As it has gotten longer since that nice-neighbourhood pathway initially
opened, my refuge has become busier and buser. While the nebulous
construction fence reclaimed silence for a while, those not willing to
creatively jump their way by have mainly found that a few blocks deeper in
there is another path that links up. The plethora of dogwalkers are very
genteel but I am not particularly after passing pedestrian dog
conversations. The edges of one mown section fringes into occasional fairy
paths through the surrounding layer of Tradescantia luring incautious feet
into the swamp beneath. One of these paths is wholly dry, except where trees
have fallen on or slid through it. Occasional human and dog prints affirm
the way. About 50 metres on there is 5 metres of clear sand/silt riverside
beach, riven by a tiny rivulet down from 6 metres of cliff. The odd shape
and scant primary succession imply a treefall into the river.
Excuse me, I do go on. This week has been both busy and unfulfilling. I
itinerantly teach in some small way. Lately I have been getting university
alumni to migrate from Notepad++ on Microsoft Windows to Emacs orgmode on a
liferaft. The university and town are absolutely owned by the Microsoft
sales rep except for an oily sheen of Google acquisitions. People that find
me come with a sense of being born behind enemy lines.
Enjoining people to orgmode is against my personal current. A hulking
monolith of writing, publishing, scheduling, tabulation, inline rendering of
images and TeX ensconsing the tangle and weave of code. I wonder if Knuth is
for or against this particular progeny of his.
What I had been planning to scribble on in this resounding new code silence
from me was constructive solid geometry (CSG). Since I first came upon it in
the context of electromagnetic finite element analysis, I largely inherited
Gmsh. Gmsh sits preferentially upon oce, being the lgpl product bait of yet
another cad software company. I like that Gmsh furnishes a light C api
rather than miring you in C++ idioms (cf cgal and its boost corollary). It
is hard to say what the best choice of CSG is. There are a handful of 3D
programs and their libraries that are GPL3+ rather than the commercial
enticement LGPL. Counter to that is the temptation to - since I know
particular algorithms and standard approaches - just defpackage myself a
little lisp rather than introducing a hard dependency on ECL's sffi. These
packages are the life's work of their major contributors though. Eventually
I will settle down but for now I am a stone doomed to rolling.
Pivoting a second time I deeply enjoyed Boris Shminke's post about deep
learning automated proofs. Deep learning success story articles often
present proofs of the nature and destination of their convergence, those
being the controversial bits of deep learning*. Deep learning converges
somewhere, eventually, and for any particular destination there is a way to
converge there, but whether the place you got and time it took to get there
was really that great can be dubious.
*I think. I would like to pre-defer to Boris on the issue. I am from
classical machine learning around receiver operating characteristic
statistics for foreground segmentation algorithms (slightly tongue in cheek,
what this means is that you come up with a completely arbitrary algorithm
then search for / invent a new receiver operating characteristic metric that
happens to make whatever your algorithm was look good. Two of my favourites
from about 10 years ago were a Chinese research group who proposed some
metric they didn't settle on, but tried something like standard ROC metrics
within subsequent dilations of gold standard foreground segmentations and
counting how the results changed. Less tantilizingly, I saw an article that
claimed to have the highest true positive fraction foreground segmentation
ever recorded! Despite its miserable false positive fraction. (If you just
say 'true' no matter what your input was you can achieve this)).
