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Category: supercomputing – Page 67

They say you can’t judge a book by its cover. But the human immune system does just that when it comes to finding and attacking harmful microbes such as the coronavirus. It relies on being able to recognize foreign intruders and generate antibodies to destroy them. Unfortunately, the coronavirus uses a sugary coating of molecules called glycans to camouflage itself as harmless from the defending antibodies.

Simulations on the National Science Foundation (NSF)-funded Frontera supercomputer at the Texas Advanced Computing Center (TACC) have revealed the atomic makeup of the coronavirus’s sugary shield. What’s more, simulation and modeling show that glycans also prime the coronavirus for infection by changing the shape of its spike . Scientists hope this basic research will add to the arsenal of knowledge needed to defeat the COVID-19 virus.

Sugar-like molecules called glycans coat each of the 65-odd spike proteins that adorn the coronavirus. Glycans account for about 40 percent of the spike protein by weight. The spike proteins are critical to cell infection because they lock onto the , giving the virus entry into the cell.

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Researchers at the Nanoscience Center and at the Faculty of Information Technology at the University of Jyväskylä in Finland have demonstrated that new distance-based machine learning methods developed at the University of Jyväskylä are capable of predicting structures and atomic dynamics of nanoparticles reliably. The new methods are significantly faster than traditional simulation methods used for nanoparticle research and will facilitate more efficient explorations of particle-particle reactions and particles’ functionality in their environment. The study was published in a Special Issue devoted to machine learning in the Journal of Physical Chemistry on May 15, 2020.

The new methods were applied to ligand-stabilized metal , which have been long studied at the Nanoscience Center at the University of Jyväskylä. Last year, the researchers published a method that is able to successfully predict binding sites of the stabilizing ligand molecules on the nanoparticle surface. Now, a new tool was created that can reliably predict based on the atomic structure of the particle, without the need to use numerically heavy electronic structure computations. The tool facilitates Monte Carlo simulations of the atom dynamics of the particles at elevated temperatures.

Potential energy of a system is a fundamental quantity in computational nanoscience, since it allows for quantitative evaluations of system’s stability, rates of chemical reactions and strengths of interatomic bonds. Ligand-stabilized metal nanoparticles have many types of interatomic bonds of varying chemical strength, and traditionally the energy evaluations have been done by using the so-called density functional theory (DFT) that often results in numerically heavy computations requiring the use of supercomputers. This has precluded efficient simulations to understand nanoparticles’ functionalities, e.g., as catalysts, or interactions with biological objects such as proteins, viruses, or DNA. Machine learning methods, once trained to model the systems reliably, can speed up the simulations by several orders of magnitude.

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MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors—silicon-based components that mimic the information-transmitting synapses in the human brain.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several , the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices—electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, , and would carry out complex computational tasks that only today’s supercomputers can handle.

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Someday, we might be able to carry around tiny, AI brains that can function without supercomputers, the internet or the cloud. Researchers from MIT say their new “brain-on-a-chip” design gets us one step closer to that future. A group of engineers put tens of thousands of artificial brain synapses, known as memristors, on a single chip that’s smaller than a piece of confetti.

In a paper published in Nature Nanotechnology, the researchers explain how their brain-inspired chip was able to remember and recreate a gray-scale image of Captain America’s shield and reliably alter an image of MIT’s Killian Court by sharpening and blurring it. Those tests may seem minor, but the team believes the chip design could advance the development of small, portable AI devices and carry out complex computational tasks that today only supercomputers are capable of.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet.”

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Physicists around the world are cracking open the proton, within the nucleus of the atom, to see what’s inside.

The proton is a fundamental building block of the atomic nucleus, and among other things it’s used as a medical probe in magnetic resonance imaging. It also has a rich inner structure made up of subatomic particles called quarks and gluons, which bind the quarks together.

Scientists are running a unique experiment involving the world’s largest particle physics laboratory and the world’s fastest university supercomputer to see and understand the dynamic world inside the proton.

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Quantum computers theoretically can prove more powerful than any supercomputer, and now scientists calculate just what quantum computers need to attain such “quantum supremacy,” and whether or not Google achieved it with its claims last year.

Whereas classical computers switch transistors either on or off to symbolize data as ones or zeroes, quantum computers use quantum bits—qubits—that, because of the bizarre nature of quantum physics, can be in a state of superposition where they are both 1 and 0 simultaneously.

Superposition lets one qubit perform two calculations at once, and if two qubits are linked through a quantum effect known as entanglement, they can help perform 22 or four calculations simultaneously; three qubits, 23 or eight calculations; and so on. In principle, a quantum computer with 300 qubits could perform more calculations in an instant than there are atoms in the visible universe.

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Microsoft announced Tuesday that it has built the fifth most powerful computer on Earth.

Packed with 285,000 and 10,000 GPUs, the was built in collaboration with San Francisco-based artificial intelligence research organization OpenAI. Microsoft announced its partnership with OpenAI last year and contributed $1 billion to the project.

The will operate as part of Microsoft’s Azure cloud computing system. The technology giant expects to achieve significantly better results from a single massive supercomputer system than from large numbers of smaller, isolated models.

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Several supercomputers in Europe have been hacked in the past few days. Attackers are thought to use these supercomputers for mining Monero (XMR).

A massive attack was carried out on some supercomputers based in Germany, the UK and Switzerland. These events first surfaced with the announcement of the University of Edinburgh on Monday. University of Edinburgh; He explained that the supercomputer known as ARCHER has detected a “vulnerability in the input nodes” and the system has been disabled. Authorities had to reset their SSH password to prevent the attack.

The attacks were not limited to this. An organization called bwHPC in Germany also made a statement on Monday, and five different supercomputers in Germany; It announced that it was closed due to “vulnerabilities” similar to those in the UK.

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With the help of the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Juliette Stecenko is exploring cosmology—a branch of astronomy that investigates the origin and evolution of the universe, from the Big Bang to today and into the future. As an intern through DOE’s Science Undergraduate Laboratory Internships (SULI) program, administered at Brookhaven by the Office of Educational Programs (OEP), Stecenko is using modern supercomputers and quantum computing platforms to perform astronomy simulations that may help us better understand where we came from.

Stecenko works under the guidance of Michael McGuigan, a computational scientist in the quantum computing group at Brookhaven’s Computational Science Initiative. The two have been collaborating on simulating Casimir energy—a small force that two electrically neutral surfaces held a tiny distance apart will experience from quantum, atomic, or subatomic fluctuations in the vacuum of space. The vacuum energy of the universe and the Casimir pressure of this energy could be a possible explanation of the origin and evolution of the universe, as well a possible cause of its accelerated expansion.

“Casimir energy is something scientists can measure in the laboratory and is especially important for nanoscience, or in cosmology, in the very early universe when the universe was very small,” McGuigan said.

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