Lifeboat Foundation

Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements. These combinations are virtually endless, and each has an infinite multitude of possible crystal structures; it is not feasible to test them all and choose the best option (for instance, the hardest compound) either in an experiment or in silico. The computational method developed by Skoltech professor Artem R. Oganov and his PhD student Zahed Allahyari solves this major problem of theoretical materials science. Oganov and Allahyari presented their method in the MendS code (stands for Mendelevian Search) and tested it on superhard and magnetic materials.

“In 2006, we developed an algorithm that can predict the crystal structure of a given fixed combination of chemical elements. Then we increased its predictive powers by teaching it to work without a specific combination — so one calculation would give you all stable compounds of given elements and their respective crystal structures. The new method tackles a much more ambitious task: here, we pick neither a precise compound nor even specific chemical elements — rather, we search through all possible combinations of all chemical elements, taking into account all possible crystal structures, and find those that have the needed properties (e.g., highest hardness or highest magnetization)” says Artem Oganov, Skoltech and MIPT professor, Fellow of the Royal Society of Chemistry and a member of Academia Europaea.

The researchers first figured out that it was possible to build an abstract chemical space so that compounds that would be close to each other in this space would have similar properties. Thus, all materials with peculiar properties (for example, superhard materials) will be clustered in certain areas, and evolutionary algorithms will be particularly effective for finding the best material. The Mendelevian Search algorithm runs through a double evolutionary search: for each point in the chemical space, it looks for the best crystal structure, and at the same time these found compounds compete against each other, mate and mutate in a natural selection of the best one.

Brain injuries can vary greatly in their severity, but assessing the extent of the damage is far from a simple undertaking. Scientists in the UK have developed a new AI algorithm that could help narrow the margin for error, with the ability to detect and categorize different types of brain lesions to gauge the impact of an injury.

One of the tools doctors use to assess brain injuries is a CT scan, which can reveal signs of damage, such as lesions, on the brain. But analyzing these scans to reach a diagnosis is a time-consuming process for radiologists, and given the complex nature of the organ, it can see tell-tale signs often overlooked.

“CT is an incredibly important diagnostic tool, but it’s rarely used quantitatively,” said Professor David Menon, from the University of Cambridge and senior author of the new study. “Often, much of the rich information available in a CT scan is missed, and as researchers, we know that the type, volume and location of a lesion on the brain are important to patient outcomes.”

That strategy was unveiled in a directive on Wednesday by the Ministry of Industry and Information Technology (MIIT), which called on local authorities in 23 provinces, five autonomous regions and four municipalities to support the establishment of these new big data centres, which will help bolster efforts to upgrade the country’s manufacturing sector.

The Ministry of Industry and Information Technology has called on local authorities in 23 provinces, five autonomous regions and four municipalities to support the establishment of new ‘industrial big data’ centres, which would bolster the digital transformation of various industries.

The present paper addresses the high Reynolds number, two-dimensional, steady laminar flow separation phenomenon near an interior (concave) corner. A very fast Alternating Direction Implicit finite difference approach is used to solve the Interacting Boundary Layer approximation to the Navier Stokes equations in a conformal plane. Solutions are presented for corner angles up to 18° for Reynolds numbers (based on forebody length) up to 10⁸. Convergence properties and accuracy levels are identified in order to provide reliability estimates of the results. Limitations to the numerical algorithm for large separation regions at high Reynolds numbers are identified.

Circa 2012

Quantum computational algorithms exploit quantum mechanics to solve problems exponentially faster than the best classical algorithms^1,2,3. Shor’s quantum algorithm⁴ for fast number factoring is a key example and the prime motivator in the international effort to realize a quantum computer⁵. However, due to the substantial resource requirement, to date there have been only four small-scale demonstrations^6,7,8,9. Here, we address this resource demand and demonstrate a scalable version of Shor’s algorithm in which the n-qubit control register is replaced by a single qubit that is recycled n times: the total number of qubits is one-third of that required in the standard protocol^10,11. Encoding the work register in higher-dimensional states, we implement a two-photon compiled algorithm to factor N = 21. The algorithmic output is distinguishable from noise, in contrast to previous demonstrations. These results point to larger-scale implementations of Shor’s algorithm by harnessing scalable resource reductions applicable to all physical architectures.

In the week of April 12–18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too —and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.

When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

An AI trained to spot objects hidden against a background is able to see through camouflage and outperforms existing algorithms at the task.

Researchers at Northwestern University have developed a garment designed to confuse digital surveillance algorithms into thinking you don’t exist.

The U.S. space agency National Aeronautics Space Administration (NASA), European Space Agency (ESA), and Japan Aerospace Exploration Agency (JAXA) are inviting coders, entrepreneurs, scientists, designers, storytellers, makers, builders, artists, and technologists to participate in a virtual hackathon May 30–31 dedicated to putting open data to work in developing solutions to issues related to the COVID-19 pandemic.

During the global Space Apps COVID-19 Challenge, participants from around the world will create virtual teams that – during a 48-hour period – will use Earth observation data to propose solutions to COVID-19-related challenges ranging from studying the coronavirus that causes COVID-19 and its spread to the impact the disease is having on the Earth system. Registration for this challenge opens in mid-May.

“There’s a tremendous need for our collective ingenuity right now,” said Thomas Zurbuchen, associate administrator for NASA’s Science Mission Directorate. “I can’t imagine a more worthy focus than COVID-19 on which to direct the energy and enthusiasm from around the world with the Space Apps Challenge that always generates such amazing solutions.”

The unique capabilities of NASA and its partner space agencies in the areas of science and technology enable them to lend a hand during this global crisis. Since the start of the global outbreak, Earth science specialists from each agency have been exploring ways to use unique Earth observation data to aid understanding of the interplay of the Earth system – on global to local scales – with aspects of the COVID-19 outbreak, including, potentially, our ability to combat it. The hackathon will also examine the human and economic response to the virus.

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