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If you thought SpaceX launching 60 Starlink satellites at once was impressive, Cornell University has managed 105 small satellites. The ChipSats, called Sprites, forming a swarm of cracker-sized nanosatellites were deployed from the Kicksat-2 CubeSat on March 18, 2019 at an altitude of 300 km (186 mi) and contact was established by Stanford University and NASA Ames engineers the next day by a Cornell satellite ground station.

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A mysterious large mass of material has been discovered beneath the largest crater in our solar system—the Moon’s South Pole-Aitken basin—and may contain metal from the asteroid that crashed into the Moon and formed the crater, according to a Baylor University study.

“Imagine taking a pile of metal five times larger than the Big Island of Hawaii and burying it underground. That’s roughly how much unexpected mass we detected,” said lead author Peter B. James.

Ph.D., assistant professor of planetary geophysics in Baylor’s College of Arts & Sciences. The itself is oval-shaped, as wide as 2,000 kilometers—roughly the distance between Waco, Texas, and Washington, D.C.—and several miles deep. Despite its size, it cannot be seen from Earth because it is on the far side of the Moon.

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Low tech sometimes is really good because when systems can be exploited then basically you see that no tech is sometimes best.


Election Systems & Software has championed electronic voting machines in the US. Now it has had a change of heart about the need for paper records of votes.

Cyber threats: Over half a million electronic machines are used in big US elections. Many produce paper copies of votes that can be used to audit electronic results, but some don’t. That’s a problem because security experts have shown that machines can be hacked.

The news: Tom Burt, Election Systems & Software’s chief executive, said in an op-ed in the political newspaper Roll Call that it will no longer sell paperless voting machines as the primary voting device in jurisdictions. Burt also called on Congress to make paper backups mandatory for all electronic votes cast, and to require all voting equipment suppliers to submit their machines to robust cybersecurity testing.

A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.

The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.

According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”

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