what can you do with a gig? - bbcmag.com · what can you do with a gig? april 5, ... •first/most...

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What Can You Do with a Gig?

April 5, 2016

William Wallace

([email protected])

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Public-private partnership

Launched at White House in 2012

501(c)(3)

What is US Ignite?

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Driving Economic Development…

Economic Development creates the conditions for economic

growth and improved quality of life by expanding the capacity of

individuals, firms, and communities to maximize the use of their

talents and skills to support innovation … and requires effective,

collaborative institutions focused on advancing mutual gain for the

public and the private sector. (EDA)

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… As reflected in early data

• Innovation and competitiveness

• GDP/employment growth

• Economic attractiveness

• Property values

• Case-by-case anecdotal evidence

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50 ms

½ sec

5 sec

1 min

10 min

100 min

16 hrs

1 week

10 weeks

2 years

KB MB GB TB PB

Commercially Available

Terra Incognita

Mobile

data

caps

Wired

data

caps

Web page

Online backup

Netflix movie

Lo

ca

vo

re

Cizzle

Synchrophaser

Remote 3D printer Fly-through data

visualization

CASA

Sim Center

Future CASA

Flood Cube Live radiology

4K video

GIGABIT FRONTIER

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BIG Quick

Sliced

Data

4K Streaming video (including VR)

IoT / CPS smart sensors

Virtual reality

Privacy

Security

Symmetric gigabit networking

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Enable experiential learning Gigabit-networked microscopy to extend reach of research universities

Differentiators

• Chattanooga STEM students gain access to researchers, 4K microscopic images,

and knowledge from 1,800 miles away

• Students able to learn about and manipulate sophisticated microscope in real time

• Analysis of Pacific micro-organisms integrated into STEM biology curriculum

• Low-latency gigabit networks enable three simultaneous streams: high-resolution

images, video conferencing, and microscope manipulation

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Train the “tech generation” work force Virtual-reality based training for wind and solar industry workers

Differentiators

• Entry-level trainees learn relevant skills via immersive, cloud-based VR platform

• Educational modules will help train thousands of workers in solar cell and small

wind turbine design, installation and maintenance tasks

• System enhances learning and reduces investment in equipment, lab space,

personnel, and field visits

• Plans to make platform available to 60+ BTOP-funded public computing centers

in Philadelphia

• Public computing centers able to offer VR-based training by being connected to

local cloud storage and compute capabilities via gigabit networks

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Enhance healthcare delivery Remote physical therapy improves patient outcomes from their homes

Differentiators

• Interactive video conferencing and 3D sensing interface links physical

therapist in a clinic to patient in a home

• Quantitative movement assessments are computed on the patient and

delivered to therapist in real time

• Patients’ balance and gait are assessed and exercise routines updated

accordingly

• Microsoft Kinect is used for both quantitative movement assessments and

video conferencing

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Extend the impact of libraries

Transforming Chattanooga Public Library into creative hub of the community

Differentiators

• First public library to offer free gigabit access to all users

• Within 4th-floor GigLab entrepreneurs gain access to 3D printers, laser

cutters, vinyl plotters, co-working space, and enterprise-level tools

• Short-session and hands-on courses offered on using available tools

• In addition, Internet2 expects to announce that it will be able to scale US

Ignite applications (e.g., software lending library, work force development)

across its network of 6000 gigabit-connected libraries

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Spur economic development & entrepreneurship Cleveland’s EDA-funded 100 Gig fiber network drives innovation and jobs

Differentiators

• 100 Gig-enabled, 3-mile Health-Tech corridor has brought new jobs from big

data companies, developers, and data centers

• Early College High School (within John Jay HS) graduation rate went from

<40% to #1 in state in part through use of gigabit video collaboration platform

• OneCommunity partnered with 40 cities in NE Ohio through its "Big Gig

Challenge" to support deployment of broadband infrastructure to downtown

innovation zones, and foster research accelerators, public safety applications,

and new services for community health clinics

• Announcements of to-be developed 10-acre health campus and health IT high

school within Health-Tech corridor may be able to be held and announced at

June event

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Participants receive: Participants agree to:

Access to applications under development Form an organizing group to serve as the focal point

Access to a US Ignite rack Develop at least two new applications to share with

the other cities

Connection to the national GENI network Commit to maintain the GENI network infrastructure

after 3-year term

Technical support and expertise Demonstrate the applications and technology at

Summit

Funding to support the ecosystem and launch

applications

Commit to hold monthly meetings, collaborate with

other cities

Smart Gigabit Communities Project (20+ cities)

• Common locavore infrastructure.

• Applications adapted to the locavore infrastructure.

• Organizational and technical capabilities bolstered.

• Best practices shared across ecosystems.

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Kansas City

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Community Overview Unique and distinguishing characteristics:

• First/most mature Google Fiber deployment

• Regional digital smart/gigabit city capacity in KC Digital Drive

• Cisco/Sprint “smart+connected” deployment along KCMO streetcar line and

Living Lab hosted by Think Big Partners

Kansas City’s advanced network(s):

• HHs uncertain, likely 20-100k; 400+ anchor institutions offered gigabit by

Google, more from other providers

• Current operational status (online and under construction)

• Google Fiber, AT&T Gigapower, Consolidated Communications, liNKCity

• GENI rack at UMKC, also at KU in Lawrence (with connections to KC-area

campuses

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Key Local Partners

• Code for KC Brigade

(Paul Barham)

• 1 Billion Bits (Ilya

Tabakh)

• Google Developers

Group

• Universities

• Other developer

meetups

• City of Kansas City, MO

• City of Kansas City, KS

• Mid-America Regional

Council (MARC)

• UMKC

• KU/KU Med

• KC Sourcelink (Maria

Meyers

• Think Big Partners

(Herb Sih, Kari Keefe)

• Mozilla (Janice Wait)

• Digital Sandbox (Jeff

Shackelford)

• KC Startup Village

(Matthew Marcus)

PARTNERS Sources of

DEVELOPERS

Community

LEADERS

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In-home Monitoring for Caregivers Target audience: caregivers for patients with dementia

Brief description

Project develops, integrates, and tests

advanced video and networking

technologies to support family

caregivers as they manage the

behavioral symptoms of family

members with dementia

Lead developer/group

KU Medical Center Alzheimer Disease

Center

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Software Lending Library Target audience: digital divide, entrepreneurs

Brief description

Using gigabit-to-the-end user to

deliver a better virtual desktop

experience, allowing greater

access to high-powered,

expensive applications

Lead developer/group

KC Public Library,

KC Digital Drive

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What we have to offer to other communities Network assets

Multiple gigabit providers, relatively large base of gigabit FTTH users

Test environments and partners

Cisco Smart+Connected, KC Living Lab, BPU Smart Meters, Center for Health Insights

Funding sources

Digital Sandbox, Gigabit Community Fund

Startup resources

Kauffman Foundation (FastTrac), Whiteboard 2 Boardroom)

Smart city vendors

Black & Veatch, Burns & McDonnell, Sprint, Rhythm Engineering, Integrated Roadways

Forum for sharing – Gigabit City Summit

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Chattanooga

April 5, 2016

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Chattanooga, Tennessee

• Southeast TN

• Former industrial town

• Renaissance beginning in 80’s

Chattanooga’s fiber optic network

• Completed in 2009

• Served by municipal utility, EPB

• EPB Fiber Optics – 82k homes and businesses

• GENI rack at University of Tennessee at Chattanooga

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Key Local Partners

• USC

• ORNL

• EPB

• Mozilla

• Entrepreneurs

• The Enterprise Center

• NSF

• USC

• ORNL

• Foundations

• City and County

• Mayors

• Entrepreneurs

• University

Leadership

• Chamber

PARTNERS Sources of

DEVELOPERS

Community

LEADERS

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Chattanooga’s Smart Grid Reducing Outage Duration by 60% on Average

Using fiber optic network as

communications backbone, the

city’s power distribution grid has

been highly automated, resulting in

improved operational efficiencies,

more options for customers and

dramatic reduction of power

outages

Developed by EPB

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Chattanooga’s Smart Grid

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Microbiology in 4K Learning from USC without the 1800 mile commute

Using the GENI rack, Chattanooga’s

fiber optic network and a 4K

microscope, kids in Chattanooga

are learning about microbiology

from professors and tech located

1800 miles away.

Developed by USC and EPB

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What we have to offer to other communities

Business Planning

Customer and/or Tech Support

Collaboration!

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Think Big: Holistic & Comprehensive Informatics

THRIVE

DataDrivenDecisions Pa4entCenteredCare HighSpeedLow-Latencynetworks,

localcloudcompu: ng

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Quote Source (Name)

TH RI VE TI M ELY H EA LTH I N D I C ATO RS U SI N G REM O TE SEN SI N G &

I N N O VATI O N FO R TH E V I TA LI TY O F TH E EN VI RO N M EN T

Why we care so much? Approximately 50 million Americans have allergic diseases, including asthma and allergic rhinitis, both of which can be exacerbated by PM2.5.

Every day in America 44,000 people have an asthma attack, and because of asthma 36,000 kids miss school, 27,000 adults miss work, 4,700 people visit the emergency room, 1,200 people are admitted to the hospital, and 9 people die.

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15 MARCH 2016 | GENEVA

An estimated 12.6 million deaths each year are

attributable to unhealthy environments

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Quote Source (Name)

Hourly Measurements from 55 countries and more than 8,000 measurement sites from 1997-present

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Quote Source (Name)

REMOTE SENSING, MACHINE LEARNING AND PM2 . 5 4

Random Forests, etc.) that can provide multi-variate non-linear

non-parametric regression or classification based on a training

dataset. We have tried all of these approaches for estimating

PM2.5 and found the best by far to be Random Forests.

B. Random Forests

In thispaper weuseoneof themost accurate machine learn-

ing approaches currently available, namely Random Forests

[53], [54]. Random forests are composed of an ensemble of

decision trees [55]. Random forests have many advantages

including their ability to work efficiently with large datasets,

accommodate thousands of input variables, provide a measure

of the relative importance of the input variables in the re-

gression, and effectively handling datasets containing missing

data.

Each tree in the random forest is a decision tree. A decision

tree is a tree-like graph that can be used for classification

or regression. Given a training dataset, a decision tree can

be grown to predict the value of a particular output variable

based on a set of input variables [55]. The performance

of the decision tree regression can be improved upon if,

instead of using a single decision tree, we use an ensemble

of independent trees, namely, a random forest [53], [54]. This

approach is referred to as tree bootstrap aggregation, or tree

bagging for short.

Bootstrapping is a simple way to assign a measure of ac-

curacy to a sample estimate or a distribution. This is achieved

by repeatedly randomly resampling the original dataset to

provide an ensemble of independently resampled datasets.

Each member of the ensemble of independently resampled

datasets is then used to grow an independent decision tree.

The statistics of random sampling means that any given tree

is trained on approximately 66% of the training dataset and

so approximately 33% of the training dataset is not used in

training any given tree. Which 66% is used is different for

each of the trees in the random forest. This is a very rigorous

independent sampling strategy that helps minimize over fitting

of the training dataset (e.g. learning the noise). In addition, in

our implementation we keep back a random sample of data not

used in the training for independent validation and uncertainty

estimation.

The members of the original training dataset not used in a

given bootstrap resample are referred to as out of bag for

this tree. The final regression estimate that is provided by

the random forest is simply the average of the ensemble of

individual predictions in the random forest.

A further advantageof decision trees is that they can provide

us the relative importance of each of the inputs in constructing

the final multi-variate non-linear non-parametric regression

model (e.g. Tables II and III).

C. Datasets Used in Machine Learning Regression

1) PM2.5 Data: As many hourly PM2.5 observations

as possible that were available from the launch of Terra

and Aqua to the present were used in this study. For

the United States this data came from the EPA Air

Quality System (AQS) http://www.epa.gov/ttn/airs/airsaqs/

TABLE IIVARIABLES USED IN THE MACHINE LEARNING ESTIMATE OF PM2.5 FOR

THE MODIS COLLECTION 5.1 PRODUCTS FOR THE TERRA AND AQUA

DEEP BLUE ALGORITHM SORTED BY THEIR IMPORTANCE. THE MOST

IMPORTANCE VARIABLE FOR A GIVEN REGRESSION IS PLACED FIRST WITH

A RANK OF 1.

Terra DeepBlue

Rank Source Variable Type

1 Population Density Input

2 Satellite Product Tropospheric NO2 Column Input

3 Meteorological Analyses Surface Specific Humidity Input

4 Satellite Product Solar Azimuth Input

5 Meteorological Analyses Surface Wind Speed Input

6 Satellite Product White-sky Albedo at 2,130 nm Input

7 Satellite Product White-sky Albedo at 555 nm Input

8 Meteorological Analyses Surface Air Temperature Input

9 Meteorological Analyses Surface Layer Height Input

10 Meteorological Analyses Surface Ventilation Velocity Input

11 Meteorological Analyses Total Precipitation Input

12 Satellite Product Solar Zenith Input

13 Meteorological Analyses Air Density at Surface Input

14 Satellite Product Cloud Mask Qa Input

15 Satellite Product Deep Blue Aerosol Optical Depth 470 nm Input

16 Satellite Product Sensor Zenith Input


18 Meteorological Analyses Surface Velocity Scale Input


20 Satellite Product Deep Blue Angstrom Exponent Land Input


22 Satellite Product Scattering Angle Input

23 Satellite Product Sensor Azimuth Input

24 Satellite Product Deep Blue Surface Reflectance 412 nm Input





29 Satellite Product Cloud Fraction Land Input




In-situ Observation PM2 . 5 Target

Aqua DeepBlue

Rank Source Variable Type

1 Satellite Product Tropospheric NO2 Column Input

2 Satellite Product Solar Azimuth Input

3 Meteorological Analyses Air Density at Surface Input

4 Satellite Product Sensor Zenith Input


6 Population Density Input


8 Meteorological Analyses Surface Air Temperature Input

9 Meteorological Analyses Surface Ventilation Velocity Input

10 Meteorological Analyses Surface Wind Speed Input



13 Satellite Product Solar Zenith Input

14 Meteorological Analyses Surface Layer Height Input





19 Satellite Product Sensor Azimuth Input

20 Satellite Product Scattering Angle Input

21 Meteorological Analyses Surface Velocity Scale Input

22 Satellite Product Cloud Mask Qa Input





27 Meteorological Analyses Total Precipitation Input



30 Satellite Product Deep Blue Angstrom Exponent Land Input

31 Meteorological Analyses Surface Specific Humidity Input

32 Satellite Product Cloud Fraction Land Input

In-situ Observation PM2 . 5 Target

detaildata/downloadaqsdata.htm and AirNOW http://www.

airnow.gov. In Canada the data came from http://www.

etc-cte.ec.gc.ca/napsdata/main.aspx. In Europe the data came

from AirBase, the European air quality database main-

tained by the European Environment Agency and the Euro-

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Long-Term Average 1997-present

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FlightonNov18,2014clearskies FlightonDec04,2014hazy/overcast

Japan USA WHO/EU

AnnualAvg.: 15μg/m3 AnnualAvg.: 12μg/m3 AnnualAvg.: 25μg/m3

24hourAvg.: 35μg/m3 24hourAvg.: 35μg/m3 AnnualAvg.:20μg/m3

PM2.5AirQualityStandards

DaywithinEPAAirQualityStandards DaywithexceedanceofEPAAirQualityStandards

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Quote Source (Name)

G EO LO C ATED A LLERG EN SEN SI N G PLA TFO RM

G A SP

Four objectives:

1. Develop and deploy an array of Internet of Things remote airborne

particle sensors within Chattanooga to be used to provide real-time

streamed data on hourly particulate levels, both pollen- sized (10-40

micron) and smaller (<2.5 micron) particles. 2. Deploy an in-situ pollen air sampler in Chattanooga to identify specific

pollen types. 3. Merge locally streamed data with already-collected, satellite-based

NASA data to complement and enhance the newly-collected particulate

data and generate Chattanooga-focused particulate maps. 4. Develop web-based visual tools to provide real-time pollen and smaller

particle alerts to end users such as asthma patients, health institutions,

and businesses and other institutions affected by elevated pollen levels.

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VA Decision Support Tools

More Than 40 Data Products from In-situ Observations, NASA Earth Observations, Earth System Models, Population Density & Emission Inventories

Personalized Alerts Dr. WatsonStaffing & Resource

Management

Machine Learning

Daily Global Air

Quality Estimates

NASA Earth Observation Data

NASA Earth System Model Products

Population Density and Other Related Products

ER AdmissionsAll ICD Codes

All Prescriptions

Machine

Learning

Machine

Learning

THRIVE Medical Environment Analytics

Engine

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OneCommunity Greater Cleveland

Ohio

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OneCommunity Overview Accelerating the speed of innovation through digital technologies

and a cultural shift that benefits the community Unique and distinguishing characteristics:

• Non-Profit Organization

• All Fiber Network

• Connecting 800 Community Anchor Institutions at 100 Gig Ready

• Community Priorities for Access and Application Development

• Focus on Big Data and IoT for Community Benefit

OneCommunity’s advanced network(s):

• Operated by Everstream 2500 route miles in NEOhio

• 12 years in operation

• GENI rack operational at Case Western Reserve University

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Key Local Partners

• University Partners

• Tech Elevator

• StartMart

• Open Cleveland

• OpenNEO

• HackCLE

• Case Western

Reserve University

• University Hospitals

• Metro Health

Hospitals

• Cleveland Clinic

• Cleveland

Metropolitan Schools

• NASA Glenn

• Ideastream PBS

• Sari Feldman,

Director Cuyahoga

County Public

Libraries

• Brad Whitehead,

President Fund for

our Economic Future

• Marco Costa

PARTNERS Sources of

DEVELOPERS Community

LEADERS

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Community-Based Chronic Disease Management

LocaVore + Connected Collaboration+ Augmented Reality

Our goal is to improve health outcomes

focused on Type II diabetes and asthma

through community wellness collaborations

enabled through the design of and

integration with interactive AR digital

technologies starting with community

anchor institutions connected to advanced

network infrastructure and local cloud

resources. OneCommunity

University Hospitals Health System

Weatherhead School of Management

Case Western Reserve University

Cuyahoga County Public Library

Healthy Cleveland Healthy Cuyahoga

Cleveland Metropolitan School District

Digital Illusions

HEALTH

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IoT and Big Data for Public Benefit Safe + Smart + Social + Health = Digitization for the Commons

We are designing and constructing a

network of IoT and Big Data demonstration

projects along Cleveland’s HTC to explore

the value of creating a public good

perspective on the emerging IoT revolution

combining Eds+Meds, Transportation,

Retails, and Public Spaces and Recreation

OneCommunity

University Circle Incorporated

Greater Cleveland Regional Transit Authority

Cleveland Public Power

University Hospitals

Case Western Reserve University

Health Tech Corridor

Cleveland Public Square

Destination Cleveland

MANUFACTURING

TRANSPORTATION

PUBLIC SAFETY

HEALTH

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Proposal for US Ignite Community - CITI-NET

A Multi-City Next Generation Network-Enabled Interactive STEM Network

Coordinated Interactive Telelearning In Networked Engineering Technology (CITI-NET)

CITI-NET location

what can you do with a gig? - bbcmag.com · what can you do with a gig? april 5, ... •first/most...

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