The one big annoyance is that there’s no switch on the floodlight, so you’ve got to turn it on and off from the mains.
Since you’re already fitting a plug, and since that sounds like it might be closer than the mains due to the short wire, you could fit a plug with a switch on it. like these:
I don’t think you need to view namedropping as an appeal to authority. The natural way to do it in a scholarly document, including a poster, would be to cite a source. That’s giving the reader valuable information—a way to check out the authority behind it.
Of course, if the reader is familiar with the author cited and knows that their work is invariably strong, they might choose to take it on authority as a shortcut, but they have the info at hand to check into it if they wish.
Aha. For each side of the pole, you can write the binary representation of 4 bits vertically, and where there’s a 1 you have a line joining it. The middle two bits both go to the middle of the pole, so they have to curve off upward or downward to indicate which they are.
So 2 is 0010, and you have no lines in the top half and one line representing the bottom of the middle, so it curves downward.
Whereas 4 is 0100, so it has the upward-curving middle-connecting line, and none in the bottom half.
The top half of a 2 might kinda look like the curve shape, and the bottom stroke of a 2 looks like a horizontal bar. So if there were partial characters hanging from the central pole, they might look a bit like those...
But… If it’s that, the curve probably only works on the bottom right anyway. So if you’re willing to mirror it for the bottom left, why not mirror to the top too?
And… That doesn’t really explain the 1 parts anyway. They’re just using “whichever part of a 2 isn’t being used for 2”.
So I guess this isn’t a complete explanation.
I have now checked through the whole top row of the expansion, and this does seem to be what’s going on.
Each glyph represents a 4 digit base 4 number.
The digits are read in the order top left, bottom left, top right, bottom right.
An empty space off the central pole is always read as a zero.
A loop that comes from the centre of the central pole and joins to the top or bottom of the pole is always a three.
The horizontal bar and the curve coming from the centre of the bar (not re-joining, to distinguish it from the loop) vary in meaning depending on whether they are at the top or the bottom. At the top, the horizontal bar is two and the curve is one; at the bottom, the horizontal bar is one and the curve is two.
So, at the top we have a glyph representing 0003_4 = 3 (followed by a point).
Then the first few along the top row can be read off as:
0210_4 = 36_10
0333_4 = 63_10
1222_4 = 106_10
2020_4 = 136_10
And Wolfram Alpha can tell us that pi in base 256 starts:
3.36:63:106:136:133:163:8:211:19:25:138:46:3..._256
Having the strokes for one and two adopt inverse meanings top and bottom adds a little wrinkle to this as a puzzle, but otherwise as a design decision if one were semi-seriously thinking of this as a way of encoding information it seems to unnecessarily add to the thought needed to decode.
Also on design, I’m torn about whether the ordering of the parts is better or worse than top then bottom. The current design keeps the most significant parts on the left, which is mostly what we expect. But the overall presentation of the number is read across the page, left to right, so there could also be a case for having the order top-left, top-right, bottom-left, bottom-right.
Out of time to check more fully, but from the first few I think it works if a curve is 1 at the top but 2 at the bottom. (And straight lines vice versa.)
Hmm. My guess was that the order from most significant digit to least significant digit was top left, bottom left, top right, bottom right; and that an absent stroke is 0, a line is 1, a curve is 2 and a loop is 3.
And it sometimes works! The third character is 0333 = (0*64)+(3*16)+(3*4)+(3*1) = 63. Which Wolfram Alpha tells me is the 3rd term in pi_256.
But it doesn’t always work. That would make the 4th character 2221_4 = 166_10. But 4th character of pi_256 is 106 (=1222_4). Which is an anagram. But maybe that’s just a coincidence.
Yup, we also have matches at characters 8 and 26, which are both 8 in pi_256.
In which case, given the 1st and 14th characters are the same, and the 14th character of pi in base 256 is 3, that’s my leading guess, pending checking a few more glyphs.
That was thinking of noggin-scratcher’s comment about the monkly glyphs for 1-9999. I hadn’t thought of their helpful analysis that there’s only 54 symbols used across 113 symbols. I guess that even if this is somewhere on the right lines, the base must be something much lower, like 64.
Putting together the observations from noggin-scratcher and Yair Halberstadt, could this be the expansion of some number like e or pi in base 256?
I think if you insert an image using markdown it’ll be displayed. But I don’t think you can draw into it directly.
Not a perfect match for your requirements, but with some features that might meet some of the same underlying goals, you might want to take a look at Zettlr.
I’m not even American, so treat this thought accordingly, but might the military recruitment numbers you’re seeing be influenced by race / ethnicity at all?
As in, if the bottom quintile has a lower-than-average proportion of white people in it (I guess this is the case in the USA), and if the military disproportionately recruits white people (I’ve no idea whether this is the case), maybe bottom-quintile white people could be over-represented, even while that quintile is a little under-represented.
An AI capable of programming might be able to reprogram itself smarter and smarter, soon surpassing humans as the dominant species on the planet.
The first moderately smart AI anyone develops might quickly become the last time that that people are the smartest things around. We know that people can write computer programs. Once we make an AI computer program that is a bit smarter than people, it should be able to write computer programs too, including re-writing its own software to make itself even smarter. This could happen repeatedly, with the program getting smarter and smarter. If an AI quickly re-programs itself from moderately-smart to super-smart, we could soon find that it is as disinterested in the wellbeing of people as people are of mice.
(Also not medically trained.)
Something missing from this analysis is that the expected probability of these conditions for any given pregnancy is not the same as the incidence in the population at large. The factor that I’ve most often heard about is increasing age being highly associated with increasing incidence of Down syndrome, though there may be others, and I’m not sure whether there are known correlates with the other conditions you mention.
That might also relate to the last point about incidence of these conditions in the wider population and the incidences that study reported. It could be that older pregnant people are more likely to opt for the test, knowing that they are at elevated probability.
You finish by suggesting people think about prevalence differences shifting by a factor of two, but from a quick Google, it looks like age can shift prevalences by orders of magnitude. The first table that popped up suggested 1 in 2000 at age 20, increasing to 1 in 100 at age 40 and 1in 10 at age 49.
A prior isn’t the termination.
That sounds like you’re thinking of priors in terms of beliefs. As Gelman recently quoted:
The prior distribution. In general, the prior distribution represents all previously available information regarding a parameter of interest. . . .
I really like that they express this in terms of “evidence” and “information” rather than “belief.”
(First sentence is something Gelman is quoting from Deke et al, p4, second sentence is Gelman’s agreement with that.)
Why is xyz your prior? The termination is the information you’ve drawn on to come to that prior.
I felt like this classification system is potentially helpful.
But I felt that the title of the article could do with being refined. I read it as meaning that there were types of akrasia that could actually be beneficial for you in some surprising way, rather than it being beneficial to categorise akrasia by this typology.
If it’s running at 500W, that’s half a kWh per hour. If electricity is a little under 40p per kWh, then the running cost should be a bit under 20p per hour. If you use it for 10 hours per day, every day, then your electricity bill might rise by about £60 per month.