Trance opera—Spente le Stellebe dramaticmore quotes

words: meaningful

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

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
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
Laan
Laha
Nian
Olce
Olly
Phry
Pide
Qisy
Qoly
Rari
Rary
Sabi
Saes
Tany
Tary
Ucte
Uida
Vatt
Vean
Waid
Xiem
Yama
Yamn
Zibi
Ziun
—5—
Babyl
Balbo
Caccy
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
Uanga
Uayda
Vaale
Wagwa
Walyg
Xilly
Xiwda
Yahye
—6—
Alelee
Babela
Bajbie
Caccye
Cacell
Dakith
Edalla
Edelah
Feliey
Felike
Garlee
Geldie
Haishe
Haline
Idelig
Idelle
Jaccey
Jatqie
Kaceey
Kacele
Laceie
Maarae
Maarla
Nabule
Olchee
Olisha
Pamber
Parell
Qoesha
Qoleen
Rabina
Rabymi
Sachie
Sacola
Tafbie
Tamima
Ulieta
Ullena
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
Jaqquil
Kaariko
Kabjine
Labelle
Labrice
Nachlee
Naqoena
Ollisha
Oralore
Panelte
Paricel
Qilonga
Qlianna
Rabette
Racelie
Sacelie
Sacelle
Tamarie
Tamarke
Ualacie
Uibelle
Vanelte
Waylena
Wazlein
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
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—
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—
Baan
Cald
Calg
Daal
Eard
Ebax
Farn
Felb
Gaht
Gart
Haan
Haco
Iane
Idae
Jaan
Jace
Kaan
Khen
Laan
Maab
Nald
Nall
Obby
Odan
Peit
Piar
Qide
Raal
Saag
Saan
Tacy
Tany
Vaen
Vaes
Waci
Waco
Ytih
—5—
Aanle
Aaton
Baane
Baart
Cabis
Cailh
Daamo
Daano
Eamon
Earis
Famry
Fandy
Gacon
Gahey
Hagre
Idail
Idris
Jacer
Karry
Keris
Laale
Laber
Maaro
Mabin
Naalo
Oaris
Ohale
Palio
Paric
Qebin
Qikel
Rabey
Sacon
Tacie
Talet
Uusse
Vaeld
Valen
Wacer
Zilal
Zloyn
—6—
Aabird
Aareno
Babton
Daapis
Dabron
Earrel
Earrre
Fabery
Faicey
Gaarrh
Haares
Habide
Ienlir
Igamar
Jaalil
Jabron
Kebitt
Kelmar
Laarco
Laarin
Maccel
Maccol
Nablan
Nacell
Ohepto
Olerrh
Paciul
Pakdon
Qicias
Qrekon
Rabwin
Saando
Tabrit
Tactan
Ulande
Uoseol
Vachon
Vacors
Waaren
Wabton
Xiklel
Zesian
—7—
Balnend
Barcick
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
Sacholh
Tahinte
Vacelle
Vagallo
Wabbent
Wacivey
Zewrave
—8—
Aarnounf
Aarruleu
Balibhat
Baravile
Carelcic
Carkocce
Dalevice
Danilian
Earrinto
Eberepto
Farricco
Gaurlnih
Gegirald
Handerus
Harelcce
Januipan
Jarcebph
Korancin
Lalenicd
Maccelce
Macchely
Nachaane
Nalaneil
Parlicco
Parreico
Randlold
Rantozer
Sachasce
Sactonae
Talentin
Tavintey
Vernilve
Vernnche
Wacellio
Waldrand
Ziliasen
—9—
Aldanoldf
Aldresdis
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—
Ccrickuctof
Llantonlolm
Lunuslinzus
Micckelammy
Triddatrerd
Waldinawwan

VIEW ALL

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.

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.

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.

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!

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.

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.

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.

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.

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

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.

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.

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.

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.