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Chemists have created a new material that self-assembles into 2D networks in a predictable and reproducible manner. They have successfully synthesized a complex material by design — paving the way for its suite of new properties to be applied in many fields. (1)

Self-assembling materials…

Self-assembling continents…

Self-assembling planets…

Self-assembling stars…

Look everything from above and you will see. That what you see is nothing but an illusion. Not because it does not exist. But because what you believe you did NOT see*, actually existed everywhere in the first place. Inside a self-assembling cosmos…

* Check for example the case of humans believing there is no water on the Moon (now we have started discovering it everywhere), that there are no other planets like Earth (now we have started discovering them everywhere), that life is rare in the universe (now we have started discovering indications for it everywhere) et cetera.

Massive filaments fuel the growth of galaxies and supermassive black holes

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Based on direct observations researchers have discovered massive filaments between galaxies in a proto-cluster, extending over more than 1 million parsecs and providing the fuel for intense formation of stars and the growth of super massive black holes within the proto-cluster. (1)

A filament fit for space: Silk is proven to thrive in outer space temperatures

The scientists who discovered that natural silks get stronger the colder they get, have finally solved the puzzle of why. (2)

Delicate structures in space.

Delicate creatures on Earth.

Holding together.

Patiently watching.

Afraid to break.

But it is not the unbreakable that God dreams of.

One day you will break.

And realize that that was what the cosmos was afraid all that time…

Delicate silk. Delicate humans.

Breaking apart.

And within their weakness.

With their cries and despair.

Rising together.

To hold the cosmos in their fragile arms…

Reading. Seeing. Seeing better!

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Reading is a recent invention in the history of human culture — too recent for dedicated brain networks to have evolved specifically for it. How, then, do we accomplish this remarkable feat? As we learn to read, a brain region known as the ‘visual word form area’ (VWFA) becomes sensitive to script (letters or characters). However, some have claimed that the development of this area takes up (and thus detrimentally affects) space that is otherwise available for processing culturally relevant objects such as faces, houses or tools.

An international research team led by Falk Huettig (MPI and Radboud University Nijmegen) and Alexis Hervais-Adelman (MPI and University of Zurich) set out to test the effect of reading on the brain’s visual system. If learning to read leads to ‘competition’ with other visual areas in the brain, readers should have different brain activation patterns from non-readers — and not just for letters, but also for faces, tools, or houses. ‘Recycling’ of brain networks when learning to read has previously been thought to negatively affect evolutionary old functions such as face processing. Huettig and Hervais-Adelman, however, hypothesized that reading, rather than negatively affecting brain responses to non-orthographic (non-letter) objects, may, conversely, result in increased brain responses to visual stimuli in general. (1)

Seeing. Reading. Learning.

In an inactive cosmos we are active.

Don’t be fooled by the super nova or the black holes colliding.

There is silence in the cosmos.

And we break that silence with our chatter.

Seeing. Seeing more. And then even more!

Learning to read in a cosmos which says nothing.

Nothing but the obvious…

Listen to your self while reading aloud.

He doesn’t truly say anything.

Except only when you stay silent and listen to him…

Chaos. Numbers. Simulations.

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Digital computers use numbers based on flawed representations of real numbers, which may lead to inaccuracies when simulating the motion of molecules, weather systems and fluids, find scientists.

The study, published today in Advanced Theory and Simulations, shows that digital computers cannot reliably reproduce the behaviour of ‘chaotic systems’ which are widespread. This fundamental limitation could have implications for high performance computation (HPC) and for applications of machine learning to HPC.

Professor Peter Coveney, Director of the UCL Centre for Computational Science and study co-author, said: “Our work shows that the behaviour of the chaotic dynamical systems is richer than any digital computer can capture. Chaos is more commonplace than many people may realise and even for very simple chaotic systems, numbers used by digital computers can lead to errors that are not obvious but can have a big impact. Ultimately, computers can’t simulate everything.”

The team investigated the impact of using floating-point arithmetic — a method standardised by the IEEE and used since the 1950s to approximate real numbers on digital computers.

Digital computers use only rational numbers, ones that can be expressed as fractions. Moreover the denominator of these fractions must be a power of two, such as 2, 4, 8, 16, etc. There are infinitely more real numbers that cannot be expressed this way. (

An irrational universe.

Full of irrational people.

Trying to analyze it rationally.

Under the illusion that number we have invented can draw a sketch of the cosmos. And yet, nothing we have invented is anywhere to be seen but on a piece of paper. Can you limit the birth of a star on a piece of paper? Can you contain the death of the universe on an equation?

We believe we can.

And sadly, we do.

And at the moment we do, the universe indeed dies…

And a small voice will whisper in our ear…

Congratulations. You have now understood it all.

How irrationally rational everything is!

And inside the darkest night you will dance.


And for a brief moment the forest will look at you.


And for a brief moment the forest will see nothing…

But an empty broken CD. Full of data. Full of life…

Seeing better. And better. And better. Until we see nothing at all…

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A few years ago, a team of scientists at EPFL’s Laboratory of Nanoscale Biology, headed by Aleksandra Radenovic in the School of Engineering, developed an algorithm that can estimate a microscope’s resolution in just a few seconds based on a single image. The algorithm’s result indicates how closely a microscope is operating to its full potential. This could be particularly useful for the automated microscopes that have started appearing in research labs. The team’s findings have just been published in Nature Methods.

The scientists used Fourier’s transform as the basis for their algorithm, but they modified it so as to extract as much information as possible from a single image.

The results indicates how closely a microscope is operating to its full potential. The algorithm performs the calculation in just a few seconds and generates a single number. “Researchers can compare this number with the microscope’s maximum possible resolution to see whether the instrument can work even better or modify the experimental conditions and observe how the resolution evolves” says Adrien Descloux, the study’s lead author. (1)

We want to see better. We want to see everything.

So we magnify.

Until we see all the details.

And more.

And more.

And more!

Pushing it to the limit! To see everything!

Until we can distinguish nothing anymore!

Isn’t it funny? The more we analyze the cosmos the more we reach absolute zero. At the end, the point is a circle with zero radius. (source) At the end, in the midst of our greatest triumph, we will see nothing.

Ghosts casting shadows…

In a cosmos without any light…

Except the light we bring on our own…

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