This is just freaking awesome. 50,000 images produced this masterpiece in 81 megapixel resolution.
4 Ways To Make Better Street Portraits While Traveling
A nice article, particularly tip 4.
How to Improve Low-Light Performance by Increasing Your ISO
I have got to try this out.
OK, this is a rant.
It annoys me to no end when people present graphs like this one. Yes, the numbers do in fact add up to 100% but does it make any sense to have so many digits after the decimal when in reality this is based on a sample size of 6? Wouldn’t 1/2, 1/3, 1/6 have sufficed? What about 0.5, 0.33 and 0.67. Do you really really have to go to all those decimal places?
Excel has made it easy for people to make meaningless graphs like this, where merely clicking a little button gives you more decimal places. I’m firmly convinced that just having more digits after the decimal point doesn’t really make a difference in a lot of situations.
Let’s start first with some definitions
accuracy is a “degree of conformity of a measure to a standard or a true value“.
precision is the “the degree of refinement with which an operation is performed or a measurement stated“.
One can be precise, and accurate. For example, when I say that the sun rises in the east 100% every single day, I am both precise, and accurate. (I am just as precise and accurate if I said that the sun rises in the east 100.000% of the time).
One can be precise, and inaccurate. For example, when I say that the sun rises in the east 90.00% of the time, I am being precise but inaccurate.
So, as you can see, it is important to be accurate; the question now is how precise does one have to be. Assume that I conduct an experiment and tabulate the results, I find that 1/2 the time I have outcome A, 1/3 of the time I have outcome B, and 1/3 of the time I have outcome C. It would be both precise, and accurate to state the results are (as shown in the pie chart above) 50.0000%, 16.66667%, and 33.33333% for the various possible outcomes.
But does that really matter? I believe that it does. Consider the following two pictures, these are real pictures, of real street signs.
This sign is on the outskirts of Mysore, in India.
This sign is in Lancaster, MA.
In the first picture (the one from Mysore, India), we have distances to various places, accurate to 0.01km (apparently). Mysore Palace is 4.00 km away, the zoo is 4.00 km away, Mangalore is 270.00 km away. What’s 0.01km? That’s about 10m (about 33 feet). It is conceivable that this is accurate (possible, not probably). So I’d say this is precise and may be accurate.
The second picture (the one from Lancaster, MA) is most definitely precise, to 4 places of the decimal point no less. The bridge is 3.3528 meters (the sign claims). It also indicates that it is 11 feet. A foot is 12 inches, an inch is 2.54 centimeters, and therefore a meter (100cm is 39.3701″) is exactly 3.2808 feet. Therefore 11 feet is 3.3528 meters exactly. So this is both precise, and accurate (assuming that the bridge does in fact have a 11′ clearance).
The question is this, is the precision (4.00km, or 3.3528m) really relevant? We’re talking about street signs, measuring things with a fair amount of error. In the case of the bridge, the clearance could change by as much as 2″ between summer and winter because of expansion and contraction of the road surface (frost heaves). So wouldn’t it make more sense to just stick with 11′, or 3.5 meters?
So back to our graph with the 50.0000%, 16.66667% and 33.33333%. Does it really matter to the person looking at the graph that these numbers are presented to a precision of 0.000001%? For the most part, given the fact that the experiment had a sample size of 6, absolutely not.
So please, when presenting facts (and numbers) please do think about accuracy; that’s important. But please make the precision consistent with the relevance. When driving a car to the zoo, is the last 33′ going to really kill me? or am I really interested in the clearance of the bridge accurate to the thickness of a human hair, or a sheet of paper?
How to run a Kubernetes cluster in OpenStack
Very often, I’ve found that it is an advantage to have Android running in a virtual machine (say on a desktop or a laptop) and use the Android applications.
Welcome to android-x86
Android runs on x86 based machines thanks to the android-x86 project. I download images from here. What follows is a simple, step-by-step How-To to get Android running on your x86 based computer with VMWare. I assume that much the same thing can be done with some other emulator (like VirtualBox).
The screenshots below show the steps involved in installing android-x86 on a Mac.
Choose “Create a custom virtual machine”
Choose “FreeBSD 64-bit”
I used the default size (for now; I’ll increase the size later).
For starters, we can get going with this.
I was installing android v8.1 RC1
Increased #cores and memory as above.
Resized the drive to 40GB.
And with that, began the installation.
Options during installation.
The options and choices are self explanatory. Screenshots show the options that are chosen (selected).
One final thing – enable 3D graphics acceleration
Before booting, you should enable 3D graphics acceleration. I’ve found that without this, you end up in a text screen at a shell prompt.
And finally, a reboot!
That’s all there is to it, you end up in the standard android “first boot” process.
The Boston Harbor hotel … http://ow.ly/i/GPnEp