From 0468a703f62310bd19a259c21090666635592dac Mon Sep 17 00:00:00 2001 From: nconrad <1194246+nconrad@users.noreply.github.com> Date: Tue, 1 Aug 2023 12:05:23 -0500 Subject: [PATCH] Deploy website - based on 469e7e3e7d9bb7f0148f600b9609b45aad85e816 --- 404.html | 8 ++++---- About.html | 8 ++++---- Publications.html | 8 ++++---- Science.html | 8 ++++---- Team.html | 8 ++++---- assets/js/{main.f51fd4c0.js => main.4fc09b42.js} | 4 ++-- ...1fd4c0.js.LICENSE.txt => main.4fc09b42.js.LICENSE.txt} | 0 docs/about/architecture.html | 8 ++++---- docs/about/overview.html | 8 ++++---- docs/category/about.html | 8 ++++---- docs/category/edge-apps.html | 8 ++++---- docs/category/installation-manuals.html | 8 ++++---- docs/category/reference-guides.html | 8 ++++---- docs/category/tutorials.html | 8 ++++---- docs/contact-us.html | 8 ++++---- docs/installation-manuals/wsn-manual.html | 8 ++++---- docs/reference-guides/dev-quick-reference.html | 8 ++++---- docs/reference-guides/pluginctl.html | 8 ++++---- docs/reference-guides/sesctl.html | 8 ++++---- docs/tutorials/access-waggle-sensors.html | 8 ++++---- docs/tutorials/accessing-data.html | 8 ++++---- docs/tutorials/cloud-compute.html | 8 ++++---- docs/tutorials/create-waggle.html | 8 ++++---- docs/tutorials/edge-apps/creating-an-edge-app.html | 8 ++++---- docs/tutorials/edge-apps/intro-to-edge-apps.html | 8 ++++---- docs/tutorials/edge-apps/publishing-to-ecr.html | 8 ++++---- docs/tutorials/edge-apps/testing-an-edge-app.html | 8 ++++---- docs/tutorials/schedule-jobs.html | 8 ++++---- index.html | 8 ++++---- news.html | 8 ++++---- news/2020/07/27/sage-ai-at-edge-workshop.html | 8 ++++---- news/2020/07/28/world-watchers.html | 8 ++++---- news/2020/07/29/sage-community-workshop.html | 8 ++++---- .../lightning-the-way-with-software-defined-radios.html | 8 ++++---- news/2020/08/12/derecho-talk-with-scott-collis.html | 8 ++++---- news/2021/02/12/ped-count-for-cross.html | 8 ++++---- news/2023/05/05/neiu-crocus-deploy.html | 8 ++++---- news/2023/05/31/scalable-ci-in-aps.html | 8 ++++---- news/archive.html | 8 ++++---- news/big-data.html | 8 ++++---- news/sage-neon-deploy-konza.html | 8 ++++---- news/tags.html | 8 ++++---- news/tags/ai-applications.html | 8 ++++---- news/tags/alcf.html | 8 ++++---- news/tags/argonne-aps.html | 8 ++++---- news/tags/big-data.html | 8 ++++---- news/tags/climate-science.html | 8 ++++---- news/tags/computational-science.html | 8 ++++---- news/tags/computer-science.html | 8 ++++---- news/tags/deployment.html | 8 ++++---- news/tags/edge-computing.html | 8 ++++---- news/tags/education.html | 8 ++++---- news/tags/in-the-news.html | 8 ++++---- news/tags/lightning-detection.html | 8 ++++---- news/tags/naise.html | 8 ++++---- news/tags/niu.html | 8 ++++---- news/tags/node.html | 8 ++++---- news/tags/sage-science.html | 8 ++++---- news/tags/talks-presentations.html | 8 ++++---- news/tags/waggle.html | 8 ++++---- news/tags/workshop.html | 8 ++++---- science/bandwidth-aware-learning.html | 8 ++++---- science/characterizing-clouds.html | 8 ++++---- science/integrating-chameleon.html | 8 ++++---- science/lightning-science.html | 8 ++++---- science/monitoring-biodiversity.html | 8 ++++---- science/nowcasting-weather.html | 8 ++++---- science/scalable-ci-in-aps.html | 8 ++++---- science/snowflake.html | 8 ++++---- science/social-distancing.html | 8 ++++---- science/traffic-state-estimation.html | 8 ++++---- science/vehicle-tracking.html | 8 ++++---- science/water-level-detection.html | 8 ++++---- science/water-segmentation.html | 8 ++++---- science/wildfire-science.html | 8 ++++---- search.html | 8 ++++---- 76 files changed, 298 insertions(+), 298 deletions(-) rename assets/js/{main.f51fd4c0.js => main.4fc09b42.js} (98%) rename assets/js/{main.f51fd4c0.js.LICENSE.txt => main.4fc09b42.js.LICENSE.txt} (100%) diff --git a/404.html b/404.html index 44be5e8e..85f63628 100644 --- a/404.html +++ b/404.html @@ -15,12 +15,12 @@ - + -
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About Sage

A new kind of NSF Mid-scale Research Infrastructure (MSRI) that supports AI at the Edge​

In September of 2019, the National Science Foundation awarded a multi-institutional team led by Northwestern University a $9 million grant to launch the Sage project, a novel cyberinfrastructure created to exploit dramatic improvements in artificial intelligence technology. The goal: to build a continent-spanning network of smart sensors.

With Sage, advanced machine learning algorithms will be moved to “the edge.” Edge computing is a way to streamline data flowing from Internet of Things (IoT) devices by providing data analysis very near the site where the data is gathered. By linking small, powerful, computers directly to high-resolution cameras, air quality and weather sensors, and experimental Light Detection and Ranging (LIDAR) systems, this new distributed infrastructure will enable researchers to analyze and respond to data almost instantly. From early detection of wildfire smoke plumes in California to identifying ultrasonic calls of bats or the patterns of pedestrians in a busy crosswalk, Sage’s artificial intelligence-enabled sensors will give scientists a new tool to understand our planet.

These distributed, intelligent sensor networks will prove essential for understanding the impacts of global urbanization, natural disasters, such as flooding and wildfires, and climate change on natural ecosystems and city infrastructure. However, many of today’s distributed systems struggle with the immense volume of streaming data that high-fidelity sensors can provide. Some systems resort to saving the data on hard drives that a technician traveling to the instrument might retrieve just a few times a year. Other systems manage the data deluge by only collecting a small portion of the valuable input and uploading it to a cloud server for analysis via a slow wireless link. To address this challenge, Sage will embed computers directly into the sensor network and rely on advancements in edge computing to analyze the torrent of sensor data as it streams past.

Sage’s name is inspired by nature: the perennial flowering plant. But it also conveys other connotations associated with the word, such as wisdom and good judgement. The new cyberinfrastructure project will be enhanced by a set of partnerships with existing scientific instruments that span contexts from urban (NSF-funded Array of Things) to continental-scale (NSF’s National Ecological Observatory Network, or NEON), and with regional instruments focused on understanding and responding to emergencies, such as severe storms (Atmospheric Radiation Measurement, or ARM) and wildfires (High-Performance Wireless Research and Education Network and WIFIRE). Sage will be able to integrate measurements from these multiple modalities. With the development of this new intelligent, or “software-defined” sensor network, researchers will be able to better collect and analyze data that is essential for understanding the impacts of wildfires, global urbanization, and climate change.

Sage test nodes will be deployed in environmental locations in California, Colorado, and Kansas and in urban settings in Illinois and Texas. The project will build on the open source technology platform used in the Array of Things project, which has deployed more than 100 sensors with edge computing capabilities within Chicago.

Reusable Cyberinfrastructure​

The research team that includes the University of Chicago, George Mason University, the University of California San Diego, Northern Illinois University, the University of Utah, the Lincoln Park Zoo, and collaborators at Argonne National Laboratory will design and build reusable software components and cyberinfrastructure services to support the new scientific measurement functions. These measures will range from in-situ analysis of high-bandwidth sensor data streams to adaptive system behaviors, such as adjusting the sampling rates and directional settings of LIDAR instruments or high-resolution cameras to capture events in more detail. While the traditional method of reducing data volume on remote NEON towers works well for steady state conditions, Sage will provide the ability to detect ecosystem changes of interest at continental scale, allowing scientists to apply more sophisticated analytics in the field, capturing much greater detail on these changes.

Sage will be programmable cyberinfrastructure — allowing edge software that can monitor everything from wildfires to wolf calls, and from urban traffic flows to rural lightning strikes. With Sage, mountaintop networking towers in Southern California, and part of the HPWREN and WIFIRE project will be able to analyze camera data for natural hazards and even autonomously redirect cameras to zoom in on regions of interest.

The reusable cyberinfrastructure running on these Sage testbeds will give climate, traffic, and ecosystem scientists new data for building models to study coupled systems. The software and hardware components developed in Sage will be open-source and provide an open architecture to enable scientists from a range of fields to build their own intelligent sensor networks. The Waggle edge computing platform, developed at Argonne National Laboratory, will be the starting point for the intelligent nodes deployed across the country. Waggle was launched in 2013 to permit a new breed of sensor-driven environmental science and smart city research.

The Sage team will also extend the current educational curriculum used in Chicago and will inspire young people — with an emphasis on women and underrepresented populations — to pursue science, technology, and mathematics careers by providing a platform for students to explore measurement-based science questions related to the natural and built environments.

The capabilities of machine learning and artificial intelligence didn’t exist a decade ago. Sage opens the door to scientists who will be able to write code that can measure a near limitless number of urban and environmental elements.

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About Sage

A new kind of NSF Mid-scale Research Infrastructure (MSRI) that supports AI at the Edge​

In September of 2019, the National Science Foundation awarded a multi-institutional team led by Northwestern University a $9 million grant to launch the Sage project, a novel cyberinfrastructure created to exploit dramatic improvements in artificial intelligence technology. The goal: to build a continent-spanning network of smart sensors.

With Sage, advanced machine learning algorithms will be moved to “the edge.” Edge computing is a way to streamline data flowing from Internet of Things (IoT) devices by providing data analysis very near the site where the data is gathered. By linking small, powerful, computers directly to high-resolution cameras, air quality and weather sensors, and experimental Light Detection and Ranging (LIDAR) systems, this new distributed infrastructure will enable researchers to analyze and respond to data almost instantly. From early detection of wildfire smoke plumes in California to identifying ultrasonic calls of bats or the patterns of pedestrians in a busy crosswalk, Sage’s artificial intelligence-enabled sensors will give scientists a new tool to understand our planet.

These distributed, intelligent sensor networks will prove essential for understanding the impacts of global urbanization, natural disasters, such as flooding and wildfires, and climate change on natural ecosystems and city infrastructure. However, many of today’s distributed systems struggle with the immense volume of streaming data that high-fidelity sensors can provide. Some systems resort to saving the data on hard drives that a technician traveling to the instrument might retrieve just a few times a year. Other systems manage the data deluge by only collecting a small portion of the valuable input and uploading it to a cloud server for analysis via a slow wireless link. To address this challenge, Sage will embed computers directly into the sensor network and rely on advancements in edge computing to analyze the torrent of sensor data as it streams past.

Sage’s name is inspired by nature: the perennial flowering plant. But it also conveys other connotations associated with the word, such as wisdom and good judgement. The new cyberinfrastructure project will be enhanced by a set of partnerships with existing scientific instruments that span contexts from urban (NSF-funded Array of Things) to continental-scale (NSF’s National Ecological Observatory Network, or NEON), and with regional instruments focused on understanding and responding to emergencies, such as severe storms (Atmospheric Radiation Measurement, or ARM) and wildfires (High-Performance Wireless Research and Education Network and WIFIRE). Sage will be able to integrate measurements from these multiple modalities. With the development of this new intelligent, or “software-defined” sensor network, researchers will be able to better collect and analyze data that is essential for understanding the impacts of wildfires, global urbanization, and climate change.

Sage test nodes will be deployed in environmental locations in California, Colorado, and Kansas and in urban settings in Illinois and Texas. The project will build on the open source technology platform used in the Array of Things project, which has deployed more than 100 sensors with edge computing capabilities within Chicago.

Reusable Cyberinfrastructure​

The research team that includes the University of Chicago, George Mason University, the University of California San Diego, Northern Illinois University, the University of Utah, the Lincoln Park Zoo, and collaborators at Argonne National Laboratory will design and build reusable software components and cyberinfrastructure services to support the new scientific measurement functions. These measures will range from in-situ analysis of high-bandwidth sensor data streams to adaptive system behaviors, such as adjusting the sampling rates and directional settings of LIDAR instruments or high-resolution cameras to capture events in more detail. While the traditional method of reducing data volume on remote NEON towers works well for steady state conditions, Sage will provide the ability to detect ecosystem changes of interest at continental scale, allowing scientists to apply more sophisticated analytics in the field, capturing much greater detail on these changes.

Sage will be programmable cyberinfrastructure — allowing edge software that can monitor everything from wildfires to wolf calls, and from urban traffic flows to rural lightning strikes. With Sage, mountaintop networking towers in Southern California, and part of the HPWREN and WIFIRE project will be able to analyze camera data for natural hazards and even autonomously redirect cameras to zoom in on regions of interest.

The reusable cyberinfrastructure running on these Sage testbeds will give climate, traffic, and ecosystem scientists new data for building models to study coupled systems. The software and hardware components developed in Sage will be open-source and provide an open architecture to enable scientists from a range of fields to build their own intelligent sensor networks. The Waggle edge computing platform, developed at Argonne National Laboratory, will be the starting point for the intelligent nodes deployed across the country. Waggle was launched in 2013 to permit a new breed of sensor-driven environmental science and smart city research.

The Sage team will also extend the current educational curriculum used in Chicago and will inspire young people — with an emphasis on women and underrepresented populations — to pursue science, technology, and mathematics careers by providing a platform for students to explore measurement-based science questions related to the natural and built environments.

The capabilities of machine learning and artificial intelligence didn’t exist a decade ago. Sage opens the door to scientists who will be able to write code that can measure a near limitless number of urban and environmental elements.

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Journal publications

Title

ARMing the Edge: Using Edge Computing and Machine Learning to Improve ARM Doppler Lidar Measurements

Jackson, R., Collis, S., Beckman, P., Ferrier. N., Sankaran, R., Kim, Y., Park, S., Shakarami, S., and Newsom, R.
(under review)

Optimizing Cloud Motion Estimation on the Edge with Phase Correlation and Optical Flow

Raut, Bhupendra A., Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis.
Atmospheric Measurement Techniques 16 (5), 1195-1209

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Dematties, Dario, Bhupendra A. Raut, Seongha Park, Robert C. Jackson, Sean Shahkarami, Yongho Kim, Rajesh Sankaran, Pete Beckman, Scott M. Collis, and Nicola Ferrier.
Artificial Intelligence for the Earth Systems. 1-34

Goal-driven Scheduling Model in Edge Computing for Smart City Applications

Kim, Yongho, Seongha Park, Sean Shahkarami, Rajesh Sankaran, Nicola Ferrier, and Pete Beckman.
Journal of Parallel and Distributed Computing 167, 97-108

Hands-On Computer Science: The Array of Things Experimental Urban Instrument

Charlie Catlett, Pete Beckman, Nicola Ferrier, Michael E. Papka, Rajesh Sankaran, Jeff Solin, Valerie Taylor, Douglas Pancoast, and Daniel Reed.
Computing in Science & Engineering 24 (1), 57-63

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Seongha Park, Yongho Kim, Nicola J. Ferrier, Scott M. Collis, Rajesh Sankaran and Pete H. Beckman.
Atmosphere 12 (3), 395

Measuring Cities with Software-Defined Sensors

Charlie Catlett, Pete Beckman, Marc G. Berman, Nicola Ferrier, Howard Nusbaum, Michael E. Papka, and Rajesh Sankaran.
IEEE Journal of Social Computing 1 (1), 14-27

Harnessing the Computing Continuum for Programming Our World

Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck.
Fog Computing: Theory and Practice (A. Y. Zomaya, A. Abbas, and S. U. Khan, eds.), John Wiley & Sons, Hoboken, NJ.

Publications in conference proceedings

Title

Addressing the Constraints of Active Learning on the Edge

Nueve, Enrique, Sean Shahkarami, Seongha Park, and Nicola Ferrier.
2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 845-849

WeatherNet: Nowcasting Net Radiation at the Edge

Nueve, Enrique, Robert Jackson, Rajesh Sankaran, Nicola Ferrier, and Scott Collis.
2021 IEEE Conference on Technologies for Sustainability (SusTech) 1-7

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Journal publications

Title

ARMing the Edge: Using Edge Computing and Machine Learning to Improve ARM Doppler Lidar Measurements

Jackson, R., Collis, S., Beckman, P., Ferrier. N., Sankaran, R., Kim, Y., Park, S., Shakarami, S., and Newsom, R.
(under review)

Optimizing Cloud Motion Estimation on the Edge with Phase Correlation and Optical Flow

Raut, Bhupendra A., Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis.
Atmospheric Measurement Techniques 16 (5), 1195-1209

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Dematties, Dario, Bhupendra A. Raut, Seongha Park, Robert C. Jackson, Sean Shahkarami, Yongho Kim, Rajesh Sankaran, Pete Beckman, Scott M. Collis, and Nicola Ferrier.
Artificial Intelligence for the Earth Systems. 1-34

Goal-driven Scheduling Model in Edge Computing for Smart City Applications

Kim, Yongho, Seongha Park, Sean Shahkarami, Rajesh Sankaran, Nicola Ferrier, and Pete Beckman.
Journal of Parallel and Distributed Computing 167, 97-108

Hands-On Computer Science: The Array of Things Experimental Urban Instrument

Charlie Catlett, Pete Beckman, Nicola Ferrier, Michael E. Papka, Rajesh Sankaran, Jeff Solin, Valerie Taylor, Douglas Pancoast, and Daniel Reed.
Computing in Science & Engineering 24 (1), 57-63

Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

Seongha Park, Yongho Kim, Nicola J. Ferrier, Scott M. Collis, Rajesh Sankaran and Pete H. Beckman.
Atmosphere 12 (3), 395

Measuring Cities with Software-Defined Sensors

Charlie Catlett, Pete Beckman, Marc G. Berman, Nicola Ferrier, Howard Nusbaum, Michael E. Papka, and Rajesh Sankaran.
IEEE Journal of Social Computing 1 (1), 14-27

Harnessing the Computing Continuum for Programming Our World

Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck.
Fog Computing: Theory and Practice (A. Y. Zomaya, A. Abbas, and S. U. Khan, eds.), John Wiley & Sons, Hoboken, NJ.

Publications in conference proceedings

Title

Addressing the Constraints of Active Learning on the Edge

Nueve, Enrique, Sean Shahkarami, Seongha Park, and Nicola Ferrier.
2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 845-849

WeatherNet: Nowcasting Net Radiation at the Edge

Nueve, Enrique, Robert Jackson, Rajesh Sankaran, Nicola Ferrier, and Scott Collis.
2021 IEEE Conference on Technologies for Sustainability (SusTech) 1-7

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Science Projects

Wildfire Science

Snowflake Classification

Water Segmentation

Monitoring Biodiversity Using Acoustic Data

Water Level Detection

Vehicle Tracking

Characterizing Clouds

Lightning Science

Bandwidth Aware Learning

Social Distancing

Nowcasting Weather

Integrating Chameleon

Traffic State Estimation

Scalable Edge Computing at APS

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Science Projects

Wildfire Science

Snowflake Classification

Water Segmentation

Monitoring Biodiversity Using Acoustic Data

Water Level Detection

Vehicle Tracking

Characterizing Clouds

Lightning Science

Bandwidth Aware Learning

Social Distancing

Nowcasting Weather

Integrating Chameleon

Traffic State Estimation

Scalable Edge Computing at APS

- + \ No newline at end of file diff --git a/Team.html b/Team.html index 08370bf5..ff516877 100644 --- a/Team.html +++ b/Team.html @@ -15,12 +15,12 @@ - + -
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Team

The Sage team brings together experts from institutions across the country.

Sage Leadership

Pete Beckman

Northwestern University / ANL
Director

Nicola Ferrier

Northwestern University / ANL
Deputy Director / ML@Edge

Daniel Reed

University of Utah
Chief Architect

Jim Olds

George Mason University
Risk Management

Scott Collis

Northwestern University / ANL
Instru. Chair / Atmosphere

Eugene Kelly

Colorado State University
Bio / Ecology (NEON)

Frank Vernon

Univ. of California-San Diego
SoCal Network and Deployment

Charlie Catlett

Univ. of Illinois Chicago / ANL
Urban / Chicago

Ilkay Altintas

Univ. of California-San Diego / SDSC
Cyberinfrastructure / Data Architect

Rajesh Sankaran

Northwestern University / ANL
Chief Platform Architect

Valerie Taylor

University of Chicago / ANL
Education Chair / Curricula

Mike Papka

University of Illinois Chicago / ANL
Education and Research Testbed

Irene Qualters

Los Alamos National Lab
Advisory Committee Chair

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Team

The Sage team brings together experts from institutions across the country.

Sage Leadership

Pete Beckman

Northwestern University / ANL
Director

Nicola Ferrier

Northwestern University / ANL
Deputy Director / ML@Edge

Daniel Reed

University of Utah
Chief Architect

Jim Olds

George Mason University
Risk Management

Scott Collis

Northwestern University / ANL
Instru. Chair / Atmosphere

Eugene Kelly

Colorado State University
Bio / Ecology (NEON)

Frank Vernon

Univ. of California-San Diego
SoCal Network and Deployment

Charlie Catlett

Univ. of Illinois Chicago / ANL
Urban / Chicago

Ilkay Altintas

Univ. of California-San Diego / SDSC
Cyberinfrastructure / Data Architect

Rajesh Sankaran

Northwestern University / ANL
Chief Platform Architect

Valerie Taylor

University of Chicago / ANL
Education Chair / Curricula

Mike Papka

University of Illinois Chicago / ANL
Education and Research Testbed

Irene Qualters

Los Alamos National Lab
Advisory Committee Chair

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a.languages.reason.function,function(e){e.languages.sass=e.languages.extend("css",{comment:{pattern:/^([ \t]*)\/[\/*].*(?:(?:\r?\n|\r)\1[ \t].+)*/m,lookbehind:!0,greedy:!0}}),e.languages.insertBefore("sass","atrule",{"atrule-line":{pattern:/^(?:[ \t]*)[@+=].+/m,greedy:!0,inside:{atrule:/(?:@[\w-]+|[+=])/}}}),delete e.languages.sass.atrule;var t=/\$[-\w]+|#\{\$[-\w]+\}/,n=[/[+*\/%]|[=!]=|<=?|>=?|\b(?:and|not|or)\b/,{pattern:/(\s)-(?=\s)/,lookbehind:!0}];e.languages.insertBefore("sass","property",{"variable-line":{pattern:/^[ \t]*\$.+/m,greedy:!0,inside:{punctuation:/:/,variable:t,operator:n}},"property-line":{pattern:/^[ \t]*(?:[^:\s]+ *:.*|:[^:\s].*)/m,greedy:!0,inside:{property:[/[^:\s]+(?=\s*:)/,{pattern:/(:)[^:\s]+/,lookbehind:!0}],punctuation:/:/,variable:t,operator:n,important:e.languages.sass.important}}}),delete e.languages.sass.property,delete e.languages.sass.important,e.languages.insertBefore("sass","punctuation",{selector:{pattern:/^([ 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