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Category: mapping – Page 55

We stored the light by putting it in a suitcase so to speak, only that in our case the suitcase was made of a cloud of cold atoms,” says physicist Patrick Windpassinger from Mainz University in Germany. “We moved this suitcase over a short distance and then took the light out again.

The storage and transfer of information is a fundamental part of any computing system, and quantum computing systems are no different – if we’re going to benefit from the speed and security of quantum computers and a quantum internet, then we need to figure out how to shift quantum data around.

One of the ways scientists are approaching this is through optical quantum memory, or using light to store data as maps of particle states, and a new study reports on what researchers are calling a milestone in the field: the successful storage and transfer of light using quantum memory.

The researchers weren’t able to transfer the light very far – just 1.2 millimetres or 0.05 inches – but the process outlined here could form the foundation of the quantum-powered computers and communication systems of the future.

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Scientists from Scripps Research and Los Alamos National Laboratory have devised a method for mapping in unprecedented detail the thickets of slippery sugar molecules that help shield HIV from the immune system.

Mapping these shields will give researchers a more complete understanding of why antibodies react to some spots on the virus but not others, and may shape the design of new vaccines that target the most vulnerable and accessible sites on HIV and other viruses.

The sugar molecules, or “glycans,” are loose and stringy, and function as shields because they are difficult for antibodies to grip and block access to the . The shields form on the outermost spike proteins of HIV and many other viruses, including SARS-CoV-2, the coronavirus that causes COVID-19, because these viruses have evolved sites on their spike proteins where glycan molecules—normally abundant in cells—will automatically attach.

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Head Image Caption: Street level view of 3D-reconstructed Chelsea, Manhattan

Historians and nostalgic residents alike take an interest in how cities were constructed and how they developed — and now there’s a tool for that. Google AI recently launched the open-source browser-based toolset “rǝ,” which was created to enable the exploration of city transitions from 1800 to 2000 virtually in a three-dimensional view.

Google AI says the name rǝ is pronounced as “re-turn” and derives its meaning from “reconstruction, research, recreation and remembering.” This scalable system runs on Google Cloud and Kubernetes and reconstructs cities from historical maps and photos.

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In this brief, at times controversial— even radical—volume. Dr. Ian C. Hale guides us through likely scenarios and gives us life-saving recommendations for effectively dealing with the next waves of the COVID-19 pandemic. This is a must read for public policy makers, medical professionals, and those mapping out their financial future in the post-corona world.

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There are four ways drones typically navigate. Either they use GPS or other beacons, or they accept guidance instructions from a computer, or they navigate off a stored map, or they are flown by an expert in control.

What do you when absolutely none of the four are possible?

You put AI on the drone and it flies itself with no outside source of data, no built-in mapping, and no operator in control.

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From the understated opulence of a Bentley to the stalwart family minivan to the utilitarian pickup, Americans know that the car you drive is an outward statement of personality. You are what you drive, as the saying goes, and researchers at Stanford have just taken that maxim to a new level.

Using computer algorithms that can see and learn, they have analyzed millions of publicly available images on Google Street View. The researchers say they can use that knowledge to determine the political leanings of a given neighborhood just by looking at the cars on the streets.

“Using easily obtainable visual data, we can learn so much about our communities, on par with some information that takes billions of dollars to obtain via census surveys. More importantly, this research opens up more possibilities of virtually continuous study of our society using sometimes cheaply available visual data,” said Fei-Fei Li, an associate professor of computer science at Stanford and director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab, where the work was done.

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