<div dir="ltr"><div class="gmail_default" style="font-size:small">Dear all,</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">I hope you are all ready and excited for the ESIP virtual summer meeting. Our cluster members will be busy during the summer meeting. There are three sessions organized by cluster members. In the July edition of our cluster newsletter, you will find a preview of these sessions and we hope we will see you in some of these sessions and other great discussions during the summer meeting, </div><div class="gmail_default" style="font-size:small">The newsletter also included exciting developments of the labeled dataset for machine learning in Earth sciences as well as COVID-19 related resources that may be relevant to the community. We hope you enjoy reading the newsletter!</div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">See you at the summer meeting!</div><div class="gmail_default" style="font-size:small">Best,</div><div class="gmail_default" style="font-size:small">Yuhan (Douglas) Rao</div><div class="gmail_default" style="font-size:small">ESIP Community Fellow - Machine Learning<br></div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small"><span id="gmail-docs-internal-guid-c4f2e05f-7fff-5308-01c9-c7b69a32e582"><p dir="ltr" style="line-height:1.44;margin-top:0pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(109,100,232);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Earth Science Information Partners</span></p><p dir="ltr" style="line-height:1.2;margin-top:10pt;margin-bottom:0pt"><span style="font-size:22pt;font-family:Roboto,sans-serif;color:rgb(40,53,146);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Machine Learning Cluster Newsletter</span></p><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">July 13, 2020</span></p><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"></p><hr><p></p><p dir="ltr" style="line-height:1.2;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(235,63,121);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><span style="border:none;display:inline-block;overflow:hidden;width:447px;height:216px"><img src="https://lh6.googleusercontent.com/XGBcmZw9mfc5B8HpW-S3AqS14q2WiqrhzJ9thXtGizY7WgDqODSehc8ckfmacVXdqMRVkITHn_MEW_mZratwNdfdmiEcwx434AOiyWJoML20x_zo1XLz3i7a3_pX9steTm0FRPnU" width="447" height="356.855902369512" style="margin-left: 0px;"></span></span></p><h1 dir="ltr" style="line-height:1.2;margin-top:20pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Greetings from Cluster Chair and Fellow</span></h1><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">We hope to see you at the ESIP 2020 Virtual Summer Meeting, starting this week. Even as it’s ESIP’s first virtual meeting, the meeting attendance list is the longest in ESIP history (with 501 registered participants)! ML Cluster members have organized three sessions focusing on machine learning and its applications, described below. Also, changes are forthcoming in Cluster leadership.</span></p><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">From outside the Cluster, more data is becoming available, such as the Radiant Earth Foundation’s large-scale collection of labeled geospatial data for land cover in ten European countries.  We also provide references to ‘</span><span style="font-size:11pt;font-family:Arial;color:rgb(41,41,41);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">plainspoken’ articles about machine learning and its applications in Earth sciences.</span></p><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Finally, resources useful for ML and COVID-19 applications in Earth Science are being made available in the form of initiatives, solicitations, data, and computing resources, we provide some references below.</span></p><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">If you have any news and topics related to machine learning in Earth sciences, please feel free to let us know so we can share it with the community.</span></p><p dir="ltr" style="line-height:1.38;margin-top:12pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Anne Wilson, Ronin Institute</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Yuhan Rao, North Carolina Institute for Climate Studies</span></p><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">July 13, 2020</span></p><h1 dir="ltr" style="line-height:1.38;margin-top:10pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Inside the cluster</span><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span></h1><p dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Highlights of ESIP-ML and cluster members’ activities</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">2020 Virtual ESIP Summer Meeting</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The ESIP 2020 Summer meeting starts this week, see the schedule here on </span><span style="text-decoration-line:underline;font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><a href="https://2020esipsummermeeting.sched.com" style="text-decoration-line:none">Sched</a></span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The ML Cluster members have organized three virtual sessions focused on machine learning and its applications in Earth sciences. </span></p></li><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">The first session, </span><a href="https://2020esipsummermeeting.sched.com/event/cIvp/machine-learning-tutorials" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Machine Learning Tutorials</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">, (July 16, 2:00 pm Eastern time) provides a brief introduction of machine learning workflow for Earth science applications and three hands-on tutorials developed through the support of 2019 Funding Friday award. This introductory session demonstrates 3 ML learning strategies and is intended for beginners interested in experimenting with machine learning for their own Earth science applications. </span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">The second session (July 21, 2:00 pm Eastern time) focuses on the discussion of </span><a href="https://2020esipsummermeeting.sched.com/event/cT76/organizational-strategies-standards-and-policies-for-ml-are-there-any" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">organizational strategies for adopting machine learning in the Earth science domain</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. We have an exciting panel consisting of representatives from government agencies (NOAA and USGS) as well as industries (Microsoft and Element 84) to discuss organizational best practices and strategies for using ML.   This session is also a lead into the follow-up session, where we’ll discuss how the Cluster can move forward to best support ESIP members, such as these organizations.    If you have any questions related to either session, please contact </span><a href="mailto:anne.wilson@ronininstitute.org" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Anne Wilson</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> and/or </span><a href="mailto:yuhan.rao@gmail.com" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Yuhan Rao</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">The third session (July 22, 4:00 pm Eastern time) led by </span><span style="font-size:11pt;color:rgb(17,85,204);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Ziheng (Jensen) Sun</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> will bring together practitioners of artificial intelligence to showcase </span><a href="https://2020esipsummermeeting.sched.com/event/cIuH/understanding-and-utilizing-ai-in-data-driven-earth-science" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">innovative applications of AI in data-driven Earth sciences</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The session aims to help the community accelerate the engagement between AI and Earth data and improve our ability to deliver value-added information faster and more accurately.</span></p></li></ul><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Passing of the Chair torch. </span><span style="font-size:11pt;color:rgb(34,34,34);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">After two years in the role of Cluster Chair, Anne is stepping down. Ziheng (Jensen) Sun has agreed to take the helm through December 2020, which is also when Yuhan's fellowship with the cluster ends.  Discussion and planning for Cluster leadership starting in January 2021 will be a topic for discussion within the Cluster this fall. If you are interested to lead the next chapter of the cluster, please let Anne and Jensen know. We would like to thank Anne for her leadership to the cluster in the past two years!</span></p></li></ul><h1 dir="ltr" style="line-height:1.38;margin-top:5pt;margin-bottom:0pt"><span style="font-size:20pt;font-family:Roboto,sans-serif;color:rgb(224,27,132);background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">All about Machine Learning</span></h1><p dir="ltr" style="line-height:1.68;margin-top:10pt;margin-bottom:0pt"><span style="font-size:12pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Information relevant to ESIP-ML community</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Radiant Earth released the BigEarthNet Benchmark Archive.</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> The Radiant Earth Foundation recently released the </span><a href="https://www.radiant.earth/?gclid=Cj0KCQjwuJz3BRDTARIsAMg-HxW9rs2N568ixNuExSMJFDW2tqtiOLJDMx_-Y7FGb-3Sgwl4_-kUgdYaAjJbEALw_wcB" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">BigEarthNet Benchmark Archive</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. It is a large-scale collection of labeled geospatial data for land cover in ten European countries. The archive is made available via </span><a href="http://www.mlhub.earth/" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Radiant MLHub</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> for free access. This dataset collection “</span><span style="font-size:11pt;color:rgb(41,41,41);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">consists of 590, 326 Sentinel-2 image patches with spectral bands at 10, 20, and 60-meter resolution. The satellite images were acquired in different seasons between June 2017 and May 2018 over Austria, Belgium, Finland, Ireland, Kosovo, Lithuania, Luxembourg, Portugal, Serbia, and Switzerland. Each patch is annotated with multiple land cover labels documenting the spatial distribution of every land cover class across the dataset region.” To learn more about the archive, please visit </span><a href="https://medium.com/radiant-earth-insights/bigearthnet-benchmark-archive-now-available-on-radiant-mlhub-the-open-repository-for-geospatial-d6c5dbe898c4" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Radiant Earth Foundation’s original post</span></a><span style="font-size:11pt;color:rgb(41,41,41);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(41,41,41);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Lacuna Fund to support the development of labeled data for agriculture</span><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Machine learning models can only be as good as its training data. There have been great initiatives in other fields to establish benchmark training data for ML model development and evaluation. Such initiatives are still at an early stage for Earth sciences. To close the data gap, Lacuna Fund recently announced its first request for a proposal to support the “the creation, expansion and maintenance of labeled data in the agriculture space”. More detailed information for this opportunity can be found on </span><a href="https://lacunafund.org/agriculture/" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Lacuna Fund website</span></a><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.  </span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Roboto,sans-serif;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">National Geographic AI for Earth Innovation Grants. </span><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">To address the many pressing scientific questions and challenges facing our planet, we must increase global understanding of how human activity is affecting natural systems and create a community of change, driven by data and cutting-edge technology. Modern technologies, such as satellite imaging, bioacoustic monitoring, environmental DNA, and genomics, can capture data at a global scale, but also produce massive, complex data sets. Artificial intelligence (AI) and cloud computing can capitalize on the potential of such data, leading to faster and more meaningful insights and creating the opportunity for transformative solutions. </span><a href="https://www.nationalgeographic.org/funding-opportunities/grants/what-we-fund/ai-earth-innovation/" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Learn more here</span></a><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. The deadline for the RFP is </span><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">July 22, 2020</span><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Selected recent plainspoken articles about machine learning and its applications in Earth sciences</span><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(41,41,41);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;color:rgb(26,26,26);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Wheeling, K. (2020), Improving atmospheric forecasts with machine learning, </span><span style="font-size:11pt;color:rgb(26,26,26);font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Eos, 101,</span><span style="font-size:11pt;color:rgb(26,26,26);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span><a href="https://doi.org/10.1029/2020EO145068" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">https://doi.org/10.1029/2020EO145068</span></a><span style="font-size:11pt;color:rgb(26,26,26);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Published on 02 June 2020.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(26,26,26);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;color:rgb(51,51,51);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Hudson, M. (2020), Core progress in AI has stalled in some fields, </span><span style="font-size:11pt;color:rgb(102,102,102);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Science, 368, </span><a href="https://science.sciencemag.org/content/368/6494/927?utm_campaign=toc_sci-mag_2020-05-28&et_rid=79608803&et_cid=3343139" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">DOI: 10.1126/science.368.6494.927</span></a><span style="font-size:11pt;color:rgb(102,102,102);font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Published on 29 May 2020.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(26,26,26);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Wheeling, K. (2020), Machine learning improves weather and climate models, </span><span style="font-size:11pt;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Eos, 101,</span><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span><a href="https://doi.org/10.1029/2020EO142422" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">https://doi.org/10.1029/2020EO142422</span></a><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Published on 07 April 2020.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(26,26,26);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Sima, R. J. (2020), Combining AI and analog forecasting to predict extreme weather, </span><span style="font-size:11pt;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Eos, 101,</span><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span><a href="https://doi.org/10.1029/2020EO140896" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">https://doi.org/10.1029/2020EO140896</span></a><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Published on 04 March 2020.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(26,26,26);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Kelvey, J. (2020), New study hints at bespoke future of lightning forecasting, </span><span style="font-size:11pt;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Eos, 100,</span><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> </span><a href="https://doi.org/10.1029/2020EO139829" style="text-decoration-line:none"><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">https://doi.org/10.1029/2020EO139829</span></a><span style="font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. Published on 13 February 2020.</span></p></li></ul></ul><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Resources for COVID-19, Earth sciences, and machine learning</span><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. As the invisible game-changer, COVID-19 continues to rock the world, we surveyed the literature looking for topics on machine learning (ML), the COVID-19 virus, and Earth science. With a portion of the $2 trillion CARES Act allocated to research and development, ML and AI are being heavily applied in health, biology, genomics, epidemiology, and logistics support, etc.    </span></p></li></ul><p dir="ltr" style="line-height:1.38;margin-left:18pt;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">But the application of ML towards COVID-related issues within the context of Earth science per se seems not yet as extensive.   One report, “</span><a href="https://techxplore.com/news/2020-06-satellite-images-pandemic-aid-at-risk.html" style="text-decoration-line:none"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Satellite images, phone data help guide pandemic aid in at-risk developing countries</span></a><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">”, describes using satellite imagery to determine the wealth of a region, its road degradation, and other proxies for the need to determine a region’s likelihood to need aid during the pandemic.</span></p><p dir="ltr" style="line-height:1.38;margin-left:18pt;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">However, resources useful for ML and COVID-19 applications in Earth Science are being made available in the form of initiatives, solicitations, data, and computing resources.  We provide some of them here.</span></p><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Initiatives & Solicitations</span></p></li><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">NASA, “</span><a href="http://www.spaceref.com/news/viewsr.html?pid=53475" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Making Innovative Use of NASA Satellite Data to Address Environmental, Economic, and/or Societal Impacts of the COVID-19 pandemic</span></a><span style="font-size:11pt;background-color:transparent;font-weight:400;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">” </span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">ESA contests for remote sensing experts, ML scientists, and the public to submit </span><a href="https://phys.org/news/2020-04-covid-satellites.html" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">ideas on using satellite data to assess COVID impacts</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">CARTO has grant money for small nonprofits, charities, and NGOs for using spatial data and “Location Intelligence”.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><a href="https://www.grants.gov/web/grants/view-opportunity.html?oppId=326034" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">DOD Newton Award for Transformative Ideas during the COVID-19 Pandemic</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. This award is presented to a single investigator or team of up to two investigators that develops a “transformative idea” to resolve challenges, advance frontiers, and set new paradigms in areas of immense potential benefit to DoD and the nation at large. ”</span></p></li></ul><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Data & Literature</span></p></li><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">ESRI has made available </span><a href="https://coronavirus-resources.esri.com" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">maps, datasets, and some applications</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> for COVID.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">C3.ai has created a COVID-19 data lake, providing free “unified, analysis-ready COVID-19 data’. They provide a knowledge graph that can be queried to understand and do some analysis on this space of datasets.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><a href="https://www.semanticscholar.org/cord19" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">CORD 19</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> provides a free corpus of scholarly articles about the virus.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><a href="https://www.covidscholar.org" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">COVID Scholar</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> is a COVID-19 literature search using natural language processing.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Digital Science is making all </span><a href="https://www.dimensions.ai/news/dimensions-is-facilitating-access-to-covid-19-research/" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">COVID-19 related published articles</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> available for free.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">EPA is expanding research on COVID in the environment through its </span><a href="https://www.epa.gov/homeland-security-research/homeland-security-research-webinar-series" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">homeland security research webinar series</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.</span></p></li></ul><li dir="ltr" style="list-style-type:disc;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Computing resources</span></p></li><ul style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">NCAR and the COVID-19 High-Performance Computing Consortium is making the NCAR-operated Cheyenne supercomputer available for COVID-related work. Click this </span><a href="https://news.ucar.edu/132724/ncar-operated-supercomputer-join-national-covid-19-computing-consortium" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">news releas</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">e for more information.</span></p></li><li dir="ltr" style="list-style-type:circle;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre"><p dir="ltr" style="line-height:1.38;margin-top:4pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">UNH has created a nice list of COVID-19 Funding Opportunities, “</span><a href="https://www.unh.edu/research/find-funding/covid-19-funding-opportunities-research-priorities-resources" style="text-decoration-line:none"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">highlighting opportunities and resources particularly suited to UNH</span></a><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">.”</span></p></li></ul></ul><p dir="ltr" style="line-height:1.2;margin-top:20pt;margin-bottom:0pt"></p><hr><p></p><p dir="ltr" style="line-height:1.68;margin-top:20pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Roboto,sans-serif;color:rgb(102,102,102);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">You are receiving this newsletter because you are part of the ESIP Machine Learning Cluster. If you have any news want to share or questions, please contact Anne Wilson (anne.wilson *at* <a href="http://ronininstitute.org">ronininstitute.org</a>) and Yuhan Rao (yuhan.rao *at* <a href="http://gmail.com">gmail.com</a>).</span></p><p dir="ltr" style="line-height:1.68;margin-top:20pt;margin-bottom:0pt"></p><br></span><div class="gmail_chip gmail_drive_chip" style="width:396px;height:18px;max-height:18px;background-color:#f5f5f5;padding:5px;color:#222;font-family:arial;font-style:normal;font-weight:bold;font-size:13px;border:1px solid #ddd;line-height:1"><a href="https://drive.google.com/file/d/1XE_osJKcqwybs1hyWnuZvGn6MoB38mFl/view?usp=drive_web" target="_blank" style="display:inline-block;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;padding:1px 0px;border:none;width:100%"><img style="vertical-align: bottom; border: none;" src="https://drive-thirdparty.googleusercontent.com/16/type/application/pdf"> <span dir="ltr" style="color:#15c;text-decoration:none;vertical-align:bottom">ML cluster newsletter - 2020 July - final.pdf</span></a></div><br></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>