I've defended my PhD thesis on 2020-11-26. After that I needed to publish my thesis in order to receive my PhD diploma. This is more complicated than it sounds.

Decades ago, when one did not have servers with PDF files, one had to publish the thesis with a lot of hard copies. And this unfortunately is still the default mode at the University of Bonn. So there are a bunch of hoops that one has to take in order to have the thesis published.

I took a couple days before I started this process. On 2020-12-01 I sent the PDF file via e-mail to the university library. They have a special address where I should sent it to. As the PDF file was so large, I have just uploaded it to my website and gave them the link.

In parallel I needed to register my thesis on some other platform of the library. There they did not accept the PDF, just the meta data. And the form had a “Submit” and a “Reset” button there. I mean, how many times does one actually want to reset a form? I thought that this “Reset” button died out decades ago. After registering my thesis there, I had to download a receipt, sign it and send it via e-mail to the university library.

On the website of the library they told me that I needed to make sure that my advisor is okay with me publish my thesis electronically. They did not mention any particular form. So I just asked my advisor and it was okay.

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Recently I have moved and since I've basically exchanged flats with somebody else and her friend, I have been driving around three locations for a whole weekend. I would try to figure out which stuff to load into the car, where to drive next, which person to bring along on the free seat. One would of course need to drive the car where the stuff should go to, but also have a sufficient number of people to carry stuff and put up furniture.

This has reminded me of the classical salad-sheep-fox riddle, which goes like this: You have a salad, a sheep and a fox. They are all on one bank of a river and you want to cross it. You have a raft which can only hold one thing at a time. But you cannot leave the sheep unattended with the fox, otherwise the fox will kill the sheep. Yet you cannot leave the salad unattended with the sheep, as the sheep will eat it. You need to figure out a way to transfer the stuff without leaving something unattended.

So you cannot start ferrying the salad, the fox will kill the sheep. You cannot start ferrying the fox, the sheep will eat the salad. You will need to ferry the sheep first. Then you ferry back. We can take either the salad or the fox. Let us try the salad. We end up with the salad and the sheep on the other bank, the fox at the start. We cannot just leave them there together unattended. But ferrying back the salad makes no sense, so we ferry back the sheep. On the starting bank we exchange the sheep for the fox, ferry over the fox. We then got the salad and the fox together on the other bank, the sheep left behind. We sail back, load in the sheep, and ferry it to the other bank. Done.

This should be solvable systematically. We have three objects, and three places where they can be. This makes for 27 states, but we can already rule out the states where more than one element is on the raft. This gives us the following states, with the original river bank on the right and the other bank on the left. The raft is in the middle.

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I have finished the text my PhD thesis at the end of August 2020. This meant that the scientific and writing part was done and from there on out it would be just formalities that I had to do. Of course there still was the defense, but first I needed to do a bunch of things in order to get there.

First one has to print the thesis and bind it. The examination office required me to hand in five copies. Four for the members of my examination board and then another one for the office. The binding and cardboard costs like 10 EUR per copy. That was the easy part.

I had to put in a formal request for an examination. For that I needed a to have an examination panel of four professors. One needs to have a second examiner from theoretical physics, a third examiner from experimental physics and a forth one outside of physics. The three people I have asked happily agreed to be on my panel and I was very glad that it went so smoothly. The hard part was to find a date. Professors are notoriously short on time, so it took one Doodle poll to find a day and another poll to find a time-of-day. But eventually I got my time slot.

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In September 2017 I have finished my Master's degree in Physics. I was offered a PhD position by my supervisor and gratefully accepted the opportunity. During the master thesis I started to work with lattice simulations, supercomputer programming and was getting into it. Although I already did not want to persue a full career in science, I still wanted to do a bit more research in physics before leaving for the industry.

The thing is that in Germany usually only 25 % of the employees in institutes have permanent positions. And these are usually people who do administration part time. These people are full time professors, administrators of machine shops, computer clusters or something else. The majority of positions are temporary contracts. The rationale seems to be that research benefits from the exchange of ideas, and if people move around the instituions, knowledge is spread. This completely ignores the fact that these are people, eventually wanting to start a familiy and the like. One usually does not get a consecutive contract at the same institution and have to move, often somewhere within the EU. The real chance for a permanent position would be to have it mixed with something permanent, we have IT administrators that are part-time administrators and part-time researchers/teachers. But then it is not really a career in research, it is merely something in academia. All these things set my long-term route, but I did not want to leave research at that moment. So I took the opportunity to do research for another three years.

At the beginning of a PhD the topic is not clear cut. My advisor had a few ideas, and I mostly started working with my valued PhD student coworker Markus to work on his project. There I helped to refactor a C++ code which did tensor contractions. Over the time I learned more of the code, had more and more ideas on improving it. Together we worked on it a lot, it was a really great time. Also I helped to improve the analysis that he needed for his data. Over time it became our analysis, I wrote most of it in the early days. He explained some of the mathematical theory behind it, I implemented a bunch of statistical transformations. On some days we sat there until very late to make some plots really readable, pretty and informative. As the project came to a conclusion, he finished up his dissertation and eventually handed it in. I was super happy for him to finish, and also sad to see him go and move to a different city.

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Wenn ein Haus mit der Front parallel zur Straße steht und gegenüber noch ein Haus ebenfalls parallel steht, ergibt sich ein wunderbarer Resonator. Im Bild sind die beiden grauen Blöcke die Häuser, das rote ein Auto auf der Fahrbahn zwischen den beiden Häusern.

Der Abstand der Hauswände ist ungefähr 15 m. Im ersten Stock sind die Fenster auf vielleicht 5 m Höhe. Damit hat man einen Winkel von 33° von der Fahrbahn direkt zum Fenster. Die Strecke, die der Schall zurücklegt ist dann 9.0 m. Aus der anderen Richtung mit Reflexion an der Hauswand ist der Steigungswinkel nur noch 13°. Die Gesamtstrecke für den Schall ist dann 23.0 m.

Wir haben also einen Gangunterschied von 14 m. Bei einer Schallgeschwindigkeit von 330 m/s sind die Resonanzfrequenzen dann Vielfache von 23.5 Hz. In einem Schallspektrum müsste man dann so Interferenzlinien sehen, wie sie beim Doppelspaltexperiment vorkommen.

Mit der Android-App Spectroid habe ich dann einfach am Fenster das Schallspektrum aufgenommen, während ein Auto vorbeigefahren ist. Die Zeit verläuft nach oben, unten ist alt, oben ist neu. Zur Seite sind die Frequenzen aufgetragen. Links sind die tiefen Frequenzen, rechts die hohen. Je heller es ist, desto stärker war diese Frequenz zu dem Zeitpunkt vertreten.

In der Ellipse sieht man, wie es erst lauter und dann wieder leiser wird. Das Auto nähert sich, und fährt wieder weg. Und dann ist da noch bei 43 Hz, also dem doppelten der grob abgeschätzten Resonanzfrequenz, ein signifikanter Beitrag. Es ist auch zeitlich beschränkt auf die Zeit, während der das Auto genau zwischen den Häusern war.

Man kann hier also gut eine Interferenz von Wellen in einem Resonanzraum zwischen zwei parallelen Häusern beobachten. Den Effekt kann man auch ohne Spektralanalyse wahrnehmen: Es wummert unangenehm, wenn ein Auto vorbeifährt.

As part of my Master and PhD theses I have used a lot of computer time on supercomputers in Jülich, Stuttgart, Bologna and the cluster in Bonn. I want to estimate the magnitude of CO₂ that this has released.

It is a bit hard to say how many core hours I have used exactly as I have already used data that existed already. Let's take like 5 Mh to pick a number. Then on JUWELS with the dual Intel Xeon Platinum 8168 with 48 cores that is around 100 kh. Each of the CPUs has a TDP of 205 W. Then there is network, file system, backup. Perhaps 750 W per node? And then there is cooling, which roughly takes the same on top, so 1.5 kW per node. That makes 150 MWh of electricity used. In Germany it seems that we would have to take 0.4 kg/kWh of CO₂. This would then give a little over 60 t of CO₂.

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I record a bunch of my activities with Strava. And there are novel routes that I try out and only have done once. The other part are routes that I do more than once. The thing that I am missing on Strava is a comparison of similar routes. It has segments, but I would have to make my whole commute one segment in order to see how I fare on it.

So what I would like to try here is to use a clustering algorithm to automatically identify clusters of similar rides. And also I would like find rides that have the same start and end point, but different routes in between. In my machine learning book I read that there are clustering algorithms, so this is the project that I would like to apply them to.

Incidentally Strava features a lot of apps, so I had a look but could not find what I was looking for. Instead I want to program this myself in Python. One can export the data from Strava and obtains a ZIP file with all the GPX files corresponding to my activities.

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I've been using clothespins all along. I know other people who do as well, and some who never use them. While discussing this over dinner, it seems there are two stances that people take:

1. Pins are not worth using at all. The clothing dries as fast as it does without them, perhaps insignificantly slower. The time needed to work with the pins does not make up for the benefit of having the laundry done faster.

2. Pins clearly must do a difference as the clothing is just in two and not four layers.

Well, I am clearly in the second team. But this is a hypotheses that one can test and negate. So apply the scientific method! As a setup I took four pieces of underwear and two t-shirts. Then I put half of them on the dryer with pins, the other just folded in half. Every now and then I measured their weight with a kitchen scale.

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Bei ALDI gibt es wegen der Mehrwertsteuersenkung aktuell 3 % auf alles. Mediamarkt hatte manchmal auch so Aktionen, bei denen es 19 % Rabatt unter dem Motto »Mediamarkt schenkt die Mehrwertsteuer« gibt. Interessant ist ja eigentlich, dass bei den Rabatten die Preise sogar noch weiter gesenkt werden als nötig.

Sei der Nettopreis $N$, dann ist der Bruttopreis $B$ bei einer Mehrwertsteuer $m$ gegeben durch $B = N \cdot (1 + m)$. Im Normalfall ist $m = 0.19$ und daher haben wir $B = 1.19 \cdot N$. Möchte man die Mehrwertsteuer erlassen, so muss man den einen Rabatt geben, der $1/1.19 \approx 0.8403361$ entspricht. Das ist aber ein Rabatt von $1 - 0.8403361 \approx 0.1596639$, also knapp unter 16 %. Würde Mediamarkt den Kunden aber nur 16 % Rabatt geben, wären wahrscheinlich viele empört. Also gibt es noch weitere 3 % Rabatt für alle, die in Prozentrechnung nicht so fit sind.

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I recently noticed by accident that the default standard deviation implementations in R and NumPy (Python) do not give the same results. In R we have this:

> x <- 1:10
> x
 [1]  1  2  3  4  5  6  7  8  9 10
> sd(x)
[1] 3.02765

And in Python the following:

>>> import numpy as np
>>> x = np.arange(1, 11)
>>> x
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
>>> np.std(x)
2.8722813232690143

So why does one get 3.02 and the other 2.87? The difference is that R uses the unbiased estimator whereas NumPy by default uses the biased estimator. See this Wikipedia article for the details.

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