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Stone Harbor Emerging Markets Total Income Fund

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Notification of Sources of Distribution

Statement Pursuant to Section 19(a) of the Investment Company Act
of 1940

NEW YORK–(BUSINESS WIRE)–On May 30, 2019, the Stone Harbor Emerging Markets Total Income Fund
(NYSE: EDI) (the “Fund”), a closed-end fund, will pay a monthly
distribution on its common stock of $0.1511 per share to shareholders of
record at the close of business on May 17, 2019. The Fund, acting in
accordance with an exemptive order received from the Securities and
Exchange Commission and with approval of its Board of Trustees, adopted
a managed distribution policy under which the Fund may utilize capital
gains, where applicable, as part of regular monthly cash distributions
to its shareholders. This policy gives the Fund greater flexibility to
realize capital gains and to distribute those gains to shareholders.

The following table sets forth the estimated amounts of the current
distribution and the cumulative distributions paid this fiscal
year-to-date from the sources indicated in the table. In addition, the
table shows the percentages of the total distribution amount per share
attributable to (i) net investment income, (ii) net realized short-term
capital gain, (iii) net realized long-term capital gain and (iv) return
of capital or other capital source. These percentages are disclosed for
the current distribution as well as the fiscal year-to-date cumulative
distribution amount per share for the Fund.

                 
Current Distribution from:            
Per Share ($) %
Net Investment Income 0.0756 50.03 %
Net Realized Short-Term Capital Gains 0.0000 0.00 %
Net Realized Long-Term Capital Gains 0.0000 0.00 %
Return of Capital or other Capital Source 0.0755 49.97 %
Total (per common share) 0.1511 100.00 %
 
Fiscal Year-to-Date Cumulative

Distributions from1:

Per Share ($) %
Net Investment Income 0.5295 58.41 %
Net Realized Short-Term Capital Gains 0.0000 0.00 %
Net Realized Long-Term Capital Gains 0.0000 0.00 %
Return of Capital or other Capital Source 0.3771 41.59 %
Total (per common share)         0.9066     100.00 %

________________

1 The Fund’s fiscal year is December 1 to November 30.
Information shown is for the period beginning December 1, 2018.

Shareholders should not draw any conclusions about the Fund’s
investment performance from the amount of this distribution or from the
terms of the Fund’s managed distribution policy.

The Fund estimates that it has distributed more than its income and
net realized capital gains; therefore, a portion of your distribution
may be a return of capital.
A return of capital may occur, for
example, when some or all of the money that you invested in the Fund is
paid back to you.
A return of capital distribution does not
necessarily reflect the Fund’s investment performance and should not be
confused with ‘yield’ or ‘income.’

The amounts and sources of distributions reported in this 19(a)
Notice are only estimates, may change over time and are not being
provided for tax reporting purposes.
The actual amounts and
sources of the amounts for tax reporting purposes will depend upon the
Fund’s investment experience during the remainder of its fiscal year and
may be subject to changes based on tax regulations. The Fund will send
you a Form 1099-DIV for the calendar year that will tell you how to
report these distributions for federal income tax purposes.

Presented below are return figures, based on the change in the Fund’s
Net Asset Value per share (“NAV”), compared to the annualized
distribution rate for this current distribution as a percentage of the
NAV on the last day of the month prior to distribution declaration date.

Fund Performance & Distribution Information

 
Annualized Distribution Rate as a Percentage of NAV^         16.12 %
Cumulative Distribution Rate as a Percentage of NAV*         8.06 %
Cumulative Total Return as a Percentage of NAV**         6.06 %
Average Annual Total Return***         0.89 %

^ Based on the Fund’s NAV as of April 30, 2019 and the May 30, 2019
distribution.

* Based on the Fund’s NAV as of April 30, 2019 and includes
distributions through May 30, 2019.

** Cumulative Total Return is the percentage change in the Fund’s NAV
including distributions paid and assuming reinvestment of these
distributions for the period December 1, 2018 through April 30, 2019.

*** Average Annual Total Return represents the compound average of the
Annual NAV Total Returns of the Fund for the five year period ending
April 30, 2019. Annual NAV Total Return is the percentage change in the
Fund’s NAV over a year including distributions paid and assuming
reinvestment of these distributions.

While the NAV performance may be indicative of the Fund’s investment
performance, it does not measure the value of a shareholder’s investment
in the Fund. The value of a shareholder’s investment in the Fund is
determined by the Fund’s market price, which is based on the supply and
demand for the Fund’s shares in the open market.

The Fund’s Board of Trustees reviews the amount of any distributions
made pursuant to the Fund’s distribution policy and considers the income
earned and capital gain realized by the Fund, as well as the Fund’s
available capital. The Board of Trustees will continue to monitor the
Fund’s distribution level, taking into consideration, among other
things, the Fund’s net asset value and market conditions. The Fund’s
distribution policy is subject to modification, suspension or
termination by the Board of Trustees at any time, which could have an
adverse effect on the market price of the Fund’s shares. The
distribution rate should not be considered the dividend yield or total
return on an investment in the Fund.

Contacts

Stone Harbor Emerging Markets Total Income Fund (EDI)
John
Dispigno, 1-877-206-0791

Cannabis

Strainprint™ Technologies Welcomes Organic Medical Cannabis Producer to Growing List of Subscribers

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Strainprint™ Technologies Ltd, the leader in cannabis data and analytics is pleased to expand their nation-wide coverage with the addition of Stewart Farms.

Stewart Farms is a late-stage applicant headquartered in Alberta that is building a 100,000 sq. ft. vertical aquaponics farm in Saint StephensNew Brunswick. They will be utilizing automation, vertical farming, and land-based aquaculture to produce medical grade organic cannabis for both recreational and medical markets in Canada. All of their products will be free of herbicides, pesticides and synthetic nutrients. At full capacity, they will reach more than 10,000 kgs per year of organically farmed cannabis and more than 200,000 kgs of organic tilapia.

Stewart Farms’ long term vision is to deliver tools and products to support  future customers and patients during their health and wellness journeys. “Through our partnership with Strainprint we gain direct access to their incomparable data and analytics tools. This will aid us in educating a wider audience on the medical benefits and best practices of cannabis-based medicine,” said Tanner Stewart, Co-founder & CEO of Stewart Farms. “We know our customers are looking to consume more than dried cannabis. We know Canadians of all legal ages are trying to sort through their personal engagement with cannabis as a medicine. What we want to know, on an ongoing basis, is what is and is not working for people. Strainprint’s patient-led data services will give us the insight needed to create custom products and further understand the benefits of our existing products. Finally, our goal is to partner with companies and teams that truly have people’s best interest at heart. Caring about a patient’s success is what makes up the core of the Strainprint team. We couldn’t be happier to move forward with them at our side.”

Stewart Farms will access the Strainprint Analytics web platform, the most sophisticated cannabis analytics platform available to improve product development. Strainprint Analytics is built on top of the largest and most granular scientific data set of its kind in the world, with more than 1.3 million anonymized, patient reported medical cannabis outcomes and more than 65 million data points on strain efficacy.

“We’re thrilled to provide our real-time, crowd sourced data to an environmentally conscious company that uses innovative, cutting edge farming technology to produce top-tier medical cannabis,” said Strainprint CEO, Andrew Muroff. “Our organizations are equally committed to improving lives using cannabis therapy, and our shared core motivation is to provide guidance and support to help cannabis patients achieve their health goals.”

 

SOURCE Strainprint Technologies Ltd.

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Field Trip Ventures Inc. Retains KCSA Strategic Communications as Public Relations Counsel

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Greenery Map, the world’s first and only cannabis search engine to allow users to search for cannabis products based on their desired mood, medicinal use, and method of consumption, is now offering users – both businesses and consumers – the opportunity to register and connect to the app in an entirely mobile way. Previously, businesses were required to access GreeneryMap.com from a desktop in order to manage accounts and connect to the inventory API system, but that road block has been removed, allowing for an entirely mobile experience through the Greenery Map app.

App users turn to Greenery Map to help them decide on the strain of Cannabis and product that is right for them depending on their desired mood or medicinal effect. Following the matching, consumers are given a detailed history and description of the strain so that they can make educated purchases, cutting down on time that budtenders need to spend with patrons and speeding up the purchase process. Consumers can then see all of the ways to purchase said product, including the nearby dispensaries that have it in stock, online options, and even delivery options through the app. Greenery Map fully integrates with a dispensary’s inventory API system to update in real-time and give consumers real-time information on dispensary stock for the item they are looking for. The Greenery Map system also provides cannabis businesses in both the B2B and B2C sectors a private database to share and sell information and products. For Cannabis businesses, Greenery Map also offers the opportunity to open bank and merchant accounts through their partnership with PayHouse Consulting Group.

 

SOURCE Greenery Map

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WeBank, IBM and Other Organizations Jointly Held the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019

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Once a concept, AI is now ushering in a key stage of application. What’s the solution to the data silos among businesses? Given the enhanced regulation on data at home and abroad, what’s the solution to data privacy and security concerns? What’s the status quo of Federated Machine Learning and how to establish an ecosystem for FML in the future?

WeBank, IBM and other organizations jointly held the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) in conjunction with the 28th International Joint Conference on Artificial Intelligence (IJCAI-19) on Aug. 12, 2019, to further discussion on these issues.

President of IJCAI, Chair of FML Steering Committee, Chief AI Officer of WeBank Professor Qiang Yang delivered opening remarks at the workshop. Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google presented keynote addresses. In the panel discussion, top scholars from WeBank, Bar-Ilan University, IBM, Squirrel AI, Google, Huawei, Clustar, Sinovation Ventures and many other renowned enterprises and universities shared and discussed their findings and experience in FML as an emerging AI technology.

This workshop received 40 papers, of which 12 were presented during the workshop, 19 presented via poster. Awards include Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award, Best Presentation Award. Selected high quality papers will be invited for publication in a special issue in the IEEE Intelligent Systems journal. All these attracted numerous scholars to engage in discussions and join efforts for building the FML ecosystem.

Experts from IBM and Google Share Groundbreaking Findings with a Focus on the Theory and Application of FML

Privacy and security are becoming a key concern in our digital age. On 25th May last year, the implementation of General Data Protection Regulation (GDPR) by the EU, the toughest Act on data privacy protection, stressed that user data collection must be open and transparent. A series of laws and regulations from China and overseas also pose new challenges to the traditional way of handling data and model for cooperation. Seeking ways for AI to adapt to this new reality became top priority, a demand that led to this workshop on FML.

A wealth of solutions and breakthroughs were shared by Dr. Shahrokh Daijavad from IBM and Dr. Jakub Konečný from Google in their speech on FML.

Besides how FML can help tackle challenges in the business world, Dr. Shahrokh Daijavad also shared the concept of Fusion AI, which means to train models on widely distributed data sets, but fuse them to produce one equivalent to what centralized training would yield. “Unlike traditional machine learning, in Fusion AI, model parameters are shared and data is not transferred, which makes Fusion AI model better than models that moving data centrally.” Given the widely distributed data, the development of Fusion AI and FML became ever important and imminent.

“FML enables machine learning engineers and data scientists to work productively with decentralized data with privacy by default,” said Dr. Jakub Konečný from Google. He also shared with us how FML works and its use cases at Google. In the case of Gboard, as on-device data is privacy sensitive or large or is more relevant than server-side proxy data, and labels can be inferred naturally via user interaction, the application of Federated RNN compared to prior n-gram model can increase the accuracy of next-word prediction by 24%, and the click rate of prediction strip by 10%.

Major Figure Panelists Discuss the Way Ahead for FML

The moderator of the panel discussion, AI Principal Scientist of WeBank Dr. Lixin Fan joined panelists including Professor Benny Pinkas from Bar-Ilan University, Dr. Shahrokh Daijavad of IBM Academy of Technology, Chief Architect of Squirrel AI Dr. Richard Tong, Research Scientist of Google Dr. Jakub Konečný, Dr. Baofeng Zhang from CTO Office of CBG Software in Huawei, Executive VP of Clustar Dr. Junxue Zhang, VP of AI Institute in Sinovation Ventures Dr. Ji Feng and other experts in a host of in-depth exchanges with attendees, to shed light on the way ahead for FML.

Experts shared thoughts in the panel discussion on questions including but not limited to: How to meet the security and compliance requirements? Is there a way to extend the value of data while observing user privacy and data security? Given the classic trade-off between data regulation and development of AI, how to achieve the long-term goal of establishing a stable and win-win business ecosystem?

List of Award-Winners

Best Theory Paper Award, Best Application Paper Award, Best Student Paper Award and Best Presentation Award selected by all attendees were announced at the closing of the workshop.

Best Theory Paper Award: 
Preserving User Privacy for Machine Learning: Local Differential Privacy or Federated Machine Learning? By Huadi Zheng, Haibo Hu & Ziyang Han;

Best Application Paper Award: 
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare. By Yiqiang ChenJindong WangChaohui YuWen Gao & Xin Qin;

Best Student Paper Award: 
Quantifying the Performance of Federated Transfer Learning. By Qinghe JingWeiyan Wang, Junxue Zhang, Han Tian & Kai Chen;

Best Presentation Award: 
Federated Generative Privacy. By Aleksei Triastcyn and Boi Faltings.

President of IJCAI, Chief AI Officer of WeBank Professor Qiang Yang, Chief Architect of Squirrel AI Dr. Richard TongandVP of AI Institute in Sinovation Ventures Dr. Ji Feng presented the awards.

“The mission of this International Federated Machine Learning Workshop is to facilitate further understanding in the academia, business community as well as legal and regulatory institutions by promoting the establishment of FML ecosystem in the hope that more businesses will join and build a platform for students aspired to work in FML to find research teams that suit them,” said Professor Qiang Yang.

Held Aug. 10-16, 2019 in Macao, China, IJCAI-19 is one of the leading International Academic Conference on AI, attracting over 3000 AI research personnel and experts. The 1st International Workshop on Federated Machine Learning (FML’19) was a highlight for experts joining this event. Visionaries in the academia and industrial sector expressed the willingness to be part of the effort for academic research, application of FML in the future, and the development and boom of AI ecosystem.

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