Friday, June 26, 2015

Sci Foo 2015

I'm in Palo Alto for this annual meeting of scientists and entrepreneurs at Google. If you read this blog, come over and say hello!

Action photos! Note most of the sessions were in smaller conference rooms, but we weren't allowed to take photographs there.

Tuesday, June 23, 2015

Schwinger meets Rabi

Seventeen year old Julian Schwinger meets Columbia professor I. I. Rabi (Nobel Prize 1944) and explains the EPR paper to him.
Climbing the Mountain: The Scientific Biography of Julian Schwinger [p.22-23] ... Rabi appeared; he invited Motz into his office to discuss 'a certain paper by Einstein in the Physical Review'! Motz introduced Julian and asked if he could bring his young friend along; Rabi did not object, and so it began.

The Einstein article turned out to be the famous paper of Einstein, Podolsky, and Rosen, with which young Julian was already familiar. He had studied quantum mechanics with Professor Wills at the City College, and discussed with him the problem of the reduction of a wave packet after additional information about a quantum system is gained from a measurement. 'Then they [Rabi and Motz] began talking and I sat down in the corner. They talked about the details of Einstein's paper, and somehow the conversation hinged on some mathematical point which had to do with whether something was bigger or smaller, and they couldn't make any progress. Then I spoke up and said, "Oh, but that is easy. All you have to do is to use the completeness theorem." Rabi turned and stared at me. Then it followed from there. Motz had to explain that I knew these things. I recall only Rabi's mouth gaping, and he said, "Oh, I see. Well, come over and tell us about it." I told them about how the completeness theorem would settle the matter. From that moment I became Rabi's protege. He asked, "Where are you studying?" "Oh, at City College." "Do you like it there?" I said, "No, I'm very bored."''

Watching young Julian demonstrate such 'deep understanding of things that were at the time at the frontier and not clearly understood,' Rabi decided on the spot to talk to George Pegram, then chairman of the physics department and dean of the graduate faculty, to arrange Julian's immediate transfer to Columbia. He and Motz left Julian waiting and went to see Pegram ...
Hans Bethe (Nobel Prize 1967) supported the transfer :-)
[p.24] Bethe provided an enthusiastic letter of support after he read Julian's notes on electrodynamics.'' Bethe's letter, dated 10 July 1935, reads as follows:
Dear Rabi,

Thank you very much for giving me the opportunity to talk to Mr. Schwinger. When discussing his problem with him, I entirely forgot that he was a sophomore 17 years of age. I spoke to him just as to any of the leading theoretical physicists. His knowledge of quantum electrodynamics is certainly equal to my own, and I can hardly understand how he could acquire that knowledge in less than two years and almost all by himself.

He is not the frequent type of man who just "knows" without being able to make his knowledge useful. On the contrary, his main interest consists in doing research, and in doing it exactly at the point where it is most needed at present. That is shown by his choice of his problem: When studying quantum electrodynamics, he found that an important point had been left out in a paper of mine concerning the radiation emitted by fast electrons. That radiation is at present one of the most crucial points of quantum theory. ...
Climbing the Mountain is one of the best scientific biographies I have read, on par with books by Pais on Einstein and Oppenheimer, and by Schweber on QED. The account of the early communication between Schwinger and Feynman about their very different formulations of QED is very interesting. See also Feynman's cognitive style and Feynman and the secret of magic.

Schwinger was one of the 64 mid-career scientists studied by Harvard psychologist Anne Roe.

Schwinger, of course, did not believe in wavefunction collapse or other Copenhagen mysticism: see Schwinger on quantum foundations.

Saturday, June 20, 2015

James Salter, 1925-2015

"Forgive him anything, he writes like an angel."

Remember that the life of this world is but a sport and a pastime.  NYTimes obituary.

From a 2011 post:

I've been a fan of the writer James Salter (see also here) since discovering his masterpiece A Sport and a Pastime. Salter evokes Americans in France as no one since Hemingway in A Moveable Feast. The title comes from the Koran: Remember that the life of this world is but a sport and a pastime ... :-)

I can't think of higher praise than to say I've read every bit of Salter's work I could get my hands on.
See also Lion in winter: James Salter.


But why a memoir?


To restore those years when one says, All this is mine—these cities, women, houses, days.


What do you think is the ultimate impulse to write?


To write? Because all this is going to vanish. The only thing left will be the prose and poems, the books, what is written down. Man was very fortunate to have invented the book. Without it the past would completely vanish, and we would be left with nothing, we would be naked on earth.

Wednesday, June 17, 2015

Hopfield on physics and biology

Theoretical physicist John Hopfield, inventor of the Hopfield neural network, on the differences between physics and biology. Hopfield migrated into biology after making important contributions in condensed matter theory. At Caltech, Hopfield co-taught a famous course with Carver Mead and Richard Feynman on the physics of computation.
Two cultures? Experiences at the physics-biology interface

(Phys. Biol. 11 053002 doi:10.1088/1478-3975/11/5/053002)

Abstract: 'I didn't really think of this as moving into biology, but rather as exploring another venue in which to do physics.' John Hopfield provides a personal perspective on working on the border between physical and biological sciences.

... With two parents who were physicists, I grew up with the view that science was about understanding quantitatively how things worked, not about collecting details and categorizing observations. Their view, though not so explicitly stated, was certainly that of Rutherford: 'all science is either physics or stamp collecting.' So, when selecting science as a career, I never considered working in biology and ultimately chose solid state physics research.

... I attended my first biology conference in the summer of 1970 at a small meeting with the world's experts on the hemoglobin molecule. It was held at the Villa Serbelloni in Bellagio, in sumptuous surroundings verging on decadence as I had never seen for physics meetings. One of the senior biochemists took me aside to explain to me why I had no place in biology. As he said, gentlemen did not interpret other gentlemen's data, and preferably worked on different organisms. If you wish to interpret data, you must get your own. Only the experimentalist himself knows which of the data points are reliable, and so only he should interpret them. Moreover, if you insist on interpreting other people's data, they will not publish their best data. Biology is very complicated, and any theory with mathematics is such an oversimplification that it is essentially wrong and thus useless. And so on... On closer examination, this diatribe chiefly describes differences between the physics and biology paradigms (at the time at least) for engaging in science. Physics papers use data points with error bars; biology papers lacked them. Physics was based on the quantitative replication of experiments in different laboratories; biology broadened its fact collecting by devaluing replication. Physics education emphasized being able to look at a physical system and express it in mathematical terms. Mathematical theory had great predictive power in physics, but very little in biology. As a result, mathematics is considered the language of the physics paradigm, a language in which most biologists could remain illiterate. Time has passed, but there is still an enormous difference in the biology and physics paradigms for working in science. Advice? Stick to the physics paradigm, for it brings refreshing attitudes and a different choice of problems to the interface. And have a thick skin. ...
Also by Hopfield: Physics, Computation, and Why Biology Looks so Different and Whatever happened to solid state physics?

See also In search of principles: when biology met physics (Bill Bialek), For the historians and the ladiesAs flies to wanton boys are we to the gods and Prometheus in the basement.

Friday, June 12, 2015

Entanglement and fast thermalization in heavy ion collisions

New paper! We hypothesize that rapid growth of entanglement entropy between modes in the central region and other scattering degrees of freedom is responsible for fast thermalization in heavy ion collisions.
Entanglement and Fast Quantum Thermalization in Heavy Ion Collisions (arXiv:1506.03696)

Chiu Man Ho, Stephen D. H. Hsu

Let A be subsystem of a larger system A∪B, and ψ be a typical state from the subspace of the Hilbert space H_AB satisfying an energy constraint. Then ρ_A(ψ)=Tr_B |ψ⟩⟨ψ| is nearly thermal. We discuss how this observation is related to fast thermalization of the central region (≈A) in heavy ion collisions, where B represents other degrees of freedom (soft modes, hard jets, collinear particles) outside of A. Entanglement between the modes in A and B plays a central role; the entanglement entropy S_A increases rapidly in the collision. In gauge-gravity duality, S_A is related to the area of extremal surfaces in the bulk, which can be studied using gravitational duals.

An earlier blog post Ulam on physical intuition and visualization mentioned the difference between intuition for familiar semiclassical (incoherent) particle phenomena, versus for intrinsically quantum mechanical (coherent) phenomena such as the spread of entanglement and its relation to thermalization.
[Ulam:] ... Most of the physics at Los Alamos could be reduced to the study of assemblies of particles interacting with each other, hitting each other, scattering, sometimes giving rise to new particles. Strangely enough, the actual working problems did not involve much of the mathematical apparatus of quantum theory although it lay at the base of the phenomena, but rather dynamics of a more classical kind—kinematics, statistical mechanics, large-scale motion problems, hydrodynamics, behavior of radiation, and the like. In fact, compared to quantum theory the project work was like applied mathematics as compared with abstract mathematics. If one is good at solving differential equations or using asymptotic series, one need not necessarily know the foundations of function space language. It is needed for a more fundamental understanding, of course. In the same way, quantum theory is necessary in many instances to explain the data and to explain the values of cross sections. But it was not crucial, once one understood the ideas and then the facts of events involving neutrons reacting with other nuclei.
This "dynamics of a more classical kind" did not require intuition for entanglement or high dimensional Hilbert spaces. But see von Neumann and the foundations of quantum statistical mechanics for examples of the latter.

Thursday, June 11, 2015

One Hundred Years of Statistical Developments in Animal Breeding

This nice review gives a history of the last 100 years in statistical genetics as applied to animal breeding (via Andrew Gelman).
One Hundred Years of Statistical Developments in Animal Breeding
(Annu. Rev. Anim. Biosci. 2015. 3:19–56 DOI:10.1146/annurev-animal-022114-110733)

Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, and many milestones are examined from historical and critical perspectives. The review concludes with a discussion of some future challenges and opportunities arising from the massive amount of data generated by livestock, plant, and human genome projects.
I've gone on and on about approximately additive genetic architecture for many human traits. These arguments are supported by the success of linear predictive models in animal breeding. But who has time to read literature outside of human genetics? Who has time to actually update priors in the face of strong evidence? ;-)

Wednesday, June 10, 2015

More GWAS hits on cognitive ability: ESHG 2015

This is a talk from ESHG 2015, which just happened in Glasgow. The abstract is old; at the talk the author reportedly described something like 70 genome wide significant hits (from an even larger combined sample) which are most likely associated with cognitive ability. This is SSGAC ... stay tuned!
Title: C15.1 - Genome-wide association study of 200,000 individuals identifies 18 genome-wide significant loci and provides biological insight into human cognitive function

Keywords: Educational attainment; genome-wide association; cognitive function

Authors: T. Esko1,2,3, on the behalf of Social Science Genetic Association Consortium (SSGAC); 1Estonian Genome Center, University of Tartu, Tartu, Estonia, 2Boston Children’s Hospital, Boston, MA, United States, 3Broad Institute of Harvard and MIT, Cambridge, MA, United States.

Abstract: Educational attainment, measured as years of schooling, is commonly used as a proxy for cognitive function. A recent genome wide association study (GWAS) of educational attainment conducted in a discovery sample of 100,000 individuals identified and replicated three genome-wide significant loci. Here, we report preliminary results based on conducted in 200,000 individuals. We replicate the previous three loci and report 15 novel, genome-wide significant loci for educational attainment. A polygenic score composed of 18 single nucleotide polymorphisms, one from each locus, explains ~0.4% of the variance educational attainment. Applying data-driven computational tools, we find that genes in loci that reach nominal significance (P < 5.0x10-5) strongly enrich for 11 groups of biological pathways (false discovery rates < 0.05) mostly related to the central nervous system, including dendritic spine morphogenesis (P=1.2x10-7), axon guidance (P=5.8x10-6) and synapse organization (P=1.7x10-5), and show enriched expression in various brain areas, including hippocampus, limbic system, cerebral and entorhinal cortex. We also prioritized genes in associated loci and found that several are known to harbor genes related to intellectual disability (SMARCA2, MAPT), obesity (RBFOX3, SLITRK5), and schizophrenia (GRIN2A) among others. By pointing at specific genes, pathways and brain areas, our work provides novel biological insights into several facets of human cognitive function.

Sparsity estimates for complex traits

Note the estimate of few to ten thousand causal SNP variants, consistent with my estimates for height and cognitive ability.

Sparsity (number of causal variants), along with heritability, determines the amount of data necessary to "solve" a specific trait. See Genetic architecture and predictive modeling of quantitative traits.

T1D looks like it could be cracked with only a limited amount of data.
Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model

PLoS Genet 11(4): e1004969. doi:10.1371/journal.pgen.1004969

Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
Table S5 below gives estimates of sparsity for various disease conditions.

Coronary Artery Disease CAD
Type 1 diabetes T1D
Type 2 diabetes T2D
Crohn's disease CD
Hypertension HT
Bipolar disorder BD
Rheumatoid arthritis RA

Replication and cumulative knowledge in life sciences

See Ioannidis at MSU for video discussion of related topics with the leading researcher in this area, and also Medical Science? Is Science Self-Correcting?
The Economics of Reproducibility in Preclinical Research (PLoS Biology)

Abstract: Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative (total) prevalence of irreproducible preclinical research exceeds 50%, resulting in approximately US$28,000,000,000 (US$28B)/year spent on preclinical research that is not reproducible—in the United States alone. We outline a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.
From the introduction:
Much has been written about the alarming number of preclinical studies that were later found to be irreproducible [1,2]. Flawed preclinical studies create false hope for patients waiting for lifesaving cures; moreover, they point to systemic and costly inefficiencies in the way preclinical studies are designed, conducted, and reported. Because replication and cumulative knowledge production are cornerstones of the scientific process, these widespread accounts are scientifically troubling. Such concerns are further complicated by questions about the effectiveness of the peer review process itself [3], as well as the rapid growth of postpublication peer review (e.g., PubMed Commons, PubPeer), data sharing, and open access publishing that accelerate the identification of irreproducible studies [4]. Indeed, there are many different perspectives on the size of this problem, and published estimates of irreproducibility range from 51% [5] to 89% [6] (Fig 1). Our primary goal here is not to pinpoint the exact irreproducibility rate, but rather to identify root causes of the problem, estimate the direct costs of irreproducible research, and to develop a framework to address the highest priorities. Based on examples from within life sciences, application of economic theory, and reviewing lessons learned from other industries, we conclude that community-developed best practices and standards must play a central role in improving reproducibility going forward. ...

Tuesday, June 09, 2015

Whither the World Island?

Alfred W. McCoy, Professor of History at the University of Wisconsin-Madison, writes on global geopolitics. The brief excerpts below do not do the essay justice.
Geopolitics of American Global Decline: Washington Versus China in the Twenty-First Century

... On a cold London evening in January 1904, Sir Halford Mackinder, the director of the London School of Economics, “entranced” an audience at the Royal Geographical Society on Savile Row with a paper boldly titled “The Geographical Pivot of History.” This presentation evinced, said the society’s president, “a brilliancy of description… we have seldom had equaled in this room.”

Mackinder argued that the future of global power lay not, as most British then imagined, in controlling the global sea lanes, but in controlling a vast land mass he called “Euro-Asia.” By turning the globe away from America to place central Asia at the planet’s epicenter, and then tilting the Earth’s axis northward just a bit beyond Mercator’s equatorial projection, Mackinder redrew and thus reconceptualized the world map.

His new map showed Africa, Asia, and Europe not as three separate continents, but as a unitary land mass, a veritable “world island.” Its broad, deep “heartland” — 4,000 miles from the Persian Gulf to the Siberian Sea — was so enormous that it could only be controlled from its “rimlands” in Eastern Europe or what he called its maritime “marginal” in the surrounding seas.

... “We didn’t push the Russians to intervene [in Afghanistan],” Brzezinski said in 1998, explaining his geopolitical masterstroke in this Cold War edition of the Great Game, “but we knowingly increased the probability that they would… That secret operation was an excellent idea. Its effect was to draw the Russians into the Afghan trap.”

Asked about this operation’s legacy when it came to creating a militant Islam hostile to the U.S., Brzezinski, who studied and frequently cited Mackinder, was coolly unapologetic. “What is most important to the history of the world?” he asked. “The Taliban or the collapse of the Soviet empire? Some stirred-up Moslems or the liberation of Central Europe and the end of the Cold War?”

... After decades of quiet preparation, Beijing has recently begun revealing its grand strategy for global power, move by careful move. Its two-step plan is designed to build a transcontinental infrastructure for the economic integration of the world island from within, while mobilizing military forces to surgically slice through Washington’s encircling containment.

The initial step has involved a breathtaking project to put in place an infrastructure for the continent’s economic integration. By laying down an elaborate and enormously expensive network of high-speed, high-volume railroads as well as oil and natural gas pipelines across the vast breadth of Eurasia, China may realize Mackinder’s vision in a new way. For the first time in history, the rapid transcontinental movement of critical cargo — oil, minerals, and manufactured goods — will be possible on a massive scale, thereby potentially unifying that vast landmass into a single economic zone stretching 6,500 miles from Shanghai to Madrid. In this way, the leadership in Beijing hopes to shift the locus of geopolitical power away from the maritime periphery and deep into the continent’s heartland.

... To capitalize such staggering regional growth plans, in October 2014 Beijing announced the establishment of the Asian Infrastructure Investment Bank. China’s leadership sees this institution as a future regional and, in the end, Eurasian alternative to the U.S.-dominated World Bank. So far, despite pressure from Washington not to join, 14 key countries, including close U.S. allies like Germany, Great Britain, Australia, and South Korea, have signed on. Simultaneously, China has begun building long-term trade relations with resource-rich areas of Africa, as well as with Australia and Southeast Asia, as part of its plan to economically integrate the world island.

... Lacking the geopolitical vision of Mackinder and his generation of British imperialists, America’s current leadership has failed to grasp the significance of a radical global change underway inside the Eurasian land mass. If China succeeds in linking its rising industries to the vast natural resources of the Eurasian heartland, then quite possibly, as Sir Halford Mackinder predicted on that cold London night in 1904, “the empire of the world would be in sight.”

Hmm... where have I seen this before?
Chung Kuo is a series of science fiction novels written by David Wingrove. The novels present a future history of an Earth dominated by China. ... Chung Kuo is primarily set 200 years in the future in mile-high, continent-spanning cities made of a super-plastic called 'ice'. Housing a global population of 40 billion, the cities are divided into 300 levels and success and prestige is measured by how far above the ground one lives. ... The ruling classes – who base their rule on the customs and fashions of imperial China – maintain traditional palaces and courts both on Earth and in geostationary orbit. There are also Martian research bases and the outer colonies, with their mining planets.

Friday, June 05, 2015

Game of Thrones at the Oxford Union

The three shows I've been following in recent years are Game of Thrones, Silicon Valley, and Mad Men (now over). Some of the Amazon Prime pilots I've seen look promising, like The Man in the High Castle.

Monday, June 01, 2015

James Simons interview

A great interview with Jim Simons. From academic mathematics to code breaking to financial markets :-)

Blog Archive