Category Archives: Math

A book recommendation, not a review

My first encounter with a topology textbook was not a happy one. I was in grad school knowing I’d leave in a few months to start med school and with plenty of time on my hands and enough money to do what I wanted. I’d always liked math and had taken calculus, including advanced and differential equations in college. Grad school and quantum mechanics meant more differential equations, series solutions of same, matrices, eigenvectors and eigenvalues, etc. etc. I liked the stuff. So I’d heard topology was cool — Mobius strips, Klein bottles, wormholes (from John Wheeler) etc. etc.

So I opened a topology book to find on page 1

A topology is a set with certain selected subsets called open sets satisfying two conditions
l. The union of any number of open sets is an open set
2. The intersection of a finite number of open sets is an open set

Say what?

In an effort to help, on page two the book provided another definition

A topology is a set with certain selected subsets called closed sets satisfying two conditions
l. The union of a finite number number of closed sets is a closed set
2. The intersection of any number of closed sets is a closed set

Ghastly. No motivation. No idea where the definitions came from or how they could be applied.

Which brings me to ‘An Introduction to Algebraic Topology” by Andrew H. Wallace. I recommend it highly, even though algebraic topology is just a branch of topology and fairly specialized at that.

Why?

Because in a wonderful, leisurely and discursive fashion, he starts out with the intuitive concept of nearness, applying it to to classic analytic geometry of the plane. He then moves on to continuous functions from one plane to another explaining why they must preserve nearness. Then he abstracts what nearness must mean in terms of the classic pythagorean distance function. Topological spaces are first defined in terms of nearness and neighborhoods, and only after 18 pages does he define open sets in terms of neighborhoods. It’s a wonderful exposition, explaining why open sets must have the properties they have. He doesn’t even get to algebraic topology until p. 62, explaining point set topological notions such as connectedness, compactness, homeomorphisms etc. etc. along the way.

This is a recommendation not a review because, I’ve not read the whole thing. But it’s a great explanation for why the definitions in topology must be the way they are.

It won’t set you back much — I paid. $12.95 for the Dover edition (not sure when).

Why some of us gamble

If you are one of the hapless schlubs who bought a Powerball ticket or two and didn’t win (like me), modern neuroscience can tell you why (but not without a bit of pompous terminology). They call a small chance of winning large amounts — e.g. Powerball along with a large chance of losing a little a positively skewed gamble. Impressed? I’m not.

Nonetheless [ Neuron vol. 89 pp. 63 – 69 ’16 ] is a very interesting paper. Functional Magnetic Resonance Imaging (fMRI – https://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging) has shown that increased blood flow in one area of the brain (the nucleus accumbent sept) predicts risk seeking choices, while increased blood flow in another (the anterior insula) predicts aversion to risk. The unproven assumption behind fMRI is that increased blood flow is due to increased neural activity.

The neurochemistry of the two regions is quite different. The accumbens gets dopamine input while the insula gets norepinephrine projections.

BTW the insula is unique to man. Our cortex has grown so much (particularly in the frontal region) that it folds over on itself burying the insular cortex — https://en.wikipedia.org/wiki/Insular_cortex.

We are now actually able to measure axon bundles (white matter fiber tracts) in the living brain, using something called diffusion weighted MRI. By and large, fiber tracts tend to have the fibers parallel and running in the same direction. It is far easier for water to flow along parallel fibers than across them, and this is what the technique measures. For the mathematically inclined, what is actually measured is a tensor field, because at any given point in the brain it varies in direction, unlike a vector field which points just one way at a given point (the mathematically inclined know that this is a simplification because vectors are actually a type of tensor).

At any rate, the work used diffusion wieghted MRI to study the white matter tracts connecting the two areas. The larger the tract between insula and accumbens was the more risk averse an individual was. The implication being that a larger tract is a better connection between the two. So your nucleus is your impulsive child and the anterior insula is your mother telling you not to do anything crazy.

Fascinating, but like all this stuff it needs to be replicated, as it probably confirms the original idea for the study.

Two Christmas Presents

Two Christmas presents for you.  Yes Christmas presents.  I refuse to be culturally castrated by the professionally aggrieved.

The first is a link to a great scientific website — https://www.quantamagazine.org. It’s primarily about math and physics, with some biology thrown in. Imagine the News and Views section of Nature or the Perspectives section of Science on steroids.

Quanta is an editorially independent division of the Simons Foundation. And what is that you enquire? It is the answer to “If you’re so smart, why ain’t you rich”. Jim Simons is both much smarter and much richer than you and I. You can read more about him in a book I’m about to review on the blog — “The Physics of Wall Street”

Simons was a very accomplished mathematician winning prizes with a friend James Ax in the 60’s and 70’s — not quite the Fields Medal but up there. The Simons Chern 3 form is part of string theory. The two founded Renaissance Technologies in the late 80’s a stock fund using mathematical techniques to beat the market. And beat it they did, averaging 40% a year (after fees which were hefty). Even in the most recent market blowout in 2008 they were up 80% for the year. The firm employs about 200 people, mostly mathematicians and physicists. It was described by an MIT math prof as ‘the best mathematics and physics department in the world”.

At any rate after becoming a multibillionaire, Simons established his foundation, of which Quanta is a small part. It’s very good, with some heavies writing for it — such as Ingrid Daubechies full prof of math at Princeton who did a good deal of the early work on wavelets.

I haven’t read it all but the math is incredible, mostly about the latest and greatest new results and why it is important placing it in context. Physics isn’t forgotten, and the lead article concerns the philosophy of science and how it’s a’changin’ a la string theory, which is light years away from an experimental test of any of it.

Your second Christmas present is a Joke

The pope visited Colorado 22 years ago. A little known fact about him is that he loved to drive. Although Colorado is famed for the Rockies, the eastern half is high plains, so flat that you can see Pike’s peak from 100 miles away across the plains. At any rate the pope was being driven by his chauffeur from Colorado Springs to Denver on the Interstate, when the pope asked if he could drive. “Only if we go out on the plains where no one will see you” said the chauffeur.

So they switched when they got about 30 miles out in the middle of nowhere with the pope driving and the chauffeur in the back seat both behind tinted opaque windows. The pope started driving, really enjoying it, going faster and faster. He got up to 85 when a state trooper pulled them over.

Where’s the fire saith the trooper. He blanched when the driver’s window came down and he saw who was driving, and called headquarters. Arrest him came the answer. The trooper said I’m not sure, this guy is very big. I don’t care how big he is, arrest him. Are you sure. Yes.

I dunno boss, this guy is so big he’s got the pope driving for him.

Merry Christmas and Happy New Year to all

Are you sure you know everything your protein is up to?

Just because you know one function of a protein doesn’t mean you know them all. A recent excellent review of the (drumroll) executioner caspases [ Neuron vol. 88 pp. 461 – 474 ’15 ] brings this to mind. Caspases control a form of cell death called apoptosis, in which a cell goes gently into the good night without causing a fuss (particularly inflammation and alerting the immune system that something bad killed it). They are enzymes which chop up other proteins and cause the activation of other proteins which chop up DNA. They cause the inner leaflet of the plasma membrane to expose itself (particularly phosphatidyl serine which tells nearby scavenger cells to ‘eat me’).

The answer to the mathematical puzzle in the previous post will be found at the end of this one.

In addition to containing an excellent review of the various steps turning caspases on and off, the review talks about all the things activated caspases do in the nervous system without killing the neuron containing them. Among them are neurite outgrowth and regeneration of peripheral nerve axons after transection. Well that’s pathology, but one executioner caspase (caspase3) is involved in the millisecond to millisecond functioning of the nervous system — e.g. long term depression of neurons (LTD), something quite important to learning.

Of course, such potentially lethal activity must be under tight control, and there are 8 inhibitors of apoptosis (IAPs) of which 3 bind the executioners. We also have inhibitors of IAPs (SMAC, HTRA2) — wheels within wheels.

Are there any other examples where a protein discovered by one of its functions turns out to have others. Absolutely. One example is cytochrome c, which was found as it shuttles electrons to complex IVin the electron transport chain of mitochondria.Certainly a crucial function. However, when the mitochondria stops functioning either because it is told to or something bad happens, cytochrome c is released from mitochondria into the cytoplasm where it then activates caspase3, one of the executioner caspases.

Here’s another. Enzymes which hook amino acids onto tRNA are called tRNA synthases (aaRs for some reason). However one of the (called EPRS) when phosphorylated due to interferon gamma activity, became part of a complex of proteins which silences specific genes (translation — stops the gene from being transcribed) involved in the inflammatory response.

Yet another tRNA synthase, when released from the cell triggers an inflammatory response.

Naturally molecular biologists have invented a fancy word for the process of evolving a completely different function for a molecule — exaptation (to contrast it with adaptation).

Note the word molecule — exaptation isn’t confined to proteins. [ Cell vol. 160 pp. 554 – 566 ’15 ] Discusses exaptation as something which happens to promoters and enhancers. This work looked at the promoters and enhancers active in the liver in 20 mammalian species — all the enhancers were rapidly evolving.

——–

Answer to the mathematical puzzle of the previous post. R is the set of 4 straight lines bounding a square centered at (0,0)

Here’s why proving it has an inside and an outside isn’t enough to prove the Jordan Curve Theorem

No. The argument for R uses its geometry (the boundary is made of straight
line segments). The problem is that an embedding f: S^1 -> R^2 may be
convoluted, say something of the the Hilbert curve sort.

An incorrect proof of the Jordan Curve Theorem – can you find what’s wrong with it?

Every closed curve in an infinite flat plane divides it into a bounded part and an unbounded part (inside and and outside if you’re not particular). This is so screamingly obvious, that for a long time no one thought it needed proof. Bolzano changed all that about 200 years ago, but a proof was not forthcoming until Jordan gave a proof (thought by most to be defective) in 1887.

The proof is long and subtle. The one I’ve read uses the Brouwer fixed point theorem, which itself uses the fact that fundamental group of a circle is infinite cyclic (and that’s just for openers). You begin to get the idea.

Imagine the 4 points (1,1),(1,-1),(-1,1) and (-1,1) the vertices of a square centered at ( 0, 0 ). Now connect the vertices by straight lines (no diagonals) and you have the border of the square (call it R).

We’re already several pages into the proof, when the author makes the statement that R “splits R^2 (the plane) into two components.”

It seemed to me that this is exactly what the Jordan Curve theorem is trying to prove. I wrote the author saying ‘why not claim victory and go home?.

I got the following back

“It is obvious that the ‘interior’ of a rectangle R is path connected. It is
only a bit less obvious – but still very easy – to show that the ‘exterior’
of R is also connected. The rest of the claim is to show that every path
alpha from a point alpha(O)=P inside the rectangle R to a point alpha(1)=Q
out of it must cross the boundary of R. The set of numbers S={alpha(i) :
alpha(k) is in interior(R) for every k≤i} is not empty (0 is there), and it
is bounded from above by 1. So j=supS exists. Then, since the exterior and
the interior of R are open, j must be on the boundary of R. So, the interior
and the exterior are separate components of R^2 \ R. So, there are two of
them.”

Well the rectangle is topologically equivalent (homeomorphic) to a circle.

So why isn’t this enough?  It isn’t ! !

Answer to follow in the next post. Here’s the link — go to the end of the post — https://luysii.wordpress.com/2015/11/10/are-you-sure-you-know-everything-your-protein-is-up-to/

Time to get busy

Well I asked for it (the answer sheets to my classmate’s book on general relativity). It came today all 347 pages of it + a small appendix “Light Orbits in the Schwarzschild Geometry”. It’s one of the few times the old school tie has actually been of some use. The real advantages of going to an elite school are (1) the education you can get if you want (2) the people you meet back then or subsequently. WRT #1 — the late 50s was the era of the “Gentleman’s C”.

It should be fun. The book is the exact opposite of the one I’d been working on which put the math front and center. This one puts the physics first and the math later on. I’m glad I’m reading it second because as an undergraduate and graduate student I became adept at mouthing mathematical incantations without really understanding what was going on. I think most of my math now is reasonably solid. I did make a lot of detours I probably didn’t need to make — manifold theory,some serious topology — but that was fun as well.

When you’re out there away from University studying on your own, you assume everything you don’t understand is due to your stupidity. This isn’t always the case (although it usually is), and I’ve found errors in just about every book I’ve studied hard, and my name features on errata web pages of most of them. For one example see https://luysii.wordpress.com/2014/05/01/a-mathematical-near-death-experience/

The many ways the many tensor notations can confuse you

This post is for the hardy autodictats attempting to learn tensors on their own. If you use multiple sources, you’ll find that they define the same terms used to describe tensors in diametrically opposed ways, so that just when you thought you knew what terms like covariant and contravariant tensor meant,  another source defines them completely differently, leading you to wonder (1) about your intelligence (2) your sanity.

Tensors involve vector spaces and their bases. This post assumes you know what they are. If you don’t understand how a vector can be expressed in terms of coordinates relative to a basis, pick up any book on linear algebra.

Tensors can be defined by the way their elements transform under a change of coordinate basis. This is where the terms covariant and contravariant come from. By the way when Einstein says that physical quantities must transform covariantly, he means they transform like tensors do (even contravariant tensors).

True enough, but this approach doesn’t help you understand the term tensor product or the weird ® notation (where there is an x within the circle) used to describe it.

The best way to view tensors (from a notational point of view) is to look on them as functions which take finite Cartesian products (https://en.wikipedia.org/wiki/Cartesian_product) of vectors and covectors and produce a single real number.

To understand what a covector (aka dual vector) is, you must understand the inner product (aka dot product).

The definition of inner product (dot product) of a vector V with itself written < V | V >, probably came from the notion of vector length. Given the standard basis in two dimensional space E1 = (1,0) and E2 = (0,1) all vectors V can be written as x * E1 + y * E2 (x is known as the coefficient of E1). Vector length is given by the good old Pythagorean theorem as SQRT[ x^2 + y^2]. The dot product (inner product) is just x^2 + y^2 without the square root.

In 3 dimensions the distance of a point (x, y, z) from the origin is SQRT [x^2 + y^2 + z^2]. The definition of vector length (or distance) easily extends (by analogy) to n dimensions where the length of V is SQRT[x1^2 + x2^2 + . . . . + xn^2] and the dot product is x1^2 + x2^2 + . . . . + xn^2. Length is always a non-negative real number.

The definition of inner product also extends to the the dot product of two different vectors V and W where V = v1 * E1 + v2 * E2 + . … vn * En, W = w1 * E1 + . . + wn * En — e.g. < V | W >  = v1 * w1 + v2 * w2 + . . . + vn * wn. Again always a real number, but not always positive as any of the v’s and w’s can be negative.

So, if you hold W constant you can regard it as a function on the vector space in which V and W reside which takes any V and produces a real number. You can regard V the same way if you hold it constant.

Now with some of the complications which mathematicians love, you can regard the set of functions { W } operating on a vector space, as a vector space itself. Functions can be added (by their results) and can be multiplied by a real number (a scalar). The set of functions { W } regarded as a vector space is called the dual vector space.

Well if { W } along with function addition and scalar multiplication is a vector space, it must have a basis. Everything I’ve every read about tensors  involves finite dimensional vector spaces. So assume the vector space A is n dimensional where n is an positive integer, and call its basis vectors the ordered set a1, . . . , an. The dual vector space (call it B) is also n dimensional with another basis the ordered set b1, . . . , bn.

The bi are chosen so that their dot product with elements of A’s basis = Kronecker delta, e.g. if i = j then  < bi | aj >
= 1. If i doesn’t equal j  then < bi | aj >  = 0. This can be done by a long and horrible process (back in the day before computer algebra systems) called Gram Schmidt orthonormalization. Assume this can be done. If you’re a true masochist have a look at https://en.wikipedia.org/wiki/Gram–Schmidt_process.

Notice what we have here. Any particular element of the dual space B (a real valued function operating on A) call it f can be written down as f1 * b1 + . . . + fn * bn. It will take any vector in A (written g1 * a1 + . . . + gn * an) and give you f1 * g1 + . . . + fn * gn which is a real number. Basically any element ( say bj) of the basis of dual space B just looks at a vector in A and picks out the coefficient of aj (when it forms the dot product with the vector in A.

Now (at long last) we can begin to look at the contrary way tensors are described. The most fruitful way is to look at them as the product of individual dot products between a vector and a dual vector.

Have a look at — https://luysii.wordpress.com/2014/12/08/tensors/. To summarize  — the whole point of tensor use in physics is that they describe physical quantities which are ‘out there’ independently of the coordinates used to describe them. A hot dog has a certain length independently of its description in inches or centimeters. Change your viewpoint and the its coordinates in space will change as well (the hot dog doesn’t care about this). Tensors are a way to accomplish this.

It’s to good to pass up, but the length of the hot dog stays the same no matter how many times you (non invasively) measure it.  This is completely different than the situations in quantum mechanics, and is one of the reasons that quantum mechanics has never been unified with general relativity (which is a theory of gravity based on tensors).

Remember the dot product concerns  < dual vector — V | vector — W > . If you change the basis of vector  W (so vector W has different coordinates) the basis of dual vector   V must also change (to keep the dot product the same). A choice must be made as to which of the two concurrent basis changes is fundamental (actually neither is as they both are).

Mathematics has chosen the basis of vector W in as fundamental.

When you change the basis of W, the coefficients of W must change in the opposite way (to keep the vector length constant). The coefficients of W are said to change contravariantly. What about the coefficients of V? The basis of V changes oppositely to the basis of W (e.g. contravariantly), so the coefficients of V must change differently from this e.g. the same way the basis of W changes — e.g. covariantly. Confused?  Nonetheless, that’s the way they are named

Vectors and convectors and other mathematical entities such differentials, metrics and gradients are labelled as covariant or contravariant by the way their numerical coefficients change with a change in basis.

So the coefficients of vector W transform contravariantly, and the coefficients of dual vector V transform covariantly. This is true even though the coefficients of V and W always transform contravariantly (e. g. oppositely) to the way their basis transforms.

An immense source of confusion.

As mentioned above, one can regard vectors and dual vectors as real valued functions on elements of a vector space. So (adding to the confusion) vectors and dual vectors are both tensors. Vectors are contravariant tensors, and dual vectors are covariant tensors.

Now we form Cartesian products of vectors W (now called V) and convectors V (hereafter called V* to keep them straight).

We get something like this V x V x V x V* x V*, a cartesian product of 3 contravariant vectors and 2 dual vectors.

To get a real number out of them we form the tensor product V* ® V* ® V* ® V ® V, where the first V* operates on the first V to produce a real number, the second operates . . . and the last V* operates on the last V to produce a real number. All real numbers produced are multiplied together to produce the result.

Why not just call  V* ® V* ® V* ® V ® V a product? Well each V and V* is an n dimensional vector space, and the tensor V ® V is a n^2 dimensional space (and  V* ® V* ® V* ® V ® V is an n^5 dimensional vector space). When we form the product of two numbers (real or complex) we just get another number of the same species (real or complex). The tensor product of two n dimensional vector spaces is not another n dimensional space, hence the need for the adjective modifying the name product. The dot product nomenclature is much the same, the dot product of two vectors is not another vector, but a real number.

Here is yet another source of confusion. What we really have is a tensor product V* ® V* ® V* ® V ® V operating on a Cartesian product of vectors and covectors (tensors themselves) V x V x V x V* x V* to produce a real number.

Tensors can either be named by their operands making this a 3 contravariant 2 covariant tensor — (3, 2) tensor.

Other books name them by their operator (e.g. the tensor product) making it a 3 covariant 3 contravariant tensor (a 2, 3) tensor.

If you don’t get this settled when you switch books you’ll think you don’t really understand what contravariant and covariant mean (when in fact you do). Mercifully, one constancy in notation (thankfully) is that the contravariant number always comes first (or on top) and the covariant number second (or on bottom).

Hopefully this is helpful.  I wish I’d had this spelled out when I started.

What is schizophrenia really like ?

The recent tragic death of John Nash and his wife warrants reposting the following written 11 October 2009

“I feel that writing to you there I am writing to the source of a ray of light from within a pit of semi-darkness. It is a strange place where you live, where administration is heaped upon administration, and all tremble with fear or abhorrence (in spite of pious phrases) at symptoms of actual non-local thinking. Up the river, slightly better, but still very strange in a certain area with which we are both familiar. And yet, to see this strangeness, the viewer must be strange.”

“I observed the local Romans show a considerable interest in getting into telephone booths and talking on the telephone and one of their favorite words was pronto. So it’s like ping-pong, pinging back again the bell pinged to me.”

Could you paraphrase this? Neither can I, and when, as a neurologist I had occasion to see schizophrenics, the only way to capture their speech was to transcribe it verbatim. It can’t be paraphrased, because it makes no sense, even though it’s reasonably gramatical.

What is a neurologist doing seeing schizophrenics? That’s for shrinks isn’t it? Sometimes in the early stages, the symptoms suggest something neurological. Epilepsy for example. One lady with funny spells was sent to me with her husband. Family history is important in just about all neurological disorders, particularly epilepsy. I asked if anyone in her family had epilepsy. She thought her nephew might have it. Her husband looked puzzled and asked her why. She said she thought so because they had the same birthday.

It’s time for a little history. The board which certifies neurologists, is called the American Board of Psychiatry and Neurology. This is not an accident as the two fields are joined at the hip. Freud himself started out as a neurologist, wrote papers on cerebral palsy, and studied with a great neurologist of the time, Charcot at la Salpetriere in Paris. 6 months of my 3 year residency were spent in Psychiatry, just as psychiatrists spend time learning neurology (and are tested on it when they take their Boards).

Once a month, a psychiatrist friend and I would go to lunch, discussing cases that were neither psychiatric nor neurologic but a mixture of both. We never lacked for new material.

Mental illness is scary as hell. Society deals with it the same way that kids deal with their fears, by romanticizing it, making it somehow more human and less horrible in the process. My kids were always talking about good monsters and bad monsters when they were little. Look at Sesame street. There are some fairly horrible looking characters on it which turn out actually to be pretty nice. Adults have books like “One flew over the Cuckoo’s nest” etc. etc.

The first quote above is from a letter John Nash wrote to Norbert Weiner in 1959. All this, and much much more, can be found in “A Beatiful Mind” by Sylvia Nasar. It is absolutely the best description of schizophrenia I’ve ever come across. No, I haven’t seen the movie, but there’s no way it can be more accurate than the book.

Unfortunately, the book is about a mathematician, which immediately turns off 95% of the populace. But that is exactly its strength. Nash became ill much later than most schizophrenics — around 30 when he had already done great work. So people saved what he wrote, and could describe what went on decades later. Even better, the mathematicians had no theoretical axe to grind (Freudian or otherwise). So there’s no ego, id, superego or penis envy in the book, just page after page of description from well over 100 people interviewed for the book, who just talked about what they saw. The description of Nash at his sickest covers 120 pages or so in the middle of the book. It’s extremely depressing reading, but you’ll never find a better description of what schizophrenia is actually like — e.g. (p. 242) She recalled that “he kept shifting from station to station. We thought he was just being pesky. But he thought that they were broadcasting messages to him. The things he did were mad, but we didn’t really know it.”

Because of his previous mathematical achievments, people saved what he wrote — the second quote above being from a letter written in 1971 and kept by the recipient for decades, the first quote from a letter written in 12 years before that.

There are a few heartening aspects of the book. His wife Alicia is a true saint, and stood by him and tried to help as best she could. The mathematicians also come off very well, in their attempts to shelter him and to get him treatment (they even took up a collection for this at one point).

I was also very pleased to see rather sympathetic portraits of the docs who took care of him. No 20/20 hindsight is to be found. They are described as doing the best for him that they could given the limited knowledge (and therapies) of the time. This is the way medicine has been and always will be practiced — we never really know enough about the diseases we’re treating, and the therapies are almost never optimal. We just try to do our best with what we know and what we have.

I actually ran into Nash shortly after the book came out. The Princeton University Store had a fabulous collection of math books back then — several hundred at least, most of them over $50, so it was a great place to browse, which I did whenever I was in the area. Afterwards, I stopped in a coffee shop in Nassau Square and there he was, carrying a large disheveled bunch of papers with what appeared to be scribbling on them. I couldn’t bring myself to speak to him. He had the eyes of a hunted animal.

Read Einstein

Devoted readers of this blog (assuming there are any) know that I’ve been studying relativity for some time — for why see https://luysii.wordpress.com/2011/12/31/some-new-years-resolutions/.

Probably some of you have looked at writings about relativity, and have seen equations containing terms like ( 1 – v^2/c^2)^1/2. You need a lot of math for general relativity (which is about gravity), but to my surprise not so much for special relativity.

Back in the early 50’s we were told not to study Calculus before reaching 18, as it was simply to hard for the young brain, and would harm it, the way lifting something too heavy could bring on a hernia. That all changed after Sputnik in ’58 (but too late for me).

I had similar temerity in approaching anything written by Einstein himself. But somehow I began looking at his book “Relativity” to clear up a few questions I had. The Routledge paperback edition (which I got in England) cost me all of 13 pounds. Routledge is a branch of a much larger publisher Taylor and Francis.

The book is extremely accessible. You need almost no math to read it. No linear algebra, no calculus, no topology, no manifolds, no differential geometry, just high school algebra.

You will see a great mind at work in terms you can understand.

Some background. Galileo had a theory of relativity, which basically said that there was no absolute position, and that motion was only meaningful relative to another object. Not much algebra was available to him, and later Galilean relativity came be taken to mean that the equations of physics should look the same to people in unaccelerated motion relative to each other.

Newton’s laws worked out quite well this way, but in the late 1800’s Maxwell’s equations for electromagnetism did not. This was recognized as a problem by physicists, so much so that some of them even wondered if the Maxwell equations were correct. In 1895 Lorentz figured out a way (purely by trying different equations out) to transform the Maxwell equations so they looked the same to two observers in relative motion to each other. It was a classic kludge (before there even were kludges).

The equation to transform the x coordinate of observer 1 to the x’ of observer 2 looks like this

x’ = ( x – v*t) / ( 1 – v^2/c^2)^1/2)

t = time, v = the constant velocity of the two observers relative to each other, c = velocity of light

Gruesome no ?

All Lorentz knew was that it made Maxwell’s equations transform properly from x to x’.

What you will see on pp. 117 – 123 of the book, is Einstein derive the Lorentz equation from
l. the constancy of the velocity of light to both observers regardless of whether they are moving relative to each other
2. the fact that as judged from observer1 the length of a rod at rest relative to observer2, is the same as the length of the same rod at rest relative to observer1 as judged from observer2. Tricky to state, but this just means that the rod is out there and has a length independent of who is measuring it.

To follow his derivation you need only high school algebra. That’s right — no linear algebra, no calculus, no topology, no manifolds, no differential geometry. Honest to God.

It’s a good idea to have figure 2 from p. 34 in front of you

The derivation isn’t particularly easy to follow, but the steps are quite clear, and you will have the experience of Einstein explaining relativity to you in terms you can understand. Like reading the Origin of Species, it’s fascinating to see a great mind at work.

Enjoy

Why we imperfectly understand randomness the way we do.

The cognoscenti think the average individual is pretty dumb when it comes to probability and randomness. Not so, says a fascinating recent paper [ Proc. Natl. Acad. Sci. vol. 112 pp. 3788 – 3792 ’15 ] http://www.pnas.org/content/112/12/3788.abstract. The average joe (this may mean you) when asked to draw a random series of fifty or so heads and tails never puts in enough runs of heads or runs of tails. This leads to the gambler’s fallacy, that if an honest coin gives a run of say 5 heads, the next result is more likely to be tails.

There is a surprising amount of structure lurking within purely random sequences such as the toss of a fair coin where the probability of heads is exactly 50%. Even with a series with 50% heads, the waiting time for two heads (HH) or two tails (TT) to appear is significantly longer than for an alternation (HT or TH). On average 6 tosses will be required for HH or TT to appear while only an average of 4 are needed for HT or TH.

This is why Joe SixPack never puts in enough runs of Hs or Ts.

Why should the wait be longer for HH or TT even when 50% of the time you get a H or T. The mean time for HH and TT is the same as for HT and TH. The variance is different because the occurrences of HH and TT are bunched in time, while the HT and TH are spread evenly.

It gets worse for longer repetitions — they can build on each other. HHH contains two instances of HH, while alterations do not. Repetitions bunch together as noted earlier. We are very good at perceiving waiting times, and this is probably why we think repetitions are less likely and soon to break up.

The paper goes a lot farther constructing a neural model, based on the way our brains integrate information over time when processing sequences of events. It takes into consideration our perceptions of mean time AND waiting times. We average the two. This produces the best fitting bias gain parameter for an existing Bayesian model of randomness.

See, you’re not as dumb as they thought you were.

Another reason for our behavior comes from neuropsychology and physiological psychology. We have ways to watch the electrical activity of your brain and find out when you perceive something as different. It’s called mismatch negativity (see http://en.wikipedia.org/wiki/Mismatch_negativity for more detail). It a brain potential (called P300) peaking .1 -.25 seconds after a deviant tone or syllable.

Play 5 middle c’s in a row followed by a d than c’s again. The potential doesn’t occur after any of the c’s just after the d. This has been applied to the study of infant perception long before they can speak.

It has shown us that asian and western newborn infants both hear ‘r’ and ‘l’ quite well (showing mismatch negativity to a sudden ‘r’ or ‘l’ in a sequence of other sounds). If the asian infant never hears people speaking words with r and l in them for 6 months, it loses mismatch negativity to them (and clinical perception of them). So our brains are literally ‘tuned’ to understand the language we hear.

So we are more likely to notice the T after a run of H’s, or an H after a run of T’s. We are also likely to notice just how long it has been since it last occurred.

This is part of a more general phenomenon — the ability of our brains to pick up and focus on changes in stimuli. Exactly the same phenomenon explains why we see edges of objects so well — at least here we have a solid physiologic explanation — surround inhibition (for details see — http://en.wikipedia.org/wiki/Lateral_inhibition). It happens in the complicated circuitry of the retina, before the brain is involved.

Philosophers should note that this destroys the concept of the pure (e.g. uninterpreted) sensory percept — information is being processed within our eyes before it ever gets to the brain.

Update 31 Mar — I wrote the following to the lead author

” Dr. Sun:

Fascinating paper. I greatly enjoyed it.

You might be interested in a post from my blog (particularly the last few paragraphs). I didn’t read your paper carefully enough to see if you mention mismatch negativity, P300 and surround inhibition. if not, you should find this quite interesting.

Luysii

And received the following back in an hour or two

“Hi, Luysii- Thanks for your interest in our paper. I read your post, and find it very interesting, and your interpretation of our findings is very accurate. I completely agree with you making connections to the phenomenon of change detection and surround inhibition. We did not spell it out in the paper, but in the supplementary material, you may find some relevant references. For example, the inhibitory competition between HH and HT detectors is a key factor for the unsupervised pattern association we found in the neural model.

Yanlong”

Nice ! ! !

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