Definitely one of my favorite parts to read of any thesis are the acknowledgements. It’s often a time to lower the “fancy academic” mask and really see the human behind the writing; something essentially impossible with the dressed-up writing of many disciplines!
I believe the first part of my own acknowledgements began with: “There are only a few interesting things in this thesis. Out of all of them, this section might be the most important.”
Sure, it’s all a mask, but there’s something more “real” behind the acknowledgements in a very distinct way than the editorialized writing of an scientific article. (Using quotes here as these things are deliberately fuzzy!)
It’s neat! To be fair, as a physicist, I did not understand the Legrendre transform essentially until taking convex optimization (where it is known as the Fenchel conjugate).
Many sources, but all of them are reasonable and give a constructive definition that actually explains what it does: we can characterize a function either by its graph, or its supporting hyperplanes (when it is a closed, convex function).
While the observation is almost silly, it has very deep consequences for different characterizations of problems and other constructions!
But you can set family members/significant others/etc as possible recovery mechanisms! This seems like a really workable solution that I don’t see people discussing in this thread?
Eh, someone just suggested that a bound of this form might break some cryptographic assumptions for finite fields of (large) prime order. (This is not obvious to me, but it's a point that I think is worth considering, and already would show that such a result is useful.)
It's very hard to know how these things end up being useful. Even if a specific piece of knowledge isn't useful by itself, it can lead to things that are useful down the line. It's both very hard to know which ones will be, or which ones do lead to actually useful results; for example, think of work on propositional logic which ends up being the bedrock of computation, work on elliptic curves (cryptography), work on PDEs (physics), quantum physics (transistors), photonics (communications), etc.
Some fields have pretty "fast-to-application" times, but it's not always clear which ones will _not_ yield useful results. Plus, and here's the real, honest answer: all of this shit is very fun! Why not do it?
I'm also a (somewhat) practical person, and that guides many of the problems I try to solve, but sometimes there's just such a tantalizing puzzle that it's hard not to resist! It's pretty damn hard to "only" work on "useful" stuff and not be just an ok academic/programmer/etc., you have to let yourself get nerd-sniped into doing random things. With very high probability, these little "side-quests" end up being very useful down the line, for one reason or another.
Wait this is pretty sick! What's the full build on that? How do you even get started on finding good cases that aren't just massive racks for a home build?
The case is "LZMOD A24 V3" - found it on caseend.com - there are smaller ITX cases, but I wanted to fit in standard components only, and not to mess with custom PSUs (for example).
The rest of the components are:
Board: AsRock Rack X570D4I-2T (2x 10GBe and IPMI!)
NVMe: 2TB Transcend TS2TMTE220S TLC
SSD: 2x 8TB Samsung 870 QVO
PSU: Seasonic SSP-300SUB (overkill, went for longevity)
CPU Cooling: Thermalright AXP-100 Series All-Copper Heatsink with Noctua NF-A12x15 PWM
Exhaust fans: 2x INEX AK-FN076 Slimfan 80mm PWM
On the air intake side, there's a filter sheet that I replace (or vacuum) once in a blue moon - the insides are still pristine after running for over a year now.
Interesting thing about cooling: one of those cases has a PSU with custom made cabling (reduced cables by about 90%). I was hoping it will reduce the temperatures a bit. Surprisingly there was basically no change. At full load all keep running at around 70 celsius.
Important: in such a small case, if you want silence you'd better disable AMD's "Core Performance Boost". This will make the CPU run at its nominal frequency, 3.4GHz for 5950x, otherwise it'll keep on jumping to it's max potential, 4.9GHz for 5950x, which will result in more heat, and more fan noise.
Yeah, a lot of scientific writing is just downright useless, and I don't just mean that in the "haha, it's hard to read, but it's ok"-sense. For example, in many fields (parts of theoretical physics, many parts of econ) publications are so hard to read that "reading" a paper looks less like "learning from the author by following what they did on paper" and more like "rederiving the same thing that the author claims to do, except by yourself with only some minor guidance from the paper." This is, frankly, absolutely insane, but it's the current state of things.
It's a fine line to walk when publishing. For example, is it ok to use the term "Hilbert space" in an article? Perhaps in physics, but not if publishing in biology - or at least in biology, a few sentences to describe the term may be more appropriate. But the use of the term is actually quite useful, as in this manufactured example the article may apply only to Hilbert spaces but not all vector spaces. So since the distinction may be important to the finding, the terminology is necessary.
Oh, no, I'm not dismissing terminology! (That's a whole separate topic, which is distinct from writing, though it can sometimes be a sign of bad writing.) I'm talking specifically about the actual writing. Iirc, there's a phrase that sums it up quite nicely: "most science is written like it hates you and wants you to stop reading immediately." Poor writing is so much the norm that people are shocked when you write a readable article. (You'd be surprised at the number of peer review comments I've received to the effect of, "the writing is surprisingly clear and direct," and I'm not like, a novelist, or essayist, or anything. English isn't even my first language! It's just not the norm to think about, and much less act on, these things.)
>"most science is written like it hates you and wants you to stop reading immediately."
As an educated lay person who dips into scientific papers occasionally I completely agree with this. And now I have a nice phrase I can remember next time I read a scientific paper and think "why does science hate me?".
To properly talk about compositionality in differential equations you absolutely need category theory. Likewise I firmly believe that PDE and numerical mathematics actually lack a good foundation in category theory, not that they don’t need one.
It is just very hard to create non-trivial general theories in sich „Applied“ fields, Functional Analysis alone is not very exciting. For an example see for example the work or Hairer (stochastic differential equations) or Costello (QFT).
Definitely one of my favorite parts to read of any thesis are the acknowledgements. It’s often a time to lower the “fancy academic” mask and really see the human behind the writing; something essentially impossible with the dressed-up writing of many disciplines!
I believe the first part of my own acknowledgements began with: “There are only a few interesting things in this thesis. Out of all of them, this section might be the most important.”
To this day, this is still true!