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2016 October 1

Nineteenth weight progress report

Filed under: Uncategorized — gasstationwithoutpumps @ 09:08
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This post is yet another weight progress report, continuing the previous one, this being the 19th since I started in January 2015.

For the past year, the trend line has had a slope of 3.76 lbs/year.

For the past year, the trend line has had a slope of 3.76 lbs/year.

weight-2016-oct-01

 

Exercise has been very low the past two month, with only 2.63 miles/day of bicycling. It is picking up again, as I’m cycling to campus 3 or 4 times a week to sit in on classes during my sabbatical. (I’m taking feedback control theory, something I’ve been meaning to learn on a formal basis for some time now.)

I skipped the report at the end of August, because I was embarrassed about the whole month being above my self-imposed target range, and I thought that September would bring me back in range.  Of course, the upshot was that September was even worse than August, so I now have two solid months above my target range. The really big spike in mid-September may not be a sudden change in weight—we moved the scale to weigh my son’s luggage before his return to college, and the scale always changes its readings after being moved. (I think that the larger  measurement is the more accurate one, though.)

I’d like to lose 5 pounds to get back to the middle of my target range, but I’m less hopeful that I’ll succeed at that than I was 2 months ago, as my trend lines keep getting steeper.

2016 September 29

GRE Analytic Writing favors bullshitters

Filed under: Uncategorized — gasstationwithoutpumps @ 22:33
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My son recently took the GRE exam to apply for grad school in computer science.  The test has changed since I took it in 1973, but it still looks a lot like the SAT exam, which has also changed since I took it in 1970.  The multiple-choice section is still primarily 9th and 10th grade material, so it is a bit surprising that only 5.5% of CS students, 11.4% of physics students, and 15.3% of math students get 170, the highest possible score, on the quantitative reasoning section. [All data in this post from https://www.ets.org/s/gre/pdf/gre_guide_table4.pdf]

The “quantitative reasoning” questions are primarily algebra and reading simple graphs, so the banking and finance students do best with 15.5% getting 170. The scores would be more valuable for STEM grad school admissions if they included some college-level math (calculus, ODEs, combinatorics, statistics, … ), but the general GRE has always been based on an extremely low math level.

The verbal scores are perhaps less surprising, with philosophy being the only major with over 3% getting a 170 (5.1%), and with some of the education and business majors doing the worst—except for computer science, where 8% get the lowest scores (130–134), with the next worst major being accounting with 2.7% having 130–134.  I wonder how much of the difference here is due to the number of native and non-native speakers, as computer science certainly attracts a lot more foreign students than most majors.

I was most interested in looking at the “Analytical Writing” scores, since I’ve not seen much correlation between them and the quality of student writing on the grad school applications I’ve reviewed over the last decade.  I was interested in two things: the mean score and the fraction that got 4.5 or better (the fraction getting 5.5 or better is too small to be worth looking at).  Again computer science and electrical engineering stand out as having extremely low means and small fractions of students having 4.5 or better.  I have not found any analyses of the GRE scores that separate native speakers of English from non-native ones—I wonder how much of the effect we see here is due to being native speakers and how much is due to curricular differences.

Here is the table of all the broad categories in the order that ETS provided them:

Subject

Mean writing

%ile ≥4.5

Computer and Information Sciences

3.1

8.8

Electrical and Electronics

3.1

6.7

ENGINEERING

3.3

12.6

Civil

3.3

13.2

Industrial

3.3

9.8

Mechanical

3.3

12.3

PHYSICAL SCIENCES

3.4

17.3

Accounting

3.4

12.3

Banking and Finance

3.4

10.7

Natural Sciences ─ Other

3.5

14.8

Materials

3.5

19.4

BUSINESS

3.5

15.2

Other

3.5

14.7

Agriculture, Natural Res. & Conservation

3.6

18.0

Mathematical Sciences

3.6

21.0

Chemical

3.6

21.6

Early Childhood

3.6

16.0

Student Counseling and Personnel Srvcs

3.6

17.3

Business Admin and Management

3.6

17.8

Health and Medical Sciences

3.7

19.0

Chemistry

3.7

23.8

Other

3.7

23.1

Other

3.7

21.3

Arts ─ Performance and Studio

3.7

24.3

Administration

3.7

21.9

Elementary

3.7

21.3

Special

3.7

19.5

Other

3.7

23.7

LIFE SCIENCES

3.8

21.3

Biological & Biomedical Sciences

3.8

26.0

Earth, Atmospheric, and Marine Sciences

3.8

25.4

Physics and Astronomy

3.8

26.8

Economics

3.8

27.8

Sociology

3.8

28.2

EDUCATION

3.8

23.9

Curriculum and Instruction

3.8

21.4

Evaluation and Research

3.8

23.6

SOCIAL SCIENCES

3.9

29.1

Psychology

3.9

26.6

Higher

3.9

29.7

Anthropology and Archaeology

4.0

34.7

Foreign Languages and Literatures

4.0

37.2

Secondary

4.0

33.9

Political Science

4.1

42.9

ARTS AND HUMANITIES

4.1

40.8

Arts ─ History, Theory, and Criticism

4.1

38.5

History

4.1

40.4

Other

4.1

38.6

English Language and Literature

4.2

45.2

Philosophy

4.3

52.7

 OTHER

Architecture and Environmental Design

3.4

13.1

Communications and Journalism

3.7

23.3

Family and Consumer Sciences

3.7

20.7

Library and Archival Sciences

4.0

34.3

Public Administration

3.8

23.7

Religion and Theology

4.2

46.5

Social Work

3.6

16.7

The table is more interesting in sorted order (say by %ile ≥4.5 on Analytical Writing):

Subject

Mean writing

%ile ≥4.5

Electrical and Electronics

3.1

6.7

Computer and Information Sciences

3.1

8.8

Industrial

3.3

9.8

Banking and Finance

3.4

10.7

Mechanical

3.3

12.3

Accounting

3.4

12.3

ENGINEERING

3.3

12.6

Architecture and Environmental Design

3.4

13.1

Civil

3.3

13.2

Other

3.5

14.7

Natural Sciences ─ Other

3.5

14.8

BUSINESS

3.5

15.2

Early Childhood

3.6

16.0

Social Work

3.6

16.7

PHYSICAL SCIENCES

3.4

17.3

Student Counseling and Personnel Srvcs

3.6

17.3

Business Admin and Management

3.6

17.8

Agriculture, Natural Res. & Conservation

3.6

18.0

Health and Medical Sciences

3.7

19.0

Materials

3.5

19.4

Special

3.7

19.5

Family and Consumer Sciences

3.7

20.7

Mathematical Sciences

3.6

21.0

Other

3.7

21.3

Elementary

3.7

21.3

LIFE SCIENCES

3.8

21.3

Curriculum and Instruction

3.8

21.4

Chemical

3.6

21.6

Administration

3.7

21.9

Other

3.7

23.1

Communications and Journalism

3.7

23.3

Evaluation and Research

3.8

23.6

Other

3.7

23.7

Public Administration

3.8

23.7

Chemistry

3.7

23.8

EDUCATION

3.8

23.9

Arts ─ Performance and Studio

3.7

24.3

Earth, Atmospheric, and Marine Sciences

3.8

25.4

Biological & Biomedical Sciences

3.8

26.0

Psychology

3.9

26.6

Physics and Astronomy

3.8

26.8

Economics

3.8

27.8

Sociology

3.8

28.2

SOCIAL SCIENCES

3.9

29.1

Higher

3.9

29.7

Secondary

4.0

33.9

Library and Archival Sciences

4.0

34.3

Anthropology and Archaeology

4.0

34.7

Foreign Languages and Literatures

4.0

37.2

Arts ─ History, Theory, and Criticism

4.1

38.5

Other

4.1

38.6

History

4.1

40.4

ARTS AND HUMANITIES

4.1

40.8

Political Science

4.1

42.9

English Language and Literature

4.2

45.2

Religion and Theology

4.2

46.5

Philosophy

4.3

52.7

Note that all the fields that call for precise, mathematical reasoning do poorly on this test, but those which call for fuzzy, emotional arguments with no mathematical foundation do well—the test is designed to favor con men. I believe that this is partly baked into the prompts (see the pool of issue topics and, to a lesser extent, the pool of argument topics), partly the result of having the writing being done entirely without access to facts (benefitting those who BS over those who prefer reasoning supported with well-sourced facts), and partly the result of having graders who are easily swayed by con men.

I believe that most of the graders are trained in the humanities, and so are more swayed by familiar vocabulary and rhetoric.  If ETS had science and engineering professors doing the grading (which they would have a hard time getting at the low rates they pay the graders), I think that the writing scores would come out quite different.

Of course, there are curricular differences, and science and engineering faculty are mostly not paying enough attention to their students’ writing (and I can well believe that CS and EE are the worst at that). But I don’t think that even engineering students who do very, very good engineering writing will necessarily score well on the GRE analytical writing test, which seems to favor rapid writing in only one style.

I will continue to give relatively little weight to Analytical Writing GRE scores in graduate admissions. The untimed essays that the students write for the applications are much closer to the sort of writing that they will be expected to do in grad school, and so much more indicative of whether their writing skills are adequate to the job. I will continue to interpret low GRE scores as a warning sign to look more closely at the essays for signs that the students are not up to the task of writing a thesis, but high GRE writing scores are not a strong recommendation—I don’t want grad students who are good at bull-shitting.

2016 September 27

Teensy 3.5 & 3.6 Kickstarter

Filed under: Uncategorized — gasstationwithoutpumps @ 16:59
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As many of the followers of my blog know, the Teensy 3.1 and Teensy LC have been my favorite microcontroller boards for the past couple of years.  The Teensy 3.1 has since been replaced by the slightly better Teensy 3.2, which has a better voltage regulator but is otherwise pretty much the same as the 3.1.  I’ve been using the Teensy LC with PteroDAQ software for my electronics course.

I’ve just noticed that PJRC has a Kickstarter campaign for a new set of boards the Teensy 3.5 and 3.6.  These will be much more powerful ARM processors (120MHz and 180MHz Cortex M4 processors with floating-point units, so at least 2.5 times faster than the Teensy 3.2, more if floating-point is used much).  The form factor is similar to before, but the boards are longer, taking up 24 rows of a breadboard, instead of just 14.  The extra board space is mainly to provide more I/O, but there is also a MicroSD card slot.

The designer is still dedicated to making the Teensy boards run in the Arduino environment, and the breadboard-friendly layout is very good for experimenting.

PJRC is positioning the new boards between the old Teensy boards and the Linux-based boards like the Raspberry Pi boards. The new Teensy boards will have a lot of raw power, but not an operating system, though I suspect that people outside PJRC will try porting one of the small real-time operating systems to the board.

The new boards are a bit pricey compared to the Teensy LC ($23–28 instead of under $12 for the Teensy LC), but still reasonable for what they provide.  PJRC also has a history of providing good software for their boards.

I probably need to get both a Teensy 3.5 and a 3.6 to port PteroDAQ to them—that looks like a $50 purchase. If the boards and the software are available in time for me do development on PteroDAQ by December, I might get it done—any later than that and I’ll have no time, as I have a very heavy teaching and service load for Winter quarter.

I suspect that the new Teensyduino software will need a newer version of the Arduino development environment, which in turn would require a newer version of the Mac operating system (my laptop is still running 10.6.8), which in turn probably means a new laptop.

I’m waiting to see if Apple releases a new, usable MacBook Pro in October, so there is a bit of built-in delay in the whole process. I’m not impressed with their recent design choices for iPhones and MacBook Air—I need connections to my laptop—so there is a strong possibility that I may be having to leave the Macintosh family of products after having been a loyal user since 1984 (that’s 32 years now).

Update 2016 Sep 27: I just watched the Kickstarter video.  They used the Karplus-Strong algorithm as a demo.  Of course, that demo could have been done on a Z80 chip from 1976 (though not easily—the sampling rate suffered a bit to run 4 strings on the Z80, and only 8-bit DACs were affordable in those days).  It would have been more instructive to do an FM synthesis algorithm, which takes a lot more processor power than Karplus-Strong.

2016 September 24

US News covers UCSC referendum on athletics

US News and World Report wrote an article,So Long, Banana Slugs? Students Cry Foul About Paying More for Sports, about the UCSC student vote last year on funding athletics.  In it they pointed out that athletics does not really benefit universities:

And while administrators often say athletics benefit their universities—and 77 percent of Americans in a Monmouth University poll said they thought big-time programs make “a lot of money for their respective schools”—the NCAA itself reports that only 24 of its 1,200 member schools take in more than they spend on sports. Even after broadcast rights, ticket sales, sponsorships, sports camp and investment income is taken into account, colleges have to subsidize a median 27.5 percent of athletic spending, much of it from student fees, the AAUP says.

“The fact is, all the data shows that many of the purported academic benefits of sports—recruitment, prestige—have all proven to not be true. They don’t exist,” Tublitz said.

One of the things that I like about UCSC is that sports is a participatory activity, not a spectator activity. A lot more students are involved in intramural sports and in individual fitness activities than bother watching the 250 or so varsity athletes, who the university has been subsidizing at a rate of $1million a year. I’m pleased to see that the national press is noticing that the subsidy of athletics by universities makes no sense, and that UCSC has an opportunity to be a leader in turning their back on this nonsense.

I’ve posted on this topic before: I’m proud of UCSC undergrads, Sports at Any Cost, and Not so proud of UCSC undergrads this year.  I am hopeful that students will realize that subsidizing a couple hundred of their fellow students to play for them is not nearly as valuable as playing themselves—that they are better off taxing themselves for equipment and facilities that all students can use than for special services (coaches, trainers, transportation) for just a few.

I also hope that the UCSC administration comes to its senses and realizes that students are having a hard time getting into the classes they need, because of all the growth in student enrollment without a corresponding growth in instructional resources, and that the $1million dollars a year they pour down the athletic drain  could be used to provide more classes.

That $1million would pay for about 100 more courses taught by lecturers, or 40–50 more taught by tenure-track faculty, about 40 more TA sections.  (Surprisingly, TAs cost departments much more than lecturers, because departments have to pay the tuition for TAs, which get recycled back into other things—like subsidizing athletics, probably.)  The money would benefit about 3000 students a year, rather than the under 300 who benefit from athletics subsidy.

I think that it is past time for UCSC to leave NCAA sports and return to having just club sports, as they did when I first started teaching at UCSC 30 years ago.

 

2016 September 23

Evolution of caffeine

Filed under: Uncategorized — gasstationwithoutpumps @ 13:07
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PZ Myers has a nice discussion of the convergent evolution of the synthesis of caffeine (separately evolved at least 5 times!), based on the paper from PNAS, Convergent evolution of caffeine in plants by co-option of exapted ancestral enzymes, by Huang, O’Donnell, Barboline, and Barkman.

Biologists used to think that there was one canonical pathway for caffeine synthesis, from xanthosine through 7-methylxanthine and theobromine to caffeine.  The paper shows that some plants use a different pathway (through 3-methylxanthine and theophylline) and that the enzymes used even on the common pathways are different.

The evolutionary model that best explains the data is that ancestral enzymes were promiscuous (which means that they had several different functions, not that they were sexual) and were eventually duplicated and specialized for caffeine production.  The researchers reconstructed some of the ancestral enzymes from the modern descendants and confirmed that this hypothesis was reasonable, as only single amino-acid substitutions were necessary to confer the two different specificities of the modern enzymes from the ancestral ones.

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