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The rule change comes as new European data privacy rules roll out in the EU next month. …read more
Rude awakening – yet again – for American DNS supremo
ICANN has been told for a second time that it must fundamentally change its Whois service to become compliant with Europe’s incoming privacy law – and do so within the next month.…
ESA wants to solve crucial riddle: What really happens when accidents occur in orbit?
The European Space Agency is launching a new research project to study satellite collisions in space.…
Sometimes the best projects are the simple, quick hits. Easily designed, fast to build, and bonus points for working right the first time. Such projects very often lead to bigger and better things, which appears to be where this low-power temperature beacon is heading.
In the world of ham radio, beacon stations are transmitters that generally operate unattended from a known location, usually at limited power (QRP). Intended for use by other hams to determine propagation conditions, most beacons just transmit the operator’s call sign, sometimes at varying power levels. Any ham that can receive the signal will know there’s a propagation path between the beacon and the receiver, which helps in making contacts. The beacon that [Dave Richards (AA7EE)] built is not a ham beacon, at least not yet; operating at 13.56 MHz, it takes advantage of FCC Part 15 regulations regarding low-power transmissions rather than the Part 97 rules for amateur radio. The circuit is very simple — a one-transistor Colpitts oscillator with no power amplifier, and thus very limited range. But as an added twist, the oscillator is keyed by an ATtiny13 hooked to an LM335 temperature sensor, sending out the Celsius and Fahrenheit temperature in Morse every 30 seconds or so. The circuit is executed in Manhattan style, which looks great and leaves plenty of room for expansion. [Dave] mentions adding a power amp and a low-pass filter to get rid of harmonics and make it legal in the ham bands.
[via Dangerous Prototypes]
An anonymous reader quotes a report from Reuters: The U.S. Environmental Protection Agency proposed a rule on Tuesday that would limit the kinds of scientific research it can use in crafting regulations, an apparent concession to big business that has long requested such restrictions. Under the new proposals, the EPA will no longer be able to rely on scientific research that is underpinned by confidential medical and industry data. The measure was billed by EPA Administrator Scott Pruitt as a way to boost transparency for the benefit of the industries his agency regulates. But scientists and former EPA officials worry it will hamstring the agency’s ability to protect public health by putting key data off limits.
The EPA has for decades relied on scientific research that is rooted in confidential medical and industry data as a basis for its air, water and chemicals rules. While it publishes enormous amounts of research and data to the public, the confidential material is held back. Business interests have argued the practice is tantamount to writing laws behind closed doors and unfairly prevents them from vetting the research underpinning the EPA’s often costly regulatory requirements. They argue that if the data cannot be published, the rules should not be adopted. But ex-EPA officials say the practice is vital.
of this story at Slashdot.
Debuting at the Beijing Motor Show and in LA this week, the sixth-generation sedan offers optional hybrid power. …read more
They probably weren’t inspired by [Jeff Dunham’s] jalapeno on a stick, but Intel have created the Movidius neural compute stick which is in effect a neural network in a USB stick form factor. They don’t rely on the cloud, they require no fan, and you can get one for well under $100. We were interested in [Jeff Johnson’s] use of these sticks with a Pynq-Z1. He also notes that it is a great way to put neural net power on a Raspberry Pi or BeagleBone. He shows us YOLO — an image recognizer — and applies it to an HDMI signal with the processing done on the Movidius. You can see the result in the first video, below.
At first, we thought you might be better off using the Z1’s built-in FPGA to do neural networks. [Jeff] points out that while it is possible, the Z1 has a lower-end device on it, so there isn’t that much FPGA real estate to play with. The stick, then, is a great idea. You can learn more about the device in the second video, below.
There’s a lot of processing going on in these tasks, just taking a slice of the video allowed processing to occur at 3 frames per second, but scaling for full size using software dropped to half that. However, [Jeff] thinks if he did the scaling in the FPGA he could easily get the rate back up to 3 frames per second.
The Economist reports of a new working paper by the Organization for Economic Co-operation and Development (OECD) that assesses the automatability of each task within a given job, based on a survey of skills in 2015. “Overall, the study finds that 14% of jobs across 32 countries are highly vulnerable, defined as having at least a 70% chance of automation,” reports Economist. “A further 32% were slightly less imperiled, with a probability between 50% and 70%. At current employment rates, that puts 210 million jobs at risk across the 32 countries in the study.” From the report: The pain will not be shared evenly. The study finds large variation across countries: jobs in Slovakia are twice as vulnerable as those in Norway. In general, workers in rich countries appear less at risk than those in middle-income ones. But wide gaps exist even between countries of similar wealth. Differences in organizational structure and industry mix both play a role, but the former matters more. In South Korea, for example, 30% of jobs are in manufacturing, compared with 22% in Canada. Nonetheless, on average, Korean jobs are harder to automate than Canadian ones are. This may be because Korean employers have found better ways to combine, in the same job, and without reducing productivity, both routine tasks and social and creative ones, which computers or robots cannot do. A gloomier explanation would be “survivor bias”: the jobs that remain in Korea appear harder to automate only because Korean firms have already handed most of the easily automatable jobs to machines.
of this story at Slashdot.