A Spare Tire Made From Countless Rolls of Duct Tape Is Surprisingly Durable

A Spare Tire Made From Countless Rolls of Duct Tape Is Surprisingly Durable

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Spare tires aren’t cheap, and do you really want to spend $80 on something you might never actually use? Not when 20 rolls of duct tape can apparently be used to make a remarkably functional spare tire, as the mechanics at YouTube’s Life OD discovered.

Do you want to take a five-hour road trip on a set of four duct tape tires? No. Do you want to attempt to drift or do burn outs with Goodyear alternatives made from the same material that’s holding your couch together? Also not a great idea. But this experiment is yet another good reason why you should never leave home without a trunk full of duct tape.

[YouTube via Likecool]

Tech

via Gizmodo http://gizmodo.com

May 30, 2018 at 08:03AM

HP gaming headset cools you down using thermoelectrics

HP gaming headset cools you down using thermoelectrics

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HP

One of the worst things about over-the-ear and on-ear headsets is that they tend to feel hot and uncomfortable after a few hours, especially if you live in a muggy environment. HP has just announced a pair of headphones that can keep you cool even during whole-day gaming sessions — unlike other similar options, though, they don’t use fans or cooling gels. At the HP Gaming Festival in Beijing, the tech giant has launched a number of new devices under its Omen gaming line, including the Mindframe headset that uses a patented thermoelectric cooling technique.

The headphones have a thermoelectric device inside their earcups that conducts heat from the acoustic chamber and directs it outside. Engadget Senior Editor Devindra Hardawar got to hold a sample of the device, and he said it was like having an AC pressed against the palm of his hand. If the technology translates well in headphones form, then the Mindframe could truly be comfortable to wear even in the midst of oppressive heat.

In addition to its cooling capabilities, the headset has a self-adjusting lightweight suspension headband for weight distribution, noise-cancelling unidirectional microphone, 3D spatial awareness and 7.1 virtual surround sound. HP didn’t reveal the Mindframe’s retail price, but it will be available sometime in the second half of 2018.

Tech

via Engadget http://www.engadget.com

May 30, 2018 at 02:06AM

Skydio’s R1 drone can autonomously follow your car, too

Skydio’s R1 drone can autonomously follow your car, too

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Skydio

Skydio’s AI-powered R1 drone can autonomously track subjects like a champ, but until now it’s been limited to following humans. Now, the company has introduced a feature called Car Follow cinematic mode that can film you on four wheels and not just two feet. Skydio said it trained the R1’s neural networks on large data sets of car images, helping the 13 cameras automatically follow your vehicle while ducking any obstacles.

The new feature lets you capture four-by-four offroading, autocross racing or even golf cart riding. The only caveat is that you have to be on a closed course, so you can’t just film yourself driving down the street.

Skydio also introduced four new cinematic modes: car tripod, car follow, quarter follow and quarter lead. It’s also improved the lead mode, optimizing the AI engine “for more intelligent behavior around obstacles,” the company said. The R1 will now show you where it’s going to land, and a new smartphone UI makes it easier to select the tracking mode.

The Skydio is already the best follow-me drone on the market, Engadget’s James Trew said, and the new features make it a lot more versatile. It’s also got an excellent 4K camera and stabilizer, and while a protective bumper protects it from obstacles, it’s excellent at avoiding them, even in tight spaces. The only caveat is the price: The R1 costs $2,499, nearly as much as DJI’s very high-end Inspire 2.

Tech

via Engadget http://www.engadget.com

May 30, 2018 at 08:06AM

Google’s Sidewalk Labs made the ultimate public transport guide

Google’s Sidewalk Labs made the ultimate public transport guide

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Bloomberg via Getty Images

Google’s parent company, Alphabet, has an offshoot called Sidewalk Labs tasked with improving urban living. The division gave birth to Coord, a spin-off which is launching a smart route planner today for people in New York City and Washington DC. The web app supports multiple modes of transportation — bus, subway and bike rentals — and will recommend different combinations based on live, street-level data. It’s a unique blend — other navigation apps don’t include dockless bike sharing services such as Spin and Jump. That means you can quickly locate the nearest two-wheeler and judge whether it would be quicker to take the bus or tube.

Coord built the app using two in-house APIs. The Bikeshare API combines data from over 100 companies into a single, comparable place. The Routing API, meanwhile, allows Coord to mix different public transport options in new, city-savvy ways. It might recommend a bicycle, for instance, if you’re boarding a train later on that accepts them. If you want to use a docked bike scheme instead — Citi Bike or Capital Bikeshare, for example — Coord will scan nearby docks and those near your destination. If either looks dicey — say you’re leaving home in the morning and there’s only one space in the dock outside your office — it’ll likely suggest something else.

For now, the web app is merely a “demo tool”. It does, however, provide insight into the company and Sidewalk Labs’ larger mission. The latter is currently drawing up plans for a smart neighborhood near the Lake Ontario waterfront in Toronto. The team’s initial pitch was full of bold ideas including modular buildings and a network of underground tunnels for delivery robots and waste removal. The document also proposed a car-free zone with, perhaps, some form of subscription for limitless bus, bike and autonomous pods access. For that to work, though, the company needs a platform that can guide and track citizens as they move around town.

Coord is a separate entity, which means it has a slightly different goal to Sidewalk Labs. The company has developed a Surveyor app, for instance, which can quickly label curb-side parking and bus stops. It could be useful for ride-hailing services such as Lyft and Uber, as well as self-driving cars that need to know where it’s safe to pick up passengers and drop off packages. Regardless, it’s easy to see how Coord’s new route planning tool could be used by Sidewalk Labs to perfect its smart neighborhood dream.

Tech

via Engadget http://www.engadget.com

May 30, 2018 at 08:06AM

Cloud-based quantum computer takes on deuteron and wins

Cloud-based quantum computer takes on deuteron and wins

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First it was the electron behavior in a hydrogen molecule, then beryllium dihydride joined the club. Now, quantum computers have been used to calculate some of the properties of an atomic nucleus: the deuterium nucleus to be precise.

What we are witnessing are two concurrent and useful processes. The first, which we have covered extensively, is the development and availability of quantum computers. But, I’ve not really discussed the second at all: the development of algorithms.

You see, theorists—the potential users of quantum computers—have a dilemma. Quantum computers hold a lot of promise. It is highly likely that a good quantum computer can calculate the properties of things like molecules and atomic nuclei much more efficiently than a classical computer. Unfortunately, the current generation of quantum computers, especially those that the average theorist can get access to, are rather limited. This gives the theorists a challenge: can they make computations less resource-intensive so that they can be performed on the currently available hardware?

Most of you will be thinking, well, duh, of course, this happens all the time. But it happens all the time with classical computers. What we are seeing now is that this process is being extended tor quantum computing algorithms too. 

Properties of the nucleus?

The nucleus is a scary place for people like me, who prefer the gentler world of whole atoms and molecules. A nucleus consists of protons and neutrons that are bound together by the strong force. The strong force’s range is so short that protons and neutrons basically have to be within a few femtometers (10-15m) of each other before they stick together. Despite this, however, the nucleus has structure. 

Picture a deuterium nucleus: it only has one proton and one neutron. The two are not stuck to each other like old leftovers at the back of your fridge, though. It is more like they are attached via a rubber band and vibrate around each other. Given a bit of energy (via an X-Ray or a gamma ray), the vibrations will get faster. The bond that holds the proton and neutron together can also snap, like an overstretched rubber band, causing the nucleus to fly apart. A sufficiently energetic gamma ray can cause this to happen. 

What we are interested in knowing is how much energy it takes to go from one vibrational state to the next. We want to know the energy at which the nucleus will fall apart. We would also like to know the minimum energy of the nucleus.

Compressing the calculation

The issue with calculating these properties is that it takes a lot of resources. The energy landscape—the rubber band that holds the neutron and proton together—is a sum of, possibly, an infinite number of terms. The number of states we can examine for the two nucleons is limited only by the length of the sum used to calculate the energy landscape.

The result is that even a small calculation requires a lot of qubits. 

There is a second consideration when it comes to quantum computations, though: how many and what type of operations do you need to perform to complete the calculation? It’s not just a matter of having a limited number of qubits; over time, they’ll lose their ability to hold quantum information, and the amount of time this takes depends on the hardware that’s being used. All algorithms have to work within this limitation, which is often considered in terms of what’s called computational depth (or volume). 

To reduce the number of operations, the researchers noted that many two-qubit operations could be replaced by single-qubit operations.To understand how, it helps to think of a qubit as an arrow that points to a location on a sphere—we don’t know the direction that the arrow is pointing, only that it has a likely value. 

A single-qubit operation rotates the arrow by a fixed amount. We still don’t know where the arrow is pointing, but we do know how much it has changed. A two-qubit operation flips the arrow of the targeted qubit, depending on the direction of the arrow of the control qubit. (Here, we have no idea where any of the the arrows are pointing.)

Qubits are not perfect, though. This essentially means that the arrow doesn’t have a precise direction; there’s some margin of error in its direction. For a single-qubit operation, this is not so bad. The operations we perform in rotating it are reasonably precise, so the error does not grow too quickly. But, for two-qubit operations, the error grows more rapidly because the error in the control qubit is copied to the target qubit. So, reducing the number of qubits involved in each operation allows a quantum computer to perform longer calculations.

Optimized

And that is what the researchers did. They reduced the calculation of nucleon energy levels to mostly single-qubit operations, with just a few two-qubit ones thrown in. From this, they were able to calculate the ground state energy and estimate the binding energy (the energy required to break up the nucleus) for a deuterium nucleus. 

As with all quantum computations, the results are statistical in nature, so the researchers have to perform the computation many times and take the average result. In this case, the researchers made use of two quantum computers—the IBM QX5 and the Rigetti 19Q—via their publicly available cloud computing APIs. This limited the number of computations that they could perform. Despite this, they obtained results within a few percent of the experimental values.

The calculation itself is nothing special. This particular nucleus has long been solvable with classical computers. The point was to start developing ways to fit calculations on small quantum computers. And this is important, because while many companies and research labs are developing quantum computers with more qubits, they’re not increasing the number of operations we can do before a large error builds up on the qubit. That means all those new qubits have to be used to correct errors rather than perform calculations.

This does not mean that quantum computers will be limited to a low number of qubits or a low number of logic operations indefinitely. Instead, you should think of this as accepting that progress may be slow, and we’re figuring out how to use what we have already as soon as possible.

Physical Review Letters, 2018, DOI: 10.1103/PhysRevLett.120.210501

Tech

via Ars Technica https://arstechnica.com

May 30, 2018 at 10:51AM

Audi E-Tron EV gets side cameras instead of mirrors for aerodynamics

Audi E-Tron EV gets side cameras instead of mirrors for aerodynamics

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It’s finally happening. After decades of cameras replacing boring old side mirrors on

concept cars

,

Audi

will bring the feature into production on the Audi E-Tron electric

crossover

SUV. They’re shown on the prototype above. The optional cameras are mounted to little winglets where the standard side mirrors would have been. As for where you see the cameras’ views, there will be an additional screen in the area to the left of the instrument cluster for at least the left camera. Audi wasn’t totally clear about the screen for the right-hand camera.

While it is cool to see a long-teased feature actually reach production, we’re not entirely sure the feature is going to be that great to use.

We’ve found the rear-view screen mirrors from Cadillac

and later

Nissan

require some adjustment to get used to, at minimum, since the screens show a fixed angle, and they don’t provide any depth perception.

Audi does say the cameras improve drag, though, which can be very important for cars. Audi says every .01 coefficient of drag reduces range by about 5 kilometers. The company revealed that the E-Tron has a Cd of 0.28. For comparison,

the Kia Niro

has a drag coefficient of 0.29. Audi made additional tweaks beyond the mirrors for aero, though. They include the various grille shutters, standard adaptive air suspension to lower the car as needed, and even tires that have numbers and logos etched into the rubber instead of raised.

And just in case you forgot, Audi is estimating

the E-Tron will go 248 miles

on a charge baseed on the WLTP test cycle (expect

EPA

testing to differ). It goes on sale in Europe by the end of the year, with U.S. sales expected sometime later. Time will tell whether these camera mirrors will even be brought to the U.S. Companies have had a hard enough time bringing over fancy adaptive LED lights. We can’t imagine the rules are friendly to eliminating side mirrors entirely.

Related Video:

Cars

via Autoblog http://www.autoblog.com

May 30, 2018 at 08:20AM