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Submission + - Marissa Mayer's reinvention of Yahoo! stumbles

schnell writes: The New York Times Magazine has an in-depth profile of Marissa Mayer's time at the helm of Yahoo!, detailing her bold plans to reinvent the company and spark a Jobs-ian turnaround through building great new products. But some investors are saying that her product focus (to the point of micromanaging) hasn't generated results, and that the company should give up on trying to create the next iPod, merge with AOL to cut costs and focus on the unglamorous core business that it has. Is it time for Yahoo! to "grow up" and set its sights lower?

Submission + - Terrestrial Gamma Ray Bursts Very Common

Rambo Tribble writes: It was long thought that gamma ray bursts were the exclusive province of deep space sources. More recently it was found that storms could produce such emissions, but such occurrences were thought rare. Now, data from NASA's Fermi satellite suggest such events happen over a thousand times a day. Per Prof. Joseph Dwyer, from the University of New Hampshire, "These are big, monster bursts of gamma rays, and one would think these must be monster storms producing them. But that's not the case. Even boring-looking, garden-variety, little storms can produce these."

Submission + - Deep neural networks are easily fooled: Is this Snowcrash for AI? (youtube.com) 1

anguyen8 writes: Deep neural networks (DNNs) trained with Deep Learning have recently produced mind-blowing results in a variety of pattern-recognition tasks, most notably speech recognition, language translation, and recognizing objects in images, where they now perform at near-human levels. But do they see the same way we do?

Nope. Researchers recently found that it is easy to produce images that are completely unrecognizable to humans, but that DNNs classify with near-certainty as everyday objects. For example, DNNs look at TV static and declare with 99.99% confidence it is a school bus. An evolutionary algorithm produced the synthetic images by generating pictures and selecting for those that a DNN believed to be an object (i.e. “survival of the school-bus-iest”). The resulting computer-generated images look like modern, abstract art. The pictures also help reveal what DNNs learn to care about when recognizing objects (e.g. a school bus is alternating yellow and black lines, but does not need to have a windshield or wheels), shedding light into the inner workings of these DNN black boxes.

Submission + - Scientists solve mystery of spontaneously combusting rubble piles in Japan quake (sciencemag.org)

sciencehabit writes: Something strange happened in the aftermath of the 2011 earthquake and tsunami that pummeled Japan. Months later, mysterious fires began breaking out in piles of brick and wood from damaged buildings. Researchers puzzled over what sparked the fires, but a new study offers a possible explanation: decomposing rice-straw flooring, called tatami mats, filled with fermenting microbes that generate large quantities of heat.

Comment Re: Unless it has support for Bitcoin... (Score 2) 156

Over here? Where is it where your banks work so well?

So much of America is in a legacy mode. And the false "we are #1" blind patriotism means that we're simply gonna stay there. Others leapfrog and innovate while 1/2 the country praises Jesus and corporate largess while denying scientific realities due to dogmatic and ignorant reasoning. Sometimes, it pays to be smaller and hungrier as opposed to being large and entrenched in old fashioned infrastructure.

Comment Re:The beaks won (Score 1) 138

Take a look at a goose's mouth. There are serrations in the mouth. I'm not sure if they are directly on bone or what the substance is which holds them.

But yeah. Less habitat for bacterial to hide out, lay down calcareous concretions and live within while they continue to secrete more acids.

Comment Re:Simplest is best (Score 2) 259

Even though the new Mac OS systems are pretty ugly UI wise, you can add tags to each file. This might be what you want.

If not, you can have a program that simply creates a hierarchy of each of your files within a folder and gives a unique ID to each file and folder within your top level.

Make a checksum on each file and apply that to the record for each file.

You can then find the file or folder again if you move it from one folder to another.

You can then create tags and apply them to the record for each file.

Also, since you have added checksums for each file, you can rebuild your library if you mistakenly move, delete or undelete a file since running a checksum on each file will create the same checksum. This will allow you to scan all the file records and map any lost record to the proper lost file. Also, as a set of backups, you can simply export this list of file references, checksums and tags.

If you have a bunch of tags, then you can search through all folders for all like tags.

So, you can use the Mac OS and add tags too your files, or you can put something like this together and use on other OSes that don't allow you to add tags to your individual files.

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