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MEMOIR

ON ELECTIONS BY BALLOT

By M. de Borda

It is a generally accepted opinion, and one against which I do not recall anyone ever having raised objections, that in an election by ballot, the plurality of votes always indicates the will of the electors—that is to say, that the candidate who obtains this plurality is necessarily the one whom the electors prefer to his competitors. But I am going to show that this opinion, which is true in the case where the election is between only two candidates, can lead to error in all other cases.

Written by ChatGPT and me.

I also had to enable temperhum as a non-root user and set up a cron job to call this script every once in a while.

Finally Qalculate has been ported to Windows. Now we can do unit-aware calculations inline, in any document, with AutoHotkey, similar to Monster.ahk but with units.

Instructions:

1. Install Qalculate
2. Install AutoHotkey
3. Add qalc.exe to your PATH. I use Rapid Environment Editor for this.
4. Run qalc.ahk

This generates cyclic/repeating/perfect loop noise by low-pass filtering white noise in a cyclic way. Useful for "random" but looping animations, etc.:

This one loops in both space and time, for instance:

Simplex noise typically has a more trapezoidal value distribution:

lmas/opensimplex#18 (comment)

My first keras experiments:

1. simplest.py — A single neuron with no activation function and no bias (so just input→weight→output, can learn y=mx functions only). Show the function for a few random weights. (Doesn't learn anything.)
2. linear with bias.py — A single neuron with no activation function but with bias (so it can learn y=mx+b functions, such as relationship between Celsius and Fahrenheit).
3. single_neuron_buffer.py — A single neuron with sigmoid activation and no bias, trained to learn a digital logic buffer.
4. single_neuron_inverter.py — A single neuron with sigmoid activation and no bias, trained to learn an inverting analog amplifier.