omenti

reflecting on the moon in the pursuit of truth

It was the beginning of Spring in 2014 and there was electricity in the air. I've only seen scientific drama at that level a few times.  First the leaks and then a well-known team made a dramatic announcement.  These dramatic events can be confirmation of a radical hypothesis or sometimes it's Nature proclaiming how limited human imagination really is with something completely unexpected.  This time it appeared to be confirmation of a beautiful idea.  If it held up it would signal the potential of a new chapter in physics and cosmology.   A few people thought about shopping for suits that would be appropriate at a formal event in Stockholm.

 

For about fifty years we've known the Big Bang couldn't be the primordial event.  It was not the beginning of our Universe.   There were a conjectures ..  the most interesting came from Alan Guth who proposed an enormous mind-blowing-even-by-the-standards-of-cosmology inflation that took the Universe from something unimaginably small to something more the size of a baseball in a tiny sliver of time.  Over the years it was modified and tweaked and, although unconfirmed, many of us fell back on it to think about the pre-Big Bang because it nicely predicated what followed.  It had become a standard bias - one that people would abandon if proven wrong - but a bias nonetheless.

 

The conjecture was developed into a testable hypothesis by a few groups.¹  The first report came from an experiment at the South Pole called BICEP2 in 2014.  They saw a high quality signal that implied cosmic inflation. I noted my own excitement at the time. 

 

During the year that followed the serious skepticism of science took hold.  The team was known to be careful.  There are several things you do in any physics experiment to check your apparatus, how you handle information, biases and errors.  Ultimately you hope that someone else has a similar result with a different approach and apparatus.

 

There's a lot of preparatory work.  You need to understand what processes might mimic what you might be looking for.  Usually it's not a blind hunt..  you're guided by theory looking for something quite specific.  Discovery is finding it or finding the unexpected. It's fun to make theorists go back to the drawing board.  Most of them agree.

 

A good deal of thought and computer modeling goes into designing the experiment and understanding exactly what you should see - rediscovering well-known and agreed upon experiments.  If you can't get this foundational piece right, no one will believe you.  It turns out this step is where many of the data analysis paths are fleshed out.  This part, by the way, is where playfulness is essential.

 

You need to understand what are known as "backgrounds"  - events that mimic what you might be looking for.    There are null tests where you put a detector in a shielded box and understand the noise it produces.  These can get sophisticated.  Then there are calibrations where you see if you can measure known signals with all parts of your experiment.  Currently a hot topic in astrophysics is understanding what went on in the Universe when the first stars formed.  Unfortunately there wasn't any light - it was a dark age.  But there's a signal from hydrogen atoms that can be detected.  The process is well-known in radio astronomy and regularly used, but tuning into the dark age is leading edge work.  To sort out the background noise astronomers pointed a radio telescope at the Moon during the last lunar eclipse.  The noise component from the Sun would be missing and they'd be able to calibrate their apparatus and computational approach.  The signal was beautiful - the reflection of the rest of the Universe from the Moon.  Most of it turned out to be the Milky Way and the animated gif shows the result.

 

There are also jack-knife tests to remove regular occurring biases.   If you were studying car traffic flow you'd note rush hours are busy and weekends aren't.  So you break up the data into chunks and examine them separately to see how they compare.  The need for good enough random numbers often turns up here.

 

You constantly have to check the state of your apparatus and filters.  In an experiment of any size there's always something broken and something malfunction.  These have to be noted and accounted for.  It's not uncommon to spend a significant amount of time looking for problems.

 

Then there's the human problem.  How broad and deep is the team?  In the case of BICEP2 there wasn't any communication with people who did observational galactic astronomy.  When the results were published they noted there was a background that hadn't been accounted for.  It seemed obscure to the team and to most of us, but in the end it was nearly fatal.  The probability of discovery and confirmation dropped dramatically.  About a year later another experiment drove the nail home.  Inflation wasn't dead, but their experimental approach couldn't detect it.

 

And then there are dragons.  If someone asks you what's in your driveway and you tell them a car, both of you are generally satisfied.  But if you tell them it's a dragon ...  well then you have some explaining to do.  Reporting dragons can color fields.  A classic example came during the 60s.

 

Joe Webber was an outstanding experimental physicist who thought he had come up with a way to detect what Einstein predicted, but said would never be detected ... gravity waves.  He carefully eliminated many sources of noise and ended up seeing a signal.  He didn't have much in the way of theoretical guidance to know how big the signal was and he thought he was careful enough, so he published.

 

The problem was no one else was able to find a signal. For the next few decades no one saw anything and the field was nearly dead quiet for about thirty years.  There were theoretical hints that big advances in detector technology might find a signal and, through luck and enormous work, the Laser Interferometer Gravitational Wave Observatory - LIGO - was born.  About twenty years later it bore fruit and an advance as important as Galileo's telescope was made.  But people had to be careful... very careful.  How do you deal with human sociology?  What if people expect something to be there - or not - and these biases get built into data taking, algorithms and the final results.  

 

They had a neat idea

 

The LIGO physicists, astrophysicists and engineers got the signal they were looking for.  About four hundred people went to work trying to prove or disprove it.  About six months later they knew they had something.  The team leaders gave their thumbs up,  champagne came out and several lengthy papers were written.   As they were getting ready to send the papers for peer review a senior researcher held a town meeting.   They had been working on a faked signal - a devilishly clever faked signal.  

 

They needed to go back and understand what they were doing at a much deeper level. They had too many biases. Since then there have been several false alarms and about a half dozen real signals.  They have developed one of the most careful cultures on the planet using their blind injection technique.

 

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Carl Sagan wrote extensively on critical thinking. Perhaps his best non-technical piece on the subject is a chapter in The Demon-Haunted World: Science as a Candle in the Dark.  The specific chapter is The Fine Art of Baloney Detection.   The type of skepticism is useful far beyond science...  I'm thinking of advertising, social media, politics...

 

The kit is brought out as a matter of course whenever new ideas are offered for consideration. If the new idea survives examination by the tools in our kit, we grant it warm, although tentative, acceptance. If you’re so inclined, if you don’t want to buy baloney even when it’s reassuring to do so, there are precautions that can be taken; there’s a tried-and-true, consumer-tested method.

 

he offers nine tools - read the chapter for the detail

 

Wherever possible there must be independent confirmation of the “facts.”

Encourage substantive debate on the evidence by knowledgeable proponents of all points of view.

Arguments from authority carry little weight — “authorities” have made mistakes in the past. They will do so again in the future. Perhaps a better way to say it is that in science there are no authorities; at most, there are experts.

Spin more than one hypothesis. If there’s something to be explained, think of all the different ways in which it could be explained. Then think of tests by which you might systematically disprove each of the alternatives. What survives, the hypothesis that resists disproof in this Darwinian selection among “multiple working hypotheses,” has a much better chance of being the right answer than if you had simply run with the first idea that caught your fancy.

Try not to get overly attached to a hypothesis just because it’s yours. It’s only a way station in the pursuit of knowledge. Ask yourself why you like the idea. Compare it fairly with the alternatives. See if you can find reasons for rejecting it. If you don’t, others will.
Quantify. If whatever it is you’re explaining has some measure, some numerical quantity attached to it, you’ll be much better able to discriminate among competing hypotheses. What is vague and qualitative is open to many explanations. Of course there are truths to be sought in the many qualitative issues we are obliged to confront, but finding them is more challenging.

If there’s a chain of argument, every link in the chain must work (including the premise) — not just most of them.

Occam’s Razor. This convenient rule-of-thumb urges us when faced with two hypotheses that explain the data equally well to choose the simpler.

Always ask whether the hypothesis can be, at least in principle, falsified. Propositions that are untestable, unfalsifiable are not worth much. Consider the grand idea that our Universe and everything in it is just an elementary particle — an electron, say — in a much bigger Cosmos. But if we can never acquire information from outside our Universe, is not the idea incapable of disproof? You must be able to check assertions out. Inveterate skeptics must be given the chance to follow your reasoning, to duplicate your experiments and see if they get the same result.

 

He goes on and notes we are vulnerable to common pitfalls of common sense and lists twenty common ones... the list is excellent and exceptionally appropriate these days.

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¹ In the physical science rough ideas are conjectures..  If you can test them they are elevated to hypothesis.  Theory usually refers to something that is well understood and tested by several approaches. It is a fairly high bar.  In particle physics it means being observed with an uncertainty lower than one part in three million - preferably by multiple approaches.  This is one of those differences in definitions that causes confusion when communicating with the public where 'theory' usually means someone's idea -no matter how good or bad.  I'll be a bit more specific as these ideas can be used to one degree or another outside of science.

I'll add that cosmology and astrophysics produce the most mind blowing graphs I've encountered.  Math can be more dramatic, but these are trying to find a visual vocabulary to describe Nature.  

 

the other scientific revolution of 1953 that radically accelerated knowledge

In the Winter of 1953 Francis Crick interrupted the lunchtime patrons of the Eagle in Cambridge to announce he and James Watson had discovered the secret of life.   In fact it wasn't far from hyperbole - they had discovered the structure of DNA and the world was forever changed.¹  Something else world-changing happened later that year.  Without it we  wouldn't have advanced past the technologies of the 1960s.  Surprisingly one of the people behind it thought it cute, but hardly worth publishing.  

 

But first a bit of historical context

 

The Manhattan project began with blackboards and experiments.  Individual researchers had their desk calculators, but it soon became necessary to work out how neutrons diffused when a critical mass of fissile material came together. A group of scientist's wives came together to perform the repetitive calculations.  Next came IBM card reading calculators and then von Neumann became interested in developments in electronic computing at Harvard, Bell Labs and the University of Pennsylvania.  There's a deep and rich history from this time on, but the important point was the meaning of "computer" was shifting from a woman with a mechanical calculator to an electronic machine.²

 

In 1953  electronic computers were still rare, mostly one-off machines that were expensive and temperamental.   It was still a world of research and development. A few people recognized practical applications, but the machines mostly weren't ready.  Except for physics.

 

Physicists were heavily involved in the birthing of electronic computers focusing on specific problems that tended to involve military projects or fundamental research to drive the hardware.  Fundamental research was seen as the tip of the R&D effort and was often funded from military budgets which were large enough to support these efforts.   Enrico Fermi  had an interesting idea.

 

He was interested in nonlinear systems thinking they might have something fundamental to do with entropy and why time only flows in one direction.  He, John Pasta and Stanislav Ulam conceived a simple toy problem - sixty four identical masses representing the atoms in a lattice connected end to end by little springs representing chemical bonds. Unfortunately calculating the restoring forces is a beast of a problem.

 

A linear system is one that can be described by a straight line on a graph.  Consider a spring.  Pull it twice as far and it exerts twice as much force. The same is true when you pluck a string on a guitar.³  Nonlinear systems have a more complex behavior.  With linear systems you just add up the contributions of each element without considering the others.  Nonlinear systems are nasty - you need to consider everything at once.  You might be able to calculate very simple systems with a few elements, but after things become very complex.

 

The three physicists wrote down a mathematical description of the physics. Think of it as a strange sort of string that you pluck and then listen to as time goes on.  Solving it would require a lot of computer time.  The big iron at Los Alamos was called the Mathematical Analyzer, Numerical Integrator, and Computer or MANAIC-1 for short.  It had been built for thermonuclear calculations, but this trio had an enormous amount of credibility and it was clearly fundamental research. 

 

The three had no experience with programming, but Mary Tsingou was a mathematical physicist who knew how to reason with  MANAIC-1.  She set to work creating a program that became an experiment simulating how the lattice behaved for sixteen, thirty two and sixty four "atoms" with various spring forces.⁴  

 

Imagine of the frequencies coming out of the system in terms of sound.  They had expected a pure note that would shift  over to more chaotic noises and finally the equivalent of just a hiss.  That didn't happen. 

 

The lattice was "plucked" and a series of overtones appeared and dissolved into each other creating a complex mixture.   Then the whole process reversed and the overtones disappeared in reverse order until the original pure note reappeared and the whole process started over again.  

 

 

Here's an illustration.  Consider the top graph.  The purple points are just the first vibration mode of a simple string - think of it as showing what would happen if it was just a simple string.  The system we're talking about with equal masses and springs is in blue.

 

Even this simple little problem that had baffled some of the greatest minds had a beautiful and completely unexpected result and a new way of doing science had been developed - the computational experiment.  The computer had become not just a bicycle, but rather a telescope for the mind.

 

Much of science is impenetrable without computer experiments like these.  It's so common, I've done thousands, that you tend to think it's natural.  But it had a beginning and without it much of what we've seen from science after the mid 1950s and the resulting technologies wouldn't have been possible.  Inefficient cars and airplanes, no electronic revolution, no cellphones, no ....

 

Fermi thought it was cute, but not fundamentally important.  That may be true for the hypothesis they tested, but how they did it changed the world.

 

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¹ There were many others who were in on this, but Crick and Watson weren't exactly the sharing kind.  Most notably the x-ray diffraction work of Rosalind Franklin.  But she was a women and died before the Nobel was awarded anyway.  

² George Dyson (Freeman Dyson's son) penned Turing's Cathedral.  An excellent and very readable history of the efforts at Princeton.  That may be the most important early thread as the computer architecture used today was developed there.  Much of the slightly later development came out of Cambridge in England. 

³ Sort of ..  you can get it to behave non-linearly, but for now consider a spring that behaves like F = kx where x is the displacement, F is force and k is a constant that tells you about the spring's stiffness.

⁴ I don't know for sure, but have been told she programmed down to the iron in machine language. 

 

 

hearts, minds and actions .. moving people

In the 1960s living conditions in some parts of the developing world were beginning to make serious progress.  It wasn't terribly uniform, but some counties in Africa and Eastern Asia were feeling the impact of the green revolution, sanitation, and widespread immunization.  Korea was at the leading edge, but there was this problem.  A large percentage of the population was rural and agrarian.  To deal with very high infant and childhood mortality rates families had an average of six or seven children.  Now it was likely that the vast majority of those children would live and the fertility rate was the same.  The Korean government was faced with a serious problem.

 

Other countries had the same problem.  The standard approach was to shame people who had too many children and provide education about contraceptives through media campaigns.  It was a tough slog with success rates somewhere between miserable and non-existant.  Korea, on the other hand, had enormous success. All of their objectives were easily met inside of a single generation.  What happened?

 

Although the Korean program was national, media campaigns were absent. It was felt a better approach would correspond to the village oriented makeup of the population.  Information was provided to a few people in each village and then moved by peer to peer contact.  Curiously, although most villages adopted some form of contraction, techniques varied widely from village to village.  Pill villages, IUD villages, condom villages and so on.    The villages with the strongest social networks saw the greatest degree of behavioral change.

 

There was an academic problem.  Work on viral diffusion of information said that weak times would be a much more efficient mechanism for radiating information and changing behavior than these messier local and strong social ties. After all - there had been a lot of success predicting diffusion of contagions  where weak ties spread diseases much more efficiently than strong ties.  Think about a single plane with someone coughing ... And now millions will know about the best current cat videos inside of a day.  And the six degrees...

 

Think about contagions.  While they spread easily, convincing people to get immunized can be a difficult task and some groups adopt anti-scientific beliefs that can lead to breakdowns in herd immunity.  Why doesn't that simple information spread as quickly?   A high level answer moving simple information is very easy and weak tie networks are very effective.  Behaviors tend to be much deeper than simple information and require richer contact often of several types.  We use our close social networks ..  the strong ties of close friends and family ..  for behavioral reference and reinforcement.

 

I know an excellent science explainer.  Make that world-class.  Her parents love the beauty of Nature and moved to Hawaii before she was born.  She returns regularly, but is a bit of the black sheep as they're religious fundamentalists and have come to believe that global warming and science in general are attacks on their belief and identify.  (This linkage is almost uniquely North American. I've talked about it before and probably will again as it's both fascinating and frustrating.)

 

She was home a few months ago when a rare Hawaiian typhoon took a swipe at their island dumping three quarters of a meter of rain in a day.  It is now possible to model how likely a storm like this could exist without global warming.  In this case it was unlikely it would have happened without the current level of global warming.  As the storm raged she broke down into tears.  She shares their love of nature and the storm was a manifestation of change and destruction.

 

They watched and for the first time heard their black sheep science loving daughter.  They're certainly not convinced. Being against a belief in global warming is part of their core identify. But she was able to chip away at their belief.   Her strong tie with them is stronger than the (less strong) tie of their religion and much stronger than widely spread scientifically accurate but weak tied information about global warming.

 

Sometimes simple coded signalings to  strong tie groups are sufficient to bring change.  It seems likely that a lot of personal technology follows this path.  Certainly one can signal to an existing tribe.  If it's done in such a way to resonate with core beliefs it is possible to bring dramatic behavioral change.   New work on  behavioral change diffusion is emerging as people realize the current diffusion models don't always apply.  Viral messaging and weak, but far reaching networked communications fail to describe much of what people actually do and particularly how behavior changes. 

 

Social science isn't a "real" science if you compare them to the "hard" sciences.  Human behavior is still far too complex.  But progress is being made and a better understanding of how behavioral spreads will have deep implications.  Contrary to what Malcolm Gladwell might say, viral information spreading isn't as effective as many think.  (I keep thinking I should write something about Gladwell's books and talks, but...)  If you wonder why your viral ad campaign didn't see any sales bump for your new gizmo, there may be a fundamental reason related to messaging.

 

chilling with light

After the last post, someone asked about laser cooling.  I'll try it without images..  just draw them in your mind.

 

First off what is hot and cold?  

 

Think of heat as the energy of motion of the atoms and molecules in something.  They can be moving through space, rotating or vibrating.  Temperature is just a measure of the average kinetic energy of the molecules of something. "hotter" means a higher kinetic energy.  Cold is just the absence of heat. .  you don't add cold to something.. you have to remove heat.

 

Light has both energy and, even though it doesn't have mass, momentum.  We don't notice the effect at our scale,  but if a molecule absorbs a photon of light it will feel a force.  

 

But isn't that adding energy and isn't using a laser something like using a flame thrower to blow out a candle?

 

Imagine a very simple system.  A single atom that can only move to the left or right.  Its temperature is directly related to its kinetic energy..  half its mass times its velocity squared.   Put a laser on the left side and point it at the atom.  Now the fun.

 

Laser light is monochromatic - one color or a single frequency.  Lasers are perfect, but in a gedanken experiment like this it can be.  Thanks to quantum mechanics it turns out atoms and molecules have very specific absorption frequencies.  If the laser light frequency isn't spot on, the atom completely ignores the light.  Now pick a special kind of laser that allows you to tune the frequency of the light.

 

If the atom was just sitting there and light at its absorption frequency strikes it, it will take the momentum of the light and move to the right.  Not exactly what we want.  We'll invoke the Doppler effect -- the fact the pitch of a siren rises as it approaches and lowers as it leaves.  The same is true for light.   Dial in a frequency just below the absorption frequency of your atom.  If the atom is moving to the left the frequency of light it "sees" is a bit higher than the frequency the laser is putting out.  If you've made the right adjustment the molecule absorbs the light.  The force is opposite the atom's leftward motion and the atom slows down a bit.  If the atom was moving to the right the frequency of the light at the atom would be lower and no absorption takes place.

 

To handle motion to the right, put another laser on the right pointed to the left. Now whichever way the atom moves it will slow down.  Since there is less kinetic energy its temperature has dropped.  You've cooled it.

 

To go to the real world imagine a cavity filled with a gas of these atoms.  There are three directions so use six lasers (or three with mirrors)  up-down, left-right, in-out.  Any motion of the atom can be described as a combination of the three directions so you are cooling the random motions of the atoms of the gas.  A refrigerator that uses light..

 

There are a number of tricks to achieve very low temperatures.  The record is a few millionths of a degree above absolute zero for a very tiny sample and something like half a degree above absolute zero for a sample large enough to see.

 

The technique turns out to be very useful for investigating quantum mechanics.. particularly of macroscopic objects like the guts of quantum computers.  It's also used in some of the most accurate atomic clocks.  The next generation atomic clocks in GPS satellites will use little laser cooled samples for much better accuracy which leads to more accurate navigation.  

 

Imagine -  a constellation of laser cooled atomic clocks 12,600 miles up so you can find your way around better with your smartphone.    

a quick tour of the 2018 nobel prize in physics

Last year we had a new way of seeing the universe ... something that Einstein's equations predicted, but so difficult to detect that he said it would never been seen.  Almost exactly a century after he published his General Theory of Relativity the first real detection of gravity waves was made.  It is a leap on the order of  Galileo's telescope.

 

This year's prize is more down to Earth, but exciting in its own way for physics and sociology.  It went to three people - half to Arthur Ashkin for optical tweezers and the other half jointly awarded to Gérard Mourou and Donna Strickland for their technique to generate ultra short laser pulses.

 

I'll start with the first.

 

You've probably seen the demonstration where you take a hair dryer and points the stream of air towards the ceiling.   Then you float a ping pong ball in the stream.  Neat, but the wicked fun part is when you tilt the stream and the ball doesn't fall out. I've done it with a leaf blower and a volleyball.  I won't get into the physics other than to note almost every science museum I've seen with a demo gets the explanation wrong.  At a high level there's a restorative force on the ball when it moves to the boundary of the stream.  If you've never seen it find some ping pong balls and a hair dryer.  Remember to set it on cold:-) 

 

An interesting feature of light is it has momentum.  Shine light on something and you transfer a bit of momentum unless the object is perfectly transparent.  The forces involved are small, but you can make sails that catch the momentum of sunlight to move a spacecraft.  With powerful laser beams you can also move small particles on Earth.  (.. those radiometers with the silvered and black paddle wheels that spin when light shines on them more for another reason)

 

It turns out you can trap very small particles in a narrowly focused beam of laser light.  The forces are different from the hair dryer and pingpong ball, but the principle is similar.  There's a restorative force when the small object gets to a boundary.  Arthur Ashkin played around with this beginning in the early 70s at Bell Labs  and figured out how to make it work well enough to manipulate biological objects like cells.  As it became an important tool, another Bell Labs guy - Steve Chu - worked to extend the technique.  His leap was to sort individual atoms by how fast they move.  He could take a sample of very cold gas molecules and separate out a slower moving subset ..  slowing them further in the process.. In other words he was cooling them.  This revolutionized lower temperature physics and a few other fields resulting in Steve being sprinkled with the Swedish holy water a few years ago.  Later he became Obama's science advisor. 

 

I was surprised Ashkin never got the prize as it was fundamental to Chu's work and had make an impact on biology and some other fields.  It was a pleasant surprise to hear his name today because that meant he was still alive,  He's 96 and apparently didn't want to be bothered with interviews because he's working on a paper and has a deadline to meet.

And now to the other half of the prize.  It's special in it's own right.  

You probably had to memorize the speed of light as 186,000 miles per second -- or 3 * 10⁸ meters per second if you took physics or aren't from the US.  Big numbers are hard to grasp.  In some of  physics and electrical engineering a more natural unit is some fraction of a billionth of a second - a nanosecond.  In a vacuum light travels about a foot in that time.¹ We used to add physical delays to signals in our experiments by running signals through small lengths of coaxial cable - eight inches is a nanosecond in coax.  Which is why some financial types use microwaves rather than fiber optics to communicate over distances of a few hundred miles.  It's also why cloud computing has some fundamental issues.

 

Very short pulses of light are useful for studying biology, chemistry and physics.  Here's another scale ..  

a fast camera shutter captures light at the millisecond level (a thousandth of a second)

many chemical reactions are on the order of a millionth of a second .. microseconds,

switching times in computers are on the billionth of a second level - nanoseconds  light travels about 30 centimeters

molecular vibrations are picosecondish..  a thousand of a billionth of  a second.  light travels about 0.3 millimeters

photosynthesis has some extremely fast aspects as does liquid water .  think a millionth of a billionth of a second .. or femtoseconds.  light travels about 0.3 microns

a billionth of a billionth of a second -  an attosecond is appropriate for some of the slower subatomic motions.  light makes it across most of what we'd call the size of a hydrogen atom.. but not by much

 

It's easy to get down to nanoseconds with electronics.  Strickland and Mourou made it much more practical to work in femtoseconds and work down to attoseconds.  Switching things on and off so quickly is very complex and their method is beautiful and simple.  Describing it is tricky as it's nonlinear optics at the core, but it has had a huge impact on pure and applied research.  It also might be used to create a relatively inexpensive way to accelerate protons and ions to energies high enough to be used in cancer treatment.  Currently these techniques are very expensive, but more effective than tradional radiation treatments. There's a potential to work on organ tumors while the organ is moving with very little or no damage to nearby organs and much more disruption to the tumor DNA.   That's probably worth another blog post.  

 

And now the neat part.  Donna Strickland is Canadian (University of Waterloo) and female.  She's only the third woman to win a Nobel Prize in Physics.  (you can probably guess the first..  I think only physicists will know the second).  Thirty years ago women were in the single digits in physics Ph.D. programs.  Now it's up to over twenty percent.  That's still far too low and there are ugly stories.  But it's finally changing.

 

She was Mourou's grad student and the prize winning work was her first published paper!  I think that's unique in science.

From this morning and worth listening to:

 

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1 It's hard to grasp what a billion is.  A reasonably close approximation of the seconds in a year is pi * 10⁷.  You're just over a billion seconds old when you turn 32.