My relatives proof-read my master thesis, I owe them quite a bit for that. That being said, they still ask me “what do you actually do?”
To be fair, I ask myself the same.
I made a geologic map of this one area.
Luckily you have a couple hard copies of your journal paper and an inked up copy of your thesis after reviewer number 2 decided to no longer like his own suggestions on how to graph the mass accuracy
I hate this because it’s true.
I recorded conversations held by non-native German speakers who learned mostly in classes and compared their particle (well, just, right, etc.) usage with that of non-native speakers who learned mostly without instruction. Then I compared those to native speakers. Now I just have to make the case for better particle instruction in German classes. It should be easy because I’ve already illustrated that particle usage is important for communication and affects the perceived language skill of the user, but ugh, writing.
Hömma! Also ja, gell, das ist halt auch… doch ja schon.
Was wollte ich eigentlich sagen?
Mach’s ruhig weiter! Ich finde es eben noch schwer, sie richtig einzusetzen.
Ich hab das Thema gewählt, weil jemand mir nach vier Jahren in Deutschland erklärt hat, genau was „ruhig” eigentlich bedeutet (mit ruhiger Seele oder ohne ein schlechtes Gewissen zu haben, für Nichtmuttersprachler). Ich dachte, es war etwas wie “ich sage niemandem, es bleibt unser kleines Geheimnis.” Aber denk mal an Situationen in denen man das sagt- ich glaube, ich hätte nie bemerkt, dass es falsch war. Ich hätte es aber bestimmt falsch benutzt 😅
Ich habe tiefen Respekt vor jedem, der Deutsch als Fremdsprache lernt. Diese Sprache ist komplett kaputt, mehr Regeln als Ausnahmen, und du brauchst m.M.n. Jahre, bis du genug “Sprachgefühl” (also, Intuition/Heuristik, wie wohl dieser Blödsinn hier konjugiert wird) hast.
Ich mache mein Master im DaF, damit ich Deutsch unterrichten kann (was ich auch schon während des Studiums mache). Du hast recht, dass man Jahre dafür braucht, welches natürlich zu Demotivation führen kann. Ich versuche, meine Studis zu beruhigen, aber man muss auch lernen, Fehler zu akzeptieren.
Ich habe das Glück, dass meine Muttersprache Englisch ist. Das hilft mit dem Sprachgefühl (das ist übrigens genau das richtige Fachwort dafür), da ich instinktiv wusste, dass es nicht “gesingt” sondern “gesungen” war, zum Beispiel. Dafür hatte ich wirklich Probleme, den Unterschied zwischen -e und -er zu hören und auszusprechen, aber das kann man einfacher trainieren.
Ich glaube, Deutsch wäre aber viel einfacher zu lernen, wenn wir es anders unterrichten würden. Es gibt zum Beispiel doch lockere Muster fürs Genus der Nomen*, aber sie sind kaum geforscht, weil Muttersprachler sie weder brauchen noch darauf aufpassen. Nichtmuttersprachler wiederum wissen meistens nicht, dass es solche gibt. Ich war schon im Master, bevor eine Lehrperson das zugegeben hat- vorher haben alle Deutschlehrer immer gesagt, dass es nur Muster für ein paar Suffixe gibt und alles sonst zufällig ist.
Ich wollte eigentlich solche Muster für meine Masterarbeit untersuchen, aber die AG hatte keine Interesse daran. Ich glaube, ich müsste aber ein bisschen fortgeschrittener sein, dem Thema gerecht zu werden, da es noch sehr nebelig ist.
- zum Beispiel: Wörter, die auf Englisch und Deutsch mit -er enden, die aber nichts mit Verben zu tun haben, wie Finger, (Stufen-)Leiter, Butter, Schulter sind oft feminin. Stoffe sind meistens neutral, und diese Regel hat Vorrang vor der letzten- Leder ist also neutral. Stoffe die sehr konkret sind, wie Stein und Stahl sind wiederum maskulin. Ich würde das so gerne wirklich untersuchen, aber niemand findet es so interessant wie ich.
Als Muttersprachler hab ich auch nie drüber nachgedacht, insbesondere was dieses ganze Sprachgefühl angeht. Mir ist erst durch Freunde, die Deutsch lernen, aufgefallen, wie sehr der Deutschunterricht darauf abzielt, Leuten, die schon 95% der Sprache passiv aufgeschnappt haben, noch das letzte Stück beizubringen. Diese ganze Sprachgefühl-Sache ist für jemanden, der mit Deutsch aufgewachsen ist, natürlich total sinnig, weil man die richtige Wendung ja schon 1000 mal gehört hat. Für DaF natürlich kompletter Schwachsinn, woher sollen die Sprachgefühl für eine neue Sprache haben. Dafür hab ich deutsche Grammatik “formal” erst durch Lateinunterricht verstanden :D
Jetzt will ich es wissen: Hast du gute Resourcen zu DaF und wie man das am wissenschaftlich sinnvollsten beibringt?
Die habe ich, aber es hängt vom Ziel ab. Wenn man die ganzen Kleinigkeiten wirklich verstehen will, sind die Duden Grammatik und www.dwds.de nicht zu unterschätzen, aber dafür muss man Deutsch auf einem gewissen Niveau (~B2-C1) schon können. Um soweit zu kommen, sollte man möglichst viel Input*, Zeit solches zu bearbeiten, und durchdachte Korrektur (jeder falscher Artikel muss nicht korrigiert werden aber Fehler, die die Kommunikation verhindern sollten erklärt werden, solange die Situation das erlaubt) kriegen.
Kurz gesagt ist mein Rat auch nicht so eigenartig. Ich glaube wir legen den Fokus zu scharf auf Auswendiglernen, aber das ist eigentlich nur relevant, wenn man fehlerfrei kommunizieren will. Es ist eine Tatsache, dass viele Menschen kein perfektes, sondern ausreichendes Deutsch können wollen, und das ist okay.
*Inputquellen, die Lernmotivation erwecken können und die ich nie als Lernende in einem Deutschkurs gesehen habe: Rammstein (nicht wirklich meins, aber etwa ein drittel der deutschlernenden sind nach meiner Erfahrung durch Rammstein zu Deutsch gekommen); gemischtes Hack, oÄ; synchronisierte Serien, die man schon kennt; Tabletopspiele sowie MtG oder DnD; und moderne Jugendliteratur (ich war ein bisschen zu alt damals für die Percy Jackson Bücher auf Englisch, aber sie waren perfekt für mein Deutschniveau und noch interessant als Erwachsene - natürlich wäre etwas unübersetztes wie Tintenwelt noch besser)- dabei ist die Regel, dass man zwischen 2-5 Wörter pro Seite nachschlagen müssen sollte.
Can’t even explain it to myself.
Let’s hear you try.
Goddammit now I want to talk about wavelets and my research 😆
By the power of being a mod, I command you to spill the beans. 🫘
Okay here are the 🫘:
Wavelets:
So the best way to begin explaining wavelets is through analogy to music. (I’m cheating a bit since this explanation is alluded to in the article 😆)
It is a nontrivial practical fact that you can express any reasonable sound as a sum of sine waves. Yes, by combining enough sine waves (which individually “move” for all time) in just the right weights, you can come up with “any” sound you want (edit: including sounds that “start” and “end”. Isn’t that wild?). And then, it turns out that if you give me just the weights, I can give you back the sound itself. And as a final physical fact, it turns out that we hear the weights of any given sound, averaged over some finite window of time (more on this window in a minute). Hence why we can pick out instruments from a band. And lastly, some phenomenon are easier to analyze by looking at the weights; music is an excellent example. In fact, when I mix music in my rapidly diminishing free time, I am often staring at a graph of the weights and seeing this these weights add together and make the instruments work together.
Formally, we use one of the Fourier transform frameworks. Each weight is associated to one unique sine with a given frequency. The size of the weight is called the frequency response at that frequency.
Now for many, many purposes, breaking up a signal in terms of sines is a perfectly appropriate choice. However, what you lose when you choose to look at just the weights is all timing information. (This is why I included the detail about the window in how you hear stuff. If you heard all frequencies over all time with no window, you would not be able to perceive rhythm.) The solution in music often is to simply impose a window on the signal and slide it as the play head moves.
However, we must now leave the realm of music to talk about wavelets in a domain where they are typically used. Now imagine you want to apply all your intuition about music [more accurately, theory of sound, not music theory] to seismic signals. Well… unfortunately, we really do care about the timing of these signals. So instead of ditching all the magical techniques of linear algebra and transform analysis, we can pick a new set of waves and decompose in terms of those. I.e., we use a transform “midway” between the Fourier transform and the identity transform (doing nothing, just working with the raw signal).
One way to do this is to start with a wavelet: any waveform with zero average and finite “length”. Then, you take this mother wavelet, and you create child wavelets by stretching and/or shifting the mother wavelet. Then, you break up your signals in terms of the wavelets. (I think you pick wavelets based on what you want to find. For example, if you want to find sharp changes, you can pick a Haar wavelet, which is basically a family of rectangles. And then, you can pick wavelets based on their statistics so that the variances and higher order statistics vanish.)
My favorite book on Wavelets, and one of my personal favorite books, is A Wavelet Tour of Signal Processing: The Sparse Way by Mallat. It’s a bit mathematically challenging, but it’s such a fun read. One of the few books I actually own in print. And it’s one those cool fields in math where you basically just start with like pure math and end up with some incredibly practical results and algorithms.
Research:
My background is in control theory. I work on analyzing dynamical systems, specifically large-scale, complicated (typically people use the word “complex”, but I really mean complicated, because all the systems I work on evolve in real spaces) systems that evolve in time according to differential equations (e.g. electronic circuits, mechanical systems, power systems) or difference equations (e.g. sampled versions of the above). The goal of my research is to make just enough assumptions and prove it using calculus so future generations don’t have to do so much calculus…because you have to do so much calculus that not even a supercomputer can solve it.
PM me for more details since I’m not quite ready to dox myself 😆
If I’m understanding your comment correctly, wavelets are a kind of discrete and/or finite quantization of the “full” infinite Fourier transform, by way of using more complex “basis vectors” than pure sine waves?
Very cool and thanks for the reading recommendation!
If I’m understanding your comment correctly, wavelets are a kind of discrete and/or finite quantization of the “full” infinite Fourier transform, by way of using more complex “basis vectors” than pure sine waves?
The second part is basically correct, but the first part needs a little bit of explanation.
So depending on what you need to do, you can actually use continuous-time (or continuous-space) wavelet transforms, or discrete-time (or discrete-space) wavelet transforms. The continuous-time wavelet transform is, practically, “just as exact” as the continuous-time Fourier transform. So instead of being “better” or “lesser” than the Fourier transform, it’s really a “different perspective” on the same space of signals by choosing a different set of “basis” vectors [1].
Also, wavelets often are “more complicated” than sine waves, but not necessarily. In fact, one of the first wavelets discovered was this, the Haar wavelet:
To be completely clear: this waveform is defined for any real number; it’s not sampled, and it is not a quantized version of some “better” wave! It is just 1 for any inputs between 0 and 1/2, -1 for any inputs between 1/2 and 1, and 0 everywhere else [2]. Although this wavelet happens to have a finite range (so not even countably infinite, you get {-1,0,1} and you don’t get upset), if you slap enough of these things together (possibly infinity of them), you can get back any “reasonable” waveform, where “reasonable” is precisely defined in Mallat’s book (it’s L2(R) if you’ve been exposed to Lp spaces).
Hope you enjoy the book as much as I have!
[1] “Basis” is in quotes because it depends on what you mean by “basis”. Typically, a “basis” in linear algebra means that you need to be able to exactly recover any element in the vector space with a weighted finite sum of the bases — a Hamel basis. In signal processing, in particular in Mallat’s book, we typically extend the notion of basis to allow for infinite linear combinations with limits. This means that we need to choose a topology, which is absolutely a reasonable requirement in signal processing, but not necessarily in “pure” linear algebra. I believe that the definition for “(orthonormal) basis” in Mallat’s book (in Appendix A) is called a(n orthonormal) Schauder basis in other parts of applied math.
By contrast, “vectors” is not in quotes above because, using the “abstract” definition of a vector space, the basis elements are indeed vectors, i.e. members of a space that you “cannot leave” by scaling or adding finite numbers of the basis elements.
Lastly, “signals” and “vectors” are mostly interchangeable within the signal processing discipline. In signal processing, we typically assume that signals have been given an inner product (“correlation”, “dot product”), therefore a norm (“length”) from the inner product, and therefore a topology (“abstract geometry”) from the norm. I.e., “signals” in signal processing usually have more structure than “vectors” in applied math. Waveform is not a mathematically precise term; here I just mean the plot of a signal.
[2] The values of the Haar wavelet at exactly {0,1/2,1} are indicate by the filled-in blue circles in the plot. However, since continuous wavelet transforms are integral transforms, the values at the points {0,1/2,1} can be changed to whatever you want as long as it’s finite. Rigorously, the Lebesgue integrals in the wavelet transform definitions are “blind to” a “small” (measure zero) set like {0,1/2,1}. From a signal processing perspective, changing the signal only on “small” sets like {0,1/2,1} is not enough to change the signal energy.
I think there is an “external” reason why the choice of values at {0,1/2,1} given in the plot makes sense. Mathematically, the choice makes the Haar mother wavelet right-continuous and upper semi-continuous, but I can’t remember off the top of my head why this is helpful for applications.
Another book added to the list… I may never get to the Silmarillion. War and Peace certain is never happening.
Good luck with your research. May you find more answers than questions.
Oh, please give me the opportunity. I love boring the shit out of people.
Do it please!
I mixed a harmless salt in water (called a tracer, because it’s easy to detect), and injected it into the ground. Then i used a huge pneumatic drill with a special geophysics tool (measuring resistivity) to chase down where the salt was moving before it diluted too much to find. Its called a Tracer Test, and it’s pretty common for characterizing aquifers. What made my test unique was proving the usefulness of a geophysics tool to chase down the changes in geochemistry.
Faster, more responsive than installing a network of monitoring wells ahead of time.
I’ve now told you enough that i may have absolutely given my identity away. Grad level science is a much smaller world than you might think.
That’s fucking cool!
I set stuff on fire and they pay me for it
Are you in New Mexico, by chance?
Naa, lot of my buds went there though. Whole lot of empty space to blow stuff up
My buddy described his engineering research job similarly lmao. He blows stuff up in the desert.
This is what my uncle and auntie do! I think it more involves chemicals and long time period trials, but it is at Los Alamos! Fun times!
