Wow, it's the second coming of Troff!
Both CO2 concentrations and the global temperature varied significantly over that period of Earth's history (~400-250 millions of years ago). Around 300 Mya, CO2 was comparable to what it is today and temperatures were…
> The only thing the degree does is get you an interview > you should have got through networking. And maybe you also gained some useful skills.
Hard link creation can in recent Linux kernels be restricted, by SECURITY_YAMA_HARDLINKS config option which seems active by default at least on Ubuntu 10.10. So that platform is not vulnerable.
Standing in front of a blackboard is known to reduce IQ temporarily but significantly.
The main reason is probably that physicists found graphene more interesting to study than buckyballs. This can be quantified by looking at the number of articles published in top physics-only journals (e.g. Physical…
Such large differences imply a difference in the algorithm Indeed, you write c = mat2.getcol(j) norms[0, j] = scipy.linalg.norm(c.A) which means (i) extract a sparse column vector, (ii) convert it to a dense vector, and…
Wow, it's the second coming of Troff!
Both CO2 concentrations and the global temperature varied significantly over that period of Earth's history (~400-250 millions of years ago). Around 300 Mya, CO2 was comparable to what it is today and temperatures were…
> The only thing the degree does is get you an interview > you should have got through networking. And maybe you also gained some useful skills.
Hard link creation can in recent Linux kernels be restricted, by SECURITY_YAMA_HARDLINKS config option which seems active by default at least on Ubuntu 10.10. So that platform is not vulnerable.
Standing in front of a blackboard is known to reduce IQ temporarily but significantly.
The main reason is probably that physicists found graphene more interesting to study than buckyballs. This can be quantified by looking at the number of articles published in top physics-only journals (e.g. Physical…
Such large differences imply a difference in the algorithm Indeed, you write c = mat2.getcol(j) norms[0, j] = scipy.linalg.norm(c.A) which means (i) extract a sparse column vector, (ii) convert it to a dense vector, and…