AA_Environmantal and Technical Studies_Final Thesis_02

TERRA MEDIA How sovereign is your building Mr. Algorithm?

Nicholas Zem bashi Diploma Un it 12 TS5 2017-18 In igo Min n s + Man ijeh Verghes e

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CONTENTS

0.0 Introduction 0.1 How Sovereign is your Building Mr. Algorithm?

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0.2 Machine Learning as Design Methodology

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0.3 Analysing In-Built Bias

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1.0 WHO ARE YOU?

17

1.1 Identity Skins

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1.2 Jus Algoritmi & China's Gamified Citizenship

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2.0 WHAT IS A SOVEREIGN BUILDING?

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2.1 Sovereign Territory Spheres of Belonging Scales of Power

2.2 The Sovereignty Spectrum

3.0 MEDIATION

29 30 31 33

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3.1 Delivering the Message

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3.2 Mr. Algorithm

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3.3 The Neuron

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4.0 RECOGNITION 4.1 TEST 01: Object Detection Neural Layers Cognition

4.2 TEST 02: Column-Detection Overview Dataset and Training Column-Detection

4.3 TEST 03: Sovereignty-Detection Overview Typologies and Sovereignty Training Detection Tests

5.0 BIAS 5.1 Not so Neutral Mr. Algorithm!

51 55 58 62 65 68 70 94 101 104 107 166 174

183

Degrees of Error Column Bias Sovereignty Bias

185 188 190 192

6.0 MANIPULATION

195

5.1 TEST 04: GAN - Generating Terra Media Generating Terra Media Flags Generating a Parliament

197 198 199

7.0 SURFACE

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7.1 Screening

207 208 210

The Weather Factory The New York Stock Exchange

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9.0 APPENDIX

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10.0 Bibliography

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How sovereign is your

MEDIATION

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RECOGNITION

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M EDIA building Mr. Algorithm?

BIAS

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MANIPULATION

SURFACE

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How Sovereign is your building Mr. Algorithm?

“I

f it were true that sovereignty and freedom are the same, then indeed no man could be free, because sovereignty, the ideal of uncompromising self-sufficiency and master ship, is contradictory to the very condition of plurality. No man can be sovereign because not one man, but men, inhabit the earth.”

Ha n n a h Ar e n d t , T he Hu m a n C o n d i t i o n

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INTRODUCTION

NS A Data Cen tre, Ut ta h , USA

HOW SOVEREIGN IS YOUR BUILDING MR. ALGORITHM ?

In 2020 the Chinese government will roll out a new form of citizenship-rating system, defined by the digital data of each individual processed through various algorithms. The scoring value it will determine is what social researcher Katika Kühnreich refers to as a form of ‘gamified control’. Given such a powerful application of algorithmic software to determine abstract human behaviours such as citizenship or sovereignty there is a great urgency to investigate machine learning practises within the design realm.

W

hy should Artificial Intelligence (A.I.) concern architecture as a technical question? Algorithms with increasing degrees of independent ‘intelligence’ have already consolidated their position as invisible mediators behind weather forecasting and stock trading practises globally, permeating beyond into every aspect of business and the web. Their design applications are becoming widely available with online communities actively encouraging an open source approach to learning and deploying A.I. Meanwhile their in-built biases are a continuing issue.

‘Big Data’, - the accumulation of any information produced by 'usercitizens' - is now put to use through various machine learning processes whereby the computing power available can handle the huge amounts of data produced and stored.

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According to the UK Parliament's report on A.I: "Increased use of Artificial Intelligence (AI) can bring major social and economic benefits to the UK. With AI, computers can analyse and learn from information at higher accuracy and speed than humans can. AI offers massive gains in efficiency and performance to most or all industry sectors, from drug discovery to logistics. AI is software that can be integrated into existing processes, improving them, scaling them, and reducing their costs, by making or suggesting more accurate decisions through better use of information.

It has been estimated that AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%.Our vision is for the UK to become the best place in the world for businesses developing and deploying AI to start, grow and thrive, to realise all the benefits the technology offers. The pioneering British computer scientist Alan Turing is widely regarded as launching and inspiring much of the development of AI. This report recommends that more is done to build on Turing’s legacy to ensure the UK remains among the leaders in AI."

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b a sed on They L i ve, J ohn Ca r p enter, 1989

MACHINE LEARNING AS DESIGN METHODOLOGY How Sovereign is your building Mr. Algorithm ?

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MEDIATION

RECOGNITION

BIAS

How Sovereign is your Building Mr. Algorithm?

Can algorithms be used to recognise and design sovereign space?

TEST1.0 Object-Recognition Object-Detection Application Program Interface (API) - Pre-trained recognition.

Testing the abilities of a Machine to read the meaning in objects in real-time.

• Defining Mediation as the process through which technologies mediate engineer’s design intents to alter behaviours.

• Defining Sovereignty as a collective power to act. Mediating technologies influence behaviours of appearance and action, thus impacting Sovereignty.

• Artificial Intelligence mediating Sovereignty?

• What is a Sovereign Building? The aesthetics of space and architectural typologies linked to degrees of Sovereignty.

TEST 2.0 Column-Recognition Teaching a Machine to learn how to read columns in real-time.

Analysing the bias Reviewing the tests and analysing the biases based on the dataset construction and the recognition output.

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TEST 3.0 Sovereignty-Recognition Teaching a Machine to interpret degrees of sovereignty based on building typologies.

TEST 4.0 Applying Sovereignty-Detection to Cityscapes Seeing urban ‘Sovereignty’ through the eyes of the algorithm

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“The major advances in civilization are processes that all but wreck the societies in which they occur” –A. N. Whitehead

SURFACE

MANIPULATION TEST4.0 Space-Generation Deep Convolutional Generative Adversarial Networks.

Teaching a Machine to generate images of ‘Sovereign’ space: Individual homes / Parliaments (interiors/exteriors)

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TEST5.0 Machine reads Machine Using the customised SovereigntyDetection algorithm to identify the Sovereignty of the MachineGenerated images.

TEST4.0 Machine reads Machine Object-Detection Application Program Interface (API) - Pre-trained recognition.

Testing the abilities of a Machine to read the meaning in objects in real-time.

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The medium of surface and the politics of appearances

Br i d gi t Ba rd ot in J ea n Luc God a rd ’s L e M épri s, 196 2

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A Fu c ki n g Di dac ti c Edu c ati on al .M OV Fi l e, stil l , b y Hito Steyer l , 2 013

ANALYSING BUILT-IN BIAS The aim of the investigation is to explore how mediation becomes a tool with which behaviours can be influenced by designed intelligence. The analysis and quantification of data will be used to determine error accuracy from algorithmic recognition of architectural elements and buildings.

The input of bias into the machine learning process will also be assessed. All this to provide a critical basis for investigating the use of algorithm's in design and their potentials.

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ias exists wherever a decision is made. The investigation will highlight various areas of bias as well as their impact on outcomes. This is particularly interesting as a forensic practise for deducing how a neural network i.e. Mr Algorithm's 'brain' works when processing images.

Furthermore the issue of bias could be applied to a more philosophical argument about where the precision and factuality of engineering which is inherent in the technical process of machine

learning, meets the abstractions of architectural concept. In other words when truth and factually defined systems falter in the face of moral values or behavioural traits.

Essentially this is where the investigation calls for design urgency in that a highly technical process such as machine learning exposes a multitude of errors and societal issues when applied even to the crude tasks explored in this document of reconsigning certain images.

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Are you bound to the soil you were born on?

Jus Soli 14

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Are you bound by your family’s blood-line?

Jus Sanguinis

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?

Who are you?

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WHO ARE YOU?

The Am e r i c a n s, T V ser ies, 2 013

IDENTITY SKINS Who are you? You are neither Jus Sanguinis or Jus Soli - neither given rights by soil nor by blood.

Jus Algoritmi is the new identifier - right by Algorithm. Since identity is as fluctuating as a skin to be shed, an algorithmic citizenship is becoming more relevant and enabled by technology.

51% M A R C H 2 018

is the benchmark for being an 'American' according to the USA's National Security Agency. If you are equal to or above this you are considered American, if you are below you are a foreigner. This then allows the NSA to abuse an 1978 amendment in the law to revoke the right to privacy and spy on anyone considered un-American.

How is this defined?

This decision making comes in the form of a quantitative confidence measure. A Washington Post analysis of the downstream “PRISM Tasking Process” notes that the NSA instructed “analysts who use the system from a Web portal at Fort Meade, Md., [to] key in ‘selectors,’ or search terms, that are designed to produce at least 51 percent confidence in a target’s ‘foreignness’” (Gellman & Poitras, 2013, “Roots in the ’70s” section, para. 6). In other words ,

if a target reaches at least 51% confidence, then she is “reasonably believed” to be, and thus actionably becomes, a non–United States person—and thus a foreigner. And as a foreigner, the user is denied any Fourth Amendment protection of privacy.

The “51% confidence” standard is to jus algoritmi as a blood quantum measurement is to jus sanguinis and a birth certificate is to jus soli. But unlike the materiality that articulates one’s sanguinis or soli belonging, like blood or birthplace, the materiality of jus algoritmi is a stream of data flowing through fiber-optic cables under the ocean and in the cloud server farms of companies such as Google. These data are then distributed into the dual frameworks of reasonable belief citizen or reasonable belief foreigner according to the NSA’s algorithmic logic.

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Land or Data

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jus algoritmi (right by algorithm) jus sanguinis (right by blood) jus soli (right by soil) jus soli abolished

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JUS ALGORITMI

Ci ti z en X, b y J a m es B r id l e

CITIZEN X & CHINA’S GAMIFIED CONTROL In 2020 the Chinese government will roll out a new form of citizenship-rating system, defined by the digital data of each individual processed through various algorithms. The scoring value it will determine is what social researcher Katika Kühnreich refers to as a form of ‘gamified control’. Artist James Bridle created a critical design project, Citizen X, to instigate a debate of the issues surrounding Jus Algoritmi using data IP addresses to make up one's online citizenship.

O

n June 14, 2014, the State Council of China published an ominous - sounding document called "Planning Outline for the Construction of a Social Credit System".

In the way of Chinese policy documents, it was a lengthy and rather dry affair, but it contained a radical idea.

What if there was a national trust score that rated the kind of citizen you were?

Imagine a world where many of your daily activities were constantly monitored and evaluated: what you buy at the shops and online; where you are at any given time; who your friends are and how you interact with them; how many hours you spend watching content or playing video games; and what bills and taxes you pay (or not). It's not hard to picture, because most of that already happens, thanks to all those data-collecting behemoths like Google, Facebook and Instagram or health-tracking apps such as Fitbit. But now imagine a system where all these behaviours are rated as either positive or negative and distilled into a single number, according to rules set by the government. That would create your Citizen Score and it would tell everyone whether or not you were trustworthy. Plus, your rating would be publicly ranked against that of the entire population and used to determine your eligibility for a mortgage or a job, where your children can go to school - or even just your chances of getting a date.

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A futuristic vision of Big Brother out of control? No, it's already getting underway in China, where the government is developing the Social Credit System (SCS) to rate the trustworthiness of its 1.3 billion citizens. The Chinese government is pitching the system as a desirable way to measure and enhance "trust" nationwide and to build a culture of "sincerity". As the policy states, "It will forge a public opinion environment where keeping trust is glorious. It will strengthen sincerity in government affairs, commercial sincerity, social sincerity and the construction of judicial credibility."

In China, certain citizens, such as government officials, will likely be deemed above the system. What will be the public reaction when their unfavourable actions don't affect their score? We could see a Panama Papers 3.0 for reputation fraud. It is still too early to know how a culture of constant monitoring plus rating will turn out. What will happen when these systems, charting the social, moral and financial history of an entire population, come into full force? How much further will privacy and freedom of speech (long under siege in China) be eroded? Who will decide which way the system goes? These are questions we all need to consider, and soon. Today China, tomorrow a place near you. The real questions about the future of trust are not technological or economic; they are ethical.

If we are not vigilant, distributed trust could become networked shame. Life will become an endless popularity contest, with us all vying for the highest rating that only a few can attain.

In composite flag above is the James Bridle, an artist concerned with issues of identity and digital media. Hecreated a google extension under the project 'Citizen X'. In response to issue such as NSA surveillance or China's imminent deployment of a data-driven citizenship, Citizen X puts together a percentage of one's identity based on the IP address they frequent online, and hence the location of the data being accessed rather than that of the user.

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A L G O R I T H M I C 24

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I D E N T I T Y M A R C H 2 018

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What is a sovereign building?

continental

global

1000_max.

100_deep-state

10_social

0_zero

min.

0

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rp or at

t lis na io na t

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1_min.

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WHAT IS A SOVEREIGN BUILDING?

Es p era nz a B a s e, Arg entinia n resea r c h sta tion, Anta r c tic a

SOVEREIGN TERRITORY If sovereignty is attached to a communal sense of freedom or bound to a collectively agreed framework such as Thomas Hobbes’ Constitution, then there is a relationship between ideas of right-giving and right-taking. Citizenship, the right to have rights, is fiercely reliant on the nation-state. There is no global citizenship. Hence the nation-state becomes the solitary entity via which maximum sovereignty is achieved. How can these 'spheres of sovereignty’ be quantified and correlated to territorial or event typological values?

S "

overeignty is more than anything else a matter of legitimacy [...that] requires reciprocal recognition.

Sovereignty is a hypothetical trade, in which two potentially conflicting sides, respecting de facto realities of power, exchange such recognitions as their least costly strategy." - Immanuel Wallerstein Sovereignty is the full right and power of a governing body over itself, without any interference from outside sources or bodies. In political theory, sovereignty is a substantive term designating supreme authority

over some polity. It is a basic principle underlying the dominant Westphalian model of state foundation.

The concepts of sovereignty have been discussed throughout history, and are still actively debated.

Its definition, concept, and application has changed throughout, especially during the Age of Enlightenment. The current notion of state sovereignty contains four aspects consisting of territory, population, authority and recognition.

earth’s orbit, moon, satellite objects national airspace

territorial waters airspace contiguous zone airspace

land territory surface internal waters surface territorial waters surface contiguous zone surface exclusive economic zone surface internal waters

land territory underground

territorial waters surface

exclusive economic zone

international airspace international waters surface

earth's surface

exclusive economic zone

continental shelf surface

extended continental shelf surface

international seabed surface

continental shelf underground

extended continental shelf surface

international seabed underground

full national jurisdiction and sovereignty (citizen rights) restrictions on national jurisdiction and sovereignty (limited citizen rights) international jurisdiction per common heritage of mankind (no citizenship) M A R C H 2 018

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Spheres of Belonging

Belongs

Does Not Belong

Nation

Non-Nation diminished transnational institution

government

Scale of Power

no power person

non-person

In order to identify a value spectrum certain spacial characteristics of sovereignty have to be laid out. Some deciding factors of doing so include the previous argument on that which belongs and that which does not under the sphere of the nation state. Also scale of ownership and wealth and scale of community. Using these factors a spectrum like the one below is devised and the question of related aesthetics or typologies can be investigated.

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Scales of Power Government

Individual

extraterritorial property

transterritorial property

national property

corporate property

global

continental

1000_max.

100_deep-state

At the scale of transnational organisations the UN is the only measure by which one can talk about a global government. It has limited sovereignty however due to the power of individual states and hence lies on the lower end of the scale.

The EU project is perhaps unique in its scale of sovereignty the closest to a meaningfully powerful continental governance of states.

It’s administrative buildings could provide a basis for a continental scale of sovereign aesthetic.

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Persona Non Grata

Sovereign State

International Groups

The state is arguably the most sovereign entity as right giver.

Deep-state organisations however could often act as states-within states. Still less power but often very flexible.

town

neighbourhood

10_social

Communes or collectives are arguably more sovereign than a lone individual who is at the bottom end of the spectrum.

After all the word 'idiot' draws meaning from the Greek 'idiots' as in privateer.

family property

object possessions

1_min.

0_zero

At the very end of the spectrum and outside the strength of the sovereign state are groups such as refugees, homeless and other dispossessed individuals. Even nomadic groups tend to fall outside of the nation-state's sovereign sphere principally due to their lack of land ownership.

Hannah Arendt places the Jews and other minorities persecuted by the Nazis in the 'stateless' field where there is no enforcing body (with the absence of a nation) to grant them rights. Hence why the UN is also at the ends of the spectrum even if at the opposite scale of collective power, it is still unable to enforce human rights when the 'nation-state' comes in its way.

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Sovereignty Spectrum outer

Terra M

Anonymous SesameCredit

Google

earth’s orbit, moon, sa national airspace

territorial waters airspace

contiguous zone airspace

International Groups

Sovereig

European Union

United Nations

China

World

+

National Security Agency

Banks

Ci

Government

extraterritorial property

transterritorial property

national property

corporate property

land territory surface internal waters surface territorial waters surface internal waters territorial waters surface land territory underground

globalist

continentalist

international

bauhaus

global

post-modern

parametric

continental

transnational corporatist

nationalist

colonial

authoritarian

1000_max

baroque

romanesque

gothic

contiguous zone surface exclusive econo continental shelf surface continental shelf underground

national corporatist

beaux-arts deconstructivist

palladian

chicag

100_deep-state

Sovereignty

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space

Media

Creative Commons

Kim Kardashian Followers

Christiania

atellite objects international airspace

Persona Non Grata

gn State

Julianne Assange

+

ity

Homeless

Object

Individual town

exclusive economic zone surface omic zone

neighbourhood

family property

art nouveau

art deco

georgian

10_social

Spectrum

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object possessions

international waters surface exclusive economic zone extended continental shelf surface international seabed surface extended continental shelf surface international seabed underground

collectivist

go school greek revival

Refugee

priveteer

queen anne

tudor revival

foresquare

novelty

1_min

nobody

refugee

0_zero

increasing rights in direction of arrow sovereign state boundary full national jurisdiction and sovereignty (citizen rights) restrictions on national jurisdiction and sovereignty (limited citizen rights) international jurisdiction per common heritage of mankind (no citizenship) Transterritorial Commons (overlapping national and extra-national jurisdiction) Extraterritorial Commons (extra-national jurisdiction - pure media/data) nicholas zembashi | T E R R A M E D I A

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GLOBAL 80%

NATIONALIST 60% NATIONALIST 60% NATIONALIST 60% NATIONALIST 60%

FASCIST 50% FASCIST 60%

FASCIST 99%

FASCIST 99%

FASCIST 99%

FASCIST 99%

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FASCIST 60%

NATIONALIST 60%

FASCIST 99%

C a s a d e l Fa c io, b y Giusep p e Ter ra g ni, Com o, Ita l y, 19 3 6

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Mediation

media media media media media media media media

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DELIVERING THE MESSAGE

An gel of the An n u n c i ati on , Frenc h Sc hool , 19 th c .

THE ANNUNCIATION

What is interesting about most traditional depictions of the annunciation is their commentary on communication.

Mary is always faced by some formal barrier demarcating her divide from God's messenger. This barrier seems to gradually dissolve through the history of depictions. Even though invisible the mediating barrier cannot leave.

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The Annunciation (from Latin annunciation), also referred to as the Annunciation to the Blessed Virgin Mary, the Annunciation of Our Lady,or the Annunciation of the Lord, is the Christian celebration of the announcement by the angel Gabriel to the Virgin Mary that she would conceive and become the mother of Jesus, the Son of God, marking his Incarnation.

Gabriel told Mary to name her son Yeshua, meaning “YHWH is salvation”.

According to Luke 1:26, the Annunciation occurred “in the sixth month” of Elizabeth’s pregnancy with John the Baptist.Many Christians observe this event with the Feast of the Annunciation on 25 March, an approximation of the northern vernal equinox nine full months before Christmas, the ceremonial birthday of Jesus. In England, this came to be known as

Lady Day, and Lady Day marked the beginning of the English new year until 1752.The 2nd-century writer Irenaeus of Lyon regarded the conception of Jesus as 25 March coinciding with the Passion.

In painting the scene always depicts the Virgin within a frame or behind a barrier usually a column. The mediation between her and the Archangel is therefore indirect or at least obstructed. Despite the various metaphors and symbols what is interesting is the commentary on the bias of communication or the barrier of ‘language’ i.e. the medium. If one is to look closely at the chronology of depictions the barrier seems to dissolve with 19th century and contemporary version having it completely removed. Like any mediating technology its bias is ever present but the perception of it has diminished.

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Peiero della Francesca 1452 - 1466 fresco Basilica di San Francesco, Arezzo

Peiero della Francesca c. 1460 Polyptich of St. Anthony National Gallery of Umbria, Perugia, Italy

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Mr. Algorithm REPRESENTATION

smart environment smart city smart home internet of things smart services

household art / entertainment medicine / care sustainability/environment transportation/logistics military science / education industry / digital production

robot-like alien-like animal-like

gynoid (woman-like) android (man-like) anthropomorphic

Infrastructure

Machine

Creaturoid

Humanoid

Hardware

+

Familiarity

humanoid robot

stuffed animal industrial robot

ROBOT

Human Likeness

Software Computer Programs

Reality Manipulation

Artificial Intelligence (AI)

Distributed Intelligence

virtual reality (VR) augmented reality (AR) mixed reality (MR)

planning

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robotics

vision

speech

language processing

machine learning

image recognition machine vision

speech to text text to speech

text generation question answering context extraction classification machine translation

deep learning unsupervised supervised

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REAL Automaton Machines Toys Clockwork

Cyborg Implant Prosthesis Bionic / Hybrid

a policeman

human figurative

the cut-out of a policeman

non-human figurative sign

a flag-waving robot

non-human figurative

a sign

non-human non-figurative sign

healthy human

Uncanny Valley

bunraku puppet

prosthetic hand

HUMAN

corpse nothing, just works in progress

zombie

a road bumper

I

n her magnum opus, The Human Condition, Hannah Arendt uses three terms uses three terms to define the basic conditions of human life that also describe the individual’s autonomous, active participation in society: labour, work, and action. While Arendt’s understanding of labour and work subsumes those (individual) goods, she describes (inter) action - language and communication - as humans greatest asset. After the three areas identified by Arendt are increasingly taken over by robots, AI

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and algorithms, Bruno Latour’s theory becomes applicable. His actor-network theory (ANT) upholds that acting is not only limited to humans (actors), but extends to non-human entities (actants). ANT is relevant here insofar as these autonomous entities do not just transfer action, they also perform it. Thus, as autonomous entities, they don’t merely participate in the society of humans but also actively (co-)construct it.

In the diagrams here, technologies on a broad spectrum are arranged to investigate their role as ‘representations’ of humans in various areas of

non-human non-figurative non-sign

activity. The technology becomes a prosthesis or a totally independent mechanism. With the ubiquity of such tools the role of communication between all these technologies and humans becomes vital, as the degree of automation and data gathering changes the way cognition works between built environments and the technologies occupying the design process.

The middle becomes a threshold for communication be it by physical means of travel or mental registries such as language. This middle is Surface.

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The Neuron

signal reception

integration

processing

signal transmission

1

2

3

4

axon hillock

endoplasmic reticulum & mitochondria

axon

nucleus

direction of data

K Super c omputer Japan

A ver a g e Hu m a n B r a i n

Mem o r y

3.5 quadrillion bytes

3.5 quadrillion bytes

Sp eed

0.95 million megaFLOPS

2.2 million megaFLOPS

Po w e r

1.2 million Watts

20 Watts

Sp a ce We i g h t

1 Server cabinet = 3.47 m 2 15 000 kg

400 cm 3 70 kg floa ting p oint op er a tions p er second ( FLOPS) 1 meg a FLOP = 1 million op er a tions

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signal reception

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n artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory

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postsynaptic potentials at neural dendrites) and sums them to produce an output (or activation, representing a neuron’s action potential which is transmitted along its axon). Usually each input is separately weighted, and the sum is passed through a non-linear function known as an activation function or transfer function. The transfer functions usually have a sigmoid shape, but they may also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often

monotonically increasing, continuous, differentiable and bounded. The thresholding function has inspired building logic gates referred to as threshold logic; applicable to building logic circuits resembling brain processing. For example, new devices such as memristors have been extensively used to develop such logic in recent times. An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.

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Mediation through communication technologies becomes a form of power that can both liberate or oppress the dispossessed in equal measure.

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Recognition column 99%

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TEST 01

OBJECT DETECTION

The theories behind machine learning in the area of object detection/recognition are decades-old. The ability however to apply this has been increasing for the past decade, owing to exponentially growing computing power, it’s cheapness and availability as well as the gargantuan accumulation of data, popularly termed as Big Data.

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bject detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection.

a person in a video. Every object class has its own special features that helps in classifying the class – for example all circles are round. Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the centre) are sought.

Object detection has applications in many areas of computer vision, including image retrieval and video surveillance. It is used in face detection and face recognition. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, tracking

Similarly, when looking for squares, objects that are perpendicular at corners and have equal side lengths are needed. A similar approach is used for face identification where eyes, nose, and lips can be found and features like skin colour and distance between eyes can be found.

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pre-trained object detection

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Object Detection Demo Welcome to the object detection inference walkthrough! This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the installation instructions (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md) before you start.

Imports In [ ]: import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile from collections import defaultdict from io import StringIO from matplotlib import pyplot as plt from PIL import Image if tf.__version__ < '1.4.0': raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')

Env setup In [ ]: # This is needed to display the images. %matplotlib inline # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..")

Object detection imports Here are the imports from the object detection module.

In [ ]: from utils import label_map_util from utils import visualization_utils as vis_util

Model preparation Variables Any model exported using the export_inference_graph.py tool can be loaded here simply by changing PATH_TO_CKPT to point to a new .pb file.

By default we use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

In [ ]: # What model to download.

MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' MODEL_FILE = MODEL_NAME + '.tar.gz' DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/' # Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt') NUM_CLASSES = 90

Download Model

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In [ ]: opener = urllib.request.URLopener() opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE) tar_file = tarfile.open(MODEL_FILE) for file in tar_file.getmembers(): file_name = os.path.basename(file.name) if 'frozen_inference_graph.pb' in file_name: tar_file.extract(file, os.getcwd())

Load a (frozen) Tensorflow model into memory.

In [ ]: detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='')

Loading label map Label maps map indices to category names, so that when our convolution network predicts 5 , we know that this corresponds to airplane . Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine In [ ]: label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True category_index = label_map_util.create_category_index(categories)

Helper code In [ ]: def load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8)

Detection In [ ]: # For the sake of simplicity we will use only 2 images: # image1.jpg # image2.jpg # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. PATH_TO_TEST_IMAGES_DIR = 'test_images' TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ] # Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

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In [ ]: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Each box represents a part of the image where a particular object was detected. detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represent how level of confidence for each of the objects. # Score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') for image_path in TEST_IMAGE_PATHS: image = Image.open(image_path) # the array based representation of the image will be used later in order to prepare the # result image with boxes and labels on it. image_np = load_image_into_numpy_array(image) # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) plt.figure(figsize=IMAGE_SIZE) plt.imshow(image_np) In [ ]:

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Object-Detection

By running the object-detection code on pre-trained datasets the test aimed at identifying the algorithm’s accuracy at recognising a collection of objects in real-

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person 63% cell phone 99%

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Neuron Value x

sigmoid function to squeeze values between -1 and 1 The Sigmoid function insures that whatever neuron value x is fed into it, the output value erf(x) is always between -1 and 1. This keeps computations in a neural network bounded and stable.

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rectified linear function to squeeze values between 0 and 1 The Rectified Linear function turns all outputs from the Sigmoid function into anything between 1 and 0. If the input numbers are positive they are kept, if negative they become zero.

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How would a neural network differentiate between different pixel arrangements ‘recognise’ a horizontal line?

The input pixels are assessed according to the value of their shading. The calculations are made as per the neural network diagram shown on the previous page. These values and their weights determine an overall positive or negative output through each layer of the network.

Eventually a well-trained neural network presents a thread through positive output values, which reinforce each other through subsequent layers, until the result is narrowed down to the expected one. There are of course errors in the evaluation and this is explored in the pages to follow.

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COLUMN How do you fare at detecting architectural elements Mr. Algorithm?

Having tested a pre-trained Object-Detection model, the investigation shifts to building a custom one. The column becomes the focus in our topic of cognitive appreciation. The tests to follow demonstrate how its meaning is defined through the tagging of a collection of images and then ran through a neural network. The aim: to achieve real-time column detection.

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TEST 02

B au ms c hu l enweg Crematori u m, b y Axel Sc hul tes a nd Cha r l ot te Fra nk , 19 98

COLUMN DETECTION Can a machine be taught to recognise architectural elements? Can it read the obstruction in the annunciation paintings? The aim is to collect and create a dataset of images depicting ‘columns’ of varying scale, from, style and from different views.

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or the Column Detection test the aim is to see how an algorithm can be taught to read architectural elements. What makes a column a column?

Before even defining an aesthetics for sovereignty something as simple as a column is surprisingly hard and prone to its own errors. Should one include the capital? Are plinths and cornices or other ornamentation considered columns? How far does 'Columness' stretch?

to know what a column is? By limiting the agenda of what kind of columns the designer is concerned with. This is already a degree of bias. Answers to all the above will unavoidably input biases at every step of the design process.

Is a column only a column when structural? The list of questions could go on and on. The column as a factual object does not exist as question surrounding its ontology unwind the whole definition. Hence how does one teach a machine

In any case the aim of the tests to follow are to investigate the process of machine learning in column detection and to assess the real-time results of a machine vision on recognising architectural elements.

In the test to follow deliberate biases are used in the dataset. Some columns are at different scales and consistency varies over the decisions. I

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Overview test 40 files: 40 .jpg images 40 .xml tag data

pic_05.xml pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

800 files: 400 .jpg images 400 .xml tag data ‘column’ 400 .xml tag data

400 .jpg images

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dataset of 400 images

Use a an online database (e.g.: google) to search for images of columns. Extract and save images, while discarding any which contain no columns. Between 100 - 300 images are a safe range for building a training data-set. In the tests to follow around 400 are usually selected to ensure even better results.

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manual ‘column’ tagging With an image tagging program open each saved image and set up a tag ‘column’. In each image draw a rectangular bounding box over whatever is considered a column.

Save as .xml file.

train 720 files: 360 .jpg images 360 .xml tag data

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10% test / 90% train 90% of the images and their corresponding .xml files will be used as training data for the algorithm.

The rest are test data for it to correlate with the training ones as it gradually learns what a ‘column’ is.

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test.csv

test_tfrecord

list of test labels

webcam

train.csv training Loss graph

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list of training labels

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.xml files into .csv This is a code that will extract all the data from the .xml files that have been tagged and arrange them into a test and training table.

filename width height class pic_001.jpg 300 300 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_003.jpg 241 300 column pic_004.jpg 300 300 column pic_005.jpg 300 300 column pic_005.jpg 300 300 column pic_006.jpg 300 300 column pic_006.jpg 300 300 column pic_007.jpg 200 300 column pic_008.jpg 249 300 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_010.jpg 255 300 column pic_011.jpg 200 300 column pic_012.jpg 300 300 column pic_013.jpg 200 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_015.jpg 94 300 column pic_016.jpg 300 300 column pic_017.jpg 225 300 column pic_018.jpg 282 300 column pic_019.jpg 300 300 column pic_020.jpg 188 300 column pic_021.jpg 292 300 column

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xmin

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xmax 1 1 17 115 26 3 17 9 1 1 27 37 14 16 13 15 1 16 13 16 40 27 16 6 16 34 13 13 21 25

generate tf records

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The test and training .csv tables are converted into tf records.

These are in a format that google’s open-source machine learning software, tensor flow, can use to train itself.

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After all data is prepared the training session may commence.

inference graph The output from the training data can be frozen into an inference graph and fed into the object-detection code.

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By running the objectdetection code it should now be trained to recognise ‘columns’ in real-time through a camera.

ymax 185 100 136 234 167 181 113 232 116 245 136 158 59 130 205 279 174 176 193 124 83 154 240 78 181 172 180 193 125 46

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column colum column colum column colum column colum column colum column colum column colum column colum colum colum colum colum columncolumncolumn columncolumncolumn columncolumncolumn columncolumncolumn 76

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column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column mn columncolumn column column column column column column column column M A R C H 2 018

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.xml to .csv

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xml_to_csv

Imports In [ ]: import os import glob import pandas as pd import xml.etree.ElementTree as ET

Convert .xml to .csv In [ ]: def xml_to_csv(path): xml_list = [] for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): value = (root.find('filename').text, int(root.find('size')[0].text), int(root.find('size')[1].text), member[0].text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text) ) xml_list.append(value) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df

Define output directory for .csv files In [ ]: def main(): for directory in ['train','test']: image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory)) xml_df = xml_to_csv(image_path) xml_df.to_csv('data/{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.')

main()

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generate tf_record

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generate_tfrecord_column

Imports In [ ]: from __future__ import division from __future__ import print_function from __future__ import absolute_import import os import io import pandas as pd import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util from collections import namedtuple, OrderedDict flags = tf.app.flags flags.DEFINE_string('csv_input', '', 'Path to the CSV input') flags.DEFINE_string('output_path', '', 'Path to output TFRecord') FLAGS = flags.FLAGS

Define Class Labels In [ ]: # TO-DO replace this with label map def class_text_to_int(row_label): if row_label == 'column': return 1 else: None

Data Group Labels In [ ]: def split(df, group): data = namedtuple('data', ['filename', 'object']) gb = df.groupby(group) return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]

Extract tagging data from .csv files to generate tf record

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generate_tfrecord_column

In [ ]: def create_tf_example(group, path): with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) image = Image.open(encoded_jpg_io) width, height = image.size filename = group.filename.encode('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in group.object.iterrows(): xmins.append(row['xmin'] / width) xmaxs.append(row['xmax'] / width) ymins.append(row['ymin'] / height) ymaxs.append(row['ymax'] / height) classes_text.append(row['class'].encode('utf8')) classes.append(class_text_to_int(row['class'])) tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(filename), 'image/source_id': dataset_util.bytes_feature(filename), 'image/encoded': dataset_util.bytes_feature(encoded_jpg), 'image/format': dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), })) return tf_example

Write tf record In [ ]: def main(_): writer = tf.python_io.TFRecordWriter(FLAGS.output_path) path = os.path.join(os.getcwd(), 'images') examples = pd.read_csv(FLAGS.csv_input) grouped = split(examples, 'filename') for group in grouped: tf_example = create_tf_example(group, path) writer.write(tf_example.SerializeToString()) writer.close() output_path = os.path.join(os.getcwd(), FLAGS.output_path) print('Successfully created the TFRecords: {}'.format(output_path))

if __name__ == '__main__': tf.app.run()

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test_labels filename width height class pic_001.jpg 300 300 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_003.jpg 241 300 column pic_004.jpg 300 300 column pic_005.jpg 300 300 column pic_005.jpg 300 300 column pic_006.jpg 300 300 column pic_006.jpg 300 300 column pic_007.jpg 200 300 column pic_008.jpg 249 300 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_010.jpg 255 300 column pic_011.jpg 200 300 column pic_012.jpg 300 300 column pic_013.jpg 200 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_015.jpg 94 300 column pic_016.jpg 300 300 column pic_017.jpg 225 300 column pic_018.jpg 282 300 column pic_019.jpg 300 300 column pic_020.jpg 188 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_022.jpg 300 300 column pic_023.jpg 82 300 column pic_024.jpg 225 300 column pic_025.jpg 300 300 column pic_026.jpg 237 300 column pic_027.jpg 300 300 column pic_028.jpg 167 300 column pic_029.jpg 300 300 column pic_030.jpg 300 300 column pic_031.jpg 300 300 column pic_032.jpg 168 300 column pic_033.jpg 300 300 column pic_034.jpg 250 250 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_036.jpg 300 300 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_038.jpg 300 300 column pic_038.jpg 300 300 column pic_039.jpg 300 300 column pic_040.jpg 110 300 column pic_041.jpg 93 300 column pic_042.jpg 300 300 column

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test.csv

xmin

ymin 117 58 102 128 74 114 57 174 53 185 65 91 13 82 156 229 81 19 110 71 52 109 186 14 116 53 103 128 61 1 82 165 247 110 13 85 78 61 115 3 120 123 113 62 116 93 8 61 114 169 218 278 102 2 34 68 96 130 161 21 113 42 8 2 120

xmax 1 1 17 115 26 3 17 9 1 1 27 37 14 16 13 15 1 16 13 16 40 27 16 6 16 34 13 13 21 25 24 24 26 13 6 26 1 13 2 2 3 2 2 29 2 1 9 12 10 16 15 16 6 3 4 3 3 3 3 2 1 1 9 1 1

ymax 185 100 136 234 167 181 113 232 116 245 136 158 59 130 205 279 174 176 193 124 83 154 240 78 181 172 180 193 125 46 129 208 290 194 69 138 246 153 185 165 179 178 184 105 184 144 54 108 160 211 271 310 187 28 60 90 123 155 186 80 297 264 99 92 177

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test_tfrecord

Sample For fu ll d oc u me nts of train_ labels and test_labels please see Appendix.

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train_labels

train.csv

train_tfrecord

Sample For full document s of t r ai n_ labels and test_labels please see Appendix.

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filename pic_043.jpg pic_044.jpg pic_044.jpg pic_044.jpg pic_044.jpg pic_044.jpg pic_045.jpg pic_045.jpg pic_045.jpg pic_045.jpg pic_046.jpg pic_046.jpg pic_046.jpg pic_046.jpg pic_046.jpg pic_046.jpg pic_046.jpg pic_047.jpg pic_048.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_049.jpg pic_050.jpg pic_051.jpg pic_052.jpg pic_053.jpg pic_053.jpg pic_054.jpg pic_055.jpg pic_056.jpg pic_057.jpg pic_058.jpg pic_059.jpg pic_060.jpg pic_061.jpg pic_062.jpg pic_063.jpg pic_064.jpg pic_064.jpg pic_065.jpg pic_065.jpg pic_065.jpg pic_066.jpg pic_067.jpg pic_068.jpg pic_069.jpg pic_069.jpg pic_069.jpg pic_069.jpg pic_069.jpg pic_069.jpg pic_069.jpg pic_070.jpg pic_070.jpg pic_070.jpg pic_070.jpg pic_070.jpg pic_070.jpg pic_070.jpg pic_071.jpg pic_071.jpg pic_071.jpg pic_071.jpg pic_071.jpg pic_071.jpg pic_071.jpg

width

height 221 219 219 219 219 219 252 252 252 252 225 225 225 225 225 225 225 75 139 285 285 285 285 285 285 285 285 285 300 165 165 300 300 300 77 300 169 300 83 70 300 92 158 287 287 269 269 269 300 113 86 177 177 177 177 177 177 177 200 200 200 200 200 200 200 248 248 248 248 248 248 248

class 166 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 300 column 300 column 300 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column

xmin

ymin 96 7 59 110 165 185 1 106 190 226 160 119 88 66 46 30 12 4 39 3 36 65 97 135 166 198 226 255 122 28 1 13 176 117 10 93 37 121 13 1 110 11 47 43 164 7 107 202 68 32 14 15 31 46 62 78 97 123 44 91 121 151 7 23 1 10 63 105 140 166 187 209

xmax

ymax

35 124 160 76 63 300 145 89 261 117 147 251 127 191 245 40 217 300 1 136 166 1 205 117 1 238 95 1 250 84 17 214 286 58 155 275 89 116 272 109 85 272 131 64 269 145 46 270 156 30 265 3 72 295 3 105 297 4 31 146 4 62 146 6 95 144 5 132 145 4 162 145 5 192 145 4 222 146 5 251 146 4 279 146 1 180 300 16 116 272 2 164 300 62 117 300 64 290 300 3 182 298 9 69 292 10 209 289 21 134 165 1 178 300 8 72 289 2 68 300 11 181 293 9 80 292 8 109 287 9 95 280 7 221 293 22 68 166 22 163 166 22 259 166 9 225 289 23 81 287 10 73 295 21 34 143 20 49 145 16 66 149 13 81 150 9 102 154 5 123 159 2 149 162 13 82 274 37 123 284 62 156 280 81 182 282 83 27 285 60 50 286 36 11 281 20 78 166 55 115 166 79 147 166 100 174 166 nicholas T E R R A M E D166 I A | 83 117zembashi | 194 129 212 166 142 226 166

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train_column

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train

Train Tensorflow Training executable for detection models.

This executable is used to train DetectionModels. There are two ways of configuring the training job: 1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file can be specified by --pipeline_config_path.

Example usage: ./train \ --logtostderr \ --train_dir=path/to/train_dir \ --pipeline_config_path=pipeline_config.pbtxt 2) Three configuration files can be provided: a model_pb2.DetectionModel configuration file to define what type of DetectionModel is being trained, an input_reader_pb2.InputReader file to specify what training data will be used and a train_pb2.TrainConfig file to configure training parameters.

Example usage: ./train \ --logtostderr \ --train_dir=path/to/train_dir \ --model_config_path=model_config.pbtxt \ --train_config_path=train_config.pbtxt \ --input_config_path=train_input_config.pbtxt

Imports In [ ]: import functools import json import os import tensorflow as tf from object_detection import trainer from object_detection.builders import input_reader_builder from object_detection.builders import model_builder from object_detection.utils import config_util tf.logging.set_verbosity(tf.logging.INFO) flags = tf.app.flags flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.') flags.DEFINE_integer('task', 0, 'task id') flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.') flags.DEFINE_boolean('clone_on_cpu', False, 'Force clones to be deployed on CPU. Note that even if ' 'set to False (allowing ops to run on gpu), some ops may ' 'still be run on the CPU if they have no GPU kernel.') flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer ' 'replicas.') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter server tasks. If None, does not use ' 'a parameter server.') flags.DEFINE_string('train_dir', '', 'Directory to save the checkpoints and training summaries.') flags.DEFINE_string('pipeline_config_path', '', 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file. If provided, other configs are ignored') flags.DEFINE_string('train_config_path', '', 'Path to a train_pb2.TrainConfig config file.') flags.DEFINE_string('input_config_path', '', 'Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '', 'Path to a model_pb2.DetectionModel config file.')

FLAGS = flags.FLAGS

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Training

train

In [ ]: def main(_): assert FLAGS.train_dir, '`train_dir` is missing.' if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) if FLAGS.task == 0: tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.train_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, train_config_path=FLAGS.train_config_path, train_input_config_path=FLAGS.input_config_path) if FLAGS.task == 0: for name, config in [('model.config', FLAGS.model_config_path), ('train.config', FLAGS.train_config_path), ('input.config', FLAGS.input_config_path)]: http://localhost:8888/notebooks/train.ipynb#Train-Tensorflow tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),

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train_config_path=FLAGS.train_config_path, train_input_config_path=FLAGS.input_config_path) if FLAGS.task == 0: for name, config in [('model.config', FLAGS.model_config_path), ('train.config', FLAGS.train_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name), overwrite=True) model_config = configs['model'] train_config = configs['train_config'] input_config = configs['train_input_config'] model_fn = functools.partial( model_builder.build, model_config=model_config, is_training=True) create_input_dict_fn = functools.partial( input_reader_builder.build, input_config) env = json.loads(os.environ.get('TF_CONFIG', '{}')) cluster_data = env.get('cluster', None) cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None task_data = env.get('task', None) or {'type': 'master', 'index': 0} task_info = type('TaskSpec', (object,), task_data) # Parameters for a single worker. ps_tasks = 0 worker_replicas = 1 worker_job_name = 'lonely_worker' task = 0 is_chief = True master = '' if cluster_data and 'worker' in cluster_data: # Number of total worker replicas include "worker"s and the "master". worker_replicas = len(cluster_data['worker']) + 1 if cluster_data and 'ps' in cluster_data: ps_tasks = len(cluster_data['ps']) if worker_replicas > 1 and ps_tasks < 1: raise ValueError('At least 1 ps task is needed for distributed training.') if worker_replicas >= 1 and ps_tasks > 0: # Set up distributed training. server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc', job_name=task_info.type, task_index=task_info.index) if task_info.type == 'ps': server.join() return worker_job_name = '%s/task:%d' % (task_info.type, task_info.index) task = task_info.index is_chief = (task_info.type == 'master') master = server.target trainer.train(create_input_dict_fn, model_fn, train_config, master, task, FLAGS.num_clones, worker_replicas, FLAGS.clone_on_cpu, ps_tasks, worker_job_name, is_chief, FLAGS.train_dir)

if __name__ == '__main__': tf.app.run()

http://localhost:8888/notebooks/train.ipynb#Train-Tensorflow

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faster_rccn_inception_v2.config

In [ ]: # Faster R-CNN with Inception v2, configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured.

model { faster_rcnn { num_classes: 1 image_resizer { keep_aspect_ratio_resizer { min_dimension: 600 max_dimension: 1024 } } feature_extractor { type: 'faster_rcnn_inception_v2' first_stage_features_stride: 16 } first_stage_anchor_generator { grid_anchor_generator { scales: [0.25, 0.5, 1.0, 2.0] aspect_ratios: [0.5, 1.0, 2.0] height_stride: 16 width_stride: 16 } } first_stage_box_predictor_conv_hyperparams { op: CONV regularizer { l2_regularizer { weight: 0.0 } } initializer { truncated_normal_initializer { stddev: 0.01 } } } first_stage_nms_score_threshold: 0.0 first_stage_nms_iou_threshold: 0.7 first_stage_max_proposals: 300 first_stage_localization_loss_weight: 2.0 first_stage_objectness_loss_weight: 1.0 initial_crop_size: 14 maxpool_kernel_size: 2 maxpool_stride: 2 second_stage_box_predictor { mask_rcnn_box_predictor { use_dropout: false dropout_keep_probability: 1.0 fc_hyperparams { op: FC regularizer { l2_regularizer { weight: 0.0 } } initializer { variance_scaling_initializer { factor: 1.0 uniform: true mode: FAN_AVG } } } } } second_stage_post_processing { batch_non_max_suppression { score_threshold: 0.0 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 300 } score_converter: SOFTMAX } second_stage_localization_loss_weight: 2.0 second_stage_classification_loss_weight: 1.0 } } train_config: { batch_size: 1 optimizer { momentum_optimizer: { http://localhost:8888/notebooks/faster_rccn_inception_v2.config.ipynb

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06/03/2018

faster_rccn_inception_v2.config

learning_rate: { manual_step_learning_rate { initial_learning_rate: 0.0002 schedule { step: 0 learning_rate: .0002 } schedule { step: 900000 learning_rate: .00002 } schedule { step: 1200000 learning_rate: .000002 } } } momentum_optimizer_value: 0.9 } use_moving_average: false } gradient_clipping_by_norm: 10.0 fine_tune_checkpoint: "faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt" from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the COCO dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } } train_input_reader: { tf_record_input_reader { input_path: "data/train.record" } label_map_path: "data/object-detection.pbtxt" } eval_config: { num_examples: 8000 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 10 } eval_input_reader: { tf_record_input_reader { input_path: "data/test.record" } label_map_path: "data/object-detection.pbtxt" shuffle: false num_readers: 1 }

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training training training training training training M A R C H 2 018

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Training Graph TotalLoss TotalLoss 2.20 2.00 1.80 1.60 1.40 1.20 1.00 0.800 0.600 0.400 0.200 0.00 -0.200 0.000

500.0

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Loss Loss/Bo

Loss/Bo

Loss/RPNLoss/localization_loss/mul_1

Loss/RPNLoss

0.200 0.160

1.20

0.160

0.120

0.800

0.120

0.0800 0.400 0.00 0.000

1.500k

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0.00 0.000

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The training process, as seen in the diagrams before, can be monitored by plotting various values. However the key curve belongs to the Total Loss. This one shows the error correction (y-axis) during the steps of training (x=axis)

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uring the training process the neural network is running between the training dataset and correlating the images with the testing dataset. At every step shifting its weight values to reach closer and closer to ‘truth’. This appears on the graph as the curve approaches zero. In general the closer it approaches zero the more accurate the training.

Overall, if it maintains a curve less than 1.0 then it is deemed to be optimal.

After certain defined time frames in the training the algorithm creates a set of 3 files each time.

These store the training data from which the frozen inference will be extracted from later on.

It is ‘froze’ because it is a conglomeration of la limited range of training data. Otherwise the algorithm could be trained indefinitely and different inference graphs can be created after longer range of training.

The inference graph is the file necessary for the object-detection code to run. It links the training data to the object-detection process and allows for the classes of objects (in this case a ’column’) to be recognised either in images, clips of film or - for the purpose of this project - in real-time.

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6.00 4.00 2.00 0.00 0.000

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parallel_read parallel_r

action_of_32_full

1.00 0.800 0.600 0.400 0.200 0.00 0.000

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queue queue/pr efetch_queue/fr action_of_5_full 1.00 0.800 0.600 0.400 0.200 0.00 0.000

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export inference graph

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export_inference_graph

Export Inference Graph Tool to export an object detection model for inference.

Prepares an object detection tensorflow graph for inference using model configuration and an optional trained checkpoint. Outputs inference graph, associated checkpoint files, a frozen inference graph and a SavedModel (https://tensorflow.github.io/serving/serving_basic.html (https://tensorflow.github.io/serving/serving_basic.html)).

The inference graph contains one of three input nodes depending on the user specified option. image_tensor : Accepts a uint8 4-D tensor of shape [None, None, None, 3] encoded_image_string_tensor : Accepts a 1-D string tensor of shape [None] containing encoded PNG or JPEG images. Image resolutions are expected to be the same if more than 1 image is provided. tf_example : Accepts a 1-D string tensor of shape [None] containing serialized TFExample protos. Image resolutions are expected to be the same if more than 1 image is provided. and the following output nodes returned by the model.postprocess(..): num_detections : Outputs float32 tensors of the form [batch] that specifies the number of valid boxes per image in the batch. detection_boxes : Outputs float32 tensors of the form [batch, num_boxes, 4] containing detected boxes. detection_scores : Outputs float32 tensors of the form [batch, num_boxes] containing class scores for the detections. detection_classes : Outputs float32 tensors of the form [batch, num_boxes] containing classes for the detections. detection_masks : Outputs float32 tensors of the form [batch, num_boxes, mask_height, mask_width] containing predicted instance masks for each box if its present in the dictionary of postprocessed tensors returned by the model.

Notes: This tool uses use_moving_averages from eval_config to decide which weights to freeze.

Example Usage: python export_inference_graph \ --input_type image_tensor \ --pipeline_config_path path/to/ssd_inception_v2.config \ --trained_checkpoint_prefix path/to/model.ckpt \ --output_directory path/to/exported_model_directory The expected output would be in the directory path/to/exported_model_directory (which is created if it does not exist) with contents: graph.pbtxt model.ckpt.data-00000-of-00001 model.ckpt.info model.ckpt.meta frozen_inference_graph.pb saved_model (a directory) """

Imports In [ ]: import tensorflow as tf from google.protobuf import text_format from object_detection import exporter from object_detection.protos import pipeline_pb2 slim = tf.contrib.slim flags = tf.app.flags flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be ' 'one of [`image_tensor`, `encoded_image_string_tensor`, ' '`tf_example`]') flags.DEFINE_string('input_shape', None, 'If input_type is `image_tensor`, this can explicitly set ' 'the shape of this input tensor to a fixed size. The ' 'dimensions are to be provided as a comma-separated list ' 'of integers. A value of -1 can be used for unknown ' 'dimensions. If not specified, for an `image_tensor, the ' 'default shape will be partially specified as ' '`[None, None, None, 3]`.') flags.DEFINE_string('pipeline_config_path', None, 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file.') flags.DEFINE_string('trained_checkpoint_prefix', None, 'Path to trained checkpoint, typically of the form ' 'path/to/model.ckpt') flags.DEFINE_string('output_directory', None, 'Path to write outputs.') tf.app.flags.mark_flag_as_required('pipeline_config_path') tf.app.flags.mark_flag_as_required('trained_checkpoint_prefix') tf.app.flags.mark_flag_as_required('output_directory') FLAGS = flags.FLAGS

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06/03/2018

export_inference_graph

Export inference graph In [ ]: def main(_): pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) if FLAGS.input_shape: input_shape = [ int(dim) if dim != '-1' else None for dim in FLAGS.input_shape.split(',') ] else: input_shape = None exporter.export_inference_graph(FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory, input_shape)

if __name__ == '__main__': tf.app.run()

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column detection

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Column_Detection

Column Detection Imports In [ ]: import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile from collections import defaultdict from io import StringIO from matplotlib import pyplot as plt from PIL import Image import cv2 cap = cv2.VideoCapture(0) # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..") from utils import label_map_util from utils import visualization_utils as vis_util

Model Preparation Any model exported using the export_inference_graph.py tool can be loaded here simply by changing PATH_TO_CKPT to point to a new .pb file.

By default we use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

In [ ]: # What model to download.

MODEL_NAME = 'column_graph' # Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt') NUM_CLASSES = 1

Load a frozen tensorflow model into memory In [ ]: detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='')

Loading label map Label maps map indices to category names, so that when our convolution network predicts 5 , we know that this corresponds to airplane . Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine In [ ]: label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True category_index = label_map_util.create_category_index(categories)

Helper Code

http://localhost:8888/notebooks/Column_Detection.ipynb

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Helper Code 06/03/2018

Column_Detection

In [ ]: def load_image_into_numpy_array(image): (im_width, im_height) = image.size http://localhost:8888/notebooks/Column_Detection.ipynb return np.array(image.getdata()).reshape( (im_height, im_width, 3)).astype(np.uint8)

Real-Time Detection In [ ]: # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. PATH_TO_TEST_IMAGES_DIR = 'test_images' TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 8) ] # Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

In [ ]: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Each box represents a part of the image where a particular object was detected. detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represent how level of confidence for each of the objects. # Score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') while True: ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) cv2.imshow('object detection', cv2.resize(image_np, (800,600))) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyALLWindows() break

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Column-Detection

After having trained the algorithm, a real-time test is applied. The aim is to see how well columns are detected and what other unexpected detections may occur, since the learning process was exclusive to columns and the algorithm has no way of knowing what is NOT a column.

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error

[3]

[2] [1]

[1] image_01

column [1] 93% [2] 59% [3] 84%

image_02

column [1] 56%

error

[2]

[1]

[1] image_03

column [1] 82%

image_04

error

column [1] 51% [2] 85%

error

[2]

[3]

[1]

[2] [3]

[1] image_05

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column [1] 97% [2] 83% [3] 78%

image_06

column [1] 51% [2] 85%

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error

error

[6] [2]

[3] error

error

[1]

[2] column [1] 90% [2] 59% [3] 84% [4] 84%

image_07

error

[3]

[1]

column [1] 92% [2] 72% [3] 98% [4] 83% [5]83% [6]58%

image_08

error [1]

[2]

[1] image_09

column [1] 99%

error

[4]

[3]

[5]

[6]

column [1] / [2] 57% [3] 70% [4] 99% [5]/[6]/

image_10

error

error

[1] [2] [1] image_11

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column [1] / [2] 99% [3] 68%

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[1] image_12

[2]

[2] column [1] / [2] / [3] 84% [4] 99%

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error

error

[1] [4]

[5] [2] [3] [1]

[5]

[4]

[6]

[2]

[2] column [1] 62% [2] / [3] 59% [4] / [5] 83% [6] /

image_13

image_14

[3] column [1] 95% [2] 99% [3] / [4] 92% [5] 96%

error

[1]

error

[1] [2] column [1] 75% [2] 90%

image_15

[2] image_16

column [1] 79% [2] 84%

error

[3]

[1]

image_17

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[1]

[2] column [1] 72% [2] 68% [3] 85%

image_18

column [1] 99%

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Analysis

The overall results seem to prove that when training the algorithm with only one dataset its success rate varies greatly.

It is high specifically when shown images with clear column content. Testament to this is how well it performed in the Annunciation paintings. However when shown more populated cityscapes or faced by shifts in scale then ‘columness’ appears where it should not, likely because of formal and scalar misreading.

Screenshot Column [1] image_01 image_02 image_03 image_04 image_05 image_06 image_07 image_08 image_09 image_10 image_11 image_12 image_13 image_14 image_15 image_16 image_17 image_18

93% 56% 82% 51% 97% 51% 90% 92% 99% / / / 62% 95% 75% 79% 72% 99%

Column [2]

Column [3]

59%

84%

85% 83% 85% 59% 72% 57% 99% / / 99% 90% 84% 68%

Column [4]

Column [5]

Column [6]

78% 84% 98%

84% 83%

83%

58%

70% 68% 84% 59% /

99%

/

/

99% / 92%

83% 96%

/

85%

Truth average

Error True = 1 average False = 0

93.0% 56.0% 82.0% 85.0% 0.00% 85.0% 71.5% 79.3% 0.00% 0.00% 83.5% 99.0% 0.00% 0.00% 75.0% 0.00% 75.0% 99.0%

71.5% 0.00% 0.00% 51.0% 86.0% 51.0% 87.0% 82.7% 99.0% 75.3% 0.00% 84.0% 72.5% 95.5% 90.0% 81.5% 0.00% 71.5%

1 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 1 1

based on true or false values, overall image truthiness = 50% 98

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“I

n my defence, I only know what a column is; nothing else. It’s like teaching a child only the letter ‘a’ and kill its ability to learn anything new. Then it’ll be seeing and communicating only through ‘a’s.”

– Mr. Algorithm

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SOVEREIGNTY Can Mr. Algorithm be taught to read deeper into the meaning of images?

In this final test of custom, real-time, Object-Detection, the investigation focuses on building a deeper understanding of space into the algorithm’s training. The sovereignty spectrum is sued to determine the labels for tagging buildings. Instead of ‘column’ or other more obvious tags, the sovereignty tags will correspond to the degrees of power inspired by the architectural typology being detected.

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1000_max 85%

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TEST 03

Cas a del Fac i o, projec tion in celebration of the c entena r y of the b ir th of Giusep p e Ter ra g ni, Ap r il 2 0 04

SOVEREIGNTY DETECTION So, how sovereign is a building Mr. Algorithm? In order to answer this question, Sovereignty must be considered as an aesthetic issue. Therefore, what Hannah Arendt refers to as a 'Politics of Appearances', for designers becomes a 'Politics of Form'.

T

M A R C H 2 018

he question of sovereignty in an age when algorithms are determining identity - or even citizenship - becomes one of aesthetics. Since so much of the data extracted is surface-value i.e. traces of actions rather than who you really are (who you date online, the products you buy etc.). there is an incredible amount of deduction to be made about what constitutes and individual. Even more abstraction is added to the mix when it comes to the issues concerning political freedom and rights.

All that without mentioning the in-built biases that these algorithms most certainly carry with them.

From a design perspective could sovereignty even be debated as an aesthetic issue? Can Arendt's politics of appearances be stretched to the politics

of form? That is what this investigation aims to discuss through the yes of machine vision. In particular, how can the algorithm be trained to recognise buildings based on typologies and how could it then assign value to them based on an aesthetic association with the sovereignty spectrum.

To do this a dataset of over 2800 images is compiled of varying images of buildings from refugee shelters to global institutions. By tagging each image with a sovereignty value the algorithm would learn to associate the abstract notion with specific aesthetic inputs. Therefore, the tests to follow aim at investigate what would a real-time sovereigntydetection algorithm see in an urban context?

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Overview pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

410

.jpg images pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

255

.jpg images pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

0_zero 408

1_min.

10_social

.jpg images

pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

400

.jpg images

100_deep-state

pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

1000_max. continental

845

.jpg images pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

global

250

.jpg images

pic_05.xml pic_04.xml pic_03.xml pic_02.xml pic_01.xml

267

410 .xml tag data

255 .xml tag data

408 .xml tag data

400 .xml tag data

845 .xml tag data

250 .xml tag data

.jpg images

267 .xml tag data

1

dataset of 2835 images Use a an online database (e.g.: google) to search for images of columns. Extract and save images, while discarding any which contain no columns. Between 100 - 300 images are a safe range for building a training data-set. In the tests to follow around 400 are usually selected to ensure even better results.

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label tagging With an image tagging program open each saved image and set up a tag ‘column’. In each image draw a rectangular bounding box over whatever is considered a column. Save as .xml file.

3

test

41.jpg images 41 .xml tag data

train

369 .jpg images 369 .xml tag data

test

26 .jpg images 26 .xml tag data

train

229 .jpg images 229 .xml tag data

test

41 .jpg images 41 .xml tag data

train

367 .jpg images 367 .xml tag data

test

40 .jpg images 40 .xml tag data

train

360 .jpg images 360 .xml tag data

test

85 .jpg images 85 .xml tag data

train

760 .jpg images 760 .xml tag data

test

25 .jpg images 25 .xml tag data

train

225 .jpg images 225 .xml tag data

test

27 .jpg images 27 .xml tag data

train train

240 .jpg images 240 .xml tag data

10% test / 90% train 90% of the images and their corresponding .xml files will be used as training data for the algorithm.

The rest are test data for it to correlate with the training ones as it gradually learns what a ‘column’ is.

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test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

test.csv train.csv test_tfrecord

train_tfrecord

4

.xml to .csv This is a code that will extract all the data from the .xml files that have been tagged and arrange them into a test and training table.

M A R C H 2 018

5

generate tf records The test and training .csv tables are converted into tf records.

These are in a format that google’s open-source machine learning software, tensor flow, can use to train itself.

6

train After all data is prepared the training session may commence.

7

inference graph The output from the training data can be frozen into an inference graph and fed into the object-detection code.

8

run detection

By running the objectdetection code it should now be trained to recognise sovereignty in real-time through a camera.

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‘0_zero’

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‘0_zero’

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‘1_min’

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‘1_min’

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‘10_social’

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‘10_social’

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‘100_deep-state’

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‘100_deep-state’

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‘1000_max’

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‘1000_max’

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‘continental’

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‘continental’

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‘global’

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‘global’

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.xml to .csv

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xml_to_csv

Imports In [ ]: import os import glob import pandas as pd import xml.etree.ElementTree as ET

Convert .xml to .csv In [ ]: def xml_to_csv(path): xml_list = [] for xml_file in glob.glob(path + '/*.xml'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): value = (root.find('filename').text, int(root.find('size')[0].text), int(root.find('size')[1].text), member[0].text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text) ) xml_list.append(value) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df

Define output directory for .csv files In [ ]: def main(): for directory in ['train','test']: image_path = os.path.join(os.getcwd(), 'images/{}'.format(directory)) xml_df = xml_to_csv(image_path) xml_df.to_csv('data/{}_labels.csv'.format(directory), index=None) print('Successfully converted xml to csv.')

main()

http://localhost:8888/notebooks/xml_to_csv.ipynb#

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generate tf_record

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generate_tfrecord

Imports In [ ]: from __future__ import division from __future__ import print_function from __future__ import absolute_import import os import io import pandas as pd import tensorflow as tf from PIL import Image from object_detection.utils import dataset_util from collections import namedtuple, OrderedDict flags = tf.app.flags flags.DEFINE_string('csv_input', '', 'Path to the CSV input') flags.DEFINE_string('output_path', '', 'Path to output TFRecord') FLAGS = flags.FLAGS

Define Class Labels In [ ]: # TO-DO replace this with label map def class_text_to_int(row_label): if row_label == '0_zero': return 1 if row_label == '1_min': return 2 if row_label == '10_social': return 3 if row_label == '100_deep-state': return 4 if row_label == '1000_max': return 5 if row_label == 'continental': return 6 if row_label == 'global': return 7 if row_label == 'person': return 8 else: None

Data Group Labels In [ ]: def split(df, group): data = namedtuple('data', ['filename', 'object']) gb = df.groupby(group) return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]

Extract tagging data from .csv files to generate tf record def create_tf_example(group, path): with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) image = Image.open(encoded_jpg_io) width, height = image.size filename = group.filename.encode('utf8') image_format = b'jpg' xmins = [] xmaxs = [] ymins = [] ymaxs = [] classes_text = [] classes = [] for index, row in group.object.iterrows(): xmins.append(row['xmin'] / width) xmaxs.append(row['xmax'] / width) ymins.append(row['ymin'] / height) ymaxs.append(row['ymax'] / height) classes_text.append(row['class'].encode('utf8')) classes.append(class_text_to_int(row['class'])) http://localhost:8888/notebooks/generate_tfrecord.ipynb#

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05/03/2018

generate_tfrecord

tf_example = tf.train.Example(features=tf.train.Features(feature={ 'image/height': dataset_util.int64_feature(height), 'image/width': dataset_util.int64_feature(width), 'image/filename': dataset_util.bytes_feature(filename), 'image/source_id': dataset_util.bytes_feature(filename), 'image/encoded': dataset_util.bytes_feature(encoded_jpg), 'image/format': dataset_util.bytes_feature(image_format), 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), 'image/object/class/label': dataset_util.int64_list_feature(classes), })) return tf_example

Write tf record In [ ]: def main(_): writer = tf.python_io.TFRecordWriter(FLAGS.output_path) path = os.path.join(os.getcwd(), 'images') examples = pd.read_csv(FLAGS.csv_input) grouped = split(examples, 'filename') for group in grouped: tf_example = create_tf_example(group, path) writer.write(tf_example.SerializeToString()) writer.close() output_path = os.path.join(os.getcwd(), FLAGS.output_path) print('Successfully created the TFRecords: {}'.format(output_path))

Write tf record In [ ]: def main(_): writer = tf.python_io.TFRecordWriter(FLAGS.output_path) path = os.path.join(os.getcwd(), 'images') examples = pd.read_csv(FLAGS.csv_input) grouped = split(examples, 'filename') for group in grouped: tf_example = create_tf_example(group, path) writer.write(tf_example.SerializeToString()) writer.close() output_path = os.path.join(os.getcwd(), FLAGS.output_path) print('Successfully created the TFRecords: {}'.format(output_path)) if __name__ == '__main__': tf.app.run()

http://localhost:8888/notebooks/generate_tfrecord.ipynb#

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test_labels

test.csv

test_tfrecord

filename filename width height width class height xminclass yminxmin xmax ymin width ymax xmax ymax height 01_pic_0_800px-Foor-Alspach_House.jpg 01_pic_0_800px-Foor-Alspach_House.jpg 800 536800 1_min 536 1_min 176 14176 713 14 505713 C_Mechanicsburg%2C_blue_sky.jpg 450505 6 01_pic_10_Mount_Clemens_Craftsman.jpg 01_pic_10_Mount_Clemens_Craftsman.jpg 640 480640 1_min 480 1_min49 74 49 472 74 369472 e.jpg 800369 5 C_Mechanicsburg.jpg 450500 6 01_pic_11_800px-Barnhardt-Bolenbaugh_House.jpg 01_pic_11_800px-Barnhardt-Bolenbaugh_House.jpg 800 536800 1_min 536 1_min 282 11282 773 11 500773 se.jpg 01_pic_12_800px-Avoca_Lodge_Denver_CO.jpg 800345 6 01_pic_12_800px-Avoca_Lodge_Denver_CO.jpg 800 370800 1_min 370 1_min16 33 16 469 33 345469 e_in_Canal_Winchester.jpg 800355 6 01_pic_12_800px-Avoca_Lodge_Denver_CO.jpg 01_pic_12_800px-Avoca_Lodge_Denver_CO.jpg 800 370800 1_min 370 1_min 448 142448 766142 355766 _House.jpg 800547 6 01_pic_12_800px-Olaf_Lee_House.jpg 01_pic_12_800px-Olaf_Lee_House.jpg 800 588800 1_min 588 1_min36 26 36 747 26 547747 rary.jpg 800506 6 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 800 600800 1_min 600 1_min 321 121321 629121 506629 use.jpg01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 800388 6 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 800 600800 1_min 600 1_min 499 195499 655195 388655 on_in_Olde_Towne_Toledo.jpg 800 4 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 800 600800 1_min 600 1_min 655 259655 735259 374735 374 on_in_Olde_Towne_Toledo.jpg 800350 4 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 01_pic_13_800px-Ginter_Park_Terrace_Foursquares.JPG 800 600800 1_min 600 1_min81 23 81 379 23 350379 on_in_Olde_Towne_Toledo.jpg 800477 4 01_pic_14_498px-108_Thayer.jpg 01_pic_14_498px-108_Thayer.jpg 498 599498 1_min 599 1_min21 77 21 461 77 477461 on_in_Olde_Towne_Toledo.jpg 800346 4 01_pic_14_800px-Floyd_and_Glenora_Dycus_House.jpg 01_pic_14_800px-Floyd_and_Glenora_Dycus_House.jpg 800 450800 1_min 450 1_min 209 67209 637 67 346637 hurch%2C_Napoleon.jpg 800522 6 01_pic_15_595px-Peter_McCourt_House.JPG 01_pic_15_595px-Peter_McCourt_House.JPG 595 600595 1_min 600 1_min54 51 54 541 51 522541 hurch%2C_Napoleon_1.jpg 800523 5 01_pic_15_800px-W.W._Logan_House.jpg 01_pic_15_800px-W.W._Logan_House.jpg 800 600800 1_min 600 1_min96 117 96 700117 523700 oulevard%2C_N.W..jpg 644501 5 01_pic_16_800px-Manuella_C._Walters_Duplex.JPG 01_pic_16_800px-Manuella_C._Walters_Duplex.JPG 800 547800 1_min 547 1_min 156 38156 742 38 501742 %2C_DC.jpg 800426 5 01_pic_17_800px-Doud_House.JPG 01_pic_17_800px-Doud_House.JPG 800 584800 1_min 584 1_min 223 67223 729 67 426729 nal_Democratic_Club.JPG 800442 5 01_pic_17_800px-Neal_House_near_Milton.jpg 01_pic_17_800px-Neal_House_near_Milton.jpg 800 600800 1_min 600 1_min91 75 91 701 75 442701 PG 800448 5 01_pic_18_610px-William_Smith_House.JPG 01_pic_18_610px-William_Smith_House.JPG 610 599610 1_min 599 1_min41 55 41 565 55 448565 _Episcopal_Church.JPG 800416 4 01_pic_18_800px-Beatrice%2C_Nebraska_610_N_11_St.JPG 01_pic_18_800px-Beatrice%2C_Nebraska_610_N_11_St.JPG 800 473800 1_min 473 1_min27 11 27 759 11 416759 hurch_of_Eckert.JPG 800 5 01_pic_19_800px-Beatrice%2C_Nebraska_602_N_11_St.JPG 01_pic_19_800px-Beatrice%2C_Nebraska_602_N_11_St.JPG 800 595800 1_min 595 1_min19 13 19 768 13 570768 570 ux_House.JPG 800468 5 01_pic_19_800px-WC_Harris_House.JPG 01_pic_19_800px-WC_Harris_House.JPG 800 550800 1_min 550 1_min71 70 71 682 70 468682 ouse.JPG 800500 5 01_pic_1_800px-1400-kenyon-avenue-onk-tn1.jpg 01_pic_1_800px-1400-kenyon-avenue-onk-tn1.jpg 800 571800 1_min 571 1_min21 47 21 722 47 500722 800509 4 01_pic_1_800px-Maui-Makawao-GomesHouse-frontsteps.JPG 01_pic_1_800px-Maui-Makawao-GomesHouse-frontsteps.JPG 800 600800 1_min 600 1_min 1 9 1 791 9 509791 e.JPG 01_pic_20_800px-Hampton_Park_Terrace_house_example1.jpg 800536 5 01_pic_20_800px-Hampton_Park_Terrace_house_example1.jpg 800 600800 1_min 600 1_min 128 60128 731 60 536731 PG 800463 3 01_pic_20_800px-House_on_Brenstrom_farmstead_%28Phelps_Co% 01_pic_20_800px-House_on_Brenstrom_farmstead_%28Phelps_Co% 800 513800 1_min 513 1_min40 37 40 749 37 463749 _House.JPG 800433 4 01_pic_21_800px-Beatrice%2C_Nebraska_802_N_7_St.JPG 01_pic_21_800px-Beatrice%2C_Nebraska_802_N_7_St.JPG 800 487800 1_min 487 1_min70 25 70 686 25 433686 pg 800409 6 01_pic_21_800px-Encampment_House.jpg 01_pic_21_800px-Encampment_House.jpg 800 503800 1_min 503 1_min 184 47184 571 47 409571 2C_Bentonville%2C_AR.jpg 800 6 01_pic_22_800px-Beatrice%2C_Nebraska_909_N_11_St.JPG 01_pic_22_800px-Beatrice%2C_Nebraska_909_N_11_St.JPG 800 470800 1_min 470 1_min73 14 73 761 14 432761 432 Grants_Pass_Oregon.jpg 800444 5 01_pic_22_800px-Snohomish%2C_WA_-_506_Avenue_B.jpg 01_pic_22_800px-Snohomish%2C_WA_-_506_Avenue_B.jpg 800 600800 1_min 600 1_min57 128 57 659128 444659 Portland.JPG 729389 6 01_pic_23_800px-Beatrice%2C_Nebraska_702_N_11_St_from_S_ 01_pic_23_800px-Beatrice%2C_Nebraska_702_N_11_St_from_S_ 800 468800 1_min 468 1_min59 20 59 747 20 389747 em_Oregon.jpg 800 6 01_pic_23_American_Foursquare_Home.jpg 01_pic_23_American_Foursquare_Home.jpg 640 480640 1_min 480 1_min88 1 88 532 1 417532 417 Grants_Pass_Oregon.jpg 800357 6 01_pic_24_800px-American_four_square.jpg 01_pic_24_800px-American_four_square.jpg 800 600800 1_min 600 1_min 241 148241 427148 357427 ouse_-_Medford_Oregon.jpg 800362 6 01_pic_24_800px-American_four_square.jpg 01_pic_24_800px-American_four_square.jpg 800 600800 1_min 600 1_min 400 99400 537 99 362537 ouse.jpg 800 5 01_pic_24_800px-American_four_square.jpg 01_pic_24_800px-American_four_square.jpg 800 600800 1_min 600 1_min 160 183160 287183 362287 362 ouse.jpg 800376 5 01_pic_24_800px-American_four_square.jpg 01_pic_24_800px-American_four_square.jpg 800 600800 1_min 600 1_min 627 109627 768109 376768 -_Medford_Oregon.jpg 800395 6 01_pic_24_800px-Hastings%2C_Nebraska_907_N_Burlington_Ave. 01_pic_24_800px-Hastings%2C_Nebraska_907_N_Burlington_Ave. 800 563800 1_min 563 1_min42 46 42 733 46 395733 nts_Pass_Oregon.jpg 800476 4 01_pic_25_800px-Hastings%2C_Nebraska_806_N_Lincoln_Ave.JPG 01_pic_25_800px-Hastings%2C_Nebraska_806_N_Lincoln_Ave.JPG 800 588800 1_min 588 1_min48 28 48 710 28 476710 Medford_Oregon.jpg 800343 6 01_pic_25_800px-Snohomish%2C_WA_-_220_Union_Ave_01.jpg 01_pic_25_800px-Snohomish%2C_WA_-_220_Union_Ave_01.jpg 800 600800 1_min 600 1_min 154 174154 613174 343613 eva_-_Medford_Oregon.jpg 800 6 01_pic_2_800px-Quaker_Hill_Historic_District_-_140_Old_Norwich 01_pic_2_800px-Quaker_Hill_Historic_District_-_140_Old_Norwich 800 578800 1_min 578 1_min 102 56102 686 56 481686 481 House_-_Gold_Hill_Oregon.jpg 799337 6 01_pic_3_800px-Dempster-house-onk-tn1.jpg 01_pic_3_800px-Dempster-house-onk-tn1.jpg 800 487800 1_min 487 1_min 216 43216 575 43 337575 shland_Oregon.jpg 780464 6 01_pic_4_800px-831Richland.jpg 01_pic_4_800px-831Richland.jpg 800 533800 1_min 533 1_min44 35 44 745 35 464745 _House_%28Dayton%2C_Oregon%29.jpg 800 5 01_pic_5_800px-Dr._John_A._Black_House_Complex.JPG 01_pic_5_800px-Dr._John_A._Black_House_Complex.JPG 800 467800 1_min 467 1_min86 18 86 748 18 442748 442 se_-_Ashland_Oregon.jpg 780482 6 01_pic_6_800px-Ferguson_House_Kalispell.jpg 01_pic_6_800px-Ferguson_House_Kalispell.jpg 800 600800 1_min 600 1_min84 61 84 628 61 482628 Portland_Oregon.jpg 800546 4 01_pic_7_800px-Doyle_Benton_House.JPG 01_pic_7_800px-Doyle_Benton_House.JPG 800 580800 1_min 580 1_min 209 51209 615 51 546615 ka_923_N_7_St.JPG 800 4 01_pic_8_448px-Johnson_House_Lynchburg_Nov_08.JPG 01_pic_8_448px-Johnson_House_Lynchburg_Nov_08.JPG 448 600448 1_min 600 1_min36 126 36 421126 459421 459 e.JPG 01_pic_8_800px-F%26G_Greenwood_House_Portland.JPG 800600 4 01_pic_8_800px-F%26G_Greenwood_House_Portland.JPG 800 600800 1_min 600 1_min59 20 59 648 20 600648 plex.jpg 800 5 01_pic_9_800px-Casiville_Bullard_House.jpg 01_pic_9_800px-Casiville_Bullard_House.jpg 800 565800 1_min 565 1_min35 7 35 705 7 523705 523 e_%28Yankton%2C_SD%29_from_SW.JPG 800189 5 0_pic_001.jpg 0_pic_001.jpg 260 194260 0_zero 194 0_zero 144 67144 260 67 189260 e_%28Yankton%2C_SD%29_from_S.JPG 800136 4 0_pic_001.jpg 0_pic_001.jpg 260 194260 0_zero 194 0_zero 105 53105 149 53 136149 use.jpg0_pic_001.jpg 0_pic_001.jpg 800 6 260 194260 0_zero 194 0_zero 84 72 84 112 72 114112 114 Pratt_House.jpg 793169 6 0_pic_002.jpg 0_pic_002.jpg 275 183275 0_zero 183 0_zero 37 1 37 275 1 169275 icello_Utah.jpg 800167 6 0_pic_002.jpg 0_pic_002.jpg 275 183275 person 183 person 174 45174 244 45 167244 ka_714_N_11_St.JPG 800 5 0_pic_003.jpg 0_pic_003.jpg 275 183275 0_zero 183 0_zero 18 10 18 202 10 179202 179 800174 5 0_pic_003.jpg 0_pic_003.jpg 275 183275 person 183 person 144 30144 185 30 174185 asper%2C_Wyoming%29_2.JPG 748 5 0_pic_003.jpg 0_pic_003.jpg 275 183275 0_zero 183 0_zero 189 19189 275 19 136275 136 ka_915_N_11_St.JPG 800148 4 0_pic_004.jpg 0_pic_004.jpg 275 183275 0_zero 183 0_zero 11 87 11 94 87 148 94 ka_616_N_11_St.JPG 799144 5 0_pic_005.jpg 0_pic_005.jpg 275 183275 0_zero 183 0_zero 84 46 84 197 46 144197 asper%2C_Wyoming%29_1.JPG 800 5 0_pic_006.jpg 0_pic_006.jpg 259 194259 0_zero 194 0_zero6 47 6 179 47 175179 175 ue.JPG 0_pic_007.jpg 0_pic_007.jpg 800 64 5 300 168300 0_zero 168 0_zero 28 25 28 103 25 64103 ka_800_N_Lexington_Ave.JPG 800 88 5 0_pic_007.jpg 0_pic_007.jpg 300 168300 0_zero 168 0_zero 181 19181 265 19 88265 ka_722_N_11_St.JPG 800 4 0_pic_008.jpg 0_pic_008.jpg 275 183275 0_zero 183 0_zero6 49 6 133 49 140133 140 ouse_c.1908.jpg 800124 6 0_pic_008.jpg 0_pic_008.jpg 275 183275 0_zero 183 0_zero 120 72120 194 72 124194 a_Street_Wetumpka_Sept10.jpg 800115 6 0_pic_008.jpg 0_pic_008.jpg 275 183275 0_zero 183 0_zero 177 59177 247 59 115247 Montgomery_July_2009.jpg 800 6 0_pic_009.jpg 0_pic_009.jpg 275 183275 0_zero 183 0_zero 19 30 19 175 30 139175 139 e_02.JPG 800160 6 0_pic_010.jpg 0_pic_010.jpg 275 183275 0_zero 183 0_zero 20 116 20 104116 160104 For f ull docum ent s of t r ai n_ ld_School_01.jpg 800 6 0_pic_010.jpg 0_pic_010.jpg 275 test_labels 183275 0_zero 84 105 84 143105 145143 145 labels and please 183 0_zero ka_923_N_11_St.JPG 800 4 see Appendix. tn1.jpg 800 5 blvd-knox-tn1.jpg 800 5 M A R C H 2 018 162 | T E R R A M E D I A | nicholas zembashi knoxville-tn1.jpg 800 4 ka_820_N_7_St.JPG 782 6

Sample

train.csv

train_labels

train_tfrecord

e filename width height width class height xmin class ymin xmin xmax ymin ymax xmax ym _139_450px-Masonic_Temple%2C_Mechanicsburg%2C_blue_sky.jpg 01_pic_139_450px-Masonic_Temple%2C_Mechanicsburg%2C_blue_sky.jpg 450 600 1_min 450 600 19 1_min 39 421 19 532 39 421 _140_800px-William_King_House.jpg 01_pic_140_800px-William_King_House.jpg 800 536 1_min 800 536 175 1_min 43 175 680 407 43 680 _142_450px-Masonic_Temple%2C_Mechanicsburg.jpg 01_pic_142_450px-Masonic_Temple%2C_Mechanicsburg.jpg 450 600 1_min 450 600 75 1_min 59 385 75 459 59 385 _146_800px-Soloman_Levy_House.jpg 01_pic_146_800px-Soloman_Levy_House.jpg 800 600 1_min 800 600 155 1_min 17 155 744 568 17 744 _150_800px-William_King_House_in_Canal_Winchester.jpg 01_pic_150_800px-William_King_House_in_Canal_Winchester.jpg 800 600 1_min 800 600 106 1_min 88 106 640 461 88 640 _151_800px-Edward_Ransbottom_House.jpg 01_pic_151_800px-Edward_Ransbottom_House.jpg 800 600 1_min 800 600 109 1_min 16 109 673 527 16 673 _152_800px-Defiance_Public_Library.jpg 01_pic_152_800px-Defiance_Public_Library.jpg 800 600 1_min 800 600 81 1_min 20 702 81 582 20 702 _153_800px-Bruns-Wynkoop_House.jpg 01_pic_153_800px-Bruns-Wynkoop_House.jpg 800 600 1_min 800 600 184 1_min 77 184 653 515 77 653 _156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 01_pic_156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 800 450 1_min 800 450 23 1_min 24 441 23 364 24 441 _156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 01_pic_156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 800 450 1_min 800 450 384 1_min 92 384 586 348 92 586 _156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 01_pic_156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 800 450 1_min 800 450 543 1_min 151 543 659 151 342 659 _156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 01_pic_156_800px-Winthrop_near_Fulton_in_Olde_Towne_Toledo.jpg 800 450 1_min 800 450 647 1_min 192 647 708 192 348 708 _161_800px-First_Presbyterian_Church%2C_Napoleon.jpg 01_pic_161_800px-First_Presbyterian_Church%2C_Napoleon.jpg 800 600 1_min 800 600 143 1_min 9 143 694 497 9 694 _162_800px-First_Presbyterian_Church%2C_Napoleon_1.jpg 01_pic_162_800px-First_Presbyterian_Church%2C_Napoleon_1.jpg 800 567 1_min 800 567 111 1_min 63 111 721 550 63 721 _165_644px-4703_MacArthur_Boulevard%2C_N.W..jpg 01_pic_165_644px-4703_MacArthur_Boulevard%2C_N.W..jpg 644 599 1_min 644 599 22 1_min 6 570 22 463 6 570 _167_800px-Potomac_Boat_Club%2C_DC.jpg 01_pic_167_800px-Potomac_Boat_Club%2C_DC.jpg 800 527 1_min 800 527 210 1_min 53 210 715 487 53 715 _179_800px-Woman%27s_National_Democratic_Club.JPG 01_pic_179_800px-Woman%27s_National_Democratic_Club.JPG 800 585 1_min 800 585 122 1_min 61 122 736 427 61 736 _195_800px-Eppich_Apartments.JPG 01_pic_195_800px-Eppich_Apartments.JPG 800 533 1_min 800 533 49 1_min 19 731 49 526 19 731 _196_800px-Hotchkiss_Methodist_Episcopal_Church.JPG 01_pic_196_800px-Hotchkiss_Methodist_Episcopal_Church.JPG 800 456 1_min 800 456 48 1_min 44 717 48 415 44 717 _197_800px-First_Presbyterian_Church_of_Eckert.JPG 01_pic_197_800px-First_Presbyterian_Church_of_Eckert.JPG 800 508 1_min 800 508 120 1_min 13 120 688 487 13 688 _198_800px-M.J._Lavina_Robidoux_House.JPG 01_pic_198_800px-M.J._Lavina_Robidoux_House.JPG 800 556 1_min 800 556 57 1_min 30 688 57 457 30 688 _200_800px-Harvey_J._Parish_House.JPG 01_pic_200_800px-Harvey_J._Parish_House.JPG 800 533 1_min 800 533 101 1_min 62 101 656 354 62 656 _201_800px-Geneva_Home.JPG 01_pic_201_800px-Geneva_Home.JPG 800 474 1_min 800 474 180 1_min 151 180 675 151 420 675 _202_800px-Knight-Wood_House.JPG 01_pic_202_800px-Knight-Wood_House.JPG 800 535 1_min 800 535 64 1_min 88 767 64 395 88 767 _203_800px-Buchtel_Bungalow.JPG 01_pic_203_800px-Buchtel_Bungalow.JPG 800 383 1_min 800 383 94 1_min 29 715 94 269 29 715 _205_800px-W.E._Heginbotham_House.JPG 01_pic_205_800px-W.E._Heginbotham_House.JPG 800 440 1_min 800 440 122 1_min 61 122 753 360 61 753 _208_800px-Jerome_School_22.jpg 01_pic_208_800px-Jerome_School_22.jpg 800 600 1_min 800 600 41 1_min 161 757 41 161 487 757 _209_800px-Linebarger_House%2C_Bentonville%2C_AR.jpg 01_pic_209_800px-Linebarger_House%2C_Bentonville%2C_AR.jpg 800 600 1_min 800 600 110 1_min 189 110 537 189 473 537 _235_800px-Calhoun_House_-_Grants_Pass_Oregon.jpg 01_pic_235_800px-Calhoun_House_-_Grants_Pass_Oregon.jpg 800 560 1_min 800 560 100 1_min 84 100 694 447 84 694 _238_729px-Hazel_Hall_House_Portland.JPG 01_pic_238_729px-Hazel_Hall_House_Portland.JPG 729 600 1_min 729 600 70 1_min 17 667 70 556 17 667 _239_800px-Daue_House_-_Salem_Oregon.jpg 01_pic_239_800px-Daue_House_-_Salem_Oregon.jpg 800 600 1_min 800 600 64 1_min 50 706 64 477 50 706 _240_800px-Clemens_House_-_Grants_Pass_Oregon.jpg 01_pic_240_800px-Clemens_House_-_Grants_Pass_Oregon.jpg 800 600 1_min 800 600 116 1_min 39 116 684 490 39 684 _241_800px-Gates-Richardson_House_-_Medford_Oregon.jpg 01_pic_241_800px-Gates-Richardson_House_-_Medford_Oregon.jpg 800 600 1_min 800 600 46 1_min 56 722 46 488 56 722 _244_800px-Walter_V._Smith_House.jpg 01_pic_244_800px-Walter_V._Smith_House.jpg 800 533 1_min 800 533 14 1_min 190 248 14 190 441 248 _244_800px-Walter_V._Smith_House.jpg 01_pic_244_800px-Walter_V._Smith_House.jpg 800 533 1_min 800 533 250 1_min 86 250 736 343 86 736 _245_800px-Toft_Joseph_House_-_Medford_Oregon.jpg 01_pic_245_800px-Toft_Joseph_House_-_Medford_Oregon.jpg 800 600 1_min 800 600 182 1_min 79 182 722 495 79 722 _247_800px-Smith_House_-_Grants_Pass_Oregon.jpg 01_pic_247_800px-Smith_House_-_Grants_Pass_Oregon.jpg 800 469 1_min 800 469 37 1_min 63 741 37 383 63 741 _248_800px-Hubbard_House_-_Medford_Oregon.jpg 01_pic_248_800px-Hubbard_House_-_Medford_Oregon.jpg 800 600 1_min 800 600 53 1_min 147 682 53 147 486 682 _251_800px-Dodge_House_Geneva_-_Medford_Oregon.jpg 01_pic_251_800px-Dodge_House_Geneva_-_Medford_Oregon.jpg 800 600 1_min 800 600 82 1_min 75 709 82 501 75 709 _253_799px-Van_Hoevenberg_House_-_Gold_Hill_Oregon.jpg 01_pic_253_799px-Van_Hoevenberg_House_-_Gold_Hill_Oregon.jpg 799 600 1_min 799 600 114 1_min 100 114 691 100 429 691 _254_780px-Badger_House_-_Ashland_Oregon.jpg 01_pic_254_780px-Badger_House_-_Ashland_Oregon.jpg 780 600 1_min 780 600 54 1_min 134 707 54 134 456 707 _255_800px-Paul_Londershausen_House_%28Dayton%2C_Oregon%29.jpg 01_pic_255_800px-Paul_Londershausen_House_%28Dayton%2C_Oregon%29.jpg 800 533 1_min 800 533 80 1_min 40 724 80 439 40 724 _257_780px-Winkler_Rental_House_-_Ashland_Oregon.jpg 01_pic_257_780px-Winkler_Rental_House_-_Ashland_Oregon.jpg 780 600 1_min 780 600 64 1_min 80 673 64 514 80 673 _260_800px-Woerner_House_-_Portland_Oregon.jpg 01_pic_260_800px-Woerner_House_-_Portland_Oregon.jpg 800 489 1_min 800 489 46 1_min 59 739 46 391 59 739 _26_800px-Beatrice%2C_Nebraska_923_N_7_St.JPG 01_pic_26_800px-Beatrice%2C_Nebraska_923_N_7_St.JPG 800 496 1_min 800 496 88 1_min 17 739 88 434 17 739 _26_800px-Martin_Walter_House.JPG 01_pic_26_800px-Martin_Walter_House.JPG 800 490 1_min 800 490 176 1_min 18 176 601 364 18 601 _271_800px-Roper_House_Complex.jpg 01_pic_271_800px-Roper_House_Complex.jpg 800 531 1_min 800 5314 1_min 26 756 4 420 26 756 _273_800px-Harold_Doyle_House_%28Yankton%2C_SD%29_from_SW.JPG 01_pic_273_800px-Harold_Doyle_House_%28Yankton%2C_SD%29_from_SW.JPG 800 522 1_min 800 522 60 1_min 23 753 60 374 23 753 _274_800px-Harold_Doyle_House_%28Yankton%2C_SD%29_from_S.JPG 01_pic_274_800px-Harold_Doyle_House_%28Yankton%2C_SD%29_from_S.JPG 800 477 1_min 800 477 66 1_min 25 743 66 421 25 743 _279_800px-Knight-Mangum_House.jpg 01_pic_279_800px-Knight-Mangum_House.jpg 800 600 1_min 800 600 55 1_min 2 677 55 556 2 677 _27_793px-Southington_J._Frank_Pratt_House.jpg 01_pic_27_793px-Southington_J._Frank_Pratt_House.jpg 793 600 1_min 793 600 104 1_min 105 104 680 105 517 680 _281_800px-Hyland_Hotel_Monticello_Utah.jpg 01_pic_281_800px-Hyland_Hotel_Monticello_Utah.jpg 800 600 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01_pic_31_800px-159_Michigan_Avenue.JPG 800 531 1_min 800 531 81 1_min 1 765 81 498 1 765 _31_800px-Hastings%2C_Nebraska_800_N_Lexington_Ave.JPG 01_pic_31_800px-Hastings%2C_Nebraska_800_N_Lexington_Ave.JPG 800 513 1_min 800 513 140 1_min 14 140 689 447 14 689 _32_800px-Beatrice%2C_Nebraska_722_N_11_St.JPG 01_pic_32_800px-Beatrice%2C_Nebraska_722_N_11_St.JPG 800 481 1_min 800 481 66 1_min 21 753 66 433 21 753 _332_800px-Abernathy-Shaw_House_c.1908.jpg 01_pic_332_800px-Abernathy-Shaw_House_c.1908.jpg 800 600 1_min 800 600 49 1_min 17 777 49 493 17 777 _335_800px-303_West_Tuskeena_Street_Wetumpka_Sept10.jpg 01_pic_335_800px-303_West_Tuskeena_Street_Wetumpka_Sept10.jpg 800 600 1_min 800 600 124 1_min 97 124 667 444 97 667 _336_800px-300s_Rose_Lane_Montgomery_July_2009.jpg 01_pic_336_800px-300s_Rose_Lane_Montgomery_July_2009.jpg 800 600 1_min 800 600 57 1_min 61 690 57 463 61 690 _338_800px-Patrick_Farrish_House_02.JPG 01_pic_338_800px-Patrick_Farrish_House_02.JPG 800 600 1_min 800 600 101 1_min 44 101 540 444 44 540 _339_800px-Tankersley_Rosenwald_School_01.jpg 01_pic_339_800px-Tankersley_Rosenwald_School_01.jpg 800 600 1_min 800 600 26 1_min 113 754 26 113 419 754 t r ai n_ 464 1_min 800 _33_800px-Beatrice%2C_Nebraska_923_N_11_St.JPG 01_pic_33_800px-Beatrice%2C_Nebraska_923_N_11_St.JPG For f ull docum ent s of800 464 56 1_min 14 720 56 414 14 720 labels and test_labels please _344_800px-125-glenwood-onk-tn1.jpg 01_pic_344_800px-125-glenwood-onk-tn1.jpg 800 589 1_min 800 589 28 1_min 88 670 28 523 88 670 see Appendix. _347_800px-2222-island-home-blvd-knox-tn1.jpg 01_pic_347_800px-2222-island-home-blvd-knox-tn1.jpg 800 517 1_min 800 517 30 1_min 102 715 30 102 461 715 _348_800px-2903-fountain-park-knoxville-tn1.jpg 01_pic_348_800px-2903-fountain-park-knoxville-tn1.jpg 800 468 1_min 800 468 60 1_min 76 714 60 381 76 714 M A R C H 2 018 nicholas zembashi | T E R R A M E D I A | 163 _34_782px-Beatrice%2C_Nebraska_820_N_7_St.JPG 01_pic_34_782px-Beatrice%2C_Nebraska_820_N_7_St.JPG 782 600 1_min 782 600 60 1_min 20 730 60 523 20 730 _34_800px-1116-1112_Cullom_Street_Dec_2012.jpg 01_pic_34_800px-1116-1112_Cullom_Street_Dec_2012.jpg 800 600 1_min 800 600 41 1_min 143 393 41 143 415 393 _34_800px-1116-1112_Cullom_Street_Dec_2012.jpg 01_pic_34_800px-1116-1112_Cullom_Street_Dec_2012.jpg 800 600 1_min 800 600 417 1_min 168 417 551 168 317 551

Sample

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train_sovereignty

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train

Train Tensorflow Training executable for detection models.

This executable is used to train DetectionModels. There are two ways of configuring the training job: 1) A single pipeline_pb2.TrainEvalPipelineConfig configuration file can be specified by --pipeline_config_path.

Example usage: ./train \ --logtostderr \ --train_dir=path/to/train_dir \ --pipeline_config_path=pipeline_config.pbtxt 2) Three configuration files can be provided: a model_pb2.DetectionModel configuration file to define what type of DetectionModel is being trained, an input_reader_pb2.InputReader file to specify what training data will be used and a train_pb2.TrainConfig file to configure training parameters.

Example usage: ./train \ --logtostderr \ --train_dir=path/to/train_dir \ --model_config_path=model_config.pbtxt \ --train_config_path=train_config.pbtxt \ --input_config_path=train_input_config.pbtxt

Imports In [ ]: import functools import json import os import tensorflow as tf from object_detection import trainer from object_detection.builders import input_reader_builder from object_detection.builders import model_builder from object_detection.utils import config_util tf.logging.set_verbosity(tf.logging.INFO) flags = tf.app.flags flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.') flags.DEFINE_integer('task', 0, 'task id') flags.DEFINE_integer('num_clones', 1, 'Number of clones to deploy per worker.') flags.DEFINE_boolean('clone_on_cpu', False, 'Force clones to be deployed on CPU. Note that even if ' 'set to False (allowing ops to run on gpu), some ops may ' 'still be run on the CPU if they have no GPU kernel.') flags.DEFINE_integer('worker_replicas', 1, 'Number of worker+trainer ' 'replicas.') flags.DEFINE_integer('ps_tasks', 0, 'Number of parameter server tasks. If None, does not use ' 'a parameter server.') flags.DEFINE_string('train_dir', '', 'Directory to save the checkpoints and training summaries.') flags.DEFINE_string('pipeline_config_path', '', 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file. If provided, other configs are ignored') flags.DEFINE_string('train_config_path', '', 'Path to a train_pb2.TrainConfig config file.') flags.DEFINE_string('input_config_path', '', 'Path to an input_reader_pb2.InputReader config file.') flags.DEFINE_string('model_config_path', '', 'Path to a model_pb2.DetectionModel config file.')

FLAGS = flags.FLAGS

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Training

train

In [ ]: def main(_): assert FLAGS.train_dir, '`train_dir` is missing.' if FLAGS.task == 0: tf.gfile.MakeDirs(FLAGS.train_dir) if FLAGS.pipeline_config_path: configs = config_util.get_configs_from_pipeline_file( FLAGS.pipeline_config_path) if FLAGS.task == 0: tf.gfile.Copy(FLAGS.pipeline_config_path, os.path.join(FLAGS.train_dir, 'pipeline.config'), overwrite=True) else: configs = config_util.get_configs_from_multiple_files( model_config_path=FLAGS.model_config_path, train_config_path=FLAGS.train_config_path, train_input_config_path=FLAGS.input_config_path) if FLAGS.task == 0: for name, config in [('model.config', FLAGS.model_config_path), ('train.config', FLAGS.train_config_path), ('input.config', FLAGS.input_config_path)]: http://localhost:8888/notebooks/train.ipynb#Train-Tensorflow tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name),

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train_config_path=FLAGS.train_config_path, train_input_config_path=FLAGS.input_config_path) if FLAGS.task == 0: for name, config in [('model.config', FLAGS.model_config_path), ('train.config', FLAGS.train_config_path), ('input.config', FLAGS.input_config_path)]: tf.gfile.Copy(config, os.path.join(FLAGS.train_dir, name), overwrite=True) model_config = configs['model'] train_config = configs['train_config'] input_config = configs['train_input_config'] model_fn = functools.partial( model_builder.build, model_config=model_config, is_training=True) create_input_dict_fn = functools.partial( input_reader_builder.build, input_config) env = json.loads(os.environ.get('TF_CONFIG', '{}')) cluster_data = env.get('cluster', None) cluster = tf.train.ClusterSpec(cluster_data) if cluster_data else None task_data = env.get('task', None) or {'type': 'master', 'index': 0} task_info = type('TaskSpec', (object,), task_data) # Parameters for a single worker. ps_tasks = 0 worker_replicas = 1 worker_job_name = 'lonely_worker' task = 0 is_chief = True master = '' if cluster_data and 'worker' in cluster_data: # Number of total worker replicas include "worker"s and the "master". worker_replicas = len(cluster_data['worker']) + 1 if cluster_data and 'ps' in cluster_data: ps_tasks = len(cluster_data['ps']) if worker_replicas > 1 and ps_tasks < 1: raise ValueError('At least 1 ps task is needed for distributed training.') if worker_replicas >= 1 and ps_tasks > 0: # Set up distributed training. server = tf.train.Server(tf.train.ClusterSpec(cluster), protocol='grpc', job_name=task_info.type, task_index=task_info.index) if task_info.type == 'ps': server.join() return worker_job_name = '%s/task:%d' % (task_info.type, task_info.index) task = task_info.index is_chief = (task_info.type == 'master') master = server.target trainer.train(create_input_dict_fn, model_fn, train_config, master, task, FLAGS.num_clones, worker_replicas, FLAGS.clone_on_cpu, ps_tasks, worker_job_name, is_chief, FLAGS.train_dir)

if __name__ == '__main__': tf.app.run()

http://localhost:8888/notebooks/train.ipynb#Train-Tensorflow

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05/03/2018

faster_rccn_inception_v2.config

In [ ]: # Faster R-CNN with Inception v2, configuration for MSCOCO Dataset. # Users should configure the fine_tune_checkpoint field in the train config as # well as the label_map_path and input_path fields in the train_input_reader and # eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that # should be configured.

model { faster_rcnn { num_classes: 1 image_resizer { keep_aspect_ratio_resizer { min_dimension: 600 max_dimension: 1024 } } feature_extractor { type: 'faster_rcnn_inception_v2' first_stage_features_stride: 16 } first_stage_anchor_generator { grid_anchor_generator { scales: [0.25, 0.5, 1.0, 2.0] aspect_ratios: [0.5, 1.0, 2.0] height_stride: 16 width_stride: 16 } } first_stage_box_predictor_conv_hyperparams { op: CONV regularizer { l2_regularizer { weight: 0.0 } } initializer { truncated_normal_initializer { stddev: 0.01 } } } first_stage_nms_score_threshold: 0.0 first_stage_nms_iou_threshold: 0.7 first_stage_max_proposals: 300 first_stage_localization_loss_weight: 2.0 first_stage_objectness_loss_weight: 1.0 initial_crop_size: 14 maxpool_kernel_size: 2 maxpool_stride: 2 second_stage_box_predictor { mask_rcnn_box_predictor { use_dropout: false dropout_keep_probability: 1.0 fc_hyperparams { op: FC regularizer { l2_regularizer { weight: 0.0 } } initializer { variance_scaling_initializer { factor: 1.0 uniform: true mode: FAN_AVG } } } } } second_stage_post_processing { batch_non_max_suppression { score_threshold: 0.0 iou_threshold: 0.6 max_detections_per_class: 100 max_total_detections: 300 } score_converter: SOFTMAX } second_stage_localization_loss_weight: 2.0 second_stage_classification_loss_weight: 1.0 } } train_config: { batch_size: 1 optimizer { momentum_optimizer: { http://localhost:8888/notebooks/faster_rccn_inception_v2.config.ipynb

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05/03/2018

faster_rccn_inception_v2.config

learning_rate: { manual_step_learning_rate { initial_learning_rate: 0.0002 schedule { step: 0 learning_rate: .0002 } schedule { step: 900000 learning_rate: .00002 } schedule { step: 1200000 learning_rate: .000002 } } } momentum_optimizer_value: 0.9 } use_moving_average: false } gradient_clipping_by_norm: 10.0 fine_tune_checkpoint: "faster_rcnn_inception_v2_coco_2018_01_28/model.ckpt" from_detection_checkpoint: true # Note: The below line limits the training process to 200K steps, which we # empirically found to be sufficient enough to train the COCO dataset. This # effectively bypasses the learning rate schedule (the learning rate will # never decay). Remove the below line to train indefinitely. num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } } train_input_reader: { tf_record_input_reader { input_path: "data/train.record" } label_map_path: "data/object-detection.pbtxt" } eval_config: { num_examples: 8000 # Note: The below line limits the evaluation process to 10 evaluations. # Remove the below line to evaluate indefinitely. max_evals: 1000 } eval_input_reader: { tf_record_input_reader { input_path: "data/test.record" } label_map_path: "data/object-detection.pbtxt" shuffle: false num_readers: 1 }

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Failed Test In this failed test it is clear from the ‘loss’ values how far off from zero the graph would deviate. In other words the training accurac y is progressively worsening.

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Successful Test As opposed to the previous test here the ‘loss’ value is successfully tending towards zero hence signifying an optimal training process.

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The training process, shown above, had successfully taught the algorithm to recognise the sovereignty spectrum values through the datasets given to it. Over 200 000 training steps and several hours later the above Total Loss graphs was produced, tending towards zero.

I

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n this test, the model was trained for several hours, This allowed the algorithm to conduct over 200 000 steps of training. As seen above the graphs tended nicely towards zero signifying a successful training rate.

In the next page a previous training example is shown whereby the session had failed to train satisfactorily.

In the first attempt to train the algorithm on the sovereignty datasets there were notable training errors. This was clear from the Total Loss graph where the curve is expect to drop below 1.0, with

accuracy increasing in the learning process as it reaches 0.0. However in this case the curve was erratic and tended to increase beyond one more often that it should have.

To rectify this failure the evaluation steps in the code (frequency of training steps) had to be increased from 10 to 1000.

Another possible reason for the failed test could have been the number of images given in the ‘test’ folder. By increasing that number as well as the evaluation steps for the training, the failure was corrected in the final session.

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clone_loss_1

Failed Test

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In the first attempt to train the algorithm on the sovereignty datasets there were notable training errors. This was clear from the Total Loss graph where the curve is expect to drop below 1.0, with accuracy increasing in the learning process as it reaches 0.0.

However in this case the curve was erratic and tended to increase beyond one more often that it should have. To rectify this failure the evaluation steps in the code (frequency of training steps) had to be increased from 10 to 1000.

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export inference graph

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export_inference_graph

Export Inference Graph Tool to export an object detection model for inference.

Prepares an object detection tensorflow graph for inference using model configuration and an optional trained checkpoint. Outputs inference graph, associated checkpoint files, a frozen inference graph and a SavedModel (https://tensorflow.github.io/serving/serving_basic.html (https://tensorflow.github.io/serving/serving_basic.html)).

The inference graph contains one of three input nodes depending on the user specified option. image_tensor : Accepts a uint8 4-D tensor of shape [None, None, None, 3] encoded_image_string_tensor : Accepts a 1-D string tensor of shape [None] containing encoded PNG or JPEG images. Image resolutions are expected to be the same if more than 1 image is provided. tf_example : Accepts a 1-D string tensor of shape [None] containing serialized TFExample protos. Image resolutions are expected to be the same if more than 1 image is provided. and the following output nodes returned by the model.postprocess(..): num_detections : Outputs float32 tensors of the form [batch] that specifies the number of valid boxes per image in the batch. detection_boxes : Outputs float32 tensors of the form [batch, num_boxes, 4] containing detected boxes. detection_scores : Outputs float32 tensors of the form [batch, num_boxes] containing class scores for the detections. detection_classes : Outputs float32 tensors of the form [batch, num_boxes] containing classes for the detections. detection_masks : Outputs float32 tensors of the form [batch, num_boxes, mask_height, mask_width] containing predicted instance masks for each box if its present in the dictionary of postprocessed tensors returned by the model.

Notes: This tool uses use_moving_averages from eval_config to decide which weights to freeze.

Example Usage: python export_inference_graph \ --input_type image_tensor \ --pipeline_config_path path/to/ssd_inception_v2.config \ --trained_checkpoint_prefix path/to/model.ckpt \ --output_directory path/to/exported_model_directory The expected output would be in the directory path/to/exported_model_directory (which is created if it does not exist) with contents: graph.pbtxt model.ckpt.data-00000-of-00001 model.ckpt.info model.ckpt.meta frozen_inference_graph.pb saved_model (a directory) """

Imports In [ ]: import tensorflow as tf from google.protobuf import text_format from object_detection import exporter from object_detection.protos import pipeline_pb2 slim = tf.contrib.slim flags = tf.app.flags flags.DEFINE_string('input_type', 'image_tensor', 'Type of input node. Can be ' 'one of [`image_tensor`, `encoded_image_string_tensor`, ' '`tf_example`]') flags.DEFINE_string('input_shape', None, 'If input_type is `image_tensor`, this can explicitly set ' 'the shape of this input tensor to a fixed size. The ' 'dimensions are to be provided as a comma-separated list ' 'of integers. A value of -1 can be used for unknown ' 'dimensions. If not specified, for an `image_tensor, the ' 'default shape will be partially specified as ' '`[None, None, None, 3]`.') flags.DEFINE_string('pipeline_config_path', None, 'Path to a pipeline_pb2.TrainEvalPipelineConfig config ' 'file.') flags.DEFINE_string('trained_checkpoint_prefix', None, 'Path to trained checkpoint, typically of the form ' 'path/to/model.ckpt') flags.DEFINE_string('output_directory', None, 'Path to write outputs.') tf.app.flags.mark_flag_as_required('pipeline_config_path') tf.app.flags.mark_flag_as_required('trained_checkpoint_prefix') tf.app.flags.mark_flag_as_required('output_directory') FLAGS = flags.FLAGS

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export_inference_graph

Export inference graph In [ ]: def main(_): pipeline_config = pipeline_pb2.TrainEvalPipelineConfig() with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f: text_format.Merge(f.read(), pipeline_config) if FLAGS.input_shape: input_shape = [ int(dim) if dim != '-1' else None for dim in FLAGS.input_shape.split(',') ] else: input_shape = None exporter.export_inference_graph(FLAGS.input_type, pipeline_config, FLAGS.trained_checkpoint_prefix, FLAGS.output_directory, input_shape)

if __name__ == '__main__': tf.app.run()

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sovereignty detection

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Sovereignty_Detection

Sovereignty Detection Imports In [ ]: import numpy as np import os import six.moves.urllib as urllib import sys import tarfile import tensorflow as tf import zipfile from collections import defaultdict from io import StringIO from matplotlib import pyplot as plt from PIL import Image import cv2 cap = cv2.VideoCapture(0) # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..") from utils import label_map_util from utils import visualization_utils as vis_util

Model Preparation Any model exported using the export_inference_graph.py tool can be loaded here simply by changing PATH_TO_CKPT to point to a new .pb file.

By default we use an "SSD with Mobilenet" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

In [ ]: # What model to download.

MODEL_NAME = 'sovereignty_graph' # Path to frozen detection graph. This is the actual model that is used for the object detection.

PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box.

PATH_TO_LABELS = os.path.join('training', 'object-detection.pbtxt') NUM_CLASSES = 8

Load a frozen tensorflow model into memory In [ ]: detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='')

Loading label map Label maps map indices to category names, so that when our convolution network predicts 5 , we know that this corresponds to airplane . Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine In [ ]: label_map = label_map_util.load_labelmap(PATH_TO_LABELS) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True category_index = label_map_util.create_category_index(categories)

Helper Code

http://localhost:8888/notebooks/Sovereignty_Detection.ipynb#

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category_index = label_map_util.create_category_index(categories)

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Helper Code

Sovereignty_Detection

In [ ]: def load_image_into_numpy_array(image): (im_width, im_height) = image.size return np.array(image.getdata()).reshape( http://localhost:8888/notebooks/Sovereignty_Detection.ipynb# (im_height, im_width, 3)).astype(np.uint8)

Real-Time Detection In [ ]: # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. PATH_TO_TEST_IMAGES_DIR = 'test_images' TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 8) ] # Size, in inches, of the output images.

IMAGE_SIZE = (12, 8)

In [ ]: with detection_graph.as_default(): with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') # Each box represents a part of the image where a particular object was detected. detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') # Each score represent how level of confidence for each of the objects. # Score is shown on the result image, together with the class label. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') while True: ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) # Visualization of the results of a detection. vis_util.visualize_boxes_and_labels_on_image_array( image_np, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8) cv2.imshow('object detection', cv2.resize(image_np, (800,600))) if cv2.waitKey(25) & 0xFF == ord('q'): cv2.destroyALLWindows() break

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Sovereignty-Detection

The test is used to determine sovereignty values in the urban context of London and York as well as the refugee crisis in a camp-site in France. This is all done in real-time by using a webcam and setting it up to view another screen with this footage on. Finally some images of buildings are also shown.

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error

[2] error

[1]

[3] [2]

image_01

[1]

sovereignty tag [1] 76% [2] 84% [3] 97%

image_02

sovereignty tag [1] 94% [2] 90%

error

error

[1]

[2]

[3]

[1] image_03

[4] sovereignty tag [1] 52% [2] 76%

image_04

sovereignty tag [1] 95% [2] 66% [3] 92% [4] 99%

[1]

[1] image_05

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sovereignty tag [1] 80%

image_06

sovereignty tag [1] 60%

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error

[1]

[3]

[1]

[2] image_07

sovereignty tag [2] 86% [2] 55%

[2]

sovereignty tag [1] 99% [2] / [3] /

image_08

[1]

[2]

[1]

[2] [2] [2] image_09

sovereignty tag [1] 84% [2] 95%

[2]

image_10

[2] sovereignty tag [1] 94% [2] 99%

[1]

[2]

[2]

[1]

sovereignty tag [1] 99%

image_11

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image_12

sovereignty tag [1] 88% [2] 99%

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error

[1]

[2]

sovereignty tag [1] 76% [2] 84% [3] 97%

image_13

error

sovereignty tag [1] 93% [2] 52%

image_14

error [2] [1]

[2]

[1] image_15

sovereignty tag [1] 63% [2] 57%

image_16

sovereignty tag [1] 89% [2] 66%

image_18

sovereignty tag [1] 77% [2] 91% [3] 69%, 77%, 66%

error [1] [2] error

[4]

[2] [2] image_17

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[2]

[3] sovereignty tag [1] 56% [2] 99% [3] 76% [4] 94%

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[2]

error error [2] [1]

[1]

image_19

sovereignty tag [1] 86% [2] 99%

image_20

sovereignty tag [1] 96% [2] 99%

image_21

sovereignty tag [1] 98%

image_22

sovereignty tag [1] 99% [2] 69%

error

error [1]

[1]

[3] error [2] [2] image_23

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[2] sovereignty tag [1] 53% [2] 96% [3] 77%

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image_24

[3] sovereignty tag [1] 95% [2] 91%, 98% [3] 53%

M A R C H 2 018

error

[1]

[2]

[1]

image_25

sovereignty tag [1] 99%

image_26

sovereignty tag [1] 99% [2] 99%

[1]

[1]

[3]

[2]

image_27

sovereignty tag [1] 99% [2] / [3] /

image_28

sovereignty tag [1] 91%

[2]

[3]

[1]

[1]

image_29

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sovereignty tag [1] 99%

image_30

sovereignty tag [1] 60% [2] 53% [3] 61%

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Analysis

Screenshot

[1]

[2]

[3]

image_01 image_02 image_03 image_04 image_05 image_06 image_07 image_08 image_09 image_10 image_11 image_12 image_13 image_14 image_15 image_16 image_17 image_18 image_19 image_20 image_21 image_22 image_23 image_24 image_25 image_26 image_27 image_28 image_29 image_30

76% 94% 52% 95% 80% 60% 86% 99% 84% 94% 99% 88% 76% 93% 63% 89% 56% 77% 86% 96% 98% 99% 53% 95% 99% 99% 99% 91% 99% 60%

84% 90% 76% 66%

97%

92%

[4]

[5]

99%

55% 95% 99% 99% 84% 52% 57% 66% 99% 91% 99% 99%

97%

69% 96% 91%

76% 69%

94% 77%

77% 98%

53%

99%

53%

61%

66%

[6]

Truth average

Error True = 1 average False = 0

90.5% 90.0% 52.0% 95.3% 80.0% 60.0% 55.0% 99.0% 89.5% 96.5% 99.0% 93.5% 90.5% 52.0% 63.0% 0.00% 77.5% 76.0% 99.0% 99.0% 98.0% 84.0% 74.5% 94.5% 99.0% 99.0% 99.0% 91.0% 99.0% 58.0%

76.0% 94.0% 76.0% 66.0% 0.00% 0.00% 86.0% 0.00% 0.00% 0.00% 0.00% 0.00% 76.0% 93.0% 57.0% 77.5% 85.0% 0.00% 86.0% 96.0% 0.00% 0.00% 77.0% 74.0% 0.00% 99.0% 0.00% 0.00% 0.00% 0.00%

1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 / 1 1 1 1

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With the successful training complete, the sovereigntydetection is tested on a series of walkthroughs through mainly urban spaces and by being shown images of buildings. Overall it seems to have developed a strong understanding of what a building is and especially distinguishing between extremes such as refugee settlements or more ornamental structures. People are detected at quite a high success rate.

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Bias Commoner: "Well, men are only men. That's why they lie.

They can't tell the truth, even to themselves" Priest: "That may be true.

Because men are weak, they lie to deceive themselves."

Ra s ho m o n , 1 95 0 , by Ak i r a Ku ro s a wa

increasing bias M A R C H 2 018

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THE ISSUE OF BIAS

NOT SO NEUTRAL MR. ALGORITHM! US law professor Bernard Harcourt notes that algorithms “…serve only to accentuate the ideological dimensions of the law and hardens the purported race, class, and power relations between certain offences and certain groups”.

Akin to any human technology, tools inevitably contain design biases; they are mirrors of their designers and users. In algorithms it is often the data.

A

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UK police force which was using an algorithm designed to help it make custody decisions has been forced to alter it amid concerns that it could discriminate against poor people. Durham Constabulary has been developing an algorithm to better predict the risk posed by offenders and to ensure that only the most “suitable” are granted police bail. But the programme has also highlighted potential social inequalities that can be maintained through the use of these big data strategies.

This might seem surprising, since an apparent feature of such programmes is that they are apparently neutral: technocratic evaluations of risk based on information that is “value-free” (based on objective calculation, eschewing subjective bias).

In practice, the apparent neutrality of the data is questionable. It has been reported that Durham

Police will no longer use postcodes as one of the data points in their model, since it has been argued that doing so perpetuates stereotypes about neighbourhoods that have negative consequences for all residents.

Algorithms rely on data that reflects – and so perpetuates – inequalities in criminal justice practice. A powerful critique of these methods by US law professor Bernard Harcourt, notes that they “…serve only to accentuate the ideological dimensions of the criminal law and hardens the purported race, class, and power relations between certain offences and certain groups”.

This one of many examples of algorithmic bias but it is true that like with any human technology the tool inevitably reflects design biases.

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Every decision comes with bias, even when it's Mr. Algorithm at the helm.

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Degrees of Error solid vertical diagonal horizontal + 1

input image

2

3

accuracy evaluation

output values

error

truth

answer

0.5

0.0

0.5

solid

0.75

0.0

0.75

vertical

horizontal line

total

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}

0.25

0.0

-0.25

diagonal

1.75

1.0

-0.75

horizontal

horizontal

3.25

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What is the mathematical process behind errors when training a neural network?

Neural networks are very efficient systems for computing vast amounts of data and requiring relatively little programming since the network basically behaves as a mediating formula between input and output values. The 'learning' process is essentially an infinitesimal error evaluation process.

During training certain differential mathematical functions are used for forward and backward propagation. What this means is they are equations allowing the neural network to process the values back and forth from inputs and expected outputs and every time adjusts the value of the weights between each of its neurons. These adjustments happen until the error between input and output values is diminished to almost zero.

Hence a neural network, like a human, has just as much of a capacity to 'lie' or make mistakes in its detections since it's definition of 'truth' is by percentile.

1

2

2

3

4

5

object capture edit copy distribution viewing Photographer's bias in camera used, resolution, composition and artistic authorship.

The data could be further edited by the photographer or other people and then replicated and re-edited.

The images may be distributed on various platforms or there may be a bias as to where and how they appear.

The viewer may input his attention bias and also the bias within the algorithm adjusting the way results appear according to his browsing history.

5

5

6

gathering

tagging

learning

The creation of dataset involves a selection process where data is edited and refined again.

The decision of 'what constitutes an object' is up to interpretive bias. This appears in the tagging stage when considering what to leave in or out of the tagging field. The tagging software and its resolution also affect how accurately the object can be defined.

The set-up and design of a neural network would further affect the accuracy of the results and there is bias in the determining of training parameters.

increasing bias

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Column Bias

1

1x1

240x88

194x259

201x251

Column capital to include or exclude? In most cases I considered this part of a column so the algorithm

275x183

2 224x224

257x196

220x229

227x222

295x171

268x188

280x180

224x224

336x150

318x159

225x225

293x172

225x225

193x261

194x259

225x225

225x225

225x225

225x225

225x225

290x174

225x225

220x229

270x187

300x168

225x225

225x225

247x204

387x130

225x225

275x183

224x224

319x158

293x172

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viewing

gathering

scale?

Usually images of columns would range from ornamental details to human-sized or building scale. In my dataset a column was valid at all scales, so form trumped scale.

4

plinth?

Most of the times the plinth or column base was not counted as column but in some cases it was this created a bias visible in some results.

5

distribution

excess space?

My tagging software could only do rectangular bounding boxes hence precision was limited. There would always be a decision on how much excess space of 'non-column' to include.

3

225x225

capital?

overlaps?

Sometimes when multiple columns in one image formed colonnades there was a decision to be made on how much bounding-box overlap to have.

tagging

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fingers are columns?

1

5 4

3

2 3

plinth? arch? shadow?

1 1

4

4 4

4

learning

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detection

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viewing

gathering

tagging M A R C H 2 018

1

materiality?

The material of the refugee tents tends to be easily mixed up with clothing and the flowing forms with organic matter

2

excess space?

At the scale of housing this dataset was affected by the consistent sameness of the photographer's angle.

Also the shape of American suburban typologies left a lot of excess space around the tagging bounding box.

3

frame

In the Christiania commune a lot of the images were cropped very close to buildings sometimes making only graffiti art visible or building elements. The framing of the tag here affects the scale of the reading and the clarity of singular structures.

4

view?

With the deep-state images the most striking bias was the view. A lot of data centres especially tended to have many aerial/drone images hence any building from this view is identified as this type.

5

quantity

The authoritarian and national typologies are the most disproportionately bias since this dataset has double the images of any of the others.

5

overlaps?

In cases like this one where the building has multiple structures it was hard to define which was the whole building or parts of smaller buildings.

5

symbols?

In some images symbols of institutions or flags would appear and as a personal choice I added my bias touch by tagging these as part of the typologies as well. Due to scalar misunderstanding though a building that might be shaped like one of these symbols could mislead the algorithm.

learning M A R C H 2 018

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Manipulation

TEST 04

IMAGE GENERATION Apart from reading there is also the read. In other words Machine Learning can be used beyond image recognition to also generate content. In the above example a Generative Adversarial Network was used to create images of bedrooms.

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ag

What would your flag look like Mr. Algorithm?

What flag would an algorithm generate?

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‘parliament_ext’

What would your Parliament look like Mr. Algorithm? Once generated what would the sovereignty detection make of the Algorithm's parliament?

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deep convolutional generative adversarial network

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In [1]: import tensorflow as tf import numpy as np import datetime import matplotlib.pyplot as plt %matplotlib inline from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/") Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz In [2]: sample_image = mnist.train.next_batch(1)[0] print(sample_image.shape) sample_image = sample_image.reshape([28, 28]) plt.imshow(sample_image, cmap='Greys') (1, 784) Out[2]: <matplotlib.image.AxesImage at 0x2873677bac8>

In [3]: def discriminator(images, reuse=False): if (reuse): tf.get_variable_scope().reuse_variables() # First convolutional and pool layers # This finds 32 different 5 x 5 pixel features d_w1 = tf.get_variable('d_w1', [5, 5, 1, 32], initializer=tf.truncated_normal_initializer(stddev=0.02)) d_b1 = tf.get_variable('d_b1', [32], initializer=tf.constant_initializer(0)) d1 = tf.nn.conv2d(input=images, filter=d_w1, strides=[1, 1, 1, 1], padding='SAME') d1 = d1 + d_b1 d1 = tf.nn.relu(d1) d1 = tf.nn.avg_pool(d1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # Second convolutional and pool layers # This finds 64 different 5 x 5 pixel features d_w2 = tf.get_variable('d_w2', [5, 5, 32, 64], initializer=tf.truncated_normal_initializer(stddev=0.02)) d_b2 = tf.get_variable('d_b2', [64], initializer=tf.constant_initializer(0)) d2 = tf.nn.conv2d(input=d1, filter=d_w2, strides=[1, 1, 1, 1], padding='SAME') d2 = d2 + d_b2 d2 = tf.nn.relu(d2) d2 = tf.nn.avg_pool(d2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # First fully connected layer d_w3 = tf.get_variable('d_w3', [7 * 7 * 64, 1024], initializer=tf.truncated_normal_initializer(stddev=0.02)) d_b3 = tf.get_variable('d_b3', [1024], initializer=tf.constant_initializer(0)) d3 = tf.reshape(d2, [-1, 7 * 7 * 64]) d3 = tf.matmul(d3, d_w3) d3 = d3 + d_b3 d3 = tf.nn.relu(d3) # Second fully connected layer d_w4 = tf.get_variable('d_w4', [1024, 1], initializer=tf.truncated_normal_initializer(stddev=0.02)) d_b4 = tf.get_variable('d_b4', [1], initializer=tf.constant_initializer(0)) d4 = tf.matmul(d3, d_w4) + d_b4 # d4 contains unscaled values return d4

In [4]: def generator(z, batch_size, z_dim): g_w1 = tf.get_variable('g_w1', [z_dim, 3136], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0. g_b1 = tf.get_variable('g_b1', [3136], initializer=tf.truncated_normal_initializer(stddev=0.02)) g1 = tf.matmul(z, g_w1) + g_b1 g1 = tf.reshape(g1, [-1, 56, 56, 1]) g1 = tf.contrib.layers.batch_norm(g1, epsilon=1e-5, scope='bn1') g1 = tf.nn.relu(g1) # Generate 50 features g_w2 = tf.get_variable('g_w2', [3, 3, 1, z_dim/2], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stdd g_b2 = tf.get_variable('g_b2', [z_dim/2], initializer=tf.truncated_normal_initializer(stddev=0.02)) g2 = tf.nn.conv2d(g1, g_w2, strides=[1, 2, 2, 1], padding='SAME') g2 = g2 + g_b2 g2 = tf.contrib.layers.batch_norm(g2, epsilon=1e-5, scope='bn2') g2 = tf.nn.relu(g2) g2 = tf.image.resize_images(g2, [56, 56]) # Generate 25 features g_w3 = tf.get_variable('g_w3', [3, 3, z_dim/2, z_dim/4], dtype=tf.float32, initializer=tf.truncated_normal_initialize g_b3 = tf.get_variable('g_b3', [z_dim/4], initializer=tf.truncated_normal_initializer(stddev=0.02)) g3 = tf.nn.conv2d(g2, g_w3, strides=[1, 2, 2, 1], padding='SAME') g3 = g3 + g_b3 g3 = tf.contrib.layers.batch_norm(g3, epsilon=1e-5, scope='bn3') g3 = tf.nn.relu(g3) g3 = tf.image.resize_images(g3, [56, 56]) # Final convolution with one output channel g_w4 = tf.get_variable('g_w4', [1, 1, z_dim/4, 1], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stdd g_b4 = tf.get_variable('g_b4', [1], initializer=tf.truncated_normal_initializer(stddev=0.02)) g4 = tf.nn.conv2d(g3, g_w4, strides=[1, 2, 2, 1], padding='SAME') g4 = g4 + g_b4 g4 = tf.sigmoid(g4) # Dimensions of g4: batch_size x 28 x 28 x 1 return g4

In [5]: z_dimensions = 100 z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions]) In [6]: generated_image_output = generator(z_placeholder, 1, z_dimensions) z_batch = np.random.normal(0, 1, [1, z_dimensions]) In [7]: with tf.Session() as sess: sess.run(tf.global_variables_initializer()) generated_image = sess.run(generated_image_output, feed_dict={z_placeholder: z_batch}) generated_image = generated_image.reshape([28, 28]) plt.imshow(generated_image, cmap='Greys')

In [8]: tf.reset_default_graph() batch_size = 50 z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions], name='z_placeholder') # z_placeholder is for feeding input noise to the generator x_placeholder = tf.placeholder(tf.float32, shape = [None,28,28,1], name='x_placeholder') # x_placeholder is for feeding input images to the discriminator Gz = generator(z_placeholder, batch_size, z_dimensions) # Gz holds the generated images Dx = discriminator(x_placeholder) # Dx will hold discriminator prediction probabilities # for the real MNIST images Dg = discriminator(Gz, reuse=True) # Dg will hold discriminator prediction probabilities for generated images

06/03/2018

GAN_MINST_Tutorial-Test

In [9]: d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dx, tf.ones_like(Dx))) d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dg, tf.zeros_like(Dg))) --------------------------------------------------------------------------ValueError Traceback (most recent call last) <ipython-input-9-23ef840fa390> in <module>() ----> 1 d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dx, tf.ones_like(Dx))) 2 d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dg, tf.zeros_like(Dg))) c:\users\nicho\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_impl.py in sigmoid_cross_entropy_wi th_logits(_sentinel, labels, logits, name) 151 # pylint: disable=protected-access 152 nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", _sentinel, --> 153 labels, logits) 154 # pylint: enable=protected-access 155 c:\users\nicho\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py in _ensure_xent_args(name, s entinel, labels, logits) 1711 if sentinel is not None: 1712 raise ValueError("Only call `%s` with " -> 1713 "named arguments (labels=..., logits=..., ...)" % name) 1714 if labels is None or logits is None: 1715 raise ValueError("Both labels and logits must be provided.")

ValueError: Only call `sigmoid_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

In [10]: g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dg, tf.ones_like(Dg))) --------------------------------------------------------------------------ValueError Traceback (most recent call last) <ipython-input-10-6036765f28de> in <module>() ----> 1 g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(Dg, tf.ones_like(Dg))) c:\users\nicho\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_impl.py in sigmoid_cross_entropy_wi th_logits(_sentinel, labels, logits, name) 151 # pylint: disable=protected-access 152 nn_ops._ensure_xent_args("sigmoid_cross_entropy_with_logits", _sentinel, --> 153 labels, logits) 154 # pylint: enable=protected-access 155 c:\users\nicho\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_ops.py in _ensure_xent_args(name, s entinel, labels, logits) 1711 if sentinel is not None: 1712 raise ValueError("Only call `%s` with " -> 1713 "named arguments (labels=..., logits=..., ...)" % name) 1714 if labels is None or logits is None: 1715 raise ValueError("Both labels and logits must be provided.")

ValueError: Only call `sigmoid_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

In [11]: tvars = tf.trainable_variables() d_vars = [var for var in tvars if 'd_' in var.name] g_vars = [var for var in tvars if 'g_' in var.name] print([v.name for v in d_vars]) print([v.name for v in g_vars]) ['d_w1:0', 'd_b1:0', 'd_w2:0', 'd_b2:0', 'd_w3:0', 'd_b3:0', 'd_w4:0', 'd_b4:0'] ['g_w1:0', 'g_b1:0', 'g_w2:0', 'g_b2:0', 'g_w3:0', 'g_b3:0', 'g_w4:0', 'g_b4:0'] In [12]: # Train the discriminator d_trainer_fake = tf.train.AdamOptimizer(0.0003).minimize(d_loss_fake, var_list=d_vars) d_trainer_real = tf.train.AdamOptimizer(0.0003).minimize(d_loss_real, var_list=d_vars) # Train the generator g_trainer = tf.train.AdamOptimizer(0.0001).minimize(g_loss, var_list=g_vars) --------------------------------------------------------------------------NameError Traceback (most recent call last) <ipython-input-12-c8ddbb4a008e> in <module>() 1 # Train the discriminator ----> 2 d_trainer_fake = tf.train.AdamOptimizer(0.0003).minimize(d_loss_fake, var_list=d_vars) 3 d_trainer_real = tf.train.AdamOptimizer(0.0003).minimize(d_loss_real, var_list=d_vars) 4 5 # Train the generator NameError: name 'd_loss_fake' is not defined

http://localhost:8888/notebooks/GAN_MINST_Tutorial-Test.ipynb

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06/03/2018

GAN_MINST_Tutorial-Test

In [13]: tf.summary.scalar('Generator_loss', g_loss) tf.summary.scalar('Discriminator_loss_real', d_loss_real) tf.summary.scalar('Discriminator_loss_fake', d_loss_fake) images_for_tensorboard = generator(z_placeholder, batch_size, z_dimensions) tf.summary.image('Generated_images', images_for_tensorboard, 5) merged = tf.summary.merge_all() logdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/" writer = tf.summary.FileWriter(logdir, sess.graph) --------------------------------------------------------------------------NameError Traceback (most recent call last) <ipython-input-13-7529b1693ed7> in <module>() ----> 1 tf.summary.scalar('Generator_loss', g_loss) 2 tf.summary.scalar('Discriminator_loss_real', d_loss_real) 3 tf.summary.scalar('Discriminator_loss_fake', d_loss_fake) 4 5 images_for_tensorboard = generator(z_placeholder, batch_size, z_dimensions) NameError: name 'g_loss' is not defined

In [14]: sess = tf.Session() sess.run(tf.global_variables_initializer()) # Pre-train discriminator for i in range(300): z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1]) _, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake], {x_placeholder: real_image_batch, z_placeholder: z_batch}) if(i % 100 == 0): print("dLossReal:", dLossReal, "dLossFake:", dLossFake) # Train generator and discriminator together for i in range(100000): real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1]) z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) # Train discriminator on both real and fake images _, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake], {x_placeholder: real_image_batch, z_placeholder: z_batch}) # Train generator z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) _ = sess.run(g_trainer, feed_dict={z_placeholder: z_batch}) if i % 10 == 0: # Update TensorBoard with summary statistics z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) summary = sess.run(merged, {z_placeholder: z_batch, x_placeholder: real_image_batch}) writer.add_summary(summary, i) if i % 100 == 0: # Every 100 iterations, show a generated image print("Iteration:", i, "at", datetime.datetime.now()) z_batch = np.random.normal(0, 1, size=[1, z_dimensions]) generated_images = generator(z_placeholder, 1, z_dimensions) images = sess.run(generated_images, {z_placeholder: z_batch}) plt.imshow(images[0].reshape([28, 28]), cmap='Greys') plt.show() # Show discriminator's estimate im = images[0].reshape([1, 28, 28, 1]) result = discriminator(x_placeholder) estimate = sess.run(result, {x_placeholder: im}) print("Estimate:", estimate) --------------------------------------------------------------------------NameError Traceback (most recent call last) <ipython-input-14-19e0be1b2d2f> in <module>() 6 z_batch = np.random.normal(0, 1, size=[batch_size, z_dimensions]) 7 real_image_batch = mnist.train.next_batch(batch_size)[0].reshape([batch_size, 28, 28, 1]) ----> 8 _, __, dLossReal, dLossFake = sess.run([d_trainer_real, d_trainer_fake, d_loss_real, d_loss_fake], 9 {x_placeholder: real_image_batch, z_placeholder: z_batch}) 10 NameError: name 'd_trainer_real' is not defined

http://localhost:8888/notebooks/GAN_MINST_Tutorial-Test.ipynb

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In [15]: saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, 'pretrained-model/pretrained_gan.ckpt') z_batch = np.random.normal(0, 1, size=[10, z_dimensions]) z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions], name='z_placeholder') generated_images = generator(z_placeholder, 10, z_dimensions) images = sess.run(generated_images, {z_placeholder: z_batch}) for i in range(10): plt.imshow(images[i].reshape([28, 28]), cmap='Greys') plt.show() INFO:tensorflow:Restoring parameters from pretrained-model/pretrained_gan.ckpt

In [ ]:

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“A

esthetically, one might describe this condition as opacity in broad daylight: you could see anything, but what exactly and why is quite unclear. There are a lot of brightly lit glossy surfaces, yet they don’t reveal anything but themselves as surface. Whatever there is — it’s all there to see but in the form of an incomprehensible, Kafkaesque glossiness, written in extraterrestrial code, perhaps subject to secret legislation. It certainly expresses something: a format, a protocol or executive order, but effectively obfuscates its meaning.

Hito S tey erl, 'The Wr e t c he d o f t he S c r e e n ' , e -f l u x jo u r n a l , 2 0 1 2

SCREENING

The We at he r Fac t or y, by L ew is F r y Ric h ardson

SURFACE AS A NEW COMMONS For the design process to follow this technical investigation, the idea of a new commons, a terra media, will be proposed. This would act as a physical space for the algorithmic citizen. The premise of this new common space is the prominence of 'surface'. Not only as literal material treatment, or as screens but also as conceptual framework for the reading and display of the politics of form.

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bout 500 years ago orality was overthrown by technology. Gutenberg's 1450 invention of metallic movable type elevated writing into a central position in the culture. Today the same shift has occurs.

More than 5 billion screens illuminate our lives.

Digital display manufacturers will crank out 3.8 billion new additional screens per year. That's nearly 1 new screen each year for every human on earth. We will start putting watchable screens on any flat surface. Words have migrated from wood pulp to pixels on computers, phones, laptops, game consoles, televisions, billboards, and tablets. Letters are no longer fixed in black ink on paper, but flitter on a glass surface in a rainbow of colours as fast

as our eyes can blink. Screens fill our pockets, briefcases, dashboards, living room walls and the sides of buildings. They sit in front of us when we work - regardless of what we do. We are now People of the Screen.

The condition of surface as an architectural and conceptual topic is, arguably, the backbone of this project. It forms an intersection between the digital and the physical and it advocates for a more polemical understanding of both the technologies of mediation and their physical impacts as a design project. We mediate, more than ever through surfaces in a process Kevin Kelly aptly defines as 'screening'.

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What would a common ground of the future look like where citizens mediate through their algorithms?

Could it be a form of identity exchange?

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Appendix

test_labels_column filename width height class pic_001.jpg 300 300 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_002.jpg 250 250 column pic_003.jpg 241 300 column pic_004.jpg 300 300 column pic_005.jpg 300 300 column pic_005.jpg 300 300 column pic_006.jpg 300 300 column pic_006.jpg 300 300 column pic_007.jpg 200 300 column pic_008.jpg 249 300 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_009.jpg 288 173 column pic_010.jpg 255 300 column pic_011.jpg 200 300 column pic_012.jpg 300 300 column pic_013.jpg 200 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_014.jpg 300 300 column pic_015.jpg 94 300 column pic_016.jpg 300 300 column pic_017.jpg 225 300 column pic_018.jpg 282 300 column pic_019.jpg 300 300 column pic_020.jpg 188 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_021.jpg 292 300 column pic_022.jpg 300 300 column pic_023.jpg 82 300 column pic_024.jpg 225 300 column pic_025.jpg 300 300 column pic_026.jpg 237 300 column pic_027.jpg 300 300 column pic_028.jpg 167 300 column pic_029.jpg 300 300 column pic_030.jpg 300 300 column pic_031.jpg 300 300 column pic_032.jpg 168 300 column pic_033.jpg 300 300 column pic_034.jpg 250 250 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_035.jpg 321 167 column pic_036.jpg 300 300 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_037.jpg 190 166 column pic_038.jpg 300 300 column pic_038.jpg 300 300 column pic_039.jpg 300 300 column pic_040.jpg 110 300 column pic_041.jpg 93 300 column pic_042.jpg 300 300 column

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ymin 117 58 102 128 74 114 57 174 53 185 65 91 13 82 156 229 81 19 110 71 52 109 186 14 116 53 103 128 61 1 82 165 247 110 13 85 78 61 115 3 120 123 113 62 116 93 8 61 114 169 218 278 102 2 34 68 96 130 161 21 113 42 8 2 120

xmax 1 1 17 115 26 3 17 9 1 1 27 37 14 16 13 15 1 16 13 16 40 27 16 6 16 34 13 13 21 25 24 24 26 13 6 26 1 13 2 2 3 2 2 29 2 1 9 12 10 16 15 16 6 3 4 3 3 3 3 2 1 1 9 1 1

ymax 185 100 136 234 167 181 113 232 116 245 136 158 59 130 205 279 174 176 193 124 83 154 240 78 181 172 180 193 125 46 129 208 290 194 69 138 246 153 185 165 179 178 184 105 184 144 54 108 160 211 271 310 187 28 60 90 123 155 186 80 297 264 99 92 177

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height 221 219 219 219 219 219 252 252 252 252 225 225 225 225 225 225 225 75 139 285 285 285 285 285 285 285 285 285 300 165 165 300 300 300 77 300 169 300 83 70 300 92 158 287 287 269 269 269 300

class 166 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 300 column

xmin

ymin 96 7 59 110 165 185 1 106 190 226 160 119 88 66 46 30 12 4 39 3 36 65 97 135 166 198 226 255 122 28 1 13 176 117 10 93 37 121 13 1 110 11 47 43 164 7 107 202 68

xmax 35 76 145 117 127 40 1 1 1 1 17 58 89 109 131 145 156 3 3 4 4 6 5 4 5 4 5 4 1 16 2 62 64 3 9 10 21 1 8 2 11 9 8 9 7 22 22 22 9

ymax 124 63 89 147 191 217 136 205 238 250 214 155 116 85 64 46 30 72 105 31 62 95 132 162 192 222 251 279 180 116 164 117 290 182 69 209 134 178 72 68 181 80 109 95 221 68 163 259 225

160 300 261 251 245 300 166 117 95 84 286 275 272 272 269 270 265 295 297 146 146 144 145 145 145 146 146 146 300 272 300 300 300 298 292 289 165 300 289 300 293 292 287 280 293 166 166 166 289

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300 column 300 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column

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23 10 21 20 16 13 9 5 2 13 37 62 81 83 60 36 20 55 79 100 117 129 142 152 1 5 5 9 12 2 23 2 20 1 1 1 1 9 15 23 26 31 35 37 39 40 42 44 44 43

81 73 34 49 66 81 102 123 149 82 123 156 182 27 50 11 78 115 147 174 194 212 226 241 7 70 163 69 170 198 158 147 161 18 40 58 81 94 107 120 134 146 158 171 186 199 211 226 237 249

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175 column 300 column 300 column 300 column 300 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 175 column 300 column 300 column 300 column 300 column 167 column 300 column 167 column 300 column 300 column 300 column 300 column 300 column 167 column 167 column 167 column 167 column 167 column 167 column 167 column 167 column 300 column 300 column 300 column 300 column 300 column

251 85 10 78 154 1 17 38 54 74 93 115 137 170 185 197 219 1 36 72 109 141 173 203 230 108 27 123 27 66 123 78 15 15 23 46 3 2 50 91 136 175 219 257 317 57 12 119 204 40

46 19 22 16 6 6 7 6 5 5 6 6 6 6 6 6 3 69 78 88 95 103 109 114 120 50 9 10 9 2 7 8 1 10 3 1 2 11 10 10 11 10 9 9 60 1 79 27 103 16

261 134 68 144 212 14 35 53 69 89 112 134 163 180 196 214 236 33 69 107 139 171 202 230 256 134 96 193 96 150 178 121 82 71 281 103 65 38 74 118 160 204 245 283 338 116 85 195 287 86

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16 1 1 1 1 19 17 11 5 1 31 26 34 21 1 6 17 19 18 13 1 1 10 12 10 1 2 11 11 11 28 26 79 79 93 55 9 1 1 1 3 88 29 10 58 110 2 51 50 53

177 33 93 157 215 189 145 61 131 210 23 54 87 121 164 208 95 202 308 179 168 102 57 126 194 93 144 54 90 128 169 206 201 52 17 99 146 203 119 168 191 74 144 131 200 138 130 125 128 30

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165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 169 column 169 column 169 column 169 column 169 column 169 column 169 column 169 column 169 column 169 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 168 column 168 column 168 column 168 column 168 column 300 column 300 column 300 column 300 column 300 column 300 column

69 117 50 36 93 48 59 73 84 116 130 25 72 155 118 89 40 70 109 149 1 43 85 130 173 20 108 59 119 94 77 87 75 85 96 109 119 126 131 18 72 128 183 236 73 11 7 54 108 173

32 33 79 256 256 255 254 253 254 1 19 26 6 14 4 4 26 20 13 7 1 1 2 1 1 5 5 23 22 31 34 34 236 237 236 236 239 241 240 72 50 36 21 4 4 69 7 7 7 10

86 136 81 41 97 52 65 78 90 183 168 171 153 217 181 136 72 108 149 194 39 83 126 169 219 90 180 111 168 134 148 126 80 91 103 115 122 128 134 38 94 148 203 262 152 43 41 92 146 207

134 136 243 281 282 282 281 282 283 300 284 300 288 279 296 169 143 147 157 164 157 162 160 166 167 294 296 285 283 206 273 218 264 263 264 264 264 264 265 154 153 155 155 151 277 189 288 256 226 289

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224 | T E R R A M E D I A

190 226 226 226 182 182 182 182 182 225 225 150 300 300 211 211 211 211 225 225 225 225 225 225 247 247 247 247 247 247 92 325 325 325 325 325 325 264 264 264 264 219 219 219 219 219 219 236 236 236

168 column 300 column 300 column 300 column 168 column 168 column 168 column 168 column 168 column 300 column 300 column 300 column 300 column 300 column 164 column 164 column 164 column 164 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 164 column 164 column 164 column

| nicholas zembashi

32 65 96 137 5 36 75 105 148 13 58 37 96 113 10 63 112 158 103 141 45 27 63 50 19 58 94 130 165 200 18 2 60 119 178 237 294 45 36 180 212 27 50 71 96 121 148 5 47 96

6 161 72 37 1 26 46 58 71 109 1 1 9 10 2 4 3 1 84 48 141 159 13 35 31 34 35 35 35 34 27 1 1 1 1 1 1 55 61 40 44 49 49 47 44 39 35 19 4 3

156 91 125 180 27 64 99 143 180 47 187 112 203 167 46 102 147 205 137 174 71 51 79 63 47 81 113 152 191 229 66 33 92 151 208 267 324 74 50 206 241 43 67 89 115 142 168 33 83 123

168 231 231 233 166 165 166 165 167 213 300 300 293 295 159 154 159 159 267 252 280 282 90 94 143 140 141 141 138 140 244 162 165 164 165 165 165 209 207 206 207 150 150 152 150 148 150 161 162 160

M A R C H 2 018

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M A R C H 2 018

236 236 261 319 319 319 319 300 250 250 250 228 228 308 308 308 308 308 308 308 308 268 268 161 102 181 179 179 179 179 179 179 300 300 300 300 300 300 300 300 300 250 250 250 250 250 250 300 300 270

164 column 164 column 300 column 177 column 177 column 177 column 177 column 300 column 177 column 177 column 177 column 178 column 178 column 177 column 177 column 177 column 177 column 177 column 177 column 177 column 177 column 177 column 177 column 300 column 300 column 300 column 161 column 161 column 161 column 161 column 161 column 161 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 161 column 161 column 161 column 161 column 161 column 161 column 300 column 300 column 161 column

138 190 47 33 144 213 264 119 12 84 172 119 17 3 32 71 110 154 197 237 281 191 29 29 15 39 1 34 70 104 133 162 31 46 67 91 109 133 157 191 228 4 64 99 127 153 201 40 135 1

11 27 3 36 49 56 64 2 21 15 15 18 15 37 35 3 35 42 33 2 37 11 17 8 2 137 1 7 21 26 29 7 97 86 71 102 93 81 60 42 23 4 1 29 61 75 1 6 88 1

172 228 192 91 192 253 295 162 74 165 242 210 105 22 51 91 138 178 217 268 304 244 82 112 82 144 24 60 88 120 151 179 47 71 95 113 134 160 192 224 265 51 94 123 143 164 243 271 165 70

161 161 298 161 145 134 129 300 169 167 170 144 145 174 173 173 173 172 173 171 175 164 168 287 286 300 161 161 161 161 161 160 290 291 290 286 291 291 291 293 296 157 139 134 132 129 158 300 276 148

nicholas zembashi | T E R R A M E D I A

|

225

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226 | T E R R A M E D I A

270 270 300 150 224 224 224 200 300 300 225 243 243 243 243 243 243 237 237 237 87 224 120 59 300 300 300 196 295 169 79 225 262 262 262 266 150 300 300 196 239 239 239 300 110 182 182 182 182 282

161 column 161 column 300 column 300 column 161 column 161 column 161 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 178 column 178 column 178 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 179 column 179 column 179 column 179 column 300 column

| nicholas zembashi

90 197 138 38 49 142 1 31 16 229 88 9 45 83 124 158 200 29 104 184 20 96 2 1 37 120 199 69 109 44 3 85 14 107 210 120 40 45 144 77 41 106 177 116 2 6 41 80 79 12

2 2 1 1 24 13 91 16 22 24 57 6 44 119 72 106 78 57 45 45 7 76 1 1 3 5 5 17 26 14 1 55 5 4 4 131 1 7 91 27 58 44 18 1 2 3 4 94 6 17

177 269 163 112 95 204 27 165 228 282 132 42 86 118 154 197 235 57 132 206 71 141 119 58 104 183 263 108 185 103 72 121 56 150 243 139 111 138 239 130 79 145 228 187 107 37 70 176 175 135

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M A R C H 2 018

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M A R C H 2 018

282 268 268 268 268 300 300 300 300 190 190 190 116 292 292 300 202 300 300 200 211 211 211 260 260 260 369 369 369 369 369 369 300 201 201 300 91 239 239 239 298 298 298 298 300 220 220 378 378 378

300 column 179 column 179 column 179 column 179 column 167 column 167 column 167 column 167 column 167 column 167 column 167 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column 300 column 300 column 180 column 300 column 300 column 179 column 179 column 179 column 166 column 166 column 166 column 166 column 300 column 166 column 166 column 166 column 166 column 166 column

147 67 150 125 1 31 142 205 251 42 127 148 28 12 93 59 85 39 185 77 2 75 161 112 131 211 39 136 165 241 186 1 124 59 34 126 3 162 62 24 29 133 201 246 114 68 166 3 47 89

17 1 1 5 1 31 45 54 58 9 32 100 13 14 34 1 60 70 74 28 1 1 2 52 9 61 1 25 27 18 1 7 3 68 15 5 7 7 15 19 30 46 54 60 17 15 1 13 12 12

271 115 184 142 16 85 178 238 277 86 136 161 104 81 277 116 123 116 267 119 64 141 203 128 160 227 81 161 188 263 237 33 175 91 164 180 82 183 83 37 87 179 239 277 186 93 194 33 73 116

253 160 137 103 123 151 136 127 121 156 148 158 276 290 272 298 241 293 292 227 300 300 299 143 162 135 180 148 145 149 180 158 297 232 180 293 287 98 91 90 149 135 128 121 295 156 166 145 143 145

nicholas zembashi | T E R R A M E D I A

|

227

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228 | T E R R A M E D I A

378 378 378 378 378 165 165 165 165 165 165 300 252 252 252 300 192 192 192 192 192 192 200 300 300 285 285 285 300 122 122 259 259 259 259 163 199 292 292 292 292 300 195 92 199 206 206 206 254 254

166 column 166 column 166 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 163 column 163 column 163 column 163 column 162 column 162 column 163 column 163 column 163 column 163 column 300 column 181 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column

| nicholas zembashi

132 169 214 251 307 63 116 145 100 39 4 122 19 107 185 127 11 36 61 110 142 176 59 36 125 33 111 190 108 68 9 90 115 138 159 20 94 37 141 205 246 116 40 24 61 11 71 136 7 51

12 12 11 10 60 63 194 216 219 192 213 9 85 83 76 22 27 26 17 25 14 1 30 28 1 27 26 24 5 55 54 1 16 25 35 8 24 31 45 51 58 6 21 13 75 26 1 32 5 6

154 198 237 276 329 95 134 158 112 58 17 172 66 149 220 168 28 54 80 126 158 191 101 260 178 92 171 249 193 97 44 119 140 157 179 147 109 85 179 235 273 187 153 72 138 67 129 190 48 90

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M A R C H 2 018

pic_236.jpg pic_236.jpg pic_236.jpg pic_237.jpg pic_238.jpg pic_238.jpg pic_239.jpg pic_240.jpg pic_240.jpg pic_240.jpg pic_240.jpg pic_240.jpg pic_240.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_241.jpg pic_242.jpg pic_242.jpg pic_242.jpg pic_242.jpg pic_242.jpg pic_243.jpg pic_244.jpg pic_245.jpg pic_246.jpg pic_246.jpg pic_246.jpg pic_247.jpg pic_248.jpg pic_249.jpg pic_250.jpg pic_251.jpg pic_252.jpg pic_253.jpg pic_253.jpg pic_253.jpg pic_254.jpg pic_254.jpg pic_254.jpg pic_255.jpg pic_255.jpg pic_255.jpg pic_255.jpg pic_256.jpg pic_257.jpg

M A R C H 2 018

254 254 254 255 232 232 300 264 264 264 264 264 264 249 249 249 249 249 249 249 249 474 474 474 474 474 128 193 85 261 261 261 300 300 225 93 300 300 247 247 247 237 237 237 299 299 299 299 225 68

300 column 300 column 300 column 300 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 161 column 161 column 161 column 161 column 161 column 300 column 300 column 300 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 178 column 178 column 178 column 178 column 178 column 178 column 300 column 300 column 300 column 300 column 300 column 300 column

99 151 200 24 10 175 99 13 58 101 147 188 231 39 82 165 146 93 101 138 135 45 167 280 358 435 6 38 24 29 91 165 78 125 91 12 63 119 48 118 197 74 142 49 41 179 38 178 95 2

7 6 8 10 36 36 18 57 49 49 50 49 50 1 21 1 21 45 60 44 57 40 28 32 29 29 1 18 87 71 71 70 9 1 22 7 1 2 27 42 52 78 1 134 27 29 172 169 19 1

138 190 250 191 54 226 214 32 75 120 163 207 248 76 96 199 162 104 108 148 139 68 191 300 379 454 122 153 68 63 131 206 197 177 143 81 253 184 68 139 218 131 225 77 127 258 127 260 136 67

285 289 284 295 273 271 278 149 148 147 149 148 149 163 133 162 132 123 117 123 116 149 148 152 152 152 300 286 286 132 140 147 287 300 290 289 300 299 144 145 145 178 178 178 130 130 274 274 209 300

nicholas zembashi | T E R R A M E D I A

|

229

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230 | T E R R A M E D I A

182 273 273 273 273 273 273 273 273 273 273 160 248 248 194 194 194 194 204 242 78 225 225 225 300 300 300 300 305 305 305 305 305 236 300 148 148 233 233 233 233 224 224 224 224 224 224 224 224 224

300 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 177 column 177 column 177 column 177 column 177 column 177 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column 180 column

| nicholas zembashi

42 3 29 59 84 110 135 166 190 220 244 40 6 171 10 102 167 83 79 28 17 50 26 14 11 83 158 234 8 71 128 189 258 52 131 29 64 10 82 155 102 9 20 31 42 55 68 83 100 117

137 4 19 6 8 9 24 5 28 6 4 3 60 59 52 42 36 42 43 23 79 15 46 108 19 19 20 20 1 1 1 1 1 30 38 74 72 3 110 139 7 80 79 78 75 72 68 65 61 56

146 28 55 86 108 137 164 188 216 244 269 105 58 224 29 123 186 93 109 213 65 177 50 24 76 144 222 295 62 119 182 243 295 208 181 70 105 59 133 205 195 18 28 38 51 64 77 94 111 129

300 130 124 128 126 130 125 129 123 125 127 297 270 271 143 138 138 138 205 287 298 280 232 195 284 285 286 285 163 169 167 168 167 177 280 287 290 293 293 295 106 138 139 139 140 140 140 141 140 139

M A R C H 2 018

pic_273.jpg pic_273.jpg pic_273.jpg pic_273.jpg pic_273.jpg pic_273.jpg pic_273.jpg pic_274.jpg pic_275.jpg pic_275.jpg pic_275.jpg pic_276.jpg pic_276.jpg pic_276.jpg pic_277.jpg pic_277.jpg pic_278.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_279.jpg pic_280.jpg pic_280.jpg pic_280.jpg pic_281.jpg pic_281.jpg pic_281.jpg pic_281.jpg pic_281.jpg pic_281.jpg pic_282.jpg pic_283.jpg pic_284.jpg pic_285.jpg pic_285.jpg pic_285.jpg pic_286.jpg pic_287.jpg pic_288.jpg pic_289.jpg pic_290.jpg pic_290.jpg pic_290.jpg pic_290.jpg pic_291.jpg pic_291.jpg

M A R C H 2 018

224 224 224 224 224 224 224 180 195 195 195 217 217 217 300 300 91 229 229 229 229 229 229 229 229 173 173 173 200 200 200 200 200 200 201 143 300 242 242 242 150 131 253 161 225 225 225 225 281 281

180 column 180 column 180 column 180 column 180 column 180 column 180 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 172 column 172 column 172 column 172 column 172 column 172 column 172 column 172 column 172 column 172 column 172 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 172 column 172 column 172 column 300 column 300 column 168 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column

138 158 170 181 187 195 202 28 9 71 133 43 120 159 28 156 2 7 26 43 66 91 122 155 184 9 61 113 88 120 163 72 50 29 87 3 110 21 107 189 45 25 112 17 15 78 125 119 99 183

52 47 53 60 62 68 71 92 79 79 80 101 101 100 23 164 1 89 83 78 72 64 54 49 38 15 16 16 55 184 207 221 188 207 84 3 5 74 74 84 72 15 22 36 66 34 12 175 68 55

149 170 181 187 196 202 208 151 65 128 186 84 152 188 122 247 89 25 42 64 86 113 142 176 208 48 102 152 121 139 179 84 67 41 112 134 189 43 137 222 100 110 145 88 113 136 195 224 111 196

139 137 136 135 134 133 132 300 280 280 282 252 254 254 282 281 299 153 153 151 151 147 144 141 138 153 153 153 247 300 289 287 300 288 216 300 294 135 147 166 300 255 151 293 300 300 176 300 123 121

nicholas zembashi | T E R R A M E D I A

|

231

pic_291.jpg pic_291.jpg pic_291.jpg pic_292.jpg pic_293.jpg pic_293.jpg pic_293.jpg pic_294.jpg pic_295.jpg pic_296.jpg pic_296.jpg pic_296.jpg pic_296.jpg pic_297.jpg pic_298.jpg pic_298.jpg pic_298.jpg pic_298.jpg pic_299.jpg pic_300.jpg pic_300.jpg pic_301.jpg pic_301.jpg pic_301.jpg pic_302.jpg pic_303.jpg pic_303.jpg pic_304.jpg pic_304.jpg pic_304.jpg pic_304.jpg pic_305.jpg pic_305.jpg pic_305.jpg pic_306.jpg pic_307.jpg pic_308.jpg pic_309.jpg pic_310.jpg pic_310.jpg pic_311.jpg pic_312.jpg pic_313.jpg pic_314.jpg pic_315.jpg pic_316.jpg pic_316.jpg pic_316.jpg pic_316.jpg pic_316.jpg

232 | T E R R A M E D I A

281 281 281 70 288 288 288 300 249 249 249 249 249 241 244 244 244 244 215 214 214 262 262 262 216 242 242 225 225 225 225 264 264 264 227 300 300 78 173 173 264 221 298 282 254 288 288 288 288 288

300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 300 column 300 column 300 column 300 column 300 column 255 column 255 column 255 column 255 column 300 column 167 column 167 column 175 column 175 column 175 column 288 column 300 column 300 column 300 column 300 column 300 column 300 column 175 column 175 column 175 column 171 column 300 column 299 column 300 column 166 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column

| nicholas zembashi

265 99 184 4 22 115 209 132 92 24 61 96 151 11 14 74 128 185 75 6 115 2 80 167 61 26 181 6 58 102 150 128 178 209 41 114 7 15 11 132 210 6 5 79 5 14 67 117 175 225

48 193 191 1 19 18 16 1 13 104 91 60 29 10 13 14 15 14 69 15 15 27 27 26 18 60 63 29 25 15 3 28 41 50 35 1 9 81 41 41 6 1 1 21 3 20 19 18 19 18

277 113 198 65 86 177 271 167 118 50 86 132 191 231 56 113 171 232 111 49 154 77 166 254 160 85 216 54 102 150 202 150 197 227 180 184 292 67 42 161 261 199 295 201 230 46 101 162 215 275

119 272 272 300 264 263 266 299 160 266 266 277 282 277 230 229 230 228 248 158 159 153 153 154 270 228 231 258 271 289 297 144 134 131 171 298 293 299 151 151 292 300 300 232 299 269 266 270 269 266

M A R C H 2 018

pic_317.jpg pic_318.jpg pic_319.jpg pic_319.jpg pic_319.jpg pic_319.jpg pic_319.jpg pic_320.jpg pic_321.jpg pic_322.jpg pic_322.jpg pic_322.jpg pic_322.jpg pic_322.jpg pic_322.jpg pic_323.jpg pic_324.jpg pic_324.jpg pic_324.jpg pic_324.jpg pic_325.jpg pic_326.jpg pic_326.jpg pic_326.jpg pic_326.jpg pic_327.jpg pic_327.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_328.jpg pic_329.jpg pic_329.jpg pic_329.jpg pic_329.jpg pic_329.jpg pic_329.jpg pic_329.jpg pic_330.jpg pic_331.jpg pic_331.jpg pic_331.jpg pic_331.jpg pic_331.jpg pic_331.jpg pic_331.jpg

M A R C H 2 018

188 141 197 197 197 197 197 300 241 225 225 225 225 225 225 241 221 221 221 221 200 269 269 269 269 277 277 207 207 207 207 207 207 207 207 200 200 200 200 200 200 200 226 133 133 133 133 133 133 133

169 column 300 column 170 column 170 column 170 column 170 column 170 column 300 column 300 column 169 column 169 column 169 column 169 column 169 column 169 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 185 column 185 column 170 column 170 column 170 column 170 column 170 column 170 column 170 column 170 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column

33 16 35 61 90 125 155 121 27 91 19 51 1 165 187 77 28 55 89 118 71 34 87 147 208 13 150 91 118 170 141 134 102 151 187 83 122 154 188 114 70 50 83 51 37 47 60 73 86 93

19 3 74 66 57 47 50 24 1 5 131 143 119 151 145 63 114 105 97 85 18 79 80 82 82 35 32 58 42 56 72 45 61 72 58 63 189 211 185 216 205 185 93 84 255 254 252 251 255 255

157 130 50 75 109 145 172 187 216 135 46 69 19 179 207 120 56 83 113 148 134 63 118 177 236 132 273 101 134 186 152 144 113 160 195 109 138 170 199 123 80 65 148 80 44 52 65 80 90 97

156 295 166 164 161 162 160 284 300 138 168 169 154 169 169 251 227 227 226 222 266 272 284 282 285 185 185 170 170 163 169 169 170 170 170 239 300 291 300 287 269 257 300 242 284 281 281 283 284 286

nicholas zembashi | T E R R A M E D I A

|

233

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234 | T E R R A M E D I A

226 255 255 255 225 225 300 300 300 183 221 148 133 225 300 288 243 243 243 243 243 294 294 294 294 294 294 294 225 225 225 225 177 177 202 179 229 239 239 239 297 297 225 245 245 245 245 245 245 245

300 column 170 column 170 column 170 column 300 column 300 column 300 column 300 column 300 column 167 column 300 column 300 column 300 column 300 column 300 column 162 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 165 column 300 column 300 column 300 column 300 column 174 column 174 column 300 column 171 column 172 column 300 column 300 column 300 column 300 column 300 column 300 column 164 column 164 column 164 column 164 column 164 column 164 column 164 column

| nicholas zembashi

29 52 144 187 37 95 84 49 27 39 56 11 19 81 20 74 17 63 108 155 199 122 177 209 228 242 252 260 33 156 145 134 30 127 12 14 24 109 142 89 61 219 65 167 150 135 118 102 84 54

18 17 40 34 33 17 2 106 194 10 104 6 12 71 13 27 35 35 36 35 35 1 14 24 32 39 42 45 30 77 109 118 37 26 57 11 11 88 58 113 51 51 43 37 34 33 35 36 36 35

102 86 171 219 54 135 134 83 43 138 134 143 101 171 287 250 43 86 132 174 218 149 198 223 241 250 259 266 86 187 158 146 49 154 48 52 186 139 181 106 89 243 160 180 161 144 129 112 96 65

300 148 117 128 217 299 300 293 298 154 297 293 292 300 283 162 130 130 130 131 132 165 156 133 122 111 105 99 288 255 206 206 150 158 267 150 171 230 265 204 214 214 260 100 93 96 96 96 96 97

M A R C H 2 018

pic_353.jpg pic_353.jpg pic_353.jpg pic_353.jpg pic_354.jpg pic_355.jpg pic_355.jpg pic_355.jpg pic_355.jpg pic_356.jpg pic_357.jpg pic_357.jpg pic_358.jpg pic_358.jpg pic_358.jpg pic_359.jpg pic_360.jpg pic_361.jpg pic_362.jpg pic_363.jpg pic_363.jpg pic_363.jpg pic_364.jpg pic_365.jpg pic_366.jpg pic_366.jpg pic_366.jpg pic_367.jpg pic_367.jpg pic_367.jpg pic_368.jpg pic_369.jpg pic_369.jpg pic_370.jpg pic_370.jpg pic_370.jpg pic_370.jpg pic_370.jpg pic_371.jpg pic_371.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_372.jpg pic_373.jpg pic_374.jpg

M A R C H 2 018

245 245 245 245 148 223 223 223 223 240 215 215 288 288 288 165 300 186 299 419 419 419 282 184 225 225 225 258 258 258 239 162 162 308 308 308 308 308 241 241 233 233 233 233 233 233 233 233 75 205

164 column 164 column 164 column 164 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 166 column 166 column 163 column 163 column 163 column 300 column 182 column 300 column 300 column 300 column 179 column 179 column 179 column 178 column 161 column 161 column 179 column 179 column 179 column 179 column 179 column 300 column 300 column 179 column 179 column 179 column 179 column 179 column 179 column 179 column 179 column 300 column 204 column

38 23 7 187 52 1 65 119 186 59 13 110 12 107 197 81 116 4 246 6 162 305 17 31 81 129 156 9 69 135 113 14 111 10 73 130 192 259 14 180 5 32 59 89 114 149 178 212 8 1

35 38 41 10 79 1 1 1 1 166 103 202 17 4 15 37 5 4 4 8 7 5 16 20 16 81 109 16 16 17 45 32 31 1 1 2 3 1 70 69 18 18 18 20 20 44 19 20 18 1

49 31 17 204 96 43 109 161 223 163 138 205 89 186 280 107 155 39 280 125 267 415 264 81 126 155 169 63 134 193 129 39 135 63 120 181 246 299 58 231 23 50 79 104 138 164 200 227 61 40

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235

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236 | T E R R A M E D I A

205 205 205 474 474 474 474 474 474 474 474 255 255 255 412 222 222 129 222 222 222 222 222 198 198 225 225 225 225 220 220 225 226 282 214 214 214 198 198 198 198 198 198 198 198 300 300 300 300 300

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| nicholas zembashi

40 141 165 307 270 248 229 213 202 190 179 76 209 1 129 149 59 21 103 151 69 62 49 4 166 136 109 98 93 18 167 2 88 29 116 39 5 90 127 159 119 65 37 1 77 10 69 123 182 220

11 65 2 1 1 1 1 1 1 1 1 73 58 91 13 1 1 3 1 1 7 17 12 58 58 4 65 85 97 42 64 1 31 17 8 75 106 48 161 180 184 162 177 162 187 13 14 12 12 9

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195 204 204 155 120 102 93 85 80 76 72 286 299 259 122 159 163 296 99 135 73 66 58 135 136 279 207 183 170 290 297 300 214 159 229 208 202 214 285 254 252 286 252 288 256 187 185 185 186 184

M A R C H 2 018

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M A R C H 2 018

226 254 254 254 254 254 218 218 218 237 237 237 167 167 295 98 295 295 295 241 241 225 242 242 242 242 225 225 225 225 225 225 225 284 273 221 221 221 221 149 275 275 275 275 275 275 275 275 275 275

300 column 300 column 300 column 300 column 300 column 300 column 164 column 164 column 164 column 300 column 300 column 300 column 164 column 164 column 166 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 182 column 182 column 182 column 182 column 300 column 300 column 300 column 300 column 300 column 300 column 300 column 182 column 182 column 166 column 166 column 166 column 166 column 300 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column 166 column

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67 6 6 5 3 6 13 1 37 40 104 117 40 48 33 49 49 81 99 67 69 47 8 48 67 76 38 139 142 147 139 142 146 24 1 50 51 55 20 19 13 7 4 10 11 6 15 18 5 12

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275 275 68 294 294 294 294 294 258 212 212 212 212 221 295 295 250 250 250 250

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| nicholas zembashi

227 250 8 14 72 130 189 245 122 63 97 110 120 20 75 194 53 98 163 205

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250 271 56 42 100 161 217 269 134 85 107 118 128 122 104 223 86 135 202 235

123 115 284 162 160 162 164 164 114 158 123 110 98 300 223 219 280 259 234 252

M A R C H 2 018

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M A R C H 2 018

xmin

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| nicholas zembashi

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M A R C H 2 018

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M A R C H 2 018

300 300 300 300 300 299 299 299 269 269 259 259 259 259 275 275 275 275 275 275 275 275 259 259 292 292 292 292 292 292 292 292 275 275 275 233 233 284 284 284 284 284 284 284 284 284 284 284 301 301 301 301 301 301 301 275 275 275 275 276 276 276 276 276 276 275 275

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| nicholas zembashi

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M A R C H 2 018

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M A R C H 2 018

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| nicholas zembashi

335 220 259 276 269 259 275 430 275 275 299 259 300 307 300 183 282 259 300 275 343 310 272 300 259 275 275 300 275 300 250 300 273 275 275 300 275 284 260 300 300 277 273 275 300 276 330 260 284 301 259 259 259 253 275 275 283 276 133 300 216 250 266 266 246 321 321

150 1000_max 149 1000_max 194 1000_max 183 1000_max 188 1000_max 194 1000_max 183 1000_max 117 1000_max 183 1000_max 183 1000_max 168 1000_max 194 1000_max 168 1000_max 164 1000_max 168 1000_max 275 1000_max 179 1000_max 195 1000_max 168 1000_max 183 1000_max 147 1000_max 163 1000_max 186 1000_max 168 1000_max 195 1000_max 183 1000_max 183 1000_max 168 1000_max 183 1000_max 168 1000_max 165 1000_max 168 1000_max 184 1000_max 183 1000_max 183 1000_max 168 1000_max 183 1000_max 177 1000_max 194 1000_max 168 1000_max 168 1000_max 182 1000_max 185 1000_max 183 1000_max 168 1000_max 183 1000_max 153 1000_max 194 1000_max 177 1000_max 167 1000_max 194 1000_max 194 1000_max 195 1000_max 199 1000_max 183 1000_max 183 1000_max 178 1000_max 183 1000_max 262 1000_max 168 1000_max 233 1000_max 189 1000_max 190 1000_max 190 1000_max 205 1000_max 157 1000_max 157 1000_max

8 19 3 13 40 24 48 18 9 28 9 21 1 1 3 18 49 65 3 74 12 1 23 5 122 47 4 1 30 74 11 1 34 16 97 16 1 22 4 133 13 75 10 15 22 50 102 15 36 35 1 8 46 4 11 13 46 15 2 2 1 3 1 1 1 2 164

8 4 56 22 11 46 27 13 6 1 3 39 14 42 26 17 45 9 40 17 11 35 16 3 59 48 10 19 11 27 51 6 17 31 18 22 1 25 25 17 40 49 1 64 46 9 9 33 18 50 5 45 40 1 54 5 41 14 5 34 9 52 2 3 9 1 1

327 198 222 262 232 240 252 414 264 269 281 247 296 306 290 170 232 251 298 200 326 304 257 290 256 225 258 298 256 251 109 292 245 236 254 203 270 267 260 269 191 246 267 264 278 269 267 252 190 291 257 257 216 253 262 262 210 265 130 300 215 250 266 261 245 158 241

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M A R C H 2 018

1000_pic_015.jpg 1000_pic_016.jpg 1000_pic_017.jpg 1000_pic_018.jpg 1000_pic_019.jpg 1000_pic_020.jpg 1000_pic_021.jpg 1000_pic_022.jpg 1000_pic_023.jpg 1000_pic_024.jpg 1000_pic_025.jpg 1000_pic_026.jpg 1000_pic_027.jpg 1000_pic_028.jpg 1000_pic_029.jpg 1000_pic_030.jpg 1000_pic_031.jpg 1000_pic_032.jpg 1000_pic_033.jpg 1000_pic_034.jpg 1000_pic_035.jpg 1000_pic_036.jpg 1000_pic_037.jpg 1000_pic_038.jpg 1000_pic_039.jpg 1000_pic_039.jpg 1000_pic_040.jpg 1000_pic_041.jpg 1000_pic_042.jpg 1000_pic_043.jpg 1000_pic_044.jpg 1000_pic_045.jpg 1000_pic_046.jpg 1000_pic_047.jpg 1000_pic_048.jpg 1000_pic_049.jpg 1000_pic_050.jpg 1000_pic_051.jpg 1000_pic_051.jpg 1000_pic_052.jpg 1000_pic_053.jpg 1000_pic_054.jpg 1000_pic_055.jpg 1000_pic_056.jpg 1000_pic_057.jpg 1000_pic_058.jpg 1000_pic_059.jpg 1000_pic_059.jpg 1000_pic_059.jpg 1000_pic_059.jpg 1000_pic_060.jpg 1000_pic_060.jpg 1000_pic_061.jpg 1000_pic_061.jpg 1000_pic_061.jpg 1000_pic_062.jpg 1000_pic_063.jpg 1000_pic_064.jpg 1000_pic_065.jpg 1000_pic_066.jpg 1000_pic_067.jpg 1000_pic_068.jpg 1000_pic_069.jpg 1000_pic_070.jpg 1000_pic_071.jpg 1000_pic_071.jpg 1000_pic_072.jpg

M A R C H 2 018

252 259 259 318 259 223 259 275 194 275 260 262 298 261 191 253 258 267 224 266 275 300 288 260 300 300 281 286 276 262 254 289 194 224 300 255 250 289 289 259 184 177 300 259 200 275 271 271 271 271 266 266 280 280 280 194 259 202 299 194 259 260 268 225 292 292 275

200 1000_max 194 1000_max 194 1000_max 159 1000_max 194 1000_max 226 1000_max 194 1000_max 183 1000_max 259 1000_max 183 1000_max 194 1000_max 193 1000_max 169 1000_max 193 1000_max 263 1000_max 199 1000_max 195 1000_max 189 1000_max 225 1000_max 189 1000_max 183 1000_max 168 1000_max 175 1000_max 194 1000_max 168 1000_max 168 1000_max 179 1000_max 176 1000_max 182 1000_max 192 1000_max 198 1000_max 174 1000_max 259 1000_max 225 1000_max 168 1000_max 198 1000_max 202 1000_max 175 1000_max 175 1000_max 194 1000_max 275 1000_max 285 1000_max 168 1000_max 194 1000_max 252 1000_max 183 1000_max 186 1000_max 186 person 186 person 186 person 189 1000_max 189 person 180 1000_max 180 1000_max 180 1000_max 259 1000_max 195 1000_max 249 1000_max 168 1000_max 259 1000_max 194 1000_max 194 1000_max 188 1000_max 225 1000_max 172 1000_max 172 1000_max 183 1000_max

14 1 2 39 4 13 13 18 117 16 4 1 165 1 12 2 2 1 4 59 4 92 1 2 86 88 35 45 13 23 4 7 31 20 36 70 41 4 119 51 6 1 104 1 5 73 25 172 172 187 2 124 17 25 245 5 61 1 1 3 100 12 1 12 31 18 11

48 2 1 20 2 3 25 22 33 1 1 24 20 1 33 56 1 1 1 7 18 10 11 8 38 2 14 44 37 13 6 6 67 18 7 15 1 91 87 16 4 25 24 138 28 1 10 26 157 155 1 29 1 56 66 2 1 1 17 4 35 13 11 34 76 9 1

220 256 246 308 253 212 249 256 194 271 254 259 295 260 168 249 251 229 221 202 261 240 277 255 267 129 183 239 261 236 247 280 152 215 251 185 250 121 220 209 181 176 235 256 199 196 270 239 188 202 131 239 266 43 261 183 256 190 295 163 165 225 266 221 222 140 264

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| nicholas zembashi

285 201 262 259 184 225 268 275 259 281 300 194 300 306 338 348 318 300 313 300 294 308 217 307 312 259 276 295 305 300 259 305 292 354 332 292 299 300 259 322 312 312 299 289 289 280 280 284 270 259 312 305 314 259 259 259 348 348 275 275 275 274 275 275 282 256 320

177 1000_max 251 1000_max 192 1000_max 194 1000_max 275 1000_max 225 1000_max 188 1000_max 183 1000_max 194 1000_max 179 1000_max 168 1000_max 260 1000_max 168 1000_max 165 100_deep149 100_deep145 100_deep159 100_deep168 100_deep161 100_deep168 100_deep171 100_deep163 100_deep232 100_deep164 100_deep162 100_deep194 100_deep183 100_deep171 100_deep165 100_deep168 100_deep194 100_deep165 100_deep173 100_deep142 100_deep152 100_deep173 100_deep168 100_deep168 100_deep194 100_deep157 100_deep162 100_deep162 100_deep168 100_deep174 100_deep174 100_deep180 100_deep180 100_deep178 100_deep187 100_deep194 100_deep162 100_deep165 100_deep161 100_deep195 100_deep194 100_deep195 100_deep145 100_deep145 100_deep183 100_deep183 100_deep183 100_deep184 100_deep183 100_deep183 100_deep179 100_deep197 100_deep157 100_deep-

1 1 41 59 1 4 6 1 5 1 2 46 1 8 15 111 25 5 20 76 12 14 36 10 98 8 61 23 17 1 1 8 12 38 43 12 68 35 45 27 35 145 11 48 84 77 44 59 15 23 61 42 39 37 15 39 114 1 43 5 5 2 9 12 7 1 76

1 1 1 1 31 1 1 1 54 1 3 23 16 28 24 9 48 50 56 26 32 15 43 65 23 4 31 25 32 62 62 23 62 26 3 32 37 10 6 52 12 6 34 12 38 4 102 56 62 103 1 20 78 56 64 19 69 78 36 79 44 10 72 21 55 11 80

281 198 256 256 183 216 229 243 249 117 272 190 298 295 326 229 261 298 290 270 287 296 174 304 301 243 251 268 303 224 257 299 275 290 306 279 237 295 225 319 159 296 290 165 239 210 81 234 258 237 255 302 278 255 258 221 334 112 274 65 263 257 272 240 280 252 307

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M A R C H 2 018

100_pic_050.jpg 100_pic_051.jpg 100_pic_051.jpg 100_pic_052.jpg 100_pic_053.jpg 100_pic_054.jpg 100_pic_055.jpg 100_pic_056.jpg 100_pic_057.jpg 100_pic_058.jpg 100_pic_059.jpg 100_pic_060.jpg 100_pic_061.jpg 100_pic_062.jpg 100_pic_063.jpg 100_pic_064.jpg 100_pic_065.jpg 100_pic_066.jpg 100_pic_067.jpg 100_pic_068.jpg 100_pic_069.jpg 100_pic_069.jpg 100_pic_070.jpg 100_pic_071.jpg 100_pic_072.jpg 100_pic_072.jpg 100_pic_073.jpg 100_pic_074.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_075.jpg 100_pic_076.jpg 100_pic_077.jpg 100_pic_078.jpg 100_pic_079.jpg 100_pic_080.jpg 100_pic_080.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_001.jpg 10_pic_002.jpg 10_pic_002.jpg 10_pic_003.jpg 10_pic_003.jpg 10_pic_003.jpg 10_pic_003.jpg 10_pic_004.jpg 10_pic_005.jpg 10_pic_006.jpg 10_pic_007.jpg 10_pic_007.jpg 10_pic_007.jpg 10_pic_007.jpg 10_pic_007.jpg 10_pic_008.jpg 10_pic_008.jpg 10_pic_008.jpg 10_pic_009.jpg 10_pic_009.jpg

M A R C H 2 018

279 279 279 258 289 323 318 259 225 259 275 279 275 259 275 275 297 300 312 259 290 290 282 275 275 275 300 313 300 300 300 300 300 300 300 291 330 259 276 312 312 300 300 300 300 300 300 300 300 300 275 275 275 275 220 275 275 275 275 275 275 275 275 275 275 275 275

181 100_deep181 100_deep181 100_deep195 100_deep174 100_deep156 100_deep158 100_deep194 100_deep225 100_deep194 100_deep183 100_deep180 100_deep183 100_deep194 100_deep183 100_deep183 100_deep170 100_deep168 100_deep162 100_deep194 100_deep174 100_deep174 100_deep179 100_deep183 100_deep183 100_deep183 100_deep168 100_deep161 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep173 100_deep153 100_deep194 100_deep183 100_deep162 100_deep162 100_deep168 10_social 168 person 168 person 168 person 168 person 168 person 168 10_social 168 10_social 168 person 183 10_social 183 person 183 person 183 person 165 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 10_social 183 10_social

1 22 138 9 25 1 66 1 59 47 7 55 16 8 1 7 1 25 7 46 1 168 22 12 149 13 8 56 95 163 126 63 2 169 105 113 60 1 23 1 150 177 201 103 158 135 126 87 100 25 95 93 60 78 1 3 8 91 54 107 165 1 31 98 47 40 110

65 39 40 9 1 1 5 63 26 5 73 55 77 54 2 16 47 46 6 73 71 98 38 16 54 46 61 56 104 97 90 95 83 84 75 8 20 121 76 54 49 96 126 125 128 128 126 19 22 74 1 103 107 110 1 3 21 118 107 93 83 124 22 105 114 101 107

273 120 259 256 141 242 251 257 202 212 246 256 270 239 217 259 287 295 308 202 167 288 276 272 275 141 292 220 169 218 182 135 65 209 161 249 305 259 229 152 312 300 219 124 173 147 135 203 224 40 275 124 78 90 219 270 256 158 92 146 204 42 256 109 73 107 147

123 143 131 156 157 120 122 163 216 166 147 109 176 144 164 150 116 137 159 123 126 123 112 161 157 157 128 113 126 111 103 117 99 95 93 136 109 159 136 104 102 148 168 168 168 161 158 47 87 111 150 183 170 164 149 129 169 176 139 117 104 183 120 154 174 151 153

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| nicholas zembashi

275 275 275 275 299 299 299 299 299 299 299 275 275 276 276 276 276 276 300 275 275 275 275 275 275 275 275 275 275 275 275 275 275 299 299 299 291 291 291 291 291 291 291 291 275 292 292 292 292 292 259 275 275 275 275 275 259 259 269 269 269 269 220 275 274 274 274

183 10_social 183 10_social 183 10_social 183 10_social 169 10_social 169 10_social 169 10_social 169 person 169 person 169 person 169 person 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 168 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 168 10_social 168 person 168 person 173 10_social 173 10_social 173 10_social 173 person 173 person 173 person 173 person 173 person 183 10_social 173 10_social 173 10_social 173 person 173 person 173 person 194 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 194 10_social 194 10_social 188 10_social 188 10_social 188 10_social 188 10_social 165 10_social 183 10_social 184 10_social 184 10_social 184 10_social

140 13 112 197 157 183 209 220 9 55 118 46 8 92 152 49 107 83 1 73 195 48 69 124 115 100 90 83 72 51 2 36 26 123 89 171 103 112 131 50 76 83 140 2 22 140 131 164 144 75 10 109 144 129 117 153 1 172 110 197 147 1 34 37 103 132 122

105 101 45 1 15 56 54 86 85 105 106 7 1 11 65 73 75 73 1 32 2 91 87 85 108 109 108 111 109 109 107 112 113 1 106 111 34 78 78 97 100 102 95 98 9 10 63 78 81 94 13 15 67 68 90 97 1 80 6 29 51 51 9 1 1 37 47

192 43 189 275 245 211 253 247 40 88 150 264 267 193 179 91 132 105 287 161 275 60 123 158 134 115 101 94 86 60 31 44 33 299 110 196 183 132 179 68 86 91 161 18 273 292 292 174 162 82 259 207 194 145 124 187 170 250 208 269 190 36 199 275 233 173 153

145 148 70 158 31 101 122 165 166 162 165 171 130 28 143 179 180 178 168 50 177 116 112 132 159 154 147 151 154 154 183 133 134 141 164 167 47 116 115 150 132 129 152 154 155 111 111 131 135 118 187 29 109 105 111 179 184 164 80 135 78 134 138 136 27 46 59

M A R C H 2 018

10_pic_026.jpg 10_pic_026.jpg 10_pic_026.jpg 10_pic_026.jpg 10_pic_026.jpg 10_pic_027.jpg 10_pic_027.jpg 10_pic_027.jpg 10_pic_027.jpg 10_pic_028.jpg 10_pic_028.jpg 10_pic_028.jpg 10_pic_029.jpg 10_pic_029.jpg 10_pic_030.jpg 10_pic_031.jpg 10_pic_031.jpg 10_pic_031.jpg 10_pic_032.jpg 10_pic_033.jpg 10_pic_034.jpg 10_pic_034.jpg 10_pic_035.jpg 10_pic_035.jpg 10_pic_036.jpg 10_pic_036.jpg 10_pic_037.jpg 10_pic_038.jpg 10_pic_039.jpg 10_pic_040.jpg 10_pic_041.jpg 10_pic_041.jpg 10_pic_041.jpg 10_pic_042.jpg 10_pic_042.jpg 10_pic_042.jpg 10_pic_043.jpg 10_pic_044.jpg 10_pic_045.jpg 10_pic_046.jpg 10_pic_046.jpg 10_pic_046.jpg 10_pic_046.jpg 10_pic_046.jpg 10_pic_046.jpg 10_pic_047.jpg 10_pic_048.jpg 10_pic_049.jpg 10_pic_049.jpg 10_pic_050.jpg 10_pic_051.jpg 10_pic_052.jpg 10_pic_053.jpg 10_pic_054.jpg 10_pic_055.jpg 10_pic_056.jpg 10_pic_057.jpg 10_pic_057.jpg 10_pic_057.jpg 10_pic_058.jpg 10_pic_058.jpg 10_pic_058.jpg 10_pic_058.jpg 10_pic_059.jpg 10_pic_060.jpg 10_pic_060.jpg 10_pic_060.jpg

M A R C H 2 018

274 274 274 274 274 275 275 275 275 275 275 275 225 225 290 259 259 259 272 259 259 259 259 259 299 299 261 300 299 284 300 300 300 290 290 290 275 275 225 275 275 275 275 275 275 290 300 276 276 275 276 450 275 275 275 311 276 276 276 284 284 284 284 194 226 226 226

184 10_social 184 10_social 184 10_social 184 10_social 184 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 225 10_social 225 person 174 10_social 194 10_social 194 person 194 person 185 10_social 194 10_social 194 10_social 194 person 194 10_social 194 10_social 168 10_social 168 person 193 10_social 168 10_social 168 10_social 177 10_social 168 10_social 168 10_social 168 10_social 174 10_social 174 person 174 10_social 183 10_social 183 10_social 225 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 174 10_social 168 10_social 183 10_social 183 10_social 183 10_social 183 10_social 112 10_social 183 10_social 183 10_social 183 10_social 162 10_social 183 10_social 183 10_social 183 person 177 10_social 177 10_social 177 person 177 person 259 10_social 170 10_social 170 person 170 person

38 108 148 140 179 99 136 82 115 101 40 15 1 52 1 2 137 148 43 1 1 153 59 23 37 143 10 144 53 70 159 132 99 38 135 191 1 23 22 1 50 60 166 186 84 1 103 90 139 1 2 241 1 25 78 128 114 64 140 1 143 189 204 68 2 33 52

43 145 143 77 167 55 58 88 106 1 113 106 1 75 2 38 156 159 13 8 1 114 1 88 31 99 1 1 9 15 27 98 105 42 64 1 16 1 50 34 38 26 93 64 71 8 86 54 98 1 1 30 14 17 15 10 57 101 119 6 29 98 101 12 2 62 70

273 119 157 156 189 135 171 191 141 275 69 43 225 84 290 259 158 181 232 249 244 182 259 62 258 171 261 300 226 211 254 183 131 118 177 290 238 185 138 275 81 103 177 198 99 121 178 165 241 109 274 331 248 254 238 285 181 188 156 145 284 208 228 194 226 54 82

157 151 150 84 173 88 93 142 121 165 183 183 108 210 149 191 189 189 176 129 167 182 133 121 141 166 140 160 163 71 114 143 139 137 174 136 130 165 173 164 50 38 117 101 76 144 135 66 146 177 144 89 131 163 166 157 75 142 168 144 144 157 160 224 135 160 170

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| nicholas zembashi

226 259 275 275 259 275 275 275 290 262 262 262 262 262 284 284 284 284 284 284 284 284 300 275 275 194 194 194 259 295 294 259 259 299 259 275 275 224 280 259 277 284 284 253 253 253 311 284 284 299 299 259 259 280 225 225 302 260 260 298 278 300 259 290 292 252 275

170 person 194 10_social 183 10_social 183 person 194 10_social 183 10_social 183 10_social 183 10_social 174 10_social 192 10_social 192 10_social 192 10_social 192 10_social 192 10_social 177 10_social 177 10_social 177 10_social 177 person 177 person 177 person 177 person 177 10_social 168 10_social 183 10_social 183 10_social 259 10_social 259 10_social 259 10_social 195 10_social 171 10_social 172 10_social 194 10_social 194 10_social 168 10_social 194 10_social 183 10_social 183 person 224 continental 180 continental 194 continental 182 continental 177 continental 177 continental 199 continental 199 continental 200 continental 162 continental 178 continental 178 continental 168 continental 168 continental 194 continental 194 continental 180 continental 225 continental 225 continental 167 continental 194 continental 194 continental 169 continental 181 continental 168 continental 194 continental 174 continental 173 continental 200 continental 183 continental

92 1 38 3 1 107 223 20 29 45 98 122 181 91 96 103 168 36 69 232 256 29 7 141 177 119 62 11 67 1 14 52 54 48 96 7 40 39 11 1 28 1 66 59 2 88 14 45 216 41 1 32 1 25 2 1 65 1 229 10 1 34 36 51 53 37 30

67 3 3 105 1 96 91 13 1 69 86 51 36 34 47 72 76 95 105 117 104 1 1 57 29 128 99 120 1 1 35 1 2 1 1 1 75 71 2 55 1 89 41 11 85 15 38 5 98 2 86 10 103 20 26 151 3 3 110 1 1 13 1 4 1 4 46

124 259 273 49 258 146 265 98 279 101 189 163 235 129 158 160 201 49 94 254 278 211 297 275 275 170 118 85 228 293 272 259 256 143 259 275 99 205 264 167 250 91 219 183 61 227 267 216 254 237 63 229 54 217 225 39 298 226 259 289 278 231 258 203 271 251 274

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M A R C H 2 018

1100_pic_024.jpg 1100_pic_025.jpg 1100_pic_026.jpg 1100_pic_026.jpg 1100_pic_027.jpg 1100_pic_028.jpg 1100_pic_028.jpg 1100_pic_028.jpg 1100_pic_029.jpg 1100_pic_030.jpg 1100_pic_031.jpg 1100_pic_032.jpg 1100_pic_032.jpg 1100_pic_033.jpg 1100_pic_034.jpg 1100_pic_035.jpg 1100_pic_036.jpg 1100_pic_037.jpg 1100_pic_037.jpg 1100_pic_038.jpg 1100_pic_039.jpg 1100_pic_040.jpg 1100_pic_041.jpg 1100_pic_041.jpg 1100_pic_042.jpg 1100_pic_042.jpg 1100_pic_043.jpg 1100_pic_044.jpg 1100_pic_044.jpg 1100_pic_045.jpg 1100_pic_046.jpg 1100_pic_046.jpg 1100_pic_047.jpg 1100_pic_048.jpg 1100_pic_049.jpg 1100_pic_050.jpg 1100_pic_050.jpg 1200_pic_001.jpg 1200_pic_002.jpg 1200_pic_003.jpg 1200_pic_004.jpg 1200_pic_004.jpg 1200_pic_005.jpg 1200_pic_006.jpg 1200_pic_007.jpg 1200_pic_007.jpg 1200_pic_007.jpg 1200_pic_007.jpg 1200_pic_008.jpg 1200_pic_009.jpg 1200_pic_010.jpg 1200_pic_011.jpg 1200_pic_011.jpg 1200_pic_012.jpg 1200_pic_013.jpg 1200_pic_013.jpg 1200_pic_014.jpg 1200_pic_014.jpg 1200_pic_014.jpg 1200_pic_014.jpg 1200_pic_014.jpg 1200_pic_014.jpg 1200_pic_015.jpg 1200_pic_016.jpg 1200_pic_017.jpg 1200_pic_017.jpg 1200_pic_018.jpg

M A R C H 2 018

300 483 259 259 259 275 275 275 300 305 259 267 267 194 204 300 198 300 300 275 273 259 267 267 292 292 275 183 183 259 275 275 292 275 300 275 275 252 244 260 267 267 194 194 259 259 259 259 259 259 193 312 312 246 262 262 259 259 259 259 259 259 177 275 307 307 194

168 continental 104 continental 194 continental 194 continental 194 continental 183 continental 183 continental 183 continental 168 continental 165 continental 194 continental 189 continental 189 continental 259 continental 247 continental 168 continental 255 continental 168 continental 168 continental 183 continental 184 continental 194 continental 189 continental 189 continental 173 continental 173 continental 183 continental 275 continental 275 continental 194 continental 184 continental 184 continental 173 continental 183 continental 168 continental 183 continental 183 continental 200 global 206 global 194 global 189 global 189 global 259 global 259 global 194 global 194 global 194 global 194 global 195 global 194 global 262 global 162 global 162 global 205 global 193 global 193 global 194 global 194 person 194 person 194 person 194 person 194 person 285 global 183 global 164 global 164 global 259 global

17 117 140 129 8 13 100 194 1 1 1 100 1 1 6 33 1 98 1 5 3 47 116 84 2 124 47 1 81 7 88 55 13 2 51 71 1 108 63 101 67 1 44 21 41 74 55 153 114 28 27 135 1 4 124 17 1 44 69 93 166 172 83 6 1 9 87

13 5 68 126 3 16 17 70 1 2 46 35 116 28 14 6 2 6 95 24 37 33 38 51 5 1 22 1 1 69 11 51 1 1 1 8 73 6 47 22 1 42 11 14 2 112 3 89 37 2 34 1 55 22 6 68 63 155 156 166 154 154 63 50 20 2 6

300 480 246 153 210 67 134 275 299 230 256 267 107 190 200 230 195 296 110 268 261 254 267 174 106 292 232 82 138 259 233 94 257 258 281 230 72 167 192 221 171 69 160 183 254 248 153 253 206 255 160 310 135 241 258 114 256 68 92 118 171 176 177 272 306 190 178

168 104 165 159 184 168 165 169 168 165 194 162 171 200 237 151 245 160 153 156 126 194 155 108 78 170 161 273 266 192 113 109 146 154 153 134 125 143 180 194 116 122 214 237 162 162 154 122 168 172 179 124 123 147 160 152 149 194 194 194 168 169 267 177 140 75 186

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1200_pic_019.jpg 1200_pic_020.jpg 1200_pic_021.jpg 1200_pic_021.jpg 1200_pic_021.jpg 1200_pic_022.jpg 1200_pic_022.jpg 1200_pic_022.jpg 1200_pic_022.jpg 1200_pic_022.jpg 1200_pic_023.jpg 1200_pic_024.jpg 1200_pic_024.jpg 1200_pic_024.jpg 1200_pic_025.jpg 1200_pic_026.jpg 1200_pic_027.jpg 1200_pic_028.jpg 1200_pic_028.jpg 1200_pic_029.jpg 1200_pic_030.jpg 1200_pic_031.jpg 1200_pic_031.jpg 1200_pic_031.jpg 1200_pic_031.jpg 1200_pic_031.jpg 1200_pic_031.jpg 1200_pic_032.jpg 1200_pic_032.jpg 1200_pic_033.jpg 1200_pic_034.jpg 1200_pic_035.jpg 1200_pic_035.jpg 1200_pic_035.jpg 1200_pic_036.jpg 1200_pic_036.jpg 1200_pic_036.jpg 1200_pic_036.jpg 1200_pic_036.jpg 1200_pic_037.jpg 1200_pic_038.jpg 1200_pic_039.jpg 1200_pic_040.jpg 1200_pic_040.jpg 1200_pic_041.jpg 1200_pic_042.jpg 1200_pic_043.jpg 1200_pic_044.jpg 1200_pic_045.jpg 1200_pic_045.jpg 1200_pic_046.jpg 1200_pic_046.jpg 1200_pic_047.jpg 1200_pic_048.jpg 1200_pic_048.jpg 1200_pic_048.jpg 1200_pic_049.jpg 1200_pic_049.jpg 1200_pic_049.jpg 1200_pic_050.jpg 1200_pic_050.jpg

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312 259 248 248 248 300 300 300 300 300 282 251 251 251 177 199 305 318 318 402 194 259 259 259 259 259 259 220 220 300 177 201 201 201 328 328 328 328 328 274 300 324 275 275 223 254 305 254 194 194 235 235 278 275 275 275 299 299 299 310 310

161 global 194 global 204 global 204 global 204 global 168 global 168 global 168 global 168 global 168 global 178 global 201 global 201 global 201 global 285 global 253 global 165 global 159 global 159 global 125 global 259 global 194 global 194 global 194 global 194 global 194 global 194 global 165 global 165 global 168 global 285 global 250 global 250 global 250 global 153 global 153 global 153 global 153 global 153 global 184 global 168 global 155 global 183 global 183 global 226 global 198 global 165 global 198 global 259 global 259 global 214 global 214 global 181 global 183 global 183 global 183 global 168 global 168 global 168 global 163 global 163 global

43 87 121 1 41 35 67 152 257 34 1 71 1 204 40 70 1 1 1 34 16 64 43 71 148 203 1 19 52 81 26 111 63 52 96 177 203 143 59 1 54 1 163 3 78 46 7 20 104 1 72 31 1 1 75 214 62 177 15 114 1

23 1 17 1 101 1 15 7 47 1 31 3 78 83 13 12 61 37 34 3 25 78 27 28 43 52 74 73 73 5 1 26 166 123 25 12 1 71 27 1 1 9 3 42 8 12 28 26 14 21 9 158 51 1 100 16 1 76 126 27 4

287 181 239 50 121 284 209 258 283 93 270 201 75 251 168 143 286 313 152 368 146 148 99 110 199 226 46 196 171 210 158 170 151 112 152 226 246 199 97 161 201 323 253 270 214 193 301 252 176 193 154 143 166 76 216 275 161 232 221 170 176

108 178 144 145 139 117 108 103 104 84 152 160 162 157 272 229 110 152 98 96 232 139 110 81 104 95 101 134 116 159 267 180 209 155 88 71 46 127 53 183 148 152 150 183 216 193 151 118 181 213 167 186 178 167 169 175 136 154 168 159 163

M A R C H 2 018

M A R C H 2 018

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width

height 450 800 450 800 800 800 800 800 800 800 800 800 800 800 644 800 800 800 800 800 800 800 800 800 800 800 800 800 800 729 800 800 800 800 800 800 800 800 800 799 780 800 780 800 800 800 800 800 800 800 793 800 800 800 748 800 799 800 800 800 800 800 800 800 800 800 800 800 800 800 782 800 800 800 800 799 800 800 800 800 800 800 800 800 800 800

class 600 1_min 536 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 450 1_min 450 1_min 450 1_min 600 1_min 567 1_min 599 1_min 527 1_min 585 1_min 533 1_min 456 1_min 508 1_min 556 1_min 533 1_min 474 1_min 535 1_min 383 1_min 440 1_min 600 1_min 600 1_min 560 1_min 600 1_min 600 1_min 600 1_min 600 1_min 533 1_min 533 1_min 600 1_min 469 1_min 600 1_min 600 1_min 600 1_min 600 1_min 533 1_min 600 1_min 489 1_min 496 1_min 490 1_min 531 1_min 522 1_min 477 1_min 600 1_min 600 1_min 600 1_min 511 1_min 533 1_min 599 1_min 475 1_min 599 1_min 587 1_min 531 1_min 513 1_min 481 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 464 1_min 589 1_min 517 1_min 468 1_min 600 1_min 600 1_min 600 1_min 600 1_min 560 1_min 599 1_min 526 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 600 1_min 598 1_min 529 1_min 600 1_min

xmin

ymin 19 175 75 155 106 109 81 184 23 384 543 647 143 111 22 210 122 49 48 120 57 101 180 64 94 122 41 110 100 70 64 116 46 14 250 182 37 53 82 114 54 80 64 46 88 176 4 60 66 55 104 124 39 146 105 19 41 109 81 140 66 49 124 57 101 26 56 28 30 60 60 41 417 72 44 50 92 148 237 544 51 88 142 123 36 79

xmax 39 43 59 17 88 16 20 77 24 92 151 192 9 63 6 53 61 19 44 13 30 62 151 88 29 61 161 189 84 17 50 39 56 190 86 79 63 147 75 100 134 40 80 59 17 18 26 23 25 2 105 128 23 42 96 21 19 87 1 14 21 17 97 61 44 113 14 88 102 76 20 143 168 44 16 228 17 43 192 184 216 49 53 18 31 102

ymax 421 680 385 744 640 673 702 653 441 586 659 708 694 721 570 715 736 731 717 688 688 656 675 767 715 753 757 537 694 667 706 684 722 248 736 722 741 682 709 691 707 724 673 739 739 601 756 753 743 677 680 696 727 571 648 754 773 700 765 689 753 777 667 690 540 754 720 670 715 714 730 393 551 731 581 621 531 631 511 724 244 736 708 715 746 669

532 407 459 568 461 527 582 515 364 348 342 348 497 550 463 487 427 526 415 487 457 354 420 395 269 360 487 473 447 556 477 490 488 441 343 495 383 486 501 429 456 439 514 391 434 364 420 374 421 556 517 423 453 383 502 412 544 481 498 447 433 493 444 463 444 419 414 523 461 381 523 415 317 516 477 544 440 434 390 390 377 374 530 528 497 496 M A R C H 2 018

01_pic_399_800px-Rock_Jenkins_House.jpg 01_pic_39_800px-Bow_Valley_SS_Peter-Paul_rectory_from_SW_1.jpg 01_pic_39_800px-Broomfield_Rowhouse_from_SW.JPG 01_pic_40_800px-Bow_Valley_SS_Peter-Paul_rectory_from_SW.jpg 01_pic_40_800px-Bow_Valley_SS_Peter-Paul_rectory_from_SW.jpg 01_pic_40_800px-Bow_Valley_SS_Peter-Paul_rectory_from_SW.jpg 01_pic_411_800px-Hunter_Avenue_East%2C_720%2C_Elm_Heights_HD.jpg 01_pic_416_800px-Washington_Street_South%2C_1300%2C_Monon_SA.jpg 01_pic_418_800px-Eighth_Street%2C_816-820%2C_University_Courts.jpg 01_pic_419_800px-Park_Avenue%2C_525-527%2C_University_Courts.jpg 01_pic_41_800px-Bow_Valley_SS_Peter-Paul_rectory_from_W.jpg 01_pic_41_800px-Bow_Valley_SS_Peter-Paul_rectory_from_W.jpg 01_pic_41_800px-Broomfield_Rowhouse_from_S_1.JPG 01_pic_420_800px-Eighth_Street%2C_804%2C_University_Courts.jpg 01_pic_421_800px-Fess_Avenue%2C_406%2C_University_Courts.jpg 01_pic_422_800px-Walnut_Street_North_804%2C_Cottage_Grove_HD.jpg 01_pic_426_800px-Howe_Street_West_830%2C_Prospect_Hill_SA.jpg 01_pic_427_800px-First_Street_East%2C_409%2C_East_Second_Street_HD.jpg 01_pic_42_732px-ElledgeMcElmonHouse-DartmouthNS.JPG 01_pic_42_800px-Broomfield_Rowhouse_from_SW_2.JPG 01_pic_433_800px-Grant_Street_North_416%2C_Furniture_Factory_SA.jpg 01_pic_435_800px-Walnut_Street_South%2C_1315%2C_Monon_SA.jpg 01_pic_437_800px-Tenth_Street_East_723%2C_Andrews_Park_SA.jpg 01_pic_43_800px-Bard_Residence%2C_Edmonton_%28front%29.JPG 01_pic_442_800px-University_Street_East%2C_726%2C_Elm_Heights_HD.jpg 01_pic_442_800px-University_Street_East%2C_726%2C_Elm_Heights_HD.jpg 01_pic_443_800px-Grant_Street_South%2C_408%2C_South_Dunn_Street_HD.jpg 01_pic_446_800px-Washington_Street_North_812-814%2C_Cottage_Grove_HD.jpg 01_pic_447_800px-Ninth_Street%2C_710-712%2C_University_Courts.jpg 01_pic_449_800px-Madison_Street_South_320%2C_Prospect_Hill_SA.jpg 01_pic_44_800px-Bard_Residence%2C_Edmonton_%28corner%29.JPG 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01_pic_515_800px-Park_Avenue_South%2C_519%2C_Elm_Heights_HD.jpg 01_pic_51_387px-Roy_Gerolamy_Residence.JPG 01_pic_521_800px-First_Street_East%2C_905%2C_Elm_Heights_HD.jpg 01_pic_522_800px-Lincoln_Street_North_610%2C_Cottage_Grove_HD.jpg 01_pic_524_800px-Walnut_Street_South%2C_1403%2C_Monon_SA.jpg 01_pic_526_800px-Fess_Avenue_South%2C_521%2C_East_Second_Street_HD.jpg 01_pic_527_800px-Washington_Street_South%2C_1401-1403%2C_Monon_SA.jpg 01_pic_529_800px-First_Street_1112%2C_Vinegar_Hill_HD.jpg 01_pic_531_800px-Eighth_Street_East_209_in_Bloomington%2C_Showers_Inn.jpg 01_pic_532_800px-Ninth_Street%2C_714%2C_University_Courts.jpg 01_pic_535_800px-Ninth_Street%2C_801%2C_University_Courts.jpg 01_pic_538_800px-First_Street_East_106_in_Bloomington.jpg M A R C H 2 018

800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 732 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 387 800 800 800 800 800 800 800 800 800 800

600 1_min 492 1_min 441 1_min 403 1_min 403 1_min 403 1_min 600 1_min 600 1_min 600 1_min 600 1_min 452 1_min 452 1_min 541 1_min 600 1_min 600 1_min 450 1_min 600 1_min 600 1_min 600 1_min 474 1_min 600 1_min 600 1_min 600 1_min 530 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 534 1_min 534 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 533 1_min 533 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 532 1_min 532 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 600 1_min 600 1_min 600 1_min 533 1_min 600 1_min 600 1_min 450 1_min 450 1_min 450 1_min 450 1_min 600 1_min 600 1_min 600 1_min 600 1_min 533 1_min 600 1_min 600 1_min 600 1_min 599 1_min 600 1_min 600 1_min 450 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 600 1_min

136 146 62 25 589 503 90 46 88 109 66 604 30 119 59 113 62 130 13 65 101 92 28 159 125 16 79 76 89 105 26 564 122 69 55 81 137 293 28 31 56 55 32 659 159 1 145 77 79 70 153 664 64 135 124 101 96 319 109 115 91 212 125 73 181 10 702 112 138 93 95 126 90 109 52 79 23 105 45 101 96 41 26 57 81 65 104

26 34 14 37 235 249 39 48 92 97 41 225 30 79 64 58 50 23 30 17 69 24 29 100 54 317 83 92 115 26 23 240 19 102 61 90 62 22 220 63 29 82 44 179 38 96 97 48 60 94 67 274 81 86 73 36 7 65 23 84 13 40 41 119 41 206 238 20 92 50 55 62 57 41 90 112 141 63 70 42 75 1 104 80 28 61 21

665 675 728 517 778 608 724 726 718 709 610 752 752 719 509 675 658 695 666 765 728 733 719 626 685 186 706 706 650 682 579 797 736 721 736 726 658 725 283 748 746 705 698 800 611 216 676 741 741 736 666 798 702 684 702 757 721 793 703 666 725 461 712 736 738 124 795 516 657 688 710 735 609 704 750 733 335 733 753 683 745 771 739 726 719 758 712

533 443 404 361 344 315 569 562 499 534 405 381 497 546 595 367 510 499 556 438 575 580 526 453 433 421 521 506 488 562 480 427 543 550 549 480 490 401 375 566 552 567 483 470 453 358 550 529 454 512 482 412 561 541 442 580 560 412 569 555 506 466 546 480 368 335 358 420 499 500 527 527 396 539 512 550 460 459 507 367 534 583 540 512 577 369 593

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| nicholas zembashi

800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 745 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 800 520 800 800 800 800 800 800 800 450 520 800 450 800 800 520 313 313 313 313 313 300 295 295 295 295 275 275 275 275 275 259 277 277 277 277 277 277

600 1_min 450 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 450 1_min 450 1_min 450 1_min 600 1_min 600 1_min 523 1_min 459 1_min 532 1_min 600 1_min 532 1_min 600 1_min 599 1_min 531 1_min 600 1_min 600 1_min 600 1_min 531 1_min 533 1_min 600 1_min 600 1_min 600 1_min 531 1_min 531 1_min 531 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 600 1_min 390 1_min 600 1_min 520 1_min 533 1_min 600 1_min 600 1_min 600 1_min 533 1_min 600 1_min 348 1_min 600 1_min 600 1_min 600 1_min 600 1_min 390 1_min 161 0_zero 161 0_zero 161 0_zero 161 0_zero 161 0_zero 168 0_zero 171 0_zero 171 0_zero 171 0_zero 171 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 194 0_zero 182 0_zero 182 0_zero 182 0_zero 182 0_zero 182 0_zero 182 person

72 126 93 116 9 29 95 28 91 45 55 118 175 41 601 111 37 90 2 403 78 268 544 50 150 64 81 59 64 11 66 111 161 102 115 21 12 102 82 36 39 56 183 577 97 64 144 124 95 1 54 66 145 163 245 597 75 88 101 26 198 68 146 91 56 3 182 256 236 84 10 25 82 124 176 212 196 134 104 79 136 74 163 188 102 214 130

19 77 47 47 41 27 45 54 68 14 77 13 186 348 381 73 82 137 52 49 52 87 216 21 12 94 120 12 133 5 19 84 207 82 53 96 154 102 57 12 55 42 122 292 261 113 88 172 79 99 75 129 34 26 168 317 144 104 200 21 130 79 22 57 73 76 87 93 81 109 7 61 56 59 52 106 111 103 104 95 99 31 43 33 22 40 79

722 564 749 695 800 764 763 746 703 729 753 696 616 185 744 679 743 704 401 800 711 583 779 764 765 778 655 617 773 725 583 652 589 633 645 790 426 416 655 737 676 565 571 771 213 661 553 715 705 686 487 592 602 688 624 722 649 595 372 506 633 432 761 733 489 79 218 302 270 161 261 98 147 175 195 270 244 183 139 120 198 131 225 210 152 275 192

517 380 540 390 562 554 422 577 539 492 490 595 559 559 550 563 521 487 398 403 409 401 396 411 390 455 462 419 526 513 508 489 463 463 522 430 423 482 533 494 479 455 408 396 393 520 447 433 448 407 346 450 417 453 441 449 476 444 437 326 512 444 540 521 345 122 111 121 108 146 155 126 110 95 108 139 138 133 127 122 143 71 71 47 55 116 150

M A R C H 2 018

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M A R C H 2 018

277 310 310 310 310 275 275 275 275 275 275 275 275 275 310 310 276 276 274 274 274 275 275 275 275 275 300 300 271 271 271 320 320 320 320 320 320 320 320 320 320 320 320 320 314 314 274 274 274 275 275 275 275 259 259 259 299 299 299 299 299 299 291 313 313 313 300 300 300 274 274 274 300 300 300 259 259 259 259 259 259 259 259 259 259 273 273

182 0_zero 163 0_zero 163 person 163 person 163 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 163 0_zero 163 person 183 0_zero 183 0_zero 184 0_zero 184 person 184 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 168 0_zero 168 0_zero 186 0_zero 186 0_zero 186 person 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 157 0_zero 160 0_zero 160 0_zero 184 0_zero 184 0_zero 184 0_zero 183 0_zero 183 0_zero 183 person 183 person 194 0_zero 194 person 194 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 173 0_zero 161 0_zero 161 0_zero 161 0_zero 168 0_zero 168 person 168 person 184 0_zero 184 0_zero 184 person 168 0_zero 168 0_zero 168 person 194 0_zero 194 0_zero 194 person 194 person 194 person 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 184 0_zero 184 person

166 1 231 212 115 29 105 99 161 118 74 32 1 214 42 217 87 56 20 100 7 77 160 138 119 13 205 152 1 110 113 119 7 227 209 264 133 115 55 23 1 1 73 131 6 167 42 161 221 1 126 211 78 88 29 182 8 174 179 130 2 238 46 1 97 151 1 149 214 138 31 18 1 103 58 138 125 112 1 222 105 11 167 208 228 1 120

40 1 103 108 1 93 108 81 77 60 83 74 61 64 26 7 7 57 1 26 4 64 40 63 62 47 65 65 50 40 76 97 94 92 79 81 79 81 77 84 88 66 64 75 59 62 72 62 49 50 48 24 16 67 90 52 46 53 34 37 14 22 51 28 79 22 29 39 67 54 45 69 7 16 15 58 55 56 62 54 31 67 79 82 78 40 80

230 114 280 236 309 110 275 177 229 175 116 67 33 233 230 286 276 118 270 189 98 133 275 184 141 240 275 201 99 229 156 233 103 320 272 295 184 145 103 55 31 37 115 155 110 251 150 274 261 80 245 275 125 239 100 259 192 244 221 168 72 274 216 103 156 313 227 216 272 222 92 85 234 287 120 186 148 130 22 240 247 135 209 230 254 146 184

76 161 161 163 160 167 182 148 113 92 111 95 176 109 156 163 182 113 173 160 137 106 183 96 83 173 101 149 105 117 146 154 157 155 128 98 106 106 105 99 128 94 93 92 112 113 130 147 66 88 183 149 116 160 176 134 167 119 64 62 134 92 139 150 124 159 162 168 168 136 80 159 149 144 159 87 81 104 103 97 156 144 118 98 95 179 184

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258 | T E R R A M E D I A

273 273 259 275 275 275 275 275 275 276 276 280 280 259 259 259 259 259 259 270 270 299 299 275 275 275 275 291 259 259 275 275 275 275 264 264 183 183 183 183 183 183 183 183 183 300 300 259 275 275 275 278 278 278 278 278 278 278 278 278 275 300 300 300 275 275 275 275 275 300 300 300 300 300 300 300 300 275 275 275 275 275 280 267 267 267 259

| nicholas zembashi

184 person 184 person 194 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 182 0_zero 182 person 180 0_zero 180 0_zero 195 0_zero 195 0_zero 195 0_zero 195 0_zero 195 0_zero 195 0_zero 187 0_zero 187 person 168 0_zero 168 person 183 person 183 0_zero 183 0_zero 183 0_zero 173 0_zero 194 0_zero 194 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 191 0_zero 191 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 275 0_zero 168 0_zero 168 person 194 0_zero 183 0_zero 183 0_zero 183 person 181 0_zero 181 0_zero 181 0_zero 181 0_zero 181 0_zero 181 0_zero 181 person 181 person 181 person 183 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 person 183 0_zero 183 0_zero 183 0_zero 180 0_zero 188 0_zero 188 0_zero 188 0_zero 194 0_zero

176 192 53 28 86 50 1 208 88 112 54 135 206 67 131 164 14 10 90 1 143 170 87 66 125 40 19 6 54 165 82 59 48 160 44 113 44 21 1 26 66 118 143 118 118 112 89 44 38 197 165 194 186 94 46 133 168 112 126 58 143 137 104 87 2 100 190 245 167 93 208 191 46 87 1 271 174 64 152 76 2 216 58 130 105 1 51

47 61 49 36 29 19 10 6 72 14 23 53 41 98 100 84 96 113 138 7 32 61 27 21 83 34 49 4 90 80 74 70 73 81 62 81 196 193 185 173 175 191 188 183 232 66 95 49 99 113 105 47 45 38 35 36 42 73 49 53 70 1 58 53 78 78 72 59 63 67 81 83 86 88 94 84 79 9 60 87 90 99 35 81 95 81 58

223 268 201 117 141 84 57 254 147 274 113 201 241 135 204 212 56 110 222 266 181 291 129 112 273 161 62 161 144 259 179 110 76 229 109 191 140 74 37 66 113 169 181 135 183 210 133 203 106 251 186 278 238 130 86 159 204 182 147 108 249 282 190 129 129 155 244 273 189 187 285 230 101 114 58 300 200 275 196 188 86 275 110 261 144 90 86

145 182 135 99 60 41 72 49 167 181 168 103 63 140 148 114 113 168 192 156 156 165 160 173 182 119 79 153 139 162 138 106 98 142 132 129 264 229 222 202 210 230 214 196 271 151 137 189 142 143 146 137 78 64 55 51 64 175 88 157 130 127 115 86 152 113 103 89 130 139 129 115 121 113 131 107 95 155 101 153 144 153 65 165 130 176 88

M A R C H 2 018

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M A R C H 2 018

259 259 275 275 275 275 275 299 300 300 300 300 300 300 300 300 271 275 275 275 259 259 194 194 194 194 194 194 194 300 300 300 300 300 300 300 275 275 275 259 262 262 262 275 275 275 275 275 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 275 275 259 259 259 258 258 258 258 275 275 275 285 285 285 259 259 259 300 300 300 300 300 294

194 0_zero 194 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 168 0_zero 168 0_zero 168 0_zero 186 0_zero 183 0_zero 183 0_zero 183 person 194 0_zero 194 0_zero 259 0_zero 259 0_zero 259 0_zero 259 0_zero 259 0_zero 259 0_zero 259 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 person 194 0_zero 192 0_zero 192 0_zero 192 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 168 person 168 person 168 person 168 person 183 0_zero 183 0_zero 194 0_zero 194 0_zero 194 0_zero 195 0_zero 195 0_zero 195 0_zero 195 0_zero 183 0_zero 183 person 183 person 177 0_zero 177 0_zero 177 0_zero 194 0_zero 194 0_zero 194 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 171 person

105 123 102 34 1 14 218 77 139 91 63 41 105 1 117 182 6 1 97 125 42 203 65 128 113 45 50 159 103 167 96 80 78 63 30 9 156 131 79 137 114 22 76 46 94 157 147 48 168 7 87 181 151 113 45 74 19 1 149 128 99 256 184 107 1 58 17 175 26 74 116 141 5 158 206 125 220 87 3 175 110 1 99 196 120 160 87

9 62 53 73 72 53 46 16 81 92 78 76 76 7 28 49 14 27 9 67 44 35 169 159 143 158 127 124 137 46 34 40 40 46 29 29 19 49 10 91 78 86 85 119 104 74 47 9 75 92 76 49 41 54 76 63 39 48 68 98 107 99 77 33 1 85 17 65 62 60 62 54 88 63 61 53 70 39 37 47 44 55 59 62 28 19 1

257 182 238 105 44 66 265 248 279 144 111 79 142 146 181 229 259 133 130 164 158 256 152 194 168 104 120 191 128 299 218 144 136 102 108 53 272 200 139 213 262 82 133 111 165 205 186 230 204 104 158 233 192 172 98 121 80 36 188 149 132 298 208 218 253 177 151 214 91 131 163 180 111 204 241 216 285 174 86 253 168 95 172 279 178 205 229

192 134 137 114 104 74 74 149 167 132 121 107 167 162 80 83 167 176 71 164 132 105 251 199 177 209 168 168 152 144 120 91 89 82 76 68 116 106 136 145 170 130 124 175 179 183 177 142 99 156 118 86 68 76 104 87 85 115 168 153 168 168 134 131 180 169 134 104 125 112 100 88 140 166 167 107 115 89 75 79 69 118 107 124 68 120 169

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260 | T E R R A M E D I A

294 276 276 277 277 277 277 275 275 275 275 275 275 275 279 279 283 283 283 283 283 283 283 275 275 300 300 300 275 275 259 259 259 259 292 292 292 292 259 259 259 259 259 259 259 259 259 196 196 275 275 275 275 275 275 275 259 259 259 259 259 259 274 274 274 259 259 259 275 275 267 267 299 299 299 299 299 259 259 259 259 259 259 304 304 304 304

| nicholas zembashi

171 0_zero 183 person 183 0_zero 182 0_zero 182 0_zero 182 person 182 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 0_zero 183 0_zero 183 person 181 person 181 0_zero 178 0_zero 178 0_zero 178 0_zero 178 0_zero 178 0_zero 178 person 178 person 183 0_zero 183 person 168 0_zero 168 person 168 0_zero 183 0_zero 183 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 172 0_zero 172 0_zero 172 0_zero 172 person 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 person 194 person 257 0_zero 257 person 183 person 183 0_zero 183 0_zero 183 person 183 person 183 0_zero 183 0_zero 195 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 184 0_zero 184 0_zero 184 person 194 0_zero 194 0_zero 194 0_zero 183 0_zero 183 person 189 person 189 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 166 person 166 person 166 0_zero 166 0_zero

37 107 6 12 99 97 192 106 150 1 42 76 219 165 81 27 113 77 146 193 223 36 149 1 176 60 255 205 18 107 1 52 102 153 1 130 184 107 81 147 217 57 17 131 5 101 68 30 129 145 72 203 141 109 34 166 104 43 86 147 177 205 136 223 4 1 68 135 16 156 86 5 231 1 69 211 64 136 101 82 64 45 28 171 131 1 196

59 45 1 40 1 29 21 93 66 99 19 61 66 27 45 5 46 35 33 37 35 24 69 13 8 44 35 63 57 76 81 73 62 75 61 70 51 63 70 67 69 67 59 61 1 91 57 97 151 21 29 40 34 26 50 42 49 69 64 64 63 61 104 76 36 90 97 94 1 58 52 12 61 86 51 55 48 71 70 71 82 70 70 82 79 85 78

293 162 273 91 208 137 277 271 263 60 104 237 269 210 168 247 259 143 180 229 249 92 214 141 235 225 297 258 128 148 94 118 164 180 108 197 292 140 158 213 259 101 34 162 219 150 126 175 162 197 133 246 174 140 110 275 250 99 135 185 211 234 274 274 95 73 136 256 275 230 156 143 299 89 122 260 99 252 163 126 95 88 58 195 151 37 246

170 131 173 108 68 84 83 182 128 163 183 172 96 161 150 178 142 89 58 58 56 170 174 157 165 141 109 93 154 106 178 126 113 102 120 129 117 144 121 120 111 92 79 79 183 131 106 227 244 178 70 72 169 167 130 160 142 99 93 85 83 80 182 105 182 148 144 148 166 150 187 189 142 151 103 79 61 155 133 124 113 107 100 142 142 110 124

M A R C H 2 018

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M A R C H 2 018

304 236 236 236 236 236 275 275 275 275 275 275 275 275 275 275 275 275 266 266 300 300 300 276 276 276 305 305 305 305 296 296 296 296 296 296 296 275 275 275 275 275 269 269 269 269 269 269 269 300 300 300 300 300 300 300 300 275 275 300 300 300 194 194 275 275 275 275 275 275 275 275 275 285 285 285 285 285 301 301 301 301 301 301 329 329 329

166 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 190 0_zero 190 person 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 person 165 0_zero 165 0_zero 165 0_zero 165 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 person 170 person 183 person 183 person 183 0_zero 183 0_zero 183 0_zero 187 0_zero 187 0_zero 187 0_zero 187 0_zero 187 0_zero 187 0_zero 187 person 168 0_zero 168 0_zero 168 0_zero 168 person 168 person 168 person 168 person 168 person 183 person 183 0_zero 168 0_zero 168 0_zero 168 0_zero 259 person 259 0_zero 183 0_zero 183 0_zero 183 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 177 0_zero 177 0_zero 177 person 177 person 177 0_zero 167 0_zero 167 0_zero 167 0_zero 167 0_zero 167 0_zero 167 person 153 0_zero 153 0_zero 153 0_zero

121 32 3 93 1 40 13 81 88 101 140 147 137 138 179 190 215 44 40 128 165 125 56 15 164 202 184 139 116 90 52 1 38 156 243 99 222 114 95 1 149 68 2 30 66 78 182 234 105 73 175 267 13 46 136 198 222 92 147 102 1 232 87 1 41 154 120 21 122 91 76 164 123 1 36 118 90 161 3 1 79 195 262 168 208 173 142

67 110 95 81 72 73 101 98 89 79 87 106 93 82 81 90 75 77 29 54 42 37 41 45 94 99 64 51 63 62 111 107 98 105 116 85 119 28 47 28 8 27 56 53 54 49 49 43 45 65 75 75 61 66 71 69 65 60 28 56 62 53 54 36 42 74 77 1 96 38 30 15 8 66 64 85 77 48 35 39 68 68 58 78 62 78 63

201 144 102 214 47 73 105 118 133 137 275 196 177 168 205 220 243 89 220 169 236 204 113 159 276 239 239 193 150 126 119 69 84 262 294 124 257 148 136 94 273 121 37 59 82 109 207 269 173 140 208 300 40 88 166 227 262 145 274 192 80 300 145 71 132 243 170 115 151 136 119 275 168 55 78 149 121 285 90 46 134 247 301 185 302 234 223

94 164 151 136 110 94 164 128 113 104 182 151 126 97 96 110 94 106 152 141 129 118 96 152 163 169 108 102 102 100 151 151 124 156 155 153 159 176 107 173 166 79 76 74 67 67 62 76 101 129 127 115 150 163 163 165 165 131 164 126 116 126 154 115 149 131 165 158 122 78 63 158 156 148 118 173 172 164 117 135 95 103 135 115 139 112 101

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262 | T E R R A M E D I A

329 329 329 284 299 299 299 299 299 299 299 299 299 275 275 275 275 275 275 275 275 275 275 321 321 321 321 321 321 321 259 259 272 272 300 300 300 300 300 300 300 300 300 300 300 300 262 300 295 295 295 259 259 259 259 259 264 264 300 300 274 274 274 274 260 260 300 300 300 300 277 277 272 272 272 272 272 275 267 267 278 278 278 290 290 290 290

| nicholas zembashi

153 0_zero 153 0_zero 153 0_zero 177 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 person 183 person 183 0_zero 183 0_zero 183 0_zero 157 0_zero 157 0_zero 157 0_zero 157 person 157 person 157 person 157 person 194 0_zero 194 0_zero 186 person 186 person 168 0_zero 168 person 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 192 0_zero 168 person 171 0_zero 171 0_zero 171 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 191 0_zero 191 person 168 0_zero 168 person 184 0_zero 184 0_zero 184 person 184 person 194 0_zero 194 person 168 0_zero 168 0_zero 168 0_zero 168 person 182 0_zero 182 0_zero 185 0_zero 185 person 185 person 185 0_zero 185 0_zero 183 person 189 person 189 0_zero 181 0_zero 181 person 181 person 174 0_zero 174 0_zero 174 0_zero 174 0_zero

55 106 195 66 1 133 109 160 166 106 209 205 251 1 172 224 146 16 104 5 1 92 133 27 89 169 151 224 267 289 1 204 1 189 142 125 63 143 28 111 40 152 171 221 209 199 86 2 1 151 166 31 88 142 191 137 26 95 108 68 168 1 79 128 3 120 132 1 201 100 1 132 142 94 31 24 222 21 117 6 7 138 199 12 72 124 207

76 50 48 18 14 62 52 51 36 37 49 44 34 66 79 69 60 100 95 92 104 69 57 54 57 74 39 42 42 45 40 36 54 67 2 43 52 35 82 74 63 66 63 42 46 56 85 30 49 88 87 97 86 87 98 133 75 120 41 17 29 7 4 40 4 67 53 65 63 39 47 79 54 43 35 49 60 19 83 1 46 64 61 94 93 92 91

108 180 240 212 151 228 154 221 201 149 252 225 287 146 211 275 191 149 153 59 166 188 199 112 139 270 179 253 289 307 170 255 191 245 296 227 139 293 147 170 92 187 194 297 279 231 178 95 168 201 207 83 173 190 248 259 249 154 283 108 240 85 115 206 253 166 212 99 254 127 146 168 230 157 98 68 254 63 179 267 245 165 233 64 124 178 241

103 81 70 134 168 129 82 87 60 59 84 60 57 182 113 115 183 176 134 141 180 124 97 107 86 129 106 91 100 93 169 69 170 178 168 85 89 156 163 110 86 85 79 165 104 86 135 162 167 146 122 134 137 121 127 191 188 185 160 162 101 80 48 159 191 133 125 131 104 128 148 109 104 179 185 95 83 97 164 171 147 160 166 121 116 117 112

M A R C H 2 018

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M A R C H 2 018

275 275 275 275 275 275 275 254 286 286 286 286 286 286 286 299 275 275 275 275 275 258 258 258 258 258 275 275 275 275 275 275 275 275 275 275 294 294 294 294 299 299 299 299 299 299 299 299 299 299 299 299 275 275 275 262 275 275 300 300 300 300 300 301 299 299 299 299 259 259 275 275 264 264 264 264 264 290 290 290 290 290 277 277 276 276 259

183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 199 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 176 0_zero 168 0_zero 183 0_zero 183 0_zero 183 person 183 0_zero 183 0_zero 196 0_zero 196 0_zero 196 0_zero 196 0_zero 196 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 person 183 person 183 person 171 person 171 0_zero 171 0_zero 171 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 168 person 168 person 183 0_zero 183 0_zero 183 0_zero 192 0_zero 183 0_zero 183 person 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 167 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 194 0_zero 194 person 183 0_zero 183 0_zero 191 0_zero 191 0_zero 191 0_zero 191 person 191 person 174 0_zero 174 0_zero 174 0_zero 174 0_zero 174 0_zero 182 0_zero 182 person 183 0_zero 183 0_zero 194 0_zero

39 7 176 213 165 99 60 1 1 93 162 229 238 120 59 82 140 106 96 26 1 34 38 188 189 100 22 174 149 214 181 135 189 233 51 18 167 1 119 235 179 144 156 120 75 16 13 82 255 68 103 155 104 55 11 59 9 124 39 148 186 205 225 9 127 94 85 76 108 187 147 53 29 72 149 126 95 222 176 166 67 1 93 207 1 110 154

68 65 73 66 74 73 6 1 103 104 85 92 108 100 94 31 81 72 65 41 31 58 55 54 40 36 92 108 82 76 86 100 116 119 105 115 1 32 32 33 7 28 20 22 33 38 32 24 26 19 67 72 56 13 9 78 5 45 17 60 68 79 78 15 78 58 75 60 51 41 98 71 63 65 44 38 36 68 55 56 51 55 77 107 40 42 63

110 51 220 265 190 112 218 149 55 150 238 280 281 151 95 227 275 195 138 143 73 108 79 258 253 183 115 243 179 251 230 154 213 261 83 54 232 102 183 294 278 177 185 146 145 66 46 105 299 87 150 213 245 147 52 176 270 171 150 190 210 226 246 249 299 226 137 132 259 246 275 95 83 95 241 173 138 290 266 211 113 73 197 224 141 170 239

104 100 102 100 92 91 175 121 134 132 133 131 136 127 115 160 164 142 136 122 107 165 101 145 89 193 153 153 105 92 115 183 173 162 181 183 165 83 78 81 89 52 39 38 77 73 55 44 60 65 149 129 160 63 31 168 183 170 149 124 112 112 110 123 162 149 115 101 191 122 183 118 104 109 137 184 175 155 131 95 112 120 165 174 170 88 117

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264 | T E R R A M E D I A

300 300 300 300 300 300 300 300 300 300 300 300 300 259 259 293 293 293 293 275 275 275 275 275 275 0 299 300 300 300 275 275 275 275 275 275 275 275 275 299 259 259 259 259 230 230 230 230 278 278 278 259 275 275 275 275 275 275 275 275 275 275 275 275 275 275 275 301 254 254 254 254 254 254 291 308 308 259 259 259 259 275 270 270 299 299 225

| nicholas zembashi

168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 194 0_zero 194 person 172 person 172 0_zero 172 0_zero 172 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 0 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 168 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 220 0_zero 220 0_zero 220 0_zero 220 person 182 0_zero 182 0_zero 182 0_zero 194 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 person 183 0_zero 183 person 183 person 183 person 183 person 183 person 183 0_zero 183 0_zero 167 0_zero 199 0_zero 199 0_zero 199 0_zero 199 0_zero 199 person 199 person 173 0_zero 163 0_zero 163 person 194 0_zero 194 0_zero 194 0_zero 194 0_zero 183 0_zero 187 0_zero 187 person 168 0_zero 168 person 225 0_zero

33 22 145 221 248 165 137 116 74 30 191 214 252 5 204 70 115 28 132 1 18 53 198 184 159 60 49 34 163 193 123 48 36 15 108 85 69 201 226 1 56 16 133 209 1 99 108 94 1 100 169 68 23 150 68 49 17 155 200 1 103 1 105 226 98 206 51 34 92 47 18 2 71 55 1 34 71 37 111 174 6 93 31 103 117 80 1

45 39 76 63 43 54 48 47 61 68 66 68 70 14 14 3 24 16 2 86 37 18 33 24 3 6 17 37 56 61 83 84 72 73 81 73 76 86 77 102 57 51 62 66 123 120 116 107 2 50 51 35 51 96 23 38 27 10 23 1 25 23 23 106 114 45 47 79 59 63 59 61 71 39 1 69 40 76 80 78 78 1 45 33 25 42 141

159 88 244 282 296 236 184 199 151 112 237 262 289 136 254 156 229 80 243 115 109 98 275 244 220 287 230 181 216 241 213 115 72 45 146 118 94 260 274 148 164 83 180 255 64 230 145 115 124 209 216 122 103 259 101 188 68 207 254 272 200 89 182 275 201 271 101 115 254 161 89 38 100 78 290 298 270 118 159 254 41 274 194 180 227 150 104

147 100 129 120 73 95 92 119 124 131 109 104 99 119 104 154 101 57 68 180 107 41 109 78 48 156 153 142 91 102 149 127 109 98 104 99 92 126 105 167 124 113 104 100 184 213 143 164 171 121 90 83 94 144 42 175 153 153 161 180 175 179 135 180 183 97 83 124 197 138 103 87 120 100 163 146 81 121 113 131 111 135 181 136 147 148 219

M A R C H 2 018

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M A R C H 2 018

225 225 225 225 225 225 299 299 299 299 273 273 273 275 275 275 275 275 275 275 310 310 310 310 310 310 310 310 310 310 310 259 259 259 275 295 295 295 295 295 275 275 275 259 259 259 259 259 275 273 273 273 273 259 275 275 275 275 275 275 275 275 275 275 275 259 259 259 259 288 281 281 281 281 259 259 259 259 259 259 259 300 300 275 275 275 275

225 0_zero 225 0_zero 225 0_zero 225 0_zero 225 person 225 0_zero 168 0_zero 168 0_zero 168 person 168 person 184 0_zero 184 0_zero 184 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 0_zero 162 person 194 person 194 0_zero 194 0_zero 183 0_zero 171 0_zero 171 person 171 person 171 person 171 person 183 0_zero 183 0_zero 183 person 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 183 0_zero 184 0_zero 184 0_zero 184 0_zero 184 0_zero 194 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 194 person 194 0_zero 194 0_zero 194 0_zero 175 0_zero 179 0_zero 179 person 179 person 179 person 194 person 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 person 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero

59 12 83 126 65 1 159 75 111 142 117 28 4 140 45 1 234 128 86 12 51 115 136 217 205 109 70 41 29 276 15 59 96 1 96 50 25 69 171 222 136 4 99 1 92 147 179 193 119 93 29 1 202 62 121 91 57 41 21 128 198 164 15 78 207 7 198 110 77 103 115 229 83 247 7 121 188 217 159 148 45 21 120 79 30 1 202

134 118 91 107 97 83 55 93 45 53 74 80 65 83 97 102 80 105 86 38 67 67 55 50 44 52 54 61 51 46 78 58 83 1 66 29 4 7 12 3 136 2 40 33 33 25 29 29 98 130 123 130 133 57 66 48 26 22 19 17 14 11 18 15 71 39 70 77 77 56 26 37 39 19 39 97 96 94 99 64 1 42 44 102 90 41 77

138 64 121 189 91 44 280 111 139 166 252 56 63 216 139 53 272 160 162 184 111 173 215 302 254 146 104 77 62 310 50 90 250 63 229 256 78 91 230 289 189 264 150 142 168 197 205 213 194 171 105 44 262 125 269 257 132 85 56 189 250 204 38 110 253 104 259 139 129 237 245 277 129 273 117 169 220 257 255 193 155 138 189 178 109 73 274

186 151 142 134 199 127 124 120 137 118 172 97 91 137 178 166 102 181 178 160 120 118 99 97 77 83 79 92 79 84 160 179 169 177 155 169 148 66 87 169 168 181 141 149 94 72 60 52 168 171 164 159 170 93 180 174 85 59 49 53 46 35 40 31 179 190 132 105 117 150 151 140 150 58 194 142 122 120 164 109 184 125 81 173 146 83 121

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266 | T E R R A M E D I A

300 300 300 300 300 300 299 299 273 273 273 292 292 292 292 292 292 292 292 292 292 275 259 300 300 274 274 274 271 271 271 271 271 271 259 275 275 275 275 259 306 306 306 306 300 300 195 318 318 318 318 318 318 318 318 318 318 318 318 300 300 300 300 300 300 305 305 305 305 305 305 305 305 305 305 305 305 305 305 305 305 305 275 360 360 360 269

| nicholas zembashi

168 0_zero 168 person 168 person 168 person 168 person 168 person 168 0_zero 168 0_zero 185 0_zero 185 0_zero 185 person 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 173 0_zero 183 0_zero 194 0_zero 168 0_zero 168 0_zero 184 0_zero 184 person 184 person 186 person 186 0_zero 186 0_zero 186 0_zero 186 0_zero 186 0_zero 194 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 194 person 165 person 165 0_zero 165 0_zero 165 0_zero 168 0_zero 168 0_zero 259 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 159 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 0_zero 165 person 165 person 183 0_zero 140 0_zero 140 0_zero 140 0_zero 187 0_zero

27 176 227 101 40 133 67 155 2 124 62 1 24 72 106 3 41 33 18 58 72 59 110 105 9 44 126 198 80 185 114 136 165 47 180 1 179 1 1 154 101 10 157 98 16 83 2 33 122 149 194 244 268 164 78 65 16 5 108 61 186 90 213 187 97 20 152 236 246 125 154 183 207 202 244 229 137 30 1 98 183 206 86 132 223 85 22

110 27 35 25 62 66 56 63 14 14 23 85 61 59 53 57 54 42 38 43 43 33 2 49 1 23 88 18 79 56 18 60 59 22 37 65 66 55 40 24 24 68 66 83 1 30 120 71 60 81 72 69 59 69 66 42 47 28 47 53 38 22 28 26 35 68 119 31 143 38 29 29 25 13 23 19 21 23 26 21 57 59 97 66 51 72 62

201 216 263 145 89 172 133 192 84 176 114 121 89 113 144 41 72 53 37 71 84 211 210 290 160 209 186 226 113 271 195 190 203 79 200 189 265 73 65 209 154 107 235 271 281 117 195 79 166 200 249 279 314 196 120 106 62 31 154 205 263 148 268 206 191 163 262 274 303 163 182 214 229 227 267 249 156 67 39 120 203 225 173 186 310 111 123

165 110 104 111 127 109 105 80 88 77 165 172 108 89 77 79 69 57 55 56 54 149 66 161 98 166 161 71 123 185 141 150 168 106 105 174 150 78 56 112 109 155 124 163 167 54 230 99 95 105 97 90 87 88 97 71 68 55 64 131 80 45 52 42 95 137 163 51 165 54 41 46 41 31 38 36 35 41 46 34 108 109 130 84 115 93 172

M A R C H 2 018

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M A R C H 2 018

269 282 282 282 282 282 282 282 282 268 268 268 268 268 268 268 275 275 275 275 275 275 275 275 259 259 259 275 275 300 275 275 275 275 275 275 275 300 300 300 300 297 297 297 297 297 297 297 297 297 297 297 274 274 274 274 308 308 259 259 259 259 275 275 275 275 275 275 275 275 275 275 275 275 275 275 326 264 264 264 264 264 316 316 316 316 316

187 0_zero 179 0_zero 179 0_zero 179 0_zero 179 person 179 person 179 person 179 person 179 0_zero 188 0_zero 188 0_zero 188 0_zero 188 0_zero 188 0_zero 188 0_zero 188 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 194 0_zero 194 0_zero 194 0_zero 183 0_zero 183 person 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 168 0_zero 168 0_zero 168 0_zero 168 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 170 0_zero 184 0_zero 184 0_zero 184 0_zero 184 0_zero 164 0_zero 164 person 194 0_zero 194 person 194 person 194 person 183 person 183 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 183 person 183 0_zero 183 0_zero 183 person 155 0_zero 191 0_zero 191 0_zero 191 person 191 person 191 person 160 0_zero 160 0_zero 160 0_zero 160 0_zero 160 0_zero

166 118 37 9 56 90 96 229 205 204 150 138 59 32 1 103 142 11 114 177 215 5 30 135 1 1 160 74 28 72 1 158 184 222 187 139 95 1 94 168 210 64 159 117 204 246 240 97 99 30 31 173 125 110 60 94 112 70 26 96 51 83 104 155 77 29 1 252 208 1 113 140 175 199 89 27 109 1 92 141 212 182 1 116 207 174 94

57 1 5 102 23 48 25 25 44 56 44 46 33 42 46 40 99 98 88 82 91 76 71 76 37 17 39 24 18 61 32 69 68 71 68 71 47 50 54 74 74 119 115 106 107 116 103 122 112 142 112 104 108 90 103 96 66 31 6 43 31 55 37 34 6 23 43 17 60 67 37 31 21 61 46 42 59 53 7 66 93 108 46 56 38 45 38

224 211 132 154 76 121 117 246 265 265 213 167 113 65 44 141 234 101 167 224 275 51 68 159 87 161 259 275 83 230 157 208 219 275 224 163 137 92 181 203 258 88 181 145 235 297 279 121 118 63 63 186 237 188 110 126 277 113 241 154 91 236 154 200 240 107 45 275 275 98 142 174 209 246 175 67 220 94 264 186 262 216 72 223 312 215 142

109 87 80 175 99 98 86 86 84 103 90 70 77 80 84 139 172 175 128 111 130 108 96 89 139 105 156 174 178 162 171 108 94 104 96 92 128 115 111 109 102 144 135 122 128 141 124 134 123 157 132 115 170 144 143 128 162 164 157 119 113 143 161 166 133 99 88 48 115 117 118 118 118 104 97 177 146 178 181 165 168 166 94 151 108 70 76

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267

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268 | T E R R A M E D I A

316 316 316 316 300 300 275 275 275 275 275 275 300 275 275 279 279 279 279 279 297 297 275 300 300 300 300 300 300 300 300 300 300 259 259 259 259 259 259 259 250 250 259 259 300 300 299 299 299 265 300 299 299 299 299 299 275 299 299 299 299 299 299 285 285 285 285 285 285 285 285 285 285 291 275 275 275 275 259 300 300 300 275 275 275 275 275

| nicholas zembashi

160 0_zero 160 0_zero 160 0_zero 160 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 183 person 168 0_zero 183 0_zero 183 0_zero 181 0_zero 181 0_zero 181 0_zero 181 person 181 person 170 0_zero 170 person 183 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 202 0_zero 202 person 194 0_zero 194 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 190 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 177 person 177 person 177 person 177 person 177 person 177 person 177 person 177 person 177 person 177 person 173 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 195 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero

40 148 90 121 1 126 5 77 146 236 213 195 90 19 153 1 211 45 86 144 108 17 85 1 78 129 136 226 214 261 129 64 208 1 67 109 149 190 206 1 100 50 47 117 92 1 1 45 146 34 79 35 41 135 250 165 38 118 210 96 41 1 23 216 190 210 177 166 151 143 129 93 1 74 173 1 115 150 1 56 120 152 56 170 24 73 119

37 43 34 34 1 43 96 97 105 102 96 97 68 57 64 56 45 40 63 52 46 17 58 54 49 40 66 58 42 43 41 46 46 48 49 38 43 45 42 60 16 31 36 60 15 24 45 1 9 14 47 43 30 24 32 27 69 56 63 60 58 57 53 52 62 47 53 61 47 63 54 37 58 96 77 78 76 76 22 48 46 39 69 71 81 78 86

98 174 112 139 129 262 82 153 200 275 229 209 154 127 253 62 279 232 137 219 285 38 267 151 129 162 262 297 280 288 159 90 231 114 172 189 202 233 242 129 203 160 113 184 148 130 87 297 197 208 220 209 103 175 297 183 137 161 246 119 69 29 39 236 208 222 188 180 165 156 137 147 67 121 214 161 156 178 253 139 167 185 216 267 82 116 149

61 59 45 45 119 140 144 145 144 142 144 143 108 124 121 132 136 133 154 160 141 80 167 167 79 65 149 146 72 65 62 68 62 193 127 98 75 71 65 145 68 200 126 127 65 166 88 165 89 183 156 166 81 50 62 43 131 85 84 82 84 84 92 133 141 70 146 159 96 122 98 176 177 133 111 164 105 99 190 95 78 61 156 141 111 105 108

M A R C H 2 018

0_pic_375.jpg 0_pic_375.jpg 0_pic_375.jpg 0_pic_375.jpg 0_pic_375.jpg 0_pic_376.jpg 0_pic_376.jpg 0_pic_376.jpg 0_pic_376.jpg 0_pic_377.jpg 0_pic_377.jpg 0_pic_377.jpg 0_pic_378.jpg 0_pic_378.jpg 0_pic_378.jpg 0_pic_379.jpg 0_pic_379.jpg 0_pic_380.jpg 0_pic_380.jpg 0_pic_381.jpg 0_pic_381.jpg 0_pic_382.jpg 0_pic_382.jpg 0_pic_383.jpg 0_pic_383.jpg 0_pic_383.jpg 0_pic_383.jpg 0_pic_383.jpg 0_pic_384.jpg 0_pic_384.jpg 0_pic_385.jpg 0_pic_385.jpg 0_pic_386.jpg 0_pic_386.jpg 0_pic_386.jpg 0_pic_386.jpg 0_pic_387.jpg 0_pic_387.jpg 0_pic_387.jpg 0_pic_388.jpg 0_pic_388.jpg 0_pic_388.jpg 0_pic_388.jpg 0_pic_388.jpg 0_pic_389.jpg 0_pic_389.jpg 0_pic_389.jpg 0_pic_390.jpg 0_pic_390.jpg 0_pic_390.jpg 0_pic_390.jpg 0_pic_390.jpg 0_pic_390.jpg 0_pic_391.jpg 0_pic_391.jpg 0_pic_392.jpg 0_pic_393.jpg 0_pic_393.jpg 0_pic_394.jpg 0_pic_394.jpg 0_pic_394.jpg 0_pic_394.jpg 0_pic_394.jpg 0_pic_394.jpg 0_pic_395.jpg 0_pic_395.jpg 0_pic_396.jpg 0_pic_396.jpg 0_pic_396.jpg 0_pic_396.jpg 0_pic_396.jpg 0_pic_396.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_397.jpg 0_pic_398.jpg 0_pic_399.jpg 0_pic_399.jpg 0_pic_399.jpg

M A R C H 2 018

275 275 275 275 275 300 300 300 300 276 276 276 275 275 275 300 300 266 266 277 277 275 275 300 300 300 300 300 276 276 300 300 259 259 259 259 183 183 183 299 299 299 299 299 299 299 299 259 259 259 259 259 259 259 259 275 287 287 274 274 274 274 274 274 279 279 225 225 225 225 225 225 300 300 300 300 300 300 300 300 300 300 300 290 299 299 299

183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 person 168 0_zero 168 0_zero 168 0_zero 168 0_zero 183 0_zero 183 0_zero 183 person 183 0_zero 183 0_zero 183 0_zero 168 0_zero 168 person 190 0_zero 190 person 182 0_zero 182 0_zero 183 0_zero 183 person 168 person 168 person 168 person 168 0_zero 168 0_zero 182 0_zero 182 0_zero 168 0_zero 168 0_zero 194 0_zero 194 0_zero 194 0_zero 194 0_zero 275 0_zero 275 0_zero 275 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 168 person 168 0_zero 168 0_zero 168 0_zero 194 0_zero 194 0_zero 194 person 194 person 194 person 194 0_zero 194 0_zero 194 0_zero 183 0_zero 176 0_zero 176 0_zero 184 0_zero 184 0_zero 184 0_zero 184 0_zero 184 0_zero 184 0_zero 180 0_zero 180 0_zero 225 0_zero 225 0_zero 225 0_zero 225 0_zero 225 0_zero 225 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 174 0_zero 169 0_zero 169 0_zero 169 0_zero

98 150 183 241 203 174 119 100 41 138 74 29 1 160 221 153 230 19 118 76 175 170 9 95 154 208 122 1 1 89 6 52 1 53 139 221 1 64 125 75 52 1 108 226 19 94 216 1 109 131 188 218 203 124 31 29 111 99 66 8 156 158 116 157 105 10 121 24 70 100 3 73 14 87 93 118 151 151 163 208 192 22 52 87 205 189 177

74 83 85 77 37 53 58 55 55 8 21 15 9 36 41 16 26 50 15 18 80 32 38 2 10 12 49 36 97 102 25 1 153 152 161 160 142 132 137 96 12 20 28 1 76 55 87 58 65 75 82 78 75 54 45 93 23 38 96 85 96 100 88 94 34 43 108 64 42 21 18 10 104 100 89 79 84 97 109 95 84 100 80 35 69 104 108

129 183 208 273 239 299 184 142 87 276 166 91 241 244 252 226 253 132 223 277 251 275 109 124 192 300 242 86 91 137 103 220 45 97 179 253 58 123 176 209 291 76 170 258 115 202 296 135 166 151 207 239 247 199 114 161 287 149 135 74 274 196 141 173 269 87 225 107 125 174 50 140 114 125 148 148 178 190 212 238 221 50 92 248 299 221 196

97 107 103 101 153 147 109 93 91 143 101 149 180 84 63 64 84 147 188 166 174 116 177 111 58 163 167 84 173 142 123 160 181 178 183 184 182 169 171 167 127 92 111 51 130 128 125 183 119 128 129 122 98 102 85 155 168 156 144 128 164 131 112 115 159 84 186 131 80 58 45 41 165 133 117 106 100 135 161 113 101 130 102 136 134 127 126

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269

0_pic_400.jpg 0_pic_400.jpg 0_pic_400.jpg 0_pic_400.jpg 0_pic_401.jpg 0_pic_401.jpg 0_pic_401.jpg 0_pic_402.jpg 0_pic_402.jpg 0_pic_403.jpg 0_pic_404.jpg 0_pic_404.jpg 0_pic_404.jpg 0_pic_404.jpg 0_pic_404.jpg 1000_pic_0086.jpg 1000_pic_0087.jpg 1000_pic_0088.jpg 1000_pic_0089.jpg 1000_pic_0090.jpg 1000_pic_0091.jpg 1000_pic_0092.jpg 1000_pic_0093.jpg 1000_pic_0094.jpg 1000_pic_0095.jpg 1000_pic_0096.jpg 1000_pic_0097.jpg 1000_pic_0098.jpg 1000_pic_0099.jpg 1000_pic_0100.jpg 1000_pic_0101.jpg 1000_pic_0102.jpg 1000_pic_0103.jpg 1000_pic_0104.jpg 1000_pic_0105.jpg 1000_pic_0106.jpg 1000_pic_0107.jpg 1000_pic_0108.jpg 1000_pic_0109.jpg 1000_pic_0110.jpg 1000_pic_0111.jpg 1000_pic_0112.jpg 1000_pic_0113.jpg 1000_pic_0114.jpg 1000_pic_0115.jpg 1000_pic_0116.jpg 1000_pic_0117.jpg 1000_pic_0118.jpg 1000_pic_0119.jpg 1000_pic_0120.jpg 1000_pic_0121.jpg 1000_pic_0122.jpg 1000_pic_0123.jpg 1000_pic_0124.jpg 1000_pic_0125.jpg 1000_pic_0126.jpg 1000_pic_0127.jpg 1000_pic_0128.jpg 1000_pic_0129.jpg 1000_pic_0130.jpg 1000_pic_0131.jpg 1000_pic_0132.jpg 1000_pic_0133.jpg 1000_pic_0134.jpg 1000_pic_0135.jpg 1000_pic_0136.jpg 1000_pic_0137.jpg 1000_pic_0138.jpg 1000_pic_0139.jpg 1000_pic_0140.jpg 1000_pic_0141.jpg 1000_pic_0142.jpg 1000_pic_0143.jpg 1000_pic_0144.jpg 1000_pic_0145.jpg 1000_pic_0146.jpg 1000_pic_0147.jpg 1000_pic_0148.jpg 1000_pic_0149.jpg 1000_pic_0150.jpg 1000_pic_0151.jpg 1000_pic_0152.jpg 1000_pic_0153.jpg 1000_pic_0154.jpg 1000_pic_0155.jpg 1000_pic_0156.jpg 1000_pic_0157.jpg

270 | T E R R A M E D I A

| nicholas zembashi

300 300 300 300 300 300 300 305 305 300 276 276 276 276 276 276 275 194 275 259 310 286 259 259 275 298 259 432 275 275 340 284 271 301 286 275 299 275 259 266 275 262 277 300 299 259 275 225 275 225 347 259 225 247 282 275 267 354 275 272 344 300 310 225 275 374 225 275 194 275 375 275 180 276 282 318 308 360 291 318 303 300 259 274 257 276 262

168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 0_zero 168 person 165 0_zero 165 0_zero 168 person 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 0_zero 183 1000_max 183 1000_max 260 1000_max 183 1000_max 194 1000_max 163 1000_max 176 1000_max 194 1000_max 194 1000_max 183 1000_max 169 1000_max 194 1000_max 116 1000_max 183 1000_max 183 1000_max 148 1000_max 177 1000_max 186 1000_max 168 1000_max 176 1000_max 183 1000_max 168 1000_max 183 1000_max 194 1000_max 190 1000_max 183 1000_max 193 1000_max 182 1000_max 168 1000_max 169 1000_max 194 1000_max 183 1000_max 225 1000_max 183 1000_max 225 1000_max 145 1000_max 194 1000_max 225 1000_max 204 1000_max 178 1000_max 183 1000_max 189 1000_max 142 1000_max 183 1000_max 186 1000_max 146 1000_max 168 1000_max 163 1000_max 225 1000_max 183 1000_max 135 1000_max 225 1000_max 183 1000_max 259 1000_max 183 1000_max 134 1000_max 183 1000_max 180 1000_max 183 1000_max 179 1000_max 159 1000_max 164 1000_max 140 1000_max 173 1000_max 159 1000_max 166 1000_max 168 1000_max 194 1000_max 184 1000_max 196 1000_max 183 1000_max 192 1000_max

56 119 227 210 138 176 180 37 169 56 19 144 215 127 196 47 1 4 34 4 19 21 2 144 22 28 15 4 12 18 27 54 21 37 28 1 28 55 1 36 1 17 14 16 32 45 1 1 9 18 10 13 17 51 76 1 6 93 1 1 55 23 77 70 44 2 6 15 32 72 35 5 41 50 75 1 65 30 7 2 33 1 6 18 1 7 31

71 85 77 82 89 95 89 82 105 4 36 49 35 39 41 38 24 56 110 61 14 8 1 61 41 7 20 15 56 37 7 47 44 1 12 24 30 16 29 42 16 37 19 61 22 38 50 50 33 45 3 8 66 26 49 22 18 17 20 58 2 13 10 11 26 15 56 56 94 50 20 71 91 3 16 3 1 4 32 20 14 25 28 39 1 17 70

108 157 283 238 166 195 185 165 241 125 88 203 276 210 226 237 175 187 164 258 281 266 124 259 246 266 253 427 242 262 314 231 253 287 273 272 269 219 235 250 273 241 264 291 258 246 179 223 267 207 328 249 180 243 227 272 255 300 271 270 297 281 226 133 236 303 211 262 157 151 295 266 178 272 201 313 292 329 273 316 264 299 245 251 175 266 248

102 107 109 103 104 104 105 136 130 60 71 77 66 65 58 144 118 168 134 125 104 110 113 117 136 160 148 108 132 154 133 106 117 137 126 108 136 136 138 131 152 145 110 140 140 99 140 144 119 164 140 164 109 143 133 173 153 91 136 133 125 153 89 131 95 114 142 148 133 87 130 125 165 166 86 103 136 63 119 96 117 141 120 108 173 177 170

M A R C H 2 018

1000_pic_0158.jpg 1000_pic_0159.jpg 1000_pic_0160.jpg 1000_pic_0161.jpg 1000_pic_0162.jpg 1000_pic_0163.jpg 1000_pic_0164.jpg 1000_pic_0165.jpg 1000_pic_0166.jpg 1000_pic_0167.jpg 1000_pic_0168.jpg 1000_pic_0169.jpg 1000_pic_0170.jpg 1000_pic_0171.jpg 1000_pic_0172.jpg 1000_pic_0173.jpg 1000_pic_0174.jpg 1000_pic_0175.jpg 1000_pic_0176.jpg 1000_pic_0177.jpg 1000_pic_0178.jpg 1000_pic_0179.jpg 1000_pic_0180.jpg 1000_pic_0181.jpg 1000_pic_0182.jpg 1000_pic_0183.jpg 1000_pic_0184.jpg 1000_pic_0185.jpg 1000_pic_0186.jpg 1000_pic_0187.jpg 1000_pic_0188.jpg 1000_pic_0189.jpg 1000_pic_0190.jpg 1000_pic_0191.jpg 1000_pic_0192.jpg 1000_pic_0193.jpg 1000_pic_0194.jpg 1000_pic_0195.jpg 1000_pic_0196.jpg 1000_pic_0197.jpg 1000_pic_0198.jpg 1000_pic_0199.jpg 1000_pic_0200.jpg 1000_pic_0201.jpg 1000_pic_0202.jpg 1000_pic_0203.jpg 1000_pic_0204.jpg 1000_pic_0205.jpg 1000_pic_0206.jpg 1000_pic_0207.jpg 1000_pic_0208.jpg 1000_pic_0209.jpg 1000_pic_0210.jpg 1000_pic_0211.jpg 1000_pic_0212.jpg 1000_pic_0213.jpg 1000_pic_0214.jpg 1000_pic_0215.jpg 1000_pic_0216.jpg 1000_pic_0217.jpg 1000_pic_0217.jpg 1000_pic_0218.jpg 1000_pic_0219.jpg 1000_pic_0220.jpg 1000_pic_0221.jpg 1000_pic_0222.jpg 1000_pic_0223.jpg 1000_pic_0224.jpg 1000_pic_0225.jpg 1000_pic_0226.jpg 1000_pic_0227.jpg 1000_pic_0227.jpg 1000_pic_0228.jpg 1000_pic_0229.jpg 1000_pic_0230.jpg 1000_pic_0231.jpg 1000_pic_0232.jpg 1000_pic_0233.jpg 1000_pic_0234.jpg 1000_pic_0235.jpg 1000_pic_0236.jpg 1000_pic_0237.jpg 1000_pic_0238.jpg 1000_pic_0239.jpg 1000_pic_0240.jpg 1000_pic_0241.jpg 1000_pic_0242.jpg

M A R C H 2 018

278 275 300 290 275 268 276 275 284 282 294 305 260 300 270 275 259 275 259 300 293 259 290 300 333 274 275 293 275 292 260 287 300 276 275 280 308 299 313 276 261 259 225 299 275 275 264 300 286 275 367 183 284 281 200 276 313 225 225 224 224 260 278 275 259 301 293 286 259 259 406 406 300 269 245 278 275 275 217 286 259 345 275 301 287 275 260

181 1000_max 183 1000_max 168 1000_max 174 1000_max 183 1000_max 188 1000_max 182 1000_max 183 1000_max 177 1000_max 179 1000_max 171 1000_max 165 1000_max 194 1000_max 168 1000_max 187 1000_max 183 1000_max 194 1000_max 183 1000_max 194 1000_max 168 1000_max 172 1000_max 194 1000_max 174 1000_max 168 1000_max 151 1000_max 184 1000_max 183 1000_max 172 1000_max 183 1000_max 173 1000_max 194 1000_max 175 1000_max 168 1000_max 183 1000_max 183 1000_max 180 1000_max 164 1000_max 168 1000_max 161 1000_max 183 1000_max 193 1000_max 194 1000_max 225 1000_max 168 1000_max 183 1000_max 183 1000_max 191 1000_max 168 1000_max 176 1000_max 183 1000_max 137 1000_max 275 1000_max 177 1000_max 179 1000_max 133 1000_max 183 1000_max 161 1000_max 225 1000_max 225 1000_max 224 1000_max 224 1000_max 194 1000_max 181 1000_max 183 1000_max 194 1000_max 167 1000_max 172 1000_max 176 1000_max 194 1000_max 194 1000_max 124 1000_max 124 1000_max 168 1000_max 187 1000_max 206 1000_max 182 1000_max 183 1000_max 183 1000_max 232 1000_max 176 1000_max 194 1000_max 146 1000_max 183 1000_max 168 1000_max 176 1000_max 183 1000_max 194 1000_max

31 19 106 28 11 110 34 24 14 13 47 15 4 7 1 1 22 115 27 1 7 63 21 107 54 1 8 72 115 5 1 1 9 9 19 10 14 81 37 1 1 10 1 1 1 1 1 40 15 21 22 27 53 1 9 1 1 74 33 13 1 1 25 33 3 90 26 12 1 31 10 288 20 39 25 1 1 12 37 4 1 23 21 8 178 29 34

46 12 48 30 19 31 23 20 25 22 36 9 7 26 1 16 58 105 20 36 42 72 24 1 17 59 21 59 70 52 42 1 14 95 8 1 31 9 28 16 22 17 15 19 1 66 3 3 57 43 20 21 11 8 22 70 23 57 70 14 168 27 68 27 28 63 7 56 32 63 20 14 33 26 18 30 25 54 51 23 7 8 13 1 84 33 12

257 256 182 260 264 267 248 230 272 271 269 285 226 292 268 274 220 188 229 298 258 189 282 298 285 174 267 224 247 290 256 282 297 267 270 266 263 187 301 274 258 248 221 161 271 271 165 251 264 265 344 181 264 268 191 235 306 213 187 220 109 182 260 206 255 278 255 278 246 234 114 405 215 258 236 276 273 261 212 264 236 331 244 290 251 257 245

96 109 140 115 168 139 147 104 136 171 109 101 108 129 183 122 133 148 91 126 149 120 134 157 142 135 169 102 111 172 132 158 141 175 116 179 110 133 100 148 146 131 149 129 162 150 184 133 110 122 125 271 141 166 82 115 88 153 169 139 222 139 148 127 164 124 123 116 133 139 104 113 108 176 190 156 168 117 122 146 169 118 150 140 135 128 177

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271

1000_pic_0243.jpg 1000_pic_0244.jpg 1000_pic_0245.jpg 1000_pic_0246.jpg 1000_pic_0247.jpg 1000_pic_0248.jpg 1000_pic_0249.jpg 1000_pic_0250.jpg 1000_pic_0250.jpg 1000_pic_0250.jpg 1000_pic_0251.jpg 1000_pic_0252.jpg 1000_pic_0253.jpg 1000_pic_0254.jpg 1000_pic_0255.jpg 1000_pic_0256.jpg 1000_pic_0257.jpg 1000_pic_0258.jpg 1000_pic_0259.jpg 1000_pic_0260.jpg 1000_pic_0261.jpg 1000_pic_0262.jpg 1000_pic_0263.jpg 1000_pic_0264.jpg 1000_pic_0265.jpg 1000_pic_0266.jpg 1000_pic_0267.jpg 1000_pic_0268.jpg 1000_pic_0269.jpg 1000_pic_0270.jpg 1000_pic_0271.jpg 1000_pic_0272.jpg 1000_pic_0273.jpg 1000_pic_0274.jpg 1000_pic_0275.jpg 1000_pic_0276.jpg 1000_pic_0277.jpg 1000_pic_0278.jpg 1000_pic_0279.jpg 1000_pic_0280.jpg 1000_pic_0281.jpg 1000_pic_0282.jpg 1000_pic_0283.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0284.jpg 1000_pic_0285.jpg 1000_pic_0286.jpg 1000_pic_0287.jpg 1000_pic_0288.jpg 1000_pic_0289.jpg 1000_pic_0290.jpg 1000_pic_0291.jpg 1000_pic_0292.jpg 1000_pic_0293.jpg 1000_pic_0294.jpg 1000_pic_0295.jpg 1000_pic_0296.jpg 1000_pic_0297.jpg 1000_pic_0298.jpg 1000_pic_0299.jpg 1000_pic_0300.jpg 1000_pic_0301.jpg 1000_pic_0302.jpg 1000_pic_0303.jpg 1000_pic_0304.jpg 1000_pic_0305.jpg 1000_pic_0306.jpg 1000_pic_0307.jpg 1000_pic_0308.jpg 1000_pic_0309.jpg 1000_pic_0310.jpg 1000_pic_0311.jpg 1000_pic_0312.jpg 1000_pic_0313.jpg 1000_pic_0313.jpg 1000_pic_0314.jpg 1000_pic_0315.jpg 1000_pic_0316.jpg 1000_pic_0317.jpg 1000_pic_0318.jpg 1000_pic_0319.jpg 1000_pic_0320.jpg

272 | T E R R A M E D I A

| nicholas zembashi

266 275 278 284 261 275 275 275 275 275 360 379 310 284 275 276 259 275 259 300 286 259 258 183 275 259 310 194 275 318 311 299 275 287 275 264 225 276 251 181 275 225 274 318 318 318 318 318 318 318 284 225 259 277 259 278 300 250 275 235 275 323 300 269 300 220 183 275 285 259 306 273 275 300 259 275 259 275 183 183 275 299 275 272 269 225 273

190 1000_max 183 1000_max 181 1000_max 177 1000_max 193 1000_max 183 1000_max 183 1000_max 183 1000_max 183 1000_max 183 person 140 1000_max 133 1000_max 163 1000_max 177 1000_max 183 1000_max 183 1000_max 194 1000_max 183 1000_max 194 1000_max 168 1000_max 176 1000_max 194 1000_max 196 1000_max 275 1000_max 183 1000_max 194 1000_max 163 1000_max 259 1000_max 183 1000_max 159 1000_max 162 1000_max 168 1000_max 183 1000_max 176 1000_max 183 1000_max 191 1000_max 225 1000_max 183 1000_max 201 1000_max 279 1000_max 183 1000_max 225 1000_max 184 1000_max 159 person 159 person 159 person 159 person 159 person 159 person 159 person 177 1000_max 225 1000_max 194 1000_max 182 1000_max 194 1000_max 181 1000_max 168 1000_max 177 1000_max 183 1000_max 154 1000_max 183 1000_max 156 1000_max 168 1000_max 187 1000_max 168 1000_max 166 1000_max 275 1000_max 183 1000_max 177 1000_max 194 1000_max 165 1000_max 185 1000_max 183 1000_max 168 1000_max 194 1000_max 183 1000_max 194 1000_max 183 1000_max 275 1000_max 275 person 183 1000_max 168 1000_max 183 1000_max 185 1000_max 187 1000_max 225 1000_max 184 1000_max

12 55 16 38 1 41 1 131 1 92 7 164 4 157 72 69 1 22 11 20 1 1 9 21 43 14 120 36 54 94 34 1 2 106 3 18 10 59 1 18 38 10 16 229 146 122 171 193 187 292 7 1 60 1 25 26 1 39 2 14 28 44 50 19 1 1 1 101 1 1 1 1 91 37 9 1 30 1 98 62 1 2 1 1 1 1 25

55 31 15 24 36 6 20 1 37 48 1 1 28 24 39 43 63 58 31 33 8 36 19 18 1 44 11 13 16 30 36 15 16 69 19 21 75 27 34 21 15 64 6 62 84 67 82 86 84 65 8 51 24 8 25 15 44 32 1 26 33 12 27 14 5 16 40 78 66 37 8 45 11 13 86 33 50 32 21 81 52 46 68 6 19 13 100

250 186 262 261 259 257 272 194 88 133 332 377 309 223 260 204 255 255 244 278 244 251 255 166 274 250 284 169 249 200 236 245 271 277 214 244 212 268 246 161 269 224 215 274 172 147 186 207 195 316 280 223 228 271 222 251 127 248 272 215 251 257 230 241 297 181 179 242 164 257 298 268 273 273 255 275 249 270 143 113 229 230 267 193 265 217 242

110 128 140 103 114 82 179 128 180 183 132 130 149 108 130 133 169 134 124 130 145 135 161 264 135 125 116 224 167 103 132 148 109 135 110 160 149 148 191 232 130 190 95 159 154 159 125 127 123 159 149 138 134 146 161 161 132 136 179 98 156 115 118 100 165 131 211 136 151 134 143 182 151 117 174 132 150 124 104 225 183 125 141 137 175 194 155

M A R C H 2 018

1000_pic_0321.jpg 1000_pic_0322.jpg 1000_pic_0322.jpg 1000_pic_0323.jpg 1000_pic_0324.jpg 1000_pic_0325.jpg 1000_pic_0326.jpg 1000_pic_0327.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0328.jpg 1000_pic_0329.jpg 1000_pic_0330.jpg 1000_pic_0331.jpg 1000_pic_0332.jpg 1000_pic_0332.jpg 1000_pic_0333.jpg 1000_pic_0334.jpg 1000_pic_0335.jpg 1000_pic_0336.jpg 1000_pic_0337.jpg 1000_pic_0338.jpg 1000_pic_0339.jpg 1000_pic_0340.jpg 1000_pic_0341.jpg 1000_pic_0342.jpg 1000_pic_0343.jpg 1000_pic_0343.jpg 1000_pic_0344.jpg 1000_pic_0345.jpg 1000_pic_0346.jpg 1000_pic_0347.jpg 1000_pic_0348.jpg 1000_pic_0349.jpg 1000_pic_0350.jpg 1000_pic_0351.jpg 1000_pic_0352.jpg 1000_pic_0353.jpg 1000_pic_0354.jpg 1000_pic_0355.jpg 1000_pic_0356.jpg 1000_pic_0357.jpg 1000_pic_0358.jpg 1000_pic_0359.jpg 1000_pic_0360.jpg 1000_pic_0361.jpg 1000_pic_0362.jpg 1000_pic_0362.jpg 1000_pic_0363.jpg 1000_pic_0364.jpg 1000_pic_0365.jpg 1000_pic_0366.jpg 1000_pic_0366.jpg 1000_pic_0367.jpg 1000_pic_0368.jpg 1000_pic_0369.jpg 1000_pic_0370.jpg 1000_pic_0370.jpg 1000_pic_0371.jpg 1000_pic_0372.jpg 1000_pic_0373.jpg 1000_pic_0374.jpg 1000_pic_0375.jpg 1000_pic_0376.jpg 1000_pic_0377.jpg 1000_pic_0377.jpg 1000_pic_0378.jpg 1000_pic_0379.jpg 1000_pic_0380.jpg 1000_pic_0381.jpg 1000_pic_0382.jpg 1000_pic_0383.jpg 1000_pic_0384.jpg 1000_pic_0385.jpg 1000_pic_0385.jpg 1000_pic_0386.jpg 1000_pic_0387.jpg 1000_pic_0388.jpg 1000_pic_0388.jpg 1000_pic_0389.jpg 1000_pic_0390.jpg 1000_pic_0391.jpg

M A R C H 2 018

300 275 275 259 275 300 200 276 194 194 194 194 194 194 194 194 275 300 258 250 250 224 262 275 286 279 275 275 258 252 275 218 218 275 259 259 300 275 276 259 285 275 276 259 275 261 275 266 361 273 261 275 275 259 275 300 275 275 259 259 337 276 276 259 259 275 300 335 268 275 275 300 275 183 259 271 299 284 191 191 347 260 300 300 308 275 259

168 1000_max 183 1000_max 183 1000_max 194 1000_max 183 1000_max 168 1000_max 238 1000_max 182 1000_max 260 1000_max 260 person 260 person 260 person 260 person 260 person 260 person 260 person 183 1000_max 168 1000_max 195 1000_max 202 1000_max 202 1000_max 224 1000_max 193 1000_max 183 1000_max 176 1000_max 181 1000_max 183 1000_max 183 1000_max 196 1000_max 200 1000_max 183 1000_max 231 1000_max 231 1000_max 183 1000_max 194 1000_max 194 1000_max 168 1000_max 183 1000_max 183 1000_max 194 1000_max 177 1000_max 183 1000_max 182 1000_max 194 1000_max 183 1000_max 193 1000_max 183 1000_max 190 1000_max 139 1000_max 184 1000_max 193 1000_max 183 1000_max 183 1000_max 194 1000_max 183 1000_max 168 1000_max 183 1000_max 183 person 194 1000_max 194 1000_max 149 1000_max 183 1000_max 183 1000_max 194 1000_max 194 1000_max 183 1000_max 168 1000_max 150 1000_max 188 1000_max 183 1000_max 183 1000_max 168 1000_max 183 1000_max 275 1000_max 194 1000_max 186 1000_max 168 1000_max 177 1000_max 264 1000_max 264 1000_max 145 1000_max 194 1000_max 168 1000_max 168 1000_max 164 1000_max 183 1000_max 194 1000_max

20 22 81 1 5 6 5 12 54 27 132 108 79 57 12 1 23 60 8 23 28 54 10 1 23 36 4 1 125 56 1 10 142 4 40 1 38 16 14 2 17 1 14 10 23 8 18 48 8 22 40 34 33 1 41 24 1 56 1 1 50 1 97 19 17 1 56 40 23 1 94 51 1 39 74 13 29 9 94 1 7 15 99 1 1 7 1

18 79 41 30 63 30 41 7 28 161 163 162 162 163 160 166 26 24 18 5 106 71 16 21 24 22 16 12 90 16 48 2 64 4 67 16 22 14 4 42 14 19 34 29 87 28 69 53 21 6 127 24 100 28 6 35 23 139 55 45 5 39 15 28 43 16 56 33 16 71 1 77 23 20 19 74 31 18 50 152 20 26 7 97 7 45 40

288 254 119 257 264 294 196 265 190 46 155 125 95 75 25 11 260 247 250 224 224 222 199 268 263 240 272 275 214 205 274 140 215 272 211 219 251 272 252 230 258 258 245 250 254 245 260 227 340 261 198 239 238 138 229 263 247 75 257 256 294 138 237 244 239 266 233 318 248 209 144 182 261 116 241 257 179 274 191 106 341 251 295 110 305 258 258

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273

1000_pic_0392.jpg 1000_pic_0393.jpg 1000_pic_0394.jpg 1000_pic_0395.jpg 1000_pic_0396.jpg 1000_pic_0397.jpg 1000_pic_0398.jpg 1000_pic_0399.jpg 1000_pic_0400.jpg 1000_pic_0401.jpg 1000_pic_0402.jpg 1000_pic_0403.jpg 1000_pic_0404.jpg 1000_pic_0405.jpg 1000_pic_0406.jpg 1000_pic_0407.jpg 1000_pic_0408.jpg 1000_pic_0409.jpg 1000_pic_0410.jpg 1000_pic_0411.jpg 1000_pic_0412.jpg 1000_pic_0413.jpg 1000_pic_0414.jpg 1000_pic_0415.jpg 1000_pic_0416.jpg 1000_pic_0417.jpg 1000_pic_0418.jpg 1000_pic_0419.jpg 1000_pic_0420.jpg 1000_pic_0421.jpg 1000_pic_0422.jpg 1000_pic_0423.jpg 1000_pic_0424.jpg 1000_pic_0425.jpg 1000_pic_0426.jpg 1000_pic_0427.jpg 1000_pic_0428.jpg 1000_pic_0429.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0430.jpg 1000_pic_0431.jpg 1000_pic_0432.jpg 1000_pic_0433.jpg 1000_pic_0434.jpg 1000_pic_0435.jpg 1000_pic_0436.jpg 1000_pic_0437.jpg 1000_pic_0438.jpg 1000_pic_0439.jpg 1000_pic_0440.jpg 1000_pic_0441.jpg 1000_pic_0442.jpg 1000_pic_0443.jpg 1000_pic_0444.jpg 1000_pic_0445.jpg 1000_pic_0446.jpg 1000_pic_0447.jpg 1000_pic_0448.jpg 1000_pic_0449.jpg 1000_pic_0450.jpg 1000_pic_0451.jpg 1000_pic_0452.jpg 1000_pic_0453.jpg 1000_pic_0454.jpg 1000_pic_0455.jpg 1000_pic_0456.jpg 1000_pic_0456.jpg 1000_pic_0457.jpg 1000_pic_0458.jpg 1000_pic_0459.jpg 1000_pic_0460.jpg 1000_pic_0461.jpg 1000_pic_0462.jpg 1000_pic_0463.jpg 1000_pic_0464.jpg 1000_pic_0465.jpg 1000_pic_0466.jpg 1000_pic_0467.jpg 1000_pic_0467.jpg 1000_pic_0468.jpg 1000_pic_0469.jpg 1000_pic_0470.jpg

274 | T E R R A M E D I A

| nicholas zembashi

275 389 275 275 306 0 280 290 306 300 289 284 259 275 259 271 257 300 268 225 275 304 312 348 323 259 299 299 275 190 225 275 275 275 275 278 321 275 308 308 308 308 308 308 308 300 291 370 280 302 357 275 299 275 275 275 259 300 286 299 286 275 183 275 299 290 260 275 275 275 186 186 274 264 309 275 275 276 311 300 266 259 275 275 259 300 275

183 1000_max 129 1000_max 183 1000_max 183 1000_max 165 1000_max 0 1000_max 180 1000_max 174 1000_max 164 1000_max 168 1000_max 174 1000_max 178 1000_max 194 1000_max 183 1000_max 194 1000_max 186 1000_max 196 1000_max 168 1000_max 188 1000_max 225 1000_max 183 1000_max 166 1000_max 161 1000_max 145 1000_max 156 1000_max 194 1000_max 168 1000_max 168 1000_max 183 1000_max 266 1000_max 225 1000_max 183 1000_max 183 1000_max 183 1000_max 183 1000_max 181 1000_max 157 1000_max 183 1000_max 164 1000_max 164 1000_max 164 person 164 person 164 person 164 person 164 person 168 1000_max 173 1000_max 136 1000_max 180 1000_max 167 1000_max 141 1000_max 183 1000_max 168 1000_max 183 1000_max 183 1000_max 183 1000_max 194 1000_max 168 1000_max 176 1000_max 168 1000_max 176 1000_max 183 1000_max 275 1000_max 183 1000_max 168 1000_max 174 1000_max 194 1000_max 183 1000_max 183 1000_max 183 1000_max 271 1000_max 271 1000_max 184 1000_max 191 1000_max 163 1000_max 183 1000_max 183 1000_max 183 1000_max 162 1000_max 168 1000_max 189 1000_max 194 1000_max 183 1000_max 183 1000_max 194 1000_max 168 1000_max 183 1000_max

12 26 1 75 10 1 68 3 1 1 103 1 32 1 1 68 1 5 1 26 6 8 28 1 32 1 44 38 46 52 28 58 1 27 6 22 6 59 76 1 95 182 157 254 114 1 31 1 1 68 25 1 40 46 1 72 95 43 49 1 1 20 1 59 16 1 15 31 6 24 7 104 9 33 8 21 3 2 1 18 7 1 20 18 80 1 10

7 54 1 17 16 2 69 43 21 32 76 10 73 14 70 23 2 8 28 20 18 1 23 17 8 2 19 24 25 43 71 70 13 15 56 41 24 29 1 56 104 103 102 105 104 5 45 1 24 3 23 57 35 61 12 31 39 8 48 24 11 36 43 10 74 9 16 29 22 36 5 44 31 50 43 72 26 57 3 22 7 58 9 95 59 20 39

230 330 273 262 303 251 229 284 295 299 251 282 255 272 257 223 255 254 265 223 189 299 282 337 268 257 289 272 253 187 137 213 273 264 266 270 312 263 227 95 112 194 172 268 131 298 270 364 268 296 330 272 260 235 273 155 229 262 259 217 283 258 182 209 292 278 253 249 273 262 182 175 118 233 305 253 273 234 283 275 258 255 254 257 219 259 264

133 110 130 105 109 171 151 127 99 130 142 131 187 181 165 112 177 141 120 171 139 129 138 126 129 146 119 124 110 178 142 132 123 109 149 115 131 113 96 102 164 164 163 157 164 128 137 120 153 165 140 183 119 138 154 135 87 128 129 140 135 118 237 170 137 118 168 167 174 167 260 157 116 138 150 149 94 107 161 114 165 174 87 174 150 109 139

M A R C H 2 018

1000_pic_0471.jpg 1000_pic_0472.jpg 1000_pic_0473.jpg 1000_pic_0473.jpg 1000_pic_0473.jpg 1000_pic_0474.jpg 1000_pic_0474.jpg 1000_pic_0475.jpg 1000_pic_0476.jpg 1000_pic_0477.jpg 1000_pic_0477.jpg 1000_pic_0478.jpg 1000_pic_0479.jpg 1000_pic_0480.jpg 1000_pic_0481.jpg 1000_pic_0481.jpg 1000_pic_0482.jpg 1000_pic_0483.jpg 1000_pic_0484.jpg 1000_pic_0485.jpg 1000_pic_0486.jpg 1000_pic_0487.jpg 1000_pic_0488.jpg 1000_pic_0489.jpg 1000_pic_0490.jpg 1000_pic_0491.jpg 1000_pic_0491.jpg 1000_pic_0491.jpg 1000_pic_0491.jpg 1000_pic_0491.jpg 1000_pic_0492.jpg 1000_pic_0493.jpg 1000_pic_0494.jpg 1000_pic_0495.jpg 1000_pic_0496.jpg 1000_pic_0497.jpg 1000_pic_0498.jpg 1000_pic_0499.jpg 1000_pic_0500.jpg 1000_pic_0501.jpg 1000_pic_0501.jpg 1000_pic_0501.jpg 1000_pic_0502.jpg 1000_pic_0503.jpg 1000_pic_0504.jpg 1000_pic_0505.jpg 1000_pic_0506.jpg 1000_pic_0506.jpg 1000_pic_0506.jpg 1000_pic_0507.jpg 1000_pic_0508.jpg 1000_pic_0509.jpg 1000_pic_0510.jpg 1000_pic_0510.jpg 1000_pic_0511.jpg 1000_pic_0512.jpg 1000_pic_0513.jpg 1000_pic_0514.jpg 1000_pic_0515.jpg 1000_pic_0516.jpg 1000_pic_0517.jpg 1000_pic_0518.jpg 1000_pic_0518.jpg 1000_pic_0519.jpg 1000_pic_0520.jpg 1000_pic_0521.jpg 1000_pic_0522.jpg 1000_pic_0523.jpg 1000_pic_0524.jpg 1000_pic_0525.jpg 1000_pic_0526.jpg 1000_pic_0527.jpg 1000_pic_0528.jpg 1000_pic_0529.jpg 1000_pic_0530.jpg 1000_pic_0531.jpg 1000_pic_0532.jpg 1000_pic_0533.jpg 1000_pic_0534.jpg 1000_pic_0535.jpg 1000_pic_0536.jpg 1000_pic_0537.jpg 1000_pic_0538.jpg 1000_pic_0538.jpg 1000_pic_0539.jpg 1000_pic_0540.jpg 1000_pic_0541.jpg

M A R C H 2 018

291 311 194 194 194 275 275 312 275 225 225 276 273 301 275 275 266 279 276 275 265 259 268 306 299 279 279 279 279 279 275 261 301 259 275 327 212 241 276 271 271 271 285 300 200 259 271 271 271 318 299 275 287 287 275 275 281 267 275 275 267 194 194 183 275 283 278 300 300 259 259 249 300 194 271 300 278 300 300 300 280 290 199 199 259 282 275

173 1000_max 162 1000_max 259 1000_max 259 1000_max 259 1000_max 183 1000_max 183 1000_max 162 1000_max 183 1000_max 225 1000_max 225 1000_max 183 1000_max 184 1000_max 167 1000_max 183 1000_max 183 1000_max 189 1000_max 180 1000_max 183 1000_max 183 1000_max 190 1000_max 194 1000_max 188 1000_max 165 1000_max 168 1000_max 181 1000_max 181 person 181 person 181 person 181 person 183 1000_max 193 1000_max 168 1000_max 194 1000_max 183 1000_max 154 1000_max 238 1000_max 209 1000_max 182 1000_max 186 1000_max 186 1000_max 186 1000_max 177 1000_max 168 1000_max 133 1000_max 194 1000_max 186 1000_max 186 1000_max 186 1000_max 159 1000_max 168 1000_max 183 1000_max 176 1000_max 176 person 183 1000_max 183 1000_max 179 1000_max 189 1000_max 183 1000_max 183 1000_max 189 1000_max 259 1000_max 259 1000_max 276 1000_max 183 1000_max 178 1000_max 181 1000_max 168 1000_max 168 1000_max 194 1000_max 194 1000_max 202 1000_max 168 1000_max 259 1000_max 186 1000_max 168 1000_max 181 1000_max 168 1000_max 168 1000_max 168 1000_max 180 1000_max 174 1000_max 253 1000_max 253 person 194 1000_max 179 1000_max 183 1000_max

5 40 1 47 109 154 79 129 23 1 20 115 1 8 3 45 4 37 45 1 1 1 1 2 26 21 78 94 115 185 1 12 95 1 23 40 16 26 5 128 17 18 8 23 3 1 1 127 189 12 16 133 49 203 1 32 2 33 10 1 32 69 1 101 87 48 54 1 3 61 40 25 17 2 84 78 16 7 39 28 93 23 10 23 1 8 12

7 44 15 30 150 66 30 62 17 33 146 5 44 13 2 9 64 48 41 29 4 31 23 40 21 12 122 117 112 117 4 6 18 19 62 9 64 24 19 54 98 51 22 1 28 20 30 70 43 13 26 3 52 99 45 28 5 28 46 29 66 53 11 6 21 25 32 52 26 59 2 64 18 1 62 19 1 10 17 34 64 51 9 152 26 35 16

251 272 189 94 129 265 114 307 247 199 188 272 261 166 272 100 263 246 223 249 252 256 266 297 262 211 89 106 131 200 272 256 294 257 240 289 141 213 257 250 146 251 278 295 198 203 87 195 269 294 266 268 240 222 164 239 242 240 199 274 234 135 190 182 249 272 215 300 298 228 255 197 227 193 194 280 250 294 259 253 192 238 186 58 248 278 249

167 110 154 106 232 141 101 139 143 126 208 115 142 118 182 91 157 121 122 142 103 161 160 144 85 150 173 171 169 164 129 127 109 155 118 92 197 161 173 137 140 141 111 164 94 134 118 106 185 105 115 183 117 176 182 131 109 116 131 111 123 165 248 169 136 127 118 135 131 128 176 131 87 257 105 107 159 164 157 118 126 148 235 236 162 153 168

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275

1000_pic_0542.jpg 1000_pic_0543.jpg 1000_pic_0544.jpg 1000_pic_0545.jpg 1000_pic_0546.jpg 1000_pic_0546.jpg 1000_pic_086.jpg 1000_pic_087.jpg 1000_pic_088.jpg 1000_pic_089.jpg 1000_pic_090.jpg 1000_pic_091.jpg 1000_pic_092.jpg 1000_pic_093.jpg 1000_pic_094.jpg 1000_pic_095.jpg 1000_pic_096.jpg 1000_pic_096.jpg 1000_pic_097.jpg 1000_pic_098.jpg 1000_pic_099.jpg 1000_pic_100.jpg 1000_pic_101.jpg 1000_pic_101.jpg 1000_pic_101.jpg 1000_pic_103.jpg 1000_pic_104.jpg 1000_pic_104.jpg 1000_pic_105.jpg 1000_pic_106.jpg 1000_pic_107.jpg 1000_pic_108.jpg 1000_pic_109.jpg 1000_pic_110.jpg 1000_pic_110.jpg 1000_pic_110.jpg 1000_pic_110.jpg 1000_pic_110.jpg 1000_pic_111.jpg 1000_pic_112.jpg 1000_pic_113.jpg 1000_pic_114.jpg 1000_pic_115.jpg 1000_pic_116.jpg 1000_pic_117.jpg 1000_pic_117.jpg 1000_pic_118.jpg 1000_pic_119.jpg 1000_pic_120.jpg 1000_pic_120.jpg 1000_pic_121.jpg 1000_pic_122.jpg 1000_pic_123.jpg 1000_pic_124.jpg 1000_pic_125.jpg 1000_pic_126.jpg 1000_pic_127.jpg 1000_pic_128.jpg 1000_pic_129.jpg 1000_pic_130.jpg 1000_pic_131.jpg 1000_pic_131.jpg 1000_pic_131.jpg 1000_pic_132.jpg 1000_pic_133.jpg 1000_pic_134.jpg 1000_pic_135.jpg 1000_pic_135.jpg 1000_pic_135.jpg 1000_pic_135.jpg 1000_pic_136.jpg 1000_pic_136.jpg 1000_pic_137.jpg 1000_pic_138.jpg 1000_pic_139.jpg 1000_pic_140.jpg 1000_pic_141.jpg 1000_pic_142.jpg 1000_pic_143.jpg 1000_pic_144.jpg 1000_pic_144.jpg 1000_pic_144.jpg 1000_pic_145.jpg 1000_pic_146.jpg 1000_pic_147.jpg 1000_pic_148.jpg 1000_pic_149.jpg

276 | T E R R A M E D I A

| nicholas zembashi

292 300 287 259 271 271 259 225 223 190 194 254 275 220 275 275 261 261 300 239 300 179 276 276 276 206 326 326 284 225 275 263 259 275 275 275 275 275 478 275 275 279 259 275 266 266 282 259 259 259 259 259 225 197 277 259 194 279 275 284 254 254 254 262 191 194 302 302 302 302 260 260 275 262 194 225 266 276 259 367 367 367 194 259 194 275 262

173 1000_max 168 1000_max 176 1000_max 194 1000_max 186 1000_max 186 person 194 1000_max 225 1000_max 226 1000_max 265 1000_max 259 1000_max 198 1000_max 183 1000_max 229 1000_max 183 1000_max 184 1000_max 193 1000_max 193 1000_max 168 1000_max 211 1000_max 168 1000_max 281 1000_max 183 1000_max 183 1000_max 183 1000_max 245 1000_max 155 1000_max 155 person 177 1000_max 225 1000_max 183 1000_max 192 1000_max 194 1000_max 183 1000_max 183 person 183 person 183 person 183 person 105 1000_max 183 1000_max 183 1000_max 181 1000_max 194 1000_max 183 1000_max 190 1000_max 190 1000_max 179 1000_max 194 1000_max 194 1000_max 194 1000_max 194 1000_max 194 1000_max 225 1000_max 256 1000_max 182 person 194 1000_max 259 1000_max 171 1000_max 183 1000_max 177 1000_max 198 1000_max 198 1000_max 198 1000_max 192 1000_max 264 1000_max 259 1000_max 167 1000_max 167 1000_max 167 1000_max 167 1000_max 194 1000_max 194 person 183 1000_max 192 1000_max 259 1000_max 225 1000_max 190 1000_max 183 1000_max 194 1000_max 137 1000_max 137 1000_max 137 1000_max 259 1000_max 194 1000_max 259 1000_max 183 1000_max 193 1000_max

109 4 8 54 44 185 3 25 61 6 1 7 31 8 1 1 13 148 1 11 1 1 131 1 168 58 1 245 7 15 35 61 39 75 152 171 137 125 37 1 4 52 11 51 101 1 9 37 96 145 58 1 38 29 9 11 11 5 55 8 3 204 5 1 5 1 107 202 48 2 8 133 82 1 6 2 20 1 1 1 263 148 1 1 4 6 42

104 15 27 71 22 75 1 13 65 2 8 5 49 1 6 1 15 99 1 24 29 1 111 1 1 46 5 3 62 5 1 13 1 2 131 131 132 132 24 42 36 28 1 6 53 25 7 22 73 34 1 21 68 45 11 4 23 17 42 5 28 36 1 1 9 55 1 76 73 110 2 126 17 1 43 52 2 1 68 2 1 15 67 78 45 1 6

273 297 285 228 228 253 258 217 209 183 191 233 274 216 271 177 146 212 297 235 297 175 154 106 275 204 245 302 254 221 257 199 218 207 168 183 144 134 441 274 239 225 195 231 265 101 137 229 143 255 200 254 208 158 256 236 171 275 216 281 50 254 252 252 185 192 190 267 94 81 243 172 189 222 192 98 228 270 255 113 367 224 188 213 193 271 184

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M A R C H 2 018

1000_pic_150.jpg 1000_pic_151.jpg 1000_pic_151.jpg 1000_pic_151.jpg 1000_pic_151.jpg 1000_pic_152.jpg 1000_pic_153.jpg 1000_pic_154.jpg 1000_pic_155.jpg 1000_pic_156.jpg 1000_pic_157.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_158.jpg 1000_pic_159.jpg 1000_pic_160.jpg 1000_pic_161.jpg 1000_pic_161.jpg 1000_pic_162.jpg 1000_pic_163.jpg 1000_pic_163.jpg 1000_pic_163.jpg 1000_pic_163.jpg 1000_pic_164.jpg 1000_pic_165.jpg 1000_pic_165.jpg 1000_pic_165.jpg 1000_pic_165.jpg 1000_pic_165.jpg 1000_pic_166.jpg 1000_pic_167.jpg 1000_pic_168.jpg 1000_pic_168.jpg 1000_pic_169.jpg 1000_pic_170.jpg 1000_pic_171.jpg 1000_pic_171.jpg 1000_pic_172.jpg 1000_pic_173.jpg 1000_pic_174.jpg 1000_pic_175.jpg 1000_pic_176.jpg 1000_pic_177.jpg 1000_pic_178.jpg 1000_pic_179.jpg 1000_pic_180.jpg 1000_pic_181.jpg 1000_pic_182.jpg 1000_pic_183.jpg 1000_pic_184.jpg 1000_pic_185.jpg 1000_pic_186.jpg 1000_pic_186.jpg 1000_pic_187.jpg 1000_pic_188.jpg 1000_pic_189.jpg 1000_pic_190.jpg 1000_pic_191.jpg 1000_pic_192.jpg 1000_pic_193.jpg 1000_pic_194.jpg 1000_pic_195.jpg 1000_pic_195.jpg 1000_pic_196.jpg 1000_pic_197.jpg 1000_pic_198.jpg 1000_pic_199.jpg 1000_pic_200.jpg 1000_pic_201.jpg 1000_pic_202.jpg 1000_pic_203.jpg 1000_pic_203.jpg 1000_pic_203.jpg 1000_pic_204.jpg 1000_pic_205.jpg 1000_pic_205.jpg 1000_pic_205.jpg 1000_pic_206.jpg 1000_pic_207.jpg

M A R C H 2 018

263 279 279 279 279 200 274 272 264 225 300 223 223 223 223 223 223 223 223 223 223 223 299 184 259 259 183 351 351 351 351 248 183 183 183 183 183 259 258 259 259 255 368 254 254 251 286 268 287 240 194 259 269 225 267 299 259 270 220 259 259 276 277 277 266 186 284 324 275 265 265 300 183 183 177 184 259 225 187 187 187 275 367 367 367 274 275

191 1000_max 180 1000_max 180 person 180 person 180 person 252 1000_max 184 1000_max 186 1000_max 191 1000_max 225 1000_max 168 1000_max 226 1000_max 226 person 226 person 226 person 226 person 226 person 226 person 226 person 226 person 226 person 226 person 168 1000_max 273 1000_max 194 1000_max 194 person 276 1000_max 143 1000_max 143 1000_max 143 1000_max 143 1000_max 203 1000_max 276 person 276 1000_max 276 person 276 person 276 person 194 1000_max 194 1000_max 194 1000_max 194 person 197 1000_max 137 1000_max 199 1000_max 199 1000_max 201 1000_max 176 1000_max 188 1000_max 175 1000_max 181 1000_max 259 1000_max 194 1000_max 187 1000_max 225 1000_max 189 1000_max 168 1000_max 194 1000_max 187 1000_max 230 1000_max 194 1000_max 194 1000_max 183 1000_max 182 1000_max 182 1000_max 190 1000_max 271 1000_max 177 1000_max 155 1000_max 183 person 190 1000_max 190 1000_max 168 1000_max 276 1000_max 275 1000_max 285 1000_max 275 1000_max 194 1000_max 225 1000_max 270 1000_max 270 1000_max 270 1000_max 183 1000_max 137 1000_max 137 person 137 person 184 1000_max 183 1000_max

1 22 153 162 228 44 94 28 59 17 73 7 69 42 14 85 103 132 91 81 4 19 1 2 57 32 30 55 39 321 217 1 76 48 2 140 162 2 1 1 101 32 23 4 1 1 40 8 1 8 51 26 4 12 45 1 31 5 4 6 65 25 20 1 63 1 51 6 18 27 1 1 1 1 5 1 1 55 2 58 5 4 1 279 242 6 108

11 22 128 130 129 25 22 7 20 90 24 1 86 112 153 153 141 142 87 94 103 104 2 5 3 83 30 60 70 77 96 1 173 160 40 113 135 72 1 1 28 5 1 17 135 4 37 16 1 7 11 34 39 70 22 40 1 20 8 8 7 9 55 4 48 1 2 1 43 5 50 6 35 41 4 1 6 10 1 1 82 33 1 44 80 19 79

262 274 164 179 238 154 202 199 172 225 300 217 82 55 42 106 116 150 104 92 24 28 299 70 259 85 137 201 50 330 316 170 107 143 49 157 177 247 253 125 172 255 356 253 123 245 250 262 281 203 156 135 263 211 221 162 255 264 209 256 195 126 182 272 182 174 210 311 166 259 73 291 157 181 170 184 255 169 185 187 185 167 189 363 312 267 160

172 132 168 167 153 228 127 177 139 223 167 123 124 148 188 224 171 181 121 127 137 134 167 245 183 170 217 129 117 115 126 164 257 185 179 181 181 164 138 191 173 131 130 124 190 173 137 131 169 181 146 133 141 146 165 166 180 163 197 160 126 182 182 182 163 270 167 128 103 171 168 126 223 244 235 266 143 174 268 107 268 95 131 98 132 153 145

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277

1000_pic_207.jpg 1000_pic_207.jpg 1000_pic_208.jpg 1000_pic_209.jpg 1000_pic_210.jpg 1000_pic_211.jpg 1000_pic_212.jpg 1000_pic_213.jpg 1000_pic_214.jpg 1000_pic_215.jpg 1000_pic_215.jpg 1000_pic_216.jpg 1000_pic_217.jpg 1000_pic_218.jpg 1000_pic_219.jpg 1000_pic_220.jpg 1000_pic_221.jpg 1000_pic_222.jpg 1000_pic_223.jpg 1000_pic_223.jpg 1000_pic_224.jpg 1000_pic_225.jpg 1000_pic_225.jpg 1000_pic_226.jpg 1000_pic_227.jpg 1000_pic_228.jpg 1000_pic_229.jpg 1000_pic_230.jpg 1000_pic_231.jpg 1000_pic_232.jpg 1000_pic_233.jpg 1000_pic_234.jpg 1000_pic_235.jpg 1000_pic_236.jpg 1000_pic_237.jpg 1000_pic_237.jpg 1000_pic_238.jpg 1000_pic_239.jpg 1000_pic_240.jpg 1000_pic_241.jpg 1000_pic_242.jpg 1000_pic_243.jpg 1000_pic_243.jpg 1000_pic_243.jpg 1000_pic_244.jpg 1000_pic_245.jpg 1000_pic_246.jpg 1000_pic_247.jpg 1000_pic_248.jpg 1000_pic_248.jpg 1000_pic_248.jpg 1000_pic_248.jpg 1000_pic_249.jpg 1000_pic_249.jpg 1000_pic_249.jpg 1000_pic_250.jpg 1000_pic_251.jpg 1000_pic_252.jpg 1000_pic_253.jpg 1000_pic_254.jpg 1000_pic_255.jpg 1000_pic_256.jpg 1000_pic_257.jpg 1000_pic_257.jpg 1000_pic_257.jpg 1000_pic_258.jpg 1000_pic_259.jpg 1000_pic_259.jpg 1000_pic_259.jpg 1000_pic_260.jpg 1000_pic_261.jpg 1000_pic_262.jpg 1000_pic_263.jpg 1000_pic_263.jpg 1000_pic_263.jpg 1000_pic_264.jpg 1000_pic_264.jpg 1000_pic_264.jpg 1000_pic_265.jpg 1000_pic_266.jpg 1000_pic_267.jpg 1000_pic_268.jpg 1000_pic_269.jpg 1000_pic_269.jpg 1000_pic_269.jpg 1000_pic_269.jpg 1000_pic_269.jpg

278 | T E R R A M E D I A

| nicholas zembashi

275 275 183 259 299 300 300 299 285 288 288 317 183 282 275 200 213 259 259 259 272 194 194 259 261 293 221 275 259 259 275 174 278 259 271 271 275 265 283 259 299 250 250 250 300 259 275 300 259 259 259 259 299 299 299 275 299 271 225 259 183 275 225 225 225 285 300 300 300 196 220 195 192 192 192 300 300 300 258 242 261 300 219 219 219 219 219

183 1000_max 183 1000_max 275 1000_max 194 1000_max 168 1000_max 168 1000_max 168 1000_max 168 1000_max 177 1000_max 175 1000_max 175 person 159 1000_max 275 1000_max 179 1000_max 183 1000_max 150 1000_max 160 1000_max 194 1000_max 194 1000_max 194 person 185 1000_max 259 1000_max 259 person 194 1000_max 193 1000_max 172 1000_max 228 1000_max 183 1000_max 194 1000_max 194 1000_max 183 1000_max 290 1000_max 182 1000_max 194 1000_max 186 1000_max 186 1000_max 183 1000_max 190 1000_max 178 1000_max 194 1000_max 168 1000_max 190 1000_max 190 1000_max 190 1000_max 168 1000_max 194 1000_max 184 1000_max 168 1000_max 194 1000_max 194 person 194 person 194 person 168 person 168 person 168 1000_max 183 1000_max 168 1000_max 186 1000_max 225 1000_max 194 1000_max 275 1000_max 183 1000_max 225 1000_max 225 1000_max 225 1000_max 177 1000_max 168 1000_max 168 person 168 person 257 1000_max 165 1000_max 259 1000_max 262 1000_max 262 1000_max 262 1000_max 168 1000_max 168 1000_max 168 1000_max 195 1000_max 208 1000_max 193 1000_max 168 1000_max 231 1000_max 231 person 231 person 231 person 231 person

1 177 1 17 27 1 5 75 118 16 147 3 1 1 72 49 46 88 4 110 19 1 106 44 120 36 1 1 128 2 1 1 5 15 172 20 23 1 1 43 1 1 208 233 48 40 1 3 1 159 102 96 92 112 5 10 1 18 14 37 38 1 98 1 200 20 3 14 276 33 1 22 68 155 1 143 1 203 5 9 19 1 1 149 177 20 208

1 1 32 22 1 2 4 1 22 6 119 9 17 39 71 38 14 31 6 110 44 13 50 31 22 2 8 2 33 26 11 4 1 41 7 30 24 13 38 3 12 45 23 25 9 45 8 1 82 140 143 142 56 105 2 5 66 43 38 1 55 1 113 37 56 8 1 91 92 6 14 6 61 113 111 41 1 3 60 98 34 25 6 138 144 191 137

89 272 180 252 155 297 295 295 180 238 162 313 177 279 236 196 82 160 257 118 264 111 191 226 261 277 204 218 208 246 244 171 233 256 262 126 237 147 283 194 299 191 226 250 296 210 275 213 255 173 108 103 140 118 243 270 178 269 128 234 151 157 200 98 223 278 296 35 293 194 209 163 124 192 48 201 139 300 252 227 249 294 171 170 192 51 219

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M A R C H 2 018

1000_pic_270.jpg 1000_pic_270.jpg 1000_pic_271.jpg 1000_pic_272.jpg 1000_pic_273.jpg 1000_pic_274.jpg 1000_pic_275.jpg 1000_pic_276.jpg 1000_pic_277.jpg 1000_pic_278.jpg 1000_pic_279.jpg 1000_pic_280.jpg 1000_pic_281.jpg 1000_pic_282.jpg 1000_pic_283.jpg 1000_pic_284.jpg 1000_pic_285.jpg 1000_pic_286.jpg 1000_pic_286.jpg 1000_pic_287.jpg 1000_pic_287.jpg 1000_pic_288.jpg 1000_pic_289.jpg 1000_pic_290.jpg 1000_pic_291.jpg 1000_pic_292.jpg 1000_pic_293.jpg 1000_pic_294.jpg 1000_pic_295.jpg 1000_pic_296.jpg 1000_pic_297.jpg 1000_pic_298.jpg 1000_pic_298.jpg 1000_pic_299.jpg 100_pic_081.jpg 100_pic_082.jpg 100_pic_082.jpg 100_pic_082.jpg 100_pic_083.jpg 100_pic_084.jpg 100_pic_085.jpg 100_pic_086.jpg 100_pic_087.jpg 100_pic_088.jpg 100_pic_089.jpg 100_pic_090.jpg 100_pic_091.jpg 100_pic_091.jpg 100_pic_092.jpg 100_pic_093.jpg 100_pic_094.jpg 100_pic_095.jpg 100_pic_095.jpg 100_pic_096.jpg 100_pic_097.jpg 100_pic_098.jpg 100_pic_099.jpg 100_pic_100.jpg 100_pic_101.jpg 100_pic_102.jpg 100_pic_103.jpg 100_pic_104.jpg 100_pic_105.jpg 100_pic_106.jpg 100_pic_107.jpg 100_pic_108.jpg 100_pic_109.jpg 100_pic_110.jpg 100_pic_111.jpg 100_pic_112.jpg 100_pic_113.jpg 100_pic_114.jpg 100_pic_115.jpg 100_pic_116.jpg 100_pic_117.jpg 100_pic_118.jpg 100_pic_119.jpg 100_pic_120.jpg 100_pic_121.jpg 100_pic_122.jpg 100_pic_123.jpg 100_pic_124.jpg 100_pic_125.jpg 100_pic_127.jpg 100_pic_127.jpg 100_pic_128.jpg 100_pic_129.jpg

M A R C H 2 018

275 275 168 269 201 262 300 236 299 300 269 225 300 177 195 259 236 299 299 300 300 269 270 188 234 268 266 263 299 291 180 267 267 267 276 254 254 254 312 301 300 260 277 299 267 299 299 299 316 300 259 270 270 308 320 284 278 280 275 259 338 259 259 328 183 314 266 284 301 276 259 312 280 300 307 267 300 249 289 275 367 368 275 251 251 291 299

183 person 183 1000_max 300 1000_max 188 1000_max 251 1000_max 192 1000_max 168 1000_max 193 1000_max 168 1000_max 168 1000_max 187 1000_max 225 1000_max 168 1000_max 284 1000_max 259 1000_max 194 1000_max 184 1000_max 168 1000_max 168 1000_max 168 1000_max 168 1000_max 188 1000_max 187 1000_max 269 1000_max 216 1000_max 188 1000_max 189 1000_max 192 1000_max 168 1000_max 173 1000_max 281 1000_max 189 1000_max 189 1000_max 189 1000_max 183 100_deep198 100_deep198 100_deep198 100_deep161 100_deep167 100_deep168 100_deep194 100_deep182 100_deep168 100_deep189 100_deep168 100_deep169 100_deep169 100_deep159 100_deep168 100_deep194 100_deep186 100_deep186 100_deep163 100_deep158 100_deep177 100_deep181 100_deep180 100_deep183 100_deep194 100_deep149 100_deep194 100_deep194 100_deep154 100_deep275 100_deep161 100_deep190 100_deep177 100_deep167 100_deep183 100_deep194 100_deep162 100_deep180 100_deep168 100_deep164 100_deep189 100_deep168 100_deep202 100_deep174 100_deep183 100_deep137 100_deep137 100_deep183 100_deep201 100_deep201 100_deep173 100_deep169 100_deep-

119 1 56 55 58 131 1 26 52 1 20 1 1 19 1 19 53 103 1 127 209 73 109 5 8 41 46 1 78 55 6 1 243 77 9 58 69 135 19 118 52 14 13 76 18 21 56 95 1 1 1 91 138 1 5 20 1 10 28 1 36 4 1 2 5 34 1 65 21 1 1 88 1 17 77 1 35 47 32 109 61 33 33 14 199 43 26

65 1 72 13 27 9 9 42 77 5 2 1 82 32 9 44 19 69 89 37 66 35 55 6 5 28 2 35 23 27 22 32 64 20 83 82 66 67 31 3 67 47 37 6 4 10 63 45 6 65 1 38 90 17 53 69 32 66 16 102 27 4 81 1 67 2 92 27 36 42 93 30 46 13 9 103 48 1 6 1 18 37 53 2 123 23 4

145 271 167 266 197 244 297 233 242 191 266 221 138 161 192 193 206 297 218 157 300 160 161 170 229 230 255 239 273 286 175 242 267 256 272 179 136 201 298 289 226 248 257 225 256 299 122 217 253 272 257 156 242 305 297 283 219 236 217 259 338 254 256 327 146 306 265 267 274 264 224 269 277 290 301 266 277 249 260 272 360 322 201 182 241 242 258

134 180 238 175 243 141 95 162 121 128 181 224 167 182 250 185 152 124 168 71 112 108 113 253 199 120 120 107 118 124 214 108 104 176 137 146 90 92 93 164 145 162 145 138 171 110 81 67 142 116 192 123 127 82 117 110 155 110 180 124 94 169 151 122 244 150 158 135 106 128 129 68 127 143 95 180 151 172 120 169 121 94 137 195 175 127 113

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100_pic_130.jpg 100_pic_131.jpg 100_pic_132.jpg 100_pic_133.jpg 100_pic_133.jpg 100_pic_133.jpg 100_pic_134.jpg 100_pic_135.jpg 100_pic_136.jpg 100_pic_137.jpg 100_pic_138.jpg 100_pic_139.jpg 100_pic_140.jpg 100_pic_141.jpg 100_pic_142.jpg 100_pic_143.jpg 100_pic_144.jpg 100_pic_145.jpg 100_pic_146.jpg 100_pic_147.jpg 100_pic_148.jpg 100_pic_149.jpg 100_pic_150.jpg 100_pic_150.jpg 100_pic_150.jpg 100_pic_151.jpg 100_pic_152.jpg 100_pic_153.jpg 100_pic_154.jpg 100_pic_155.jpg 100_pic_156.jpg 100_pic_157.jpg 100_pic_158.jpg 100_pic_159.jpg 100_pic_160.jpg 100_pic_160.jpg 100_pic_161.jpg 100_pic_162.jpg 100_pic_163.jpg 100_pic_164.jpg 100_pic_164.jpg 100_pic_165.jpg 100_pic_166.jpg 100_pic_167.jpg 100_pic_168.jpg 100_pic_168.jpg 100_pic_168.jpg 100_pic_169.jpg 100_pic_170.jpg 100_pic_171.jpg 100_pic_172.jpg 100_pic_173.jpg 100_pic_174.jpg 100_pic_175.jpg 100_pic_175.jpg 100_pic_176.jpg 100_pic_177.jpg 100_pic_178.jpg 100_pic_179.jpg 100_pic_180.jpg 100_pic_181.jpg 100_pic_182.jpg 100_pic_182.jpg 100_pic_183.jpg 100_pic_183.jpg 100_pic_184.jpg 100_pic_185.jpg 100_pic_186.jpg 100_pic_186.jpg 100_pic_187.jpg 100_pic_188.jpg 100_pic_189.jpg 100_pic_190.jpg 100_pic_191.jpg 100_pic_192.jpg 100_pic_193.jpg 100_pic_194.jpg 100_pic_195.jpg 100_pic_196.jpg 100_pic_197.jpg 100_pic_198.jpg 100_pic_199.jpg 100_pic_200.jpg 100_pic_201.jpg 100_pic_202.jpg 100_pic_203.jpg 100_pic_204.jpg

280 | T E R R A M E D I A

| nicholas zembashi

308 321 246 259 259 259 229 275 299 320 282 305 333 276 275 275 318 275 310 275 275 275 275 275 275 288 187 279 295 327 225 275 271 300 377 377 298 275 275 259 259 301 186 275 275 275 275 277 274 276 275 287 287 366 366 358 312 259 259 328 281 183 183 296 296 328 343 259 259 275 275 290 259 299 321 266 267 259 291 270 303 275 259 300 467 258 291

163 100_deep157 100_deep205 100_deep194 100_deep194 100_deep194 person 220 100_deep183 100_deep168 100_deep157 100_deep179 100_deep165 100_deep151 100_deep183 100_deep183 100_deep183 100_deep159 100_deep183 100_deep163 100_deep183 100_deep183 100_deep183 100_deep183 100_deep183 100_deep183 100_deep175 100_deep270 100_deep181 100_deep171 100_deep154 100_deep225 100_deep183 100_deep186 100_deep168 100_deep134 100_deep134 100_deep169 100_deep183 100_deep183 100_deep194 100_deep194 100_deep167 100_deep271 100_deep183 100_deep183 100_deep183 100_deep183 100_deep182 100_deep184 100_deep182 100_deep183 100_deep176 100_deep176 100_deep138 100_deep138 100_deep141 100_deep162 100_deep194 100_deep195 100_deep154 100_deep179 100_deep275 100_deep275 100_deep170 100_deep170 100_deep154 100_deep147 100_deep194 100_deep194 person 183 100_deep183 100_deep174 100_deep194 100_deep168 100_deep157 100_deep190 100_deep188 100_deep194 100_deep173 100_deep187 100_deep166 100_deep183 100_deep194 100_deep168 100_deep108 100_deep195 100_deep173 100_deep-

2 14 66 65 175 173 106 112 1 1 23 1 73 1 44 94 10 1 15 34 8 36 149 61 47 2 6 8 5 25 32 2 1 31 71 1 4 17 30 50 181 1 24 10 27 64 106 6 119 28 16 10 32 119 200 4 93 2 7 1 18 11 34 87 25 20 1 48 63 34 26 1 40 1 12 19 16 20 59 55 3 24 3 35 81 1 29

9 46 7 72 5 152 1 1 1 1 21 24 74 26 57 78 36 40 21 41 2 6 9 72 129 6 24 61 1 3 35 18 48 29 1 35 90 39 19 60 1 2 36 47 49 79 100 5 14 12 45 59 21 51 5 52 1 33 4 12 6 50 1 14 13 59 44 51 126 63 1 11 5 7 40 64 6 63 24 26 80 29 14 67 1 58 60

308 318 243 146 258 182 228 273 186 307 245 303 179 169 178 265 278 272 246 161 273 242 243 148 61 287 184 277 238 295 193 273 269 236 320 83 295 253 234 221 258 268 173 274 158 198 252 273 274 267 204 279 272 198 355 343 307 191 256 311 272 103 181 275 76 315 333 258 76 203 244 268 243 262 312 239 252 253 252 250 298 254 185 265 413 256 177

149 114 205 155 162 177 220 162 130 124 166 149 107 146 105 149 132 132 80 127 172 174 177 174 170 167 264 142 92 154 193 146 141 93 117 90 140 96 103 123 128 131 216 113 94 122 162 177 138 123 169 158 171 87 95 110 162 194 174 150 179 225 227 96 35 140 107 132 178 156 174 145 184 118 117 125 161 138 108 166 132 160 133 111 101 158 150

M A R C H 2 018

100_pic_204.jpg 100_pic_205.jpg 100_pic_205.jpg 100_pic_206.jpg 100_pic_207.jpg 100_pic_208.jpg 100_pic_209.jpg 100_pic_210.jpg 100_pic_211.jpg 100_pic_212.jpg 100_pic_212.jpg 100_pic_212.jpg 100_pic_212.jpg 100_pic_213.jpg 100_pic_214.jpg 100_pic_215.jpg 100_pic_216.jpg 100_pic_217.jpg 100_pic_218.jpg 100_pic_219.jpg 100_pic_220.jpg 100_pic_220.jpg 100_pic_220.jpg 100_pic_220.jpg 100_pic_221.jpg 100_pic_222.jpg 100_pic_222.jpg 100_pic_223.jpg 100_pic_224.jpg 100_pic_225.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_226.jpg 100_pic_227.jpg 100_pic_228.jpg 100_pic_229.jpg 100_pic_229.jpg 100_pic_229.jpg 100_pic_229.jpg 100_pic_230.jpg 100_pic_231.jpg 100_pic_232.jpg 100_pic_233.jpg 100_pic_234.jpg 100_pic_235.jpg 100_pic_235.jpg 100_pic_236.jpg 100_pic_236.jpg 100_pic_237.jpg 100_pic_237.jpg 100_pic_237.jpg 100_pic_238.jpg 100_pic_239.jpg 100_pic_240.jpg 100_pic_241.jpg 100_pic_242.jpg 100_pic_243.jpg 100_pic_244.jpg 100_pic_245.jpg 100_pic_246.jpg 100_pic_247.jpg 100_pic_248.jpg 100_pic_249.jpg 100_pic_250.jpg 100_pic_250.jpg 100_pic_250.jpg 100_pic_251.jpg 100_pic_252.jpg 100_pic_252.jpg 100_pic_252.jpg 100_pic_253.jpg 100_pic_254.jpg 100_pic_255.jpg 100_pic_256.jpg 100_pic_257.jpg 100_pic_258.jpg 100_pic_259.jpg 100_pic_260.jpg 100_pic_261.jpg 100_pic_262.jpg 100_pic_263.jpg

M A R C H 2 018

291 265 265 224 297 307 225 298 275 275 275 275 275 278 278 275 252 310 284 300 284 284 284 284 313 268 268 265 225 273 301 301 301 301 301 301 301 301 301 299 263 298 298 298 298 299 310 299 276 205 305 305 259 259 293 293 293 304 278 355 290 259 262 299 251 230 327 301 275 300 300 300 285 225 225 225 275 275 299 275 275 299 259 259 372 339 289

173 100_deep190 100_deep190 100_deep224 100_deep170 100_deep164 100_deep225 100_deep169 100_deep183 100_deep183 100_deep183 100_deep183 100_deep183 100_deep181 100_deep181 100_deep183 100_deep200 100_deep162 100_deep177 100_deep168 100_deep177 100_deep177 100_deep177 100_deep177 100_deep161 100_deep188 100_deep188 100_deep190 100_deep225 100_deep185 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep168 100_deep191 100_deep169 100_deep169 100_deep169 100_deep169 100_deep168 100_deep162 100_deep168 100_deep183 100_deep246 100_deep165 100_deep165 100_deep194 100_deep194 100_deep172 100_deep172 100_deep172 100_deep166 100_deep181 100_deep142 100_deep174 100_deep194 100_deep192 100_deep169 100_deep201 100_deep219 100_deep154 100_deep167 100_deep183 100_deep168 100_deep168 100_deep168 100_deep177 100_deep224 100_deep224 100_deep224 100_deep183 100_deep183 100_deep168 100_deep183 100_deep183 100_deep169 100_deep194 100_deep194 100_deep135 100_deep148 100_deep174 100_deep-

78 27 91 145 28 74 50 8 4 27 29 131 233 6 6 19 21 1 1 138 30 113 169 77 45 21 172 30 65 58 58 143 116 25 37 81 154 135 224 23 4 128 223 95 64 51 21 1 38 6 21 24 89 70 1 1 1 19 7 16 27 42 22 1 13 20 27 3 1 3 99 211 3 109 36 8 83 2 108 25 1 30 1 24 53 48 52

27 71 90 1 27 1 7 56 46 5 4 27 52 19 18 21 26 22 55 1 88 86 90 115 32 75 23 61 20 3 100 101 111 67 46 32 36 24 18 22 86 9 55 41 92 1 33 38 17 178 46 47 58 7 34 2 84 14 29 27 32 12 1 13 20 1 8 27 38 79 65 87 17 90 89 98 82 46 71 31 23 66 28 48 3 56 47

213 216 208 222 275 238 171 283 266 274 130 236 275 278 270 235 242 293 284 296 97 171 244 169 229 220 250 265 225 272 144 244 224 204 197 162 224 214 275 271 250 223 289 126 224 299 258 245 273 199 296 256 181 123 227 280 217 297 278 322 206 228 260 244 239 228 323 285 275 209 246 287 258 224 114 39 218 275 240 247 267 292 256 183 372 320 221

59 110 152 214 151 145 179 106 139 126 131 119 121 169 158 183 185 119 121 122 136 116 147 138 133 166 162 158 202 178 129 130 149 103 70 49 55 42 31 132 189 102 147 97 146 167 109 117 177 233 97 81 175 134 105 83 124 145 165 82 130 154 175 117 201 204 150 133 147 149 88 121 175 195 190 177 183 145 152 134 163 133 109 131 133 112 153

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100_pic_263.jpg 100_pic_264.jpg 100_pic_264.jpg 100_pic_265.jpg 100_pic_266.jpg 100_pic_267.jpg 100_pic_268.jpg 100_pic_269.jpg 100_pic_270.jpg 100_pic_270.jpg 100_pic_270.jpg 100_pic_271.jpg 100_pic_272.jpg 100_pic_272.jpg 100_pic_273.jpg 100_pic_274.jpg 100_pic_275.jpg 100_pic_276.jpg 100_pic_277.jpg 100_pic_278.jpg 100_pic_279.jpg 100_pic_280.jpg 100_pic_281.jpg 100_pic_282.jpg 100_pic_283.jpg 100_pic_284.jpg 100_pic_285.jpg 100_pic_286.jpg 100_pic_287.jpg 100_pic_288.jpg 100_pic_289.jpg 100_pic_290.jpg 100_pic_291.jpg 100_pic_292.jpg 100_pic_293.jpg 100_pic_294.jpg 100_pic_295.jpg 100_pic_296.jpg 100_pic_297.jpg 100_pic_298.jpg 100_pic_299.jpg 100_pic_300.jpg 100_pic_301.jpg 100_pic_302.jpg 100_pic_303.jpg 100_pic_304.jpg 100_pic_305.jpg 100_pic_306.jpg 100_pic_306.jpg 100_pic_307.jpg 100_pic_308.jpg 100_pic_309.jpg 100_pic_310.jpg 100_pic_311.jpg 100_pic_312.jpg 100_pic_313.jpg 100_pic_314.jpg 100_pic_315.jpg 100_pic_315.jpg 100_pic_316.jpg 100_pic_317.jpg 100_pic_318.jpg 100_pic_319.jpg 100_pic_320.jpg 100_pic_321.jpg 100_pic_322.jpg 100_pic_323.jpg 100_pic_324.jpg 100_pic_325.jpg 100_pic_326.jpg 100_pic_327.jpg 100_pic_328.jpg 100_pic_328.jpg 100_pic_329.jpg 100_pic_329.jpg 100_pic_330.jpg 100_pic_331.jpg 100_pic_332.jpg 100_pic_333.jpg 100_pic_334.jpg 100_pic_335.jpg 100_pic_336.jpg 100_pic_337.jpg 100_pic_338.jpg 100_pic_339.jpg 100_pic_340.jpg 100_pic_341.jpg

282 | T E R R A M E D I A

| nicholas zembashi

289 318 318 312 308 275 345 333 288 288 288 305 259 259 183 276 287 182 277 275 260 275 255 276 246 312 275 300 300 358 328 284 275 274 384 282 259 200 262 258 252 276 299 278 277 309 329 259 259 263 259 311 300 294 300 194 266 318 318 259 281 327 300 311 340 293 319 299 282 227 275 300 300 274 274 299 300 301 275 295 259 205 322 251 285 269 294

174 100_deep158 100_deep158 100_deep162 100_deep163 100_deep183 100_deep146 100_deep151 100_deep175 100_deep175 100_deep175 100_deep165 100_deep194 100_deep194 100_deep275 100_deep183 100_deep176 100_deep276 100_deep182 100_deep183 100_deep194 100_deep183 100_deep197 100_deep183 100_deep205 100_deep162 100_deep183 100_deep168 100_deep168 100_deep141 100_deep154 100_deep177 100_deep184 100_deep184 100_deep131 100_deep179 100_deep194 100_deep252 100_deep192 100_deep195 100_deep200 100_deep183 100_deep169 100_deep181 100_deep182 100_deep163 100_deep153 100_deep194 100_deep194 100_deep192 100_deep194 100_deep162 100_deep168 100_deep171 100_deep168 100_deep259 100_deep190 100_deep159 100_deep159 100_deep194 100_deep179 100_deep154 100_deep168 100_deep162 100_deep148 100_deep172 100_deep158 100_deep168 100_deep179 100_deep222 100_deep183 100_deep168 100_deep168 100_deep184 100_deep184 100_deep168 100_deep168 100_deep167 100_deep183 100_deep171 100_deep194 100_deep246 100_deep156 100_deep201 100_deep177 100_deep187 100_deep172 100_deep-

222 193 1 79 95 43 15 1 56 198 15 31 1 141 20 32 1 14 25 1 24 11 42 39 36 1 43 38 113 83 1 5 71 5 116 1 33 62 21 1 1 1 29 7 1 34 8 1 139 21 44 5 18 66 1 1 1 25 204 30 17 101 16 7 50 19 7 86 7 9 14 30 11 130 9 25 1 13 11 1 30 36 1 28 63 6 1

52 5 2 18 54 42 22 18 50 106 108 1 1 38 57 15 17 9 32 51 39 1 72 19 1 5 1 44 1 1 11 1 34 12 12 40 1 44 61 1 59 31 53 1 77 29 17 57 54 43 1 31 24 2 33 4 15 8 42 53 1 1 67 27 35 35 80 19 32 62 50 42 1 4 72 1 67 8 20 66 12 154 73 8 45 38 59

263 312 113 311 273 226 269 314 189 270 272 300 142 257 141 261 284 151 274 268 229 267 235 239 186 310 264 297 291 297 320 136 253 269 367 278 231 139 240 254 251 276 299 276 277 291 317 119 258 238 225 303 295 256 129 182 244 173 315 258 235 270 282 301 318 279 306 267 265 227 264 300 300 246 129 299 298 301 228 295 247 145 319 226 281 253 292

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M A R C H 2 018

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M A R C H 2 018

294 294 281 281 270 290 259 302 276 265 308 308 275 275 294 276 259 284 286 204 271 275 259 328 275 259 288 274 300 302 328 279 299 248 410 410 299 386 386 386 259 275 221 413 413 244 275 312 259 271 299 299 299 299 351 312 285 261 259 266 275 300 300 300 300 183 275 275 275 275 188 188 259 254 259 259 327 327 266 275 259 259 259 275 275 275 275

172 100_deep172 100_deep179 100_deep179 100_deep187 100_deep174 100_deep194 100_deep167 100_deep183 100_deep190 100_deep163 100_deep163 100_deep183 100_deep183 100_deep171 100_deep183 100_deep194 100_deep177 100_deep176 100_deep246 100_deep186 100_deep183 100_deep194 100_deep154 100_deep183 100_deep194 100_deep175 100_deep184 100_deep168 100_deep167 100_deep154 100_deep181 100_deep168 100_deep203 100_deep123 100_deep123 100_deep169 100_deep131 100_deep131 100_deep131 100_deep194 100_deep183 100_deep228 100_deep122 100_deep122 100_deep207 100_deep183 100_deep162 100_deep194 100_deep186 100_deep168 100_deep168 100_deep168 100_deep168 person 144 100_deep162 100_deep177 100_deep193 100_deep194 100_deep190 100_deep183 100_deep168 100_deep168 100_deep168 100_deep168 100_deep275 100_deep183 100_deep183 100_deep183 100_deep183 100_deep268 100_deep268 100_deep194 100_deep199 100_deep194 100_deep194 100_deep154 100_deep154 100_deep190 100_deep183 100_deep194 10_social 194 person 194 person 183 10_social 183 10_social 183 person 183 person

1 189 20 150 13 1 1 58 69 44 1 156 50 24 25 1 17 33 35 33 11 71 26 16 1 19 12 49 5 41 135 25 89 40 200 1 13 44 45 207 22 24 2 224 1 8 36 3 28 36 93 226 1 245 28 25 34 1 1 1 28 18 19 127 147 67 1 189 16 45 68 1 1 4 1 1 4 5 48 9 1 125 108 40 22 83 160

60 81 92 19 62 2 1 1 30 10 21 13 76 59 53 24 1 100 73 12 22 16 10 8 34 29 50 5 61 4 1 9 3 63 13 4 2 3 33 2 36 38 23 13 1 7 29 10 27 57 59 73 91 121 4 21 32 39 6 47 1 26 29 61 34 4 1 66 35 34 6 206 12 1 44 19 61 60 38 37 1 104 114 3 1 97 101

188 294 120 281 260 276 224 278 254 227 306 291 250 268 256 167 250 232 153 182 263 256 231 322 275 255 174 200 300 230 326 241 284 213 410 185 260 185 130 368 256 244 80 405 220 240 232 293 218 244 226 299 109 263 334 290 283 245 240 265 271 257 155 209 256 172 184 275 261 229 133 66 227 233 259 144 312 232 206 161 259 150 130 275 275 94 182

147 145 142 147 104 161 185 164 117 183 150 51 140 117 120 147 189 146 123 245 169 127 179 154 145 136 118 182 103 166 154 180 116 203 100 94 169 74 76 104 169 143 211 92 101 185 133 158 188 142 132 144 127 164 144 158 133 163 141 154 172 126 122 133 94 244 160 151 115 183 247 242 120 187 170 146 127 81 109 154 177 187 184 171 144 148 150

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284 | T E R R A M E D I A

275 275 275 275 275 275 343 407 275 275 275 275 275 276 276 276 276 276 276 276 276 276 276 276 276 259 275 275 194 276 284 274 274 259 259 259 400 400 400 400 400 400 400 400 275 181 275 275 275 275 275 275 275 225 275 275 275 275 275 275 260 260 260 294 294 294 275 275 275 275 275 275 275 247 247 247 259 259 259 262 274 259 259 259 259 220 220

| nicholas zembashi

183 person 183 person 183 person 183 person 183 person 183 10_social 147 10_social 124 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 183 person 194 10_social 183 10_social 183 person 259 10_social 183 person 177 person 184 person 184 person 194 10_social 194 10_social 194 10_social 126 10_social 126 10_social 126 10_social 126 10_social 126 10_social 126 person 126 person 126 person 183 10_social 278 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 225 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 194 10_social 194 10_social 194 10_social 171 10_social 171 10_social 171 10_social 183 10_social 183 person 183 person 183 person 183 10_social 183 10_social 183 10_social 204 10_social 204 person 204 person 194 10_social 194 10_social 194 10_social 192 10_social 184 10_social 194 10_social 194 10_social 194 10_social 194 10_social 159 10_social 159 10_social

188 89 11 39 193 22 29 46 1 26 107 115 42 101 51 101 175 199 113 141 165 195 51 37 1 157 155 115 2 13 92 2 124 55 9 151 90 172 232 251 317 209 57 175 84 17 20 178 198 34 54 142 155 57 1 161 55 31 22 194 128 151 127 99 142 127 87 99 79 68 103 15 141 1 69 98 81 113 40 16 1 72 109 97 157 1 120

102 57 45 50 1 10 6 23 2 1 1 91 89 1 22 73 76 62 106 110 108 110 108 102 104 135 25 81 2 1 1 2 74 58 106 1 46 32 15 29 32 55 60 91 91 144 1 104 103 103 104 105 105 55 19 42 30 50 93 1 50 112 114 25 85 85 4 122 118 114 58 113 23 2 158 162 65 133 141 2 1 50 98 101 2 1 37

205 121 43 68 273 247 292 354 86 274 275 140 71 270 146 142 211 230 143 170 194 224 73 58 25 217 275 153 194 276 284 274 167 127 152 259 143 218 259 304 369 270 110 204 165 169 189 198 215 48 66 152 164 192 261 237 87 90 122 275 224 260 153 194 246 146 272 116 99 83 244 27 204 97 94 122 192 257 203 262 257 146 152 110 259 40 220

158 166 178 149 172 158 124 123 183 172 157 157 169 110 77 102 101 84 157 159 155 164 142 138 142 177 129 183 258 181 141 143 172 75 150 170 78 64 40 63 63 125 119 126 183 262 119 170 164 148 147 138 137 148 179 83 36 73 177 177 64 168 162 42 128 116 148 174 179 162 118 144 45 193 204 204 82 181 182 140 127 62 136 132 178 125 125

M A R C H 2 018

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M A R C H 2 018

250 283 0 266 261 259 259 259 275 194 194 194 194 194 194 259 284 275 275 275 277 266 275 291 291 291 259 259 275 300 300 300 300 300 300 300 300 300 300 300 275 275 275 275 275 275 275 275 275 275 275 275 275 291 290 275 275 275 275 275 275 275 275 275 194 194 194 194 194 194 275 275 275 275 263 263 263 263 263 259 294 294 294 294 290 290 290

167 10_social 178 10_social 0 10_social 189 10_social 193 10_social 194 10_social 194 10_social 194 10_social 183 10_social 259 10_social 259 person 259 person 259 person 259 person 259 person 194 10_social 177 10_social 183 10_social 183 10_social 183 10_social 182 10_social 190 10_social 183 10_social 173 10_social 173 10_social 173 person 194 10_social 194 10_social 183 10_social 168 10_social 168 10_social 168 person 168 person 168 person 168 person 168 person 168 person 168 person 168 person 168 person 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 10_social 173 10_social 174 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 10_social 259 10_social 259 10_social 259 person 259 person 259 person 259 person 183 10_social 183 10_social 183 10_social 183 10_social 191 10_social 191 10_social 191 person 191 person 191 person 194 10_social 172 10_social 172 person 172 person 172 person 174 10_social 174 10_social 174 10_social

12 14 1 105 113 2 127 179 2 15 70 62 54 40 26 108 1 6 91 4 46 5 31 165 122 167 1 146 49 1 92 8 35 62 122 147 189 203 170 274 189 162 223 185 156 123 138 149 101 62 81 89 4 68 1 94 123 81 39 50 35 70 178 116 57 144 92 124 115 154 106 140 151 193 13 109 106 139 223 1 161 135 75 232 126 109 80

1 5 1 24 65 81 70 79 2 17 211 210 209 206 211 2 58 8 110 96 68 36 5 1 102 127 9 36 1 1 1 80 80 82 81 82 81 79 75 72 65 93 102 108 103 104 105 102 103 106 108 108 1 3 12 1 107 96 97 98 97 92 101 1 84 149 188 188 175 177 96 145 142 1 2 12 107 93 75 2 36 64 77 60 52 82 93

249 245 247 237 249 68 170 233 275 193 83 72 64 49 36 259 79 182 188 87 263 266 270 291 175 187 147 211 254 103 219 39 63 98 146 164 202 217 180 294 251 275 244 203 172 140 150 160 125 79 91 104 266 254 197 274 142 103 51 61 65 99 204 275 152 168 113 140 123 165 183 152 237 275 112 230 129 167 244 259 196 167 114 256 196 202 110

167 173 167 132 138 134 140 128 162 244 248 242 247 241 237 164 155 107 179 164 113 187 121 164 122 170 172 156 107 108 86 168 168 168 130 131 114 115 109 131 83 150 165 158 167 169 168 163 169 150 149 150 153 72 129 136 169 162 149 142 174 172 183 143 106 199 221 219 201 207 110 175 183 183 154 112 170 165 112 192 99 156 138 146 67 139 130

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286 | T E R R A M E D I A

290 290 290 259 300 300 259 186 275 300 300 300 275 260 260 260 260 275 275 259 200 259 259 259 259 259 259 259 259 259 259 259 274 332 275 275 259 259 259 259 279 275 275 183 324 324 260 260 260 260 260 260 259 299 257 285 285 285 285 285 275 275 275 275 275 275 275 275 275 275 275 275 275 259 259 259 259 259 194 194 276 300 300 259 276 276 276

| nicholas zembashi

174 person 174 person 174 person 194 10_social 168 10_social 168 person 194 10_social 271 10_social 183 10_social 168 10_social 168 10_social 168 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 10_social 183 10_social 183 10_social 194 10_social 200 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 10_social 194 10_social 194 10_social 194 10_social 184 10_social 152 10_social 183 10_social 183 person 194 10_social 194 person 194 10_social 194 10_social 181 10_social 183 10_social 183 person 275 10_social 155 10_social 155 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 10_social 168 10_social 196 10_social 177 10_social 177 person 177 person 177 person 177 person 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 259 10_social 259 person 183 10_social 168 10_social 168 person 194 10_social 183 10_social 183 10_social 183 10_social

91 79 177 28 233 121 10 17 69 138 190 174 68 74 100 113 194 92 1 1 15 77 111 90 67 164 33 10 2 20 72 2 22 7 1 218 55 163 2 42 3 1 224 2 89 28 107 115 113 126 164 35 1 9 44 99 190 219 161 131 9 152 140 195 225 99 109 19 84 216 246 199 84 27 62 95 5 2 1 124 2 20 161 38 76 108 82

111 113 105 9 34 59 11 13 81 15 77 77 31 49 121 117 2 78 1 1 17 56 128 129 157 159 157 155 52 102 2 3 1 50 1 79 27 145 6 14 1 1 98 3 2 39 49 80 118 119 122 119 1 22 14 1 73 69 77 79 69 111 98 113 103 111 16 21 96 146 80 69 1 1 48 2 98 1 1 159 1 3 56 15 5 74 76

102 89 194 235 300 175 259 174 195 245 300 192 149 176 112 183 257 185 199 242 198 194 259 113 89 183 47 26 26 74 254 257 270 325 211 261 259 182 259 242 257 129 254 183 324 89 189 260 127 144 178 47 200 265 219 285 219 237 192 162 98 187 167 231 252 129 199 111 111 252 268 215 165 259 128 258 127 255 97 142 274 256 191 226 187 194 108

142 143 153 178 138 168 146 113 107 35 130 116 54 67 148 156 190 102 168 95 173 73 181 178 194 194 194 194 148 148 131 192 168 91 183 151 169 184 155 162 165 127 160 187 94 85 60 149 167 161 167 156 156 142 157 90 173 161 164 159 108 133 117 135 120 134 89 79 162 181 166 112 108 151 70 165 141 163 256 214 116 144 156 173 27 119 123

M A R C H 2 018

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M A R C H 2 018

276 276 187 187 187 300 300 300 300 300 300 300 300 300 300 275 275 260 260 260 260 260 260 260 260 275 275 275 275 275 183 259 225 275 416 416 275 275 259 259 259 259 259 259 259 259 259 259 259 259 259 259 299 299 299 259 259 259 259 262 194 275 183 275 273 273 273 273 260 275 259 259 259 259 275 276 278 225 225 225 225 259 259 259 259 275 266

183 person 183 person 270 10_social 270 10_social 270 person 168 10_social 168 10_social 168 person 168 person 168 person 168 person 168 person 168 person 168 person 168 person 183 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 person 183 10_social 183 10_social 183 person 183 person 183 person 275 person 194 10_social 225 10_social 183 10_social 121 person 121 person 183 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 person 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 168 10_social 168 10_social 168 10_social 194 10_social 194 10_social 194 10_social 194 10_social 192 10_social 259 10_social 183 10_social 276 10_social 183 10_social 185 10_social 185 10_social 185 10_social 185 person 194 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 10_social 183 10_social 183 10_social 181 10_social 225 10_social 225 10_social 225 10_social 225 person 194 10_social 194 10_social 194 10_social 194 person 183 10_social 190 10_social

132 3 44 16 56 56 187 56 191 109 129 152 214 249 95 1 25 66 86 180 112 128 93 85 46 1 70 191 205 251 121 155 27 30 65 177 1 1 76 130 188 37 62 133 172 126 125 183 76 49 30 68 67 2 130 33 18 126 3 1 11 60 2 4 85 120 136 130 22 1 82 139 124 3 9 42 101 80 91 41 49 3 83 182 234 4 2

88 99 104 44 178 1 63 80 84 75 81 84 85 79 82 1 2 59 127 5 149 153 148 149 144 73 50 114 112 109 67 1 47 10 39 41 49 1 47 61 29 83 87 89 92 89 26 3 97 114 116 117 62 52 50 4 57 62 68 1 10 2 27 2 27 102 105 123 4 1 47 95 47 23 10 1 40 70 122 33 129 10 39 92 113 7 24

154 32 181 168 82 228 300 77 216 134 148 168 240 280 113 182 273 166 169 260 125 137 101 93 65 66 244 206 219 273 152 259 194 209 105 212 162 220 102 259 259 57 83 180 205 144 213 259 212 61 43 79 130 69 227 259 255 191 140 262 192 275 183 240 204 130 273 194 257 274 166 247 259 60 275 275 175 168 221 213 72 82 132 257 244 268 46

172 170 235 64 254 113 117 143 147 158 157 145 151 158 146 156 169 77 172 191 180 183 178 175 184 182 147 150 145 177 153 163 177 160 121 120 173 69 100 125 129 136 159 194 165 155 48 155 136 151 149 151 112 121 108 174 126 141 156 158 239 162 234 130 44 148 155 185 148 171 59 144 185 181 149 179 52 84 176 164 195 182 162 134 144 168 69

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287

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288 | T E R R A M E D I A

275 183 183 287 287 287 287 287 287 194 259 275 275 259 262 262 259 259 259 259 259 259 259 259 259 259 286 286 286 294 269 220 225 225 225 225 259 259 259 259 225 225 225 375 275 289 259 299 259 259 259 259 259 274 225 276 276 263 263 263 263 263 276 276 276 276 276 276 276 276 276 276 276 259 275 275 275 275 275 275 275 275 260 260 260 275 275

| nicholas zembashi

183 person 275 10_social 275 person 175 10_social 175 10_social 175 person 175 person 175 person 175 person 259 10_social 194 10_social 183 10_social 183 10_social 194 10_social 192 10_social 192 person 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 person 194 person 194 10_social 176 10_social 176 10_social 176 10_social 171 10_social 187 10_social 147 10_social 225 10_social 225 10_social 225 10_social 225 person 194 10_social 194 10_social 194 10_social 194 10_social 225 10_social 225 10_social 225 person 134 10_social 183 10_social 174 10_social 194 10_social 168 10_social 194 10_social 194 person 194 person 194 person 194 10_social 184 10_social 224 10_social 183 10_social 183 person 192 10_social 192 10_social 192 10_social 192 10_social 192 10_social 182 10_social 182 10_social 182 10_social 182 person 182 person 182 person 182 person 182 person 182 person 182 person 182 person 194 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 194 10_social 194 person 194 person 183 10_social 183 10_social

205 3 56 3 181 254 105 150 175 1 2 126 64 46 1 17 1 16 220 229 88 153 184 111 70 19 63 8 218 2 30 25 116 98 2 11 89 98 182 99 45 33 70 1 43 21 3 94 2 174 1 136 8 107 67 76 158 161 9 14 109 149 132 165 28 78 82 122 136 148 188 216 231 2 129 122 99 186 66 37 119 156 57 28 49 17 53

130 4 143 2 24 66 71 73 68 19 89 65 52 23 102 113 2 1 49 96 97 98 99 98 98 5 39 45 56 7 27 59 38 133 85 83 69 106 2 42 1 93 110 2 4 4 1 6 7 78 69 97 17 79 55 41 20 61 92 123 132 121 56 67 66 88 50 84 84 84 85 85 93 33 59 84 87 1 104 107 117 123 11 125 127 3 3

213 182 109 91 269 287 125 170 198 194 103 228 131 218 125 65 259 226 259 259 108 175 200 126 84 208 225 68 286 293 269 180 225 125 49 93 155 196 259 205 185 196 137 256 218 194 212 256 185 233 71 171 241 199 192 166 198 232 76 96 160 194 168 191 87 130 126 143 151 164 213 233 257 257 183 210 121 275 86 58 147 188 259 45 70 262 273

165 201 248 98 74 174 97 122 120 231 191 144 143 167 167 175 190 118 109 191 155 150 154 139 139 181 140 120 128 114 154 112 151 162 180 187 81 144 177 119 22 160 225 133 173 166 181 128 115 190 194 194 178 125 137 103 182 110 135 192 163 141 98 95 98 182 149 177 138 136 146 142 145 173 71 120 120 139 165 165 157 157 163 179 188 165 116

M A R C H 2 018

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M A R C H 2 018

259 259 259 275 183 183 271 271 271 271 275 259 259 259 259 259 259 273 273 266 266 260 260 268 259 259 275 275 275 272 259 275 275 275 259 259 259 259 259 259 259 259 259 259 259 259 275 217 294 294 183 183 318 318 318 318 318 310 310 180 270 270 267 267 267 267 194 276 300 300 300 275 209 209 209 277 275 291 291 291 291 291 291 291 291 291 291

194 person 194 person 194 person 183 10_social 276 10_social 276 person 186 10_social 186 10_social 186 10_social 186 10_social 183 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 184 10_social 184 10_social 189 10_social 189 10_social 194 10_social 194 10_social 188 10_social 194 10_social 194 10_social 183 10_social 183 10_social 183 10_social 185 10_social 194 10_social 183 10_social 183 10_social 183 person 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 person 194 10_social 194 10_social 183 10_social 232 10_social 171 10_social 171 person 275 10_social 275 person 159 10_social 159 10_social 159 10_social 159 person 159 person 162 10_social 162 10_social 280 10_social 187 10_social 187 10_social 189 10_social 189 10_social 189 person 189 person 259 10_social 183 10_social 168 10_social 168 10_social 168 10_social 184 10_social 241 10_social 241 10_social 241 10_social 182 10_social 183 10_social 173 10_social 173 10_social 173 10_social 173 person 173 person 173 person 173 person 173 person 173 person 173 person

97 169 32 132 5 50 1 67 60 165 80 39 93 70 94 112 154 143 226 1 81 109 80 66 99 90 48 198 138 30 4 8 172 114 35 160 1 5 1 13 96 145 130 112 1 96 2 70 47 157 4 84 67 48 14 74 239 91 23 1 76 99 71 124 128 151 19 104 146 33 2 3 52 99 78 10 31 1 167 204 241 166 197 155 114 138 76

109 108 103 71 7 121 1 144 167 153 1 51 69 97 94 94 91 94 15 10 112 53 89 11 51 97 22 58 66 70 26 3 24 98 1 43 1 1 35 104 106 108 108 114 51 48 1 63 50 139 49 121 12 59 65 67 58 19 44 58 60 107 24 93 122 123 53 107 36 77 87 1 56 131 133 72 1 2 35 40 69 60 66 62 58 60 61

122 198 69 173 183 119 214 93 89 197 220 57 102 85 103 119 168 273 273 169 93 177 211 232 176 221 236 253 175 205 252 173 262 137 254 249 215 235 206 38 119 162 144 126 93 259 275 147 193 170 80 113 149 201 46 106 290 302 91 165 159 220 183 265 146 170 191 134 236 135 59 271 163 164 103 259 274 101 207 253 269 196 214 176 138 153 100

171 192 192 105 249 276 186 164 182 170 92 126 98 150 122 119 140 179 80 142 153 71 135 188 65 140 56 163 102 127 144 134 128 150 193 194 172 145 133 171 161 133 141 145 133 119 163 86 159 171 151 185 31 113 112 150 159 143 102 249 75 155 42 156 178 180 187 113 164 142 163 111 75 184 182 161 107 88 65 92 146 166 140 157 160 141 152

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289

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290 | T E R R A M E D I A

| nicholas zembashi

275 259 259 236 275 275 286 276 290 290 292 292 259 259 263 300 266 284 271 271 300 300 318 300 300 300 292 275 259 275 275 275 264 311 300 318 275 267 282 282 278 278 288 275 290 290 275 275 372 275 275 275 275 286 300 292 292 275 194 284 284 259 405 322 300 318 275 275 275 259 182 300 209 209 275 270 251 275 311 283 283 250 250 354 272 272 275

183 continental 194 continental 194 continental 213 continental 183 continental 183 continental 176 continental 183 continental 174 continental 174 continental 173 continental 173 continental 194 continental 194 person 191 continental 168 continental 190 continental 177 continental 186 continental 186 continental 168 continental 168 continental 159 continental 168 continental 168 continental 168 continental 173 continental 183 continental 194 continental 183 continental 183 continental 183 continental 191 continental 162 continental 168 continental 159 continental 183 continental 189 continental 179 continental 179 continental 181 continental 181 continental 175 continental 183 continental 174 continental 174 continental 183 continental 183 continental 136 continental 183 continental 183 person 183 person 183 person 176 continental 168 continental 173 continental 173 continental 183 continental 259 continental 177 continental 177 continental 194 continental 124 continental 157 continental 168 continental 159 continental 183 continental 183 continental 183 continental 194 continental 278 continental 168 continental 241 continental 241 continental 183 continental 187 continental 201 continental 183 continental 162 continental 178 continental 178 continental 175 continental 175 continental 142 continental 186 continental 186 continental 183 continental

1 21 1 46 61 225 7 1 1 246 94 1 1 58 54 36 57 1 1 148 1 180 47 2 81 6 11 5 1 92 1 54 49 128 1 13 33 48 19 235 53 123 2 2 77 1 95 1 61 1 163 120 10 38 115 61 2 1 1 1 166 3 95 1 79 23 72 170 3 1 1 8 11 62 3 1 56 11 54 66 1 67 1 37 28 126 119

14 13 127 6 1 85 2 9 2 34 1 116 6 138 22 6 26 1 45 2 5 33 1 2 63 10 1 34 91 1 38 104 39 3 1 5 60 5 4 2 35 57 8 2 7 65 19 97 33 1 97 94 95 6 35 2 21 1 94 2 1 61 44 1 1 1 64 2 1 3 3 1 8 184 4 43 29 29 7 21 96 2 102 2 86 11 1

226 247 62 203 234 262 285 274 150 290 292 100 259 144 202 251 209 284 148 271 137 295 226 145 227 255 282 178 257 273 135 89 234 270 201 318 269 156 235 282 144 217 123 264 190 277 265 97 360 238 197 156 43 275 220 172 93 272 192 125 284 167 325 276 259 295 169 275 100 249 137 250 99 98 275 221 213 214 214 248 87 250 68 354 131 250 253

183 164 173 210 160 151 176 171 142 116 172 170 183 194 130 156 158 174 173 178 168 165 144 168 131 126 163 183 193 183 167 136 191 138 168 157 183 189 163 162 76 89 124 183 74 123 164 155 105 103 183 183 183 123 112 99 99 161 257 173 173 167 117 156 153 159 124 110 148 191 261 163 201 229 153 164 124 141 162 164 167 140 151 133 164 159 120

M A R C H 2 018

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M A R C H 2 018

275 275 433 433 231 275 297 297 270 287 287 275 259 327 327 474 283 283 283 283 284 289 284 274 300 300 275 275 318 318 318 271 310 310 259 259 275 190 275 292 275 275 483 276 259 259 253 284 276 194 348 275 225 225 274 274 183 275 294 290 290 308 199 183 183 275 238 270 199 275 275 292 284 284 331 300 300 292 292 328 189 284 275 258 258 392 392

183 continental 183 continental 116 continental 116 continental 219 continental 183 continental 170 continental 170 continental 187 continental 175 continental 175 continental 183 continental 194 continental 154 continental 154 continental 106 continental 178 continental 178 continental 178 continental 178 continental 177 continental 175 continental 177 continental 184 continental 168 continental 168 continental 183 continental 183 continental 159 continental 159 continental 159 continental 186 continental 163 continental 163 continental 194 continental 194 continental 183 continental 266 continental 183 continental 173 continental 183 continental 183 continental 104 continental 183 continental 194 continental 194 continental 199 continental 177 continental 183 continental 259 continental 145 continental 183 continental 225 continental 225 continental 184 continental 184 continental 275 continental 183 continental 171 continental 174 continental 174 continental 163 continental 254 continental 275 continental 275 continental 183 continental 212 continental 187 continental 254 continental 183 continental 183 continental 173 continental 177 continental 177 continental 152 continental 168 continental 168 person 173 continental 173 continental 154 continental 267 continental 177 continental 183 continental 195 continental 195 continental 129 continental 129 continental

87 1 65 131 116 5 3 42 1 101 1 1 41 97 132 3 58 153 206 1 5 22 1 31 122 1 30 199 72 1 46 15 38 220 1 168 5 60 15 16 62 185 130 47 1 140 25 1 1 26 111 33 1 174 78 4 5 1 192 22 39 26 21 12 94 13 3 11 31 107 25 1 95 1 11 89 213 14 220 34 29 6 48 12 145 110 185

34 73 2 2 20 1 17 4 14 46 100 2 11 39 31 1 58 36 56 27 38 1 13 5 22 1 76 83 67 6 7 1 48 6 98 8 4 67 6 3 9 6 4 12 1 30 19 1 5 14 1 77 40 3 3 61 1 1 5 1 1 37 28 10 187 11 1 3 28 40 56 5 8 97 7 44 39 10 36 12 19 50 3 19 32 3 16

203 86 185 424 169 269 279 142 259 286 116 233 248 232 327 473 115 264 283 57 284 229 278 271 262 166 183 275 232 113 318 270 209 275 171 259 255 163 272 292 161 275 432 230 79 172 204 284 203 170 310 261 173 225 187 90 183 240 269 290 236 308 199 74 109 269 174 255 171 187 142 247 284 96 311 300 295 219 292 288 189 284 272 257 221 388 218

120 122 116 111 105 176 160 84 144 127 131 182 170 129 151 105 138 83 134 136 175 163 144 168 132 145 142 119 109 76 157 167 132 45 186 189 179 144 181 109 176 173 104 147 163 50 150 175 178 243 130 163 180 179 158 153 252 156 136 148 169 116 176 62 197 127 212 169 197 97 110 144 146 147 130 132 168 173 151 131 241 141 171 195 63 129 60

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292 | T E R R A M E D I A

| nicholas zembashi

259 259 172 318 259 259 259 183 183 274 290 299 299 194 194 194 194 194 194 265 265 265 364 275 275 275 275 275 262 299 299 299 299 299 299 340 194 279 279 264 299 243 329 187 299 299 299 299 259 177 177 259 190 309 309 286 300 300 274 192 192 192 192 299 299 299 309 309 300 300 267 272 272 194 257 275 275 300 300 300 300 276 259 259 221 312 312

194 global 194 global 294 global 159 global 194 global 194 global 194 global 275 global 275 global 184 global 174 global 168 global 168 global 259 global 259 global 259 person 259 person 259 person 259 global 190 global 190 global 190 global 139 global 183 global 183 global 183 global 183 global 183 global 192 global 168 global 168 global 168 global 168 global 168 global 168 global 148 global 259 global 181 global 181 global 191 global 168 global 207 global 153 global 270 global 168 global 168 global 168 global 168 global 194 global 284 global 284 global 194 global 265 global 163 global 163 global 176 global 168 global 168 global 184 global 262 global 262 global 262 global 262 global 168 global 168 global 168 global 163 global 163 global 168 global 168 global 189 global 185 global 185 global 259 global 196 global 183 global 183 global 168 global 168 global 168 global 168 global 183 global 194 global 194 global 228 global 162 global 162 global

65 1 29 77 47 139 41 29 1 77 1 70 131 76 1 159 121 105 30 142 1 80 1 68 2 25 176 94 71 119 1 220 40 138 220 1 1 150 1 16 57 34 33 40 95 157 242 16 61 19 31 39 31 100 110 69 94 93 85 32 115 74 58 119 29 211 88 102 117 9 61 171 10 60 2 170 1 34 135 233 3 1 76 1 1 2 177

12 110 14 27 49 140 148 35 111 26 62 1 94 3 78 191 196 192 13 10 9 107 1 38 11 1 5 64 40 54 1 1 55 6 81 51 1 5 98 7 26 35 18 40 25 1 36 40 1 45 199 8 10 70 113 8 84 47 65 96 158 16 178 12 78 79 52 118 32 108 7 6 103 10 1 2 94 24 22 47 71 1 9 72 117 20 2

259 93 138 246 108 224 146 168 29 195 290 217 299 144 161 182 143 124 159 242 81 145 127 221 223 178 253 268 203 216 113 299 115 224 262 339 129 279 244 247 163 139 97 163 169 226 285 53 212 128 177 206 172 132 156 256 139 203 197 114 182 119 187 211 135 278 132 167 215 130 244 236 213 161 257 251 217 124 232 300 43 221 223 71 220 180 312

191 194 270 98 149 162 163 187 192 140 173 160 167 165 193 259 259 258 245 129 122 127 137 147 183 163 86 158 113 168 168 159 168 168 168 135 174 168 179 127 168 126 134 270 152 123 117 143 157 223 237 194 259 122 125 130 168 148 148 208 180 58 212 127 166 134 129 134 166 168 178 142 171 210 195 151 153 121 106 104 124 178 168 162 170 106 109

M A R C H 2 018

1200_pic_198.jpg 1200_pic_198.jpg 1200_pic_199.jpg 1200_pic_200.jpg 1200_pic_200.jpg 1200_pic_200.jpg 1200_pic_201.jpg 1200_pic_202.jpg 1200_pic_203.jpg 1200_pic_203.jpg 1200_pic_204.jpg 1200_pic_204.jpg 1200_pic_204.jpg 1200_pic_204.jpg 1200_pic_205.jpg 1200_pic_206.jpg 1200_pic_207.jpg 1200_pic_207.jpg 1200_pic_208.jpg 1200_pic_209.jpg 1200_pic_210.jpg 1200_pic_211.jpg 1200_pic_212.jpg 1200_pic_213.jpg 1200_pic_214.jpg 1200_pic_214.jpg 1200_pic_215.jpg 1200_pic_216.jpg 1200_pic_217.jpg 1200_pic_218.jpg 1200_pic_218.jpg 1200_pic_218.jpg 1200_pic_219.jpg 1200_pic_220.jpg 1200_pic_220.jpg 1200_pic_220.jpg 1200_pic_221.jpg 1200_pic_222.jpg 1200_pic_222.jpg 1200_pic_222.jpg 1200_pic_223.jpg 1200_pic_223.jpg 1200_pic_224.jpg 1200_pic_224.jpg 1200_pic_224.jpg 1200_pic_224.jpg 1200_pic_225.jpg 1200_pic_225.jpg 1200_pic_226.jpg 1200_pic_226.jpg 1200_pic_227.jpg 1200_pic_228.jpg 1200_pic_229.jpg 1200_pic_230.jpg 1200_pic_231.jpg 1200_pic_232.jpg 1200_pic_233.jpg 1200_pic_234.jpg 1200_pic_235.jpg 1200_pic_236.jpg 1200_pic_237.jpg 1200_pic_237.jpg 1200_pic_238.jpg 1200_pic_238.jpg 1200_pic_238.jpg 1200_pic_239.jpg 1200_pic_240.jpg 1200_pic_240.jpg 1200_pic_241.jpg 1200_pic_242.jpg 1200_pic_243.jpg 1200_pic_243.jpg 1200_pic_244.jpg 1200_pic_245.jpg 1200_pic_245.jpg 1200_pic_246.jpg 1200_pic_246.jpg 1200_pic_247.jpg 1200_pic_248.jpg 1200_pic_249.jpg 1200_pic_250.jpg 1200_pic_251.jpg 1200_pic_252.jpg 1200_pic_252.jpg 1200_pic_252.jpg 1200_pic_252.jpg 1200_pic_252.jpg

M A R C H 2 018

269 269 267 299 299 299 275 259 300 300 262 262 262 262 267 305 180 180 275 275 276 180 272 275 0 0 286 300 379 290 290 290 335 273 273 273 274 268 268 268 240 240 294 294 294 294 299 299 253 253 186 300 251 276 96 266 317 275 262 300 275 275 340 340 340 324 299 299 429 275 192 192 200 275 275 266 266 269 275 177 225 262 259 259 259 259 259

187 global 187 global 189 global 168 global 168 global 168 global 183 global 194 global 168 global 168 global 192 global 192 global 192 global 192 global 189 global 165 global 281 global 281 global 183 global 183 global 183 global 281 global 186 global 183 global 0 global 0 global 176 global 168 global 133 global 174 global 174 global 174 global 150 global 185 global 185 global 185 global 184 global 188 global 188 global 188 global 160 global 160 global 172 global 172 global 172 global 172 global 168 global 168 global 199 global 199 global 271 global 168 global 201 global 183 global 202 global 189 global 159 global 183 global 192 global 168 global 183 global 183 global 148 global 148 global 148 global 156 global 168 global 168 global 117 global 183 global 262 global 262 global 200 global 183 global 183 global 189 global 189 global 187 global 183 global 284 global 225 global 193 global 194 global 194 global 194 person 194 person 194 person

162 1 1 27 228 1 1 13 195 239 75 37 139 182 103 91 20 67 102 80 91 77 1 1 61 122 116 98 108 18 196 66 124 54 65 193 126 28 12 84 1 128 51 1 126 201 1 176 100 10 101 186 100 26 23 20 6 108 26 113 157 1 104 1 219 2 118 13 2 120 116 112 4 6 47 52 78 9 117 9 25 45 114 1 39 226 212

18 116 28 1 3 3 61 1 22 110 54 65 50 66 5 2 58 195 33 38 5 71 9 36 33 177 53 68 28 1 95 125 1 5 140 130 8 24 104 98 7 100 1 32 1 7 20 2 1 101 117 17 85 78 3 3 37 75 89 51 1 113 62 1 1 82 1 35 3 2 1 116 7 1 41 27 131 50 101 142 31 33 44 1 120 122 120

251 218 153 229 299 37 275 254 224 257 114 78 183 229 234 301 100 180 181 196 274 159 268 275 140 176 208 201 276 207 290 285 227 142 191 273 216 66 78 128 130 236 119 57 202 270 178 299 247 220 155 300 171 231 88 246 317 175 52 250 269 193 239 119 340 324 213 130 384 275 159 190 199 264 217 124 186 178 167 122 161 182 253 116 56 237 225

178 177 102 96 95 98 119 150 131 131 91 87 94 91 176 145 208 219 85 122 179 210 127 106 174 231 110 155 133 174 125 174 150 162 163 159 152 104 119 112 160 160 64 67 56 60 153 153 154 172 227 167 144 175 158 179 133 124 106 168 166 178 142 146 144 130 107 140 110 163 160 253 149 183 150 147 149 177 146 243 169 146 115 121 173 170 168

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293

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294 | T E R R A M E D I A

266 266 266 281 275 275 275 275 275 275 275 275 275 275 275 275 267 259 259 259 259 194 194 275 275 275 275 300 300 275 275 275 275 259 259 259 259 256 299 299 299 275 275 275 275 194 275 0 275 275 275 275 300 300 300 300 300 300 300 299 299 299 299 299 275 275 275 279 279 279 287 287 287 287 287 287 259 275 259 407 407 194 275 275 275 275 275

| nicholas zembashi

190 10_social 190 10_social 190 10_social 179 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 10_social 183 10_social 183 10_social 183 10_social 183 person 189 10_social 194 10_social 194 10_social 194 10_social 194 10_social 259 10_social 259 person 183 10_social 183 10_social 183 10_social 183 10_social 168 10_social 168 10_social 183 10_social 183 10_social 183 person 183 person 194 10_social 194 10_social 194 10_social 194 person 197 10_social 168 10_social 168 person 168 person 184 10_social 184 10_social 184 person 184 person 259 10_social 183 10_social 0 10_social 183 10_social 183 10_social 183 10_social 183 10_social 168 10_social 168 10_social 168 person 168 person 168 person 168 person 168 person 168 10_social 168 10_social 168 10_social 168 10_social 168 10_social 183 10_social 183 10_social 183 10_social 180 10_social 180 10_social 180 10_social 176 person 176 person 176 person 176 person 176 person 176 person 194 10_social 183 10_social 194 10_social 124 10_social 124 10_social 259 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person

90 166 30 42 153 119 173 111 89 73 63 1 94 51 33 177 97 1 29 67 121 14 142 96 152 111 101 9 154 8 2 183 245 72 37 157 54 7 1 130 92 127 128 150 168 1 7 2 2 98 128 202 111 122 87 17 106 120 156 98 125 24 73 202 43 93 179 145 95 19 73 107 127 174 183 205 4 146 144 66 1 1 49 128 108 86 92

26 103 134 1 65 23 95 99 93 94 100 48 36 85 87 37 38 73 46 9 3 111 174 62 1 111 115 15 25 3 51 126 123 81 107 4 128 8 6 44 45 31 83 106 104 65 51 2 1 57 108 5 19 56 90 89 89 89 94 71 26 56 3 5 74 26 84 40 82 121 99 99 96 105 107 68 3 24 1 2 19 31 68 50 105 108 127

132 254 117 229 228 234 195 127 111 94 79 50 149 156 56 233 204 27 68 121 259 193 168 172 275 181 112 156 256 264 236 247 272 126 163 259 71 209 113 157 120 216 275 168 188 77 168 273 274 168 174 272 195 269 110 48 124 142 168 218 182 99 136 290 101 181 261 248 279 98 99 125 150 184 197 231 256 262 259 364 69 194 221 215 265 112 106

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M A R C H 2 018

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M A R C H 2 018

275 184 184 184 300 275 275 284 284 275 259 258 300 300 259 262 262 262 271 271 271 318 318 259 185 272 272 259 259 259 259 259 275 275 259 259 259 259 259 259 259 259 285 285 360 259 259 259 259 259 295 202 259 259 259 259 290 275 275 275 275 275 275 275 275 260 260 259 259 259 259 259 260 260 260 260 259 259 259 259 275 194 275 275 275 259 259

183 person 274 10_social 274 10_social 274 10_social 168 10_social 183 10_social 183 10_social 160 10_social 160 person 183 10_social 194 10_social 196 10_social 168 10_social 168 person 194 10_social 193 10_social 193 person 193 person 186 10_social 186 person 186 person 159 10_social 159 10_social 194 10_social 272 10_social 185 10_social 185 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 183 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 person 194 person 194 person 177 10_social 177 10_social 140 10_social 194 10_social 194 10_social 194 10_social 194 person 194 10_social 171 10_social 250 10_social 194 10_social 194 10_social 194 10_social 194 10_social 174 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 183 person 183 person 194 10_social 194 person 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 person 194 person 194 10_social 194 10_social 194 person 194 person 194 person 183 10_social 259 10_social 183 10_social 183 10_social 183 10_social 194 10_social 194 10_social

77 41 120 5 94 35 170 144 32 17 160 40 1 152 1 84 66 78 77 127 134 2 181 123 1 160 93 60 117 134 174 215 6 159 62 92 108 134 121 102 71 49 60 47 85 36 31 133 60 1 224 9 2 18 172 206 2 1 187 1 94 47 176 3 28 1 96 136 143 29 85 116 5 17 1 2 141 42 87 114 2 26 187 219 205 80 127

88 125 174 178 49 10 18 1 32 1 150 1 1 78 7 27 107 101 29 92 97 2 24 1 55 10 78 83 89 85 78 76 46 65 3 77 76 100 108 103 103 101 39 105 8 26 81 83 102 2 1 165 2 19 79 48 3 1 5 1 49 101 1 116 120 1 76 54 79 89 86 81 3 88 99 19 67 51 58 72 1 24 27 69 67 12 90

102 176 143 40 189 160 200 284 72 273 233 256 296 189 240 228 82 95 207 134 141 156 286 259 185 272 272 121 168 174 208 244 164 185 179 110 251 170 138 119 105 64 207 278 360 219 121 196 83 259 295 175 35 99 255 259 287 163 272 98 168 172 273 27 47 256 142 202 184 94 132 152 253 38 19 260 259 78 107 140 273 191 255 274 229 190 259

134 251 183 232 109 80 102 130 154 179 173 188 114 168 192 138 168 157 113 116 113 116 36 120 243 78 122 134 108 94 98 92 133 121 21 112 125 171 175 172 178 136 65 177 124 173 143 133 153 193 128 230 129 111 140 86 174 173 134 140 71 145 169 177 171 165 191 105 108 140 124 111 164 148 150 128 132 171 123 137 183 180 40 109 108 34 128

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295

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296 | T E R R A M E D I A

| nicholas zembashi

291 291 291 275 275 275 275 275 274 274 276 275 264 264 264 264 264 275 275 275 275 275 275 275 275 275 275 275 275 225 259 259 259 259 407 259 271 271 277 259 259 259 275 270 288 288 272 275 275 275 201 275 275 275 259 259 249 249 259 275 275 259 259 203 337 182 288 288 288 275 275 275 236 254 275 259 259 259 259 259 183 183 194 194 259 259 225

173 person 173 person 173 person 183 10_social 183 10_social 183 10_social 183 10_social 183 10_social 184 10_social 184 10_social 183 10_social 183 10_social 191 10_social 191 10_social 191 10_social 191 person 191 person 183 10_social 183 10_social 183 10_social 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 person 183 10_social 225 10_social 194 10_social 194 10_social 194 person 194 10_social 124 10_social 194 10_social 186 10_social 186 10_social 182 10_social 194 10_social 194 10_social 194 10_social 183 10_social 180 10_social 175 10_social 175 10_social 185 10_social 183 10_social 183 10_social 183 person 250 10_social 183 10_social 183 10_social 183 person 194 10_social 194 10_social 202 10_social 202 10_social 194 10_social 183 10_social 183 10_social 194 10_social 194 10_social 249 10_social 150 10_social 277 10_social 175 10_social 175 10_social 175 10_social 183 10_social 183 10_social 183 10_social 177 10_social 198 10_social 183 10_social 194 10_social 194 10_social 194 10_social 194 10_social 194 10_social 276 10_social 276 10_social 260 10_social 259 10_social 194 10_social 194 10_social 225 continental

92 64 38 1 87 107 131 185 5 212 14 117 11 3 94 166 194 1 94 102 32 46 103 145 158 198 207 219 243 21 83 41 5 121 8 2 1 114 60 53 115 15 1 2 83 1 26 113 170 158 1 1 45 112 5 46 75 38 32 77 172 48 118 81 63 1 122 130 87 29 136 76 18 47 43 33 116 1 69 202 1 103 45 28 1 176 8

66 70 67 4 48 59 48 1 59 22 61 1 4 40 22 87 87 2 52 8 83 80 75 72 69 73 73 75 38 78 1 62 63 31 3 2 38 53 1 53 12 55 19 5 40 15 35 17 77 108 6 9 8 98 1 21 65 91 8 15 101 66 80 1 7 14 9 14 53 48 54 45 3 8 16 1 97 98 113 112 96 145 143 18 49 50 27

112 75 61 93 111 131 162 275 163 274 253 262 244 117 264 189 217 91 148 166 46 60 126 162 178 211 223 233 275 205 250 189 19 259 387 242 111 271 273 255 193 57 275 270 261 77 254 221 275 175 170 101 202 148 259 243 228 71 222 191 275 86 212 194 155 182 222 284 124 140 213 195 236 254 240 238 207 76 115 235 54 135 137 162 89 256 215

136 119 127 178 117 99 91 169 133 141 174 118 157 102 108 156 152 113 105 93 116 118 112 129 134 110 122 109 110 150 65 134 92 140 124 190 113 109 182 147 31 104 168 163 150 170 153 32 138 167 166 59 150 170 146 158 157 115 166 51 152 161 165 249 136 181 66 141 108 156 151 183 119 152 134 193 159 139 154 137 226 207 195 247 127 121 204

M A R C H 2 018

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M A R C H 2 018

299 299 275 275 259 299 336 336 274 275 275 341 341 274 347 300 284 284 259 275 319 299 259 302 259 292 278 270 275 276 420 420 259 300 296 296 275 268 433 250 259 278 278 278 314 278 275 259 275 275 276 276 259 183 259 425 275 275 275 294 294 415 252 276 410 290 290 290 290 290 292 259 270 275 310 310 300 360 222 179 275 192 318 275 275 225 318

168 continental 168 continental 183 continental 183 continental 194 continental 168 continental 150 continental 150 continental 184 continental 183 continental 183 continental 148 continental 148 continental 184 continental 145 continental 168 continental 177 continental 177 continental 194 continental 183 continental 158 continental 168 continental 194 continental 167 continental 194 continental 173 continental 181 continental 186 continental 183 continental 182 continental 120 continental 120 continental 194 continental 168 continental 170 continental 170 continental 183 continental 188 continental 116 continental 202 continental 194 continental 182 continental 182 continental 182 continental 160 continental 182 continental 183 continental 195 continental 183 continental 183 continental 182 continental 182 continental 194 continental 276 continental 195 continental 119 continental 183 continental 183 continental 183 continental 171 continental 171 continental 121 continental 200 continental 183 continental 123 continental 174 continental 174 continental 174 continental 174 continental 174 continental 173 continental 194 continental 187 continental 183 continental 163 continental 163 continental 168 continental 140 continental 227 continental 281 continental 183 continental 263 continental 159 continental 183 continental 183 continental 225 continental 158 continental

122 183 57 1 5 1 100 1 6 1 3 132 21 2 160 184 135 55 29 1 2 160 4 7 38 61 38 69 29 13 193 20 12 4 52 121 94 24 76 32 5 134 1 67 29 145 5 23 1 154 1 142 37 2 44 140 131 1 12 141 42 226 5 146 115 67 99 151 221 11 36 97 11 40 1 121 7 71 64 1 1 62 1 186 21 5 5

1 1 23 47 3 12 1 53 14 2 7 24 51 25 1 6 18 42 12 18 14 39 14 80 5 15 25 8 30 15 2 56 6 4 16 35 7 5 4 20 5 33 94 63 27 8 18 17 1 1 6 13 4 10 1 8 1 90 5 20 52 2 101 9 1 43 102 46 113 118 1 9 26 7 3 2 12 16 36 28 22 12 2 46 68 16 5

209 299 188 127 257 109 235 122 264 267 268 253 188 268 287 278 229 172 258 275 248 204 257 302 177 266 274 159 230 241 403 266 256 275 152 235 188 235 364 246 88 276 146 88 264 249 273 236 98 272 168 194 248 176 259 408 274 128 272 267 188 296 252 226 366 130 172 218 242 70 244 174 190 239 59 291 291 286 147 149 135 131 172 221 177 224 313

166 167 128 141 173 161 121 122 165 183 181 105 117 184 77 48 86 100 111 137 158 67 194 164 164 168 173 125 144 171 118 118 184 162 78 86 57 153 107 156 191 147 164 86 160 176 170 163 183 181 173 55 192 272 156 119 105 112 146 109 129 110 196 151 119 151 150 152 147 152 162 115 150 155 148 143 167 106 206 281 155 228 135 108 92 207 124

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297

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298 | T E R R A M E D I A

| nicholas zembashi

277 183 183 300 183 262 275 283 283 283 195 250 194 275 259 259 275 300 181 183 259 259 259 259 259 259 259 259 259 259 259 259 259 262 299 299 397 397 397 225 259 259 280 280 280 280 275 275 275 304 177 275 275 275 276 275 275 275 275 277 177 225 259 275 275 266 299 277 277 317 259 259 266 259 296 266 180 180 269 299 299 259 284 299 299 299 299

182 continental 275 continental 275 continental 168 continental 275 continental 192 continental 183 global 178 global 178 global 178 global 259 global 201 global 259 global 183 global 194 global 194 global 183 global 168 global 278 global 275 global 194 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 195 global 192 global 168 global 168 global 127 global 127 global 127 global 225 global 194 global 194 global 180 global 180 global 180 global 180 person 183 global 183 global 183 global 166 global 285 global 183 global 183 global 183 global 183 global 183 global 183 global 183 global 183 global 182 global 285 global 225 global 195 global 183 global 183 global 190 global 168 global 182 global 182 global 159 global 194 global 194 global 189 global 194 global 170 global 189 global 280 global 280 global 188 global 168 global 168 global 194 global 177 global 168 global 168 global 168 global 168 global

1 20 117 114 39 27 15 26 213 222 32 122 37 14 5 148 71 128 5 1 72 39 7 132 133 134 135 133 189 191 190 190 188 8 13 191 4 108 324 11 10 13 71 1 184 125 1 87 175 5 28 58 1 199 102 67 1 203 14 10 38 93 52 27 33 38 137 70 1 118 1 193 43 1 80 56 16 108 71 1 117 31 1 82 171 237 1

1 60 49 4 34 27 74 22 61 92 4 5 10 9 2 1 38 37 1 1 49 29 5 53 83 113 143 173 53 83 113 142 174 70 71 1 1 1 2 1 2 4 7 52 52 123 3 104 1 1 194 65 1 1 5 46 2 1 44 31 74 5 13 1 10 66 7 31 51 10 2 1 33 58 1 35 18 63 47 2 72 12 27 1 63 2 54

235 69 155 294 141 250 275 212 231 283 195 181 158 258 101 258 207 203 164 167 258 119 43 157 156 156 153 154 212 212 210 210 212 262 258 281 130 318 397 224 131 249 183 73 280 150 98 191 275 227 114 211 65 275 190 206 69 275 218 265 141 219 172 118 273 234 192 179 69 154 213 259 253 257 170 211 113 172 128 120 299 239 161 170 233 293 93

117 237 235 134 233 169 178 144 130 138 216 126 241 180 127 155 155 102 254 235 126 168 36 71 100 132 162 190 69 101 131 160 192 125 157 141 125 119 87 181 123 170 145 149 142 178 170 173 173 147 256 168 162 171 178 125 130 129 139 161 174 225 195 158 183 180 145 139 141 113 177 178 181 194 137 168 233 175 163 168 165 160 122 110 112 167 143

M A R C H 2 018

1200_pic_148.jpg 1200_pic_149.jpg 1200_pic_150.jpg 1200_pic_151.jpg 1200_pic_151.jpg 1200_pic_153.jpg 1200_pic_153.jpg 1200_pic_153.jpg 1200_pic_153.jpg 1200_pic_153.jpg 1200_pic_154.jpg 1200_pic_155.jpg 1200_pic_156.jpg 1200_pic_157.jpg 1200_pic_157.jpg 1200_pic_157.jpg 1200_pic_158.jpg 1200_pic_158.jpg 1200_pic_158.jpg 1200_pic_158.jpg 1200_pic_159.jpg 1200_pic_160.jpg 1200_pic_161.jpg 1200_pic_162.jpg 1200_pic_163.jpg 1200_pic_164.jpg 1200_pic_164.jpg 1200_pic_164.jpg 1200_pic_165.jpg 1200_pic_165.jpg 1200_pic_165.jpg 1200_pic_166.jpg 1200_pic_167.jpg 1200_pic_167.jpg 1200_pic_167.jpg 1200_pic_168.jpg 1200_pic_168.jpg 1200_pic_168.jpg 1200_pic_168.jpg 1200_pic_168.jpg 1200_pic_169.jpg 1200_pic_169.jpg 1200_pic_170.jpg 1200_pic_171.jpg 1200_pic_172.jpg 1200_pic_172.jpg 1200_pic_173.jpg 1200_pic_173.jpg 1200_pic_174.jpg 1200_pic_175.jpg 1200_pic_175.jpg 1200_pic_176.jpg 1200_pic_176.jpg 1200_pic_176.jpg 1200_pic_177.jpg 1200_pic_178.jpg 1200_pic_179.jpg 1200_pic_179.jpg 1200_pic_180.jpg 1200_pic_181.jpg 1200_pic_181.jpg 1200_pic_182.jpg 1200_pic_183.jpg 1200_pic_183.jpg 1200_pic_184.jpg 1200_pic_184.jpg 1200_pic_184.jpg 1200_pic_185.jpg 1200_pic_185.jpg 1200_pic_186.jpg 1200_pic_187.jpg 1200_pic_188.jpg 1200_pic_188.jpg 1200_pic_188.jpg 1200_pic_188.jpg 1200_pic_189.jpg 1200_pic_190.jpg 1200_pic_191.jpg 1200_pic_192.jpg 1200_pic_193.jpg 1200_pic_193.jpg 1200_pic_194.jpg 1200_pic_194.jpg 1200_pic_195.jpg 1200_pic_197.jpg 1200_pic_197.jpg 1200_pic_197.jpg

M A R C H 2 018

248 275 262 274 274 261 261 261 261 261 259 275 289 259 259 259 300 300 300 300 425 300 274 329 225 275 275 275 250 250 250 300 270 270 270 275 275 275 275 275 269 269 183 265 347 347 331 331 275 274 274 262 262 262 379 248 263 263 279 259 259 266 259 259 299 299 299 179 179 293 328 313 313 313 313 260 179 179 267 180 180 259 259 261 266 266 266

203 global 183 global 193 global 184 global 184 global 193 global 193 global 193 global 193 global 193 global 194 global 183 global 174 global 194 global 194 global 194 global 168 global 168 global 168 global 168 global 118 global 168 global 184 global 153 global 224 global 183 global 183 global 183 global 171 global 171 global 171 global 168 global 186 global 186 global 186 global 183 global 183 global 183 global 183 global 183 global 187 global 187 global 275 global 190 global 145 global 145 global 152 global 152 global 183 global 184 global 184 global 192 global 192 global 192 global 133 global 204 global 192 global 192 global 181 global 194 global 194 global 189 global 194 global 194 person 168 global 168 global 168 global 281 global 281 global 172 global 154 global 161 global 161 global 161 global 161 global 194 global 282 global 281 global 189 global 280 global 280 global 194 global 194 global 193 global 189 global 189 global 189 global

50 100 31 104 126 84 123 145 188 47 2 8 47 78 1 100 67 5 143 215 6 30 1 32 3 95 1 189 28 13 135 26 1 106 177 69 6 117 150 205 81 127 64 53 169 204 51 195 96 148 1 27 15 104 2 137 49 92 1 181 54 88 1 42 134 41 224 30 58 29 1 62 22 167 240 125 23 18 123 51 15 135 11 154 110 125 195

76 26 62 10 132 54 84 50 62 73 64 5 1 3 1 37 37 73 36 51 1 37 3 17 81 10 1 96 14 130 116 9 2 80 1 21 43 63 19 38 11 135 49 25 4 101 1 114 10 5 86 35 126 125 21 19 13 151 62 46 7 13 72 115 1 16 42 15 193 13 2 51 76 49 77 72 63 7 26 18 214 65 2 24 47 127 124

220 187 56 179 198 125 146 188 235 86 259 268 275 135 54 259 135 76 217 281 371 224 172 98 223 184 105 275 92 136 229 299 109 183 270 117 74 149 206 264 157 239 134 231 237 303 198 324 140 242 194 79 107 190 378 248 132 214 276 252 177 177 259 54 222 142 299 151 178 280 326 124 71 239 308 222 158 113 177 138 171 216 30 216 165 205 247

137 177 129 159 171 99 99 98 99 97 134 168 174 106 113 107 113 119 101 103 106 164 183 135 206 153 152 151 128 149 140 141 100 103 109 79 81 82 82 76 146 160 175 190 115 119 152 152 166 147 145 134 148 140 102 201 159 175 173 166 106 148 125 144 85 111 88 219 256 149 137 122 121 110 110 132 259 210 143 215 248 194 22 130 141 145 139

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BOOKS Vitra Design Museum, Hello Robot: Design Between Human and Machine, (Vitra, MAK, 2017) Bratton, B. H., The Stack: On Software and Sovereignty (Cambridge, MA, MIT Press, 2016) Susskind, R. & Susskind, D., The Future of the Professions: How Technology Will Transform the Work of Human Experts, (Oxford, Uk, Oxford University Press, 2015) Kelly, K., The Inevitable: Understanding The 12 Technological Forces That Will Shape Our Future (New York, Penguin Books, 2016) Arendt, H., The Human Condition (Chicago, University of Chicago Press, 1958) Arendt, H., The Origins of Totalitarianism, (New York, Harcourt Publishing, 1985) Oslo Architecture Triennale 2016, After Belonging: The Objects, Spaces, and Territories of the Ways We Stay in Transit, (Lars Muller Publishers, 2016) Funk, W., Gross, F. and Huber, I.(eds.), The Aesthetics of Authenticity: Medial Constructions of the Real, (Bielefeld, Transcript Verlag, 2012) Gabriel, M., Why The World Does Not Exist, (Cambridge, UK, Polity Press, 2015) Latour, B. and Porter, C., We Have Never Been Modern, (3rd edn. Cambridge, MA, Prentice Hall / Harvester Wheatsheaf, 1993) Yates, F., The Art of Memory, (London, The Random House Group, 1966) McLuhan, M., Understanding Media: The Extensions of Man (London and New York) Derrida, tans. Spivak, C, Of Grammatology, (The John Hopkins University Press, 1997, USA) Derrida, J., Stiegler, B., Ethnographies of Television, (Polity Press, 2002, France)

WEB Big data meets Big Brother as China moves to rate its citizens - http://www.wired.co.uk/article/chinese-governmentsocial-credit-score-privacy-invasion Jus Algoritmi: How the National Security Agency Remade Citizenship, John Cheney-Lippold, University of Michigan, USA, 2016, - http://ijoc.org/index.php/ijoc/article/viewFile/4480/16 Citizen X - http://citizen-ex.com/ Object Detection Definition, Wikipedia - https://en.wikipedia.org/wiki/Object_detection How Machines Learn - https://www.youtube.com/ watch?v=R9OHn5ZF4Uo&index=24&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf How Deep Neural Networks Work - https://www.youtube.com/ watch?v=ILsA4nyG7I0&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=25 Intro - TensorFlow Object Detection API Tutorial p.1 - https://www.youtube.com/watch?v=COlbP62-B-U&list=PLGl2Rq vUbOuyLaQEnsJS_8WpifXiHxVWf&index=3 Adapting to video feed - TensorFlow Object Detection API Tutorial p.2 - https://www.youtube.com/ watch?v=MyAOtvwTkT0&index=4&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf Tracking Custom Objects - TensorFlow Object Detection API Tutorial p.3 - https://www.youtube.com/watch?v=K_ mFnvzyLvc&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=5 Creating TFRecords - TensorFlow Object Detection API Tutorial p.4 - https://www.youtube.com/watch?v=kq2Gjv_ pPe8&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=6 Training Custom Object Detector - TensorFlow Object Detection API Tutorial p.5 - https://www.youtube.com/ watch?v=JR8CmWyh2E8&index=7&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf Testing Custom Object Detector - TensorFlow Object Detection API Tutorial p.6 - https://www.youtube.com/ watch?v=srPndLNMMpk&index=8&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf How to Make a Tensorflow Image Classifier (LIVE) - https://www.youtube.com/ watch?v=APmF6qE3Vjc&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=10 Decentralized AI Live Talk - https://www.youtube.com/ watch?v=5ORIRJCPbmk&index=14&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf Generative Adversarial Networks (LIVE) - https://www.youtube.com/ watch?v=0VPQHbMvGzg&index=15&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf Generating Pokemon with a Generative Adversarial Network - https://www.youtube.com/ watch?v=yz6dNf7X7SA&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=16 How to Train Your Models in the Cloud - https://www.youtube.com/watch?v=Bgwujw-yom8&list=PLGl2RqvUbOuyLa QEnsJS_8WpifXiHxVWf&index=17 Decentralized Artificial Intelligence - https://www.youtube.com/ watch?v=GWOdAAFoSFE&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&index=18 DeepFakes Explained - https://www.youtube.com/ watch?list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf&v=7XchCsYtYMQ How to Generate Art - Intro to Deep Learning #8 - https://www.youtube.com/ watch?v=Oex0eWoU7AQ&index=20&list=PLGl2RqvUbOuyLaQEnsJS_8WpifXiHxVWf

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