Jan Domanski

A few months ago I bought a new hard-drive. When you encrypt a drive, you need to set a password – but the trick here is to remember the password. Your hard-drive won't send you a "forgot password?" email. So you set that password, enter it, the drive mounts and then you work away happily for weeks without rebooting your machine. Then you reboot... and you need to re-enter that password. You slowly realize that those weeks ago you set a new unique password for that bloody hard-drive that you don't remember for the love of god... What to do? Well one, don't be a complete idiot like me – remember the password. But if you do, here is what to do? [Spoiler alert: I failed to recover the password but it's a pretty interesting ride...] The new Ubuntu 16.04 comes with this handy too called 'bruteforce-luks', here are some scenarios. Note you have to use it as root. "I remember the start/end of the password and I sort of know what's in the midd

With gromacs there are all these things you have to do when benchmarking, it's a little bit of a mess. One question that I always wondered about was – well – how long do you have to run to get a reliable performance number, in ns/day? 1e0 MD step is certainly too short but 1e7 steps seems like unnecessarily excessive. Second question was, could one get away with measuring benchmarking speed of just a box of water? Currently people use system like DHPR or APOA1 (protein in water) to asses but those are arbitrary.  A box of water has dramatically simpler topology than a protein but maybe it doesn't matter? One thing that this asses is absolute performance of gromacs: the ns/day below are low but that's because an old workstation is used, without GPU acceleration. The answer are pretty simple: 1e4–1e5 steps are required to 'converge' the performance estimate, for my test system. Also,  a water box of very similar dimensions (and # of particles) but without the p

Just visited an old-favourite blog of mine – the excellent "In the pipeline" by Derek Lowe. By going though the last 5 or so pages, it's clear what's the hiring state in the industry that once was Merck Cuts Chemistry Layoffs at Takeda AstraZeneca Cutting Back Again Layoffs at Medimmune To balance that there was one positive news Merck Expanding on the West Coast The blog posts are for the span between July 2016 and Sept 2016. Now, this is no hard numbers but gives me an "anecdotal nudge" that field/industry is in atrophy. When is the last time you saw any of the computer-science shops with similar headlines? "Google cuts down on software engineers", "Facebook re-organizes and lays-off data scientists",  anybody? It simply doesn't happen, not at this scale. If you're a young student, grad or post grad in life sciences, consider your options carefully. Academia is marred with fierce competition over small resources.