iano

Not entirely related to the post, but what's the sane use case for doing:

  return (expr, expr)

like in the first example? I cannot think of a good reason to do this.

Along the same vane from the math world is how natural numbers (which can then be used to build the reals, etc) are represented using only empty sets. Pretty awesome.

See http://en.wikipedia.org/wiki/Set-theoretic_definition_of_nat...

Can someone explain to me how the first queue example's (using singly link lists) dequeue method gives the expected result? Isn't head->m_pNext always NULL, so the new head will always be NULL and tail will be set to NULL losing the rest of the queue?

Seems like you should need to traverse the list to dequeue.

Where did I go wrong?

Credit card companies are a good example of the this too. Since they're responsible for fraudulent charges, there's a huge incentive for them to detect them.

Maybe it depends on the language? I could see something like this in python:

  if not callable(something): make_callable(something)

Which can (sortof) be read off in English.

This happens everywhere, not just in Silicon Valley. Maybe I'm missing it, but where does this article give evidence that this is the biggest in the US?

My home town has 20k people, and has winters with weeks of sustained below 0F temperature. Yet, there is a forested section behind a low budget grocery store where a 30-50 person homeless community exists perpetually. I know this is smaller than the 175 mentioned in the article, but its in a 20k person winter town in the middle of nowhere.

ROS development was handed off to OSRF [1] earlier this year. [2]

[1] http://osrfoundation.org/ [2] http://osrfoundation.org/blog/ros-at-osrf.html

A similar issue was responsible for one of the V22 crashes ([1], page 4). In that case the gyro wires were hooked up backwards, instead of the unit installed backward. Both cases are good examples of small design details that make or break a project.

[1] - http://www.fas.org/man/dod-101/sys/ac/v22-report.pdf

I thought the same, but as chance should have it I finally installed Ubuntu on my mpb yesterday. It went off without a hitch, and as far as I can tell everything works out of the box (wireless, dual monitors, sound, even all of the Mac function keys!).

It was a surprisingly pleasant experience.

Ah, alright. Thanks for the response.

The "I want to do something else!" button does nothing for me. I have many "MUST" todos and only 1 "WANT" while trying this button. The button just brings me to the same single "WANT" I have.

Yeah, sorry. Having cyclic pitch control on the 4 rotors of a quadrotor makes even less sense - then you really really might as well just be a helicopter.

There aren't a lot of quadrotors that use pitch control. That's half of their appeal; hook 4 brushless motors up to props and you're good to go. If you add in pitch control, then you either need 8 dof (4 motors, 4 pitch servos) or 5 dof (1 motor with a complicated power train to drive the 4 rotors, and 4 servos for pitch control). Both are more complicated and expensive, and again you might as well just be a helicopter.

Unfortunately quadrotors don't scale up well. As the rotors get larger control input delay increases (because with this type of quadrotor you get your control input by rapidly changing the angular speed of the rotors). You can fix this by turning each of the 4 rotors into a conventional helicoptor rotor with collective pitch control, but at that point it makes sense to just go with a helicopter.

Maybe I'm not reading the pyramid properly, but it looks like Clojure is on average ~2x as lengthy as Mathematica? IE: The entries in the horizontal Clojure row are ~1/2 the entries in the horizontal Mathematica row.

Definitely a confusing table.

In short: Learn constant coefficient 2nd order differential equations, and you can design a whole slew of mechanical, electrical, and hydraulic contraptions.

A real application of this: before computers, mechanical systems control engineers would write the equations of their mechanical systems, write their controllers, and then implement their controllers around the electrical circuit that duplicated the behavior of their mechanical system. Sort of an analog computer way of simulating a system.

Applications of algorithms are patentable, not the algorithms themselves. (un-cited e.g. using machine learning for handwriting recognition)