Today’s dashboard snapshot (more data here). My thoughts today are about supression and containment, which are laid out in the UK’s Imperial College COVID-19 report. The TL;DR is that suppression is the only strategy that doesn’t overwhelm healthcare. Suppression is fever clinics, contact tracing, and enforced isolation, away from all others (in China, this was special isolation clinics/dorms). This has clearly worked in China (and a town in Italy), though Hong Kong and Singapore seem to be succeeding in different (more cultural?) ways. The thing that strikes me is that suppression is just putting a lid on things. The moment the lid comes off, then infections start up again? I guess we’ll see over the next few months in China.
There appear to be vaccines in (human already!) testing. Normally, there is an extensive evaluation process to see if the treatment is dangerous, but that was sidestepped during the AIDS crisis (the parallel track policy). I wonder if at risk populations (People older than 70?), will allowed to use less-tested drugs. My guess is yes, probably within a month.
Finished all the dissertation revisions and made a document that contains only those revisions. Need to make a change tableand then send (full and revisions only) to Wayne today.
Whoops! No I didn’t. After putting together the change table, I realize there are still a few things to do. Dammit!
Update SDaaS paper as per John’s edits
Phone call with Darren at 2:00
Start a google doc that has all the parts of a proposal, plus a good introduction.
Also the idea of sims came up again as ways to define, explain, train ML, and test a problem/solutions
Today’s view of the dashboard. Looking at the numbers, it’s pretty clear that China has things under control, which means that we can get an idea of what it will look like in the US on the other side. The symptomatic population was (3,111 deaths + 55,987 recovered) = 59,098. That means that the mortality rate for that (infected? symptomatic?) population (59,098/3,111) is 5.26%. The median age in China is 38.4 years. Interestingly, that’s about the same as the USA.
Working from home for the duration of the COVID-19 pandemic. It’s estimated that we are approximately 10 days behind Italy, So I’m hoping that when things start to get better there, it will be a head’s up that things might start to get better here.
Modeling immensely complex natural phenomena such as how subatomic particles interact or how atmospheric haze affects climate can take many hours on even the fastest supercomputers. Emulators, algorithms that quickly approximate these detailed simulations, offer a shortcut. Now, work posted online shows how artificial intelligence (AI) can easily produce accurate emulators that can accelerate simulations across all of science by billions of times.
Spent the last few days at GSAW 2020. Got to present a paper/extended abstract, and learned a lot about the ground station community. For example, I learned that The Aerospace Corporation was a Thing. Also participated in a panel on machine learning and got to tell the autonomous vehicles in a fire story. The audience paid attention! Basically, I pitched Charles Perrow a lot.
AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open-source, cross platform, and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. Similarly, we have an experimental release for a Unity plugin.
Added notes for the dissertation revisions
Working on the GVSETS paper – meeting at 3:00. Got everything into SVN and coordinated across machines.
Combine the Limitations and Future Work chapters into a new chapter that explores where my research has landed. I think that a beachhead analogy might work here.
Preliminary, early work that lives in the space between computational sociology and HCC (socio-technical, from From Keyword Search to Exploration). Add discussions about wormholes, weathermaps, and maps that connect distant places, like air travel maps.
Gathering data on online consensus in various sized groups and within different cultures
Extending simulations into human belief spaces, such as with GPT-2 agents
Add a “my hut, revisited” section to the contributions that discusses the contributions from the perspective of the spaces defined by Kauffman, Martindale, and Bacharach in particular, but also the literature in general
Tie into this as well
Some general reworking of the contributions text to reflect the slides: This also requires expansion of the current text in CH11. In particular slides 49-52 are a stronger synopsis than is provided in your CH10 discussion. Slides 53-56 are a more thoughtful framing of your contributions, both theoretical and practical (can you weave this back in your literature from CH2 to show specific knowledge contribution?). The “bookend” revisiting ofthe trustworthy anonymous citizen journalism was also more effective in the presentation than the document. Consider capturing this.