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Why is XOR the default way to combine hashes?
...om (1-bit) inputs, the AND function output probability distribution is 75% 0 and 25% 1. Conversely, OR is 25% 0 and 75% 1.
The XOR function is 50% 0 and 50% 1, therefore it is good for combining uniform probability distributions.
This can be seen by writing out truth tables:
a | b | a AND b
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Android phone orientation overview including compass
...ow I think (hope) I have a better feeling for it again but I am still not 100%. I will try and explain my patchy understanding of it and hopefully people will be able to correct me if I am wrong in parts or fill in any blanks.
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Heroku + node.js error (Web process failed to bind to $PORT within 60 seconds of launch)
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1043
Heroku dynamically assigns your app a port, so you can't set the port to a fixed number. Herok...
How to calculate the angle between a line and the horizontal axis?
... the positive Y axis at P1).
angleInDegrees = arctan(deltaY / deltaX) * 180 / PI
But arctan may not be ideal, because dividing the differences this way will erase the distinction needed to distinguish which quadrant the angle is in (see below). Use the following instead if your language includes ...
List comprehension on a nested list?
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answered Aug 6 '13 at 6:05
Andrew ClarkAndrew Clark
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UIView bottom border?
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CALayer *bottomBorder = [CALayer layer];
bottomBorder.frame = CGRectMake(0.0f, 43.0f, toScrollView.frame.size.width, 1.0f);
bottomBorder.backgroundColor = [UIColor colorWithWhite:0.8f
alpha:1.0f].CGColor;
[toScrollView.layer addSublayer:bottomBor...
How to strip HTML tags from a string in SQL Server?
I've got data in SQL Server 2005 that contains HTML tags and I'd like to strip all that out, leaving just the text between the tags. Ideally also replacing things like &lt; with < , etc.
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dropping infinite values from dataframes in pandas?
...nf, -np.inf])
In [12]: df.replace([np.inf, -np.inf], np.nan)
Out[12]:
0
0 1
1 2
2 NaN
3 NaN
The same method would work for a Series.
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Naming returned columns in Pandas aggregate function? [duplicate]
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107
This will drop the outermost level from the hierarchical column index:
df = data.groupby(...)....