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language + fiction

Dark Matter of the English Language—the unwords

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

unnames

These are names generated from the US Census list of names using a char-rnn recurrent neural network.

The names generated by the network appear neither in the list of names nor in a 479,000 list of English words. The names may be words or names in another language, however.

female first names that don't exist

Your friends discouraged you from naming your first daughter "Ginavietta Xilly Anganelel" but you didn't listen. When you named your second daughter "Nabule Yama Janda" everyone wanted to know what your secret to having such successful children was.

Below are the alphabetically first 3–10 letter female unnames for each letter. In some cases, no names of a given length were generated for a given letter.

—3—
Bei
Cac
Cau
Daa
Deu
Edz
Ele
Fea
Fri
Hea
Hhi
Ied
Ien
Jea
Kau
Kec
Ldo
Leb
Maz
Mec
Nie
Nin
Oro
Ota
Reu
Ric
Seu
Sia
Tix
Tuu
Uan
Uid
Vad
Vea
Wie
Wil
Xai
Xon
Yka
Yra
—4—
Aaya
Abal
Bbhy
Beli
Cani
Caro
Daee
Dayn
Eann
Ebha
Gori
Guda
Hael
Hari
Idek
Idla
Joga
Joon
Kace
Kade
Laan
Laha
Mada
Madi
Nado
Nian
Olce
Olly
Phry
Pide
Qisy
Qoly
Rari
Rary
Sabi
Saes
Tany
Tary
Ucte
Uida
Vatt
Vean
Wada
Waid
Xiem
Yama
Yamn
Zibi
Ziun
—5—
Adree
Adyla
Babyl
Balbo
Caccy
Cadda
Dadel
Dader
Eddra
Ededr
Fibee
Fleei
Gelan
Guita
Hacie
Haela
Idale
Idena
Janda
Jerly
Kaaly
Kacee
Laala
Lacee
Maala
Mabie
Nayle
Nelli
Olako
Olise
Payna
Phaci
Qinsy
Qolee
Raane
Racey
Sacti
Sacul
Tabbr
Tabuy
Uanga
Uayda
Vaale
Vadia
Wagwa
Walyg
Xilly
Xiwda
Yabuy
Yahye
—6—
Adeley
Alelee
Babela
Bajbie
Caccye
Cacell
Dadela
Dakith
Edalla
Edelah
Feliey
Felike
Garlee
Geldie
Haishe
Haline
Idelig
Idelle
Jaccey
Jatqie
Kaceey
Kacele
Laceie
Ladira
Maarae
Maarla
Nabule
Nadera
Olchee
Olisha
Pamber
Parell
Qoesha
Qoleen
Rabina
Rabymi
Sachie
Sacola
Tafbie
Tamima
Ulieta
Ullena
Vadeta
Vandie
Waghel
Wandie
Xaique
Xillia
Yaketo
Yameka
—7—
Alenlis
Alissea
Barelah
Barmeta
Cacalla
Caccayc
Dalecee
Dalleer
Ebeccii
Eeenera
Farleen
Ferreda
Ganalel
Griagne
Harlean
Hayceda
Iellina
Ienetka
Jadquen
Jaqquil
Kaariko
Kabjine
Labelle
Labrice
Maadite
Maadrae
Nachlee
Naqoena
Ollisha
Oralore
Panelte
Paricel
Qilonga
Qlianna
Rabette
Racelie
Sacelie
Sacelle
Tamarie
Tamarke
Ualacie
Uibelle
Valerte
Vanelte
Waylena
Wazlein
Yadalie
Yakkina
—8—
Aleretha
Allalera
Bamberah
Battynkb
Caccelle
Cacellen
Dacheele
Dameline
Eetenere
Eethelie
Feairice
Gaannele
Gelneria
Hacylone
Hecticie
Iachelie
Ilabetth
Jacquine
Jaqqueyn
Kabrenee
Kacalyne
Laloytha
Langella
Maarmila
Mabylere
Nadalena
Nadmelle
Orotenne
Parleeta
Parmicia
Quettine
Rachilde
Racierda
Saaleych
Saccelle
Tasharia
Tathrika
Uuguetta
Uussuida
Valtonda
Vassicha
Wapreida
Willenee
Yaumette
Yeholaki
—9—
Anganelel
Bathueyna
Bealyakha
Caccalren
Caleniqsa
Dalerisha
Dannerele
Eferwrace
Elaberosh
Genelnice
Helmarita
Hemaricia
Ieanerise
Ilbebette
Jatquelyn
Kacalenne
Kacelynen
Lasheudde
Laverethe
Macarelze
Macbalica
Nompterla
Porpencia
Ramancina
Rarashera
Saccellne
Sanelline
Thashinda
Tizkiqhie
Ususuista
Uussautti
Velletita
Vellotina
—10—
Camalincia
Ccarleetta
Deliqheeda
Elatoresha
Elisamerie
Ginavietta
Iimameline
Ilollinina
Karestanet
Kariamarie
Lelagrelie
Lelerateta
Maccelline
Maceannica
Retaqyelle
Saraquetta
Shelolesne
—11—
Cciccinelda
Cclarleette
Elisazetlie
Elisebethle
Ikekzikeina
Ilizeblelle
Kimbhrresty
Lichiabetta
Liebetreide
Mamiammalan
Marianceran
Sherleenene
Sisselletta

male first names that don't exist

You name your first child "Babton Laarco Tabrit". You name your second "Ferandulde Hommanloco Kictortick". Both see infinite success in life and you wonder why you haven't discovered neural networks sooner.

Below are the alphabetically first 3–10 letter male unnames for each letter. In some cases, no names of a given length were generated for a given letter.

—3—
Aad
Aan
Bil
Bre
Cas
Ces
Daa
Dax
Ede
Eey
Har
Hhe
Ial
Iir
Jac
Jal
Kel
Kib
Lal
Lel
Mah
Meh
Nal
Nas
Oid
Oon
Phy
Pys
Roz
Ruf
Sas
Sih
Tes
Tey
Vay
Ven
Wal
Wil
Zes
Zin
—4—
Aado
Admo
Baan
Badl
Cald
Calg
Daad
Daal
Eard
Ebax
Farn
Felb
Gaht
Gart
Haan
Haco
Iane
Idae
Jaan
Jace
Kaan
Khen
Laad
Laan
Maab
Madi
Nald
Nall
Obby
Odan
Peit
Piar
Qide
Raal
Rady
Saag
Saan
Tacy
Tany
Vaen
Vaes
Waci
Waco
Ytih
—5—
Aanle
Aaton
Baane
Baart
Cabis
Cailh
Daamo
Daano
Eamon
Earis
Famry
Fandy
Gacon
Gahey
Hadel
Hagre
Idail
Idris
Jacer
Jadio
Karry
Keris
Laale
Laber
Maaro
Mabin
Naado
Naalo
Oaris
Ohale
Palio
Paric
Qebin
Qikel
Rabey
Radey
Sacon
Sadne
Tacie
Talet
Uusse
Vaeld
Valen
Wacer
Wadle
Zilal
Zloyn
—6—
Aabird
Aareno
Babton
Badunt
Cadlor
Cadron
Daapis
Dabron
Earrel
Earrre
Fabery
Faicey
Gaarrh
Gadano
Haares
Habide
Ienlir
Igamar
Jaalil
Jabron
Kebitt
Kelmar
Laarco
Laarin
Maccel
Maccol
Nablan
Nacell
Ohepto
Olerrh
Paciul
Pakdon
Qicias
Qrekon
Rabwin
Raciad
Saando
Saddon
Tabrit
Tactan
Ulande
Uoseol
Vachon
Vacors
Waaren
Wabton
Xiklel
Zesian
—7—
Aabrado
Aarsado
Balnend
Barcick
Caduuse
Caliulo
Daalius
Daarrol
Eanondo
Earesle
Falbeus
Faloric
Ganunle
Garlard
Haameno
Habrenc
Icoolse
Idonald
Jaendie
Jajuian
Kodavio
Korgell
Laarnel
Laarrec
Maccalo
Machual
Nabtumo
Nachale
Oimolan
Ollisee
Paberto
Palducb
Quitius
Radlond
Radullo
Sacholh
Saconad
Tadrine
Tahinte
Vacelle
Vagallo
Wabbent
Wacivey
Zewrave
—8—
Aarnounf
Aarruleu
Balibhat
Baravile
Carelcic
Carkocce
Dalevice
Danilian
Earrinto
Eberepto
Fadonocf
Farricco
Gaurlnih
Gegirald
Handerus
Harelcce
Januipan
Jarcebph
Korancin
Laleaddo
Lalenicd
Maccelce
Macchely
Nachaane
Nalaneil
Parlicco
Parreico
Randlold
Rantozer
Sachasce
Sactonae
Talentin
Tavintey
Vernilve
Vernnche
Wacellio
Waldrand
Ziliasen
—9—
Aldanoldf
Aldresdis
Barrkimad
Berganton
Carlercca
Carmencan
Darriscce
Dauguslus
Edgaronte
Eeletento
Flandinco
Flilnendy
Galrinand
Gerarmovo
Hefarordo
Helaphhey
Jeenforue
Jeffersol
Lannendan
Lanuullan
Marricice
Marridcce
Nathanaal
Oberverto
Qoaberucc
Rallisten
Rardusler
Salcieley
Salvinten
Teliberel
Tewraslel
Wiccelele
Willofvis
—10—
Alfandrone
Atthaaneel
Brantisard
Castushart
Caucerucce
Eeverielti
Elerdrolde
Ferandulde
Flarericco
Hommanloco
Kictortick
Licoonicio
Llenelvind
Nattonanal
Oriccoomon
Rarvondard
Renaldordo
Sawvarcsas
Wengortwen
—11—
Cadrielccar
Ccrickuctof
Llantonlolm
Lunuslinzus
Micckelammy
Triddatrerd
Waldinawwan

VIEW ALL

news + thoughts

Hola Mundo Cover

Sat 21-09-2019

My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Hola Mundo by Hannah Fry. Cover design is based on my 2013 `\pi` day art. (read)

Curious how the design was created? Read the full details.

Markov Chains

Tue 30-07-2019

You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts

A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Markov Chains. (read)

Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current state—the future is independent of the past if the present is known.

Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.

This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.

Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.

1-bit zoomable gigapixel maps of Moon, Solar System and Sky

Mon 22-07-2019

Places to go and nobody to see.

Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
3.6 gigapixel map of the near side of the Moon, annotated with 6,733. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixel and 10 gigapixel map of the Solar System on 20 July 2019, annotated with 758k asteroids, 1.3k comets and all planets and satellites. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Northern Celestial Hemisphere, annotated with 44 million stars, 74,000 deep-sky objects and 3,000 exoplanets. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Southern Celestial Hemisphere, annotated with 69 million stars, 88,000 deep-sky objects and 1000 exoplanets. (details)

Quantile regression

Sat 01-06-2019
Quantile regression robustly estimates the typical and extreme values of a response.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Quantile regression. (read)

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nature Methods 12:999–1000.

Analyzing outliers: Robust methods to the rescue

Sat 30-03-2019
Robust regression generates more reliable estimates by detecting and downweighting outliers.

Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Analyzing outliers: Robust methods to the rescue. (read)

We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.

Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.

Background reading

Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.

Two-level factorial experiments

Fri 22-03-2019
To find which experimental factors have an effect, simultaneously examine the difference between the high and low levels of each.

Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.

They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Two-level factorial experiments. (read)

Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).

Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.