Meanwhile, this year's publication history:
Published manuscripts: 2
Submitted manuscripts: 0
In-preparation manuscripts: 1
Abstracts submitted: 2
Conference papers written: 2
Conference presentations: 1
Invited lectures: 1
SUBMITTED MANUSCRIPTS = 0.0, this isn't so good. I have a waiting list of whatever comes right before "in-preparation", though.
Okay, what did I learn today:
Well, I built a model of adapted image quality (blur/normal/sharp) matching yesterday, and fixed it up today. It does just what it should: it "normalizes" when adapted to one or another type of input, though for now its starting point is "blank adapted" which isn't quite right. It also displays the loss of blur/sharp gain that I found in the matching experiment (which accounts for 4 of the above objects: paper in preparation, abstract accepted, presentation and lecture given).
The model is your basic contrast transducer array, a set of Foley functions (Stromeyer-Foley, Naka-Rushton, etc.) with thresholds set by a standard function. I've built it several times before, but this is the first time I came up with a good way of implementing the adaptation part. This is the transducer function, with w in the denominator standing in for some added (only added, yes) gain control function:
The idea is that the system wants R to be kept relatively constant, at a particular level above threshold but not terribly near saturation - but C keeps changing, so how to keep R in that ideal range? Yes, we adapt, and here adaptation basically means setting the value of w. That's easy to do, just solve for w. This introduces probably the most important free parameter in the model, R, because I don't know what it should be, though I have a good idea of the range, and luckily the thing only really behaves if I put it in that range. So okay, it works!
So what I learned is that the third time you build something, it might actually work. From now on I need to make sure to build everything at least three times.
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