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Showing posts with label Computing. Show all posts
Showing posts with label Computing. Show all posts

Sunday, 2 February 2020

The laws of physics that explain political polarization in elections

The study of political polarization, which has emerged around the world, may have much to gain from the use of some tools and formulas used by physics. [Image: MIT]

It may seem surprising, but theories and formulas derived from physics can be useful tools for understanding how democratic elections work, including how these systems fail to deliver on their promises and how they can be improved.

Alexander Siegenfeld (MIT) and Yaneer Bar-Yam (New England Institute of Complex Systems) took political-electoral data and analyzed it using various well-known laws of physics as tools. And they demonstrated how these laws can be used to describe the behavior of the data.

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The application of several of the physics formulas to the US electoral system revealed that the elections went through a transition in 1970, from a condition in which the election results reasonably captured the electorate's greatest political preferences, to a period of increasing instability, in which very small changes in voter preferences have led to significant changes towards more extreme political results in both directions.

The two physicists found that the Ising model , developed to explain the behavior of ferromagnets and other physical systems, is mathematically equivalent to certain election models and accurately describes the onset of instability in electoral systems.

In this regime of "unstable" elections, "a small change in voter opinion can dramatically alter the outcome of the election, just as the direction of a small push on a rock at the top of a hill can dramatically change its final location," said Siegenfeld .

"What happened in 1970 is a phase transition just like boiling water. The elections went from stable to unstable," added Bar-Yam.

Negative representation

The analysis shows that this instability can be associated with an unexpected situation in which the results oscillate in the opposite direction of how people's real preferences are changing. In other words, a small movement in the predominant opinions towards the left can result in a result more to the right and vice versa - a situation that the researchers call "negative representation".

"Our country seems more divided than ever, with the election results looking like a pendulum swinging with increasing strength," said Siegenfeld.

This long-term shift from a stable electoral situation to one marked by instability is similar to what happens with ferromagnetic metal exposed to a magnetic field, adds Siegenfeld, and can be described by the same mathematical formulas.

Predict the whole without knowing the parts

But why can the derived formulas for such different subjects be relevant to the political field?

Siegenfeld says that it is because in Physics it is not always necessary to know the details of the underlying objects or mechanisms in order to produce useful and significant results. He compares this to how physicists were able to describe the behavior of sound waves - which are essentially the aggregate movements of atoms - with great precision, long before they knew about the existence of atoms.

"When we apply physics to understand the fundamental particles of our Universe, we don't really know the underlying details of the theories," he said. "However, we can still make incredibly accurate predictions."

Likewise, researchers do not need to understand the reasons and opinions of each individual voter in order to conduct a meaningful analysis of their collective behavior.

As the pair's article states, "understanding the collective behavior of social systems can benefit from methods and concepts in physics, not because humans are similar to electrons, but because certain behaviors on a large scale can be understood without understanding small-scale details."


Article: Negative representation and instability in democratic elections

Authors: Alexander F. Siegenfeld, Yaneer Bar-Yam

Magazine: Nature Physics

DOI: 10.1038 / s41567-019-0739-6

Monday, 9 December 2019

The era of printed electronics is beginning

Large scale integrated circuit (LSI) prototypes straight out of the printer. [Image: Thor Balkhed]

Printed electronics

Swedish researchers say they have taken the missing step to bring electronic circuit printing from the laboratory to the factories, making it possible to apply organic electronics on a large scale.

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The decisive step was the integration between the new field of printed electronics and traditional silicon-based electronics manufactured by traditional mask and lithography techniques.

"This is a decisive step for a technology that was born at Linkoping University just over 17 years ago," said Professor Magnus Berggren.

"The advantage we have here is that we don't have to mix different manufacturing methods: Everything is done by screen printing and in relatively few processing steps. The key is to make sure the different layers finish in exactly the right place," added his colleague Peter Ersman.

Printing electronic circuits

Printing fully functional electronic circuits - they can be printed on flexible, transparent plastics or virtually any other material - has required a number of innovations over the past 17 years.

A first step was the creation of screen-printing screens that let you print extremely thin lines so that semiconductor inks can form components with precision and high density per area.

At least three additional challenges have since been faced: Reduce circuit size, increase quality so that the probability of all transistors in the circuit working is as close as possible to 100%, and - not least - integrating with the silicon-based circuits needed to process signals and communicate with the environment.

"One of the major advances is that we have been able to use printed circuits to interface with traditional silicon-based electronics. We have developed various types of printed circuits based on organic electrochemical transistors. One of them is the shift register, which can interface and handle contact between the silicon-based circuit and other electronic components such as sensors and displays. This means that we can now use a silicon chip with fewer contacts, which requires a smaller area and thus is much cheaper. , "said Berggren.

The internet of things will be the first major beneficiary of print electronics.

IoT and screens

The development of semiconductor inks was another decisive element for the miniaturization process and also for higher quality. "We can now place more than 1,000 organic electrochemical transistors on an A4 size plastic substrate and connect them in different ways to create different types of printed integrated circuits," said team member Professor Simone Fabiano.

These large-scale integrated circuits, or LSIs, can be used, for example, to power electrochromic screens themselves manufactured as printed electronics.

The big expectation, however, is that printed electronics will give the final push to make the low cost, low power circuits required by the internet of things.


Article: All-Printed Large-Scale Integrated Circuits Based on Organic Electrochemical Transistors
Authors: Peter Andersson Ersman, Roman Lassnig, Jan Strandberg, Deyu You, Vahid Keshmiri, Robert Forchheimer, Simone Fabiano, Goran Gustafsson, Magnus Berggren
Journal: Nature Communications
Vol .: 10, Article number: 5053
DOI: 10.1038 / s41467-019-13079-4

Saturday, 7 December 2019

Quantum light processors are demonstrated in practice

Interlaced 3D light beams allow for quantum operations at room temperature and macro scale

Optical quantum processor

Two international teams, working separately, built prototypes of quantum processors made of light.

Qubits formed by intertwining laser beams are expected to make quantum computers less error prone and allow scalability, that is, scaling up processors to a large number of qubits.

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"While today's quantum processors are impressive, it's unclear whether today's designs can scale to extremely large sizes. Our approach starts with extreme scalability - built in from the start - because the processor, called a cluster state, is made of light. , "said Professor Nicolas Menicucci of RMIT University in Australia and leader of one of the teams.

A cluster state is a large collection of intertwined quantum components that perform quantum calculations when measured in a specific way - all operating at macroscopic scale using normal photonic components.

Both teams met the two fundamental requirements for cluster state operation, which comprise a minimum amount of qubits and quantum entanglement in the proper structure for their use in computational calculations.

To this end, specially designed crystals convert common laser light into a type of quantum light called compressed light , which is woven into a cluster state by a network of mirrors, light splitters, and optical fibers.

While the light compression levels achieved so far - which are a measure of photonic processor quality - are too low to solve practical problems, the design is compatible with approaches to achieving next-generation compression levels.

"Our experiment demonstrates that this design is workable - and scalable," said Professor Hidehiro Yonezawa of the University of New South Wales.

Animation showing the temporal evolution of the cluster state generation scheme

Quantum processor at room temperature

Mikkel Larsen and his colleagues at the Technical University of Denmark prefer to call his optical quantum processor prototype a "light carpet."

This is because, instead of the threads of an ordinary carpet, the processor is in fact a carefully crafted web of thousands of intertwined pulses of light.

"Unlike traditional cluster states, we use the temporal degree of freedom to achieve a two-dimensional interlaced network of 30,000 light pulses. The experimental setup is really surprisingly simple. Most of the effort has gone into developing the idea of ​​state generation. cluster, "said Larsen.

The Danish team has also been able to make its light carpet handle quantum entanglement at room temperature, noting that, in addition to error correction and simplification of technology, quantum optical processors can be cheaper and more powerful as they will allow the rapid increase in the number of qubits.

An optical quantum computer, therefore, does not require the expensive and complicated cooling technology used by superconducting qubits. At the same time, light-based qubits, which carry information in laser light, hold the information longer and can transmit it over long distances.

"By distributing the state of the cluster generated in space and time, an optical quantum computer can also scale more easily to contain hundreds of qubits. This makes it a potential candidate for the next generation of larger and more powerful quantum computers," reinforced Professor Ulrik Andersen.


Article: Generation of time-domain-multiplexed two-dimensional cluster state
Authors: Warit Asavanant, Yu Shiozawa, Shota Yokoyama, Baramee Charoensombutamon, Hiroki Emura, Rafael N. Alexander, Shuntaro Takeda, Jun-Ichi Yoshikawa, Nicolas C. Menicucci , Hidehiro Yonezawa, Akira Furusawa
Magazine: Science
Vol. 373-376
DOI: 10.1126 / science.aay2645

Article: Deterministic generation of a two-dimensional cluster state
Authors: Mikkel V. Larsen, Xueshi Guo, Casper R. Breum, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen
Journal: Science
Vol. 366, Issue 6463, p. 369-372
DOI: 10.1126 / science.aay4354

Saturday, 23 November 2019

What can make artificial intelligence really intelligent?

Automated stupidity

Despite the many concerns about artificial intelligence and its growing role in society, the fact is that today's generation of artificial intelligence programs is not at all intelligent .

There are basically two types of machine learning: deep neural networks, those responsible for the famous "deep learning", and reinforcement learning networks. Both are based on system training, using huge amounts of data, to perform a specific task, for example making a decision.

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During training, the desired result is provided along with the task. Over time, the program learns to solve the task with ever faster accuracy, although no one understands exactly how the program works - it's the so-called "black box" of artificial intelligence .

"The problem with these machine learning processes is that they are basically completely dumb," says Professor Laurenz Wiskott of Ruhr University in Germany. "The underlying techniques date back to the 1980s. The only reason for today's success is that we have more computing power and more data at our disposal today."

But Professor Wiskott's team is trying to eliminate the stupidity of artificial intelligence and make it really smart.

Unsupervised artificial intelligence

Today artificial intelligence can be superior to humans specifically in the one task for which each program has been trained - it cannot generalize or transfer its knowledge even to similar tasks.

"What we want to know is, how can we avoid all this absurd and long training? And most of all: how can we make machine learning more flexible?" said Wiskott.

The strategy is to help machines autonomously discover structures in data. Tasks can include, for example, category formation or detection of gradual changes in videos. The idea is that this unsupervised learning allows computers to autonomously explore the world and perform tasks for which they have not been trained in detail.

"A task could be, for example, forming clusters," explains Wiskott. To do this, the computer is instructed to group similar data in search, for example, of a face in a photo. Turning the pixels into points in a three-dimensional space means grouping points whose coordinates are close to each other. If the distance between coordinates is greater, they will be allocated to different groups. This dispenses with the enormity of photos and their descriptions as used today.

This method offers more flexibility because this cluster formation is applicable not only to pictures of people, but also to cars, plants, houses or other objects.

Slow Principle

Another approach taken by the team is the slowness principle. In this case, it is not the photos that constitute the input signal, but moving images: If all the very slowly changing features are extracted from a video, structures appear that help construct an abstract representation of the environment. "Here, too, the goal is to pre-structure the input data," says Wiskott.

Eventually, researchers combine the two approaches in a modular way with supervised learning methods to create more flexible yet much more accurate applications.

"Greater flexibility naturally results in loss of performance," admits the researcher. "But in the long run, flexibility is indispensable if we want to develop robots that can handle new situations."

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Tuesday, 25 June 2019

New form of computing with light does not waste energy

The material inside the cube forms complex patterns that give the answer to the calculation directly. [Image: Hudson et al. - 10.1038 / s41467-019-10166-4]

Computation with light

Canadian researchers have developed an unprecedented and incredibly simple form of computing.

The entrances are provided through standard beams of light and shade, known as bands or fringes, which are fired across different facets of a hub containing a plastic material.

To know the result of the calculation just read the combined light fringes that emerge on the other side of the cube.

So far, the team has been able to use its new optical computing process to perform simple addition and subtraction operations.

The computing is highly localized, does not need energy source and operates completely within the spectrum of visible light.

The material in the cube reads and reacts "intuitively" to light, much like a plant does when it turns to the sun or as an octopus changes the color of its skin to adapt to the environment.

"We are very excited to be able to do addition and subtraction in this way, and we are thinking of ways to do other computational functions," said Professor Kalaichelvi Saravanamuttu of McMaster University. "These are autonomous materials that respond to stimuli and perform intelligent operations.

Visualization of the fringes of light emerging from the various faces of the cube. [Image: Hudson et al. - 10.1038 / s41467-019-10166-4]

Smart Objects

The technique, inspired by the natural biological systems it recalls, represents a completely new form of computation, which, according to the team, has the potential to perform complex and useful functions, and even others to be imagined, possibly organized along structures of neural networks.

The technology is based on a branch of chemistry called nonlinear dynamics, and uses materials designed and manufactured to produce specific reactions to light - a class of artificial materials known as metamaterials .

The amber polymeric artificial material is encapsulated within a glass cube about the size of a die used in a board game. The polymer begins as a liquid and turns into a gel in reaction to light.

The beam of light passes through the hub, exiting the opposite face toward a camera, which reads the results. The results are produced as light is refracted by the material inside the cube, whose components spontaneously form in thousands of filaments that react to light patterns to produce a new dimensional pattern that expresses the result.

As computation is embedded in the material, this optical computing technique will not replace current computers, but it can yield intelligent objects that give instant solutions to specific problems.

"We do not want to compete with existing computing technologies. We are trying to build materials with smarter and more sophisticated responses," said Fariha Mahmood, a co-author of the paper.


A soft photopolymer cuboid that computes with binary strings of white light
Alexander D. Hudson, Matthew R. Ponte, Fariha Mahmood, Thomas Pena Ventura, Kalaichelvi Saravanamuttu
Nature Communications Vol. 10, Article number: 2310 
DOI: 10.1038 / s41467- 019-10166-4

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