Harvey Devereux, PhD Student (University of Warwick), software engineer, app developer

Personal/Professional Contact: harvey@devereux.io

University Work Contact: h.devereux@warwick.ac.uk

SPP: A CUDA and OpenCL acceleration library for Active Matter (unreleased) [C++][CUDA][OpenCL]

Consensus algorithms in Python, applied to Flocking Consensus [Python]

DNA to text converter (and execute DNA as code!): DNAText.jl [Julia]

Alpha shapes in Julia: AlphaShapes.jl [Julia]

Time Rich List (Selenium) Scrapper: TimesRichList2020 [Python,Selenium]

Triangular Meshes of the Sphere: HTM [C++]

CUDA accelerated self-propelled particle models

Physical models of collective motion

Large scale simulation of intelligent agent systems with visual input

Devereux HL, Twomey CR, Turner MS, Thutupalli S. 2021 Whirligig beetles as corralled active Brownian particles. J. R. Soc. Interface 18: 20210114. https://doi.org/10.1098/rsif.2021.0114

A blog for recreational maths and programming

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### Consensus 13/06/2020

Part of this post (the application to flocking) is implemented on my Github

Consensus is "a generally accepted opinion or decision among a group of people" (Cambridge Dictionary). More interestingly the state of consensus around a particular issue can evolve over time, through discussion and debate of ideas.

To think about this mathematically we will first take a leap into graph theory. A group of people can be described as a network, where each person is connected to some (or maybe all) of the others. The most obvious example of this is the social network we construct between friends and family, here "connectedness" just means person \(A\) is friends with person \(B\).

In the visual representation the people (nodes) are circles and the friendship status (connectivity) between people are represented by lines drawn between them.

Moving back to consensus we can use the network by saying that each person has a current decision \(x_{i}(t)\), where \(i\) is an integer representing the individual and \(t\) is time. Here we've implied that a persons "decision" is represented as some number, that can also vary as time progresses. The way in which this number varies for each person (the different \(i\)'s) will be linked to the edges in the network.

If we imagine that \(x_{2}(0)\) is a representation of person \(2\)'s opinion about a topic at time \(0\), it would make sense if the people person \(2\) is friends with, \(3\) and \(5\) from the network, could change person \(2\)'s opinon i.e change \(x_{2}(0)\). As a first go we might say that a person's opinion changes proportionaly to the difference between their opinion, and the average of the friend's opinions. We can even rigPPht this down for person \(2\) $$ x_{2}(1) = \frac{x_{3}(0)-x_{2}(0) + x_{5}(0) - x_{2}(0)}{2} = \frac{x_{5}(0)+x_{3}(0)}{2}-x_{2}(0) $$ Intuitively this means if person \(2\)'s opinion is higher (remember "opinions" are numbers here) than \(3\) and \(5\)'s then \(2\)'s opinion will decrease towards \(3\) and \(5\)'s based upon how different their views are (on average).

Borrowing some more graph theory we can summarise "who knows who" by a \(N\cdot N\) grid of numbers, if there are \(N\) people, called an "Adjacency Matrix". Think of this as a table with a row and a column for each person, as we read one row \(2\) across the columns, when there is a \(1\) this tells us person \(2\) is friends with the person represented by this column, when there is a \(0\) person \(2\) is not friends with them.

The image above show a visual representation of this adjacency matrix, here a black square means "not friends" and a yello one means "friends", or as numbers \(0\) and \(1\). Mathematically we can write this down as $$ \boldsymbol{A} = \begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 1 \\ 0 & 0 & 0 & 0 & 1 & 0 \end{bmatrix}$$ Where notice that unhelpfully the mathematics convention has it flipped with repspect to the image! Either way we can now say \(\boldsymbol{A}_{32}=1\), or in plain language the value at the \(3\)rd row and \(2\)nd column is \(1\).

So how does this relate to our consensus problem? Well we cna begin to genralise how we write down how each person influences the others, e.g for the example from before we can now write $$ x_{2}(1) = \frac{A_{23}}{2}(x_{3}(0)-x_{2}(0)) + \frac{A_{25}}{2}(x_{5}(0)-x_{2}(0)) $$ This hasn't changed much yet, but it means we can write the general equation for any \(i\)! $$ x_{i}(t) = \frac{\sum_{j\neq i}\boldsymbol{A}_{ij}(x_{j}(t-1)-x_{i}(t-1))}{\sum_{j\neq i}\boldsymbol{A}_{ij}} $$ Where the mathematical notation \(\sum_{i}\) means a summation over the index \(i\), so \(\sum_{i=1}^{5}i = 1 + 2 + 3 + 4 + 5\), and \(\neq\) means not equal to, therefore \(\sum_{j\neq 2}\boldsymbol{A}_{2j}\) means summing over the columns of the grid of numbers ignoring where the column is equal to the row \(2\), i.e the number of friends person \(2\)! Which is \(2\)

To wrap up, we have a general equation for how each person influences the others opinions in their friendship group, but what is consensus? Looking back at our original definition consensus would mean that everyone shares the same opinion, i.e $$ x_{1}(t) = x_{2}(t) = x_{3}(t) = x_{4}(t) = x_{5}(t) = x_{6}(t) $$ And it turns out that we can prove how long it will take before this consensus condition is met, that is when a graph is fully connected, in our example person \(1\) is on their own and so won't change their opinion ever under this model!

As a final note, the maths here does not just describe social networks and opinions, we can apply the same reasoning to any process by which multiple "agents" choose a desision and influence the decisions of others, some examples are politics (voter models), collective motion (e.g bird flocks), distributed computation, and swarm robotics.

The pdf below contains the full detail of mathematics, and some applications.

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### DNA 16/07/2020

#### What is it?

DNA (Deoxyribonucleic Acid) is the physical mechanism by which many organisms
store instructions for their developement and function, and this genetic code is resonsible for traits
such as eye colour and many others in humans. Skipping the detailed chemistry, many will be familiar
with the idea of DNA as the double helix; two chains of molecules linked together and forming
helical twists. The molecules forming DNA in humans go by the names of
Adenine (A), Thymine (T), Guanine (G), and Cytosine (C) and are themselves complicated molecular structures.

For our purpose we will take it as given that DNA is formed of these molecules in two chains: one chain which encodes actual information, and a second chain which exists to aid stability. This second chain is formed by two simple rules (in standard DNA), simply if the first chain has an A at a particular position then the second will have a T (and vice versa) but if a G or C is in the first chain the second will have a C or G in the same position respectively. So an example of a valid pair of chains is $$ ACACTCAGCGGT $$ $$ TGTGAGTCGCCA $$ and an invalid example is $$ GCGGTTGATTAA $$ $$ C{\color{red}C}CCAAC{\color{red}G}AATT $$

#### The Maths: Number Systems and Bases

Mathematically speaking the interesting part about DNA is that we have exactly four characters to
play with. Consider computers, in the computer world all information (including numbers) is represented
with just a sequence of \(1\)s and \(0\)s; ON and OFF signals in an electrical circuit. The language of
computers is *Binary*. So then what, mathematically speaking, is the language of DNA? Well there
are four characters, so it's *four*-ary more commonly known as *Quarternary* numbers. What we
are dealing with here are different number systems, called *bases*.

Let's take a step back a second and think about the numbers we are all familiar with, the*decimal*
numbers \(0,1,2,3,4,5,6,7,8,9,10,11,\ldots\). We call these base-\(10\), because there are \(10\)
digits (\(0\) to \(9\)) that make up how we write down any number, although we of course use a \(.\) to
represent fractional numbers but this is a notational convenience. So let's throw down a few equations
$$ 0 = 0\times 10^{0}, \enspace\enspace 6 = 6\times 10^{0}, \enspace\enspace 27 = 2\times 10^{1}+7\times 10^{0}, \enspace\enspace 172 = 1\times 10^{2} + 7\times 10^{1} + 2\times 10^{0} $$
These appear obvious, and they are, but there is a pattern here. Notice we are writing base-\(10\) numbers
and we see \(10\) appearing in every number. Specifically we can write any base-\(10\) number
as a sum of powers of \(10\) each multiplied by just one of the of the \(10\) digits \(0,1,2,3,4,5,6,7,8,9\).
and really our notation \(325\) is a shorthand for "3 lots of 100, 2 lots of 10, and 5 lots of 1". Again remarking
on this it's obvious, what's the point it's just how it is? The point is that for *any* base we can
generalise this to write numbers in that base, so in Binary , we have to character \(0\) and \(1\) and instead
of powers of \(10\) a number will be a sum of powers of \(2\) each multiplied by either a \(0\) or a \(1\) e.g
$$ 101_{2} = 1\times 2^2 + 0\times 2^{1} + 1\times 2^{0} = 5 = 5\times 10^{0} = 5_{10} $$
where I'm now writing a subscript \(x_{b}\) to say that the number \(x\) is to be understood as
being written in base \(b\). So in maths we like to genralise everything, so we can now write down
any (integer for now) number in any base as the sum
$$x_{b} = d_{N-1}d_{N}\cdots d_{1}d_{0} = d_{N-1}\times b^{N-1} + d_{N-2}\times b^{N-2} + \cdots + d_{1}\times b^{1} +d_{0}\times b^{0} = \sum_{i=0}^{N}d_{i}b^{i} $$
where \(x_{b}\) is a number in base \(b\) with \(N\) digits \(d_{0},d_{1},d_{2},\ldots d_{N-1}\). For
fractional values we can relax the low bound of the sum index \(i\) to be -\(M\), where \(M\) is the number
of decimal places, notice this will naturally give negative powers and so fractions.

Armed with this we can return to DNA, we already noticed that DNA has for characters therefore we can think of each character as a digit in a base four number system. Identifying \(A=0,C=1,G=2,T=3\) we can write a string of DNA as a number $$ ATCGTGC \equiv 0312321_{4} = 3513_{10}$$ Since we have represented DNA in base four, we can convert it to any other base. Tricks exist for conversion, and I'm not going to write these down in detail, essentially to go from base \(10\) to base \(b\) we can use devision and remainder to convert e.g (with all numbers in base \(10\)) $$ 143 / 4 = 35 \text{ remainder } 3 $$ $$ 35 / 4 = 8 \text{ remainder } 3 $$ $$ 8 / 4 = 2 \text{ remainder } 0 $$ $$ 2 / 4 = 0 \text{ remainder } 2 $$ and (you can check for yourself) $$ 143_{10} = 2033_{4} $$

#### Text to DNA and Back Again

Now that we have learned all of this what can we do with it? Well one fun application is
that since computers use base \(2\), DNA can be written in base \(4\), and we can
convert bases we can write DNA as Binary or Binary as DNA. More interestingly since computers
often display text, text must be represented as (Binary) numbers somehow, so can we convert text
into a DNA sequence? Yes!

Unfortunately there is one slight bump in the road, yes text is written in Binary for computers, but for humans who program computers it is actually more convenient to encode text as base-16 numbers! Of course, we can actually just ignore that for this post since we know how to convert bases (in the future I may even write about base-16, AKA Hexadecimal numbers).

For now though before playing with the code, there is one point to clear up. We can convert from text (Binary/Hex) to base-\(4\) easy enough, and we know how base pairs form so the second DNA strand will be automatically taken care of, but how can we decode the information conatining strand? How do we know when when a sequence of DNA should be read as a character? Since there are more than four letters we want to encode, a single letter is going to be formed of multiple base-\(4\) digits. DNA also has this problem, it exists to be read, so DNA evolved a solution (which is also studied in the theory of codes like Morse-code) stop sequences. A stop sequence tells us that the characters we have read up to now should be interpreted as a single piece of information (a letter in our case), DNA itself also includes start sequences for extra safety. One DNA stop sequence is \(TAG\), which is what we will be using. Let's look at an example, say (arbitrarily) that the sequences \(ATAC, GCAT, AC, CTG\) represent the letters "dna!" to encode this as a DNA sequence (with our stop characters so that we can decode it later) we simply write down the sequence. $$ \text{"dna!" } \equiv ATAC{\color{green}TAG}GCAT{\color{green}TAG}AC{\color{green}TAG}CTG{\color{green}TAG}$$ But this introduces a new problem, what if a letter is represented by \(TAGC\) or \(GTAG\)? Programmatically we can handle this by reading the DNA sequence in fours, we read a set of four DNA characters and convert it to a letter, then we skip the next three characters which should be \(TAG\) and read the next four until the end of the sequence. We can even check that the three characters after a letter are \(TAG\) to make sure the DNA sequence is not corrupted somehow.

The grand finale then is a program which we can use to re-write any sequence of text as a strand of DNA! As mentioned before I've implemented this in Julia (DNAText.jl [Julia]), and also on this site I've made a Javascript application you can try for yourself!
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### Gaussian Moments 20/08/20

#### Normal Distributions

The Gaussian distribution (or normal distribution) is ubiquitous in mathematics and its
applications. Most outside of science will have encountered it as the "Bell curve" when taking
or talking about IQ tests
and other standardised measurements, or perhaps in an introductory statistics course.

In the wild normal distributions crop up all the time, due to a result known as the central limit theorem. Which states that when we take a sample \(\{X_{i}\}_{i=1}^{N}\) of random numbers with some (finite) mean \(\mu\) and (finite) variance \(\sigma^{2}\) the variable \(Z = \frac{\frac{1}{N}\sum_{i}X_{i}-\mu}{\sigma/\sqrt{N}}\) will be normally distributed when \(N\) is sufficiently large. This is useful for statistical models of real world processes. Since even if data is not "normal" we can "standardise" it, by transforming to \(Z(\{X_{i}\})\), this standardised data can then be put through the various statistical tests statisticians have developed. This was particulary useful before computers, where any sample (sufficiently large!) could be standardised and compared to the same book of "statistical tables".

A great example of a wild normal distribution is the Galton board, shown as a gif below. What we see here are pebbles falling through a set of pegs. On hitting a peg we can assume a pebble has two options, fall left or right with some probability \(p\), and given enough pebbles we hope this probability is roughly consistent (despite pebble-pebble collisions). Thankfully it does work, and we end up with a Binomial distribution in the final slots. What's more, given enough pebbles this approximates a normal distribution by the central limit theorem!

#### The Maths

Let's look at the functional form of a normal distribution.
$$ p(x) = \frac{1}{\sqrt{2\pi\sigma^2}}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}, \enspace -\infty \leq x \leq \infty $$
The exponential gives us a strong concentration of density around the mean \(\mu\) which
decays of depending on the magnitude of the variance \(\sigma^{2}\). High variance
means more spread, and a low variance mean a stronger peak and a thinner spread,
with the special case of \(\sigma \rightarrow 0\) being a dirac delta functional
picking out the mean.

#### Shape

These ideas of location and shape are not just applicable to the Gaussian distribution,
in fact they form an integral part of how distributions are categorised. Let's
remind ourselves what the mean and variance actually are in terms of \(p(x)\). If the random
variable \(X\) is defined by the probability density function \(p(x)\), on the
range \(\Omega\) the the mean and variance are
$$ \mathbb{E}[X] = \int_{x\in\Omega} x p(x)dx$$
$$ \text{Var}[X] = \int_{x\in\Omega} (x-\mathbb{E}[X])^{2}p(x)dx$$
For the mean the equation translates as "the sum of all possible values \(x\) of \(X\) multiplied by their
probabilities of occuring", for the variance the equation means "the sum of all *square deviations* of
all possible values of \(x\) of \(X\) from the mean of \(X\) multiplied by their
probability of occuring". It's a bit of a mouthfull but essentially it tells us
the average *distance* between realisations of a random variable \(X\) from
the mean value.

Notice that both these integral functions actually come from the same family $$ \biggl\{\int_{x\in\Omega}(x-c)^{k}p(x)dx : \enspace c \in\Omega, \enspace k \in \mathbb{N}\biggr\} $$ Where \(\mathbb{N} = \{1,2,3,\ldots\}\). In particular the mean is the element with \(k=1\) and \(c=0\) and the variance is the element with \(k=2\) and \(c=\mathbb{E}[X]\). This family is known as the \(k\)-th moments centered on \(c\), higher moments are also useful (but more complicated!) such as \(k=3\) which is related to the Skewness of a distribution, and \(k=4\) which is related to the peakedness of the distribution (kurtosis). The next figure shows a few of these values off, for the Normal \(\mathcal{N}\), Exponential \(Exp\) and Binomial \(Bin\) distributions.

#### Fractional Moments

So far so good, but what about the restriction on \(k\) that we slipped in? Namely
\(k\in\mathbb{N}\). Well the "problem" of course is that, when \(k=1/2\) say, some
distributions like the normal distribution, are defined over negative values. Since
\(\sqrt{-1}\) is a *complex* number suddenly some of the moments are not
so easy to interpret. Fractional moments may not be quite as intutive as
their integer cousins, but nothing is gained without exploring. For the standard
normal distribution the fractional moments are defined by the function \(z(k):\mathbb{R}\mapsto\mathbb{C}\)
$$ z(k) = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}x^{k}e^{-\frac{x^{2}}{2}}dx $$
To get an idea of what this thing does, we can approximate this integral
numerically. The plots below are valid for steps of \(dx=0.01, dk=0.005\) over the range
\((-100,100)\). So not quite \((-\infty,\infty)\), but still beautiful!

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### Implementing Automatic Differentiation 27/11/2020

#### Differentiation

Calculus is ubiquitous in mathematics applications because it defines
a system for quantifying change. Consider the movement of a ball, the growth
of a portfolio, spread of disease, growth of a bacterial colony, or learning
artificial neural network. All of these problems are intricately linked
to a quantity that changes; to the derivative of a function \(f(x)\).

As humans (or a very advanced AI) we can learn to compute the rate of change of a function, for example if $$ f(x) : \mathbb{R}\mapsto\mathbb{R} = x^2 $$ then \(f\)'s rate of change with respect to a change in \(x\) is $$ \frac{df(x)}{dx} = 2x $$ This comes from the definition of a derivative $$ f'(x) = \frac{df(x)}{dx} = \lim_{h\rightarrow 0} \frac{f(x+h)-f(x)}{h} $$ i.e the slope of the line \(f(x)\) evaluated between two infinitely close points. It is possible to define a programming language capable of analytic differentiation (Mathematica and Wolfram Alpha), this is not always practical or useful, as such most applications of the derivative resort to some approximation of the limit definition. Most simply this can be done by taking an \(h\), say \(h=0.01\), and plugging in the numbers. This is fine but will lead to errors.#### Dual Numbers To the Rescue

It would be nice if for any \(f\) we could know \(f'\) at \(x\) on the fly,
without having to derive it ourselves, approximating it, or asking a computer to chug away and
possibly find the analytic solution itself. This is where dual
numbers come in and reveal "Automatic Differentiation" (AD).

A dual number is a "new" type of number analogous to complex numbers, but now instead of using \(a+bi\) where \(i=\sqrt{-1}\) we write $$ a+b\epsilon, \enspace \text{ where } \epsilon^{2}=0, \epsilon \neq 0 \enspace \text{ for } a,b\in\mathbb{R} $$ This seemingly trivial academic pursuit it incredibly fruitfull. Consider \(f(x)=x^2\), notice $$f(a+b\epsilon) = a^2 + b^2\cdot(0) + 2ab\epsilon = a^2 + 2ab\epsilon$$ again nothing too crazy, yet, but see that $$ f(a) = a^2, \text{ and } f'(a) = 2a $$ so if we had taken \(a+\epsilon\) inplace of \(a+b\epsilon\) then we would get $$f(a+\epsilon) = f(a)+f'(a)\epsilon$$ This is no accident, in fact this result applies to any \(f\) where \(f'\) exists! $$f(a+b\epsilon) = \sum_{n=0}^{\infty}\frac{f^{(n)}(a)b^n\epsilon^n}{n!} = f(a) + bf'(a)\epsilon + \epsilon^{2}\sum_{n=1}^{\infty}\frac{f^{(n)}(a)b^n\epsilon^n}{n!}$$ So in general evaluating a function of a dual number gives you the value of the function as it's real part and the*exact* derivative as it's dual part!
#### Implementing Dual Numbers

Practically we can make use of these results with a simple object called Dual that
just smashes two Numbers together.
#### References for Further Reading

[1] - Forward-Mode Automatic Differentiation in Julia,Revels, J. and Lubin, M. and Papamarkou, arXiv:1607.07892 [cs.MS], T. , 2016

Consensus is "a generally accepted opinion or decision among a group of people" (Cambridge Dictionary). More interestingly the state of consensus around a particular issue can evolve over time, through discussion and debate of ideas.

To think about this mathematically we will first take a leap into graph theory. A group of people can be described as a network, where each person is connected to some (or maybe all) of the others. The most obvious example of this is the social network we construct between friends and family, here "connectedness" just means person \(A\) is friends with person \(B\).

In the visual representation the people (nodes) are circles and the friendship status (connectivity) between people are represented by lines drawn between them.

Moving back to consensus we can use the network by saying that each person has a current decision \(x_{i}(t)\), where \(i\) is an integer representing the individual and \(t\) is time. Here we've implied that a persons "decision" is represented as some number, that can also vary as time progresses. The way in which this number varies for each person (the different \(i\)'s) will be linked to the edges in the network.

If we imagine that \(x_{2}(0)\) is a representation of person \(2\)'s opinion about a topic at time \(0\), it would make sense if the people person \(2\) is friends with, \(3\) and \(5\) from the network, could change person \(2\)'s opinon i.e change \(x_{2}(0)\). As a first go we might say that a person's opinion changes proportionaly to the difference between their opinion, and the average of the friend's opinions. We can even rigPPht this down for person \(2\) $$ x_{2}(1) = \frac{x_{3}(0)-x_{2}(0) + x_{5}(0) - x_{2}(0)}{2} = \frac{x_{5}(0)+x_{3}(0)}{2}-x_{2}(0) $$ Intuitively this means if person \(2\)'s opinion is higher (remember "opinions" are numbers here) than \(3\) and \(5\)'s then \(2\)'s opinion will decrease towards \(3\) and \(5\)'s based upon how different their views are (on average).

Borrowing some more graph theory we can summarise "who knows who" by a \(N\cdot N\) grid of numbers, if there are \(N\) people, called an "Adjacency Matrix". Think of this as a table with a row and a column for each person, as we read one row \(2\) across the columns, when there is a \(1\) this tells us person \(2\) is friends with the person represented by this column, when there is a \(0\) person \(2\) is not friends with them.

The image above show a visual representation of this adjacency matrix, here a black square means "not friends" and a yello one means "friends", or as numbers \(0\) and \(1\). Mathematically we can write this down as $$ \boldsymbol{A} = \begin{bmatrix} 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 1 \\ 0 & 0 & 0 & 0 & 1 & 0 \end{bmatrix}$$ Where notice that unhelpfully the mathematics convention has it flipped with repspect to the image! Either way we can now say \(\boldsymbol{A}_{32}=1\), or in plain language the value at the \(3\)rd row and \(2\)nd column is \(1\).

So how does this relate to our consensus problem? Well we cna begin to genralise how we write down how each person influences the others, e.g for the example from before we can now write $$ x_{2}(1) = \frac{A_{23}}{2}(x_{3}(0)-x_{2}(0)) + \frac{A_{25}}{2}(x_{5}(0)-x_{2}(0)) $$ This hasn't changed much yet, but it means we can write the general equation for any \(i\)! $$ x_{i}(t) = \frac{\sum_{j\neq i}\boldsymbol{A}_{ij}(x_{j}(t-1)-x_{i}(t-1))}{\sum_{j\neq i}\boldsymbol{A}_{ij}} $$ Where the mathematical notation \(\sum_{i}\) means a summation over the index \(i\), so \(\sum_{i=1}^{5}i = 1 + 2 + 3 + 4 + 5\), and \(\neq\) means not equal to, therefore \(\sum_{j\neq 2}\boldsymbol{A}_{2j}\) means summing over the columns of the grid of numbers ignoring where the column is equal to the row \(2\), i.e the number of friends person \(2\)! Which is \(2\)

To wrap up, we have a general equation for how each person influences the others opinions in their friendship group, but what is consensus? Looking back at our original definition consensus would mean that everyone shares the same opinion, i.e $$ x_{1}(t) = x_{2}(t) = x_{3}(t) = x_{4}(t) = x_{5}(t) = x_{6}(t) $$ And it turns out that we can prove how long it will take before this consensus condition is met, that is when a graph is fully connected, in our example person \(1\) is on their own and so won't change their opinion ever under this model!

As a final note, the maths here does not just describe social networks and opinions, we can apply the same reasoning to any process by which multiple "agents" choose a desision and influence the decisions of others, some examples are politics (voter models), collective motion (e.g bird flocks), distributed computation, and swarm robotics.

The pdf below contains the full detail of mathematics, and some applications.

For our purpose we will take it as given that DNA is formed of these molecules in two chains: one chain which encodes actual information, and a second chain which exists to aid stability. This second chain is formed by two simple rules (in standard DNA), simply if the first chain has an A at a particular position then the second will have a T (and vice versa) but if a G or C is in the first chain the second will have a C or G in the same position respectively. So an example of a valid pair of chains is $$ ACACTCAGCGGT $$ $$ TGTGAGTCGCCA $$ and an invalid example is $$ GCGGTTGATTAA $$ $$ C{\color{red}C}CCAAC{\color{red}G}AATT $$

Let's take a step back a second and think about the numbers we are all familiar with, the

Armed with this we can return to DNA, we already noticed that DNA has for characters therefore we can think of each character as a digit in a base four number system. Identifying \(A=0,C=1,G=2,T=3\) we can write a string of DNA as a number $$ ATCGTGC \equiv 0312321_{4} = 3513_{10}$$ Since we have represented DNA in base four, we can convert it to any other base. Tricks exist for conversion, and I'm not going to write these down in detail, essentially to go from base \(10\) to base \(b\) we can use devision and remainder to convert e.g (with all numbers in base \(10\)) $$ 143 / 4 = 35 \text{ remainder } 3 $$ $$ 35 / 4 = 8 \text{ remainder } 3 $$ $$ 8 / 4 = 2 \text{ remainder } 0 $$ $$ 2 / 4 = 0 \text{ remainder } 2 $$ and (you can check for yourself) $$ 143_{10} = 2033_{4} $$

Unfortunately there is one slight bump in the road, yes text is written in Binary for computers, but for humans who program computers it is actually more convenient to encode text as base-16 numbers! Of course, we can actually just ignore that for this post since we know how to convert bases (in the future I may even write about base-16, AKA Hexadecimal numbers).

For now though before playing with the code, there is one point to clear up. We can convert from text (Binary/Hex) to base-\(4\) easy enough, and we know how base pairs form so the second DNA strand will be automatically taken care of, but how can we decode the information conatining strand? How do we know when when a sequence of DNA should be read as a character? Since there are more than four letters we want to encode, a single letter is going to be formed of multiple base-\(4\) digits. DNA also has this problem, it exists to be read, so DNA evolved a solution (which is also studied in the theory of codes like Morse-code) stop sequences. A stop sequence tells us that the characters we have read up to now should be interpreted as a single piece of information (a letter in our case), DNA itself also includes start sequences for extra safety. One DNA stop sequence is \(TAG\), which is what we will be using. Let's look at an example, say (arbitrarily) that the sequences \(ATAC, GCAT, AC, CTG\) represent the letters "dna!" to encode this as a DNA sequence (with our stop characters so that we can decode it later) we simply write down the sequence. $$ \text{"dna!" } \equiv ATAC{\color{green}TAG}GCAT{\color{green}TAG}AC{\color{green}TAG}CTG{\color{green}TAG}$$ But this introduces a new problem, what if a letter is represented by \(TAGC\) or \(GTAG\)? Programmatically we can handle this by reading the DNA sequence in fours, we read a set of four DNA characters and convert it to a letter, then we skip the next three characters which should be \(TAG\) and read the next four until the end of the sequence. We can even check that the three characters after a letter are \(TAG\) to make sure the DNA sequence is not corrupted somehow.

The grand finale then is a program which we can use to re-write any sequence of text as a strand of DNA! As mentioned before I've implemented this in Julia (DNAText.jl [Julia]), and also on this site I've made a Javascript application you can try for yourself!

In the wild normal distributions crop up all the time, due to a result known as the central limit theorem. Which states that when we take a sample \(\{X_{i}\}_{i=1}^{N}\) of random numbers with some (finite) mean \(\mu\) and (finite) variance \(\sigma^{2}\) the variable \(Z = \frac{\frac{1}{N}\sum_{i}X_{i}-\mu}{\sigma/\sqrt{N}}\) will be normally distributed when \(N\) is sufficiently large. This is useful for statistical models of real world processes. Since even if data is not "normal" we can "standardise" it, by transforming to \(Z(\{X_{i}\})\), this standardised data can then be put through the various statistical tests statisticians have developed. This was particulary useful before computers, where any sample (sufficiently large!) could be standardised and compared to the same book of "statistical tables".

A great example of a wild normal distribution is the Galton board, shown as a gif below. What we see here are pebbles falling through a set of pegs. On hitting a peg we can assume a pebble has two options, fall left or right with some probability \(p\), and given enough pebbles we hope this probability is roughly consistent (despite pebble-pebble collisions). Thankfully it does work, and we end up with a Binomial distribution in the final slots. What's more, given enough pebbles this approximates a normal distribution by the central limit theorem!

Notice that both these integral functions actually come from the same family $$ \biggl\{\int_{x\in\Omega}(x-c)^{k}p(x)dx : \enspace c \in\Omega, \enspace k \in \mathbb{N}\biggr\} $$ Where \(\mathbb{N} = \{1,2,3,\ldots\}\). In particular the mean is the element with \(k=1\) and \(c=0\) and the variance is the element with \(k=2\) and \(c=\mathbb{E}[X]\). This family is known as the \(k\)-th moments centered on \(c\), higher moments are also useful (but more complicated!) such as \(k=3\) which is related to the Skewness of a distribution, and \(k=4\) which is related to the peakedness of the distribution (kurtosis). The next figure shows a few of these values off, for the Normal \(\mathcal{N}\), Exponential \(Exp\) and Binomial \(Bin\) distributions.

As humans (or a very advanced AI) we can learn to compute the rate of change of a function, for example if $$ f(x) : \mathbb{R}\mapsto\mathbb{R} = x^2 $$ then \(f\)'s rate of change with respect to a change in \(x\) is $$ \frac{df(x)}{dx} = 2x $$ This comes from the definition of a derivative $$ f'(x) = \frac{df(x)}{dx} = \lim_{h\rightarrow 0} \frac{f(x+h)-f(x)}{h} $$ i.e the slope of the line \(f(x)\) evaluated between two infinitely close points. It is possible to define a programming language capable of analytic differentiation (Mathematica and Wolfram Alpha), this is not always practical or useful, as such most applications of the derivative resort to some approximation of the limit definition. Most simply this can be done by taking an \(h\), say \(h=0.01\), and plugging in the numbers. This is fine but will lead to errors.

A dual number is a "new" type of number analogous to complex numbers, but now instead of using \(a+bi\) where \(i=\sqrt{-1}\) we write $$ a+b\epsilon, \enspace \text{ where } \epsilon^{2}=0, \epsilon \neq 0 \enspace \text{ for } a,b\in\mathbb{R} $$ This seemingly trivial academic pursuit it incredibly fruitfull. Consider \(f(x)=x^2\), notice $$f(a+b\epsilon) = a^2 + b^2\cdot(0) + 2ab\epsilon = a^2 + 2ab\epsilon$$ again nothing too crazy, yet, but see that $$ f(a) = a^2, \text{ and } f'(a) = 2a $$ so if we had taken \(a+\epsilon\) inplace of \(a+b\epsilon\) then we would get $$f(a+\epsilon) = f(a)+f'(a)\epsilon$$ This is no accident, in fact this result applies to any \(f\) where \(f'\) exists! $$f(a+b\epsilon) = \sum_{n=0}^{\infty}\frac{f^{(n)}(a)b^n\epsilon^n}{n!} = f(a) + bf'(a)\epsilon + \epsilon^{2}\sum_{n=1}^{\infty}\frac{f^{(n)}(a)b^n\epsilon^n}{n!}$$ So in general evaluating a function of a dual number gives you the value of the function as it's real part and the

```
struct Dual{t1<:Number,t2<:Number}
a::t1
b::t2
end
```

Of course the real work is done in defining operators on our objects, in
this case \(+,-,/,*\) for addition, subtraction, division, and multiplication.
```
import Base.+, Base.*, Base.^, Base.-, Base./
# addition and subtraction are easy
+(x::Dual,y::Dual) = Dual(x.a+y.a,x.b+y.b)
-(x::Dual,y::Dual) = Dual(x.a-y.a,x.b-y.b)
# multiplication (remember that binomial theorem?)
*(x::Dual,y::Dual) = Dual(x.a*y.a,x.a*y.b+x.b*y.a)
*(x::Number,y::Dual) = Dual(x*y.a,x*y.b)
*(y::Dual,x::Number) = *(x::Number,y::Dual)
# division a bit more of a pain
/(x::Dual,y::Dual) = Dual(x.a/y.a,(x.b*y.a-x.a*y.b)/(y.a^2))
/(x::Number,y::Dual) = /(Dual(x,0.),y)
/(y::Dual,x::Number) = /(y,Dual(x,0.))
# positive integer powers
function ^(x::Dual,n::Int)
y = x
for i in 1:(n-1)
y = y*x
end
return y
end
```

At this point the only curve-ball is division, after a bit of algebra
you'd find the expression
$$ \frac{a+b\epsilon}{c+d\epsilon} = \frac{a}{c} + \frac{bc-ad}{c^2}\epsilon $$
which is what we have in the code.
With these definitiions we can make use of AD for functions using integer
powers multiplication division and summation, e.g for powers
```
julia> [Dual(2^b,1)*Dual(2^b,1) for b in 1:8]
8-element Array{Dual{Int64},1}:
Dual{Int64}(4, 4)
Dual{Int64}(16, 8)
Dual{Int64}(64, 16)
Dual{Int64}(256, 32)
Dual{Int64}(1024, 64)
Dual{Int64}(4096, 128)
Dual{Int64}(16384, 256)
Dual{Int64}(65536, 512)
```

and for \(f(x)=\frac{1}{x}, f'(x)=-\frac{1}{x^2}\)
```
julia> f = (x) -> 1.0/x
julia> g = (x) -> -(1.0/x^2)
julia> [(1.0/Dual(2^b,1),(f(2^b),g(2^b))) for b in 1:8]
8-element Array{Tuple{Dual{Float64,Float64},Tuple{Float64,Float64}},1}:
(Dual{Float64,Float64}(0.5, -0.25), (0.5, -0.25))
(Dual{Float64,Float64}(0.25, -0.0625), (0.25, -0.0625))
(Dual{Float64,Float64}(0.125, -0.015625), (0.125, -0.015625))
(Dual{Float64,Float64}(0.0625, -0.00390625), (0.0625, -0.00390625))
(Dual{Float64,Float64}(0.03125, -0.0009765625), (0.03125, -0.0009765625))
(Dual{Float64,Float64}(0.015625, -0.000244140625), (0.015625, -0.000244140625))
(Dual{Float64,Float64}(0.0078125, -6.103515625e-5), (0.0078125, -6.103515625e-5))
(Dual{Float64,Float64}(0.00390625, -1.52587890625e-5), (0.00390625, -1.52587890625e-5))
```

Great. But what about other functions like \(\cos\), \(\sin\), \(\exp\), \(\log\)
and real powers (roots etc)? Generally for any function we would make
use of it's Taylor seris (or a faster converging approximation). Let's
look at \(\exp\). The Taylor seris simplifies heavily, i.e
$$e^{a+b\epsilon} = \sum_{n=0}^{\infty}\frac{(a+b\epsilon)^{n}}{n!}=
\sum_{n=0}^{\infty}\frac{1}{n!}\sum_{k=0}^{n}\binom{n}{k}a^{n-k}(b\epsilon)^{k} \\
= \sum_{n=0}^{\infty}\frac{1}{n!}\bigl(a^{n} + na^{n-1}b\epsilon\bigr) = e^{a}(1+b\epsilon)$$
So \(\exp(x)\) can be found exactly with a few muls!
This also means exponentiation to any real power is trivial if we know log...
$$(a+b\epsilon)^{x} = e^{x\log(a+b\epsilon)}$$
$$ \text{Let } \log(a+b\epsilon) = c+d\epsilon$$
$$ (a+b\epsilon)^x = e^{xc}e^{xd\epsilon} = e^{xc}(1+xd\epsilon)$$
Thankfully we can (admittedly approximately) find the natural log just from integer powers, \(*\), and \(/\) !
$$\log(x) = 2\sum_{k=0}^{\infty}\frac{1}{2k+1}\biggl(\frac{x-1}{x+1}\biggr)^{2k+1}$$
This series in particular works nicely for \(x\) about \(1\), which
we can handle by using \(\log(ab) = \log(a)+\log(b)\) to reduce (or increase) the logand
to be about \(1\)
```
function Log(z::Dual,k=10)
ln2 = 0.6931471805599453094172321214581765680755001343602552541206800094933936219696947156058633269964186875420014810205706857336855202
b = 0.
m = z.a
# use log(ab) = log(a) + log(b) to reduce the magnitute of the logand
if m > 1
# find smallest power of 2 larger
while 2. ^b < m
b += 1
end
elseif m < 1
# find smallest power of -2 smaller
while m < 2. ^b
b -= 1
end
end
# reduce the logand
z = z / 2. ^b
L = Dual(0.0,0.0)
# compute the series with the reduced logand for accuracy
for i in 0:k
L += (1/(2*i+1))*((z-Dual(1,0))/(z+Dual(1,0)))^(2*i+1)
end
# factor in the logand reduction
return 2*L + Dual(b*ln2,0.0)
end
function ^(x::Dual,n::AbstractFloat)
e = 2.7182818284590452353602874713526624977572470936999595749669676277240766303535475945713821785251664274274663919320030599218174135
y = Log(x,10)
c = e^(n*y.a)
return Dual(c,c*(n*y.b))
end
```

This code gives us a quick implementation of dual numbers that we can get some use
out of, but it's far from complete. A real implementation in Julia can be found
as a subpackage of ForwardDiff.jl,
and a great use case for industry is the very elegant machine learning solution Flux.jl
A small curated list of interesting papers I've found along the way.

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Peter Turchin , **Disentangling the evolutionary drivers of social complexity: A comprehensive test of hypotheses**, Science Advances, 2022, 10.1126/sciadv.abn3517

Lahtinen, Maria , **Excess protein enabled dog domestication during severe Ice Age winters**, Scientific reports, 2021, 10.1038/s41598-020-78214-4

Oliver Smith , **The effect of renewable energy incorporation on power grid stability and resilience**, Science Advances, 2022, 10.1126/sciadv.abj6734

Srivastava, Anuj , **Shape analysis of elastic curves in euclidean spaces**, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 10.1109/TPAMI.2010.184

Turing, Alan Mathison , **The chemical basis of morphogenesis**, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 1952, 10.1098/rstb.1952.0012

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Turing, A. M., **Computing machinery and intelligence**, Mind, 1950, 10.1093/mind/LIX.236.433

Kevin Aslett , **News credibility labels have limited average effects on news diet quality and fail to reduce misperceptions**, Science Advances, 2022, 10.1126/sciadv.abl3844

Ye Li , **A unique stone skipping-like trajectory of asteroid Aletai**, Science Advances, 2022, 10.1126/sciadv.abm8890

Inbal Ravreby , **There is chemistry in social chemistry**, Science Advances, 2022, 10.1126/sciadv.abn0154

Bryan S. Obst , **Kinematics of phalarope spinning**, Nature, 1996

Vilk, Ohad , **Ergodicity Breaking in Area-Restricted Search of Avian Predators**, Phys. Rev. X, 2022, 10.1103/PhysRevX.12.031005

Hossam ElGindy , **Slicing an ear using prune-and-search**, Pattern Recognition Letters, 1993, https://doi.org/10.1016/0167-8655(93)90141-Y

Junhui Law , **Microrobotic swarms for selective embolization**, Science Advances, 2022, 10.1126/sciadv.abm5752

Hao Li , **Performing calculus with epsilon-near-zero metamaterials**, Science Advances, 2022, 10.1126/sciadv.abq6198

Rosato, Anthony , **Why the Brazil nuts are on top: Size segregation of particulate matter by shaking**, Phys. Rev. Lett., 1987, 10.1103/PhysRevLett.58.1038

Gajjar, Parmesh , **Size segregation of irregular granular materials captured by time-resolved 3D imaging**, Scientific reports, 2021

Trivedi, Drupad K , **Discovery of volatile biomarkers of Parkinson’s disease from sebum**, ACS central science, 2019

Sawyer, James S, **Man-made carbon dioxide and the “greenhouse” effect**, Nature, 1972