… Data aequatione quotcunque fluentes quantitates involvente fluxiones invenire et vice versa …

## Category: Life of a mathematician

### TeXLive — on Android

I’ve just done something that is, admittedly, rather silly. And I am somewhat surprised that it actually worked.

I managed to have TeXLive running on my new Samsung Galaxy Tab A, which runs Android.

It is painfully slow (speed is comparable to my old netbook from 2010). But in a pinch, I now know that it works. And that gives me some (minor) peace of mind.

### Workflow

Recently I started rethinking how I organize my incomplete and under development notes.

I know full well the inherent dangers of such an exercise in terms of my actual productivity. But now that I have completed my newest workflow I think I’ve finally found one that works well. (Fingers crossed.)

Before I describe what I do now, I’d like to document what I used to do, what I changed the last time, and why I am changing it again.

### In defense of integration by parts

A prominent academic, who happens not to be a mathematician, visited my home institution recently and gave a public address about the role of the university in the modern world. Most of what he said concerning our teaching mission are the usual platitudes about not being stuck in the past and making sure that our curricular content and learning objectives are aligned with what we would expect a 21st century college graduates to need.

It however bugged me to no end that the recurring example this particular individual returns to for something old-fashioned and “ought not be taught” is integration by parts; and he justifies this by mentioning that computer algebra systems (or even just google) can do the integrals faster and better than we humans can.

I don’t generally mind others cracking jokes at mathematicians’ expense. But this particular self-serving strawman uttered by so well-regarded an individual is, to those of us actually in the field teaching calculus to freshmen and sophomores, very damaging and disingenuous.

I happened to have just spent the entirety of last year rethinking how we can best teach calculus to the modern engineering majors. Believe me, students nowadays know perfectly well when we are just asking them to do busywork; they also know perfectly well that computer algebra systems are generally better at finding closed-form integral expressions than we can. Part of the challenge of the redesign that I am involved in is precisely to convince the students that calculus is worth learning in spite of computers. The difficulty is not in dearth of reason; on the contrary, there are many good reasons why a solid grounding of calculus is important to a modern engineering students. To give a few examples:

1. Taylor series are in fact important because of computers, since they provide a method of compactly encoding an entire function.
2. Newton’s method for root finding (and its application to, say, numerical optimization) is build on a solid understanding of differential calculus.
3. The entirety of the finite element method of numerical simulation, which underlies a lot of civil and mechanical engineering applications, are based on a variational formulation of differential equations that, guess what, only make sense when one understand integration by parts.
4. The notion of Fourier transform which is behind a lot of signal/image processing requires understanding how trigonometric functions behave under integration.

No, the difficulty for me and my collaborator is narrowing down a list of examples that we can not only reasonably explain to undergraduate students, but also have them have some hands-on experience working with.

When my collaborator and I were first plunged into this adventure of designing engineering-specific calculus material, one of the very first things that we did was to seek out inputs from our engineering colleagues. My original impulse was to cut some curricular content in order to give the students a chance to develop deeper understanding of fewer topics. To that end I selected some number of topics which I thought are old-fashioned, out-dated, and no longer used in this day and age. How wrong I was! Even something like “integration by partial fractions” which most practicing mathematicians will defer to a computer to do has its advocates (those who have to teach control theory insists that a lot of fundamental examples in their field can be reduced to evaluating integrals of rational functions, and a good grasp of how such integrals behave is key to developing a general sense of how control theory works).

In short, unlike some individuals will have you believe, math education is not obsolete because we all have calculators. In fact, I would argue the opposite: math education is especially pertinent now that we all have calculators. Long gone was the age where a superficial understanding of mathematics in terms of its rote computations is a valuable skill. A successful scientist or engineer needs to be able to effectively leverage the large toolbox that is available to her, and this requires a much deeper understanding of mathematics, one that goes beyond just the how but also the what and the why.

There are indeed much that can be done to better math education for the modern student. But one thing that shouldn’t be done is getting rid of integration by parts.

### Abusing JabRef to manage snipplets of TeX

I use JabRef as my reference manager. In this post, however, I will discuss how we can abuse it to do some other things.

The problem

Let’s start with a concrete example: I keep a “lab notebook”. It is where I document all my miscellaneous thoughts and computations that come up during my research. Some of those are immediately useful and are collected into papers for publication. Some of those are not, and I prefer to keep them for future reference. These computations range over many different subjects. Now and then, I want to share with a collaborator or a student some subset of these notes. So I want a way to quickly search (by keywords/abstract) for relevant notes, and that compile them into one large LaTeX document.

Another concrete example: I am starting to collect a bunch of examples and exercises in analysis for use in my various classes. Again, I want to have them organized for easy search and retrieval, especially to make into exercise sheets.

The JabRef solution

The “correct” way to do this is probably with a database (or a document store), with each document tagged with a list of keywords. But that requires a bit more programming than I want to worry about at the moment.

JabRef, as it turns out, is sort of a metadata database: by defining a customized entry type you can use the BibTeX syntax as a proxy for JSON-style data. So for my lab notebook example, I define a custom type lnbentry in JabRef with

• Optional fields: keywords, abstract

I store each lab notebook entry as an individual TeX file, whose file system address is stored in the file field. The remaining metadata fields’ contents are self-evident.

(Technical note: in my case I actually store the metadata in the TeX file and have a script to parse the TeX files and update the bib database accordingly.)

For generating output, we can use JabRef’s convenient export filter support. In the simplest case we can create a custom export layout with the main layout file containing the single line

\\input{\file}

with appropriate begin and end incantations to make the output a fully-formed TeX file. Then one can simply select the entries to be exported, click on “Export”, and generate the appropriate TeX file on the fly.

(Technical note: JabRef can also be run without a GUI. So one can use this to perform searches through the database on the command line.)

### Simulating closed cosmic strings (or yet another adventure in Julia)

One of my current research interests is in the geometry of constant-mean-curvature time-like submanifolds of Minkowski space. A special case of this is the study of cosmic strings under the Nambu-Goto action. Mathematically the classical (as opposed to quantum) behavior of such strings are quite well understood, by combining the works of theoretical physicists since the 80s (especially those of Jens Hoppe and collaborators) together with recent mathematical developments (such as the work of Nguyen and Tian and that of Jerrard, Novaga, and Orlandi). To get a better handle on what is going on with these results, and especially to provide some visual aids, I’ve tried to produce some simulations of the dynamics of closed cosmic strings. (This was also an opportunity for me to practice code-writing in Julia and learn a bit about the best practices for performance and optimization in that language.)

The code

After some false starts, here are some reasonably stable code.

function MC_corr3!(position::Array{Float64,2}, prev_vel::Array{Float64,2}, next_vel::Array{Float64,2}, result::Array{Float64,2}, dt::Float64)
# We will assume that data is stored in the format point(coord,number), so as a 3x1000 array or something.
num_points = size(position,2)
num_dims = size(position,1)

curr_vel = zeros(num_dims)
curr_vs = zeros(num_dims)
curr_ps = zeros(num_dims)
curr_pss = zeros(num_dims)

pred_vel = zeros(num_dims)
agreement = true

for col = 1:num_points  #Outer loop is column
if col == 1
prev_col = num_points
next_col = 2
elseif col == num_points
prev_col = num_points - 1
next_col = 1
else
prev_col = col -1
next_col = col + 1
end

for row = 1:num_dims
curr_vel[row] = (next_vel[row,col] + prev_vel[row,col])/2
curr_vs[row] = (next_vel[row,next_col] + prev_vel[row,next_col] - next_vel[row,prev_col] - prev_vel[row,prev_col])/4
curr_ps[row] = (position[row,next_col] - position[row,prev_col])/2
curr_pss[row] = position[row,next_col] + position[row,prev_col] - 2*position[row,col]
end

beta = (1 + dot(curr_vel,curr_vel))^(1/2)
sigma = dot(curr_ps,curr_ps)
psvs = dot(curr_ps,curr_vs)
bvvs = dot(curr_vs,curr_vel) / (beta^2)
pssps = dot(curr_pss,curr_ps)

for row in 1:num_dims
result[row,col] = curr_pss[row] / (sigma * beta) - curr_ps[row] * pssps / (sigma^2 * beta) - curr_vel[row] * psvs / (sigma * beta) - curr_ps[row] * bvvs / (sigma * beta)
pred_vel[row] = prev_vel[row,col] + dt * result[row,col]
end

agreement = agreement && isapprox(next_vel[:,col], pred_vel, rtol=sqrt(eps(Float64)))
end

return agreement
end

function find_next_vel!(position::Array{Float64,2}, prev_vel::Array{Float64,2}, next_vel::Array{Float64,2}, dt::Float64; max_tries::Int64=50)
tries = 1
result = zeros(next_vel)
agreement = MC_corr3!(position,prev_vel,next_vel,result,dt)
for j in 1:size(next_vel,2), i in 1:size(next_vel,1)
next_vel[i,j] = prev_vel[i,j] + result[i,j]*dt
end
while !agreement && tries < max_tries
agreement = MC_corr3!(position,prev_vel,next_vel,result,dt)
for j in 1:size(next_vel,2), i in 1:size(next_vel,1)
next_vel[i,j] = prev_vel[i,j] + result[i,j]*dt
end
tries +=1
end
return tries, agreement
end

This first file does the heavy lifting of solving the evolution equation. The scheme is a semi-implicit finite difference scheme. The function MC_Corr3 takes as input the current position, the previous velocity, and the next velocity, and computes the correct current acceleration. The function find_next_vel iterates MC_Corr3 until the computed acceleration agrees (up to numerical errors) with the input previous and next velocities.

Or, in notations:
MC_Corr3: ( x[t], v[t-1], v[t+1] ) --> Delta-v[t]
and find_next_vel iterates MC_Corr3 until
Delta-v[t] == (v[t+1] - v[t-1]) / 2

The code in this file is also where the performance matters the most, and I spent quite some time experimenting with different algorithms to find one with most reasonable speed.

function make_ellipse(a::Float64,b::Float64, n::Int64, extra_dims::Int64=1)  # a,b are relative lengths of x and y axes
s = linspace(0,2π * (n-1)/n, n)
if extra_dims == 0
return vcat(transpose(a*cos(s)), transpose(b*sin(s)))
elseif extra_dims > 0
return vcat(transpose(a*cos(s)), transpose(b*sin(s)), zeros(extra_dims,n))
else
error("extra_dims must be non-negative")
end
end

function perturb_data!(data::Array{Float64,2}, coeff::Vector{Float64}, num_modes::Int64)
# num_modes is the number of modes
# coeff are the relative sizes of the perturbations

numpts = size(data,2)

for j in 2:num_modes
rcoeff = rand(length(coeff),2)

for pt in 1:numpts
theta = 2j * π * pt / numpts
for d in 1:length(coeff)
data[d,pt] += ( (rcoeff[d,1] - 0.5) *  cos(theta) + (rcoeff[d,2] - 0.5) * sin(theta)) * coeff[d] / j^2
end
end
end

nothing
end

This file just sets up the initial data. Note that in principle the number of ambient spatial dimensions is arbitrary.

using Plots

pyplot(size=(1920,1080), reuse=true)

function plot_data2D(filename_prefix::ASCIIString, filename_offset::Int64, titlestring::ASCIIString, data::Array{Float64,2}, additional_data...)
x_max = 1.5
y_max = 1.5
plot(transpose(data)[:,1], transpose(data)[:,2] , xlims=(-x_max,x_max), ylims=(-y_max,y_max), title=titlestring)

end
end

png(filename_prefix*dec(filename_offset,5)*".png")
nothing
end

function plot_data3D(filename_prefix::ASCIIString, filename_offset::Int64, titlestring::ASCIIString, data::Array{Float64,2}, additional_data...)
x_max = 1.5
y_max = 1.5
z_max = 0.9
tdata = transpose(data)
plot(tdata[:,1], tdata[:,2],tdata[:,3], xlims=(-x_max,x_max), ylims=(-y_max,y_max),zlims=(-z_max,z_max), title=titlestring)

plot!(tdata[:,1], tdata[:,2], tdata[:,3])
end
end

png(filename_prefix*dec(filename_offset,5)*".png")
nothing
end

This file provides some wrapper commands for generating the plots.

include("InitialData3.jl")
include("MeanCurvature3.jl")
include("GraphCode3.jl")

num_pts = 3000
default_timestep = 0.01 / num_pts
max_time = 3
plot_every_ts = 1500

my_data = make_ellipse(1.0,1.0,num_pts,0)
perturb_data!(my_data, [1.0,1.0], 15)
this_vel = zeros(my_data)
next_vel = zeros(my_data)

for t = 0:floor(Int64,max_time / default_timestep)
num_tries, agreement = find_next_vel!(my_data, this_vel,next_vel,default_timestep)

if !agreement
warn("Time $(t*default_timestep): failed to converge when finding next_vel.") warn("Dumping information:") max_beta = 1.0 max_col = 1 for col in 1:size(my_data,2) beta = (1 + dot(next_vel[:,col], next_vel[:,col]))^(1/2) if beta > max_beta max_beta = beta max_col = col end end warn(" Beta attains maximum at position$max_col")
warn("   Beta = $max_beta") warn(" Position = ", my_data[:,max_col]) prevcol = max_col - 1 nextcol = max_col + 1 if max_col == 1 prevcol = size(my_data,2) elseif max_col == size(my_data,2) nextcol = 1 end warn(" Deltas") warn(" Left: ", my_data[:,max_col] - my_data[:,prevcol]) warn(" Right: ", my_data[:,nextcol] - my_data[:,max_col]) warn(" Previous velocity: ", this_vel[:,max_col]) warn(" Putative next velocity: ", next_vel[:,max_col]) warn("Quitting...") break end for col in 1:size(my_data,2) beta = (1 + dot(next_vel[:,col], next_vel[:,col]))^(1/2) for row in 1:size(my_data,1) my_data[row,col] += next_vel[row,col] * default_timestep / beta this_vel[row,col] = next_vel[row,col] end if beta > 1e7 warn("time: ", t * default_timestep) warn("Almost null... beta = ", beta) warn("current position = ", my_data[:,col]) warn("current Deltas") prevcol = col - 1 nextcol = col + 1 if col == 1 prevcol = size(my_data,2) elseif col == size(my_data,2) nextcol = 1 end warn(" Left: ", my_data[:,col] - my_data[:,prevcol]) warn(" Right: ", my_data[:,nextcol] - my_data[:,col]) end end if t % plot_every_ts ==0 plot_data2D("3Dtest", div(t,plot_every_ts), @sprintf("elapsed: %0.4f",t*default_timestep), my_data, make_ellipse(cos(t*default_timestep), cos(t*default_timestep),100,0)) info("Frame$(t/plot_every_ts):  used $num_tries tries.") end end And finally the main file. Mostly it just ties the other files together to produce the plots using the simulation code; there are some diagnostics included for me to keep an eye on the output. The results First thing to do is to run a sanity check against explicit solutions. In rotational symmetry, the solution to the cosmic string equations can be found analytically. As you can see below the simulation closely replicates the explicit solution in this case. The video ends when the simulation stopped. The simulation stopped because a singularity has formed; in this video the singularity can be seen as the collapse of the string to a single point. Next we can play around with a more complicated initial configuration. In this video the blue curve is the closed cosmic string, which starts out as a random perturbation of the circle with zero initial speed. The string contracts with acceleration determined by the Nambu-Goto action. The simulation ends when a singularity has formed. It is perhaps a bit hard to see directly where the singularity happened. The diagnostic messages, however, help in this regard. From it we know that the onset of singularity can be seen in the final frame: The highlighted region is getting quite pointy. In fact, that is accompanied with the “corner” picking up infinite acceleration (in other words, experiencing an infinite force). The mathematical singularity corresponds to something unreasonable happening in the physics. To make it easier to see the “speed” at which the curve is moving, the following videos show the string along with its “trail”. This first one again shows how a singularity can happen as the curve gets gradually more bent, eventually forming a corner. This next one does a good job emphasizing the “wave” nature of the motion. The closed cosmic strings behave like a elastic band. The string, overall, wants to contract to a point. Small undulations along the string however are propagated like traveling waves. Both of these tendencies can be seen quite clearly in the above video. That the numerical solver can solve “past” the singular point is a happy accident; while theoretically the solutions can in fact be analytically continued past the singular points, the renormalization process involved in this continuation is numerically unstable and we shouldn’t be able to see it on the computer most of the time. The next video also emphasizes the wave nature of the motion. In addition to the traveling waves, pay attention to the bottom left of the video. Initially the string is almost straight there. This total lack of curvature is a stationary configuration for the string, and so initially there is absolutely no acceleration of that segment of the string. The curvature from the left and right of that segment slowly intrudes on the quiescent piece until the whole thing starts moving. The last video for this post is a simulation when the ambient space is 3 dimensional. The motion of the string, as you can see, becomes somewhat more complicated. When the ambient space is 2 dimensional a point either accelerates or decelerates based on the local (signed) curvature of the string. But when the ambient space is 3 dimensional, the curvature is now a vector and this additional degree of freedom introduces complications into the behavior. For example, when the ambient space is 2 dimensional it is known that all closed cosmic strings become singular in finite time. But in 3 dimensions there are many closed cosmic strings that vibrate in place without every becoming singular. The video below is one that does however become singular. In addition to a fading trail to help visualize the speed of the curve, this plot also includes the shadows: projections of the curve onto the three coordinate planes. ### Adventures in Julia Recently I have been playing around with the Julia programming language as a way to run some cheap simulations for some geometric analysis stuff that I am working on. So far the experience has been awesome. A few random things … Juno Julia has a decent IDE in JunoLab, which is built on top of Atom. In terms of functionality it captures most of the sort of things I used to use with Spyder for python, so is very convenient. Jupyter Julia interfaces with Jupyter notebooks through the IJulia kernel. I am a fan of Jupyter (I will be using it with the MATLAB kernel for a class I am teaching this fall). Plots.jl For plotting, right now one of the most convenience ways is through Plots.jl, which is a plotting front-end the bridges between your code and various different backends that can be almost swapped in and out on the fly. The actual plotting is powered by things like matplotlib or plotlyJS, but for the most part you can ignore the backend. This drastically simplifies the production of visualizations. (At least compared to what I remembered for my previous simulations in python.) Automatic Differentiation I just learned very recently about automatic differentiation. At a cost in running time for my scripts, it can very much simplify the coding of the scripts. For example, we can have a black-box root finder using Newton iteration that does not require pre-computing the Jacobian by hand: module NewtonIteration using ForwardDiff export RootFind function RootFind(f, init_guess::Vector, accuracy::Float64, cache::ForwardDiffCache; method="Newton", max_iters=100, chunk_size=0) ### Takes input function f(x::Vector) → y::Vector of the same dimension and an initial guess init_guess. Apply Newton iteration to find solution of f(x) = 0. Stop when accuracy is better than prescribed, or when max_iters is reached, at which point a warning is raised. ### Setting chunk_size=0 deactivates chunking. But for large dimensional functions, chunk_size=5 or 10 improves performance drastically. Note that chunk_size must evenly divide the dimension of the input vector. ### Available methods are Newton or Chord # First check if we are already within the accuracy bounds error_term = f(init_guess) if norm(error_term) < accuracy info("Initial guess accurate.") return init_guess end # Different solution methods i = 1 current_guess = init_guess if method=="Chord" df = jacobian(f,current_guess,chunk_size=chunk_size) while norm(error_term) >= accuracy && i <= max_iters current_guess -= df \ error_term error_term = f(current_guess) i += 1 end elseif method=="Newton" jake = jacobian(f, ForwardDiff.AllResults, chunk_size=chunk_size, cache=cache) df, lower_order = jake(init_guess) while norm(value(lower_order)) >= accuracy && i <= max_iters current_guess -= df \ value(lower_order) df, lower_order = jake(current_guess) i += 1 end error_term = value(lower_order) else warn("Unknown method: ", method, ", returning initial guess.") return init_guess end # Check if converged if norm(error_term) >= accuracy warn("Did not converge, check initial guess or try increasing max_iters (currently: ", max_iters, ").") end info("Used ", i, " iterations; remaining error=", norm(error_term)) return current_guess end end This can then be wrapped in finite difference code for solving nonlinear PDEs! ### LaTeX runtime for NeoVim I’ve just recently migrated to using NeoVim instead of traditional Vim. One of the nice features in NeoVim (or nvim) is that it now supports asynchronous job dispatch. This makes it a bit nicer to call external previewers for instance (otherwise the previewer may block the editing). So here are the latest LaTeX runtime code that I use, modified for NeoVim. function Dvipreview() let dviviewjob = jobstart(['xdvi', '-sourceposition', line(".")." ".expand("%"), expand("%:r") . ".dvi"]) endfunction function PDFpreview() let pdfviewjob = jobstart(['evince', expand("%:r") . ".pdf"]) endfunction au BufRead *.tex call LaTeXStartup() function LaTeXStartup() set dictionary+=~/.config/nvim/custom/latextmp/labelsdictionary set iskeyword=@,48-57,_,: call SimpleTexFold() set completefunc=CompleteBib set completeopt=menuone,preview runtime custom/latextmp/bibdictionary call SetShortCuts() endfunction function SimpleTexFold() exe "normal mz" 1 set foldmethod=manual if search('\\begin{document}','nW') 1,/\\begin{document}/-1fold if search('\\section','nW') /\\section/1 endif while search('\\section','nW') .,/\\section/-1fold /\\section/1 endwhile .,$fold
endif
if search('\\begin{entry}','nW')
/\\begin{entry}/1
while search('\\begin{entry}','nW')
.,/\\begin{entry}/-1fold
/\\begin{entry}/1
endwhile
.,$fold endif exe "normal g`zzv" endfunction function SetShortCuts() " Map <F2> to save and compile imap <F2> ^[:w^M:! latex -src-specials % >/dev/null^M^Mi " Map S-<F2> to save and compile as PDF " apparently <S-F2> sends the same keycode as <F12>? imap <F12> ^[:w^M:! pdflatex % >/dev/null^M^Mi " Map <F3> to Dvipreview() imap <F3> ^[:call Dvipreview()^M " Map S-<F3> to PDFpreview() " apparently <S-F3> = <F13> imap <F13> ^[:call PDFpreview()^M " Map <F4> to bibtex imap <F4> ^[:! bibtex "%:r" >/dev/null^M^Mi " Map <F5> to change the previous word into a latex \begin .. \end environment imap <F5> ^[diwi\begin{^[pi<Right>}^M^M\end{^[pi<Right>}<Up> " Map <F6> to 'escape the current \begin .. \end environment imap <F6> ^[/\\end{.*}/e^Mi<Right> " Map <F7> to search the labels dictionary for matching labels imap <F7> ^[diwi\ref{^[pi<Right>^X^K " Map <F8> to rebuild the labels dictionary imap <F8> ^[:w^M:! ~/.config/nvim/custom/latexreadlabels.sh %^M^Mi " Map <F9> to search using the bibs dictionary imap <F9> ^[diwi\cite{^[pi<Right>^X^U imap <S-Tab>C ^[diwi\mathcal{^[pi<Right>} imap <S-Tab>B ^[diwi\mathbb{^[pi<Right>} imap <S-Tab>F ^[diwi\mathfrak{^[pi<Right>} imap <S-Tab>R ^[diwi\mathrm{^[pi<Right>} imap <S-Tab>O ^[diwi\mathop{^[pi<Right>} imap <S-Tab>= ^[diWi\bar{^[pi<Right>} imap <S-Tab>. ^[diWi\dot{^[pi<Right>} imap <S-Tab>" ^[diWi\ddot{^[pi<Right>} imap <S-Tab>- ^[diWi\overline{^[pi<Right>} imap <S-Tab>^ ^[diWi\widehat{^[pi<Right>} imap <S-Tab>~ ^[diWi\widetilde{^[pi<Right>} imap <S-Tab>_ ^[diWi\underline{^[pi<Right>} endfunction Pay attention that the control characters did not copy-paste entirely correctly in the SetShortCuts() routine. Those need to be replaced by the actual control-X sequences. The read labels shell script is simply #!/bin/sh grep '\label{'$1 | sed -r 's/.*\\label\{([^}]*)\}.*/\1/' > ~/.config/nvim/custom/latextmp/labelsdictionary

(I probably should observe the proper directory structure and dump the dictionary into ~/.local/share/ instead.)

### So starts the svn to git migration…

For five years now I have been a happy user of svn to manage my research work, and I probably would have remained so if it weren’t for my next job favoring git instead. So in the past few weeks I have been reading up on git and in the process discovering all sorts of things that I have been doing wrong, or at least sub-optimally. So here are just some notes on what I’ve just figured out (yay slow me!).

Each paper should be a repository

Previously I keep one single giant repository for all my research work. I’ve discovered that this is not the best idea for multiple reasons:

• Collaboration: one of the great things about version control systems is that it makes collaboration easier to manage. But your collaborators are not a static set and you probably don’t want them to peek at every one of your research ideas. The easiest way to share individual projects with only those who should be allowed to see and edit them is to have one repo for each paper. (I got away with what I did mostly because I failed to convince any of my collaborators to use a VCS beyond that built-in support in Dropbox.)
• Organisation: to keep track of papers I have them stored in subdirectories, some of which are “stuff I am working on” and some of which are “stuff that is finished from year X” and some of which are “stuff that is being refereed”. It is a bit silly that I have to do svn mv changes to “graduate” a project from one subdirectory to the next. By keeping each paper in its own (git) repository, the local directory representation of the storage is immaterial. And this makes more sense to me.
• (In)compatibility: here’s something that I changed my mind on. Previously I thought it a great idea to keep a single up-to-date bibtex file containing all the references that I can ever need, and a single up-to-date version of my custom LaTeX class and style files. The advantage of course is that I just need to issue one svn up to get the newest versions of everything. But the disadvantage is that when upgrading my class and style files, or when updating my bibtex files, I have to maintain backward compatibility. And when I do break the compatibility, it is then required that I keep a copy of the old versions of the files along with the LaTeX source that uses them, which, when you think about it, defeats the purpose of having a single up-to-date version in one repo completely.

So my new workflow, instead of one giant repository, is that I will create a repo for each paper/project. My LaTeX class and style files will be itself a separate Git repo, on which I can upgrade and develop to my hearts desire. When I start a new paper I will simply make a copy of the current version of the files (with git archive instead of git clone because I won’t need the previous versions, nor will I want to track the changes). This also allows me to set-up my “development environment” (via .gitattributes and .gitignore) quickly.

Keyword substitution is not necessary

The papers I keep in my svn repo I have been using the svn and svn-multi packages to add time-stamp and versioning information to the PDF files. Both of those packages rely on the “keyword substitution” capabilities of the svn system at commit time. Naturally when I wanted to start using git, I looked for a replacement. The obvious one is gitinfot2. One thing I don’t like is that unlike the keyword replacements, this package does not directly modified the source LaTeX file; instead it creates (via commit and checkout hooks) a supplementary file in the .git/ directory which it searches for and inserts when building the PDF file. This makes it a bit more of a hassle when uploading stuff to the arXiv, for example.

So I started reading up on how one can actually imitate keyword expansion using commit and checkout filters. And I went so far as to implement something for LaTeX. And then I read the discussion by the kernel devs on this issue, and Linus Torvalds’ comments left an impression on me. In short:

• When you are working on the code in a git repository, you don’t need this tagging since you can just “ask git”.
• Conversely, this sort of tagging is only needed when your code is ready to leave the repository (upload to arXiv or sent to non-git-using collaborators, for example).

So philosophically it is much less useful to have something that work on the working copy compared to something that works on an exported archive. And while git, by design, cannot and will not do keyword expansion on commits, it is perfectly happy to do keyword expansion when one exports the repo. Furthermore, since the export substitution can be essentially formatted arbitrarily, this moots the need for something like svn or svn-multi to parse the string generated by the RCS: we can make the string appear how we want to start with. The only hiccup is that before the substitution (i.e. when you are working in the working copy), the syntax for the export substitution is not exactly compatible with LaTeX, and requires a little mucking about with catcodes. But with that problem solved, and with the workflow now accounting for each paper as a separate repository, for arXiv uploads the easiest thing will actually be to simply issue git archive and upload the resulting tarball.

### So I will be living in Michigan for the next few years…

This year I have had a little bit of sucess with my job search. At the end I sent in around 45 applications in total (though goodness for MathJobs, though a few applications were dealt with differently), from which I got 5 interviews which resulted in 2 offers, 1 rejection, and 2 “we cannot solve your two body problem so let’s not bother going through the motions”.

Starting mid August I will be affiliated with Michigan State University in East Lansing, Michigan.

All in all, a pretty stressful 6 months (job season starts around mid September, and my decision was sent in mid March), and now a slightly less stressful few months of preparing an international move from Switzerland to the US.

I’ve noticed that it is somewhat fashionable nowadays for early career individuals who have found jobs to post their application material on their websites, to serve as a sample for graduate students and new PhDs who are on the market. I think it is a pretty good idea. This was my fourth time applying for jobs. (Last year I applied also, despite there still being time left on my contract here. In the current market that is something I would recommend.) And looking back on the previous research statements I have written, the ones I wrote for my first two times applying for jobs really weren’t that great: they are both too narrowly focussed and too technical. Of course, a chunk of this has to do with my maturing as a professional mathematician. But some blame must be put on the lack of “models” to which I can compare my writing. Similarly, until this year I think my approach to the teaching statement has been on the naive and flippant side, which again partly has to do with me not knowing any better. (And this is to say nothing of the excuse of a cover letter I wrote 6 years ago.)

So below I share my research and teaching statements from this year (no, you don’t get to see the awful stuff I’ve written in the years prior). If you find it in anyway helpful, don’t hesistate to let me know.

2014 Research Statement (please note that this was written around September/October of 2014; the field of research in which I work has seen already some very interesting developments since then, so don’t treat this in anyway as an up-to-date survey!)

2014 Teaching Statement

### Using Prezi

I was introduced to the use of Prezi for doing presentations (I know, I am slightly behind the times) from a MOOC I am taking, and so for my recent trip to Cambridge I decided to give it a try. The audience seemed to like it, and I felt that in this particular instance, the use of Prezi vastly improved my talk. On the other hand, I don’t think in the future I will do every one of my presentations as Prezis: that presentation style is suitable for some topics but traditional (be it slides or board talks) methods do shine for others. Read the rest of this entry »