HELLO!

My name is YANO, I am a visual coder and artist based in Basel.
My work is usually explorative and revelatory.

I like to understands the mechanics and functions of things.

By things I mean any object or concept that catches my interest.
I have been working a lot with photography, I learned to build cameras, and to destroy images. This was the gateway practice that led me to what I do now: building tools that explore the mechanics of things.
I also do a lot of that usually make no sense.
Currently I work a lot with , that led me to a growing interest in High Frequency Trading algorithms.
I am having a long term relationship ❤️️️️️️❤️❤️ with the algorithm, and I also do some in the form of workshops, lectures and talks.

2019 Workshop 9 Hysterical Media Truths You’ll Never Believe! @Transmediale, resulting in a micro-publication availale here
2019 Talk Phantasms of Decentralization? Conversations About Commoning With Coming Media @Transmediale
2018 New Face Award @Japan Media Art Festival + Exhibition + talk
2017 Collective exhibition @The Wrong Digital Art Biennale
2017 Collective exhibition @H3k for the 2017 Regionale
2017 Talk "The Dither is Naked" @TEOK
2015 – 2017 MA in Visual Communication and Iconic Theory @HGK, Basel
2014 The new portrait Zine by Mathea Millman
2014 Naturae by Steve Bisson
2014 Dazed Digital
2013 Der Greif Photomagazine issue #7
2013 finalist of 17th edition of the "vfg Nachwuchsförderpreis für Fotografie"
2012 Collective exposition, Piombino Social Photo Fest, Piombino.
2012 Collective exposition, Diladdarte, Firenze.
2011 La Luna Di Traverso, online magazine.
2007 – 2011 BA in Photography at Università degli Studi di Firenze


For inquiries drop a line @
yannpatrickmartins [at] gmail [dot] com

My web presence manifests on Github.

ABM

Since january 2018 I am part of a research group at the Critical Media Lab, focusing on "Thinking (Toys or Games) for Commoning": a project looking at the complexity of commoning as a subject and making it understandable through playing and gaming.
As creative coder of the team I am developing agent based models that

"stimulate reflection on the intuitively incomprehensible complexity of commoning"



I am currently programming the games and models with JavaScript and p5.js.
I also curate the design of our website.
And here a link to our project repository with the developed models.

TEACHING!

I teach [critical] creative coding classes at the Academy of Art and Design in Basel (Hochschule für Gestaltung und Kunst, FHNW) together with Shintaro Miyazaki.

Additional to that I also give some workshops

GENEALOGY OF THE DITHER IS NAKED!

If you don't want to read the whole text just click the following links to see the different stages of the project.

My first experience with dither was during my my first year of my Masters. During the first year I needed to print a gradient, but our printer was not good enough. The prints were full of artifacts due to the large amount of colors in a gradient. I decided therefore to use the Photoshop built-in tool for colour palette reduction. The tool provided various options to choose between different dither algorithms. This was the result I had while playing with the tool.

WOW!
EYE BLEEDING!

But it was olnly one year later that I mastered the Floyd–Steinberg dithering algorithm. This was possible also thanks to Daniele Piccone, Daniel Temkin and their amazing work. Last and but not least this text about "digital halftoning, or dithering" was quite helpful to understand the mechanics of dithering.
Floyd–Steinberg dithering works by using an error diffusion algortihm

meaning it pushes (adds) the residual quantization error of a pixel onto its neighboring pixels.

It spreads the error according to some hard-coded values as follows:

[ ] [ ] [ ]
[ ] [*] [7/16]
[3/16] [5/16] [1/16]

The pixel with the * indicates the pixel being currently scanned by the algorithm.

Those values became my playground for the exploration of the dithering algorithm. My first step was to change the common divisor (16) of the those hard-coded values.

[ ] [ ] [ ]
[ ] [*] [7/ 16]
[3/ 16] [5/ 16] [1/ 16]

This lead to the creation of a Tumblr-Bot, posting dithered gradients with random values as dividend in the error quantization algorithm.

The next step was to create a Twitter-Bot, reacting to the #prettyDither hashtag, followed by two colors, i.e. #prettyDither red green

In order to gain a better control over the algorithm I decided to control also the the divisor of the error quantization algortihm

divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend
divisor
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dividend

I implemented this to control the the calculation of the quantization error among all the neighboring pixels, not only the 4 in use in the Floyd–Steinberg algorithm. this lead to the development of this website, that allows user to control each part of the algorithm.

here some results






The interesting thing about the dithering algorithm, is its camouflaging attitude. What do I mean? Good dithering algorithms, when properly developed, should remain unnoticed by the viewers eyes. Curiously enough when the same algorithm is applied to gradients its camouflaging qualities get weaker revealing the algorithm structure in the form of colorful patterns. The additional control on the quantization error algortihm allows for more in depth analysis of the dithering process.

DESPITE THE VISUAL RESULTS OF THE EXPLOITATION OF THE LATTER ALGORITHM, MY INTERESTS LIE ALSO IN THE REVELATORY QUALITY OF THE PROJECT EXPOSING THE ALGORITHM AS ITSELF.

FISIOGNOMIC HOROSCOPE

The Fisiognomic Horoscope was created for the Yami-Ichi Flea Market @H3K. Thanks to Wekinator, a machine learning software, it was possible to train a model with celebrities faces, and their astrological sign. People can sit in front of the camera and let their face be scanned. The trained model matches their face traits with the ones in the database. At the end the matching horoscope is printed. The project was done to bring awareness about the use of machine learning in the facial recognition field. Some Ph.D students already applied machine learning to determine if someone could be a criminal or not. This was possible by training a model with the portraits of already convicted criminals.

THIS RESEARCH LEADS BACK TO THE TIME WHERE PHYSIOGNOMY AND PHRENOLOGY WERE CONSIDERED REAL SCIENCES, DESPITE DISCOVERING LATER ON THAT THEY WERE NOT.


I am currently trying to port this project to JavaScript with tensorFlow.js.

FUNNY GAMES!

Despite the encouraging title, maybe those games are not funny at all.

BUT I LIKE THEM ANYWAY, THEY STILL MAKE ME LAUGH!

Those games are developed to be played on handheld devices – yes, like your phone – they use the gyroscope of your device to steer and control the elements in it.
There is no score or goal in the games.
(Maybe they aren't games at all...)


Growing up as omega–male* is harsh!
* I hope that the self irony is clear!




Oh software, you **are** kind of magic!




Running gag!