The first space shuttle prototype (Enterprise) started construction in 1974. The first shuttle launched in 1981. To the best of my knowledge, there were no major upgrades to the design over its career, save avionics. So even though the space shuttle was “serious space development,” it’s been a long time since a new human rated vehicle has been designed.
It would be great if that tool existed, but it doesn’t seem to right now. I can appreciate the instinct to improve packaging, but from an occasional Python developer’s perspective things are getting worse. I published a few packages before the pandemic that had compiled extensions. I tried to do the same at my new job and got so lost in the new tools, I eventually just gave up.
One of Python’s great strengths is the belief there should be one, obvious right way to things. This lack of unity in the packing environment is ruining my zen.
As someone who works in numerical optimization, this is a dirty little secret of our profession. The optimization algorithms in the literature are great at finding local minima, but often are very sensitive to the initialization as to how small the objective is. Good heuristics for initialization are thus critical for finding a good (small objective) minimizer. Sometimes this gets to the point where the local optimization algorithm does a trivial refinement of the heuristic’s solution.
Once I was working on a government funded small business grant trying to do something that was mathematically impossible (and literally the first example of intractability in textbooks of the field). The only goal was for the company to collect overhead.
Many of the algorithms in BLAS are not easily parallelized. For example, a QR factorization an inherently sequential algorithm. Optimizing BLAS performance comes mainly from rewriting the sequential algorithm into larger blocks so as to efficiently access memory. As Jim Demmel is fond of saying, floating point optimizations are cheap, memory movement is expensive.
This is a nice exposition, but it would have been more clear if they laid out the difference between inertial and gravitational masses. So far, these two varieties of mass are equivalent in all our observations, but they need not be so. Negative inertial mass is pretty weird, as the examples illustrate; but negative gravitational mass (i.e., normal and negative mass repel according to inverse square law) would be something exciting to observe.
For reference, a typical adjunct (non tenure track instructor) will make $5000 per course per semester with no benefits. Tenured and tenure track faculty in STEM are typically paid around $60-120k per year with benefits and will teach, at most, six courses a year.
The job of a tenured STEM faculty member is not the same as contract lecturer.
Tenured faculty do research and run the university as well as teach. At a major research school a STEM faculty member will often raise hundreds of thousands of dollars a year in outside research funds, which will pay upwards of ten graduate students and the other running costs of a research lab. They will do and publish new research, usually while training graduate students, review the work of others, run conferences, sit on hiring committees,, take turns being Director of this or that (ie. management positions).
Many STEM professors raise more external money than their salary costs. They are dollar positive to the school before they teach anything. And they teach too.
Yes, it has a salary better than contract teaching.
research, rarely. running their department, designing courses and materials for other faculty to teach with, as well as a lot of organizational housekeeping chores (of the sort handled by non-faculty staff) at 4 year schools? constantly.
It's a bit more than six courses a year at community colleges and teaching-oriented 4-year colleges (though you're still right that adjuncts are much cheaper). In California, most full-time faculty at community colleges have a 15 credit per semester teaching obligation, which works out to ten 3-credit courses a year. Some do get credit in lieu of a course for taking on other responsibilities (dept chair, director of a program, etc.), but that's the baseline workload. Even the Cal State system, which is made up of 4-year schools, has a baseline teaching load of eight classes a year ("4/4").
I've adapted the same work flow as well. It's really nice to have one script that generates data (often taking a few minutes or hours) and then another (in TeX) that configures display. That way when I recycle plots from papers into slides for a talk, I can reconfigure these easily.
