Here we are now at the middle of the fourth large part of this talk.get nowheremore quotes

# stars: fun

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

# visualization + design

The 2018 Pi Day art celebrates the 30th anniversary of $\pi$ day and connects friends stitching road maps from around the world. Pack a sandwich and let's go!

# $\pi$ Day 2017 Art Posters - Star charts and extinct animals and plants

2018 $\pi$ day shrinks the world and celebrates road trips by stitching streets from around the world together. In this version, we look at the boonies, burbs and boutique of $\pi$ by drawing progressively denser patches of streets. Let's go places.
2017 $\pi$ day
2016 $\pi$ approximation day
2016 $\pi$ day
2015 $\pi$ day
2014 $\pi$ approx day
2014 $\pi$ day
2013 $\pi$ day
Circular $\pi$ art

On March 14th celebrate $\pi$ Day. Hug $\pi$—find a way to do it.

For those who favour $\tau=2\pi$ will have to postpone celebrations until July 26th. That's what you get for thinking that $\pi$ is wrong.

If you're not into details, you may opt to party on July 22nd, which is $\pi$ approximation day ($\pi$ ≈ 22/7). It's 20% more accurate that the official $\pi$ day!

Finally, if you believe that $\pi = 3$, you should read why $\pi$ is not equal to 3.

All art posters are available for purchase.
I take custom requests.

Caelum non animum mutant qui trans mare currunt.
—Horace

This year: creatures that don't exist, but once did, in the skies.

And a poem Of Black Body.

This year's $\pi$ day song is Exploration by Karminsky Experience Inc. Why? Because "you never know what you'll find on an exploration".

## create myths and contribute!

Want to contribute to the mythology behind the constellations in the $\pi$ in the sky? Many already have a story, but others still need one. Please submit your stories!

Here I make available all the files you need to reconstruct the chart. All files are plain text and designed to be easily parsable.

Star chart of the first 12,000,000 digits of $\pi$. The 80 constellations honor extinct animals and plants. Azimuthal equidistant projection. (BUY ARTWORK)

For the simplest chart, you'll need the star catalogue which already provides the longitude and latitude coordinates for each star. It'll be up to you to choose and calculate a projection.

You can then layer constellations, which are defined by a list of edges. If you like, you can draw boundaries around each constellation, which are also provided.

Star-to-constellation mapping is also given, which allows you to create labels for the stars within each constellation based on relative brightness.

Finally, you can get the species details for each constellation, including the Latin name of the species, Wikipedia URL and (for many) the mythology of the constellation.

## star catalogue

The star catalog generated from the first 12 million digits of $\pi$.

$DOWNLOAD # idx digits name x y z long lat dist mabs mapp 0 314159265358 a -1859 926 35 145.339 38.384 2077.157 3.00 14.59 1 979323846264 b 4793 -2616 126 -38.404 -39.555 5461.884 -1.00 12.69 2 338327950288 c -1617 -2205 -472 -110.162 32.164 2774.797 3.00 15.22 3 419716939937 d -803 -3307 493 -105.489 3.655 3438.620 2.00 14.68 ... 999996 601538500580 cexhk 1015 -1150 -442 -48.144 -14.704 1596.273 -5.00 6.02 999997 420478142596 cexhl -796 2814 -241 97.939 14.471 2934.330 1.00 13.34 999998 278256213419 cexhm -2218 621 -159 156.900 46.719 2308.776 4.00 15.82 999999 453839371943 cexhn -462 -1063 -306 -95.924 26.937 1198.770 -2.00 8.39$

## constellation definitions

Constellations were manually defined. Each constellation has a name and abbreviation (first 3 characters unless longer is required to uniquely specify it). Shown next is the number of stars used to define the constellation and their names, as appear in the star catalogue file above. Next is the number of edges and the star pairs that define the edges of the constellation. The edges are not in any particular order and have no direction. Any spaces in names are encoded with _.

$DOWNLOAD # idx n_stars n_edges name abbrev stars edges ... 2 3 3 alaotra ala blts,btosf,cbkbw btosf-cbkbw,blts-btosf,blts-cbkbw 3 4 4 alloperla all ghyr,pwkn,ssrx,ugwt pwkn-ssrx,pwkn-ghyr,ugwt-pwkn,ssrx-ghyr 4 2 1 aplonis apl cbocd,rllm rllm-cbocd ...$

You can use this file to quickly search for certain shapes. For example, triangular constellations are those that have 3 stars and 3 edges.

$> grep " 3 3" constellations.def.txt 2 3 3 alaotra ala blts,btosf,cbkbw btosf-cbkbw,blts-btosf,blts-cbkbw 16 3 3 camptor camp benxf,bqvh,bwqed bqvh-benxf,bwqed-bqvh,bwqed-benxf 26 3 3 desmodus des bfnqu,mork,zwzy bfnqu-mork,zwzy-bfnqu,zwzy-mork 27 3 3 ectopistes ect bopmt,cbquf,wmnw cbquf-bopmt,wmnw-cbquf,wmnw-bopmt 30 3 3 hoopoe hoo bpsop,bvmjh,tryh tryh-bvmjh,bpsop-tryh,bpsop-bvmjh 31 3 3 huia hui ccteo,xbvq,yqet xbvq-yqet,ccteo-xbvq,ccteo-yqet 39 3 3 malpaisomys mal bucqd,likq,nqn nqn-bucqd,likq-nqn,likq-bucqd 41 3 3 mariana mar gmps,jydb,pcjx pcjx-gmps,jydb-pcjx,jydb-gmps 49 3 3 palaeoaldrovanda pal bedae,oife,saz bedae-saz,oife-bedae,oife-saz 63 3 3 rhynia rhy bpviv,cenuk,hivz bpviv-hivz,cenuk-bpviv,cenuk-hivz 65 3 3 silphium sil bjesg,bmquw,bpxia bmquw-bpxia,bjesg-bmquw,bjesg-bpxia 69 3 3 tadorna tad bukqe,cbtrx,epdx cbtrx-epdx,bukqe-cbtrx,bukqe-epdx 72 3 3 traversia tra fcnw,fywb,puib fywb-fcnw,puib-fywb,puib-fcnw 80 3 3 yersinia yer colq,ibls,zgvy ibls-zgvy,colq-ibls,colq-zgvy$

## constellation boundaries

The boundaries were manually defined. Shown here, for each constellation, is the constellation's area, perimeter center and boundary $(x,y)$ pairs, delimited by : and represent a closed polygon that encloses the constellation's stars.

All values are longitude and latitude. The three constellations listed below are ones with smallest area.

$DOWNLOAD # abbrev area perimeter centroid_xy boundary_xy_pairs ... com 74.83 34.96 -102.50,26.25 -100.00,30.00:-102.50,30.00:-105.00,30.00:-107.50,30.00:-107.50,27.50: -107.50,25.00:-107.50,22.51:-105.00,22.51:-102.50,22.51:-100.00,22.51: -97.51,22.51:-97.51,25.00:-97.51,27.50:-97.51,30.00:-100.00,30.00 pal 50.02 30.00 3.43,-40.93 5.00,-37.49:2.50,-37.49:0.00,-37.49:0.00,-40.00:0.00,-42.50:0.00,-45.00: 2.50,-45.00:5.00,-45.00:5.00,-42.50:7.49,-42.50:7.49,-40.00:7.49,-37.49:5.00,-37.49 sil 37.57 25.02 115.00,-51.25 115.00,-47.50:112.49,-47.50:112.49,-50.00:112.49,-52.49:112.49,-55.00: 115.00,-55.00:117.50,-55.00:117.50,-52.49:117.50,-50.00:117.50,-47.50:115.00,-47.50$

The boundary polygons abut but do not overlap and they cover the entire sky. There is one polygon per consetllation. The total area of all constellations is $360 × 180 = 36800$.

## constellation star membership

This is a list of all the stars on the chart and their constellation membership. A star is considered to be in a constellation if it falls within the constellation boundary.

The $i$ and $j$ indexes give the relative brightness of the star on the map and in the constellation, respectively. If a star is used to define the constellation edges it gets a + otherwise -.

$DOWNLOAD # abbreviation star i j mapp on_edge? aep bkawv 35 0 1.34 + aep gql 65 1 1.63 + aep cavix 72 2 1.71 + aep bqxvm 137 3 2.25 + aep tjow 158 4 2.31 + aep beelq 176 5 2.39 + ... yer jjlj 39365 412 7.24 - yer bgswm 39464 413 7.24 - yer ittu 39546 414 7.24 - yer wakp 39556 415 7.24 - yer bedks 39667 416 7.25 - yer gxzo 39817 417 7.25 -$

To lookup the 10 brightest stars, sort on the i index. Here we see that megal (Megalodon) has the brightest star in the sky, jkxo with apparent magnitude $-2.05$. The next two brighest stars are in mam (Mammuthus) and ara (Araucaria).

$> cat constellations.stars.txt | sort -n +2 -3 | head -10 megal jkxo 0 0 -2.05 + mam btsqy 1 0 -0.73 + ara ccijs 2 0 -0.38 + rap btaum 3 0 0.26 + urs bxlss 4 0 0.26 + tec bgrdk 5 0 0.43 + cop itwr 6 0 0.45 + ara mrvq 7 1 0.54 + phe loju 8 0 0.54 + mam bhlbw 9 1 0.55 +$

To get a list of the brightest star in each constellation, just search for " 0 ". Below I show this list sorted by brightness.

$> cat constellations.stars.txt | grep " 0 "| sort -n +4 -5 megal jkxo 0 0 -2.05 + mam btsqy 1 0 -0.73 + ara ccijs 2 0 -0.38 + rap btaum 3 0 0.26 + urs bxlss 4 0 0.26 + tec bgrdk 5 0 0.43 + cop itwr 6 0 0.45 + phe loju 8 0 0.54 + kel bnhwx 11 0 0.59 + spe rtep 13 0 0.71 + ... nes vxou 299 0 2.80 - aur bmjvf 307 0 2.81 + tra puib 318 0 2.83 + pip cecq 358 0 2.91 + pal oife 389 0 2.97 + swa gvr 463 0 3.12 + hui ccteo 485 0 3.17 + ple bzqur 506 0 3.20 + com ygrn 875 0 3.64 + car yjkn 933 0 3.68 +$

For example, tec (Tecopa) has bgrdk as its brightest star, which is 6th brightest in the sky with an apparent magnitude of 0.43.

The constellation whose brightest star is dimmest of all first brightest stars is car (Caracara). Its brightest star is yjkn which is 934th brightest in the sky with an apparent magnitude of 3.68.

To get the number of stars in each constellation, just add the number of times the constellation abbreviation appears. Bron has the most stars of any constellation, more than twice as many as the next one, archaeo (Archaeopteryx). Both car (Caracara) and por (Porzana) have only 7 stars each, the fewest of all constellations.

$5230 bro 2287 archaeo 2205 thy 2155 kim 1838 archaea 1789 came 1768 ard ... 20 megal 19 swa 17 mar 12 rhy 8 sil 7 por 7 car$

## constellation names, stories and links

The constellations are in no particular order in this file.

$DOWNLOAD # constellation name # hemisphere (n north, s south, b both) # common name # Latin name # extinction date # URL # optional story aplonis n mysterious bird of Ulieta Aplonis ulietensis 1774-1850 https://en.wikipedia.org/wiki/Raiatea_starling desmodus n Giant Vampire Bat Desmodus draculae Pleistocene or early Holocene https://www.thoughtco.com/recently-extinct-shrews-bats-and-rodents-1092147 It is thought that each night Desmodus flies up against the dome of the sky, looking for a way to escape. ...$
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.