This was one of the profile pictures that Ciro Santilli used as part of his campaign.
Ciro later went on to prefer the "unmodified" Xi Jinping photo cover of some edition Xi Jinping Though, which also reminds Ciro very much of religious devotional pictures, e.g. those of Li Hongzhi.
Ciro understood that the best propaganda against a dictatorial enemy is recontextualized unmodified propaganda produced by the enemy itself. Their propaganda speaks for itself
Like most people in the West, Ciro has always been for political freedom of speech, and therefore against the Chinese government's policies.
Scatter plot of Stack Overflow user reputation vs profile views in March 2019 with Ciro Santilli marked as A
. The A is towards the top right corner.
Ciro feels that the view count started increasing more slowly since 2020 compared to his reputation, likely every single Chinese user has already viewed the profile.
When this kind of non-financial data is embedded into a blockchain some people called an "inscription". The study or "early" inscriptions had been called a form of "archaeology"[ref][ref]. Since this is a collection of archeological artifacts, we call it a "museum"!
One really cool thing about inscriptions is that because blockchains are huge Merkle trees, it is impossible to censor any one inscription without censoring the entire blockchain. It is also really cool to see people treating the Bitcoin blockchain basically like a global social media feed!
Starting on December 2022, ordinal ruleset inscriptions took the bitcoin blockchain by storm, and dwarfed in volume all other previous inscriptions. This museum focuses mostly on non-ordinals, though certain specific ordinal topics that especially interest he curators may be covered, e.g. Ordinal ruleset inscription porn and ordinal ASCII art inscription.
Hidden surprises in the Bitcoin blockchain by Ken Shirriff (2014) is a mandatory precursor to this article and contains the most interesting examples of the time. But much happened since Ken's article which we try to cover. This analysis is also a bit more data oriented through our usage of scripting.
to prevent their publication ideas from being stolen. Who would steal publication ideas with public proof in an issue tracker without crediting original authors? Academia is broken. Academia should be the most open form of knowledge sharing. But instead we get this silly competition for publication points.
to prevent noise from non-collaborators. But they only get like 2 issues as year on such a meganiche subject... Did you know that you can ignore people, and even block them if they are particularly annoying? Much more likely is that no one will every hear about your project and that it will die with its last graduate student slave.
The project has a partial dependency on the proprietaryoptimization softwareCPLEX which is freeware, for students, not sure what it is used for exactly, from the comment in the requirements.txt the dependency is only partial.
This project makes Ciro Santilli think of the E. Coli as an optimization problem. Given such external nutrient/temperature condition, which DNA sequence makes the cell grow the fastest? Balancing metabolites feels like designing a Factorio speedrun.
Everything in this section refers to version 7e4cc9e57de76752df0f4e32eca95fb653ea64e4, the code drop from November 2020, and was tested on Ubuntu 21.04 with a docker install of docker.pkg.github.com/covertlab/wholecellecolirelease/wcm-full with image id 502c3e604265, unless otherwise noted.
This article gives an idea of how this kind of biological experiment feels like to a software engineer who has never done any biology like Ciro Santilli.
a concrete and precise hello world operation can be understood in 30 minutes.
Although there are several types of quantum computer under development, there exists a single high level model that represents what most of those computers can do, and we are going to explain that model here. This model is the is the digital quantum computer model, which uses a quantum circuit, that is made up of many quantum gates.
Beyond that basic model, programmers only may have to consider the imperfections of their hardware, but the starting point will almost always be this basic model, and tooling that automates mapping the high level model to real hardware considering those imperfections (i.e. quantum compilers) is already getting better and better.
The way quantum programmers think about a quantum computer in order to program can be described as follows:
the input of a N qubit quantum computer is a vector of dimension N containing classic bits 0 and 1
the quantum program, also known as circuit, is a 2n×2nunitary matrix of complex numbersQ∈C2n×C2n that operates on the input to generate the output
the output of a N qubit computer is also a vector of dimension N containing classic bits 0 and 1
Each time you do this, you are literally conducting a physical experiment of the specific physical implementation of the computer:
setup your physical system to represent the classical 0/1 inputs
let the state evolve for long enough
measure the classical output back out
and each run as the above can is simply called "an experiment" or "a measurement".
The output comes out "instantly" in the sense that it is physically impossible to observe any intermediate state of the system, i.e. there are no clocks like in classical computers, further discussion at: quantum circuits vs classical circuits. Setting up, running the experiment and taking the does take some time however, and this is important because you have to run the same experiment multiple times because results are probabilistic as mentioned below.
Unlike in a classical computer, the output of a quantum computer is not deterministic however.
But the each output is not equally likely either, otherwise the computer would be useless except as random number generator!
This is because the probabilities of each output for a given input depends on the program (unitary matrix) it went through.
Therefore, what we have to do is to design the quantum circuit in a way that the right or better answers will come out more likely than the bad answers.
We then calculate the error bound for our circuit based on its design, and then determine how many times we have to run the experiment to reach the desired accuracy.
The probability of each output of a quantum computer is derived from the input and the circuit as follows.
First we take the classic input vector of dimension N of 0's and 1's and convert it to a "quantum state vector" qin of dimension 2n:
We are after all going to multiply it by the program matrix, as you would expect, and that has dimension 2n×2n!
Note that this initial transformation also transforms the discrete zeroes and ones into complex numbers.
For example, in a 3 qubit computer, the quantum state vector has dimension 23=8 and the following shows all 8 possible conversions from the classic input to the quantum state vector:
if the classic input is 000, then we are certain that all three bits are 0.
Therefore, the probability of all three 0's is 1.0, and all other possible combinations have 0 probability.
if the classic input is 001, then we are certain that bit one and two are 0, and bit three is 1. The probability of that is 1.0, and all others are zero.
and so on
Now that we finally have our quantum state vector, we just multiply it by the unitary matrixQ of the quantum circuit, and obtain the 2n dimensional output quantum state vector qout:
And at long last, the probability of each classical outcome of the measurement is proportional to the square of the length of each entry in the quantum vector, analogously to what is done in the Schrödinger equation.
For example, suppose that the 3 qubit output were:
This interpretation of the quantum state vector clarifies a few things:
the input quantum state is just a simple state where we are certain of the value of each classic input bit
the matrix has to be unitary because the total probability of all possible outcomes must be 1.0
This is true for the input matrix, and unitary matrices have the probability of maintaining that property after multiplication.
Unitary matrices are a bit analogous to self-adjoint operators in general quantum mechanics (self-adjoint in finite dimensions implies is stronger)
This also allows us to understand intuitively why quantum computers may be capable of accelerating certain algorithms exponentially: that is because the quantum computer is able to quickly do an unitary matrix multiplication of a humongous 2N sized matrix.
If we are able to encode our algorithm in that matrix multiplication, considering the probabilistic interpretation of the output, then we stand a chance of getting that speedup.
As we could see, this model is was simple to understand, being only marginally more complex than that of a classical computer, see also: quantumcomputing.stackexchange.com/questions/6639/is-my-background-sufficient-to-start-quantum-computing/14317#14317 The situation of quantum computers today in the 2020's is somewhat analogous to that of the early days of classical circuits and computers in the 1950's and 1960's, before CPU came along and software ate the world. Even though the exact physics of a classical computer might be hard to understand and vary across different types of integrated circuits, those early hardware pioneers (and to this day modern CPU designers), can usefully view circuits from a higher level point of view, thinking only about concepts such as:
as modelled at the register transfer level, and only in a separate compilation step translated into actual chips. This high level understanding of how a classical computer works is what we can call "the programmer's model of a classical computer". So we are now going to describe the quantum analogue of it.
This tutorial explains the very basics of how paging works, with focus on x86, although most high level concepts will also apply to other instruction set architectures, e.g. ARM.
The goals are to:
demonstrate minimal concrete simplified paging examples that will be useful to those learning paging for the first time
