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listen; there's a hell of a good universe next door: let's go.e.e. cummingsgo theremore quotes

words: meaningful


In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017


language + fiction

Dark Matter of the English Language—the unwords

Words are easy, like the wind;
Faithful friends are hard to find.
—William Shakespeare

unanimals

Critters that definitely don't exist but, perhaps, should.

The backal is probably a feisty biter while the cakmiran probably has a quizzical look. And I would completely avoid the fangol—he sounds like trouble.

A great exercise for kids and the comedic-at-heart would be to try to draw some of these. What would a gakrin look like? Or a gorderish?

Below are the alphabetically first 4–10 letter single-word unanimals for each letter. In some cases, no names of a given length were generated for a given letter.

—4—
aytt
bebe
bick
caen
calb
dalh
dlol
fibl
file
galg
gaon
haen
hale
ilpa
jang
kall
laot
laro
mard
mean
naal
neat
orot
oton
pate
peof
qaid
radc
ranl
saol
shal
tial
walr
weil
—5—
acter
alome
boloo
brata
cabal
capir
dacwo
daxol
fimat
fogon
gatey
geass
haore
heisa
ihire
kardo
kouse
lalpy
lante
malbe
morci
nlreg
nriwe
oacda
omita
paric
ponga
radka
ramep
saage
saako
teart
ufuse
wease
weatl
—6—
acukoe
agtalt
backal
banher
caidat
calepe
dearle
dolpin
eyrita
fangol
gafala
gakrin
haamet
hadnel
iykile
jacang
kagcet
kurdot
lalper
largoz
mamket
mander
narnla
oammim
ooceat
palyus
patble
rarman
ravlil
seaise
seikol
tarbaa
tonele
usrenl
valiss
waatir
whagit
—7—
amreron
apunaed
baadber
balsidd
cadtole
calfasf
daldaug
dalfiso
eolgeal
eomrarf
fondard
gallish
gamymly
hankrer
hokloru
itarato
jatfish
jatwoss
kaister
lamushe
leittoy
madarle
malfash
narddco
nhucasf
oncigut
ootfoto
pakline
parcata
qicsoor
raacbor
raipins
sablrod
sabrilr
tenlrit
tonmede
vansoar
vatkifh
waldfil
walslil
—8—
anlonfow
arnbwict
baieslel
barnnkor
caeffuse
cakmiran
disteale
erhadiol
geepbuwl
golshowo
halalale
hocscist
loicpalt
lruzgind
mannforl
marppuse
obberose
oosgerle
pandleie
perphist
raaldope
ragprerd
saelling
saistiet
tolrfish
valcunle
wadmfish
wasshail
—9—
anlfilher
beigartal
cacdockud
cagccride
gardefand
gorderish
ipilfoyor
keosildor
laechinee
lhallaeye
malpandie
maltreuge
okrerblon
pallanmer
penrhapor
shipopish
shorgeone
ugoflifes
waadarall
waamesder
—10—
asdrosquod
cackemorel
canzlitbar
gaotemtirh
gorofoshew
hirkaflarl
honkerfosh
mapobanadl
moalarfesg
nearretlee
qoarrorule
raccistech
sancockese
sealdhicnh
waagelidhe
weendefish
—11—
condlidilin
cotarleweer
galafonllar
geatingtink
rellswobgry
soridioatar
wolfendelad

Here are all some lists with common suffixes

*ish camfish gallish gawlish gohfish gurrish jatfish mipkish polmish wamfish gorderish shipopish slarmish soulfish tolrfish wadmfish weendefish

*ile halile iykile weadnrile cragiile file gile

*ale anmale calilale disteale halalale hale saale

*use bampuse caeffuse marppuse kouse ufuse

*her banher coocher lorsher anlfilher wher

*tar codtar mistar soridioatar wortautar

*ole rorole cadtole wurkole cole

*ise seaise shoise guceyrise

VIEW ALL

news + thoughts

Statistics vs Machine Learning

Tue 03-04-2018
We conclude our series on Machine Learning with a comparison of two approaches: classical statistical inference and machine learning. The boundary between them is subject to debate, but important generalizations can be made.

Inference creates a mathematical model of the datageneration process to formalize understanding or test a hypothesis about how the system behaves. Prediction aims at forecasting unobserved outcomes or future behavior. Typically we want to do both and know how biological processes work and what will happen next. Inference and ML are complementary in pointing us to biologically meaningful conclusions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Statistics vs machine learning. (read)

Statistics asks us to choose a model that incorporates our knowledge of the system, and ML requires us to choose a predictive algorithm by relying on its empirical capabilities. Justification for an inference model typically rests on whether we feel it adequately captures the essence of the system. The choice of pattern-learning algorithms often depends on measures of past performance in similar scenarios.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Statistics vs machine learning. Nature Methods 15:233–234.

Background reading

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: supervised methods. Nature Methods 15:5–6.

...more about the Points of Significance column

Happy 2018 `\pi` Day—Boonies, burbs and boutiques of `\pi`

Wed 14-03-2018

Celebrate `\pi` Day (March 14th) and go to brand new places. Together with Jake Lever, this year we shrink the world and play with road maps.

Streets are seamlessly streets from across the world. Finally, a halva shop on the same block!

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A great 10 km run loop between Istanbul, Copenhagen, San Francisco and Dublin. Stop off for halva, smørrebrød, espresso and a Guinness on the way. (details)

Intriguing and personal patterns of urban development for each city appear in the Boonies, Burbs and Boutiques series.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
In the Boonies, Burbs and Boutiques of `\pi` we draw progressively denser patches using the digit sequence 159 to inform density. (details)

No color—just lines. Lines from Marrakesh, Prague, Istanbul, Nice and other destinations for the mind and the heart.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Roads from cities rearranged according to the digits of `\pi`. (details)

The art is featured in the Pi City on the Scientific American SA Visual blog.

Check out art from previous years: 2013 `\pi` Day and 2014 `\pi` Day, 2015 `\pi` Day, 2016 `\pi` Day and 2017 `\pi` Day.

Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Background reading

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column

Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

Machine learning: a primer

Thu 18-01-2018
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column

Snowflake simulation

Tue 16-01-2018
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

The work is described as a wintertime tale in In Silico Flurries: Computing a world of snow and co-authored with Jake Lever in the Scientific American SA Blog.

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.