Never makes it to 7 letters words.
All words in 2018 start with P.
1547408965 PLACED Sun Jan 13 11:49:25 2019
1543566578 PIZZLE Fri Nov 30 00:29:38 2018
...
1528992094 PABLUM Thu Jun 14 09:01:34 2018
1511124996 OZONIC Sun Nov 19 12:56:36 2017
September 9th was full of words. There will be no more until October.
1540710226 PHARMA Sun Oct 28 00:03:46 2018
1540702422 PHALLI Sat Oct 27 21:53:42 2018
1540695700 PHAGES Sat Oct 27 20:01:40 2018
1536787497 PEYOTL Wed Sep 12 14:24:57 2018
1536787490 PEYOTE Wed Sep 12 14:24:50 2018
1536700131 PEWTER Tue Sep 11 14:08:51 2018
1536686416 PEWITS Tue Sep 11 10:20:16 2018
1536680692 PEWEES Tue Sep 11 08:44:52 2018
It takes a lot of humility to realize how utterly stupid human brains are. They are actually pretty trash, but while you're thinking with one, you think they are absolutely flawless and inerrant but that's exactly the problem.
Though the Bible is filled with prognostications about the premature exaltation of these mortal coils, this God fellow extends this much-promised rapture like a guy who thinks he is himself 'God's gift to Mankind' but is excessively focused one tiny pointless strip in the desert.
Missing a double entendre is when, in social intercourse, there is another meaning upon the lips, that one fails to fully wrap their head around or penetrate the whole of the available depth, a sort of premature interpretation.
Within this oral intercourse,
There is a seeming, source
Of a premature interpretation.
Flip it with a second destination.
On those lips, left wanting,
of a deeper thrust,
a pair of content's taunting,
their points securely trussed.
Beyond the naked point's, another.
A spectrum of awaited breadth,
It's hard to grasp
the whole, of the penetrable depth.
When in oral intercourse there is pair of points upon the lips, yet a failure to grasp the whole of the penetrable depth.
Double Entendre: n. pair of material and both points are grasped, and the whole of available depth is penetrated.
Double Entendre: when there is considerable pair, and both points are grasped
--- My hope here was to make a good and quippy definition for what a double entendre is, but I dunno, these seem a bit lackluster.
CS folks are enamored of dividing 2D space in 2D space chunks? It makes all of the math needlessly harder, even if it makes the math-sense and understandability easier. You divide 2D space into two instances of 1D space. The algorithms don't require trees, you can still easily access the needed data in log(n) time, update them as easy as a sorted list can be updated, and with a bit of style you can easily prove points don't need to be checked. It's weird because no algorithms do this, but you can do an amazing amount of very simple math to solve all the traditional problems in computational geometry with simpler algorithms.
You can build the space in d*n*log(n) time (sorting lists).
With sorted lists of points you can insert in as easy as inserting into a list.
You can remove as easily as removing from a list.
You can find the nearest neighbor to a point in a short algorithm in log(n) time.
You can find k nearest neighbors to a point in a similar short algorithm in ~k*log(k)*log(n) time.
You can do basically all the AABB math quite easily too. You can easily find in n time the bounding boxes relevant to a point. And update that point very easily.
You can find if a point is inside a polygon.
You can find all points within an axis aligned rectangle in trivial time.
But the really really pressing bits of this is that categorically these algorithms are tiny. Like they don't take much work at all. They are exceptionally short. And the datastructure is what modern memory systems are optimized for so you won't be cache missing all the time. And it's so common it's implemented in like four lines of code. So why the hell are all games doing collision detection of divided up 2D space in 2D chunks or 3D chunks in 3D space? Also, these algorithms obviously scale really easily with the degree of dimensionality. They don't violate theoretical points though. If you do a Nearest Neighbor Search in 3D it's basically identical code with the steps as the 2D code but since we're dealing with 3D, it can easily end up checking most all the points anyway simply because you can't rule out the remaining checks as quickly.
I needed a nearest neighbor search for my android app to find the nearest point to a point and do this many many times in real time. But, when a point is moved it required a lot to fix the pre-processed datastructure since they were organized into trees so the nodes needed to be fiddled with etc. This allowed for nodes to be updated en masse, and the lists to simply be very quickly resorted in a trivial amount of time and perfectly ready for the next tick.
I hit upon this algorithm largely because I had also done considerable work with filling monotone polygons using a scanline, and used a sort of step-raytrace with line segments which is another brilliant algorithm (it's very similar to the good solution to the skyline problem), it's largely just a single list of all the points sorted along one axis. Then you scan through the list, keeping a working list of active line segments.
This solves the perpendicular rays and finds all intersections of that perpendicular ray (it's literally the intersections of the active list of line segments) in fast enough time that a little android phone can do the ray traced lines in real time.
To speed up my attract tool (requires many nearest neighbor searches), I previously made an interesting nearest neighbor search with a point quadtree which equally used the same general idea that you could prove the invalidity of other parts of the tree by showing that the distance in just 1 dimension is greater than the shortest distance already found. I thought this was awesome as nobody seemed to have any nearest neighbor algorithms based on that rather rare datastructure.
But, if you could do this with a tree it would be much easier to do this with 2 different 1D sorted lists. Just binary search into the list, and go to each point in the +X, +Y, -X, -Y directions until the distance in just dX is greater than your best candidate so far. You can also conclude things like if +X and -X are exhausted then there can be no closer point, as you proved all points exhausted (the points found in the other dimensions are going to be duplicates of the ones you ruled out). But, the point is implementing all of that is trivial compared to the other ways. And all the tricks in computational geometry are simply proving you don't have to check those other points, which is can be done with the same trick over and over, can this point further along this axis possibly be closer or included? For the K-nearest neighbor algorithm, you store the candidates in a heap sorted by distance and only compare the worst of them to the new potential candidates until just that one direction along is enough to be worse in which case no more points along that axis can possibly be better.
For Axis Aligned Bounding Boxes (AABB) you can rather simply add the start points and end points of the shapes. Then when you cross a threshold by traveling along the axis (only going to the next relevant point always as that's how the traversal works), you can trivially check if the relevant point is within that bounding box. So you can rather easily linearly iterate into the list and you'll know exactly which bounding boxes that point is within tracking them basically exactly as one would the skyline problem. Then moving that point within the space equally quite quick and just means updating any active elements as you cross thresholds (and if you don't cross any relevant boundaries, changing nothing at all). Equally it solves a number of common problems in collision detections like not updating something that moved in time to see that it struck something. As the object moves it needs to find it's place in the lists, which means you must process each element in order, which means you know if you hit anything, regardless whether you would now be completely on the other side of that object now.
I totally understand that it's easier to think in 3d space in 3d and 2d space in 2d so when you're partitioning space you are wildly more likely to divide the space in ways that does not screw up the dimensionality. It's easy to think of dividing a square into two half-squares but it's a bit weird to divide it into two equally lengthed lines. But the math is easier if you divide it into two lines for obvious reasons, even if it's harder to conceptualize.
Wrote this elsewhere as personal correspondence, but it makes some points I'd rather not forget for future reference.
I'm a computer scientist and one of my notable skills is understanding algorithms that underlie things. In 2007, I watched a pretty great TED talk by Jeff Hawkins ( Jeff Hawkins on how brain science will change computing. ) which had some really critical ideas about understanding intelligence and it made me realize that if brains predict the future, and that seems right, then the algorithm for intelligence is necessarily: evolution. Now, I don't mean this to say intelligence evolved, I mean this to say that a child from infancy to adulthood how they work in the brain is fundamentally an implementation of evolution. And I don't mean that lightly. There's a few people who note that artificial neural nets have a somewhat evolutionary strengthening, or folks who argue that brains work kind of like the adaptive immune system. I mean this strongly like algorithmically evolution and intelligence are identical.
It seems a bit weird at first, but the more you let it infect your mind the more you realize it explains things. Creationists always look at trees and insist they were intelligently designed. Because they look at trees and the products of intelligence and say; the causes of these things are the same. We turn around and say we know the tree formed through evolution and therefore that statement is not true; but I think this is wrong. I think this rather shockingly strong *illusion* of intelligence is no fiction at all. Sometimes the opposite of a profound truth is another profound truth. The error we make is in assuming we know what the fuck intelligence is and are therefore correct in dismissing Darwinian evolution as a form of it; I think it is a form of it because at the core the two are the same thing. The argument from design says that the two things are the same. We say that argument is wrong because the tree is the result of evolution by natural selection. But, there's a missing premise there required to deny the argument: intelligence is not evolution. And I think that premise is false; It is evolution.
This also gives us a few interesting requirements about what is required between senses and minds; namely there's no requirement for a connection at all. It isn't as if the environment programs DNA of various species. There's no direct connection there at all. Rather the things that work effectively are strengthened and those that don't do not. Likewise if brains are making predictions of things fairly randomly and are only being confirmed by your senses, then we necessarily do not need our senses to direct our actions at all. Without senses we'd start hallucinating wildly, with them we hallucinate coherently. Our senses only need to confirm things after the fact. So some weird items like how we can swing a bat in less time than we could have decided to, just sort of evaporate. And seemingly profound thought experiments like Searle's Chinese Box become rather trite. Undoubtedly you can think of dozens more examples that quickly get explained away.
So the posted article contains things like "Your mind filters, sorts, selects, contains, and bestows meaning to an element in the endless stream of external data flooding your senses." -- This is completely pointless and wrong. Your mind doesn't need to do any of that because our senses don't do any of that. We invent a world inside our own heads with various theories and ideas and understandings, and then our senses tell us whether that stuff is right or not and we react accordingly. This is also largely why in sensory deprivation chambers or our sleep we kind of go a little crazy, because nothing ends up registering as being more true than anything else so we start hallucinating. We also get basically every form of hallucination for free, from excess details that we can't really see to the blind spot in our eyes, and why we make up intermediate steps between events even if they didn't exist. Under my theory, we not only get these for free; they are required to be true.
It also broadly explains why "there is a difference between what you experience, and what exists out there: The virtual can never be real. The essence of your understanding is, in effect, rooted in this difference. Where does difference arise?" -- The difference arises because the stuff we perceive is completely made up. Everything about our understanding of the world what we think the world actually is, is a confabulation that was made up inside our head, and whittled down by our senses. Just as every nucleotide in our very amazing DNA was a random mutation. It sounds like this should completely unmoor our understanding of reality from reality itself and it does, but our senses narrow that range of possibilities and those elements, just as the process of selection culls random mutations into "endless forms most beautiful".
If you take this idea seriously, you will quickly realize that it starts knocking down barriers by its mere existence in your head. It not only explains the veil but why it must exist. Our very nature as intelligent beings requires that this is how we work. Most of the objections to George Berkeley's sort of idealism ((1) We perceive ordinary objects (houses, mountains, etc.). (2) We perceive only ideas. Therefore, (3) Ordinary objects are ideas.) are generally that reality is real and it is, but if you understand what intelligence is you can thread the needle aptly enough to take the correct points from idealism and from materialism. It's entirely true that reality comes only from us internally and that reality is not somehow the result of our senses grabbing reality and pulling it into our head. Rather the reality in our head is influenced by our senses and our ability to predict what we well sense next requires us to come up with an internal view of reality that need not be anything like actual reality (in fact we know a lot of aspects like color are just ad hoc inventions and do not exist outside our heads) it's filled in with a bunch of hallucinations and randomness because the internal view of reality need not have anything to do with actual reality, except that we have consistent ideas about reality that have evolved and they are remarkably resilient. Just as evolved forms need not be efficient hunters or energy gatherers but because of the nature of evolution that's how they end up. We need not have a good grasp of reality and make correct predictions and correct understandings, but because of the nature of what evolution does, the more efficient forms are preferred. In other words, under this understanding intelligence will end up being as robust as the web of life itself.
Update, based on a reply discussing evolutionary epistemology:
No. I am not at all saying evolutionary epistemology. Fucking obviously evolution formed our senses, but that's not even remotely the point. I'm thinking I missed the point if you couldn't get your head around it enough to stop you from jumpping down one of the many different lines of similar but all equally inexact thoughts. I mean what I say more profoundly than that, like expelling the hobgoblins there and replacing the whole shebang. I wrote it well and tried to stress the points to help get your head around it, but apparently it still missed the mark. Imagine if you would that there is no such thing as intelligence. That that which we call intelligence is entirely just evolution taking place in neurons. Not that senses are evolved or brains are evolved or that brains use vaguely reinforcing neurons that are akin to evolutionary processes; no. Everything about intelligence is just evolution taking place in the medium of brains. That the mind is running an evolutionary algorithm. That's how it entirely works.
You sir are a donkey fucker. Okay, maybe you're not a donkey fucker, since there's no evidence of that. You sir, are a chicken fucker. Wait, no evidence there either. You sir are a squirrel fucker. -- Look, I have my conclusion, you fuck some kind of animal and when that type of animal is shot-down, I'm going to shop around for another type of animal. Because that's how this shit works apparently. --- Make some shit up, when that gets demolished, make different similar shit up, with generally the same conclusion.
The idea is that rather than minimize the extrema of the error we can instead minimize the total error. Graph out formula for the error and calculate what would be under the curve as a whole. So rather than positive and negative error equal it would seek to minimize the error overall. It's not too different from the other metrics. But, rather than trying to keep the first pixel from being wrong, it tries to keep the pixels as a whole from being wrong for as long as possible. It's different than the number for the extrema because oddly the graph is symmetric at 0.5. It's not symmetric with regard to the y axis, and has different amounts areas of positive and negative and the error shifts as the value of c shifts.
Mortensen's value. Even extrema point.
0.55191502449 at 0.0000001 increments
Total error:1180.57375326880434046054832219597498652621901536765190
Samples: 10000001
My calculated value, brute force.
0.55201372171 at 0.0000001 increments
Total error:1159.83397426356807198675826572857280613256846239728729
Samples: 10000001
Naive geometric value, with purely positive error ((4/3)*(sqrt(2) - 1).
0.552284749 at 0.0000001 increments
Total error:1401.62730758375303300973494715872257045283590056376636
Samples: 10000001
As we can see the total error over a million samples gives us an improvement of 20. Compared to the naive value for all error being positive we gain 242 which is better than Mortensen's gain of 222.
We're talking literally fractions of percents here, but this number has another advantage. You can call it .552 which is a much shorter fraction. Besides the more slices I do the more I narrow in on the value, but it's still a bit off. I'm pretty sure on most of those digits but without actually doing the calculus I can't get much better than that.
0.552 at 0.0000001 increments
Total error:1160.26180861006145200701189677079769078925976995284068
Samples 10000001
You'll notice my value is only 0.4279 different than that in total error over a million samples. And since that truncation lowers it, it will only make it a bit closer to the even extrema point, which is a fine metric.
Under this metric the cubic value something like: 0.92103099 which might be more important because really flattening out the error might matter in that case a lot more than in the cubic case. I'll call it 0.921
Basic geometry allows one to solve for the best solution to use a value of C to make a bezier curve close to an arc. The naive solution using Geometry is
This is the solution to the question of we have anchor points on the curve and the curve touches the circle at the center, what value of C for a control point on the quadrant curve (0,1),(C,C),(1,0). Gives us the best fit to the curve.
However Mortenson points out that we are better if we minimize the error rather than allow all the error to be on one side of the circle.
This issue allows a few more percent with the Cubic Bezier form. But, it's more important if we're using one fewer control points. The same is also true at one fewer yet. If we have a square, what square looks most similar to a circle.
The if the naive geometry solution is the same we get:
. Where the corners touch the circle but do not exceed it. The other way you could do it is to have them only touch at the corners and just make the curve larger than the circle.
And finally we have Mortensen's solution to it, by making the metric for closest be the average error across the entire graph.
Well, solving this for 1 control point means doing the same thing he did. Which isn't super-trivial because it means calculating min and max error and adjusting various things. So I wrote a program to do it.
Having the program I refigured it for Cubic:
0.55191502449351057074356272279256664233618039472430889733698053746758709885277817592685338345358001614300815257463095148547403466508799941938848910944812198495136713728128014911200347760723733292314480
Having a deviation in both directions of: ±0.00019607646987687817401874512914923 ....
Mortensen gave this as,
0.551915024494
Since we might well be using doubles I'd give it as:
0.5519150244935106
This is much better than the more naive value:
0.91421356237 which is effectively unusable. While Mortensen's use for the cubic is great, it changes the quad naive to almost usable, generally not, but *almost* usable.
The Mortensen-optimized value for quads is off by max 0.007767318 whereas the naive value is off by 0.010781424258 which is 28% better. Hm. That's the same value Mortensen got for the cubic.
Naive Quad: For comparison.
1 dimensional fractals all look the same. That's why they are fractals. Get it? Because they are all lines. And fractals look the same. And all 1d anything is a line which looks like all the other lines. HAHAHAHAHAHA!
Velocity is the direction and speed of an object. Changing the direction because of the maximum velocity was reached must be considered a defect. This also clearly causes a flaw in that a diagonal velocity is faster than the orthogonal one.
The flaw here is that if your velocity in the X direction exceeds the maxVelocity it is changed equal maxVelocity and the same for the Y. But, that means that if we are going at 20° angle and at a speed of 200, and our maxVelocity is 20. Our velocity is changed to be 20*sqrt(2) at a 45° angle. The correct answer is to scale the mXVelocity and mYVeloicity by the ratio of the actual velocity and maxVelocity.
I checked a few of the Table Talk quotes and found the same thing Richard Carrier found. That they were generally bullshit. But, I didn't know how far the rabbit hole went or how crap they were to start with. http://www.richardcarrier.info/archives/10978
----- Behold.
Admittedly it'll fill the side two garbage pixels on the right with looped garbage but it doesn't bother to slow down to check when those happen. Stride is typically equal to width.
This is going to make some pretty interesting L-System fractals pretty trivial to generate. F, F:=F-F-F+ then at the end replace all F with h10 all + with b90 and all - with b-90, and you're totally done.
Update: This did not make it into the SVG 2.0 final Spec.
