Things may get more expensive, but if more Americans can live a middle class life even accounting for the inflation of consumer goods I think that is a good tradeoff.
There is a joke in applied mathematics that we’re like Taco Bell. We all use the same six ingredients, mixing them in different ways.
For myself, I’ve found several techniques I use over and over again. Some of this is a “when you’re a hammer, everything looks like a nail.” But fundamentally there are only a handful of ideas. One professor of mine once said the only groundbreaking result in the past few decades was compressive sensing.
When I was working with NREL back in 2017, they were thinking about coordinating water heater electricity use with a “smart grid.” Each device attached to the smart grid would measure the electricity spot price and would “store” energy to minimize cost. At the time the goal was to reduce peak load on the grid, but the same ingredients to maximize power use from intermittent power sources.
I concur. As a postdoc for many years adjacent to this work, I was similarly unimpressed.
The best part about PINNs is that since there are so many parameters to tune, you can get several papers out of the same problem. Then these researchers get more publications, hence better job prospects, and go on to promote PINNs even more. Eventually they’ll move on, but not before having sucked the air out of more promising research directions.
I taught numerical linear algebra in grad school and was really frustrated that even the applied math department took so long to build up to solving linear systems and eigen-decompsotions. The ordering of the material in the textbook is great, focusing on algorithms and decompositions.
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.
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.
Only on a short term basis. As the snow is melting, this creates a large number of small, shallow ponds where mosquitoes can breed without being eaten by fish. As the year progresses, these ponds dry up, reducing habitat to those permanent lakes which likely have fish present that predate the water part of the mosquito lifecycle. Moreover, there is a lag introduced by the predator-prey dynamics, in particular short lived species like dragonflies and mosquito hawks that predate the flying stage. Thus, it can take a few weeks for these predators to start reducing the number of mosquitoes.
Source: I’m hiking the Pacific Crest Trail and I have been swarmed by mosquitoes on several occasions. Mosquitoes were particularly bad in Yosemite in late June and Washington in early August; mosquitoes are finally letting up now that theses processes are taking place. Let me just say mosquitoes make the most of that interval before predators dominate.
https://www.nbcnews.com/news/us-news/1-person-killed-tesla-a...