PostgreSQL requires you to define a SQL stored procedure: stackoverflow.com/questions/28149494/is-it-possible-to-create-trigger-without-execute-procedure-in-postgresql Their syntax may be standard compliant, not sure about the
EXECUTE part. Their docs: www.postgresql.org/docs/current/sql-createtrigger.htmlSQLite does not support SQL stored procedures at all, so maybe that's why they can't be standard compliant here: stackoverflow.com/questions/3335162/creating-stored-procedure-in-sqlite
SQL:1999 11.38 covers "Trigger definition". The Abstract syntax tree starts with the
CREATE TRIGGER and ends in:<triggered SQL statement> ::=
<SQL procedure statement>This is defined at 13.5 "SQL procedure statement", but that is humongous and I'm not sure what it is at all.
70,000 28x28 grayscale (1 byte per pixel) images of hand-written digits 0-9, i.e. 10 categories. 60k are considered training data, 10k are considered for test data.
This is THE "OG" computer vision dataset.
Playing with it is the de-facto computer vision hello world.
It was on this dataset that Yann LeCun made great progress with the LeNet model. Running LeNet on MNIST has to be the most classic computer vision thing ever. See e.g. activatedgeek/LeNet-5 for a minimal and modern PyTorch educational implementation.
But it is important to note that as of the 2010's, the benchmark had become too easy for many applications. It is perhaps fair to say that the next big dataset revolution of the same importance was with ImageNet.
The dataset could be downloaded from yann.lecun.com/exdb/mnist/ but as of March 2025 it was down and seems to have broken from time to time randomly, so Wayback Machine to the rescue:but doing so is kind of pointless as both files use some crazy single-file custom binary format to store all images and labels. OMG!
wget \
https://web.archive.org/web/20120828222752/http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz \
https://web.archive.org/web/20120828182504/http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz \
https://web.archive.org/web/20240323235739/http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz \
https://web.archive.org/web/20240328174015/http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
OK-ish data explorer: knowyourdata-tfds.withgoogle.com/#tab=STATS&dataset=mnist
In the 2010's/2020's, many people got excited about getting children in to electronics with cheap devboards, notably with Raspberry Pi and Arduino.
While there is some potential in that, Ciro Santilli always felt that this is very difficult to do, while also keeping his sacred principle of backward design in mind.
The reason for this is that "everyone" already has much more powerful computers at hand: their laptops/desktops and even mobile phones as of the 2020s. Except perhaps if you are thing specifically about poor countries.
Therefore, the advantage using such devboards for doing something that could useful must come from either:
- their low cost. This would be an important consideration if you were to mass produce your product, but that is not going to be the case for learners, at least initially.
- their portability, and closely linked their ability to act as sensors
- their ability to act as actuators, which is often missing from regular computers
- them having hardware accelerators that are not normally present in regular computers, e.g. FPGAs or AI accelerators. And then the demo project must demonstrate that the project is able to do something significantly faster/cheaper on the devboard than on a desktop computer.
Whenever Ciro Santilli learns about molecular biology, he can't help but to feel that it feels like programming, and notably systems programming and computer hardware design.
In some sense, the comparison is obvious: DNA is clearly a programmable medium like any assembly language, but still, systems programming did give Ciro some further feelings.
- The most important analogy perhaps is observability, or more precisely the lack of it. For the computer, this is described at: The lower level you go into a computer, the harder it is to observe things.And then, when Ciro started learning a bit about biology techniques, he started to feel the exact same thing.For example when he played with E. Coli Whole Cell Model by Covert Lab, the main thing Ciro felt was: it is going to be hard to verify any of this data, because it is hard/impossible to know the concentration of each element in a cell as a function of time.More generally of course, this is exactly why making any biology discovery is so hard: we can't easily see what's going on inside the cell, and have to resort to indirect ways of doing so..This exact idea was highlighted by I should have loved biology by James Somers:
For a computer scientist, a biologist's methods can seem insane; the trouble comes from the fact that cells are too small, too numerous, too complex to analyze the way a programmer would, say in a step-by-step debugger.
And then just like in software, some of the methods biologists use to overcome the lack of visibility have direct software analogues:- add instrumentation to cells, e.g. GFP tagging comes to mind
- emulation, e.g. E. Coli Whole Cell Model by Covert Lab
- The boot process is another one. E.g. in x86 the way that you start in 16-bit mode, largely compatible into the 70's, then move to 32-bit and finally 64, does feel a lot the way a earlier stages of embryo development looks more and more like more ancient animals.
Ciro likes to think that maybe that is why a hardcore systems programmer like Bert Hubert got into molecular biology.
Some other people who mention similar things:
- I should have loved biology by James Somers highlights the computer abstraction layer analogy between the two:
The elliptic curve group of all elliptic curve over the rational numbers is always a finitely generated group.
The number of points may be either finite or infinite. But when infinite, it is still a finitely generated group.
For this reason, the rank of an elliptic curve over the rational numbers is always defined.
TODO example.
See form.
Analogous to a linear form, a multilinear form is a Multilinear map where the image is the underlying field of the vector space, e.g. .
www.biorxiv.org/content/10.1101/2022.09.19.508583v1.fullIt is also interesting to see how they are interested in co-culture with HeLa cells, presumably to enable infectious bacterial disease studies.
CVI-syn3B strains differ from JCVI-syn3.0 by the presence of 19 additional non-essential genes that result in a more easily manipulated cell. JCVI-syn3B additionally includes a dual loxP landing pad that enables easy Cre recombinase mediated insertion of genes
At biology.indiana.edu/news-events/news/2023/lennon-minimal-cells.html (2023) they let it re-evove to it it would regain some fitness, and it did.
My brother, Richard: How he came to be so smart interview with Joan Feynman by Web of Stories (2019)
Source. Ah, shame to see Joan so old. Some good stories. The tiles game thing was not mentioned in Genius: Richard Feynman and Modern Physics by James Gleick (1994) I think. How to use an Oxford Nanopore MinION to extract DNA from river water and determine which bacteria live in it External links to this page Updated 2025-05-21 +Created 1970-01-01
- 2021-03-25: Oxford Nanopore Technologies retweeted this article, that's awesome!
- 2021: hackaday.com/author/wd5gnr1/ "SEQUENCING DNA FOR METAGENOMICS" by Al Williams (2021). This came after Ciro Santilli self promoted at: stackoverflow.blog/2021/02/03/sequencing-your-dna-with-a-usb-dongle-and-open-source-code/#comment-1411921
Funding:
The Story of John Bardeen at the University of Illinois (2010)
Source. - youtu.be/OyV8qSwGUHU?t=976 of when Bardeen demoed the transistor in class is particularly memorable
- youtu.be/OyV8qSwGUHU?t=1105 some of his golf colleagues didn't know he had won a Nobel Prize!
Maximum current that can flow across a Josephson junction, as can be directly seen from the Josephson equations.
Is a fixed characteristic value of the physical construction of the junction.
Discrete quantum effect observed in superconductors with a small insulating layer, a device known as a Josephson junction.
To understand the behaviour effect, it is important to look at the Josephson equations consider the following Josephson effect regimes separately:
Bibliography:
- www.youtube.com/watch?v=cnZ6exn2CkE "Superconductivity: Professor Brian Josephson". Several random excerpts from Cambridge people talking about the Josephson effect
Two equations derived from first principles by Brian Josephson that characterize the device, somewhat like an I-V curve:where:
- : Josephson current
- : the Josephson phase, a function defined by the second equation plus initial conditions
- : input voltage of the system
- : current across the junction, determined by the input voltage
Note how these equations are not a typical I-V curve, as they are not an instantaneous dependency between voltage and current: the history of the voltage matters! Or in other words, the system has an internal state, represented by the Josephson phase at a given point in time.
To understand them better, it is important to look at some important cases separately:
- AC Josephson effect: V is a fixed DC voltage
Transaction retries are inevitable, as some sQL isolation levels
Doesn't seem to have any simple built-in mechanism?
Unlisted articles are being shown, click here to show only listed articles.