## Musings on $\pi$ Day

#### 1. The Ubiquity of $\pi$      or, Life, the Universe, and Everything: A Simple Statement of Fact

$\pi$ is everywhere you look. It is even the case that there is $\pi$ in the sky. We need $\pi$ in order to live and function. These three observations are fundamental to the way our universe is put together.

#### 2. The Value of $\pi$      or, How Much is that Round Thing in the Window?

(with apologies to Patti Page) So, for fun, let’s calculate $\pi$ using Ramanujan’s famous infinite series formula, and check the error against a clever, arbitrary-precision algorithm for $\pi$, based on the Chudnovsky brothers’ improvement on Ramanujan’s series approximation, and which is correct to as many digits as we care to specify. While we’re at it, we’ll include just a straight-up implementation of the Chudnovsky brothers’ series approximation, too.

Ramanujan’s formula (see also here, and here):

\dfrac{1}{\pi} = \dfrac{2\sqrt 2}{9801}
\sum_{n=0}^{\infty} \dfrac{\left(4 n\right)!}{\left(n!\right)^4}
\dfrac{1103 + 26390\,n}{396^{4n}} \label{eq:ram}

As mentioned here, the Chudnovsky brothers derived a Ramanujan-like formula that converges considerably faster(!) than Ramanujan’s original:

\dfrac{1}{\pi} = \dfrac{1}{53360\sqrt{640320}}
\sum_{n=0}^{\infty} \left(-1\right)^n
\dfrac{\left(6 n\right)!}{\left(n!\right)^3\left(3 n\right)!}
\dfrac{13591409 + 545140134\,n}{640320^{3n}} \label{eq:chud}

We can take advantage of Python’s decimal module for exact arithmetic to as many digits of precision as we might want in calculating each term of the series. Doing so, we find the following errors after each successive iteration of the two series (note the exponents!):

      Ramanujan   Chudnovsky  n   Rpi(n)-pi    Cpi(n)-pi --  ----------  -----------  0    7.642E-8   -5.903E-14  1   6.395E-16    3.078E-28  2   5.682E-24   -1.721E-42  3   5.239E-32        1E-56  4   4.944E-40   -5.959E-71  5   4.741E-48    3.609E-85  6   4.599E-56   -2.212E-99  7     4.5E-64   1.368E-113  8   4.433E-72  -8.515E-128  9   4.391E-80   5.331E-142 10    4.37E-88  -3.353E-156 11   4.364E-96   2.117E-170 12  4.372E-104  -1.341E-184 13  4.393E-112   8.513E-199 14  4.424E-120   -5.42E-213

As we can see, Ramanujan’s formula, eq. \eqref{eq:ram}, gives eight orders of improvement (i.e., eight more digits of accuracy) per successive iteration, while the Chudnovsky formula, eq. \eqref{eq:chud}, yields fourteen orders of precision per iteration!

To illustrate, after fifteen Chudnovsky series terms, the difference between the series approximation and the actual value of $\pi$ is:

-0.00000 00000 00000 00000 00000 00000 00000 00000 00000 00000   00000 00000 00000 00000 00000 00000 00000 00000 00000 00000   00000 00000 00000 00000 00000 00000 00000 00000 00000 00000   00000 00000 00000 00000 00000 00000 00000 00000 00000 00000   00000 00000 00542...

Even just the first Chudnovsky term by itself (or just the first two Ramanujan terms) gives $\pi$ to almost machine precision ($2^{-52}\approx 2.22\!\times\!10^{-16}$) on a 64-bit computer.

For another perspective (thanks for the idea, Daniel Greenspan), let’s calculate (roughly!) the total number of atoms in the universe. As you might imagine, this will be a big number. We’ll break it down into two parts.

First, how many stars are in the universe? This is a number we can estimate from observations of galaxies and the amount of light that they emit. Essentially, since we can determine distances to galaxies, we just add it all up. Modern astronomical estimates for the equivalent number of solar-mass stars in our universe, based on the amount of light we detect coupled with the distances to the objects (galaxies) emitting that light, all come in at around

N_{stars} \approx 2\!\times\!10^{23} \label{eq:Nstars}

This is equivalent to the mass of the visible universe divided by the mass of the Sun.

The amount of baryonic—that is, visible, or what we think of as “normal”—matter in the universe is only a small fraction of the total mass of the universe. Our universe, based on several different kinds of observations, is $68.3\%$ dark energy, $26.8\%$ dark matter, and $4.9\%$ ordinary matter. But that’s another story. We’ll just stick to the ordinary matter that we can detect via the electromagnetic radiation it emits.

Second, how many atoms are in a star the mass of our Sun? Now, the Sun has a measured mass of $M_{\odot} = 1.9884\!\times\!10^{30}$kg() and is composed of about $74.9\%$ hydrogen and $23.8\%$ helium by mass(). For this exercise, we will assume that the mass contributions of electrons and the other elements besides hydrogen and helium are negligible. The mass of a proton is $1.00784$amu, and the mass of a helium nucleus is $4.002602$amu. One amu (atomic mass unit) is $1.66053904\!\times\!10^{-27}$kg. The approximate number of atoms in the Sun, $N_{\odot}$, is then

N_{\odot} \approx
\dfrac{0.749 M_{\odot}}{1.00784\mathrm{amu}} +
\dfrac{0.238 M_{\odot}}{4.002602\mathrm{amu}}
\approx 9.6 \!\times\!10^{56} \mathrm{atoms} \label{eq:Nsuns}

Hence, combining \eqref{eq:Nstars} and \eqref{eq:Nsuns}, the number of atoms in the universe, $N_{universe}$, is, roughly,

N_{universe} \approx N_{stars}\cdot N_{\odot}
\approx 1.9\!\times\!10^{80} \mathrm{atoms}

Notice from the table above that the error of the Chudnovsky series after only the first six terms is about one part in $2.8\!\times\!10^{84}$—a number that is several orders of magnitude larger than the number of atoms in the entire universe!

#### 3. A Short Introduction to Astrophysics     or, Is there Really $\pi$ in the Sky?     or, Here are the Footnotes

You might not have seen this coming. But here it is, wherein we demonstrate that, indeed, there is $\pi$ in the sky.

(†) We can determine the mass of the Sun by measuring the motions of the planets and asteroids in our Solar System, and then using Newton’s Law of Gravity. As Kepler discovered from Tycho Brahe’s meticulous observations, and Newton proved mathematically after he invented calculus and then turned his attention to the Moon’s motion, the orbital period $P$ of a body of mass $m$ and its mean distance $a$ from the Sun with mass $M_{\odot}$ are related by

P^2 = \dfrac{4 {\pi}^2}{G\left(M_{\odot}+m\right)} a^3

Look at that: $\pi$ is in this equation that describes what we see in the sky.

(‡) We can determine the relative abundances of the elements that make up the Sun (and almost any star) by measuring, with a spectroscope, the amount of radiation absorbed by those elements in the atmosphere of the Sun (called the photosphere). Every element has its own discrete spectral signature in the form of absorption lines at specific sets of wavelengths. The amount of radiation absorbed by an element, relative to the other elements present, and in combination with the measured temperature, luminosity, and mass of a star, tells us what fraction of the star’s photosphere consists of that element. (We also need to know the distance to the star, but that’s a long story.)

Stars are, roughly speaking (i.e., ignoring the radiation absorbed by the elements in their photospheres), black body radiators. This means we can relate their luminosity (total radiated energy per unit time) to their radius $R$ and their effective surface temperature, $T_{eff}$. Simply put, the luminosity is the surface area of the star ($4\pi R^2$) times the amount of radiation emitted per unit surface area of the star:

L = 4\pi R^2 \sigma T_{eff}^4 \label{eq:L}

where $\sigma = \dfrac{2\pi^5 k^4}{15 c^2 h^3}$ is the Stefan-Boltzmann constant, $k=1.38064852\!\times\!10^{−23}$ Joules per degree Kelvin ($J\cdot K^{-1}$) is the Boltzmann constant, $c$ is the speed of light in vacuum, and $h=6.62607015\!\times\!10^{−34} J\cdot s$ is the Planck constant from quantum mechanics. Eq. \eqref{eq:L} is a consequence of the physics of black body radiation.

Look at that: $\pi$ is integral to these relations that describe what we see in the sky, too.

(Don’t ask about the quantum mechanics connection. You can go down that rabbit hole by following the provided links. Quantum mechanics hurts my head.)

#### 4. Full Disclosure      or, So This is Where That Came From

The Python code that produces the Ramanujan and Chudnovsky results (table and plot) is:


import decimal
from decimal import Decimal as D
from utils import mutils
from utils import mplot as plt

prec = 300  # Set the number of digits of precision
# for calculations.
decimal.getcontext().prec = prec

def dfac(n):
""" Arbitrary digits factorial. """
m = D('1')
for k in range(1,n+1):
m *= k
return m

def Rpi(n):
"""
Calculate pi using n iterations of Ramanujan's
formula.
"""
s = D('0')
for k in range(n+1):
facterm = dfac(4*k)/dfac(k)**4
num = D('1103') + D('26390')*k
den = D('396')**(4*k)
s += facterm*num/den
s *= D('8').sqrt()/D('9801')
return 1/s

def Cpi(n):
"""
Calculate pi using n iterations of the Chudnovsky
brothers' Ramanujan-like formula.
"""
s = D('0')
for k in range(n+1):
facterm = dfac(6*k)/(dfac(k)**3*dfac(3*k))
num = D('13591409') + D('545140134')*k
den = D('640320')**(3*k)
s += D('-1')**k*facterm*num/den
s *= D('1')/(D('53360')*D('640320').sqrt())
return 1/s

# Print a table of the error of n iterations
# of Ramanujan's formula.
print('     Ramanujan   Chudnovsky')
print(' n   Rpi(n)-pi    Cpi(n)-pi')
print('--  ----------  -----------')
fmt = '{:2d}  {:>10s}  {:>11s}'
c4  = decimal.Context(prec=4)
rerrs = []
cerrs = []
for n in range(15):
exact_pi = D(mutils.pi_chudnovsky(prec))
errR = Rpi(n) - exact_pi*D('1e-{:d}'.format(prec))
errC = Cpi(n) - exact_pi*D('1e-{:d}'.format(prec))
normerrR = errR.normalize(c4)
normerrC = errC.normalize(c4)
print(fmt.format(n, str(normerrR), str(normerrC)))
rerrs.append(float(errR))
cerrs.append(float(errC))

fig = plt.figure(figsize=(8.2, 5))
xlab = ['$\mathrm{number\ of\ series\ terms}\ n$', 12]
ylab = ['$\mid f(n) - \pi \mid$', 12]
pt = ['$\mathrm{\pi\ series\ approximation\ error}$',
14]
labs = [['$f(n) = \mathrm{Ramanujan}$', 10],
'$f(n) = \mathrm{Chudnovsky}$']
xticks = np.arange(15)
yticks = np.array([0.1**k for k in range(0, 240, 30)])
ylim = (1e-220, 1e-1)
plt.lineplot([np.array(rerrs), abs(np.array(cerrs))],
np.arange(15), ['k-', 'r-'], [1, 1],
ylim=ylim, logy=True, xlab=xlab,
ylab=ylab, xticks=xticks, yticks=yticks,
doxticks='bottom', doyticklabels='both',
dolegend=True, labels=labs, plottitle=pt)
fname = (os.environ['PYTHONPATH'] +
'/misc/Ramanujan pi.jpg')
plt.savefig(fname, dpi=300)



## Snow, Mountains, Telescope

Snow on Flagstaff’s San Francisco Peaks, and a picture of the 61-inch telescope at the U.S. Naval Observatory — Flagstaff Station. Click on an image to enlarge; right-click to open the full-resolution version in a new tab.

## Moonlit Snowscape

The Moon illuminates a snowy scene (my back yard) in the pre-dawn darkness. This is an 80 second exposure at 35mm f/3.2 and ISO 125. The yellow-gold color on the background trees is from low-pressure sodium streetlights on the next street over. Click on the image to enlarge; right-click to open the full-resolution version in a new tab.

## Twilight Dome Fun

Here are a few images from goofing around yesterday evening with the NOFS 61-inch telescope, dome, and twilight. Images were taken with a Canon G3 X at ISO 125 on 2019-02-07 MST. Click on an image to enlarge; right-click to open the full-resolution version in a new tab. Ordering is reverse-chronological because I like the 7:02pm image best.

All images © 2019 Marc A. Murison: CC-BY-NC-ND

## If Aesop Were a Modern-Day Scientist, This Would Be a Fable.

When you wake up, get up. When you get up, do something.
—one of my favorite quotes, from Viola Babbitt

Wake up.

Sit with the cat for a bit. Because Cat Time. “Is necessary, hooman. Sit.” Wait for cat to reach inevitable “bored now” condition…lick warning…condescending departure to stretch. Stretching is important. There is no moral analogy in this.

Get up.

Shuffle around the stretching kitty to the computer in the next room. Shuffle back to get your glasses from the nightstand. Shuffle around the feline a third time, oblivious to its withering stare.

Do something.

Start the computation job you were too tired run at 3am. Rely on the setup you already did. At 3am.

“Initializing”…?

Grogginess is measured by the amount of time it takes to realize one has done something unfortunate. It is an accurate measure. Being after the fact, though, it is not helpful towards the prevention of unfortunate acts. Lassitude can be assessed by the time needed to recognize that feeling in the pit of one’s stomach and say “Oh, hi again, you”. These are the same.

So herein lies the moral of our tale: Coffee. Do coffee first.

## The Bear and the Butterfingers, the Exploding Dinosaur, Fred, (and Fred, and Fred,) and the Orb Weavers

An always delightfully engaging friend asks of the Hive Mind on Facebook:

What is the first wildlife you remember encountering in the actual wild (wild is broadly meant here–not a zoo)? Maybe don’t count squirrels. Or maybe do. I’m pretty sure mine was a squirrel. Or a pot gut up at Brighton.

Childhood memories. Such hole-y, but ever-tantalizing, constructs (say cognitive neuroscientists, whom I’ve no reason to question, although I’d like to).

My timeline ordering is necessarily diaphanous from this far vantage of almost five decades, but the set of vivid memories poking out from the gray mists comprises the following most likely non-boring contenders (for current purposes, deer = boring, although at first-sighting they, too, were exciting):

1. various SoCal orb weavers — hence my early and lasting fascination with and fondness of arachnids and the beguiling, interrupted symmetry of spider webs (was this the seed of my later captivation by mathematics?);
2. praying mantises — hence, subsuming arachnids, my early and lasting chitinous fascination with and fondness of arthropods generally;
3. a brown Desert tortoise, whom we named Fred, my brother and I, and who hung out in the grass and ivy of our suburban Los Angeles back yard one summer, happily devouring our fig tree droppings (Fred did not seem to care for fallen apricots) — hence, yada yada, reptiles;
[Later, I had an alligator lizard, also named Fred. He was brown, too. Much later, my wife and I had a cat, also named Fred, whom we rescued from the wilds of our Washington, DC, suburb. Fred the cat was also brown. Fred is the name of all the best brown pets. One day, alligator Fred laid an egg. It had a soft, leathery shell, cream-white with brown stains. Not at all like a chicken egg from the grocery store. Fred with the hard shell and a fondness for brown-black figs laying in the sun did not lay an egg. Fred’s egg never hatched; not understanding why, I was crestfallen. (And cats, silly, don’t lay eggs. But an ancestor that cats deign to share in common with us laid eggs. I think that’s cool. I bet it was brown, that ancestor.)]
4. a hawk, perched on top of a tall party-line telephone pole (it seemed impossibly tall to an eight-or-nine-year-old kid from the L.A. suburbs, and the isolated farmhouses in eastern Washington wheat country were all connected by party lines, and church socials) across and down the draw — that’s what my grandmother always called it, The Draw, as if that was its name — a mile or so south and west from my grandparents’ farmhouse one hot summer day, which one of my ancestral uncles shot, from an impressive distance, with his childhood .22 rifle with the well-worn brown wood stock, the bird’s feathers suddenly exploding — a long time, it seemed, after the rifle hammer made the bullet’s gunpowder explode — and fluttering down against the hot still air, shimmering silhouettes arcing in and out of existence against an infinite deep blue sky (back then, deep blue skies were infinite, especially on hot summer days), the way blue and black dinosaur feathers do when they explode — hence my early and lasting dislike of firearms, and of immature, inconsiderate mentalities;
[The hawk flew away, I would guess surprised at the sudden disappearance of tail feathers and angry at such a rude interruption of its respite. One of the feathers, like my favorite lucky agate, I kept for many years.]
5. and, indirectly, an American black bear, who, some time in the dead of an inky starlit night in the Sierras,
1. quietly and neatly tore a perfectly square, hand-sized opening in the corner of a fellow Boy Scout member’s tent (pretty sure it was a blue tent, like mine, but it could have been green) and took his illicit stash of Butterfingers (yes, that kid, who had red hair and freckles and I’m sure a name, and whom I never did much like because he was kind of a bully, was a fucking idiot), and
2. quietly and neatly tore the left rear door off a neighboring camper’s metallic-blue Toyota sedan and took the food from the subsequently undamaged blue and white Coleman cooler sitting under a blanket on the back seat (the sedan owner wasn’t too bright, either, though, unlike freckles the idiot, he had black horn rimmed glasses and was a really nice guy)

— hence, my early and lasting appreciation for the wide range of manual dexterity among bears.

## Twilight Jewels: Moon and Venus

I took these from up on the catwalk of the U.S. Naval Observatory Flagstaff Station‘s 61-inch astrometric telescope in Flagstaff, on the evening of 2018-04-17. In all, I got 43 decent exposures, from which I picked four that I liked and that spanned the range of useful twilight brightness. All times are MST. Camera: Canon G3 X.

Images ©Marc A. Murison, CC BY-NC-ND.

Click on an image to enlarge; right-click to view at full resolution in a separate browser tab.

This first image was fairly early, when the Sun was just 6.8 degrees below the horizon (just past the end of civil twilight) and the twilight sky was still bright. Visually, you could just discriminate the reflected Earth light on the dark part of the Moon’s disk from the twilight sky background.

This next exposure was when the Sun was 8.8 degrees below the horizon, about halfway through nautical twilight. Right around this time, the lighting was at its most ethereal and, I think, beautiful. It was a wondrous sight.

This next one is a closeup of just the Moon, showing reflected Earth light from the non-sunlit part of the Moon’s disk. Reflected Earth light is visually striking when the Moon is a thin crescent — look for it the next time you see a thin crescent Moon. The faint arc to the left of the Moon is an internal camera lens reflection from the bright, sunlit sliver. The star on the left is 5 Tau, magnitude 4.1. The fainter one below-left is HD 21379, magnitude 6.25. The stars appear as short trails, and the features of the Moon are blurred, due to the Earth’s rotation during the 2 seconds that the camera shutter was open.

This final image shows the sky when the Sun was 11.8 degrees below the horizon — just before the end of nautical twilight and the beginning of astronomical twilight. The end of astronomical twilight, when the Sun is 18 degrees below the horizon, is when the sky is typically dark enough for astronomers to begin observing. Although you can still see a faint twilight glow near the western horizon even at the end of astronomical twilight, the sky is bright enough to interfere with observations only close to the horizon — where we almost never point telescopes anyway. (The usual rule-of-thumb limit is 30 degrees above the horizon. Lower than that, and you’re looking through too much of the Earth’s turbulent atmosphere for most types of observations to be scientifically useful.)

## Foibles in Finding Fault

A driverless car hit a woman who was walking her bicycle across a street at night. Numerous articles, some less useful than others, but also some more helpful to a better understanding, describe this incident. For example, in order of increasing technical detail,

Police In Arizona Release Dashcam Video Of Fatal Crash Involving Self-Driving Car

Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam

How a Self-Driving Uber Killed a Pedestrian in Arizona

Uber Self-Driving Car Fatality Reveals the Technology’s Blind Spots

I have tried to find articles that do not center on or advance irresponsible histrionics, egregious ideological bias, or insulting, simplistic thinking. Critical thinking matters, especially when people’s lives are at stake.

Given the infancy of driverless vehicle technology, how significant is this incident? All knowledgeable observers have been expecting, and dreading, that something like this would happen. Now that it has, how do we put this in a context that makes sense? That is helpful? That is at least somewhat productive for society? Certainly, snap judgments and scapegoating are the opposite. So let’s first consider a few facts.

According to the CDC, in 2015 (latest official U.S. mortality data) the lower bound on the number of pedestrians killed by people-driven cars was 5,719. Another 17,008 were killed by ‘unspecified’ means involving motor vehicles, so the real number is probably higher than 5,719. Another 8,313 vehicle occupants died in accidents, cars killed 4,431 motorcyclists, 675 bicyclists, and 15 ‘other’. Of necessity, those are all lower bounds as well. The total number of motor vehicle deaths in the U.S. in 2015 was therefore 36,161. Further, 94% of traffic accidents are due to human error (i.e., insufficiently sound judgment).

However, the number of driverless cars on the road is as yet statistically minuscule, and the miles driven under realistic circumstances even more statistically minuscule. So any direct comparison is invalid in the absence of substantially further data — estimates put the number of miles necessary for statistically significant discrimination at hundreds of millions of miles or more — and better-discriminating data measures — such as number of fatal accidents per mile driven — with which to compare. The significance of this incident, for now, lies elsewhere.

In the immediate aftermath of this unfortunate and terrible accident, many of us have several questions in common: what might have gone wrong? Where should we look for correctable fault? With whom should we lay blame? Is attempting to lay blame on someone even useful, to anybody? I would argue that the answer to that last question, at least, is easy and should be obvious: no. The situation is complicated in several arenas (technical, political, psychological, among probably others), with many confounding factors. So, based on experience if nothing else, we know it is likely there are no easy or quick answers. Certainly, simplistic thinking is neither productive nor beneficial.

With regard to the political context around this particular event in this particular state: As so often has happened in the past couple of decades, we could again be looking at the sad, unnecessary consequences of conservative values enacted as irresponsible and reckless public policy: private profits tend to matter more than people’s lives. But that is an entirely separate, infuriating, and thoroughly expletive-laden subject, at least for me. Here, let us choose not to go there.

Could we also, or maybe instead, be looking at technology advance pursued at the knowing expense of public safety? Of people’s lives? The implication being that tech advance, and science more generally, is pursued by heartless elites who disregard public concerns? In short: no. This is an ignorant, flagrantly dishonest strawman. No. This is not that. Just no. It is never, in the real world, that. The people who manufacture and push this false cultural meme on us are dishonest; they seek to steer an often unknowing society toward their own self-serving agenda — an agenda that cannot survive the light of rational, fair, ethical examination, hence their sly dishonesty. The real world is never this simple-minded, one-dimensional fiction. In the real world, scientists and technologists are not evil, are of necessity if nothing else the opposite of dishonest, have neither time nor inclination for shady, conspiratorial ulterior motives. That story, however superficially enticing, is pure fiction. Deep down, under whatever emotions, ideological bent, and biased noise might be pummeling our conscious minds, I think we all know this.

With regard to sensors, the Wikipedia article on driverless cars sheds some light but, surprisingly, not much:

Typical sensors include lidar, stereo vision, GPS and IMU.[42] Visual object recognition uses machine vision including neural networks.

The last two journalistic articles linked above are more helpful. In this context, “vision” can mean optical or infrared or both (and/or even some other wavelength range, such as radar). I would have been utterly shocked if these systems did not use both IR and optical sensors. It’d surely be the height of both stupidity and irresponsibility if they did not. It is worth pointing out that engineers are not stupid, and rarely irresponsible, while corporate upper management sometimes is — and politicians almost certainly can be counted on being — both stupid and the epitome of irresponsibility. Given the current apparently poor regulation in this area, I suppose stupidity and irresponsibility are therefore potentially viable likelihoods, despite the no doubt multiple layers of safety protocols that smart people, down at the technical levels inside the companies, have nevertheless managed to put in place despite upper-level idiots (if any).

If this particular car was outfitted with both visible and IR sensors (lidar would necessarily be IR in this context), then the fault must be either 1) with the chosen sensor sensitivities, the chosen sensor ranges, or the chosen sensor fields of view, or some combination thereof; or 2) within the AI decision assessment of the filtered and cleaned input signals; or 3) some combination of both. That’s it; those are the options. Yes, it’s complicated.

(I’m assuming there was not a sensor failure, which would be both a manufacturing testing and reliability fault as well as a redundancy failure in the design. This is a different topic I won’t address here, but at this point it’s also a possibility.)

Indeed, as we learn from the articles above, this vehicle used optical, IR, and radar sensors, as one would reasonably and correctly think should be the case. Whether or not the production design had been recklessly limited to just optical wavelengths — I find it unthinkably unlikely that any significant U.S. company would be that self-destructively reckless, but suppose so anyway — then you must arguably add, and otherwise could reasonably add, a fourth potential likelihood: the fault lies foremost with a cost/safety trade-off decision or series of decisions that some fucking idiot in a position of overriding power might have made — undoubtedly (if this even happened) over the vehement objections of the design engineers and other technical experts. Trade off decisions are ultimately subjective assessments, whether wisely made or not, therefore a thick morass of difficulties and ambiguities. (As an aside, optical-only would be, and many other potential technical shortcomings could be, a direct consequence of allowing privatization in the absence of regulation of technological advances, in spite of all the obvious public risks. Ayn Rand was a fucktard and an awful human.)

But here’s the point I wish to make: either way, the woman who was struck and killed was NOT at fault, no matter how careless or distracted she may or may not have been in that terrible moment. Keep in mind that we can never really know her state of mind or level of distractedness anyway. The available data are insufficient for attempting any such assessment, even if it could have been useful (it is not). But even so that judgment, however tempting for some people when emotions run high, is not relevant. In addition, it is not relevant that she was not at a cross walk; those exist to primarily to counter (or, rather, partly contain) human driver error. Nor is it relevant that it was dark instead of broad daylight: a paucity of optical photons does not affect two of the three sensor wavelength ranges, while optical sensors that are extremely sensitive at low light levels are a well-established and inexpensive technology. It even is not relevant that the safety driver was provably distracted and then failed to react and take control. None of these are relevant if a fatal fault lies further up the precedence hierarchy.

I watched the video clip of the incident, compiled from on-board optical cameras, several times. It is crystal clear to me that this accident should never have happened, because first and foremost something either is or went wrong (or is at least insufficiently comprehensive, therefore still wrong) with the sensor design, or the sensor data analysis software (this is where noise filtering takes place), or the software decision module (the machine learning and AI part), or — and this I think is most likely — some combination of those. Even if every reasonable precaution had been allowed — and we all know that every precaution engineers deemed necessary and reasonable very likely was not allowed, for a variety of reasons (some good, some bad) — this still is where the fault lies that we should all care most about right now. It is a complex system operating in a complex, time-variable setting, and somewhere therein lies a problem, a bug, or an oversight that unambiguously takes precedence over whatever may or may not have been happening with that poor, tragically unlucky woman.

We don’t know yet what, exactly, happened. But we know where we should be looking. It behooves all of us — society — to allow unhindered gathering of all possibly relevant data around the event and give the experts the access, resources, and time that, according to them, they need to hunt down the real cause and find ways to fix it. The technical problem, wherever it lies, is the only valid first priority. Setting up red herrings (“it’s her fault!”, “no, it’s Uber’s fault!”, “no, it’s capitalism’s fault!”, “no, it’s the idiot Arizona legislators’ faults for once again valuing corporate profit over human lives!”, etc.) only uselessly distracts from — and maybe even will prevent — tracking down the technical problem.

Complex systems can behave in unexpected ways. Probably several things — perhaps even each one innocuous in isolation — had to combine for this accident to have happened. It is likely every single-fault failure mode was identified and mitigated by the design and test engineers. Those failure points are relatively straightforward to deal with, and, again, engineers are far from stupid. However, multiple chained events leading to unexpected, even unpredictable, behavior in a complex system can be extremely difficult and time-consuming to debug, both before and after the fact — especially in the presence of insufficient testing or design resources. This is every engineer’s absolute, hands down, most disturbing nightmare. It is THE thing that keeps engineers up at night.

Yet even if every conceivable precaution had been (allowed to be) taken, dependent multimode failures can and do still happen anyway. It is inherent in the very natures of technological advance and sophisticated systems.

Sometimes, nobody is to blame.

The best anybody can ever do — and therefore the most anybody can ever ask — is to implement every reasonably knowable precaution and perform every reasonably knowable relevant test, and iterate sufficiently many of these design-test-correct cycles to satisfy everyone’s most important misgivings, in order to ferret out all (you hope) of the important gotchas you undoubtedly didn’t or couldn’t, for whatever reasons, think of beforehand. Even then, surprising shit will happen. To further compound things, in the real world you very rarely (as in: never) can afford to fully implement these to everybody’s satisfaction, which just makes bad unexpected events all the more unavoidably likely.

Nobody wants it to happen, or even to be a possibility, but bad shit is very likely going to happen no matter what. You do your best — and, even in the real world, almost everybody in this line of work does — given the current context, available knowledge, and available resources, and you hope that you’ve managed to mitigate the severity of the consequences of the inevitable but unpredictable bad things to a sufficient extent that in the end you have navigated this unavoidable minefield without anybody getting hurt or killed.

That didn’t happen, this time. But nobody should be surprised. Nor should anybody be at all quick in pronouncing — nor is anybody entitled to pronounce — ill-informed judgments.

This is the way it is. You learn from the problems that, despite all your efforts, hit you and your team; you fix them; you become wiser (if more saddened); and despite the paralyzing pit in your stomach you move on.

My dad was an aircraft flight test instrumentation engineer, so I grew up seeing him and his friends live this process, several times. As a kid I did not fully comprehend what had happened, and he had a tendency for understatement in place of emphasis in serious events. One of the incidents occupies a vividly chiseled volume in my brain. In a freak accident, a chase plane — a standard safety measure — was nicked on the wing by a test helicopter’s main rotor blade. As it happened this time, Dad was not on board the helicopter but directing the chorus of test data from the ground. The helicopter pilot managed, somehow, to recover control, but the chase jet went down, hitting the ocean surface off the California coast (another standard safety precaution — never expose the public to even the slightest possible risk). Out of long experience, the seasoned test pilot habitually flew with his harness buckled but loosened. This habit saved his life. His copilot was a different story.

The stunned test engineers, as my Dad later relayed to me, listened to this event unfold, from routine start to grisly finish, on the comms radio, while pitiless equipment monitored pilot life signs data. The test pilot and his copilot both survived the crash. The cockpit canopy had automatically released upon impact. As the plane sank the pilot immediately unbuckled and escaped. The descent of the plane-turned-anchor was too rapid for any hope of him to turning around and helping his copilot (he tried anyway). The apparent size of the crippled jet shrank with distance, and it disappeared from sight, his friend and colleague methodically struggling in the dark, finger-numbing cold and the exponentially rising pressure. (Test pilots are a uniquely cool-headed lot in dire circumstances.) The well liked twenty-five year old, ironically trapped by his snugly tightened safety harness, sank with his plane, fighting to release himself until he lost consciousness and died in the unforgiving abyss.

Imagine the pilot having to witness this. Imagine the test team looking up from their instruments, in dawning horror informed by knowing dread, to each other’s faces, one after another, hoping for some sign from the more experienced team leaders — in this case my Dad — that, despite what their rational brains were telling them, things really would be okay, that nobody was really going to die. For any engineer, nothing is more inexpressibly awful, nor more dreaded, sitting there in the backs of their minds, nor more devastating when it one day happens, than  to have to live through — and, afterwards, continue to live with — somebody getting killed. But it happens. It will happen. And sometimes, nobody is to blame.

## Facts Matter: We Know Why Only the U.S. Suffers Frequent Mass Shootings

For the umpteen-gazillionth time, here is a known fact: America has more mass shootings — more gun murders — than any other country on Earth, because America has more guns per person than any other country on Earth[3,6,8,11,10,12,13,15]. Our mass shootings problem is not due to any other factor.

It really is this straightforward: more guns, more gun murders; fewer guns, fewer gun murders.

This uniquely American obscenity — frequent mass shootings — is not related to mental illness. The rates of mental illness in the U.S. do not differ from those of other nations[2,11]. It is not related to crime rate. The U.S. is not more prone to crime than other developed countries[6,11,13,17]. It is not related to racial diversity issues. Pointedly, in this era of blatant self-serving liars infesting our nation’s statehouses, Congress, and the White House, America’s gun problem is not related to immigration[4,7,14]. Every study that examines this has found that recent immigrants — documented or not — are less prone to violent crime. Our gun problem is also not related to video games[5]. It is none of these things, nor is it primarily due to anything else. America is suffering a disastrous public health problem[16], and we have long known how to substantially reduce it[1,11]. The largest causative factor for mass killings is simply this: the number of guns. More guns, more gun murders.

A corollary of this fact is inescapable: reducing the rate of gun ownership reduces the rate of gun murders. The relation is clean, and its interpretation is unambiguous[11].

Every other developed nation on Earth has already not just discovered this but has acted upon it to protect and to better their own societies — and it has, with no exceptions, worked. Further, every evidence-based, peer-reviewed study of this relation has verified that the relation is causal — that it is factually true.

More guns, more gun murders.

What about mass killing lethality — the body count per mass shooting? This is not directly related to the question we’re examining here of why we, alone among all nations, have so many mass killings. But any discussion of mass killings in America would be remiss if it did not point out that the death toll from our daily rate of mass shootings — 347 in calendar 2017, for example[9] — is as high as it is not just because of the frequency of mass shootings but also because of the nearly unrestricted lethality of our readily available weaponry:

In my large file of mass murders, if you look decade by decade, the numbers of victims are fairly small up until the 1960s. That’s when the deaths start going way up. When the AK-47s and the Kalashnikovs and the Uzis — all these semiautomatic weapons, when they became so easily accessible.
— Dr. Michael Stone, forensic psychiatrist, Columbia University[2]

(Emphasis mine.) Ready availability of guns is the American Problem. The unjustifiable, immoral lethality of automatic and semi-automatic weaponry that is so easily available to Americans greatly compounds this American Problem of mass shootings.

Another notable difference about the U.S. and guns is this: America, along with only two other countries in the world (Guatemala and Mexico), adopts a most peculiar stance — an attitude that has proven to have been horrifically corrosive to American society. Alone from all others, we think that gun ownership — a priori, with no justification — is an inalienable right that stands alongside such real inalienable rights as life, liberty, equality, freedom of thought, and the freedom to pursue one’s own happiness.

This “right” to unrestricted ownership and use of firearms, this uniquely American intellectual and cultural obscenity, is the polar opposite of the rest of the world’s view, which concludes that gun ownership is a privilege we must first earn by tested demonstration of knowledge and responsibly competent behavior. To compound our error, American gun advocates erroneously — dishonestly — base our “right” on the willful truncation and misinterpretation of an antiquated second amendment that is merely an anachronism, irrelevant to any modern society. Perhaps the worst error of all is that we as a society reliably fail to call out this immoral stance on its (absence of) merits, this corrosive posturing of willing simpletons.

The resulting difference and its consequence — an unfortunate but inescapable conclusion — between us and nearly everybody else in the world? Between our culture on guns and almost everybody else’s culture on guns? The result is this: America deems senseless slaughter and mass murder, even of children in their classrooms, acceptable — a worthwhile price, dead bodies as tokens of our “freedom”. The slaughter of innocents is of less consequence than being able to own a lethal firearm with few to no constraints and with no more legal, ethical, or moral justification than merest whim.

This is worth repeating, to give the horror of it more time to sink in: In America, we view as acceptable the routine, senseless slaughter and mass murder of innocentsIn America, mass-murder rampages, enormous numbers of shattered, blood-soaked bodies, are of less concern to us than being able to own lethal firearms under few to no constraints, and with no more justification for ownership than thoughtless fancy. Does this conclusion shock you? Does it strike you as shrieking hyperbole? Yet it is an inescapable logical result that directly follows from valid, evidence-based premises. Mass murder is an okay trade for idle gun ownership.

More guns, more gun murders; fewer guns, fewer gun murders. This relation is not just common sense, nor does its validity stop at correlation. It is established fact[10]. But many, especially among the ideological right, actively deny this fact, or they willfully ignore it (which is the same as denial), and they frequently throw an infantile fit whenever their proven wrong stance is called out on the facts (this is a particularly immature form of denial). Besides being fallacious logic, this behavior — denial — no matter how vociferous or “sincerely felt”, does not alter the facts, does not change the truth. Nor does denial alter what these facts tell us about the hideous visage that is modern American gun culture. Nor — tellingly — does it change what the facts imply about the moral character of those who willingly choose to bury their heads in the dirt of denial.

To deny verified truth is to be in the wrong — in both senses of that word. It is wrong not only on the facts themselves, but, according to traditional American values — above all, honesty — it is morally wrong. If you deny established truth, your values are incompatible with foundational American values that real Americans all hold dear — those very same values that once had made America the envy of the world, that once had made America a great nation. Due to our enduring, stubborn denial of evidence and truth, we have made of ourselves and our nation a fallen, sad laughing-stock.

It is often said that the root of all evil is the love of money[19]. But one of the hallmarks of the greedy and the avaricious is dishonesty; greedy people always utilize dishonesty to get more wealth. Another hallmark of greed is denial of reality. To be a greedy bastard is to dishonestly deny that societal needs, that the suffering of other people, matter if one is to count oneself a moral and ethical being. Greed and denial both are incompatible with morality and with ethics. Further, to exist, greed and denial both need dishonesty.

Since dishonesty is a prerequisite for greed, dishonesty is the more fundamental characteristic. More broadly, one cannot help but notice that a core requirement for every kind of evil — not just greed — is dishonesty. Dishonesty is the universal common element of evil — the seed from which all evil grows. It is little wonder that dishonesty plays so central a role in the mythologies of every culture. In the third chapter of Genesis, for example, the serpent’s lie — dishonesty — instigates the Fall of Man[18].

So I would point out that the root of all evil is not love of money, not greed and avarice, but dishonesty. As an aside, this is precisely why the very most abhorrent, most reviled character trait in all of science — ask any scientist — is dishonesty. We can conclude, then, that one of the most loathsome human characteristics to find within oneself is dishonesty.

Now that this horse has been sufficiently flogged, we can better appreciate the weight of the moral implication of this statement: if, upon learning that you are factually wrong, you still choose, for whatever reason, to continue to ignore and/or deny the established facts, then you are dishonest. This is a disconcerting result.

More guns, more gun murders.

If you know someone among the reality deniers — the dishonorable and unprincipled — regarding this uniquely American of problems, then you know that something is wrong with where their head is at. Assuming they are a normal human being capable of introspection, this wrongness, you would think, should cause them to fundamentally question why they feel such a need to deny reality. But it is a defining and quite stubborn characteristic of a person in denial to not question the sources of their denial. Therein lies the rub.

We should be clear that in this case complicating factors are not a valid excuse. Studies have repeatedly shown that one of the most consequential facts of the American Problem is a simple and robust relation, one that is also perfectly in line with the most basic of “common” sense. Nothing about it is unintuitive. As with anything in the real world, there are complications, confounding factors. But all known confounding factors have been proven minor with respect to this simple relation. More guns, more gun murders; fewer guns, fewer gun murders. No complicating factor changes this relation; the evidence shows that it is solid — what scientists call robust. So one does not get to hide behind diversions, red herrings — dishonest changes of subject. And we should not allow them to.

For the good of the country we all care about, and for the good of those close to us, as well as for ourselves — we all have to face this at some point, to ask ourselves this question: where does our denial come from? What is it based on? Somebody else’s dishonest information, perhaps? Ideological bias? What are we actually basing our stance on? Only we, within the quiet of our own selves, can answer this. And once we have discovered it, we must ask ourselves this: does our answer — our truth — sit well with our conscience?

[1] Beauchamp, Z. (2016), “A huge international study of gun control finds strong evidence that it actually works” , Vox, February 29, 2016.

[2] Carey, B. (2017), “Are Mass Murderers Insane? Usually Not, Researchers Say” , The New York Times, November 8, 2017.

[3] Chalabi, M. (2012), “Gun homicides and gun ownership listed by country” , The Guardian, July 22, 2012.

[4] Chattanooga, W.W. (2015), “Not here to cause trouble” , The Economist, July 10, 2015.

[5] Fisher, M. (2012), “Ten-country comparison suggests there’s little or no link between video games and gun murders” , The Washington Post, December 17, 2012.

[6] Fisher, M., and Keller, J. (2017), “What Explains U.S. Mass Shootings?” , The New York Times, November 7, 2017.

[7] Florida, R. (2011), “The Geography of Gun Deaths” , The Atlantic, January 13, 2011.

[8] Gilson, D. (2013), “10 Pro-Gun Myths, Shot Down” , Mother Jones, January 31, 2013.

[10] Lankford, A. (2015), “Are America’s public mass shooters unique? A comparative analysis of offenders in the United States and other countries” , International Journal of Comparative and Applied Criminal Justice 40, 171-183. PDF available here: https://goo.gl/5yLL9C.

[11] Lopez, G. (2018), “I’ve covered gun violence for years. The solutions aren’t a big mystery.” , Vox, February 21, 2018.

[12] Lopez, G. (2017), “America’s unique gun violence problem, explained in 17 maps and charts” , Vox, October 2, 2017.

[13] Lopez, G. (2015), “America’s gun problem, explained” , Vox, October 3, 2015.

[14] Nowrasteh, A. (2015), “Immigration and Crime – What the Research Says” , Cato Institute, July 14, 2015.

[15] Willingham, A.J., and Ahmed, S. (2016), “Mass shootings in America are a serious problem” , CNN, June 13, 2016.

[16] Zhang, S. (2018), “Why Can’t the U.S. Treat Gun Violence as a Public-Health Problem?” , The Atlantic, February 15, 2018.

[17] Zimring, F.E., and Hawkins, G. (1997), Crime Is Not the Problem: Lethal Violence in America, Oxford University Press.

[18] Genesis 3:1-5 (KJV):

Now the serpent was more subtil [sic] than any beast of the field which the Lord God had made. And he said unto the woman, Yea, hath God said, Ye shall not eat of every tree of the garden?

And the woman said unto the serpent, We may eat of the fruit of the trees of the garden:

But of the fruit of the tree which is in the midst of the garden, God hath said, Ye shall not eat of it, neither shall ye touch it, lest ye die.

And the serpent said unto the woman, Ye shall not surely die:

For God doth know that in the day ye eat thereof, then your eyes shall be opened, and ye shall be as gods, knowing good and evil.

[19] 1 Timothy 6:10 (KJV):

For the love of money is the root of all evil…

## cretins, fucktards(*), and liars

Trigger warning: I am about to use facts. If you are a conservative, the following will be foreign to your way of thinking and therefore will induce apoplexy. You should probably stop reading NOW.

Looky there, my pay check has changes, as promised! Trump and the Republicans have been incessantly, obsessively, and noisily claiming that we, the 99 percent, will see real benefits, as a result of Republican changes to Obama-era public policy, in three major economic areas that loom large for us: salary, federal taxes, and health insurance premiums. Let’s call them on their fakery and see just how these stack up when we consider the facts of a typical upper middle class case.

• I got a pay raise! Due to a purported “cost of living” increase, my (civil servant) salary went up by 1.68%. But inflation in 2017 was 2.1%. Result: my new pay rate, in constant money (2017 dollars), is smaller by 0.41%, meaning I make $17.43 less per biweekly pay check than before. • My federal taxes changed! Oh, gee, it looks nothing like what Trash Trump and the Robber Republicans have trumpeted, since I am a member of the 99 percent. Result: as a proportion of my salary, my federal tax rate went up by 0.18%. • My health insurance rate changed! Surprise, surprise, it went up. In 2017, my biweekly premium was$240.77 (or 5.6 percent of my 2017 salary). The 2018 premium is $257.81 (or 5.9 percent of my 2018 salary). In 2017 dollars, that’s$252.51. So my health insurance rate went up by (252.51-240.77)/240.77 = 5.48 percent in constant dollars, which is par for what others like me have seen.

The upshot: the net change of my biweekly income due to these three factors — salary, federal taxes, health insurance — expressed in 2017 dollars, is a decrease of \$35.01. This is a 2017 salary fractional decrease of 1.0 percent. So, where are my promised “real benefits” due to Republican policy changes from the Obama era?

Gee, thanks, Trash Trump and Robber Republicans, you cretins, fucktards(*), and liars. Every one of your claims and bragging points is FALSE. As is always the case with you. You are liars. You are never not liars. Did you really think we would somehow fail to notice your trademark fraud, mendacity, and trumpery?

As my (Republican) dad would have put it, you bet your sweet bippy I will remember this — and your uncountable(**) earlier and ongoing cretinous acts against the American people — come November.

(*) fucktard (noun): A person of unbelievable, inexcusable, and indescribable stupidity (stupidity being defined as “knowing better yet doing it wrong anyway”). Note: a character trait, not a physical or physiological defect or shortcoming.

(**) uncountable (adjective): said of a set which has more elements than the set of integers.

## My Dear Moore Letter

Hey, Roy Moore,

Here are the numbers (WaPo, NYT, and all manner of Fake News):

Jones......671,151
Moore......650,436
write-in....22,819
military.....8,700

You lost. It was not a fair contest, since you Republicans prevented thousands upon thousands of those people from exercising their constitutional right to vote. Even so, you lost.

Not so fast, you say? Okay, sure. I can be all lawyery and numbery and elitist and argue totally for you and see where that gets us. This will prove you won, as is only right and fair and just, right? Well, no, but what the hey, I haven’t had my morning covfefe yet, so here we go.

Blanket Declaration*:

Now, let’s just declare, up front and all obvious-like, that ALL assumptions below are true and in your favor. You might have won after all! (Finally got your attention, didn’t I?)

* That means this is important for you to remember, Roy.

The military votes have yet to be counted. But, taking the tallied votes as reported by 100% of precincts, which is the overwhelming majority of possible legal votes, 49.92% voted for Jones, 48.38% voted for the asshole and child molester—for a difference of 1.54%—and 1.70% threw their vote away in justifiable disgust. A result that differs by 0.5% or less triggers a recount.

Guess what, Roy? You can’t guess? Here, I’ll make it easy: 1.54 is bigger than 0.5—in anybody’s version of reality, including even your magical Land of Nod.

Let’s try another way to view it, which I know is familiar to you and which even someone like you might grok: I believe in my heart of hearts—my heart of hearts, Roy!—that the simple, second-grade math above is true. So, therefore, it is true: you lost. There, does that convince you?

There’s wiggle room!, you say. Well, I’m pretty sure I heard something like that somewhere in your spittle-flinging ranting. Okay—we’re now going to start using the blanket declaration above—suppose ALL of the eligible military voters sent in ballots, and further suppose that ALL of those ballots are valid, and, even further, suppose ALL of those valid ballots were cast for you, the pedophile. Then we have 49.60% for Jones, 48.71% for the Alabamer prevert, and 1.69% for legitimate disgust. 49.60 minus 48.71 is 0.89, which in anybody’s version of reality is also bigger than 0.5. You still lost.

But wait, there could be more, Mr. Moore! (I figure you could never actually think, never mind think of this next possibility all by your widdle sewf, but hey, I’m a nice guy, capable of empathy (I know that word is alien to your kind, but bear with me here), and you’re pitifully pathetic even for a Republican, so here, this is me helping you out.) Suppose, after election officials—granted, election officials work for the evil gubment, so they’re pinko commie socialists committed to restoration of the Great Conspiracy—suppose all of them see the Light of your Righteous Cause and examine each write-in ballot and “discover” that, to everybody’s shock, the ballots are ALL valid, and that they are ALL for Moore the Misogynist. Let’s further suppose, as before, that ALL eligible military voted, ALL those ballots are also one hundred percent valid, and ALL are for precioussss little you.

The probability of this is not significantly different from zero, but let’s suppose all of it is true anyway, since you believe in miracles. Yes, I know, you don’t know what those big therefore bad and evil words mean, so you hatesss them, you do—trust me, that’s okay, I know you’d ignore the concept even if the words consisted of just monosyllabic grunts, your native language. Anyway, the tally then would be 49.60% for Jones and 50.40% for you Mr. Monster! That differs by 0.80%, which is again bigger than 0.5%, but this time it’s in your favor!

Yay! You win! You win!

Just kidding—you know every bit as well as the rest of us that you lost. As is true for everybody of your mental-midget ilk (that means people like you), you’re a shitty liar.

You’re welcome anyway to wait for the secret agent socialist election officials to examine the write-in ballots, and for that commie USPS to finish delivering the military ballots to the secret agents for tallying, and see if all those improbable things above come true. I have lots of popcorn, and I’m pretty sure everybody else does, too, so we’re all good with that.

Those impossible things won’t happen, though. Sorry-not-sorry, but miracles don’t exist. Reality does. So suck it up and be a Marlboro Man, asshole: you lost. As even Mike Huckabee—Mike Huckabee—said this morning,

“In elections everyone does NOT get a trophy.”

See those quotation marks? That means Huckabee really did write that.

Now, about that miracle recount for which you still insist on throwing a tantrum. Do you even grasp, yet, how dull-witted and infantile this makes you look? No, of course you don’t. Privation of even minimal cognition and mental agility is your problem. (Your therapist can explain this to you.) As mentioned, you don’t qualify for an automatic recount on the taxpayer’s dime. But you could still pay for a recount out of your own pocket, right? People have done that, and you can, too, right? Especially since you’ve been wronged—wronged, how dare they!

Well . . . WRONG, dude. You were seeking federal office. That matters in Alabama. You don’t get to demand a recount for a margin greater than 0.5%, even if you could convince some idiot to pay for it for you (assuming that’s even legal—but remember the blanket declaration at the top of this letter). Alabama state law says this, and it’s crystal clear on the matter. If you have any groveling flunkies left, get one to look it up for you.

You lost. Grow up.

## Red, White, and Green, and the Children Scream, and it is Impossible to Breathe

Here is one man’s poem. His poetry is not easy to listen to. It is poetry, which means shining a light on uncomfortable things, dark things, things which are hard to look at. But we are human, most of us, and so we look, and listen — as we must.

In the words of Nick Laird at the Guardian, poetry . . . “lets you – it makes you – experience in words the feelings of others. And then it makes you do it again.”

In the words of JFK (who was assassinated three weeks later), “When power leads men towards arrogance, poetry reminds him of his limitations. When power narrows the areas of man’s concern, poetry reminds him of the richness and diversity of his existence. When power corrupts, poetry cleanses, for art establishes the basic human truths which must serve as the touchstones of our judgment.”

JFK’s speech at Amherst led Johnson to establish the NEA two years later. Which conservatives, being among other things profoundly uncomfortable with truth, have been trying to kill ever since.

Nothing ever changes, does it?

If you’re not squirming, or crying, or ashamed, or raging during this, this is how you can know that you are a dead thing.

#### Further Reading

Listen to JFK’s speech.

National Endowment for the Arts

## Human Faces of the Flagstaff March for Science

A companion collection, Signs of the Flagstaff March for Science, is here.

## Signs of the Flagstaff March for Science

Now, hey you, mister, can’t you read? You’ve got to have a shirt and tie to get a seat You can’t even watch, no you can’t eat You ain’t supposed to be here The sign said you got to have a membership card to get inside Ugh! Sign, sign, everywhere a sign Blockin’ out the …

## A Physicist Contemplates Training Wheels

Motion catches the corner of my eye. Look up. Over there, beyond Spike the cat on his high perch, out the front window, past the rough-barked pine trees. A little girl struggles to get her bicycle going again. It is an impossible bright pink, with incongruous black rims and stout black spokes. Pink helmet, blue-jeans shorts and sneakers, a pink shirt with a pattern I can’t quite make out, probably animals. She pushes and shoves, single-minded, her entire world encompassed by this instant, realigns with the center of her sidewalk highway, and is on her way again, oddly upright. She is gone before I figure out the oddness plucking tunelessly at the back of my mind: her winged Pegasus, pink bike with the black rims and thick spokes, has training wheels.

A minute or two later a couple strolls by, chatting and laughing quietly, comfortable with their daughter on the fluorescent bicycle roaming her sidewalk highway in search of adventures. He is tall—very tall, I realize—dark-haired and relaxed and fills an oversized baggy white shirt and oversized baggy blue gym shorts. She is short, dark-complexioned as well, with long Mesoamerican black hair flowing down a relaxed back over a shapeless baggy garment of muted earthen colors. Her being animates the facial features of a woman from a Central American country, a newborn swaddled to her chest. He laughs again. Whatever world they inhabit in this moment, it seems pleasant—fitting for a quiet mountain neighborhood on this sunny, breezy, unexpectedly warm and easygoing March day. Sharp contrast to the cold hardness and wantonly inflicted greed newly risen from a vat of putrescent bile left simmering in the underbelly of our country.

I can’t help but smile in response to a brief respite in this couple’s welcoming sunny bubble, even at a distance. Vicarious pleasure is still pleasure; escape is sometimes necessary. After perusing the news after telling myself that I would not peruse the news, which seems always horrific in this dank hegemony, seeing the chatting and laughing couple is a surprising if welcome balm, a salve for psychic ills and the hurt billowing across the world.

A study in contrasts, this, but a common theme emerges: disposition emanates and infects, whether one realizes it or not. I think of my leaden, lowering demeanor this morning, then of their easy laughter. I am glad that they can infect me, and not I them, in this chance one-way encounter. We humans are a spongy mirror, the physicist’s black body, absorbing and re-emitting packets of dark or light that happen to intersect our surface—venom or laughter, pain or comfort, bigotry or celebration.

Choices.

But do we create? Can we create? To what extent do we have a choice? If all of us absorb and reflect, then what is the source of the light, what is the source of the dark, these quantized carriers of sadness and joy that scatter through our society, random-walking among neighbors, among couples, among children riding pink steeds along narrow weathered-cement roadways?

It dawns on me that this chatting and laughing couple, this reflection of what is, or can be, right in the realm, is the essence of resistance—resistance to the advancing wave of hard dark bone-seeping cold things, predatory denizens swimming a fouled and murky miasma, this newly-erupted, unwelcoming abyss. This couple and their fleet daughter, emitters of packets of wonder and humanity, whatever their origins, are what saves us. Hold on to this and do not let go, I say to myself, however violent and turbulent the buffetings to come.

I wonder if they are new to the neighborhood. Two world lines intersecting defines a point in space and time, a before and an after, that changes both in the instant, in the mystery, of interaction. Perhaps we will cross walking paths. I think I would like that. Perhaps, even, the training wheels will be off.

#### Further reading

“Black Body”, Wikipedia, https://en.wikipedia.org/wiki/Black_body

“Quantum”, Wikipedia, https://en.wikipedia.org/wiki/Quantum

“Sunlight Is Way Older Than You Think!”, Sten Odenwald, The Huffington Post (2014), https://goo.gl/dBlKFy

“World Line”, Wikipedia, https://en.wikipedia.org/wiki/World_line

“Overview of the fundamental interactions”, Wikipedia, https://goo.gl/sptt9p