Slashdot Mirror

According to NSERC no (NSERC Grant Award IP Policy) Specifically, "The Agencies do not retain or claim any ownership of, or exploitation rights to, intellectual property or copyright developed with grant funds. These rights are owned by the Institution and/or by the inventor." You need to check with policies you have in place with your institution. Many universities do claim IP and it is usually addressed in the student handbook, or somewhere in university policy. Look at stanford they own google's page rank patend.

Actually,

In fact, relative to body weight these colourful birds have brain sizes that are on par with chimpanzees and orangutans.... "Humans have these really big brains, but guess what, parrots have really big brains too. In fact, if you overlay a graph of brain size to body mass for parrots on top of one for non-human primates, they sit in a perfect line"

Source

If you'll be studying in Canada, you need to look at the Natural Science and Engineering Research Council's web site. They are the major source of academic funding in Canada, offering several types of scholarships.

The basic scholarship that everyone applies to is the PGS Masters (or Doctoral). This year, it was worth $17,300. Competition is very tough for them and applications are usually due in September to start in the next year.

If you're willing to put in the work and earn a 4.0 grade point average, you are elligible for an Industrial Postgraduate Scholarship (IPS). They are worth a minimum of $21,000/yr for two years. The idea is that you work with a company (very similar to co-op for undergrads) who pays part of the scholarship in return for 20% of your time spent on a research project. There is no application deadline for this scholarship and it usually takes about a month for approval. I was pre-approved for one of these scholarships when I unsuccessfully applied for a PGS.

Depending on your specific discipline, they offer other scholarships as well. I suggest that you check it out for yourself.

Disclaimer: I work for maple, as a developer but not in R&D. I speak from my knowledge of this company but I'm sure the others have similar situations.

The problem with OSS for something like a mathematics package (especially a package with symbolics) is they contain IP obtained with research partnerships with institutions. You won't find a OSS solution that competes with any of the big 3 (Maple, Matlab and Mathematica) because the the algorithms for symbolics and so forth are just too complex and important as IP to the companies.

All 3 of the packages I believe/know have much reduced student pricing ($100 USD for a downloadable on Mac, Linux or Win) and many of the schools in North America either have a partnership or you can obtain the software directly from your department. Also many of the calculus texts include 4 or 8 month trial versions.

I know this doesn't help much and it is unfortunate that you can't obtain OSS alternatives for software that has a educational purpose, but on the otherside with out the 1000's of freshman in Calc101 I wouldn't have a salary.

Two things:

There's a difference between 'weather' and 'climate'. The latter has longer time scales (which makes things harder) and much less spatial resolution (which makes things easier).

As an analogy, say you pour cream into a hot cup of coffee. It's supremely difficult to model exactly how the cream swirls around in the cup due to the nonlinearity of turbulence. But it's pretty easy to accurately model the avg. concentration of cream, and the average temperature, etc., of that coffee cup 20 minutes later. That's not to say that climate models are supremely accurate, just that their success is not necessarily closely coupled with weather prediction.

Also, in regard to skepticism, there's this fundamental problem of certainty. Because environmental (like biological) systems are so frickin' complex, and it's so hard to do controlled experiments (here's an excellent counter-example), it's really hard to judge things by the reproducable results standard (and even when you do, polluters can muddy the waters anyway).

So what to do? I don't think it's inappropriate, in the face of provacative but uncertain evidence, to take measures to mitigate the risk. Does this indicate a lack of skepticism, or prudent risk management?

Look at this.

or simply read the qoute below:

"In the past, people have been puzzled by the significant variations in sea levels in different parts of the world," says Jerry Mitrovica, a professor of geophysics at the University of Toronto and lead author of the study. "Like throwing water in a bathtub, many scientists assumed that if polar ice melting were contributing to sea level rise, it would present itself evenly and uniformly across the Earth's oceans."

But that assumption, he says, is simply wrong. Mitrovica uses Greenland as an example. It was assumed that if the ice caps on Greenland were melting, all coastal locations would flood evenly. "In fact," Mitrovica says, "if the entire Greenland ice cap melted, then places relatively close by, like Britain and Newfoundland, would actually see sea levels fall. The reason is fairly simple: despite its small size, the Greenland ice sheet exerts a strong gravitational pull on the seas. As the polar sheet melts, it will exert less pull, resulting in lower - not higher - sea levels around Greenland. Of course, sea levels will rise on average, and as the meltwater moves away from Greenland it will create problems for countries in the Southern Hemisphere. In the same way, melting from the Antarctic will raise sea levels in the Northern Hemisphere, but not in places like Australia."

or this. Quote:

"Re-applying a 19th century idea, Dr. Mitrovica and his colleagues showed that each ice sheet has a distinct 'sea level fingerprint.' In general sea levels rise in the opposite hemisphere to the melting ice due to the reduction in the gravitational pull of the ice mass.

"The very idea that sea levels should rise uniformly if the ice sheets are melting is wrong. It's dramatically non-uniform," he says. "If the Greenland ice sheet melted tomorrow there'd be flooding in the southern hemisphere but a sea-level fall in Scotland and Newfoundland.""

Oh yeah, here's a really interesting article on cannibalistic mating.

From the article: The original work along these lines dates back to the late 1980s and early 1990s and was done by Peter F. Brown and his colleagues at IBM's Watson Research Center.

IBM's pioneering work was written up in a student-friendly workbook available online. Feel free to try coding it and see how well you do. Do remember though, the state of the art has progressed a lot since IBM's work. This workbook only covers the basics.

You will find that debugging statistical translation system is really hard. You can write test cases, but they take one hour to run each time. You can look at the result of your test cases, but since you cannot work the answer out by hand, you can never by sure if the numbers you are computing are correct. As an example of how tricky it can get, in Brown university's cs241 last fall, amongts the four teams, only two teams managed to correctly implement Model-3, and the workbook goes up to Model-5.

There are two reason why a three way translation is a bad idea. First, it is already difficult to find large amounts of text translated two-way and available in digital format. Restricting your approach to three-way translated text would reduce the amount of text you could train on so much, it would offset the advantage you would get from the three-way text.

Second, training for statistical translation is really expensive. If running one single test case can take an hour, running a full training can take a whole week. Under these conditions, you are always very careful how you spend your cpu cycles. Until better cpus come along, training three-way and cross referencing each language with the other could well take a month of processing (or two).