Six principles of nonviolence (Happy MLK Jr. Day)

17 Jan

A day late but here we go anyway!  Last weekend I went to volunteer training for the Women’s March On Austin scheduled for this Saturday.  I was planning on blogging about biology today, but talking about what we went over in training seems more timely.  The Austin march is one of 616 sister marches around the world planned to coincide with the one in Washington, D.C. (so there might be one near you!)  Here’s an excerpt from the mission statement of the women’s march on Washington, emphasis added:

In the spirit of democracy and honoring the champions of human rights, dignity, and justice who have come before us, we join in diversity to show our presence in numbers too great to ignore. The Women’s March on Washington will send a bold message to our new government on their first day in office, and to the world that women’s rights are human rights. We stand together, recognizing that defending the most marginalized among us is defending all of us.

I was really impressed and inspired by the event coordinators- the person who trained us in nonviolent protest has been involved with protests and activism since she was 8 (Austin native) and read this excerpt aloud, stressing the end: that we need to support all marginalized groups to move forward, instead of looking only at cis, heterosexual white women (a.k.a. “white feminism”– that was a link to an article by an academic; this is a link to a HuffPo video explaining the term).

Next we went through practical things about how to march safely (link arms, use a buddy, if something happens decide as a group to stay and sit down, linked or go away very quickly, report anything suspicious to block marshalls), volunteer jobs (I’m at the check-in table!), and then the six principles of nonviolent protest.  She was careful to say that these have been used for a long time by not just MLK, Jr. (examples: suffragists, Gandhi) but he happened to write them down in a way that’s very nice for teaching activism to new people.  So here they are (in bold), plus some thoughts

  1. Nonviolence is a way of life for courageous people.  Often not fighting back requires bravery.  You can be nonviolent and still be aggressive, just not physically aggressive.
  2. Nonviolence seeks to win friendship and understanding. The goal here is to make a community, to win over the people who are against you.  A good way to not do it: tell people “you’re wrong!”  A good way to start to do it: listen.  Also, make eye contact.  Be a human and show people that you are a human and you recognize their humanity as well.
  3. Attack forces of evil, not people doing evil.  This was when our presenter reiterated that this is not an Anti-Trump march, but a pro-women, pro-LGBTQ, pro-immigrant, pro-marginalized people march.  “Trump is a symptom, not the disease.  We want to defeat the disease.”
  4. Accept suffering without retaliation.  This is basically, don’t fight back.  When people see you suffering an injustice, you’ve communicated to them that this matters, and hopefully they extrapolate that you matter.
  5. Nonviolence chooses love instead of hate.  Another way that people put this is to avoid internal violence as well as external violence.  Come at this with love and hope for reconciliation in your heart instead of hatred and hope for retribution.  Keep up morale in a positive manner, not a negative manner.
  6. The universe is on the side of justice.  Believe this.  A volunteer said that this was the easiest principle to keep up, and another said #2 is the hardest- she wants to snap back instead of listen.

I got a little teary at the end of the nonviolence training, when we practiced chanting “HEAR OUR VOICE.”  I’m not a big crowds person so being in the middle of a room of such positive energy and solidarity really affected me.

Here’s a video of a training that happened later that day (not for volunteers), which starts with 20 minutes of Q&A and then an hour of Simone going through these principles etc.  She’s really good:

So if you’re in or near one of the cities with a sister march, consider heading over there this weekend and checking it out!  Ours will have some awesome speakers and music after (Wendy Davis!  Lizzie Velasquez!  More!) and should be really cool.  I am also not actually planning on marching the 1.5 miles in 80 degree heat with 22,000 other people (definitely a recipe for very pregnant me fainting), but I’ll be there beforehand so if you’re around come say hi!

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And now for something completely different-cognitive neuroscience!

10 Jan

I sometimes trawl arxiv.org for short math papers to read, and occasionally I even blog about them (see: curve complex I and II), though generally my math blog posts arise from interesting talks I’ve seen (see: most of the rest of my math posts).  Recently a friend sent me a job listing that would require a Ph.D. in biology or similar, but the real job requirement is an ability to read biology papers.  The only related category on arxiv is “quantitative biology,” so I thought I’d try to bring up a short paper and read it and blog about it to see how I do.  Any cognitive neuroscientists who might read this, let me know if my reading is correct!

This post is based on the paper “Deep driven fMRI decoding of visual categories” by Michele Svanera, Sergio Benini, Gal Raz, Talma Hendler, Rainer Goebel, and Giancarlo Valente.  First, here’s my schematic of the paper:

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We’ll read this schematic from top to bottom, left to right.

  1. On top is the experiment: they had a lot of people watch 5-10 minute movies.  The left white arrow indicates that the people were in fMRI machines (I know a fMRI machine does not look like an EEG but that’s the picture you get) and so they have a bunch of data sitting around from that.  The right white arrow indicates that they used a computer algorithm (“math!”) to extract information directly from the movies [this is the fc7 data].  So far they haven’t contributed anything new to the literature; just used existing techniques to come up with raw data.
  2. The orange diagonal arrows are when things get interesting.  The fMRI data and fc7 data comes in giant matrices, and they use another math algorithm to come up with a set of “decoding” matrices.  Not pictured in schematic: they test these matrices using some of the data.
  3. The goal is indicated by the green arrows: to use the brain data and these matrices they came up with to reconstruct what people are seeing and classify these things (aka are subjects seeing people’s faces on the screen, or entire human figures?)

Now for a few details on each of the steps.

0. The motivation behind the paper seems to be to link the brain imaging community (those who work the fMRI, EEG, etc. data) with the deep neural network community (computer people) to answer questions that involve both.  The main question they have is: how do people associate low-level information like colors, shapes, etc. with semantic concepts like car, person, etc.?  Here’s the picture:

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Eyes see a vague shape + different colors [low-level information]; brain tells us whether it’s a person or a tree with the sun behind it [semantic concepts]

There’s a lot of work in both communities on answering this question, and this paper uses work from both sides to form a decoder model: with an input of fMRI data, the model spits out predictions about what the subjects are seeing.  Specifically, the model is supposed to tell if subjects were looking at human faces or full human figures.  This is hard!  Those are pretty similar categories.

  1. The data: they grabbed a bunch of existing data from other experiments, where scientists took 5-10 minute clips from five different movies (side note I would never want to be in these studies because one of the clips was from The Ring 2) and showed them to subjects (ranging from 27 to 74 participants in each movie) and recorded all the fMRI data, which creates a huge three-dimensional datasetevery 3 seconds.  Then they threw the movie frames into a computer algorithm (called the faster R-CNN method) which detects objects in the video frames (with varying confidence levels) and spits out a 4096-dimensional vector for each frame.  They averaged these vectors over 15 frames so that the two datasets could match up (the movies were shown at 5 frames per second so this makes sense).  These vectors form the fc7 data.
  2. The math: they use an algorithm called Canonical Correlation Analysis (CCA) to spit out two orthogonal matrices and which are highly correlated (hence the middle C).  Looks like linear algebra with some linear projection!  The schematic is fMRI \cdot A = U \\ fc7 \cdot B = V.  To do this, they took a subset (about 75%) of the fMRI data and the corresponding fc7 data and plugged it into the math.  The goal of this step (the training step) is actually to get the helper matrices and B.  To make sure these matrices are a-OK, they used the remaining fMRI data to reconstruct the fc7 data within a reasonable margin of error fMRI \cdot A = U \rightarrow V \cdot B^{-1} = fc7.  Remember U and V are highly (in fact maximally) correlated so that middle arrow actually makes sense in this step (the testing step).
  3. The result: For one movie, they did the training math step using different subsets of data (they did it 300 times) to make sure those helper matrices and are the best possible ones.  Then to show that this whole paper does what they want it to do, they do the testing step using the other movies.  [The whole point of a decoding method is to predict what people are seeing].  They then try to classify whether subjects see faces or bodies using their method (the fancy fc7 method) and another method (some linear thing) and show that their method is way better at this discriminating task than the other method.  Fun caveat that they had to think about: it takes people a little while to react to stimuli, so they had to toss in time-shifts for the fMRI data, and also throw in another regulatory parameter to normalize the data.

Conclusion: their method works on this preliminary result (faces versus bodies)!  They want to expand to other movies and other semantic concepts in the future.

General keywords: machine learning, fMRI, linear algebra.  Also CCA, faster R-CCN, fc7 but those are keywords for specialists.

My conclusion: this was cool and fun!  I like reading new things and learning.  I hope you do too!

2016 Book Roundup

3 Jan

I keep track of the books I read (aside from baby books, which we read by the handful everyday), so I thought this year I’d try something new and share my thoughts on some of the books.  For a few years I’ve been trying to read more female authors, more non-American authors, and more nonfiction.  This year I read less than previous years (or did a worse job of keeping track), with 29 books over the year.  Of 19 unique authors, I had 14 women (best yet!) and 5 men; 11 Americans and 8 non-Americans.  I did a bad job of reading nonfiction this year- a paltry 3 books out of the 29.  I also got REALLY obsessed with a few scifi series, so let’s talk about those first.

SCIENCE FICTION

Books by Octavia Butler: Fledgling, Wild Seed, Mind of My Mind, Clay’s Ark, Patternmaster.  Inspired in part by the city of LA celebrating Octavia Butler during 2016, I went on a binge with this masterful fable-teller who works in race, gender, interesting power dynamics, commentary on social structures, and surprising action scenes into her imaginative worlds.  The latter four are part of a loose series; the first one is about racist vampires (Yes, you read that right).  Plus she wrote this awesome note to herself:

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From the magnificent Huntington Library in Pasadena, which I recommend visiting.

I still haven’t read her most famous books, Kindred and the Parable series.  There’s plenty of Octavia Butler to explore and she’s fantastic; can’t recommend her enough.

The Expanse series: Leviathan Wakes, Caliban’s War, Abaddon’s Gate, Cibola Burn, Nemesis Games, Babylon’s Ashes.  This series written by two dudes in New Mexico is now a TV show on ScyFy!  I have not watched it because the books are sort of gory, tense, and scary, and I don’t handle that well in TV (despite reading stuff like The Walking Dead, Preacher, Watchmen, etc. in graphic novel format).  These pleasantly popcorn-y books hide smart literary themes and a surprisingly deep study of their characters- Nemesis Games is my favorite one because each of our protagonists goes on their own adventure and you can appreciate how much they’ve affected each other.  The characters feel fully fleshed out and fun, with lots of flaws and learning about relationships and communication.  Also, they have to constantly save all of humanity from itself/evil aliens/evil people trying to use alien technology.  So that’s a blast.  Definitely recommend for sci-fi enthusiasts.

Unrelated science fiction/fantasy books: 

The Left Hand of Darkness by Ursula K Leguin is great.  Perhaps the beginning of feminist science fiction?  This is a classic that took me too long to get around to reading. Also great for non-science fiction nerds.

The Ocean at the End of the Lane by Neil Gaiman was the first book I read in 2016.  Creepy classic Gaiman.  He did a reading/speaking event here in Austin near the beginning of the year and we saw him read a few of his short stories and poems; it was astonishing.

Assassin’s Apprentice and Royal Assassin by Robin Hobb: these are the first two pulpy fantasy books in a series.  The first book is super fun and includes puppies (not really a spoiler: main character can talk to dogs), but the second book drags.  My spouse read the third and said everything stops making sense.  The first one is super fun if you want a quick fantasy read with puppies in it!  I cried (in a good way).

Speaker for the Dead by Orson Scott Card: I had read Ender’s Game and Ender’s Shadow and never came around to this third book.  I really enjoyed it!  The big reveal is SUPER COOL (if no one spoils it for you/you don’t read the prologue) and the pedantic self-righteousness that can plague OSC books is at a minimum (still there but not horrible).

Year’s Best Science Fiction, edited by Gardner Dozois: there are 32 of these; we have maybe 25 of them.  Fantastic huge collections of the best short stories from that year in science fiction; Dozois does a really good job of including a huge array of diverse voices.  Some of these stories haunt me for years, which means they’re really good.  If you run across one of these volumes it’s worth a buy; you can read and reread these for years.

THE BEST AND MOST IMPORTANT BOOK I READ IN 2016: AMERICANAH BY CHIMAMANDA NGOZI ADICHE.  By this point I think I’m on my fourth copy of this book- I keep giving it away to people and telling them that it’s important and that they should read it and pass it on.  Plenty of people this year read Between the World and Me by Ta-Nehisi Coates, which is an important non-fiction book but I hear is a bummer (I have not read it yet and will likely not until my very strong pregnancy hormones taper away).  This book is fiction, which I think is a little bit easier to swallow and still get similar messages across.  Race, immigrant experiences, gender, education, society, and mental health are all tied in to this love story.  If you read a single book from the books I have read in 2016, make it this one.

THE SECOND BEST BUT NOT THAT IMPORTANT BOOK I READ IN 2016: OUTLINE BY RACHEL CUSK.  This is an experimental-feeling novel where not much happens but you feel a lot of feelings.  Beautiful and evocative prose; I read and reread this book and told several others to read it.  It feels like having a big glass of water while standing in a waterfall- you aren’t sure where the book (the waterfall) ends and you (glass of water) begin, but you’re enjoying a refreshing experience.

Mom books: 

How to Talk so Kids will Listen and Listen so Kids Will Talk by Adele Faber and Elaine Mazlish is a great resource for humans; you could easily replace “kids” with “people” in the title and get the same result.  And it includes comic strips if you don’t want to read the whole thing (which is what my spouse did)!  Takeaways: feelings are legitimate and difficult to handle, and we need to validate each other and help each other instead of shutting each other down.  No one likes getting nagged all the time; people like having some autonomy/power/choice, and thrive off of responsibility/reasonable expectations.  The book is chock full of specific and concrete anecdotes and exercises.  It’s from 1980 but it doesn’t feel too dated (everyone in it is white though).

Please Look After Mom by Kyung-Sook Shin is a South Korean novel (one of two I read this year) which is haunting and sad and about the sacrifices of motherhood.  I would definitely not read it when in a heightened emotional state.  It also makes you want to call your mom

The Hen Who Dreamed She Could Fly by Sun-mi Hwang is the other South Korean novel I read this year.  This is GREAT.  I gave my copy to my mother-in-law.  It’s a wonderful short fable about motherhood and would make a great mother’s day gift to tell someone how much you appreciate them.  Plot synopsis: a hen really wants a baby; she adopts a duck egg and spends her life helping her baby learn to fly/fit in where he belongs. It’s adorable and wonderful.  Would also be great for adopted parents.

All other books:

An Abundance of Catherines by John Green: I adored The Fault in Our Stars as a book and movie (and blog post) but hated this book.  I’m not sure why I finished it.

Still Alice by Lisa Genova- every few years I reread this hauntingly beautiful and soft first-person novel about a neuroscientist with early onset Alzheimer’s.  That should tell you how good I think this book is.

The Devil You Know by Claire Kilroy- a satire about the housing crisis and 2008 recession as it affected Ireland.  Generally I like reading non-American authors because it feels like a dip into another culture while still celebrating the universality of the human experience; this one was pretty heavy on the “other culture” part.  Always surprising (in a good way) with an Anglophone country.  I identified far more with the South Korean novels than this one; maybe because I’m Asian or maybe because this was super Irish.

Station Eleven by Emily St. John Mandel- a very popular science fiction post-apocalyptic book by a not-science-fiction author.  Unfortunately with my delving into sci-fi over the past few years it’s become harder for me to enjoy these sorts for books, because a small voice inside me (that sounds like my spouse) is screaming “how does this make sense?!?!?!”  I read this lightly and enjoyed a lot of it, but there are a few pages which sum up what I hated about the book: at some point, several of the characters chat about how they didn’t really pay attention in science class and don’t know how things work.  Despite, you know, BOOKS and LIBRARIES and LEARNING.

The Faster I Walk, The Smaller I Am by Kjersti Annesdatter Skomsvold- talk about weird other culture experiences!  This Icelandic book is extremely sad and absurd.  I’ve enjoyed absurdist Icelandic music and movies but literature might be a bit too much for me.

So What are You Going to Do With That? by Susan Elizabeth Basalla – I wrote about this in my ‘finding a job’ blog post.

Ashley Turner by Dean Koontz- absolutely the worst thing I’ve read since that Justin Bieber book.

The Road to Little Dribbling by Bill Bryson- calming and funny and smart.

This blog post got really long!  Wow!  Happy New Year folks!  Goals for my year: survive this pregnancy, make sure my kid survives it, have a baby, get a job, keep up a every-other-week schedule on the blog (versus every week for the past two years).  Thanks to all who have contacted me about job leads- please keep them coming if you hear of anything in the Charlotte, NC area or remote work that you think I would be interested in/awesome at.

The employers’ argument for parental leave

22 Dec
As you may be aware, I’m having a baby in three months.  My spouse’s work offers one week of parental leave to non-primary caretakers of new babies, so I decided to put an hour in of internet research to see if I could make a little report that he could send on to HR or someone who might be interested.  And then since I did that, I figured I’d post it to here!  Happy holidays dear reader!  Maybe this will be helpful to someone.
The main sources are all freely available on the internet.
Overall takeaways:
  • Jobs that offer paid parental leave are increasingly important for Millennials and young workers and increase employee retention, as seen in the CWF study as well as the California experiment.
  • Paid parental leave has no negative economic impact on employers, as in California experiment.
  • Longer parental leave increases employee satisfaction with work-life balance, which increases worker happiness and thus productivity.
  • For employees who are partners with other employees, leave for the non-gestating employee increases work-life balance satisfaction for the gestating employee and retention for that employee.
Here’s the Department of Labor briefing on paternity leave: https://www.dol.gov/asp/policy-development/paternityBrief.pdf.  Selected quotes (sources mentioned are available at the end of this short briefing:)
  • “In one study of working fathers in the U.S., those who took leaves of two weeks or more were much more likely to be actively involved in their child’s care nine months after birth – including feeding, changing diapers, and getting up in the night.6 Studies from other countries have confirmed that fathers who take more paternity leave have higher satisfaction with parenting and increased engagement in caring for their children.7”
  •  “Fathers are increasingly concerned about work-life balance, and nearly half of men surveyed report that the demands of work interfere with family life.11”
  • “In a 2014 study of highly educated professional fathers in the U.S., nine of out ten reported that it would be important when looking for a new job that the employer offered paid parental leave, and six out of ten considered it very or extremely important. These numbers were even higher for millennial workers.23”
Here’s a 2010 California study on Paid Family Leave, which was implemented in 2004 and offered employees six weeks of leave at 55% of salary (followed by New Jersey and Washington): http://cepr.net/documents/publications/paid-family-leave-1-2011.pdf.  This offers concrete evidence of the effects of paid family leave in general.
  • Most employers report that PFL had either a “positive effect” or “no noticeable effect” on productivity (89 percent), profitability/performance (91 percent), turnover (96 percent), and employee morale (99 percent).
  • Relevant pages: 7-10
Here’s a Center for Work and Family (out of Boston College) report on paternity leave: http://www.thenewdad.org/yahoo_site_admin/assets/docs/BCCWF_The_New_Dad_2014_FINAL.157170735.pdf
  • Look at this chart, based on over 1000 worker fathers who were mostly (over 90%) well-educated professionals:Inline image 1
  • “Only 20% of the study participants felt that all of the time off should be taken consecutively beginning with the birth of their children. More than 75% preferred the option to take the paid time off when it was most needed after the birth, within a specified period of time such as six months. For example, over a six month period after the birth of their child, they could take two weeks at the beginning and then additional days off as needed up to the maximum amount allowed.”
  • Pages 1-14 are about worker desires, pages 15-20 are about employers’ implementations, including spotlights on Ernst and Young, Deloitte, and American Express.  “• For policies that didn’t differentiate between primary and secondary caregivers, fathers were given an average of two weeks of paid leave • For policies that did have designated provisions for fathers who were primary or secondary caregivers, fathers as primary caregivers were given an average of about eight weeks, which was approximately three times as much as the leave offered to secondary caregivers”
  • Takeaways: “Nearly three quarters of the fathers believed that the most appropriate amount of time for fathers to have off for paternity leave is between two and four weeks… 76% of fathers would prefer the option of not taking all their time off immediately following the birth of their children”
  • Pages 27-28 are concrete recommendations for employers.
If no one wants to read this post , here’s a great and easy to read article from ThinkProgress that similarly cites a bunch of studies and includes anecdotal evidence titled “How Everyone Benefits When New Fathers Take Paid Leave.”  I highly recommend this article.

Coconut cream pie

13 Dec

It’s been a little while since I made a pie, and I love pie!  In an unusual fit of activity last week I bought a bag of coconut, a can of mandarin oranges, and a can of pineapple and made ambrosia (yum).  I had some leftover coconut, so decided to make a coconut cream pie.  Growing up I never had dried shredded sweetened/unsweetened coconut and I still find the texture a bit strange, but I have fallen in love with coconut macaroons [wow I can’t believe I haven’t blogged those!  That’s on the list now] and am coming around to having a twiggy texture in my sweets.

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This is vaguely tropical, so I bet it’s a PIErate’s favorite dessert.

Coconut cream pie is a basic custard pie: you bake a pie crust (I love the make-in-pan lazy one but I actually rolled and chilled one for this pie), slowly make a creamy set custard to fill it, and top the whole thing with whipped cream.  Next time I think I’ll make this a banana cream pie instead of coconut, just omitting the coconut and tossing in sliced bananas.  And by next time I mean tonight.  And by tonight I mean I paused writing this blog post, went and bought some whipping cream and milk, and then made this pie before continuing the post.

First you want to make the crust (or buy a store bought one as I often do).  I use a food processor to pulse together very cold butter with flour and salt.  Probably don’t use frozen butter because your food processor isn’t that strong, but fridge butter is great.  Do that (or use two knives or a pastry blender) until it’s pretty well mixed and your remaining bits of butter are maximum 2-3 pea sized.  Then drop in a tablespoon of ice water and press the dough together with your hands just until it holds together.  It’s gonna get real crumbly in there, and try not to knead it too much- we want flaky pie crust, not gluey pie crust.  Wrap it up in plastic wrap and stuff it in the fridge.

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If I saw one of the main characters of “Empire” throwing a piece of scrap paper in the trash, I’d just have to say “That’s the way the Cookie crumb-ples”

Fun “trick” for this- I add the ice water directly to the food processor, press a little bit, and then dump the whole thing onto a big piece of plastic wrap on my counter.  Minimize mess.  Then you can wrap it up in the plastic wrap (bonus- if the piece is big enough, you can use it later to roll out the dough!)

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Are you feeling sad?  No need to be so dough-lful, pie is on its way!

While the dough chills, you can measure you all your ingredients for the filling.  Also preheat your oven.  Usually I’m a lazybones and avoid recipes like this, where you have to stir a lot/mind something on the stove, but I cooked dinner while making this pie and it was perfect.  First, microwave your milk (I did two minutes) and while that’s going, measure and mix your dry ingredients in a pot on the stove.

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The best way to make your dreams come true as you reach for the stars… is the (coconut) milky way

We’re slowly incorporating sugar, milk, coconut milk, coconut, and flour together, adding a bit more at a time, until we have the texture we want.  Then at the end you stir in your egg yolks for richness and cook until everything is set.  This can actually take about as long as you want it to.  The first time I made this it was luscious and creamy and not a clump to be found in my custard, and it took about 35 minutes.  The second time (just now) it took maybe 15 and it’s a little lumpy but still delicious.

So mix your flour, coconut, sugar, and pinch of salt in the dry pot, then add 1/3 of the milk and stir over medium-low (35 minute method) or medium (15 minute method) until the flour has started cooking into the liquid and it’s a little bit thickened.  Then add 1/3 of the milk and stir stir stir again, until that’s thickened.  Finally add your third batch of milk and stir.  Each thickening takes 2-10 minutes depending on heat.

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I didn’t constantly stir, just every minute or so.  So once the pudding got started, I pulled out the chilled dough and rolled it out thinly between two sheets of plastic wrap (this is not necessary; it just makes it so you don’t have to wash the rolling pin and counter).  Lay it into your pie tin, poke a few fork holes in it so it doesn’t puff up, and toss it in your preheated oven.

Once the third thickening has happened, add a little of the hot pudding to some beaten egg yolks so that they don’t curdle when you put their eggy deliciousness into the custard, and cook for another couple minutes until you’re juuuuust about to bubble/boil (but don’t boil, ever!).  Turn off the heat, dump in half a stick of butter and some extracts (this is the key to flavor), and let cool.  Pull your pie crust and let it cool too.  Should take 20-30 minutes depending on how hot your kitchen is for both things to cool off enough to put one inside the other, and then stick it in the fridge.

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We can see the inside of this pie; it’s naked!  Oh the improPIEty!

After a few hours, make some whipped cream (I won’t tell if you use the ready made stuff, but the homemade stuff is GOOD) with a little sugar and vanilla in it, and put it on your chilled custard.  Chill again, overnight is best.  Toast some coconut in a dry pan on the stove for 30 seconds or so (til brown and fragrant), let cool, and decorate your pie with it.  Or don’t!

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I’m not so great with speeches but I would definitely call myself a toastmaster.

 

YUM.

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Here I am eating it out of the pan for breakfast.  Because I’m pregnant?  Or because it’s pie?

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Coconut cream pie, adapted from Rock Recipes:

Crust:

1/2 c (1 stick) butter

1 1/4 c flour

2-4 TB ice water

Using a pastry blender, two knives, or a food processor, cut butter into flour until pebbly and well mixed.  Add ice water 1 TB at a time just until the dough holds when you push it together with your hands.  Knead once or twice (just to hold together), wrap in plastic wrap, and chill.

Filling:

1 can coconut milk (1 3/4 c), light if you want

1 1/4 c whole milk

1/3 c flour

2/3 c sugar

1 c unsweetened dried shredded coconut

1/4 tsp salt

3 egg yolks

4 TB (1/2 stick) butter

2 tsp vanilla extract

1/4 tsp ALMOND extract

  1. Preheat oven to 400 for the pie crust.  Microwave milks for 2 minutes.  Mix flour, sugar, coconut, and salt in a pot.
  2. Over medium or medium-low, add 1 c of milks to pot and stir until combined, then stir occasionally  until slightly thickened.
  3. Add next 1 c of milks and stir occasionally until thickened.
  4. Roll out chilled pie crust, put in a pie pan, and bake for 11-15 minutes until golden brown, and then let cool completely.
  5. Repeat step 3.  In between stirrings, lightly beat the egg yolks, measure out the butter and extracts.  Add 1/4-1/2 c of hot pudding to the egg yolks (whatever measuring cup is handy) and stir, then add the egg yolk mixture to the pudding and cook for another few minutes until pudding-like.
  6. Turn off heat, add butter and extracts and stir, and let sit until cool.
  7. Put cool custard into cool pie crust and put into fridge for several hours

Whipped cream and topping:

1 c heavy whipping cream

2 TB powdered sugar

1 tsp vanilla

1/2 c shredded coconut

  1. Beat cream, sugar, and vanilla together for a few minutes until it turns into whipped cream (peaks hold).
  2. Meanwhile, toast coconut in dry pan over medium heat for 30-45 seconds until fragrant and light brown.
  3. Spread whipped cream over chilled pie.
  4. Sprinkle with cooled toasted coconut.

“Strange” escapism- media thoughts

23 Nov

This post is like two posts put into one.  A way for me to process my feelings about the election by not talking about current events at all.  Part 1 is about news media, Part 2 is about Dr. Strange.

Over the past few months I’ve been getting more and more exhausted with the “OUTRAGEOUS! OUTRAGE!” tone in my usual liberal news-thinkpiece sites (I read a lot of Slate and Jezebel); I think those sites have also started getting sick of being constantly outraged by the latest something that some conservative bigot/racist/misogynist/generally bad person has done.  Or maybe they’ve become more leery of the language of outrage since the death of Gawker (covered in Slate with a line that’s stuck with me: “The Internet masses had found a new vice, outrage, to replace our voyeurism.”)  Links on this topic: a super long, fairly defensive reflection in NY Magazine by the former editor in chief of Gawker, a surprisingly newsy story on what happened in the Daily Beast, and a slow-to-load but funnily Gawker-like list of responses to Gawker’s death, including this quote: “I think Gawker did for some what Trump does for some. Both let their friends and supporters be comfortable in being terrible people.” (Erick Erickson, noted conservative blogger/radio host).

Anyway, to help combat my liberal outrage-fatigue, I thought I’d try to read some conservative news sites (which I’ve blogged about before, maybe notably in my Yale reflection).  So I started with Breitbart and Drudge Report, but the angry rhetoric there mirrored exactly the outraged rhetoric on the liberal sites- those whiny, thin skinned snowflake liberals who don’t believe people can make their own decisions.  Then someone turned me on to National Review, which is now my go-to conservative news site.  They also make fun of lefties, but they’re less obsessed with it and more focused on how to continue to progress on the conservative agenda.  I like it when people think about actions and what to do in the future, even if I disagree with their premises.  I just opened the site and a random article, and here’s a line that popped out at me: “Here’s some free advice for all the liberals insisting that Trump was elected by racists: The more you say that, the more you help Trump.”  Also, if like me you are a dirty liberal, you’ll be soothed by the way the National Review covers Republican infighting.  If you are not like me, you might just like the National Review!  They’re thoughtful and logical and much less angry-outrageous-clickbaity than other sites I’ve seen [I am also open to more suggestions!  I read the libertarian site Reason Magazine sometimes, but they think we’re all idiots so I get tired of that rhetoric as well].

So my main goal of writing this post was to tell you about the escapism that we did last weekend- we went and saw Dr. Strange at the movie theatre (this is a big deal to us!  We very rarely go out to the movies now because of babysitter requirements).  I got distracted thinking about the liberal media, which touched on the whitewashing that happens in this movie a few months ago.  It first came to my attention on the blog Angry Asian Man, and has been covered in a bunch of other various sites (for instance: cinema blend, variety, fusion-this is my favorite one).  Basically, the Ancient One is a main character/jedi master for padawan Dr. Strange, and was created by Lee and Ditko as an ultra-stereotypical mystical Tibetan old dude.  Most of the lines that Tilda Swinton says in the movie would be ridiculous if an Asian person said them (they’re already ridiculous, but Tilda Swinton can pull them off just as well as she can pull of her slightly agender character).  Lots of stuff that sounds like someone read the Tao Te Ching and imagined Mr. Miyagi.

So, to avoid making a racist and offensive caricature of the character, Marvel decided to change the Ancient One into a Celtic woman.  But inexplicably keep the whole Himalayas/white guy travels to the Far East for mystical wisdom and returns to the civilized West to save the world trope.  Here are two different reactions to this situation by critics/writers:

Pro-Derrickson/whitewashing (I love that this article starts with the “yellow elephant in the room”)

While this desire to keep from reinforcing negative Asian stereotypes is commendable, it also created a Catch-22 situation for the director. Does he create a “dragon lady” that causes Asians to protest the perpetuation of a negative Asian stereotype? Or does he remove the Asian aspect of this character, but in doing so, cause Asians to protest the removal of what was supposed to be an iconic Asian character. There is no easy answer either way.  So Derrickson decided to take the path that he thought was best.

So before we bring out the pitchforks, it is important to consider intent. If Derrickson was pressured into recasting The Ancient One as a white woman to make the film more commercial, then I’ll be the first one to pick up a torch. But if he made this decision based solely on his own creative vision of the film, then I think it’s fair to withhold judgment on this subject and critique the movie solely on its cinematic merits. Because ultimately, as long as Swinton’s performance improves the film (and in this case, it most definitely does), it’s hard to fault the director for his artistic choice.

I actually think the next pull quote addresses this situation pretty well, but I’ll also make a nod to myself and a past blog post, and say your intentions don’t matter as much as the effects of your work.  So if you say you didn’t intend to be racist, that doesn’t alleviate that you hurt someone with a racist action.  Plus most people outside the KKK (and maybe also inside) won’t ever say “Oh, I meant to be racist” so saying “I didn’t mean to be racist” is about as deep as saying “the sky is up.”  Later in this post I will tell you that I LOVED Tilda Swinton as the Ancient One and I have a solution.

Anti Derrickson/whitewashing: (but they loved the movie, as did I)

Why did Derrickson feel his only options to portray the Ancient One were to either make the character one racist stereotype or another? This wasn’t some Catch 22. You could still write the ancient mystic leader role with nuance.  And for the most part, Derrickson and his co-writers succeeded — but with a performer who isn’t Asian….

And they elevated the Wong character (played by Benedict Wong, the film’s only Asian in a speaking role). In the comics, Wong was merely Doctor Strange’s cabana boy (yet another stereotype, the Asian sidekick). But in the film, he is Strange’s philosophical superior. Wong has agency in the plot, answers to almost no one except his own work and also owns the funniest sequence in the film. The director and writers clearly have the talent and imagination to dodge or upend stereotypes.

Other things: I loved the Pop Culture Happy Hour podcast on this topic, if you’re more of a listener than a reader.  I also like this tweet:

Okay, so you’re all caught up on your reading now (I really do like the Fusion article).  So here’s my take on what they should have thought about doing instead of what happened.  First, here’s a cover of a comic book showing Dr. Strange; image from Wikipedia:

strange

Note that Dr. Strange here is totally red, and has lots of facial hair.  So… just looking at this picture, I don’t see it tied to a particular ethnicity.  Let’s take a look at the original Ancient One (also from wikipedia):

ancientone

This dude is Tibetan, but they turned him into a Celtic lady.  So there’s no reason to not change the Dr. Strange of ambiguous ethnic background into …

johnchoTHIS GUY.

Hear me out (also, this headshot was taken from the inimitable starringjohncho.com).  Instead of going to the Himalayas for absolutely no reason, our hero Dr. Strange hears about this Celtic Ancient One and heads to Ireland.  This also makes more sense if the Ancient One is Celtic.  I’ve read a Wrinkle in Time and the Chronicles of Narnia; I’m pretty sure there’s as much magic in the British Isles as there is in Tibet.  Now we haven’t whitewashed out a character because OUR MOVIE HAS AN ASIAN LEADING MAN, and we get to keep Tilda Swinton who really is great in the movie.  I have nothing against Benedict Cumberbatch except he’s going the way Jude Law did in 2004, when he appeared in six big movies.  Too much Benedict Cumberbatch, not enough John Cho (or other Asian actor).

This is the obvious solution to me, anyway.  The reviews of Dr. Strange praise the visual effects, the side characters (POC Mordo and Wong), Tilda Swinton, but I haven’t seen a lot of praise for Benedict Cumberbatch.  They just needed a white guy to lead the movie.  So why not grab an Asian guy instead?  This should be a rule: if you ever whitewash a character of X ethnic background, you should change a white character into that X background.

I think I could just keep writing and thinking about this topic, so I’ll stop here instead.  Happy Thanksgiving, reader!  Good luck with politics and family.

The (2,3,7) Triangle Group

8 Nov

Hi!  Today we’re going to use some stuff we learned about a long time ago (non-Euclidean geometry, manifolds, and groups) and put it together to explore a particular group.  This is based on a talk given by my dear friend and co-organizer Michelle Chu.  “Co-organizer?  Of what?” you ask.  Well thanks for asking!  Last weekend we did held the first Texas Women in Mathematics Symposium – we had over 60 people come, lots of talks, lots of networking, and lots of food.  By the end of the day I got to add “annual” to that description, and it seems like a lot of schools were interested in hosting it in future years.  Maybe some time I’ll write a post about how to found an annual conference (this is my second!).

Anyways, let’s first talk about tilings by triangles.  We first choose some integers p, q, r and set the three angles of a base triangle equal to \frac{\pi}{p}, \frac{\pi}{q}, \frac{\pi}{r}.  Now reflect over each of the three sides to start tiling your space.  Turns out this tiling will lead to a group.  Here’s an example with p=q=4 and r=2 (so we have a right isosceles triangle):

firstimgs

Start with the pink triangle, and reflect it over each of the three sides to make the colored triangles as shown.

secondimgs

Now do the reflections again.  I kept the pink base triangle and grayed out the first images.  Note that I colored the bottom left image yellow, for reflecting over the vertical side of the bottom orange triangle, but I also could color it orange, for reflecting over the horizontal side of the left yellow triangle.  This means that yellow+orange = orange+yellow in the group.

thirdimgs

A third iteration of the same process; there are more relations here (that I didn’t color)

I picked a particularly good example, so that my triangles could tile the Euclidean plain.  We learned some time ago about non-Euclidean geometries: the space is spherical if the sum of triangle angles is more than \pi, and hyperbolic if triangles are thin and their sum of angles is less than \pi.  So based on how I choose my p, q, and r, I’ll find myself tiling different spaces.  Here’s an example of one iteration on a sphere for p=q=2 and r=5:

This slideshow requires JavaScript.

It’s pretty easy to find the integer solutions for p, q, r to tile each space.  The only triangles that tile the flat plane are when (p,q,r) = (2,3,6), (2,4,4), and (3,3,3).  We already saw (2,4,4), and I’ll just tell you that (3,3,3) is when you use equilateral triangles (so there are 6 around each vertex), and (2,3,6) are those 30-60-90 triangles we learned about back in trigonometry class: here’s the picture from wikipedia:

Similarly there are only a few (relatively) that tile the sphere: (2,3,3), (2,3,4), (2,3,5), and (2,2, n), where is some number bigger than 1.  Of course this forms an infinite family of tilings, since you can choose n.  In the example above I chose n=5, and if is bigger the base pink triangle just gets skinnier.

But I say there’s only a few that tile the sphere because everything else tiles the hyperbolic plane.  That’s a lot of choices!  It might also make you think, “hm, maybe the hyperbolic plane is interesting.”

Let’s bring us back to groups.  How does a tiling of a space lead to a group?  Well, let the reflections over the (extended) sides of the base triangle be the generators of our group.  If I name these a, b, and c, I immediately get the relators a^2=b^2=c^2=1.  Next we have to figure out the rest of the relators.  I hinted at them in the pictures above.  They are (ab)^p=(bc)^r=(ca)^q.  Now we have a group presentation [link for definition] R\triangle(p,q,r)\cong \langle a, b, c \ | a^2=b^2=c^2=(ab)^p=(bc)^r=(ca)^q=1\rangle.

Also, fun coincidence: if you create the dual tree to the tiling by putting a vertex inside each triangle and connecting two vertices by a line if one triangle is the image of another under one of the reflections, you get something that looks a lot like the Cayley graph of the reflection triangle group.  The only difference is that each edge needs to be two edges (like a little loop) to reflect that each generator has order 2.

So what’s special about the (2,3,7) triangle group?  We know from above that it tiles the hyperbolic plane.  Check out this great picture from wikipedia of the tiling:

h2checkers_237

Maybe we’ll take a second here to point out that you can see the p, q, r values in the tilings, both in this picture and the other wikipedia picture above for (2,3,6): pick your favorite triangle, and look at its three vertices.  Each vertex is adjacent to other triangles, and since there are 2\pi angle around any vertex, we can figure out that p,q,r are just \frac{n}{2}.

Also, of all the integers you can pick for p, q, r, it turns out that 2, 3, and 7 maximize the sum \frac{\pi}{p}+\frac{\pi}{q}+\frac{\pi}{r} while staying less than \pi.  [It ends up giving you \frac{41\pi}{42} for those of you keeping track at home.]

So we maximize something with the numbers 2, 3, 7.  Well it turns out we do more than that- we also minimize the volume of the resulting quotient-we touched on and defined those in this post about manifolds.  And this is unique (up to conjugation/fiddling), and makes the smallest possible quotient.  Huzzah!

On a personal note, I’ve had a demonic cold keeping me in bed for the past two weeks, so forgive me if I messed up (pretty sure I did on the reflections; I’ll try to fix those soon).  Also, hope you voted today!  I voted a week and a half ago.

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