Except math is almost never actually done that way in practice. Euclid was wonderful, but almost all modern math does not work that strictly (and Euclid really should have been more careful with the parallel postulate -- there's "more than one thing at a time" involved there). Yes, proofs are careful and detailed, but so is, say, technical writing in English. Except for a few cases (check out metamath.org, or Homotopy Type Theory) almost no-one actually pedantically lays out all the formal steps introducing "only one concept at a time".

I don't think you need math even to be a great programmer. I do think a lot of great programmers are people who think in mathematical terms and thus benefit from mathematics. But I also believe you can be a great programmer and not be the sort of person who thinks in those terms. I expect the latter is harder, but then I'm a mathematician so I'm more than read to accept that I have some bias in this topic.

Math is a language. Just because you can frame things in that language doesn't mean that that language is necessary. Recipes are often in English. English is sequencing (words are a serial stream after all). That doesn't mean English is necessary for programming (there seem to many competent non-english speaking programmers as far as I can tell).

Disclaimer: I am a professional research mathematician; I do understand math just fine.

Of course there was a time when college education was supposed to be education and not just vocational training.

The other part of the problem is that math is so diverse. There's calculus and engineering math with all kinds of techniques for solving this or that PDE; there's set theoretic foundations; there's graph theory and design theory and combinatorics and a slew of other discrete math topics; there's topology and metric spaces and various abstractions for continuity; there's linear algebra and all the finer points of matrices and matrix decompositions and tensors and on into Hilbert spaces and other infinite dimensional things; there's category theory and stacks and topos theory and other esoterica of abstraction. On and on, and all very different and I can't even pretend to have anything but cursory knowledge of most of them ... and I have a Ph.D. in math and work for a research institute trying to stay abreast of a decent range of topics. The people who actually study these topics in depth are all called "mathematicians", but if you're an algebraic geometer then sure, you're probably familiar with category theory and homological algebra; if you do design theory and graph theory then those seem like the most useful subject available.

Calculus is perhaps not the best measure however. Depending on where you go in the programming field calculus is likely less useful than some decent depth of knowledge in graph theory, abstract algebra, category theory, or combinatorics and optimization. I imagine a number of people would chime in with statistics, but to do statistics right you need calculus (which is an example of one of the directions where calculus can be useful for programming).

Of course the reality is that you don't need any of those subjects. Those subjects can, however, be very useful to you as a programmer. So yes you can certainly be a programmer, and even a very successful and productive one without any knowledge of calculus, or graph theory say. On the other hand, there may well be times when graph theory, or calculus, or statistics could prove very useful. what it comes down to is whether you are inclined to think that way -- and if so it can be a benefit; if not it won't be the way you think about the problem anyway.

You may want to look into pandas as a middle ground. It's great for sucking in tabular or csv data and then applying statistical analysis tools to it. It has a native "dataframe" object which is similar to database tables, and has efficient merge, join, and groupby semantics. If you have a ton of data then a database and SQL is the right answer, but for a decent range of use cases in between pandas is extremely powerful and effective.

Python has the default implementation CPython which compiles python to an interpreted bytecode; there's also Jython which compiles to JVM, and IronPython which compiles Microsoft's CLR. There's also Cython (which requires extra annotations) which compiles to C and thence to machine code, and numba which does compilation to LLVM. Finally there's Pypy which is a python JIT compiler/interpreter written in a restricted subset of Python.

This is interesting. I was just discussing this with my friend last night, and proposed this exact solution. However, it's still a reactive solution. It will detect that you may be the victim of a stingray attack, but it won't stop your phone from connecting in the first place. But there is another potential solution, I just don't have enough experience developing android roms to say how it would have to be implemented. The idea is this: maintain a database of all know cell towers (your link to OpenCellID would do nicely, they offer their DB for download). Using a rooted or fully custom ROM, such as cyanogenmod, have the phone compare any new tower to the database prior to connecting. If it doesn't exist in the database, red flag it and don't connect.

The question is, can this be done on the OS level, or does it have to happen on the driver level? If it can be done at the OS level, easy peasy, just modify the code to establish tower connections to include this check. If it has to happen on a driver level, it gets trickier. Most phones use proprietary binary drivers for their cell radios, so they couldn't be readily modified. However, it may be possible to load an intermediate driver, which in turn loads the proprietary driver. If it could be determined which driver calls involved connecting to a new tower, you could just pass through everything else, and only pass through calls to the tower connect function if they passed your database lookup. Trickier, but doable. Because really, you want to avoid connecting to these things at all. Nice though it is to see you're being attacked, it's better to stop the attack before it starts.

Do you live in the area? I do. The cabs here can suck. A cab in the middle of summer that smells of old smoke, with no AC, in 95F and 10% humidity in the middle of summer is a not good thing.

That said, there are some amazing cabs too - but it's a guessing game. I've been (illegally) kicked out of cabs because my destination was too far or too "dangerous". Cabs get very picky during peak hours on who they pick up and in what neighborhoods they pick up in. Only this year do DC cabs take credit cards reliably, and only because of much-hated and delayed regulation changes based on Uber entering the game.

Do I think Uber/Lyft/etc. need to join in to regulations? Sure. That's a good direction. But sorry D/M/V cab industry, maybe you should have upped your game a long time ago. I have much respect for a good cabbie, but not much for the industry.

A government's just a body of people. Usually, notably ungoverned.

Episodes I through III are only on here so our wise poll overlords can track down anyone who clicks on them, freeze them in carbonite, and ship them off to re-education camps.

Rigorous testing is helpful, but I think it's the wrong approach. The problem here was lack of requirements and/or rigorous design. In the physical engineering disciplines, much effort is done to think about failure modes of designs before they are implemented. In software, for some reason, the lack of pre-implementation design and analysis is endemic. This leads to things like Heartbleed - not language choice, not tools, not lack of static testing.

I would also go as far as saying if you're relying on testing to see if your code is correct (rather than verify your expectations), you're already SOL because testing itself is meaningless if you don't know the things you have to test - which means up-front design and analysis.

That said, tools and such can help mitigate issues associated with lack of design, but the problem is more fundamental than a "coding error."

They are memes in the sense that they are specifically finding words and phrases that are frequently inherited by papers (where "descendant" is determined by citation links), and rarely appear spontaneously (i.e. without appearing in any of the papers cites by a paper). An important feature is that their method used zero linguistic information, didn't bother with pruning out stopwords, or indeed, do any preprocessing other than simple tokenisation by whitespace and punctuation. Managing to come out with nouns and complex phrases under such conditions is actually very impressive. You should try actually reading the paper.