“cool” as information flow

“Coolness” is a measure of creative information’s degree of flow through a social network.

Individuals merely channel and contribute to this flow as it progresses through such networks, they do not contain or retain coolness within themselves.

However some individuals prove more receptive to the incoming information and more effective at transmitting it forward than others, and we often label these people as “cool”. But this focuses attention toward the wrong place—the individual—where in reality coolness only resides in the experience of its movement through the network’s nodes.

And when the network consists of many such open, creative people sending signals back and forth, we accurately describe the network as cool.

It’s a jam session. Whether I’m working on a collaborative science project or improvising with a rock band, it’s a jam session. Creative information flows through the network of participants—and we experience “cool” through the exchange.

tracking my gender transition through computational linguistics and machine learning

I wrote 299 blog posts in the last decade, roughly half on badassdatascience.com and half on genderpunk360.com. Produced most of the Badass Data Science content while publicly expressing as a man, and most of the Gender Punk 360 content as a woman. Some articles appear on both blogs—for example this one—and in the analysis described below I account for such duplication.

My speech therapist observed that I successfully employ feminine language in my recent video “radical forgiveness”. This led me to thinking: Has the language I use in my prose evolved as I blossomed into femininity? I detail my attempt to answer this question using mathematical analysis below.

Two Caveats

I make two major assumptions in this analysis, assumptions I will address in future work:

First, I assume my writing skill remained constant throughout the last ten years. Not a great assumption in the long haul but necessary to simplify the math for this “back of the envelope” analysis.

Second, the two blogs cover different subjects, and the first one even contains source code on occasion. This may distort the clustering process described below. Again, ignoring this concern proves acceptable for this “quick-and-dirty” calculation to enable exploration of the problem domain.



Method

I download each of my blog posts and then calculated the part of speech (POS) for each word in the post. After that I computed the frequency distribution of the POSs. I then performed hierarchical clustering using a similarity matrix defined by the dot product of each pair of posts’ POS use frequency distribution vectors. The resulting dendrogram looks like:

I recommend downloading the image to view it at full size.

Each vertical line represents a blog post, and the trees linking the vertical lines indicate the degree of similarity between any two blog posts. For example, in the above image, the cyan and magenta colored posts prove similar but the green and black posts diverge significantly in terms of their POS use frequency distributions. The asterisks indicate posts created after I started expressing publicly as a woman full-time. The colors divide the tree into sections that group similar blog posts. Please note that I chose the grouping threshold manually (but rationally).

Results

By visually inspecting the density of these asterisks for the different color groups we derive an indication of how “feminine” or how “masculine” we might regard each group of blog posts. For example, we see sparse femininity in the green, yellow, and black groups; while we see enriched femininity in the cyan and purple group. The algorithm clearly found little distinction between the posts within the large red group, but even there we visually recognize sections of diminished femininity and sections of enhanced femininity.

So a linguistical difference between my pre- and post-transition writing appears to exist. But is it real? Can we conclude that my prose grew more feminine after my public transition? Not so fast! We must build a model that includes time as a variable to cancel out possible influence of improvement in my writing skill, and then test that model for significance. I’ll save this work for a later date.

Grrl on Grrl Podcast interviewed me!

Today my interview with Grrl on Grrl Podcast came out!  We discuss, among other things,

  • The science of gender identity
  • The music of Axis Evil
  • “Ladylike” behavior as a source of personal empowerment
  • Cultural appropriation
  • Psychosexuality
  • Model minorities

Big thanks to June Owatari of Grrl on Grrl Podcast for working so hard to put this together! The music presented during the interviews may be downloaded here.

 

artificial intelligence in fashion (part two: a first step)

In my recent post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list.

This post doesn’t specifically cover an artificial intelligence solution, but it lays groundwork that I need in place to get to an AI-based style recommendation engine based on body shapes that I’d like to build. Essentially, most fashion dictums take the form of IF-THEN-ELSE rules, where the IF clause generally starts with specifying one’s body shape.



So I needed a way for many individuals at once to determine their body shape, which led to creation of a web-based body shape calculator, pictured below. Several of these already exist, but I really needed my own for my AI project for the following reasons:

  • I can include this work into larger AI software pipelines.
    • Cannot easily include others’ tools, by comparison.
  • I understand the computational method behind what I’m offering.
    • Others’ tools are black boxes.
  • The computation method I used comes from academic literature, so it is peer-reviewed.
  • I can show ads to users to generate some cash flow.

Here is a picture of the web-application I created for this task. Click here to use the application!

numb penis

To prepare for gender affirmation surgery, I’m having hair removed from my testes and penile shaft through electrolysis. To manage pain, I numb the area prior to each session using tetracaine.

I like having a numb penis. Then I can’t feel a part of my body that I don’t particularly want, don’t feel particularly attached to.

artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual:

  1. Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine).
  2. User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes.
  3. Upload an image of a dress you want to buy, and specify the dress’s given size. At the same time, upload your measurements. The algorithm then tells you the likelihood of fit.
  4. Upload your measurements. The algorithm searches for clothes that fit well.
  5. Upload your measurements. The algorithm searches for clothes that flatter your body shape.
  6. User submits 10+ images of dresses they like, with the option to add more. Moreover, they submit their measurements. The algorithm then designs dresses for them.
  7. Automate difficult design tasks. My model here is the AI drummer in GarageBand which provides very sophisticated beats, and which I use in all my songs.
  8. Enhance design. Algorithms can produce combinations that have not been thought of before. Here I envision designer as “pilot” and algorithm as “vehicle”.
  9. Create fiber optic dresses that light up responsively to movement, such that the changes in lighting accentuate curves.



Collaborate!

If you would like to collaborate with me to these or similar ideas happen, I’m an extremely experienced data scientist and would love to work with you!  Please contact me through Facebook if you are interested.

AI-Driven Fashion Show

Holding a fashion show for AI-created styles sounds fabulous!

Next Steps

See what tools exist already. See what APIs exist. Determine if measurement statistics are known. Investigate the Computer Science and Home Economics academic literature.

What data is out there?

See Also

body shape calculator

“distorted and out of tune” – a troll (sort of) gets it

I received the following response from a troll regarding music [4] I released recently:

The music is “distorted” and dissonant—and would sound “out of tune” to many who lack musical sophistication. That is intentional. A compositional choice.

And the dissonance intentionally speaks to the experience of feeling “out of tune” as a transgender person. So our troll is correct in his surface interpretation of the music.

But he fails to see where the problem lies. I assume that his interpretation is that the transgender person is “sick” in some way, compared to some standard of wellness that he and most individuals presumably meet.

But the transgender person is actually fine biologically and psychologically, as I’ve demonstrated in my scientific articles [1, 2, and 3]. So the problem emits from living within an unsupportive and hostile environment. This experience causes sickness in all individuals—a feature of being human, not a trait specific to the transgender population.

I sing these songs today so that transgender folks among future generations need not feel “distorted and out of tune”.

About “Axis Evil”

I perform as “Axis Evil“, the musical arm of my outreach work. Please follow the feed on Facebook and Twitter.

Update 20 November 2017

I further discuss the dissonant and distorted features of my music as it pertains to the transgender experience in my March 2017 post “this is transgender music”, which goes into far more detail than this text does.

References

  1. the science of gender identity (part 1: genetics)
  2. the science of gender identity (part 2: brain anatomy)
  3. the science of gender identity (part 3: psychology)
  4. the music discussed above:

delivering sex appeal to a job interview

About a week ago I attended my first job interview as a woman. There was so much more to think about than before:

  • Fear of discrimination as a woman interviewing for a technical and scientific position
  • Fear of discrimination as a transgender person
  • Keeping my voice in a feminine pitch range for the duration of the interview
  • What to wear

I dealt with the first two concerns by just giving my best absolute possible performance. Nothing else I can do. Similarly, I held the voice up as best I could—really can’t sustain a feminine voice beyond an hour. In other words, nothing much I could do about that than the constant practicing I’ve been doing.

The only real leverage I felt was in choosing what to wear. I originally was going to wear a suit. However, I bluntly decided to favor showing sex appeal instead, choosing an outfit that shows legs and curves (but still wholly appropriate). The company is made solely of men at this point, and I wanted to get into their heads in more ways than just intellectually.

We’ll see how this worked shortly. Here is what I wore:

hypothesis #1

Hypothesis: Women generally excel at mindfulness over men because living in a patriarchy forces us to.

I can envision an experiment to test the first part of this hypothesis:  Put statistically representative samples of men and women through a battery of psychological tests to measure mindfulness, and then compare the sample medians.

However, establishing the proposed causality would prove tremendously difficult.

vocal frequency response

I now can speak consistently for an hour in a feminine voice—decent pitch, resonance, and inflection—before needing to rest. Moreover, my voice now passes on the phone.

So my voice therapist and I decided to tackle my singing range, to feminize that as well. (Followers of Axis Evil know I sing with a masculine voice despite functioning in all other parts of my life using a feminine one).

I needed data to see where I stand currently:

Starting at D3 (146.832 Hz), which lies in the gender-neutral pitch range, I recorded myself singing the words “I am Emily” up the scale in half-step intervals until D5 (587.330 Hz). (But I couldn’t make it that far in practice). I used a synthesizer to provide the pitch at each interval.

I then cut the synthesizer track and ran the vocal track through a frequency analysis algorithm to get a frequency response (Bode) plot:

As you can see from the plot, I can hold it up to about middle C, but can’t currently sustain volume beyond that.

Good baseline information.

estrogen deficit disorder

Potential correlation: I’ve recently upped my estrogen dose, and have recently been happier than I’ve been at anytime in the last two years. What if the two are related? What if my brain expects a certain baseline level of estrogen to function best that it never received until now?

There is evidence that hormone administration improves psychological functioning in transgender people (see my post “the science of gender identity (part 3: psychology)” for a discussion of this evidence.

Perhaps I finally hit a (psychiatrically) clinical dose.

at the genetics conference

We (scientists), suspect a genetic component to gender dysphoria. While that may not be the full explanation, it might be a factor. See “the science of gender identity (part 1: genetics)” for my previous analysis of this subject.

CRISPR/Cas9 technology allows us to edit genomes early in life. The idea is that we can replace genetic mutations that correlate to future disease with nucleotide sequences that correlate to healthy outcomes.

Last October I presented a poster at the American Society of Human Genetics’ annual conference. Most of the workshops I attended were deeply technical, but I attended one about the ethics of using CRISPR/Cas9.

There were hundreds of people in the workshop’s audience, and many big names in the human genetics field. Researchers and M.D.-PhDs filled the room. We agreed that the CRISPR/Cas9 technology could be beneficial to disease prevention.

I stepped up to the microphone at that point (audience feedback was invited) and stated that

We must be careful not to confuse diversity and disease. I like being transgender and would not want that to have been eradicated. Similarly, we must not be hasty to “treat” other socially challenging conditions such as homosexuality and Asperger’s syndrome.

I made it clear that just because someone, somewhere finds my identity distasteful and culturally and/or religiously problematic, it should never be “cured” a priori as if it were a disease.

My statements elicited a full chorus of cheers from the audience, and many complemented me for my courage afterwards.

DIY brain anatomy: looking for transgender features (part 1)

This is my pre-hormone-taking adult brain:

In my post “the science of gender identity (part 2: brain anatomy)“, I describe evidence that certain regions of transsexual brains resemble in size the regions of their cisgender counterparts in the transsexuals’ gender identity.

So I would like to measure my brain region sizes to see how they stack up. Enter image recognition:

This is someone else’s brain, but it shows a visual representation of an image recognition procedure’s results. I discovered I can use the same procedure on my MRI (but without being able to generate the cool image).

I ran the procedure on nine separate images of mine and compared the results by brain region. Here are the Spearman R values for the comparisons:

These results are very consistent, telling me that the image recognition program is consistent. This gives me confidence in using the results in later analyses comparing my brain region sizes to that of a population of cisgender brain MRIs.

Code, Data, and Procedure

Data, code, and instructions necessary to produce these results are attached.

code_and_data