To be clear, Rowan is not a good boy. She’s a good grrl.
The rowan, or mountain ash (Sorbus aucuparia), of the rose family — much beloved by the Ents and a favorite of
Quickbeam, of the Fangorn Forest.
Rowan excels in 360 spin zoomies and dock diving into water. That, and frog catch/release.
photo of tri-color Australian Shepherd looking to side of camera with blue sky in upper background
Rowan… intrepid woof, Dragon Soccer star, enthusiastic hiking companion, fearless creek wader…
#dogs
#dogsofbluesky
#AustralianShepherd
Plots of realizations of a Wiener process $X(t)$ starting at $X(0) = 0$ with (left) $\tau = 0.1$ and (right) $c \tau = 10$ for scaling factor $c = 100$, showing self-similar geometry over different time scales.
to launch into more advanced foundational topics — kinematics of the particle flux, master equations, advection and diffusion, considerations of entropy, etc. — thence to the second part of the book focused on applications.
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(ii) basic Markov processes (birth-death processes, and Wiener, Langevin and Ornstein-Uhlenbeck processes)
(iii) survival analysis (focused on particle disentrainment)
Together with Chapters 2 and 3, this fourth chapter completes the basic probabilistic (stochastic) material needed...
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eBook Preview (continued): Statistical Physics of Sediment Particle Motions and Transport
Chapter 4: Basic Stochastic Processes
Highlights of this chapter include:
(i) counting processes (Poisson, simple renewal, and additive Levy processes)
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cdn.vanderbilt.edu/t2-my/my-prd...
I have fond memories of time spent in Budapest — the people, the city, the Danube, the countryside. This is all truly wonderful news.
Here is a nice 2025 article that addresses the matter of switching together with options. I opted for Linux Mint.
3/3
www.zdnet.com/article/the-...
So this is a brief note of encouragement for those contemplating a similar migration in reaction to the continuing enshittification of computer/IT things. Linux works well. It is trustworthy and stupid-free. And there are superior open-source alternatives to MS Office stuff, Adobe stuff, etc.
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As an emeritus faculty I minimally maintain a Windows system for reasons related to legacy software and engagement with campus IT. But I have otherwise nearly completed my migration to Linux and open-source software. (I have avoided most MS software for years.)
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we will lose a special set of wild friends when the little pink elephants are gone
en.wikipedia.org/wiki/Pedicul...
“This fragile landscape—like so many across our planet—is on the brink of huge change.”
www.motherjones.com/environment/...
and if possible, contributor/editor
I have wondered if the inevitable homogenization will eventually be formally measurable using methods of information theory, for example, via the Shannon surprisal function and information entropy. Or maybe people are already examining this? I’d be surprised if not.
The author’s response to this question is absolutely brilliant!
“It is a letter that arrives every few years from the government, asking a question that is medically absurd and philosophically insulting: "Are you still disabled?"”
sightlessscribbles.com/posts/the-pa...
An Open Letter to Georgetown Students, In Response to Recent Announcements by the University about “Generative AI”
medium.com/center-on-pr...
lol they definitely hid the jelly beans
I suppose the Quanta folks expect us to know that a Dirac function centered on zero can be obtained from a Gaussian in the limit of zero variance
lol
For those interested in sediment transport, Sect 3.5 presents implications of i.i.d. random variables, the law of large numbers, a summary of the classical & generalized central limit theorems, & an explanation of the ubiquitous emergence of Gaussian behavior
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cdn.vanderbilt.edu/t2-my/my-prd...
Yes, the central limit theorem might just be as close as one can get to real mathemagic! Indeed, here is the opening sentence of material on the CLT that I recently posted (the link appears in 2/2 below):
“The central limit theorem is among the crown jewels of mathematics and science.”
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Yes, that’s the issue. I’ve been trying to find a way to articulate this: to the extent that LLMs serve a purpose in education, it’s an indictment of the education system, not a praiseworthy feature of LLMs.
“The company seemed to want it both ways — to benefit from the implication of an association with prominent writers while distancing itself in the fine print.”
yes… “Feed this into your slop machine, assholes”
oh… so we’re doing this again
that was quick
wait. my dog understands L'Hôpital's rule and taking limits
“…if you’re an AI advocate and you’re not also talking seriously about the very valid reasons so many people are pissed off, you’re not really talking seriously about the subject at all. You’re in marketing.”
yup
![\textbf{2.4.3 Ensemble Averaging}
\noindent The far-reaching significance of ensemble averaging was cemented in physics by the work of \textcolor{blue}{Gibbs (1902)}. As \textcolor{blue}{Kittel (1958)} notes, ``the scheme introduced by Gibbs... [provides a formal way] to replace \textit{time averages} over a single system by \textit{ensemble} averages, which are averages at a fixed time over all systems in an ensemble.'' With specific reference to the statistical mechanics of gas particles, \textcolor{blue}{Gibbs (1902)} defined an ensemble as ``a great number of independent systems, identical in nature, but differing in phase, that is, in their condition with respect to configuration and velocity.'' For context we briefly outline below the concept of a Gibbs ensemble in relation to gas particles, and then develop this concept more fully in Chapter 9. A key point of this section is to illustrate that the concept of a Gibbs ensemble as applied in stochastic systems analysis is now much broader than what Gibbs originally envisioned, owing to the clear interpretive value of ensemble averaging. For our purposes we may define a Gibbs-like ensemble as a great number of independent but nominally identical systems, each evolving independently but in the same probabilistic manner.
In appealing to a Gibbs-like ensemble, we often are referring to a set of imagined systems that are nominally identical to a real prototype system. In this case the number of imagined systems may be arbitrarily large. In certain situations, as outlined in examples below, we can identify an ensemble consisting of real systems, although the ensemble size might be small relative to an imagined large ensemble. Nonetheless, this situation lends realism to the idea of an \textit{ensemble distribution} and ensemble averaging.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:6ui77txzzc5prhvlfcvrzqqb/bafkreibtnnu62zwtzoiub3vi3kdvsgz6v4hwxzn4tpuy4t7wwp5jzwkyqm)
\textbf{2.4.3 Ensemble Averaging} \noindent The far-reaching significance of ensemble averaging was cemented in physics by the work of \textcolor{blue}{Gibbs (1902)}. As \textcolor{blue}{Kittel (1958)} notes, ``the scheme introduced by Gibbs... [provides a formal way] to replace \textit{time averages} over a single system by \textit{ensemble} averages, which are averages at a fixed time over all systems in an ensemble.'' With specific reference to the statistical mechanics of gas particles, \textcolor{blue}{Gibbs (1902)} defined an ensemble as ``a great number of independent systems, identical in nature, but differing in phase, that is, in their condition with respect to configuration and velocity.'' For context we briefly outline below the concept of a Gibbs ensemble in relation to gas particles, and then develop this concept more fully in Chapter 9. A key point of this section is to illustrate that the concept of a Gibbs ensemble as applied in stochastic systems analysis is now much broader than what Gibbs originally envisioned, owing to the clear interpretive value of ensemble averaging. For our purposes we may define a Gibbs-like ensemble as a great number of independent but nominally identical systems, each evolving independently but in the same probabilistic manner. In appealing to a Gibbs-like ensemble, we often are referring to a set of imagined systems that are nominally identical to a real prototype system. In this case the number of imagined systems may be arbitrarily large. In certain situations, as outlined in examples below, we can identify an ensemble consisting of real systems, although the ensemble size might be small relative to an imagined large ensemble. Nonetheless, this situation lends realism to the idea of an \textit{ensemble distribution} and ensemble averaging.
I can’t help myself 😊
A teaser from Chapter 2 — the opening paragraphs of the section on ensemble averaging — for serious-minded students/aficionados of sediment transport
I got this section just right
cdn.vanderbilt.edu/t2-my/my-prd...
best AI workflow ever 😂
This is the Platonic ideal of a social media exchange, never again to be equaled or even attempted:
the doctrine and practice of manifest destiny never ended
this short thread with article links definitely merits reposting