As a researcher in computational chemistry, I am quite familiar with this problem. Only a minority of chemists and physicists are interested in (or any good at) programming, while CS students/grads usually haven't taken much of the physics that they need for our research.
The biggest problem in attracting well-qualified candidates is salary (or stipend). In my field, the going rate is about $18k for grad students and $25k for postdoctoral researchers. Not at all competitive with what industry can offer a talented programmer.
The only reason we can attract anyone at all with these stipends is that we're not hiring employees, we're training students and researchers. They're here for the education, not for the earnings. Or at least they should be.
It's unrealistic to expect someone who already has all the skills I need to be interested in working in my lab for peanuts.
By the way -- if anyone is interested, I'm currently looking for a postdoctoral researcher to work on a comp. chem. project involving object-oriented parallel processing in F90. Email me or see web page below for details.
This writeup has a sample of Brutus.1's work. It does read pretty well. From this and several other examples, though, it appears that Brutus only knows one plotline.
The concept that there could be a "universal" curve behind all statistical phenomena silly. About as silly as the implication that scientists currently think Gaussian curves are universally applicable.
Every statistical process has a different distribution. Some are Gaussian, some become Gaussian under certain limits, and some can be approximated as Gaussians. But many others are simply something completely different: Lorentzian, Poissonian, etc. (Many of which have fatter tails than a Gaussian distribution.)
These guys may or may not have come up with something. But there's certainly no news value to the claim that some distributions are non-Gaussian.
I don't buy the argument that anyone is suppressing cold fusion research. It's just that nobody (in academia) really cares any more. There was no shortage of dollars and careers invested in cold fusion when it was a hot topic. But nothing ever panned out, so most people moved elsewhere. And there are relatively few grant dollars which would be in immediate jeapordy if cold fusion were to be funded -- government agencies often fund multiple competing attempts to solve the same problem. As they should.
Just use Occam's Razor: those of us in academic research don't have anything to lose if cold fusion works (and quite a lot to gain, actually). On the other hand, McKubre et al. have their entire livelihood at stake if they don't keep people believing there's something to this. And at this stage in the game, that requires claims of underhanded conspiracies.
The most compelling evidence that there's nothing to cold fusion? The University of Utah can't give away the licensing rights to Pons' and Fleischmann's process.
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The biggest problem in attracting well-qualified candidates is salary (or stipend). In my field, the going rate is about $18k for grad students and $25k for postdoctoral researchers. Not at all competitive with what industry can offer a talented programmer.
The only reason we can attract anyone at all with these stipends is that we're not hiring employees, we're training students and researchers. They're here for the education, not for the earnings. Or at least they should be.
It's unrealistic to expect someone who already has all the skills I need to be interested in working in my lab for peanuts.
By the way -- if anyone is interested, I'm currently looking for a postdoctoral researcher to work on a comp. chem. project involving object-oriented parallel processing in F90. Email me or see web page below for details.
-Steve Stuart
http://radar.ces.clemson.edu
This writeup has a sample of Brutus.1's work. It does read pretty well. From this and several other examples, though, it appears that Brutus only knows one plotline.
The concept that there could be a "universal" curve behind all statistical phenomena silly. About as silly as the implication that scientists currently think Gaussian curves are universally applicable.
Every statistical process has a different distribution. Some are Gaussian, some become Gaussian under certain limits, and some can be approximated as Gaussians. But many others are simply something completely different: Lorentzian, Poissonian, etc. (Many of which have fatter tails than a Gaussian distribution.)
These guys may or may not have come up with something. But there's certainly no news value to the claim that some distributions are non-Gaussian.
-Steve Stuart
I don't buy the argument that anyone is suppressing cold fusion research. It's just that nobody (in academia) really cares any more. There was no shortage of dollars and careers invested in cold fusion when it was a hot topic. But nothing ever panned out, so most people moved elsewhere. And there are relatively few grant dollars which would be in immediate jeapordy if cold fusion were to be funded -- government agencies often fund multiple competing attempts to solve the same problem. As they should.
Just use Occam's Razor: those of us in academic research don't have anything to lose if cold fusion works (and quite a lot to gain, actually). On the other hand, McKubre et al. have their entire livelihood at stake if they don't keep people believing there's something to this. And at this stage in the game, that requires claims of underhanded conspiracies.
The most compelling evidence that there's nothing to cold fusion? The University of Utah can't give away the licensing rights to Pons' and Fleischmann's process.