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visualization
**+** math

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. I sympathize with this position and have `\tau` day art too!

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.

For the 2014 `\pi` day, two styles of posters are available: folded paths and frequency circles.

The folded paths show `\pi` on a path that maximizes adjacent prime digits and were created using a protein-folding algorithm.

The frequency circles colourfully depict the ratio of digits in groupings of 3 or 6. Oh, look, there's the Feynman Point!

Download the HP lattice simulation binary. You'll need one of the three 2D methods — I used `rem2dm`

, which does local and pull moves. If you'd like to learn more about the algorithm, read the publication.

A replica exchange Monte Carlo algorithm for protein folding in the HP model. Chris Thachuk, Alena Shmygelska and Holger H Hoos, BMC Bioinformatics 2007, 8:342 (17 Sep 2007).

Download the batch file for 64- or 768-digit folding.

When you run the 64-digit simulation, you're likely to find a path with `E=-23`

, which is the lowest energy I've been able to sample. On my Intel Xeon E5540 (2.53 GHz) it takes anywhere from 1-30 seconds to find a `E=-23`

path (there are many possible paths at this energy), depending on the random seed. Here's the output of a typical run of the 64-digit folding simulation

> rem2dm -seq=hppphphphhhpphphhhppphpphhphhhphphppppphppphpphhhpphphpphpppphph -maxT=220 -numLocalSteps=500 -eng=100 -maxRunTime=60 -traceFile=pi.64 -minT=160 -expID=pi.64 -numReps=10 REMC-HP2D-M Begin Simulation 0.01: Current Best Solution: -8 0.01: Current Best Solution: -10 0.01: Current Best Solution: -13 0.02: Current Best Solution: -15 0.03: Current Best Solution: -16 0.03: Current Best Solution: -17 0.04: Current Best Solution: -18 0.04: Current Best Solution: -19 0.16: Current Best Solution: -20 0.27: Current Best Solution: -21 0.69: Current Best Solution: -22 36.23: Current Best Solution: -23 Real time: 120 ggslrrsrllssrrlrrllsrrlrrlslslrrsrlssrrsllrslrrlrsllsrsrrlsrssrs p--h--p | | h--h h--p--p--p | | p--p h H h--p--p | | | | | p--h h--h--p p p--p | | | p--p--h h--p p--p p | | | | | h--h h h--p--h h--p | | | p--h h h--p--H h--p | | | | p--p p p--h--h | | p p--h--p | | p--p--h h | | p--p End Simulation

If you want to apply this to different number (e.g.
φ
or
e
), you'll need to replace the digits with either `p`

or `h`

. Remember, the simulation will try to group the `h`

's together. You can download 1,000,000 of
π
,
φ
and
e
.

The best path I could find for 768 digits is one with `E=-223`

. In 1000s of simulations this solution came up only once. I also saw one path at `E=-222`

. After that, there were many solutions at each of the less optimal energy levels.

If you manage to find a better one, let me know right away!

If you obtain a segmentation fault,

> ./rem2dlm REMC-HP2D-LM Begin Simulation Real time: 0 Segmentation fault

don't panic just yet. The folding binaries don't do a lot of error checking, so you have to get the input parameters correct.

For example, if you do not include the `-eng`

parameter, the code will segfault.

Try one of the batch files above (64 digit batch file, 768 digit batch file) or the following simple job

> bin/rem2dm -seq=hhpppphhhhpppphh -maxRunTime=5 -eng 10 REMC-HP2D-M Begin Simulation 3.13877e-17: Current Best Solution: -2 5.49284e-17: Current Best Solution: -3 1.0201e-16: Current Best Solution: -4 1.33398e-16: Current Best Solution: -5 Real time: 5 ggrllslsssrllsls p--p--p | | h h--p | | H h | H h | | p--h h | | p--p--p

If this segfaults, then you'll need to recompile the code (see below).

Precompiled binaries are available for download directly: rem2dm, rem2dlm, rem2dpm, rem3dm, rem3dlm, rem3dpm.

If these don't work on your system, you need to recompile them. Download the the protein folding code and see INSTALL.txt for compilation instructions.

news
**+** thoughts

We'd like to say a ‘cosmic hello’: mathematics, culture, palaeontology, art and science, and ... human genomes.

*All animals are equal, but some animals are more equal than others. —George Orwell*

This month, we will illustrate the importance of establishing a baseline performance level.

Baselines are typically generated independently for each dataset using very simple models. Their role is to set the minimum level of acceptable performance and help with comparing relative improvements in performance of other models.

Unfortunately, baselines are often overlooked and, in the presence of a class imbalance5, must be established with care.

Megahed, F.M, Chen, Y-J., Jones-Farmer, A., Rigdon, S.E., Krzywinski, M. & Altman, N. (2024) Points of significance: Comparing classifier performance with baselines. *Nat. Methods* **20**.

sunflowers ho!

Celebrate π Day (March 14th) and dig into the digit garden. Let's grow something.

*Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.*

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reﬂects SDSS’s sky coverage.”

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function *Astrophysical Journal* **953**:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics **2020**:023.

Sloan Digital Sky Survey Data Release 12

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

*H*_{0} = 67.4 km/(Mpc·s), *Ω*_{m} = 0.315, *Ω*_{v} = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

*It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle*

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. *Nat. Methods* **20**.

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

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. *Nat. Methods* **13**:541–542 (2016).

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

*Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman*

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. *Nature Methods* **20**:1269–1270.

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods **20**:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods **13**:541–542.

Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences Centre ⊂ BC Cancer Research Center ⊂ BC Cancer ⊂ PHSA

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