AL_TU

Ted Talk - computation

2010-05-10T16:09:00.001-07:00

Ted-Talk - social-networks

2010-05-10T15:28:00.001-07:00

CANactions Presentations

2009-12-07T09:23:00.000-08:00

This summer we have been invited to present our studio work (DIA-Dessau Institute of Architecture - StudioX - Prof. Christos Passas) at the CANactions festival for young architects, Kiev, Ukraine.

This is a link to the videos of all the presentations. Feel free to browse through them since you'll find some very interesting ones.

This link on the other hand is to our presentation.

NASA RESEARCH

2009-10-28T07:01:00.000-07:00

For more info click HERE

Neurons behaviour

2009-10-21T12:11:00.000-07:00

The Emergence of Intelligence in the Neocortical Microcircuit

2009-10-20T07:18:00.001-07:00

Full conference

Mind 08

Henry Markram: Designing the Human Mind - Watch more Videos at Vodpod.

Articles

2009-10-20T05:40:00.000-07:00

Networks, Swarms, Multitudes

Part One

Eugene Thacker

Mutations in the Body Politic

A question: are we connected because we are collective, or are we collective because we are connected? Another, related question: in the "network society," is it possible to reformat the body politic without resorting to the paradigm of modern sovereignty?

For the whole article click on the title

Melanie Mitchell - Publications

Visual Complexity - visual projects

Kokkugia - networking tutorial (processing -> rhino)

Research

2009-10-19T09:02:00.000-07:00

Pirate Utopia

Pirates created a world of their own making, where they had "the choice in themselves" - a world of solidarity and fraternity, where they shared the risks and the gains of life at sea, made decisions collectively and seized their life for themselves in the present, denying its use to the merchants as a tool for the accumulation of dead property.

Pirate ships operated on a 'No Prey, No Pay' basis, but when a vessel was captured the booty was divided up by a share system.

Pirate captains were elected and could be de-elected at any time for abuse of their authority. The captain enjoyed no special privileges

Libertatia

Libertatia (also known as Libertalia) is said to have been a free communalist colony forged by pirates under the leadership of Captain James Misson in the late 1600s. Whether or not Libertatia actually existed is disputed. It is described in the book A General History of the Pyrates by Captain Charles Johnson, an otherwise unknown individual who may have been a pseudonym of Daniel Defoe.[1] Much of the book is a mixture of fact and fiction, and it is possible the account of Libertatia is entirely fabricated.

Libertatia is said to have consisted of an enclave within north Madagascar and to have lasted for about twenty five years. The precise location is not known, however, most sources say it stretched from the Bay of Antongil to Mananjary, including Ile Sainte Marie and Foulpointe. Thomas Tew, the Provençal Misson and an Italian Dominican priest named Caraccioli were involved in founding it.

Living Neural Networks (collaboration with Elisha Moses)

Neurons form complex webs of connections. Dendrites and axons extend, ramify, and form synaptic links with their neighbors. This complex wiring diagram has caught the attention of physicists and biologists for its resemblance to problems of percolation. Important questions are the critical distance that dendrites and axons have to travel in order to make the network percolate, i.e., to establish a path from one neuron of the network to any other, or the number of bonds (connections) or sites (cell bodies) that can be removed without critically damaging the functionality of the circuit.

In the brain, neural networks display such robust flexibility that circuits tolerate the destruction of many neurons or connections while keeping the same, though degraded, function. For example, it is currently believed that in Parkinson's disease, up to 70% of the functionality of the neurons in the affected areas can be lost before behavioral symptoms appear. At the core of the experiments and the model is a percolation approach. In our work, we consider a simplified model of a neural network in terms of bond-percolation on a graph.

Kohonen self-organizing network

Main article: Self-organizing map

The self-organizing map (SOM) invented by Teuvo Kohonen performs a form of unsupervised learning. A set of artificial neurons learn to map points in an input space to coordinates in an output space. The input space can have different dimensions and topology from the output space, and the SOM will attempt to preserve these.

Self-organizing map

From Wikipedia, the free encyclopedia

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A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different than other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.

A self-organizing map showing US Congress voting patterns visualized in Synapse. The first two boxes show clustering and distances while the remaining ones show the component planes. Red means a yes vote while blue means a no vote in the component planes (except the party component where red is Republican and blue is Democrat).

This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. [1]

Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.

A self-organizing map consists of components called nodes or neurons. Associated with each node is a weight vector of the same dimension as the input data vectors and a position in the map space. The usual arrangement of nodes is a regular spacing in a hexagonal or rectangular grid. The self-organizing map describes a mapping from a higher dimensional input space to a lower dimensional map space. The procedure for placing a vector from data space onto the map is to find the node with the closest weight vector to the vector taken from data space and to assign the map coordinates of this node to our vector.

While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation.

Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes. It has been shown that while self-organizing maps with a small number of nodes behave in a way that is similar to K-means, larger self-organizing maps rearrange data in a way that is fundamentally topological in character.

It is also common to use the U-matrix. The U-matrix value of a particular node is the average distance between the node and its closest neighbors. In a rectangular grid for instance, we might consider the closest 4 or 8 nodes.

Large SOMs display properties which are emergent. Therefore, large maps are preferable to smaller ones. In maps consisting of thousands of nodes, it is possible to perform cluster operations on the map itself.[2]

Growing self-organizing map

From Wikipedia, the free encyclopedia

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A growing self-organizing map (GSOM) is a growing variant of the popular self-organizing map (SOM). The GSOM was developed to address the issue of identifying a suitable map size in the SOM. It starts with a minimal number of nodes (usually 4) and grows new nodes on the boundary based on a heuristic. By using the value called Spread Factor (SF), the data analyst has the ability to control the growth of the GSOM.

All the starting nodes of the GSOM are boundary nodes, i.e. each node has the freedom to grow in its own direction at the beginning. (Fig. 1) New Nodes are grown from the boundary nodes. Once a node is selected for growing all its free neighboring positions will be grown new nodes. The figure shows the three possible node growth options for a rectangular GSOM.

metaphormosis

2009-10-19T08:39:00.000-07:00

Lorenz Strange Attractor Evolution

2009-10-19T08:37:00.000-07:00

Structure Formation

2009-10-19T08:01:00.000-07:00

Neural Nets simulation

2009-10-19T07:27:00.000-07:00

sites Array

2009-10-19T07:00:00.000-07:00

south pole steams

2009-10-19T06:53:00.000-07:00

Tribes

2009-10-19T06:01:00.001-07:00

Talks James Balog: Time-lapse proof of extreme ice loss

2009-10-19T05:14:00.000-07:00

Studio description

2009-10-19T05:04:00.000-07:00

Studio X- Christos Passas, Yevgeniy Beylkin, Liss Werner
ALGORITHMIC MODULATIONS;
Simultaneous forms of unity and dis-unity

As the studio work now expands to one year, Studio X takes this opportunity to explore deeper and more formally abstract topics within the Algorithmic Design agenda.
This year’s agenda is to explore the topic of algorithmic configurations within a more elaborate framework. The studio will examine unified and continuous compositions as a starting point but will concentrate on diversified compositions that propose radical shifts, raptures and discontinuities within the set framework; we are looking to discover a world beyond perfection, beyond the suffocation of prediction, patterned growth; we seek those instances where unity becomes discontinuous and the separateness becomes continuous.

Watercube

2009-03-14T15:05:00.001-07:00

The Watercube book on issuu

MATA4 making of movie

2009-03-08T07:00:00.000-07:00

A movie showing the process of getting a 3d model to a laser cutted model.

Making of MATA4 from Tudor Cosmatu on Vimeo.

Final

2009-02-05T07:35:00.000-08:00

The final.

Architecture should be people related. Architecture should be directly influenced by the user

Olympic stadiums and stadiums in general all have several attributes in common: people, emotions, cheering. All these can be reduced to people and sound, since the people are the ones creating emotions through sound. A stadium without one of these elements loses its meaning.

For creating the 2012 London Olympic Stadium we are making use of the people (users), as sound emitters and of the sound, as a form generating device. By spectral analysis and 3D soundscapes the shape of the stadium becomes tangible, as a concentration point of sound waves.

Sound is dynamic; therefore the skin of the stadium will react to its emitters through both movement deformations and color changes. The stadium skin is no longer a protective shell or a border, but a bridge, a linkage, between the inside and the outside. Through this exteriorization of the inside happenings, the Olympic Games will be sensed from the exterior as well as from the interior.

The usage of the stadium after the Olympic Games is not predefined and not limited, offering the possibility to change the interior layout.

Generative approach

Starting with a functional diagram, predefining the position of the sound emitters according to the importance of the event, the general layout of the stadium is set.

Returning to the primordial statement that particle emitters are simulations of sound emitters and considering the emitted spheres to be sound waves moving and deforming through space, the idea of letting exactly them determine the interior space emerged. Therefore several study models were needed in order to analyze different spatial layouts.

In order to achieve an improved visual and sound perception sport areas are being separated in semi-individual amphitheatres which are self supporting and flowing into the stadium skin. Through this also the running track is being deformed according to the same laws.

Analyzing the sound waves and the way they interfere (highs, lows and ripples at the intersection points), the site is also being created, as an Olympic park, creating space for all other typical functions as well as other functions like skate parks, recreation areas, amphitheatres, piers, etc.

The interior as well as the whole stadium structure become part of a dynamic space, a place for people and through people.

just follow the link in order to view the final booklet.

Get your own - Open publication

Sound Vibration Creates Form ( David Icke )

2008-12-24T08:07:00.000-08:00

click here to see video

SEATINGS GENERATION

2008-12-22T06:55:00.000-08:00

sound element

2008-12-22T06:45:00.000-08:00

MIDTERM PRESENTATION

2008-12-12T13:31:00.000-08:00

In order ti view the presentation click this link.

Pre-midterm presentation

2008-11-29T05:23:00.000-08:00

Some other stuff

2008-11-27T17:43:00.000-08:00

Not related to the stadium really, some paneling exercises we were working on in a 3 person group.

Studies

2008-11-25T06:24:00.000-08:00

8 possible designs for the general form of the sound-based stadium. Particle spreading and congregation were analyzed in order to choose the most representative one. Work still in progress.

Further studies - still to follow

2008-11-24T09:50:00.000-08:00

2012 London Olympics Stadium: Studies

2008-11-14T22:19:00.001-08:00

Architecture should be people related. Architecture should be directly influenced by the user

Olympic stadiums and stadiums in general all have some things in common: people, emotions, cheering. All these can be reduced to people and sound. A stadium without one of these elements looses its meaning.

Sound is dynamic, therefore the skin of the stadium will react to its emitters through both movement deformations and color changes. The stadium skin is no longer a protective shell or a border, but a bridge, a linkage, between the inside and the outside. Therefore the Olympic Games will be sensed from the exterior as well as from the interior.

Based on the same logics, the usual set-up of an olympic stadium is denied and replaced by a grid of modules of 40 by 40 cm which creates the interior deformable structure. The usage of the stadium after the Olympic Games is not predefined and not limited, offering the possibility to change the interior layout.

Concept

2008-11-12T19:49:00.001-08:00

Architecture should be people related. Architecture should be directly influenced by the user

For generating the London 2012 Olympic Stadium, the “stadium sound” will be used as a 3D soundscape. Everything around and about the stadium will be influenced by the same laws. Therefore the stadium will perform as a sound capturing device, which will transform real time sound in a 3d motion of the skin. Seating areas as well as lightning all-around the stadium will be controlled by sound related information.

Stadiums live through their occupants. What connects every stadium around the world is atmosphere, emotion, entertainment. Emotion due to the stadium atmosphere is created by people, which generate sound.

Stadium as it is, will no longer be. Why classical orders? Why usual sitting areas? Why stadium as a border? Why stadium just on the inside?

picture presentation