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Pebblebrook Hotel Trust Completes Sale of Onyx Hotel

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BETHESDA, Md.–(BUSINESS WIRE)–lt;a href=”https://twitter.com/search?q=%24PEB&src=ctag” target=”_blank”gt;$PEBlt;/agt; lt;a href=”https://twitter.com/hashtag/PEB?src=hash” target=”_blank”gt;#PEBlt;/agt;–Pebblebrook Hotel Trust (NYSE: PEB) (the “Company”) announced that it
closed on the sale of the 112-room Onyx Hotel in Boston, Massachusetts
for $58.3 million on May 29, 2019.

The sale price of $58.3 million reflects a 15.3x EBITDA multiple and a
5.9% net operating income capitalization rate (after an assumed annual
capital reserve of 4.0% of total hotel revenues) based on the hotel’s
operating performance for 2018.

Proceeds from the sale of the Onyx Hotel will be utilized for general
business purposes which may include reducing the Company’s outstanding
debt. As a result of this completed sale, the Company estimates its
total net debt to trailing 12-month corporate EBITDA will be
approximately 4.7 times at the end of the second quarter 2019.

About Pebblebrook Hotel Trust

Pebblebrook Hotel Trust (NYSE: PEB) is a publicly traded real estate
investment trust (“REIT”) and the largest owner of urban and resort
lifestyle hotels in the United States. The Company owns 60 hotels,
totaling approximately 14,500 guest rooms across 16 urban and resort
markets with a focus on the west coast gateway cities. For more
information, visit www.pebblebrookhotels.com
and follow us at @PebblebrookPEB.

For further information about the Company’s business and financial
results, please refer to the “Management’s Discussion and Analysis of
Financial Condition and Results of Operations” and “Risk Factors”
sections of the Company’s SEC filings, including, but not limited to,
its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q,
copies of which may be obtained at the Investor Relations section of the
Company’s website at
www.pebblebrookhotels.com.

This press release contains certain “forward-looking statements” made
pursuant to the safe harbor provisions of the Private Securities Reform
Act of 1995.
Forward-looking statements are generally
identifiable by use of forward-looking terminology such as “may,”
“will,” “should,” “potential,” “intend,” “expect,” “seek,” “anticipate,”
“estimate,” “approximately,” “believe,” “could,” “project,” “predict,”
“forecast,” “continue,” “assume,” “plan,” references to “outlook” or
other similar words or expressions. Forward-looking statements are based
on certain assumptions and can include future expectations, future plans
and strategies, financial and operating projections and forecasts and
other forward-looking information and estimates.
Examples of
forward-looking statements include the following: the Company’s net debt
and EBITDA; descriptions of the Company’s plans; forecasts of the
Company’s future economic performance and its share of future markets;
forecasts of hotel industry performance; and descriptions of assumptions
underlying or relating to any of the foregoing expectations including
assumptions regarding the timing of their occurrence.
These
forward-looking statements are subject to various risks and
uncertainties, many of which are beyond the Company’s control, which
could cause actual results to differ materially from such statements.

These risks and uncertainties include, but are not limited to, the
state of the U.S. economy and the supply of hotel properties, and other
factors as are described in greater detail in the Company’s filings with
the Securities and Exchange Commission, including, without limitation,
the Company’s Annual Report on Form 10-K for the year ended December 31,
2018.
Unless legally required, the Company disclaims any
obligation to update any forward-looking statements, whether as a result
of new information, future events or otherwise.

For further information about the Company’s business and financial
results, please refer to the “Management’s Discussion and Analysis of
Financial Condition and Results of Operations” and “Risk Factors”
sections of the Company’s SEC filings, including, but not limited to,
its Annual Report on Form 10-K and Quarterly Reports on Form 10-Q,
copies of which may be obtained at the Investor Relations section of the
Company’s website at
www.pebblebrookhotels.com.

All information in this press release is as of May 30, 2019. The
Company undertakes no duty to update the statements in this press
release to conform the statements to actual results or changes in the
Company’s expectations.

For additional information or to receive press releases via email,
please visit our website at
www.pebblebrookhotels.com

 
Pebblebrook Hotel Trust
Onyx Hotel
Reconciliation of Hotel Net Income to Hotel EBITDA and Hotel Net
Operating Income
Trailing Twelve Months
(Unaudited, in millions)
 

Twelve months ended
December 31,

2018  
 
Hotel net income $2.4
 
Adjustment:
Depreciation and amortization 1.4
 
Hotel EBITDA $3.8  
 
Adjustment:
Capital reserve (0.4 )
 
Hotel Net Operating Income $3.4  
 
 

This press release includes certain non-GAAP financial measures
as defined under Securities and Exchange Commission (SEC) rules.
These measures are not in accordance with, or an alternative to,
measures prepared in accordance with U.S. generally accepted
accounting principles, or GAAP, and may be different from non-GAAP
measures used by other companies. In addition, these non-GAAP
measures are not based on any comprehensive set of accounting
rules or principles. Non-GAAP measures have limitations in that
they do not reflect all of the amounts associated with the hotel’s
results of operations determined in accordance with GAAP.

The Company has presented trailing twelve-month hotel EBITDA
and trailing twelve-month hotel net operating income after capital
reserves because it believes these measures provide investors and
analysts with an understanding of the hotel-level operating
performance. These non-GAAP measures do not represent amounts
available for management’s discretionary use, because of needed
capital replacement or expansion, debt service obligations or
other commitments and uncertainties, nor are they indicative of
funds available to fund the Company’s cash needs, including its
ability to make distributions.

The Company’s
presentation of the hotel’s trailing twelve-month EBITDA and
trailing twelve-month net operating income after capital reserves
should not be considered as an alternative to net income (computed
in accordance with GAAP) as an indicator of the hotel’s financial
performance. The table above is a reconciliation of the hotel’s
trailing twelve-month EBITDA and net operating income after
capital reserves calculations to net income in accordance with
GAAP. Any differences are a result of rounding.

 
 
Pebblebrook Hotel Trust
Historical Operating Data
($ in millions, except ADR and RevPAR)
(Unaudited)
         
 
Historical Operating Data:
 
First Quarter Second Quarter Third Quarter Fourth Quarter Full Year
2018 2018 2018 2018 2018
 
Occupancy 76% 87% 89% 77% 82%
ADR $237 $262 $260 $246 $252
RevPAR $180 $228 $230 $190 $207
 
Hotel Revenues $346.2 $432.3 $431.1 $377.2 $1,586.8
Hotel EBITDA $97.3 $160.4 $157.3 $110.8 $525.8
Hotel EBITDA Margin 28.1% 37.1% 36.5% 29.4% 33.1%
 
First Quarter
2019
 
Occupancy 75%
ADR $250
RevPAR $188
 
Hotel Revenues $361.4
Hotel EBITDA $99.7
Hotel EBITDA Margin 27.6%
 
 

These historical hotel operating results include information
for all of the hotels the Company owned as of May 29, 2019. These
historical operating results include periods prior to the
Company’s ownership of the hotels. The information above does not
reflect the Company’s corporate general and administrative
expense, interest expense, property acquisition costs,
depreciation and amortization, taxes and other expenses. Any
differences are a result of rounding.

The
information above has not been audited and has been presented only
for comparison purposes.

 

Contacts

Raymond D. Martz, Chief Financial Officer, Pebblebrook Hotel Trust –
(240) 507-1330

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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