Penyngton

Here's a link you can use to explain the topic to people who make this mistake: https://www.cais-soas.com/CAIS/Languages/persian_not_farsi.h...

I believe that the 'official answer' is that SageMath is the best of the free software CASs. I personally use Maxima, because it's good enough for my use cases (which involve a lot of finite sets and not many inequalities) and I've found it simpler to understand.

Obviously, Mathematica is the leader in academia generally, as far as I can tell, but I personally haven't had good experiences with it myself, but I also have a strong preference for free software, so I haven't tried to hard to get used to it.

I might be interested. How many people are you? What is your background (professionals/students/etc)? Are you planning to work in Java, C, or C++?

One of the few surviving industries we've got.

Yes and no. Maybe I should have quoted the following part of the parent's comment:

> whenever civil protection and economic freedom gives them any semblance of a choice, the number of children they get goes way down.

Which is clearly falsified by the link I posted.

I certainly agree with you that the economics of raising children is not right in western countries, but this still doesn't deter women from wanting more children than they're having, so I don't think it's at all fair to say that fewer are willingly choosing to have them.

> Having children is a horrible deal for women though

Women in every rich country have fewer children than they desire:

https://medium.com/migration-issues/how-big-is-the-fertility...

I've personally read An Introduction to Mathematical Reasoning: Numbers, Sets and Functions Paperback by Peter Eccles and Chapter Zero by Carol Schumacher and would recommend them both.

Sadly, this is not an easy thing to learn and requires a lot of work and most importantly discipline. You mustn't let yourself be at all complacent. It's very easy when doing exercises by yourself to believe that you've "got the idea" and you can "see how it works", but the key is to actually write _everything_ down, so that there is no room for handwaving at all. This is not easy to force yourself to do, but it is necessary.

I'm not quite in the situation you're looking for, because I have got an undergraduate maths degree, but due to my personal situation at the time, I wasn't a good student and I have done a lot of self-teaching in the years since then. Mainly in mathematics, but also theoretical computer science and some programming.

One thing I'd say is don't overlook books. Textbooks may seem less appealing than slides or videos, but seriously sitting and reading a maths book from cover to cover is a very powerful experience. It may take months, but you will learn something significant from doing it. And if you do that for say Thomas's Calculus and Anton's Elementary Linear Algebra, that's about half of a maths degree already.

Anyway, I'm happy to discuss these topics further with you (or anyone else reading this). I'd personally love to find a serious online community for studying mathematics, but it seems very hard to coordinate such a thing.

I've actually recently read "FUNCTIONAL PROGRAMMING: Application and Implementation", by Peter Henderson from 1980, which contains a description of the SECD machine and a compiler for a simple purely-functional Lisp dialect called Lispkit.

This <https://github.com/carld/lispkit> looks like a good repo if you want to know more, although I'm not related to it in any way.

I can't say how it compares the book by Kogge mentioned in this post since I haven't read that, but I can say I think it's an excellent book and worth reading if you're interested in this kind of thing.

I agree with you, but this is how bullies get their way.

William Shawcross's Independent Review of Prevent [0] has on page 24

> 3.46 While the products related to Islamist terrorism focus on the most serious material relating to violent Islamist ideology, mostly Islamic State and al-Qa’ida, much of the material covering Extreme Right-Wing falls well below the threshold for even non-violent extremism.

> 3.47 This material tends to deal with broader themes and often covers content that relates to narratives on social media. These products not only covered non-violent far right extremism, but also examples of centre-right debate, populism, and controversial or distasteful forms of right-leaning commentary and intolerance. Some of this material falls well short of the extremism threshold altogether.

> 3.48 I saw one RICU analysis product from 2020 on Right-Wing terrorist and extremist activity online which referenced books by mainstream British conservative commentators as “key cultural nationalist ideological texts”. The same document listed “key texts” for white nationalists as including historic works of the Western philosophic and literary canon.

Emphasis mine.

So it is certain that some mainstream books were considered signifiers of right-wing extremism by Prevent's Research Information and Communications Unit (RICU), but I haven't seen any official publication of exactly what the list contained.

[0] https://www.gov.uk/government/publications/independent-revie...

SML is probably my favourite programming language, and ML for the Working Programmer is probably my favourite programming book. It is indeed sad to think that I'll probably never use it seriously or even meet another SML programmer in my life (I met one at university in a larger city once, but it seems unlikely to happen again where I am now).

OCaml is good, though I personally dislike its complexity compared to SML ("Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away." and all that). There are also remote OCaml jobs in the world, so it's not completely impossible I could do something like that in the future.

Your own link says:

> However, a large number of epidemiologic studies (both case-control and cohort studies) in humans have found no consistent evidence that dietary acrylamide exposure is associated with the risk of any type of cancer (9, 14).

Apparently, this is a myth: https://www.cancerresearchuk.org/about-cancer/causes-of-canc...

I did that a bit more than a decade ago and everything worked except for the "picture language" section from chapter 2, but that's a very minor part of the course. There may have been an occasional definition I had to add to get code to work, but it was certainly nothing difficult.

I feel like I live in a society without a culture, let alone a counterculture.

> That can also happen in two-year long programs as well, especially in those that allow you for less than a semester from proposal to defense.

I can imagine that. My own programme had two terms with exams in January and May/June, then the dissertation from June till September and it was a nightmare!

> I can certainly perceive a scaling issue in academia. Will MOOC-style initiatives be able to solve this problem in the future?

If I were running a university, I'd definitely move the large early undergrad courses to MOOCs to free up lecturers time for more advanced/specialised courses and direct supervision of students. It seems crazy to me that there are thousands of basic algorithms, calculus, etc courses being taught across the world by people who could use their time a lot more productively teaching the more specialised knowledge they have acquired through their research.

Yes, my own MSc dissertation was very rushed and of low quality, unfortunately...

The problem is that universities, at least in my experience here in the UK, are just getting more bureaucratised and less committed to pure (non-corporate/"enterprise") research. I don't trust them to reform anything and actually improve it.

Since I haven't seen many discrete maths books, he's my list:

Beginner: NL Biggs, Discrete Mathematics, Oxford University Press

Intermediate: PJ Cameron, Combinatorics: Topics, Techniques, Algorithms, Cambridge University Press

Advanced: JH van Lint & RM Wilson, A Course in Combinatorics, Cambridge University Press

I discovered Autocode from the following passage of [0]:

> Alick Glennie (UK)

> In their contribution "Programming for high-speed digital calculating machines" (1953), the British computer scientists J. M. Bennett and Alick E. Glennie mention the name Autocode. According to Christopher S. Strachey, this compiler was used from September 1952 with the Manchester Mark 1 computer.

> Hopper published her article before Glennie. Nevertheless, Knuth considers Glennie to be the creator of the first true compiler which was actually implemented and used (see Donald E. Knuth; Luis Trabb Pardo: The early development of programming languages, in: Nicholas Constantine Metropolis; Jack Howlett; Gian-Carlo Rota (editors): A history of computing in the twentieth century, Academic Press, New York, London etc. 1980, pages 218 ff.).

[0] : https://cacm.acm.org/blogs/blog-cacm/268001-did-grace-hopper...

I've already started improving my C++ knowledge with www.learncpp.com and Stroustrup's "The C++ Programming Language (4th Edition)" because my PhD supervisor is into C++ and I've got a few projects with him coming up where I may need to read and write C++ code.

One of the projects will also involve using a supercomputer, so I think I'll have to learn some of the various ways of interacting with that (Slurm, OpenMP, MPI, CUDA, GNU Parallel? I don't really know anything about it yet). I imagine I'll mostly learn about this from dipping into various guides and manuals and playing with examples rather than a proper book or course.

Depending on your definition of `Computer Science', [2] is already the link you're looking for. Church, Turing, von Neumann, McCarthy, Shannon and many other founding fathers of Computer Science are there. In fact, from your link, I believe the only people mentioned who are not in the Mathematics Genealogy Project are Lovelace and Boole, and that's because the PhD system didn't really exist in England at the time, so a specifically Computer Science based database wouldn't help with that.

Sadly, that's not something easy to find if you're not born into one...

I'm surprised not to see any mention of satisfiability here. There are a few papers I've seen about it, but this blog post seems to give a reasonable overview:

https://codingnest.com/modern-sat-solvers-fast-neat-underuse...

Then you just need to start counting models, which is the #SAT (sharp SAT) problem:

https://en.wikipedia.org/wiki/Sharp-SAT

Using software such as:

https://github.com/marcthurley/sharpSAT

Google's Common Lisp style guide says "Ideally, use the default indentation settings provided by slime-indentation".

<https://google.github.io/styleguide/lispguide.xml>

I'm sorry that the phrase riled you. I didn't intend it to be pithy, but simply to demonstrate that it's well known that what OP is asking for doesn't exist.

> Is there a course or series of courses I can take that can build my math skill level to solve such problems with ease?

It seems to me that this question assumes that there is a "royal road" of courses that will turn him into the mathematician he wants to be, but I don't believe that's the case. The only way to get where he wants to be is through a long and difficult process. And in my comment I tried to give what advice I could, however inadequate.

There's no royal road to geometry. You just have to keep trying things and keep suffering. Sadly, I'm not familiar with that book or particularly with the topics you've mentioned, so I can't recommend specific books, but this is the basic recipe that I've used to teach myself some amount of mathematics:

Start with a book you want to read. If you get stuck, then buy another book (hopefully aimed at a lower level) on that topic and repeat the process with the new book.

Don't be afraid to read "easy" books. You should probably aim to start reading books where you look at the contents page and think you know 80-90% of the material already. I've wasted a lot of time trying to read books that were above my level. The path of least resistance is longer, but in my experience it pays off.

"Do the exercises" is good advice, but don't be too obsessive about it. Be more obsessive about regularly working on the topic, even if that means skipping exercises or jumping between books (on the same topic). You can often find the answer to an exercise in one book in a different book's presentation of the same topic, or on a website or in a paper. As long as you can integrate these discoveries into your conceptual framework of the subject, that's not cheating, it's success.

Writing things out in a lot of detail and working out examples in a lot of detail in a notebook can really help. This is like designing your own exercises and can be better than doing exercises in a book sometimes.

I personally really like Alfred Tarski's book "Introduction to Logic and to the Methodology of Deductive Sciences". The first Polish edition was written in 1936 and the English version is still available from Dover: https://store.doverpublications.com/048628462x.html