matplotlib already does this in conjunction with Numpy and Scipy - its plotting quality and flexibility compares favourably to Matlab.

Its biggest drawback is that it is pretty glacial even by Matlab's standards when rendering large datasets (think millions of points). I'm not sure whether matplotlib or the interactive backend is at fault, but anything DARPA can do to improve the situation would be welcome.

Read the original SETI report (NASA SP-419) - from its inception, SETI didn't expect to overhear information-bearing signals. Instead, it was looking for deliberately generated 'beacons' - high-powered, spectrally dense transmissions. These should be detectable over distances of the order of several thousand light-years.

I seem to remember Autocad doing something like this. They used the dongle to decrypt a look-up table used in geometric transforms. If the LUT wasn't decrypted, subtle and cumulative errors would be introduced into the design which would only become noticeable after a week or two of use.

If used correctly, dongles are pretty damn hard to defeat. Ideally you use them to decrypt hunks of both your data and executable, then hide the significant decryption data amongst a blizzard of dummy calls to the dongle.