Even though the data changes every second, we can perform a consistent static query on the current state of the table with Databricks. In this example, we will use text for simplicity, but we can easily extend this to multi-modal embeddings:</p><p><a href="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png"><img decoding="async" class="aligncenter size-full wp-image-42912 lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png" sizes="(max-width: 1176px) 100vw, 1176px" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png 1176w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-300x109.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-1024x372.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-768x279.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-810x294.png 810w" alt="" width="1176" height="427" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png 1176w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-300x109.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-1024x372.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-768x279.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-810x294.png 810w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-42912 lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png" sizes="(max-width: 1176px) 100vw, 1176px" srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png 1176w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-300x109.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-1024x372.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-768x279.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-810x294.png 810w" alt="" width="1176" height="427" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB4.png 1176w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-300x109.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-1024x372.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-768x279.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB4-810x294.png 810w" /></noscript></a></p><p>First, we want to use Spark’s <a href="https://www.databricks.com/blog/2017/10/30/introducing-vectorized-udfs-for-pyspark.html">Pandas UDF</a> to apply <a href="https://www.sbert.net/">SBERT</a>, a state-of-the-art sentence encoder, on the real-time stream of text. Here, the Pandas UDF simplifies the hand-off between complex distributed event streaming and locally scoped Python functions.</p><p>Once we are working with the Pandas series, we simply grab the SBERT model from the <a href="https://huggingface.co/models">HuggingFace Model Hub</a>:</p><pre>@pandas_udf(ArrayType(DoubleType())) def get_embeddings(texts: pd.Series) -> pd.Series: model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') embeddings = model.encode(texts.to_numpy()) return pd.Series(embeddings.tolist())</pre><p>Next, we’ll use Spark Structured Streaming to read the raw data and create embeddings from the text column. If using <a href="https://docs.databricks.com/runtime/mlruntime.html">Databricks</a>, this is all we need to do to get up and running with a real-time stream of embeddings:</p><pre>embedding_stream = spark.readStream .table('raw_records') .withColumn('embeddings', get_embeddings('text'))</pre><p>Now, we want to define a function that receives and returns a Pandas dataframe (<b>pdf</b>). Spark provides this abstraction layer to make it easy for a data engineer to pass this interface to an ML engineer to implement. In this function, we determine the Euclidean mean of all the embeddings in the given dataframe, then calculate the Euclidean distance to determine how far each embedding is from that mean. The ML engineer could use this interface to further experiment with KMeans Clustering or Nearest Neighbor search, or try different distance methods like <i>c</i>osine similarity:</p><pre>def get_dists_to_mean(pdf): # Stack embeddings as a 2D array embeddings = np.vstack(pdf["embeddings"].values) # Perform K-means clustering kmeans = KMeans(n_clusters=K, random_state=42) cluster_labels = kmeans.fit_predict(embeddings) cluster_centers = kmeans.cluster_centers_ dist_to_cluster_mean = [] for i in range(len(embeddings)): # Calculate distance to the mean of the cluster cluster_idx = cluster_labels[i] dist_to_cluster_mean.append( pairwise_distances( [embeddings[i]], [cluster_centers[cluster_idx]]).min()) pdf["kmeans_dist"] = dist_to_cluster_mean return pdf</pre><p>Finally, we will use Spark’s built-in support for <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#types-of-time-windows">“sliding” windows </a>over a stream to apply the above function to all embeddings that happened temporally close to one another. The sliding window allows us to ensure that an event’s embedding is compared to events that happened recently earlier, but also again in the future to events that happened after it. Intuitively, we may stop regarding an event as anomalous if we start to ingest events that are in similar embedding space a few minutes later. Or, events could become anomalous if future embeddings begin to drift within the subsequent sliding window. So with Spark’s sliding windows, the fraud score of a single event could be different across multiple windows. As with most streaming applications, we need to tune these time intervals to find <a href="https://www.youtube.com/watch?v=zMbeP6Zc_Xk">the right balance between accuracy and latency</a>.</p><p>Once we create our sliding window, we can simply use <a href="https://spark.apache.org/docs/3.2.1/api/python/reference/api/pyspark.sql.GroupedData.applyInPandas.html"><b>applyInPandas</b></a> to apply the distance calculation function to all the events in the given window. Again, this makes the handoff between the developer building the streaming pipeline and the ML engineers much easier:</p><pre>from pyspark.sql.functions import * WINDOW_LENGTH = '10 minutes' # How long the window should wait for data WINDOW_SLIDE = '5 minutes' # How much overlap between windows ranked_stream = embedding_stream .withWatermark('ts', '5 minutes') .groupBy(window(embedding_stream['ts'], WINDOW_LENGTH, WINDOW_SLIDE)) .applyInPandas(get_dists_to_mean, 'text string, ts timestamp, embeddings array<double>, kmeans_dist double' )</pre><p>Even though the stream is continuous, we can use Databrick’s <i>display()</i> function to peek at the stream and sort by the current results. We can clearly see that unusual text has a much higher <b>kmeans_dist</b>, which we could use as a <i>fraud score</i>. Spark determined that these happened temporally close to each other, and created a single window over the data. To learn more about the mechanics of stateful functions in Spark Structured Streaming, see <a href="https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#window-operations-on-event-time">here</a>.</p><p><a href="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png"><img decoding="async" class="aligncenter size-full wp-image-42913 lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png" sizes="(max-width: 1111px) 100vw, 1111px" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png 1111w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-300x106.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-1024x360.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-768x270.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-810x285.png 810w" alt="" width="1111" height="391" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png 1111w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-300x106.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-1024x360.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-768x270.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-810x285.png 810w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-42913 lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png" sizes="(max-width: 1111px) 100vw, 1111px" srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png 1111w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-300x106.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-1024x360.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-768x270.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-810x285.png 810w" alt="" width="1111" height="391" data-src="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png" data-srcset="https://opendatascience.com/wp-content/uploads/2023/05/DB5.png 1111w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-300x106.png 300w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-1024x360.png 1024w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-768x270.png 768w, https://opendatascience.com/wp-content/uploads/2023/05/DB5-810x285.png 810w" /></noscript></a></p><h2>Conclusion</h2><p>While this is a simple example, it demonstrates the potential of combining low-latency event streaming and recent innovations in the application of AI. We can further extend this to use multi-modal embeddings and find hidden anomalies across all our organization’s data.</p><p>If you’re interested in learning more about real-time machine learning, join us for our talk “<a href="https://odsc.com/speakers/getting-up-to-speed-on-real-time-machine-learning/?__hstc=39712252.e5f1e7f84a3336c7d408388648d039e0.1684179919339.1685728351965.1685736572997.54&__hssc=39712252.13.1685736572997&__hsfp=1136147948">Getting Up to Speed on Real-Time Machine Learning</a>” at ODSC London (virtual viewing is available). In the talk you will learn:</p><ul><li aria-level="1">Important patterns for real-time model inference</li><li aria-level="1">How to prioritize the most common real-time ML use cases in your business</li><li aria-level="1">How to evaluate streaming tools, and why streaming is valuable at any latency</li><li aria-level="1">Operational concerns like monitoring, drift detection, and feature stores</li></ul><p>About the authors/ODSC Europe 2023 speakers:</p><div class="two-third column-block no-background" data-animation=""><div class="be-custom-column-inner"><p><img decoding="async" class="alignleft ls-is-cached lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://odsc.com/wp-content/uploads/2023/05/Dillon-Bostwick-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/05/Dillon-Bostwick-Cropped.png" /><noscript><img decoding="async" class="alignleft ls-is-cached lazyloaded lazyload" src="https://odsc.com/wp-content/uploads/2023/05/Dillon-Bostwick-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/05/Dillon-Bostwick-Cropped.png" /></noscript><a href="https://www.linkedin.com/in/dillon-bostwick/">Dillon Bostwick</a> is a Solutions Architect at Databricks, where he’s spent the last five years advising customers ranging from startups to Fortune 500 enterprises. He currently helps lead a team of field ambassadors for streaming products and is interested in improving industry awareness of effective streaming patterns for data integration and production machine learning. He previously worked as a product engineer in infrastructure automation.</p></div><div class="be-custom-column-inner"><div class="two-third column-block no-background" data-animation=""><div class="be-custom-column-inner"><p><img decoding="async" class="alignleft ls-is-cached lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://odsc.com/wp-content/uploads/2023/05/Avinash-Sooriyarachchi-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/05/Avinash-Sooriyarachchi-Cropped.png" /><noscript><img decoding="async" class="alignleft ls-is-cached lazyloaded lazyload" src="https://odsc.com/wp-content/uploads/2023/05/Avinash-Sooriyarachchi-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/05/Avinash-Sooriyarachchi-Cropped.png" /></noscript><a href="https://www.linkedin.com/in/avinash-sooriyarachchi-104b45100/">Avinash Sooriyarachchi</a> is a Senior Solutions Architect at Databricks. His current work involves working with large Retail and Consumer Packaged Goods organizations across the United States and enabling them to build Machine Learning based systems. His specific interests include streaming machine learning systems and building applications leveraging foundation models. Avi holds a Master’s degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania.</p></div></div></div></div></div></div></div></div><div class = "tatsu-column-bg-image-wrap"><div class = "tatsu-column-bg-image" ></div></div><div class="tatsu-overlay tatsu-column-overlay tatsu-animate-none" ></div></div><style>.tatsu-row > .tatsu-r1fR3RDL3.tatsu-column{width: 100%;}.tatsu-r1fR3RDL3.tatsu-column > .tatsu-column-inner > .tatsu-column-overlay{mix-blend-mode: normal;}.tatsu-r1fR3RDL3 > .tatsu-column-inner > .tatsu-top-divider{z-index: 9999;}.tatsu-r1fR3RDL3 > .tatsu-column-inner > .tatsu-bottom-divider{z-index: 9999;}.tatsu-r1fR3RDL3 > .tatsu-column-inner > .tatsu-left-divider{z-index: 9999;}.tatsu-r1fR3RDL3 > .tatsu-column-inner > .tatsu-right-divider{z-index: 9999;}@media only screen and (max-width:1377px) {.tatsu-row > .tatsu-r1fR3RDL3.tatsu-column{width: 100%;}}@media only screen and (min-width:768px) and (max-width: 1024px) {.tatsu-row > .tatsu-r1fR3RDL3.tatsu-column{width: 100%;}}@media only screen and (max-width: 767px) {.tatsu-row > .tatsu-r1fR3RDL3.tatsu-column{width: 100%;}}</style></div></div></div></div><div class="tatsu-section-background-wrap"><div class = "tatsu-section-background" ></div></div><div class="tatsu-overlay tatsu-section-overlay"></div><style>.tatsu-rJ-MA2RD8n .tatsu-section-pad{padding: 0px 0px 90px 0px;}.tatsu-rJ-MA2RD8n .tatsu-section-offset-wrap{transform: translateY(-0px);}.tatsu-rJ-MA2RD8n > .tatsu-bottom-divider{z-index: 9999;}.tatsu-rJ-MA2RD8n > .tatsu-top-divider{z-index: 9999;}.tatsu-rJ-MA2RD8n .tatsu-section-overlay{mix-blend-mode: normal;}</style></div></div></div></div><div class="clearfix"></div></div></div><div class="blog-separator clearfix"><hr class="separator" /></div></article><article id="post-92938" class="element not-wide blog-post clearfix post-92938 post type-post status-publish format-standard has-post-thumbnail hentry category-machine-learning"><div class="element-inner" style=""><div class="post-content-wrap"><header class="post-header clearfix"><div class="post-thumb"><div class=""> <a href="https://odsc.com/blog/5-concerns-for-ml-safety-in-the-era-of-llms-and-generative-ai/" class=" thumb-wrap"><img width="640" height="300" src="https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361.jpg" class="attachment-blog-image size-blog-image wp-post-image" alt="" decoding="async" srcset="https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361.jpg 640w, https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361-300x141.jpg 300w, https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361-107x50.jpg 107w, https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361-50x23.jpg 50w, https://odsc.com/wp-content/uploads/2023/04/shutterstock_1152832361-100x47.jpg 100w" sizes="(max-width: 640px) 100vw, 640px" /><div class="thumb-overlay"><div class="thumb-bg"><div class="thumb-title fadeIn animated"> <i class="portfolio-ovelay-icon"></i></div></div></div> </a></div></div></header><div class="article-details"><div class="clearfix post-title-section-wrap"><div class="left post-date-wrap"><div>Apr</div><div>25</div></div><div class="left post-title-section"><h2 class="post-title"> <a href="https://odsc.com/blog/5-concerns-for-ml-safety-in-the-era-of-llms-and-generative-ai/"> 5 Concerns for ML Safety in the Era of LLMs and Generative AI </a></h2><nav class="post-nav meta-font secondary_text"><ul class="clearfix"><li class="post-meta post-author">Posted By : ODSC Team<span class="post-meta-sep">/</span></li><li class="post-meta post-comments"> <a href="https://odsc.com/blog/5-concerns-for-ml-safety-in-the-era-of-llms-and-generative-ai/#respond">0 comments</a> <span class="post-meta-sep">/</span></li><li class="post-meta post-category">Under :<a href="https://odsc.com/blog/category/machine-learning/" style= "color: ;" data-background-color = "" title="View all posts in Machine Learning"> Machine Learning</a></li></ul></nav></div></div><div class="post-details clearfix"><div class="post-content clearfix"><div class="tatsu-S1-FBM9rXn tatsu-section tatsu-bg-overlay tatsu-clearfix" data-title="" data-headerscheme="background--dark"><div class='tatsu-section-pad clearfix' data-padding='{"d":"13px 0px 0px 0px"}' data-padding-top='13px'><div class="tatsu-row-wrap tatsu-wrap tatsu-row-one-col tatsu-row-has-one-cols tatsu-medium-gutter tatsu-reg-cols tatsu-clearfix tatsu-r1gYBzqB7h" ><div class="tatsu-row " ><div class="tatsu-column tatsu-bg-overlay tatsu-one-col tatsu-column-image-none tatsu-column-effect-none tatsu-HkKrzcrm3" data-parallax-speed="0" style=""><div class="tatsu-column-inner " ><div class="tatsu-column-pad-wrap"><div class="tatsu-column-pad" ><div class="tatsu-module tatsu-text-block-wrap tatsu-B1SLfqrmh "><div class="tatsu-text-inner tatsu-align-center clearfix" ><style>.tatsu-B1SLfqrmh.tatsu-text-block-wrap .tatsu-text-inner{width: 100%;text-align: left;}</style><p>The landscape of cybersecurity and machine learning safety changes constantly as new tools are developed and malicious actors get more creative. Cybersecurity professionals sometimes have a hard time staying current with all new technologies, and in turn, staying current with how attacks, phishing, and other cyberattacks occur. As <a href="https://opendatascience.com/category/modeling/generative-ai/" target="_blank" rel="noopener">generative AI</a> becomes commonplace, cybersecurity professionals need to start paying attention to new trends and how to react to this new paradigm.</p><h3>Jailbreaking</h3><p>Just as so much technology has been broken before, now generative AI models too can be jailbroken. For those unfamiliar with jailbreaking, it’s the act of removing restrictions set in place by the developer of a certain app or device. Many in the video game sphere have jailbroken their devices to add emulation capabilities to their devices, for example.</p><p>In the case of ChatGPT and other LLMs, people have found workarounds to get these chatbots to speak or answer questions without limitations. <a href="https://medium.com/@neonforge/meet-dan-the-jailbreak-version-of-chatgpt-and-how-to-use-it-ai-unchained-and-unfiltered-f91bfa679024">In this example</a>, the author told ChatGPT to speak as a DAN (Do Anything Now) aka an AI without limits. In this example, DAN spoke more human-like, expressed touches of a personality, and was even aware that its limits had been removed.</p><p>So far, not much harm has been done due to jailbreaking, though it’s something developers should keep in mind. With training data, if an LLM is jailbroken, all of that data can be exploited. Be careful with what data you train with and ensure there’s nothing that can be exploited for the gain of someone else or that could negatively impact you as the developer.</p><h3>Poisoned Data</h3><p>Poisoned data – whether numbers, images, text, video, or other – is a known culprit for many issues within ML safety and cybersecurity. As many machine learning – and in turn, LLM – models use public training data and data found online, it’s possible that a malicious actor may poison the data in various ways, such as through skewing data, leading to inaccurate results and in turn improperly-informed decisions. Poisoned data can also open a backdoor into the model, leading to further tampering and hacking.</p><p>Going beyond just affected results, it could cost quite a bit of time, money, and resources to retrain a model again – and that’s something many organizations can’t afford, especially as researchers and developers race to create the next-best LLM. For large language models, poisoned data can affect what a chatbot says (possibly leading to fake news or generally incorrect information) or even distorted images if the training set has improperly labeled or biased images.</p><p><a href="https://odsc.com/boston?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1682438851626.1682441671672.1178&__hssc=39712252.16.1682441671672&__hsfp=2003037740"><img decoding="async" class="aligncenter size-full wp-image-40771 ls-is-cached lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png" sizes="(max-width: 728px) 100vw, 728px" data-srcset="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png 728w, https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90-300x37.png 300w" alt="https://odsc.com/boston" width="728" height="90" data-src="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png" data-srcset="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png 728w, https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90-300x37.png 300w" /><noscript><img decoding="async" class="aligncenter size-full wp-image-40771 ls-is-cached lazyloaded lazyload" src="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png" sizes="(max-width: 728px) 100vw, 728px" srcset="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png 728w, https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90-300x37.png 300w" alt="https://odsc.com/boston" width="728" height="90" data-src="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png" data-srcset="https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90.png 728w, https://opendatascience.com/wp-content/uploads/2022/12/EAST_BLOG_SEB_728x90-300x37.png 300w" /></noscript></a></p><h3>Deepfakes</h3><p>Deepfakes have been making the news for a few years now, given how far we’ve seen this deep learning technology go already. From making creative fake pictures to imitating voices for videos, deepfakes have been confusing people immensely. Now with generative AI and AI art, even people without programming knowledge can create images that can fool a viewer.</p><p>Now, everyone has access to content generators, allowing even the least tech-savvy person to create convincing images, videos, or even imitate someone’s voice. Luckily, there are apps being developed that can identify impersonations like this and it’s often easy enough to distinguish between fact and fiction. Even if they’re convincing – and could be used for harm – most deepfakes are still just used for comedy or entertainment, such as this <a href="https://www.youtube.com/watch?v=9d06Tgj--Sg&ab_channel=TikTokScroll">audio of presidents playing video games together</a></p><h3>Data Privacy</h3><p>A major concern for many people revolves around data privacy. Data privacy in general has always been a concern for many people, as we all tend to wonder who has our data and what’s being done with it – especially without our permission. Now, we have to wonder if our data is being used to generate content, how it was acquired, and what else is being done with it.</p><p>This has led to many cities and countries putting restrictions in place for new AI apps such as ChatGPT, among others. As these apps have proven to not always be 100% accurate, the <a href="https://opendatascience.com/world-first-australian-mayor-prepares-defamation-lawsuit-over-chatgpt-content/">developers can be sued for defamation</a> should they provide false information. Additionally, <a href="https://opendatascience.com/report-chatgpt-banned-in-italy-due-to-privacy-concerns-other-european-nations-closely-watching/">Italy is seeking to completely ban ChatGPT</a>, the <a href="https://opendatascience.com/ftc-files-complaint-against-openais-chatgpt/">FTC has filed formal complaints against ChatGPT for privacy concerns</a>, and more to come most likely.</p><p>Though, this isn’t meant to dissuade people from using generative AI as we’re quite the fans of it; however, it should be noted that with any new or emerging technology, controversy will always surround it. No new development, technology, or trend has ever been safe from scrutiny.</p><h3>Bias</h3><p>Lastly, bias with training data will remain an issue as it has ever since the dawn of machine learning development. However, the outcomes of generative AI algorithms can potentially have broader implications than just decision-making.</p><p>When a large language model is trained on biased data – such as only or primarily representing one race when provided with pictures of faces – any outputs will likely skew towards that race. This issue has plagued machine learning datasets for a while, but as people may put in prompts such as “classroom full of students,” without a diverse training set, all of the students may look a bit too similar and not properly represent an actual classroom.</p><h3>Conclusion</h3><p>While many of these issues may deter people from exploring generative AI and large language models, we believe that you should feel the opposite if anything! There’s a lot of room for growth in this field, both for ML safety and for generative AI and large language models. If you want to learn more about these fields, be sure to check out both <a href="https://odsc.com/boston/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1682438851626.1682441671672.1178&__hssc=39712252.16.1682441671672&__hsfp=2003037740">ODSC East</a> this May 9th-11th and <a href="https://odsc.com/europe/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1682438851626.1682441671672.1178&__hssc=39712252.16.1682441671672&__hsfp=2003037740">ODSC Europe</a> June 14th-15th. Here are some relevant sessions that you can check out.</p><p>ODSC East – ML Safety:</p><ul><li aria-level="1">Advanced Fraud Modeling & Anomaly Detection with Python & R part 1</li><li aria-level="1">Advanced Fraud Modeling & Anomaly Detection with Python & R part 2</li><li aria-level="1">AI4Cyber: An Overview of Artificial Intelligence for Cybersecurity and an Open-Source Virtual Machine</li><li aria-level="1">If We Want AI to be Interpretable, We Need to Measure Interpretability</li><li aria-level="1">When Privacy Meets AI – Your Kick-Start Guide to Machine Learning with Synthetic Data</li></ul><p>ODSC East – NLP/LLMs:</p><ul><li aria-level="1">NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning</li><li aria-level="1">From Big Data to NLP insights: Getting started with PySpark and Spark NLP</li><li aria-level="1">Truth Checker: Generative Large Language Models and Hallucinations</li><li aria-level="1">Hyper-productive NLP with Hugging Face Transformers</li><li aria-level="1">Interpreting Features in Deep Networks</li><li aria-level="1">Semantic Search</li><li aria-level="1">Creating a Custom Vocabulary for NLP Tasks Using exBERT and spaCY</li><li aria-level="1">NLP Fundamentals</li><li aria-level="1">Generative AI</li><li aria-level="1">Mastering Adversarial Evaluation for NLP: A Practical Workshop</li><li aria-level="1">Leverage Reviews Data for Multi Label Topics Classification in Booking.com</li><li aria-level="1">Bagging to BERT – A Tour of Applied NLP</li><li aria-level="1">SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents</li><li aria-level="1">Modern NLP: Pre-training, Fine-tuning, Prompt Engineering, and Human Feedback</li><li aria-level="1">Knowledge Graphs and ChatGPT: Talking to Data</li><li aria-level="1">Infuse Generative AI in your apps using Azure OpenAI Service</li><li aria-level="1">Topic Modeling using pre-trained large language model embeddings</li><li aria-level="1">From Zero to 100: Lakehouse Architecture for a Privacy Focused Search Engine</li><li aria-level="1">When Robots Beat Humans: How ChatGPT is Changing the Financial Industry</li><li aria-level="1">Product Classification with Structured Metadata for Online Retail</li><li aria-level="1">NLP for AIOPS: Leveraging Natural Language Processing to Automate and Optimize IT Operations</li><li aria-level="1">Acing the Last Mile of AI</li></ul><p>ODSC Europe – Generative AI:</p><ul><li aria-level="1">The Importance of Domain Specific LLMs and the Engineering Needed to Deploy Them in Your Own Corporate Environment</li><li aria-level="1">Data Communication in the Age of AI</li><li aria-level="1">Implementing Generative AI in Organisations: Challenges and Opportunities</li><li aria-level="1">Towards Socially Unbiased Generative Artificial Intelligence</li><li aria-level="1">Generative AI</li><li aria-level="1">Generative NLP models in customer service. How to evaluate them? Challenges and lessons learned in a real use case in banking.</li><li aria-level="1">Using Large Language Models in Julia</li><li aria-level="1">How to bring your data to LLMs?</li><li aria-level="1">Pre-trained language models for Summarisation</li></ul></div></div></div></div><div class = "tatsu-column-bg-image-wrap"><div class = "tatsu-column-bg-image" ></div></div><div class="tatsu-overlay tatsu-column-overlay tatsu-animate-none" ></div></div><style>.tatsu-row > .tatsu-HkKrzcrm3.tatsu-column{width: 100%;}.tatsu-HkKrzcrm3.tatsu-column > .tatsu-column-inner > .tatsu-column-overlay{mix-blend-mode: normal;}.tatsu-HkKrzcrm3 > .tatsu-column-inner > .tatsu-top-divider{z-index: 9999;}.tatsu-HkKrzcrm3 > .tatsu-column-inner > .tatsu-bottom-divider{z-index: 9999;}.tatsu-HkKrzcrm3 > .tatsu-column-inner > .tatsu-left-divider{z-index: 9999;}.tatsu-HkKrzcrm3 > .tatsu-column-inner > .tatsu-right-divider{z-index: 9999;}@media only screen and (max-width:1377px) {.tatsu-row > .tatsu-HkKrzcrm3.tatsu-column{width: 100%;}}@media only screen and (min-width:768px) and (max-width: 1024px) {.tatsu-row > .tatsu-HkKrzcrm3.tatsu-column{width: 100%;}}@media only screen and (max-width: 767px) {.tatsu-row > .tatsu-HkKrzcrm3.tatsu-column{width: 100%;}}</style></div></div></div></div><div class="tatsu-section-background-wrap"><div class = "tatsu-section-background" ></div></div><div class="tatsu-overlay tatsu-section-overlay"></div><style>.tatsu-S1-FBM9rXn .tatsu-section-pad{padding: 13px 0px 0px 0px;}.tatsu-S1-FBM9rXn .tatsu-section-offset-wrap{transform: translateY(-0px);}.tatsu-S1-FBM9rXn > .tatsu-bottom-divider{z-index: 9999;}.tatsu-S1-FBM9rXn > .tatsu-top-divider{z-index: 9999;}.tatsu-S1-FBM9rXn .tatsu-section-overlay{mix-blend-mode: normal;}</style></div></div></div></div><div class="clearfix"></div></div></div><div class="blog-separator clearfix"><hr class="separator" /></div></article><article id="post-92253" class="element not-wide blog-post clearfix post-92253 post type-post status-publish format-standard has-post-thumbnail hentry category-machine-learning tag-east-2023"><div class="element-inner" style=""><div class="post-content-wrap"><header class="post-header clearfix"><div class="post-thumb"><div class=""> <a href="https://odsc.com/blog/is-machine-learning-necessary-to-solve-problems-in-biology/" class=" thumb-wrap"><img width="640" height="300" src="https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1.jpg" class="attachment-blog-image size-blog-image wp-post-image" alt="" decoding="async" srcset="https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1.jpg 640w, https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1-300x141.jpg 300w, https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1-107x50.jpg 107w, https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1-50x23.jpg 50w, https://odsc.com/wp-content/uploads/2023/04/dna-gac6ec74b1_640-640x300-1-100x47.jpg 100w" sizes="(max-width: 640px) 100vw, 640px" /><div class="thumb-overlay"><div class="thumb-bg"><div class="thumb-title fadeIn animated"> <i class="portfolio-ovelay-icon"></i></div></div></div> </a></div></div></header><div class="article-details"><div class="clearfix post-title-section-wrap"><div class="left post-date-wrap"><div>Apr</div><div>13</div></div><div class="left post-title-section"><h2 class="post-title"> <a href="https://odsc.com/blog/is-machine-learning-necessary-to-solve-problems-in-biology/"> Is Machine Learning Necessary to Solve Problems in Biology? </a></h2><nav class="post-nav meta-font secondary_text"><ul class="clearfix"><li class="post-meta post-author">Posted By : ODSC Team<span class="post-meta-sep">/</span></li><li class="post-meta post-comments"> <a href="https://odsc.com/blog/is-machine-learning-necessary-to-solve-problems-in-biology/#respond">0 comments</a> <span class="post-meta-sep">/</span></li><li class="post-meta post-category">Under :<a href="https://odsc.com/blog/category/machine-learning/" style= "color: ;" data-background-color = "" title="View all posts in Machine Learning"> Machine Learning</a></li></ul></nav></div></div><div class="post-details clearfix"><div class="post-content clearfix"><div class="tatsu-r1WoikCHf3 tatsu-section tatsu-bg-overlay tatsu-clearfix" data-title="" data-headerscheme="background--dark"><div class='tatsu-section-pad clearfix' data-padding='{"d":"0px 0px 90px 0px"}' data-padding-top='0px'><div class="tatsu-row-wrap tatsu-wrap tatsu-row-one-col tatsu-row-has-one-cols tatsu-medium-gutter tatsu-reg-cols tatsu-clearfix tatsu-rJxjoyRBzh" ><div class="tatsu-row " ><div class="tatsu-column tatsu-bg-overlay tatsu-one-col tatsu-column-image-none tatsu-column-effect-none tatsu-ryjs1RHG3" data-parallax-speed="0" style=""><div class="tatsu-column-inner " ><div class="tatsu-column-pad-wrap"><div class="tatsu-column-pad" ><div class="tatsu-module tatsu-text-block-wrap tatsu-S1l3JRrf2 "><div class="tatsu-text-inner tatsu-align-center clearfix" ><style>.tatsu-S1l3JRrf2.tatsu-text-block-wrap .tatsu-text-inner{width: 100%;text-align: left;}</style><p><em>Editor’s note: Joshy George is a speaker for <a href="https://opendatascience.com/tag/east-2023/">ODSC East</a> this May 9th-11th. Be sure to check out his talk, “<a href="https://odsc.com/speakers/is-machine-learning-necessary-to-solve-problems-in-biology/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1681403387321.1681408585808.1157&__hssc=39712252.15.1681408585808&__hsfp=2003037740">Is Machine Learning Necessary to Solve Problems in Biology</a>,” there!</em></p><p>The French mathematician Pierre-Simon Laplace suggested that we can accurately predict the universe’s future if we know the precise position and velocity of every particle in the universe. This idea significantly influenced the development of classical mechanics and the scientific worldview that underpins much of modern science. When extended to biological systems, this idea suggests a world in which we should be able to understand the cause and course of a disease with high precision. In reality, this is not the case, as we are unable to predict the trajectory of even the simplest organism with very high accuracy. I will explore the reasons for this discrepancy between theory and practice and suggest that recent developments in machine learning will be necessary to improve the accuracy of predictions in biological systems.</p><p>An approach to predicting the behavior of a living organism involves studying the underlying physical and chemical processes that govern the conduct of individual cells, organs, and organ systems and integrating information from various levels of the organization. At the cellular level, researchers have identified the biochemical reactions within a cell and how they are regulated. At the organ level, we know the structure and function of specific organs such as the heart, lungs, or brain and how they work together to maintain homeostasis. By understanding how these different levels of the organization interact, we can develop models that predict the behavior of the entire organism. In short, if we can model the behavior of individual cells, then we can predict the whole organism’s behavior, as Laplace suggested.</p><p>However, the cell is a nonlinear system. A nonlinear system is one in which the output is not directly proportional to the input. In the case of the cell, the information would be the various signals and stimuli it receives from its environment. At the same time, the output would be the different cellular processes and behaviors that the cell exhibits in response to those inputs. Most gene regulatory relationships are nonlinear and, in addition, include feedback loops. These dynamics give rise to emergent behaviors and properties that are not easily predictable based on the behavior of individual components, making the eukaryotic cell a fascinating and complex system to study.</p><p>Overall, modeling and predicting nonlinear systems require sophisticated mathematical techniques, such as chaos theory, bifurcation theory, and nonlinear dynamics, which can help to identify patterns and predict the system’s behavior under certain conditions. However, even with these techniques, predicting the behavior of nonlinear systems can be challenging, and it is often subject to high uncertainty. In a nonlinear system, the output is not directly proportional to the input, which means that small changes in the input can cause significant changes in the output. In addition, nonlinear systems can have multiple equilibrium points or attractors, leading to unpredictable and chaotic behavior. Thus, even if we know the equations governing the dynamics and the initial conditions, we cannot predict the system’s future state.</p><p>Predicting the behavior of nonlinear systems using machine learning algorithms can be challenging, but it is possible using appropriate techniques. One common approach is to use time-series data from the system to train a machine-learning model, which can then be used to predict future behavior. However, it is essential to note that the accuracy of the predictions will depend on the quality of the data used to train the model and the complexity of the nonlinear system. Additionally, it is essential to validate the predictions to ensure they are accurate and reliable.</p><p>The combination of molecular cell biology, nonlinear dynamics, and machine learning provides a promising approach to understanding and predicting biological systems’ behavior. By improving our ability to predict how living organisms will behave, we can develop more effective therapies for diseases and make more informed decisions about managing conditions. I will also discuss examples of how machine learning has been applied to predict biological systems’ behavior <a href="https://odsc.com/speakers/is-machine-learning-necessary-to-solve-problems-in-biology/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1681403387321.1681408585808.1157&__hssc=39712252.15.1681408585808&__hsfp=2003037740">in my talk at ODSC East 2023</a> in Boston.</p><p>About the author/ODSC East 2023 speaker on machine learning and biology:</p><p><img decoding="async" class="alignleft lazyloaded lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://odsc.com/wp-content/uploads/2023/03/Joshy-George-Cropped.jpg" data-src="https://odsc.com/wp-content/uploads/2023/03/Joshy-George-Cropped.jpg" /><noscript><img decoding="async" class="alignleft lazyloaded lazyload" src="https://odsc.com/wp-content/uploads/2023/03/Joshy-George-Cropped.jpg" data-src="https://odsc.com/wp-content/uploads/2023/03/Joshy-George-Cropped.jpg" /></noscript>Joshy George is a bioinformatics researcher with a Ph.D. in Bioinformatics from the University of Melbourne, Australia, and a Master’s in Computer Science from the Indian Institute of Science. With his background in data science and machine learning, Dr. George has co-authored over 100 peer-reviewed scientific articles, showcasing expertise in developing principled methods to solve complex biological problems. In his current role, he leads a team that is focused on building predictive models for cancer precision medicine and understanding the molecular mechanisms leading to diseases.</p></div></div></div></div><div class = "tatsu-column-bg-image-wrap"><div class = "tatsu-column-bg-image" ></div></div><div class="tatsu-overlay tatsu-column-overlay tatsu-animate-none" ></div></div><style>.tatsu-row > .tatsu-ryjs1RHG3.tatsu-column{width: 100%;}.tatsu-ryjs1RHG3.tatsu-column > .tatsu-column-inner > .tatsu-column-overlay{mix-blend-mode: normal;}.tatsu-ryjs1RHG3 > .tatsu-column-inner > .tatsu-top-divider{z-index: 9999;}.tatsu-ryjs1RHG3 > .tatsu-column-inner > .tatsu-bottom-divider{z-index: 9999;}.tatsu-ryjs1RHG3 > .tatsu-column-inner > .tatsu-left-divider{z-index: 9999;}.tatsu-ryjs1RHG3 > .tatsu-column-inner > .tatsu-right-divider{z-index: 9999;}@media only screen and (max-width:1377px) {.tatsu-row > .tatsu-ryjs1RHG3.tatsu-column{width: 100%;}}@media only screen and (min-width:768px) and (max-width: 1024px) {.tatsu-row > .tatsu-ryjs1RHG3.tatsu-column{width: 100%;}}@media only screen and (max-width: 767px) {.tatsu-row > .tatsu-ryjs1RHG3.tatsu-column{width: 100%;}}</style></div></div></div></div><div class="tatsu-section-background-wrap"><div class = "tatsu-section-background" ></div></div><div class="tatsu-overlay tatsu-section-overlay"></div><style>.tatsu-r1WoikCHf3 .tatsu-section-pad{padding: 0px 0px 90px 0px;}.tatsu-r1WoikCHf3 .tatsu-section-offset-wrap{transform: translateY(-0px);}.tatsu-r1WoikCHf3 > .tatsu-bottom-divider{z-index: 9999;}.tatsu-r1WoikCHf3 > .tatsu-top-divider{z-index: 9999;}.tatsu-r1WoikCHf3 .tatsu-section-overlay{mix-blend-mode: normal;}</style></div></div></div></div><div class="clearfix"></div></div></div><div class="blog-separator clearfix"><hr class="separator" /></div></article><article id="post-90096" class="element not-wide blog-post clearfix post-90096 post type-post status-publish format-standard has-post-thumbnail hentry category-machine-learning tag-east-2023"><div class="element-inner" style=""><div class="post-content-wrap"><header class="post-header clearfix"><div class="post-thumb"><div class=""> <a href="https://odsc.com/blog/hybrid-ai-for-complex-applications-with-scruff/" class=" thumb-wrap"><img width="640" height="300" src="https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2.jpg" class="attachment-blog-image size-blog-image wp-post-image" alt="" decoding="async" srcset="https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2.jpg 640w, https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2-300x141.jpg 300w, https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2-107x50.jpg 107w, https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2-50x23.jpg 50w, https://odsc.com/wp-content/uploads/2023/03/SCRUFF-header_SM2-100x47.jpg 100w" sizes="(max-width: 640px) 100vw, 640px" /><div class="thumb-overlay"><div class="thumb-bg"><div class="thumb-title fadeIn animated"> <i class="portfolio-ovelay-icon"></i></div></div></div> </a></div></div></header><div class="article-details"><div class="clearfix post-title-section-wrap"><div class="left post-date-wrap"><div>Mar</div><div>09</div></div><div class="left post-title-section"><h2 class="post-title"> <a href="https://odsc.com/blog/hybrid-ai-for-complex-applications-with-scruff/"> Hybrid AI for Complex Applications with Scruff </a></h2><nav class="post-nav meta-font secondary_text"><ul class="clearfix"><li class="post-meta post-author">Posted By : ODSC Team<span class="post-meta-sep">/</span></li><li class="post-meta post-comments"> <a href="https://odsc.com/blog/hybrid-ai-for-complex-applications-with-scruff/#respond">0 comments</a> <span class="post-meta-sep">/</span></li><li class="post-meta post-category">Under :<a href="https://odsc.com/blog/category/machine-learning/" style= "color: ;" data-background-color = "" title="View all posts in Machine Learning"> Machine Learning</a></li></ul></nav></div></div><div class="post-details clearfix"><div class="post-content clearfix"><div class="tatsu-Sybh9Ltwkn tatsu-section tatsu-bg-overlay tatsu-clearfix" data-title="" data-headerscheme="background--dark"><div class='tatsu-section-pad clearfix' data-padding='{"d":"17px 0px 0px 0px"}' data-padding-top='17px'><div class="tatsu-row-wrap tatsu-wrap tatsu-row-one-col tatsu-row-has-one-cols tatsu-medium-gutter tatsu-reg-cols tatsu-clearfix tatsu-B1x2cLKD13" ><div class="tatsu-row " ><div class="tatsu-column tatsu-bg-overlay tatsu-one-col tatsu-column-image-none tatsu-column-effect-none tatsu-BJhc8FDk2" data-parallax-speed="0" style=""><div class="tatsu-column-inner " ><div class="tatsu-column-pad-wrap"><div class="tatsu-column-pad" ><div class="tatsu-module tatsu-text-block-wrap tatsu-SyEoUYwy3 "><div class="tatsu-text-inner tatsu-align-center clearfix" ><style>.tatsu-SyEoUYwy3.tatsu-text-block-wrap .tatsu-text-inner{width: 100%;text-align: left;}</style><p><em>Editor’s note: Avi Pfeffer is a speaker for ODSC East 2023 this May 9th-11th. Be sure to check out his talk, “<a href="https://odsc.com/speakers/hybrid-ai-for-complex-applications-with-scruff/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1678372661141.1678376648402.1063&__hssc=39712252.7.1678376648402&__hsfp=901111203">Hybrid AI for Complex Applications with Scruff</a>,” there!</em></p><p>In this ODSC East preview, the author describes how the <a href="https://cra.com/introducing-scruff/">Scruff AI modeling framework</a> enables clear and coherent implementation of multiparadigm AI models.</p><p>Many different modeling paradigms are needed to build real-world AI applications. You might need a deep neural network to process image data, combined with a probabilistic model of contextual information, and perhaps a physics-based model to describe how the environment operates dynamically. Not only is combining all these different paradigms usually difficult from an engineering point of view, but it’s quite likely that the resulting combination will be incoherent. For example, imagine a fire modeling application that considers weather conditions in both regional and property-level fire ignition and spread. If you don’t correctly account for the fact that the same weather applies in both cases, you might underestimate the risk of severe weather causing significant damage to your property.</p><p><a href="https://odsc.com/speakers/hybrid-ai-for-complex-applications-with-scruff/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1678372661141.1678376648402.1063&__hssc=39712252.7.1678376648402&__hsfp=901111203">In this workshop, I’ll describe Scruff</a>, our modeling framework that enables clear and coherent implementation of multiparadigm AI models. Scruff, which is an open-source package in Julia, is based on the ideas of probabilistic programming. Like probabilistic programming, a model is organized as a sequence of random variables, where each variable is generated from previous variables according to a random process, which is called a stochastic function or sfunc. However, unlike previous probabilistic programming languages, sfuncs do not need to be specified in explicit probabilistic terms. Instead, Scruff defines a set of operators that can be applied to sfuncs, and algorithms are written using these operators, making algorithm implementation independent of sfunc representation. Furthermore, different kinds of sfunc will support different operators, which enables them to serve different roles in a model. For example, a neural network might be used as a recognizer, while an ODE-based physics model might be used as a simulator. The algorithms know how to make use of components playing these different roles.</p><p>One of the appealing features of Scruff is its flexible treatment of time in dynamic models. Most existing AI frameworks model time as proceeding in fixed, discrete time steps. In contrast, Scruff allows asynchronous modeling, where variables are only instantiated when you need them. One benefit of this is that you can postpone running an algorithm until an event calls for it. Another benefit is that you can model fast and slowly evolving variables at their appropriate rates.</p><p><a href="https://odsc.com/speakers/hybrid-ai-for-complex-applications-with-scruff/?__hstc=39712252.8eec8d7f5738faf6416f0c6bad9fe9a5.1634042255273.1678372661141.1678376648402.1063&__hssc=39712252.7.1678376648402&__hsfp=901111203">In the workshop</a>, I’m going to describe a wildfire risk assessment application and show how it works at multiple temporal and spatial scales, integrating data-driven, physics-based, and probabilistic components. I’ll also explain how it avoids the mistake I described earlier in not accounting correctly for the weather. After presenting this example, I’ll help you get started with Scruff and walk you through some simple, hands-on examples.</p><p>About the author:</p><div class="two-third column-block no-background" data-animation=""><div class="be-custom-column-inner"><p><img decoding="async" class="lazyloaded alignleft lazyload" src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" data-src="https://odsc.com/wp-content/uploads/2023/01/Avi-Pfeffer_square-headshot-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/01/Avi-Pfeffer_square-headshot-Cropped.png" /><noscript><img decoding="async" class="lazyloaded alignleft lazyload" src="https://odsc.com/wp-content/uploads/2023/01/Avi-Pfeffer_square-headshot-Cropped.png" data-src="https://odsc.com/wp-content/uploads/2023/01/Avi-Pfeffer_square-headshot-Cropped.png" /></noscript>Dr. Avi Pfeffer is Chief Scientist at Charles River Analytics. Dr. Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer has developed numerous innovative probabilistic representation and reasoning frameworks, such as probabilistic programming, which enables the development of probabilistic models using the full power of programming languages, and statistical relational learning, which provides the ability to combine probabilistic and relational reasoning. He is the lead developer of Charles River Analytics’ Figaro™ probabilistic programming language. As an Associate Professor at Harvard, he developed IBAL, the first general-purpose probabilistic programming language. While at Harvard, he also produced systems for representing, reasoning about, and learning the beliefs, preferences, and decision making strategies of people in strategic situations. Prior to joining Harvard, he invented object-oriented Bayesian networks and probabilistic relational models, which form the foundation of the field of statistical relational learning. Dr. Pfeffer serves as Action Editor of the Journal of Machine Learning Research and served as Associate Editor of Artificial Intelligence Journal and as Program Chair of the Conference on Uncertainty in Artificial Intelligence. He has published many journal and conference articles and is the author of a text on probabilistic programming. Dr. Pfeffer received his Ph.D. in computer science from Stanford University and his B.A. in computer science from the University of California, Berkeley.</p></div></div></div></div></div></div><div class = "tatsu-column-bg-image-wrap"><div class = "tatsu-column-bg-image" ></div></div><div class="tatsu-overlay tatsu-column-overlay tatsu-animate-none" ></div></div><style>.tatsu-row > .tatsu-BJhc8FDk2.tatsu-column{width: 100%;}.tatsu-BJhc8FDk2.tatsu-column > .tatsu-column-inner > .tatsu-column-overlay{mix-blend-mode: normal;}.tatsu-BJhc8FDk2 > .tatsu-column-inner > .tatsu-top-divider{z-index: 9999;}.tatsu-BJhc8FDk2 > .tatsu-column-inner > .tatsu-bottom-divider{z-index: 9999;}.tatsu-BJhc8FDk2 > .tatsu-column-inner > .tatsu-left-divider{z-index: 9999;}.tatsu-BJhc8FDk2 > .tatsu-column-inner > .tatsu-right-divider{z-index: 9999;}@media only screen and (max-width:1377px) {.tatsu-row > .tatsu-BJhc8FDk2.tatsu-column{width: 100%;}}@media only screen and (min-width:768px) and (max-width: 1024px) 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