Martin E. P. Seligman From a psych paper on writing that was recommended to me. I like the right-wrong examples. I can’t ever find it posted on its own when I want to find it, so for my own use here it is. Here are some errors to avoid: Vacant Lead Sentences. The first sentences of each section, and the first sentences of each paragraph as well, are the most important sentences....
Sevgi Zubeyde Gurbuz, Hugh D. Griffiths, Alex Charlish, Muralidhar Rangaswamy, Maria Sabrina Greco, Kristine Bell. Bottom Line Up Front I rate this 4.4. Too much ink spilled on context and definitions. For a Past, Present, and Future paper, it felt like it was about 70% past, about 20% present, and a few tweets anthropophormizing radars tossed in at the end. It takes too much reading between the lines to find “things a researcher could do in this area”....
Aaron J Corcoran, Jesse R. Barber, William E. Conner https://www.science.org/doi/epdf/10.1126/science.1174096 I found this while googling for different types of radar jamming acronyms at the bottom of a wikipedia article. Summary Bats use echolocation to pinpoint airborne insects in darkness. Some bugs do other things. Tiger Moths click ultrasonically in response to attacking bats. 3 possible reasons: Startle, warning, and jamming. Clicking moths are juicy, so bad warning. Bats aren’t startled long....
ECM is Electronic Counter Measures W.D. Blair (cited in a lot of these benchmark things in Jack’s book), G. A. Watson, T. Kirubarajan, Y. Bar-Shalom 1996, published 1998. This is the “Benchmark 2” paper. Thoughts So these are all from the late 1990s, but there’s a bizarre glimmer of hope that I have that I’ll be able to run them myself. I think worst case if I want to steer my masters into “just make a benchmark for RRM”, seeing how other benchmarks were implemented/interfaced with would be beneficial....
6-chapter book by Peter Moo and Zhen Ding. Thoughts: Definitions aren’t consistent in this book, task is sometimes == look, other times not. Sometimes it’s a sensor, a radar, or a node. It’s clear it is always referencing some other work, but it is a lot for somebody coming in with no prior experience. I’m most excited about the waveform selection. In my head, an AI that is tasked with a relatively tight set of waveform selections that is rewarded for correctly tracking targets could be an elegant, if black box, way to just step over a lot of this....
Do winners pick government? How scale-up experience shapes entrepreneurs’ assessments of innovation policy mixes https://doi.org/10.1093/scipol/scad030 Steven Denney, Travis Southin, David A Wolfe Bottom Line Up Front Scaleups prefer grants to tax incentives and want government to ‘pick winners’ instead of spreading out the love. They’re positive and negative about everything. Summary Analyze interviews with entrepreneurs from Canadian technology firms. Is there a disconnect between objectives and instruments employed by the government?...
Alexander Charlish, Karl Woodbridge, Hugh Griffiths Bottom Line Up Front Run a quick auction to adjust the sizes of tasks, then dump the resulting tasks into earliest deadline first. It works better than randomly dropping tasks, it costs less computationally than Q-RAM. Summary multiplexed in time and angle time/power budget multidimensional parameter selection problem task revisit interval task dwell duration Part 2 of CDAPS (Continuous Double Action Param Selection) paper has good problem formulation....
R. Stuart Geiger. Bottom Line Up Front I rate this 7.7. Does what it says on the tin. Summary 5500 randomly sampled respondents from 3800 github repos. 500 non-random, non-github responses. 50 questions. paper is really an ipynb https://github.com/staeiou/github-survey-analysis/blob/master/github-survey-descriptive-stats.ipynb They cite Python 3.6 as a 1995 paper by guido? Also a bizarre subset of all the ipynb greatest hits all as papers. That’s a first for me. Truly bizarre. Amos’s Thoughts It’s mostly a bunch of figures....
Chris N. Potts, Luk N. Van Wassenhove Bottom Line Up Front I rate this 3.2. A period piece only readable in 1985, it did lead me to “Single Machine Weighted Earliness-Tardiness Scheduling Problem” though. I should have considered branching, trying to one-shot it was foolish. Next stop: brushing up on mixed integer linear programming/constraint programming. Make something slow. If too slow, tabu search. Summary Total Weighted Tardiness Problem n jobs, numbered 1 to n, must be processed without interruption on a single machine that can only handle one job at a time....
Andreas Zeller, Thomas Zimmermann, Christian Bird. Bottom Line Up Front I rate this 0.7. It’s an almost funny tweet dragged out for 4 pages. I’m not sure why I read this… hopefully discussion in class reveals something interesting. Summary Introduction Failures follow a Pareto distribution. Cost of consequence: If I know a module is failure-prone because it frequently changes, should I stop changing it? This paper proposes moving work earlier, to time of typing code....