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Primeur weekly 2017-02-20

Focus

HPC expert Genias Benelux to show its skillful expertise in brandnew website ...

Are billion Euro Flagships the right way to finance innovative areas like graphene, human brain research and quantum computing? ...

Exascale supercomputing

Advanced fusion code led by PPPL selected to participate in Early Science Programmes on three new DOE Office of Science pre-exascale supercomputers ...

Focus on Europe

From robotics to particle physics: Data analytics gets the spotlight in Distinguished Talk series at ISC 2017 ...

A new spin on electronics ...

Data mining tools for personalized cancer treatment ...

Why host HPC in Iceland to tackle Big Data for life sciences at Earlham Insititute ...

Biological experiments become transparent - anywhere, any time ...

Middleware

IBM delivers new platform to help clients address storage challenges at massive scale ...

Hewlett Packard Enterprise unveils most significant 3PAR Flash storage innovations to date ...

Hardware

Tokyo Institute of Technology partners with DDN on Tsubame3.0 to build forward-looking AI and Big Data computing infrastructure ...

Mellanox demonstrates four times improvement in crypto performance with Innova IPsec 40G Ethernet network adapter ...

Supermicro launches BigTwin - the industry's highest performing Twin multi-node system supporting the full range of CPUs, maximum memory and all-flash NVMe ...

Applications

Researchers catch extreme waves with higher-resolution modelling ...

Researchers are creating software to 'clean' large datasets, making it easier for scientists and the public to use Big Data ...

Designing new materials from 'small' data ...

Success by deception ...

DNA computer brings 'intelligent drugs' a step closer ...

'Lossless' metamaterial could boost efficiency of lasers and other light-based devices ...

Perimeter Institute researchers apply machine learning to condensed matter physics ...

When treating brain aneurysms, two isn't always better than one ...

Real-time MRI analysis powered by supercomputers ...

Analyzing data for transportation systems using TACC's Rustler, XSEDE ECSS support ...

NCSA facilitates performance comparisons with China's nr. 1 supercomputer ...

IBM delivers Watson for cyber security to power cognitive security operations centres ...

The Cloud

Optimizing data centre placement and network design to strengthen Cloud computing ...

Dutch start-up solution impacts data centres ...

OpenFog Consortium releases landmark reference architecture for Fog computing ...

IBM brings machine learning to the private Cloud ...

IBM accelerates hybrid Cloud adoption by enabling channel partners to offer VMware solutions ...

Oracle launches Cloud service to help organisations integrate disparate data and drive real-time analytics ...

Success by deception

13 Feb 2017 Zurich - When computers independently identify bodies of water and their outlines in satellite images, or beat the world's best professional players at the board game Go, then adaptive algorithms are working in the background. Programmers supply these algorithms with known examples in a training phase: images of bodies of water and land, or sequences of Go moves that have led to success or failure in tournaments. Similarly to how our brain nerve cells produce new networks during learning processes, the special algorithms adapt in the learning phase based on the examples presented to them. This continues until they are able to differentiate bodies of water from land in unknown photos, or successful sequences of moves from unsuccessful ones.

Until now, these artificial neural networks have been used in machine learning with a known decision-making criterion: we know what a body of water is and which sequences of moves were successful in Go tournaments.

Now, a group of scientists working under Sebastian Huber, Professor of Condensed Matter Theory and Quantum Optics at ETH Zurich, have expanded the applications for these neural networks by developing a method that not only allows categorisation of any data, but also recognises whether complex datasets contain categories at all.

Questions of this kind arise in science: for example, the method could be useful for analysis of measurements from particle accelerators or astronomical observations. Physicists could thus filter out the most promising measurements from their often unmanageable quantities of measurement data. Pharmacologists could extract molecules with a certain probability of having a specific pharmaceutical effect or side-effect from large molecular databases. And data scientists could sort huge masses of disordered data ripples and obtain usable information (data mining).

The ETH researchers applied their method to an intensively researched phenomenon of theoretical physics: a many-body system of interacting magnetic dipoles that never reaches a state of equilibrium - even in the long term. Such systems have been described recently, but it is not yet known in detail which quantum physical properties prevent a many-body system from entering a state of equilibrium. In particular, it is unclear where exactly the boundary lies between systems that reach equilibrium and those that do not.

In order to locate this boundary, the scientists developed the "act as if" principle: taking data from quantum systems, they established an arbitrary boundary based on one parameter and used it to divide the data into two groups. They then trained an artificial neural network by pretending to it that one group reached a state of equilibrium while the other did not. Thus, the researchers acted as if they knew where the boundary was.

They trained the network countless times overall, with a different boundary each time, and tested the network's ability to sort data after each session. The result was that, in many cases, the network struggled to classify the data as the scientists had. But in some cases, the division into the two groups was very accurate.

The researchers were able to show that this sorting performance depends on the location of the boundary. Evert van Nieuwenburg, a doctoral student in Huber's group, explained this as follows: "By choosing to train with a boundary far away from the actual boundary (which I don't know), I am able to mislead the network. Ultimately we're training the network incorrectly - and incorrectly trained networks are very bad at classifying data." However, if by chance a boundary is chosen close to the actual boundary, a highly efficient algorithm is produced. By determining the algorithm's performance, the researchers were able to track down the boundary between quantum systems that reach equilibrium and those that do not: the boundary is located where the network's sorting performance is highest.

The researchers also demonstrated the capabilities of their new method using two further questions from theoretical physics: topological phase transitions in one-dimensional solids and the Ising model, which describes magnetism inside solids.

The new method can also be illustrated in simplified form with a thought experiment, where we want to classify red, reddish, bluish and blue balls into two groups. We assume that we have no idea of how such a classification might reasonably look.

If a neural network is trained by telling it that the dividing line lies somewhere in the red region, then this will confuse the network. "You try to teach the network that blue and reddish balls are the same and ask it to differentiate between red and red balls, which it simply isn't able to do", stated Sebastian Huber.

On the other hand, if you place the boundary in the violet colour spectrum, the network learns an actual difference and sorts the balls into red and blue groups. However, one does not need to know in advance that the dividing line should be in the violet region. By comparing the sorting performance at a variety of chosen boundaries, this boundary can be found with no prior knowledge.

The paper titled "
Source: ETH Zurich

Back to Table of contents

Primeur weekly 2017-02-20

Focus

HPC expert Genias Benelux to show its skillful expertise in brandnew website ...

Are billion Euro Flagships the right way to finance innovative areas like graphene, human brain research and quantum computing? ...

Exascale supercomputing

Advanced fusion code led by PPPL selected to participate in Early Science Programmes on three new DOE Office of Science pre-exascale supercomputers ...

Focus on Europe

From robotics to particle physics: Data analytics gets the spotlight in Distinguished Talk series at ISC 2017 ...

A new spin on electronics ...

Data mining tools for personalized cancer treatment ...

Why host HPC in Iceland to tackle Big Data for life sciences at Earlham Insititute ...

Biological experiments become transparent - anywhere, any time ...

Middleware

IBM delivers new platform to help clients address storage challenges at massive scale ...

Hewlett Packard Enterprise unveils most significant 3PAR Flash storage innovations to date ...

Hardware

Tokyo Institute of Technology partners with DDN on Tsubame3.0 to build forward-looking AI and Big Data computing infrastructure ...

Mellanox demonstrates four times improvement in crypto performance with Innova IPsec 40G Ethernet network adapter ...

Supermicro launches BigTwin - the industry's highest performing Twin multi-node system supporting the full range of CPUs, maximum memory and all-flash NVMe ...

Applications

Researchers catch extreme waves with higher-resolution modelling ...

Researchers are creating software to 'clean' large datasets, making it easier for scientists and the public to use Big Data ...

Designing new materials from 'small' data ...

Success by deception ...

DNA computer brings 'intelligent drugs' a step closer ...

'Lossless' metamaterial could boost efficiency of lasers and other light-based devices ...

Perimeter Institute researchers apply machine learning to condensed matter physics ...

When treating brain aneurysms, two isn't always better than one ...

Real-time MRI analysis powered by supercomputers ...

Analyzing data for transportation systems using TACC's Rustler, XSEDE ECSS support ...

NCSA facilitates performance comparisons with China's nr. 1 supercomputer ...

IBM delivers Watson for cyber security to power cognitive security operations centres ...

The Cloud

Optimizing data centre placement and network design to strengthen Cloud computing ...

Dutch start-up solution impacts data centres ...

OpenFog Consortium releases landmark reference architecture for Fog computing ...

IBM brings machine learning to the private Cloud ...

IBM accelerates hybrid Cloud adoption by enabling channel partners to offer VMware solutions ...

Oracle launches Cloud service to help organisations integrate disparate data and drive real-time analytics ...