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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

These are maladies sampled from my list of about 883,500 English unwords.

Medical jargon has never been so opaque.

phobias

Don't feel like going to that party? It could be a conveniently timed attack of ictophobia or myconomascophobia.

—9—
abophobia
beophobia
cgyphobia
deophobia
epophobia
etaphobia
kanphobia
miophobia
—10—
alraphobia
beexphobia
bemophobia
cemophobia
cetophobia
dalophobia
degephobia
eccophobia
eedophobia
ficaphobia
galophobia
ganyphobia
helophobia
hilophobia
ictophobia
illyphobia
kelophobia
manophobia
menophobia
oxiophobia
perophobia
pisophobia
tegophobia
tenophobia
—11—
acctophobia
alonophobia
beonyphobia
beprephobia
caanophobia
cachophobia
deginphobia
deinephobia
ecquephobia
egycaphobia
geceophobia
geneophobia
inymophobia
iponephobia
kennophobia
lleiophobia
mactophobia
maenephobia
oeriophobia
optigphobia
paliophobia
parrophobia
reglophobia
scavophobia
starophobia
taloaphobia
teonaphobia
—12—
antotophobia
begatophobia
birroophobia
cabolophobia
canchophobia
dachoiphobia
debriophobia
elimiaphobia
enconophobia
fansoaphobia
fatenophobia
gedicophobia
gercomphobia
heralephobia
hyscrophobia
inchrophobia
ingonophobia
konetophobia
lamphophobia
lepinophobia
menoxiphobia
mezalophobia
neponophobia
oppetophobia
parkiophobia
preotaphobia
saconophobia
temphophobia
tilophphobia
uemanaphobia
vobitophobia
—13—
achythophobia
acteliophobia
becrosophobia
benoprophobia
cembaniphobia
chegenophobia
dambotophobia
denyarophobia
ecenytophobia
eerichophobia
fatenylphobia
galarsophobia
gannotophobia
hotoidophobia
iconopephobia
inbictophobia
kentylophobia
mallygophobia
manismyphobia
nonditophobia
oophalaphobia
ototarophobia
peltosaphobia
penconophobia
rechapophobia
rechorophobia
stimotophobia
stomosophobia
testanophobia
thidotophobia
vertulophobia
—14—
ancometophobia
antionsyphobia
cenctopaphobia
cheinonophobia
dempabiophobia
denolaryphobia
ecenytrophobia
edalycrophobia
gebrocomphobia
geoparyophobia
heegoscophobia
hindancophobia
ichicotyphobia
ideuschophobia
maromaniphobia
myolegrophobia
octonenophobia
onechotophobia
persectophobia
phetholyphobia

And here are some very long fears. I'm sure that one of these is the fear of long fears.

fires in the brain

Those thoughts you're having might not be yours. It could be talmatomania.

—7—
demania
gomania
namania
—8—
adomania
diomania
dipmania
fermania
giomania
maomania
peomania
thomania
—9—
adiamania
agnymania
benamania
brepmania
calemania
capumania
dipamania
disomania
egzomania
expomania
facomania
galamania
gamemania
hytomania
iglimania
ihyomania
ledimania
macemania
mallmania
najumania
pedamania
poelmania
susimania
tegomania
tigymania
uztimania
vicimania
—10—
agniomania
ampiamania
besmomania
ceedomania
centomania
deglimania
deykomania
eodaimania
fathemania
fertomania
galarmania
geolemania
inasamania
inorimania
leliomania
maaromania
mariomania
nedramania
nodiomania
plolomania
precamania
sereimania
tachymania
tattomania
viliomania
—11—
acculamania
antisumania
beedromania
beranimania
cemchomania
chetiomania
degonomania
demitemania
endonomania
eyleromania
flodiomania
gastdomania
gelyzymania
ingatomania
lamphomania
lamphymania
malatomania
maracamania
opostomania
perciomania
prathomania
teouromania
tieanomania
—12—
anchodymania
anchonomania
cirminomania
conchromania
degotrsmania
demminomania
encoctomania
eyotoxomania
gremgormania
mardisimania
mibeliomania
nenadiomania
parmulamania
pestriomania
talmatomania
thyminomania
wirgilamania
—13—
bepporanmania
berandromania
caconylymania
calebitomania
deborphomania
icepatromania
inferonomania
megazidomania
pecatisomania
peribacomania
seergenamania
stentosomania

stuff is definitely wrong

I'm not sure what's going on, but it might be an acute attack of ophalosis or that chronic icabulosis you've been struggling with.

—5—
cosis
gosis
iosis
mosis
—6—
axosis
buosis
byosis
ciosis
coosis
diosis
ecosis
egosis
feosis
geosis
idosis
irosis
keosis
meosis
moosis
pathyc
pathym
taosis
tiosis
—7—
algiang
algiate
bedosis
beeosis
calgias
cavosis
dehosis
didosis
edosist
eemosis
facosis
ferosis
galosis
gasosis
hedosis
hemosis
ichosis
idaosis
madosis
magosis
odiosis
odlosis
panosis
pecosis
roposis
saxosis
tacosis
talosis
vinosis
vorosis
—8—
acshosis
ademosis
bemposis
benosise
cactosis
caliosis
deagosis
demiosis
ecosises
egglosis
fattosis
fedlosis
gareosis
gariosis
harrosis
helposis
icerosis
ichnosis
leliosis
macosism
mactosis
nefkosis
neocosis
obbiosis
ocirosis
pathybic
pathymia
rechosis
tachosis
tendosis
—9—
acctrosis
acyclosis
balolosis
befalgian
cacacosis
cafulosis
danknosis
decarosis
echenosis
econeosis
falgnosis
fanulosis
gabarosis
galoxosis
heepathyl
hegriosis
icemosism
ichapathy
kelutosis
kenatosis
lamomosis
lebytosis
mabacosis
macalosis
nenylosis
nercrosis
odiosises
olohosism
panosises
partosism
quoetosis
regoosism
rhagnosis
saconosis
saumalgia
tacopathy
talthosis
undylosis
vhilalgia
vhinnosis
—10—
acaporosis
aceutiosis
bariakosis
becrotosis
cabesmosis
cachinosis
daiocrosis
daiphyosis
ecraniosis
eeminalgia
falogiosis
fedophosis
gaggonosis
gamancosis
heltonosis
heniflosis
icabulosis
icayulosis
kasphosise
kemoptosis
lephanosis
maccolosis
macosistic
namancosis
neocosises
oditopathy
omosinosis
palanrosis
panhidosis
rechoposis
rechosises
segiphosis
setactosis
tahradosis
taronosism
unodylosis
vhitatosis
vhoripathy
—11—
acdioenosis
actypyrosis
ballfrosise
becosistate
cacymalosis
caliphiosis
dalmatosise
dalomicosis
econeosises
ecrpharosis
facunolosis
fadiettosis
gacochrosis
gadersiosis
heleignosis
helipolosis
ibbiognosis
ichabolosis
jabimarosis
kedamirosis
kelodialgia
lelidalosis
macolobosis
macomycosis
nacocynosis
nectianosis
octoperosis
octorsposis
palevomosis
palygulosis
recocolosis
reglogyosis
sabrichosis
saconosises
teclagnosis
tellodrosis
untochosise
vurocylosis
—12—
abzibellosis
achythoposis
bakiomatosis
bedolignosis
cabolophosis
caconoacosis
dansepicosis
dantachrosis
ecchonylosis
ecctometosis
fansoaphosis
fentivorosis
gaelopyrosis
ganactomosis
hecthlinosis
hedophonosis
icaneoptosis
icepleitosis
karocalcosis
keloecatosis
lelemosisine
lelephotosis
macophagosis
maerozooosis
naniquinosis
nescroblosis
octorschosis
olethydrosis
pastiosising
pastiosismes
rechopatosis
rechopyrosis
segamethosis
seprorinosis
taronosismic
tedophirosis
venotormosis
vicypartosis
—13—
abobiolanosis
abrophyclosis
bailligirosis
beetheritosis
caccoparcosis
caconylycosis
decantophosis
decretharosis
ecccombulosis
ecyphenolosis
fansoecosises
feritoverosis
gaoduroniosis
gecivericosis
helatosteosis
heneotermosis
icenaronrosis
icernicycosis
kaumebedrosis
kelumbeenosis
lamphymbiosis
lelemosiseous
maelmicolosis
mafectermosis
neodylemnosis
noriotyphosis
octonepidosis
oepetymstosis
paccurognosis
paliascilosis
rerexomatosis
rimipathylene
stammomycosis
stolosibeosis
tadiotennosis
talhagunnosis
uneloplerosis
unmedunulosis
vhoriolecosis
vyrteobulosis
xlocrodynosis
—14—
acciritorrosis
accorbotolosis
bacocentamosis
becrotenicosis
caliphogniosis
calmicocenosis
danduocardosis
dantachromosis
ectinoliphosis
eggenopertosis
fematosishalle
fenhhaocamosis
galettonemosis
gaslogastrosis
hendimorphosis
henoptermeosis
icliotoidlosis
iddomethemosis
karapiapulosis
kichymarenosis
maccombuscosis
macophomacosis
nercrodidiosis
nondexicalosis
omestospidosis
ooracholosises
pacolimidrosis
pastinooctosis
qansinquirosis
rechanucenosis
saxinifactosis
secrotopolosis
tachomorphosis
tanadavicuosis
vericatorrosis
vhioductylosis

it's probably just age

Most of these are natural conditions associated with aging. I know mediatopathy definitely is and it always makes my bellotalgia flare up.

—7—
byalgia
—8—
alealgia
dymalgia
gelalgia
mydalgia
myzalgia
tinalgia
—9—
aditalgia
coomalgia
cyonalgia
diptalgia
drycalgia
egyralgia
enaralgia
inoralgia
irivalgia
mochalgia
myolalgia
saumalgia
sempalgia
vhilalgia
—10—
antoralgia
camoxalgia
ceavoalgia
detotalgia
dogosalgia
eeminalgia
goemoalgia
hotodalgia
ictetalgia
opennalgia
paniyalgia
pumonalgia
—11—
bellotalgia
bontaralgia
cicroyalgia
cinodyalgia
digratalgia
diodymalgia
eppersalgia
kelodialgia
progetalgia
pyrponalgia
surianalgia
—12—
artaramalgia
ceminonalgia
chempemalgia
degoestalgia
dehyzydalgia
fidetisalgia
genaroyalgia
geolaycalgia
manchomalgia
oorcastalgia
—13—
addigothalgia
conomethalgia
coronydralgia
doiocystalgia
epalanthalgia
keneopatalgia
manciscialgia
merycromalgia
olettrosalgia
oxosinchalgia
phiorhicalgia
proodustalgia

—7—
gepathy
mipathy
pupathy
—8—
dempathy
deopathy
geopathy
oxypathy
penpathy
—9—
antopathy
betopathy
bilypathy
cypopathy
detopathy
dipapathy
endopathy
finypathy
henopathy
ichapathy
inrepathy
manopathy
melopathy
notopathy
orcopathy
oviopathy
phiopathy
prempathy
tacopathy
tebopathy
—10—
acynopathy
aderipathy
biplopathy
cecropathy
cectopathy
deptopathy
deylopathy
egenopathy
eodyopathy
geneopathy
geolopathy
illyopathy
maliopathy
meddopathy
oditopathy
perropathy
phelopathy
teadopathy
vhoripathy
—11—
cherropathy
conchopathy
delphopathy
demylopathy
euchlopathy
helloopathy
omperopathy
phogiopathy
tyoptopathy
—12—
blatalopathy
cepigropathy
comyslopathy
ditheropathy
enceptopathy
mediatopathy
mompulopathy
orchypopathy
perrctopathy
prophropathy
—13—
chaldipopathy
chillitypathy
driboatopathy
miteligipathy

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.