Sabine Hossenfelder’s new book Lost in Math should be starting to appear in bookstores around now. It’s very good and you should get a copy. I hope that the book will receive a lot of attention, but suspect that much of this will focus on an oversimplified version of the book’s argument, ignoring some of the more interesting material that she has put together.
Hossenfelder’s main concern is the difficult current state of theoretical fundamental physics, sometimes referred to as a “crisis” or “nightmare scenario”. She is writing at what is likely to be a decisive moment for the subject: the negative LHC results for popular speculative models are now in. What effect will these have on those who have devoted decades to studying such models?
Decades after physicists happened upon a stunning mathematical coincidence, researchers are getting close to understanding the link between two seemingly unrelated geometric universes.
An interesting story in that physicists found the connection first and mathematicians are tying the two areas together after the fact. More often it’s the case that mathematicians come up with the theory and then physicists are applying it to something. I’m not sure I like some of the naming conventions laid out, but it’ll be another decade or two after it’s all settled before things have more logical sounding names. I’m a bit curious if any category theorists are playing around in either of these areas.
After having spent the last couple of months working through some of the “rigidity” (not the best descriptor in the article as it shows some inherent bias in my opinion) of algebraic geometry, now I’m feeling like symplectic geometry could be fun.
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL )
A summer school for advanced undergraduates
June 11-22, 2018 @ Princeton University
What would it mean to have a physicist’s understanding of life?
How do DYNAMICS and the EMERGENCE of ORDER affect biological function?
How do organisms process INFORMATION, LEARN, ADAPT, and EVOLVE?
See how physics problems emerge from thinking about developing embryos, communicating bacteria, dynamic neural networks, animal behaviors, evolution, and more.
Learn how ideas and methods from statistical physics, simulation and data analysis, optics and microscopy connect to diverse biological phenomena.
Explore these questions, tools, and concepts in an intense two weeks of lectures, seminars, hands-on exercises, and projects.
What happens when several thousand distinguished physicists, researchers, and students descend on the nation’s gambling capital for a conference? The answer is "a bad week for the casino"—but you'd never guess why.
The year was 1986, and the American Physical Society’s annual April meeting was slated to be held in San Diego. But when scheduling conflicts caused the hotel arrangements to fall through just a few months before, the conference's organizers were left scrambling to find an alternative destination that could accommodate the crowd—and ended up settling on Las Vegas's MGM grand.
Totally physics clickbait. The headline should have read: “Vegas won’t cater to physics conferences anymore because they’re too smart to gamble.”
I’d read a portion of this in the past, but thought I’d circle back to it when I saw it sitting on the shelf at the library before the holidays. It naturally helps to have had lots of physics in the past, but this has a phenomenally clear and crisp presentation of just the basics in a way that is seldom if ever seen in actual physics textbooks.
Highlights, Quotes, & Marginalia
Lecture One: The Nature of Classical Physics
There is a very simple rule to tell when a diagram represents a deterministic reversible law. If every state has a single unique arrow leading into it, and a single arrow leading out of it, then it is a legal deterministic reversible law.
Highlight (yellow) – 1. The Nature of Classical Physics > Page 9
There’s naturally a much more sophisticated and subtle mathematical way of saying this. I feel like I’ve been constantly tempted to go back and look at more category theory, and this may be yet another motivator. Added on Wednesday, January 4, 2018 late evening
The rule that dynamical laws must be deterministic and reversible is so central to classical physics that we sometimes forget to mention it when teaching the subject. […] minus-first law [: …] undoubtedly the most fundamental of all physics laws–the conservation of information. The conservation of information is simply the rule that every state has one arrow in and one arrow out. It ensures that you never lose track of where you started.
Highlight (yellow) – 1. The Nature of Classical Physics > Page 9-10
This is very simply and naturally stated, but holds a lot of complexity. Again I’d like to come back and do some serious formalization of this and reframe it in a category theory frameork. Added on Wednesday, January 4, 2018 late evening
There is evan a zeroth law […]
Highlight (gray) – 1. The Nature of Classical Physics > Page 9
spelling should be even; I’m also noticing a lot of subtle typesetting issues within the physical production of the book that are driving me a bit crazy. Spaces where they don’t belong or text not having clear margins at the tops/bottoms of pages. I suspect the math and layout of diagrams and boxes in the text caused a lot of problems in their usual production flow. Added on Wednesday, January 4, 2018 late evening
Guide to highlight colors
Yellow–general highlights and highlights which don’t fit under another category below Orange–Vocabulary word; interesting and/or rare word Green–Reference to read Blue–Interesting Quote Gray–Typography Problem Red–Example to work through
Imagine a world in which it is possible for an elite group of hackers to install a “backdoor” not on a personal computer but on the entire U.S. economy. Imagine that they can use it to cryptically raise taxes and slash social benefits at will. Such a scenario may sound far-fetched, but replace “backdoor” with the Consumer Price Index (CPI), and you get a pretty accurate picture of how this arcane economics statistic has been used.
Tax brackets, Social Security, Medicare, and various indexed payments, together affecting tens of millions of Americans, are pegged to the CPI as a measure of inflation. The fiscal cliff deal that the White House and Congress reached a month ago was almost derailed by a proposal to change the formula for the CPI, which Matthew Yglesias characterized as “a sneaky plan to cut Social Security and raise taxes by changing how inflation is calculated.” That plan was scrapped at the last minute. But what most people don’t realize is that something similar had already happened in the past. A new book, The Physics of Wall Streetby James Weatherall, tells that story: In 1996, five economists, known as the Boskin Commission, were tasked with saving the government $1 trillion. They observed that if the CPI were lowered by 1.1 percent, then a $1 trillion could indeed be saved over the coming decade. So what did they do? They proposed a way to alter the formula that would lower the CPI by exactly that amount!
When: Wednesday, October 25, 2017, from 4:30 PM – 6:30 PM PDT
Where: UCLA California NanoSystems Institute (CNSI), 570 Westwood Plaza, Los Angeles, CA 90095
While there is no mathematical formula for writing television comedy, for the writers of The Simpsons, Futurama, and The Big Bang Theory, mathematical formulas (along with classic equations and cutting-edge theorems) can sometimes be an integral part of those shows. In a lively and nerdy discussion, five of these writers (who have advanced degrees in math, physics, and computer science) will share their love of numbers and talent for producing laughter. Mathematician Sarah Greenwald, who teaches and writes about math in popular culture, will moderate the panel.
The event will begin with a lecture by bestselling author Simon Singh (The Simpsons and Their Mathematical Secrets), who will examine some of the mathematical nuggets hidden in The Simpsons (from Euler’s identity to Mersenne primes) and discuss how Futurama has also managed to include obscure number theory and complex ideas about geometry.
Tickets: Tickets are $15 each and seating is limited, so reserve your seat soon. Tickets can be purchased here via Eventbrite (ticket required for entry to the event).
A limited number of free tickets will be reserved for UCLA students. We ask that students come to IPAM between 9:00am and 3:00pm on Friday, October 20, to present your BruinCard and pick up your ticket (one ticket per BruinCard, nontransferable). If any tickets remain, we will continue distributing free tickets to students on Monday, Oct. 23, starting at 9:00am until we run out. Both your ticket and BruinCard must be presented at the door for entry.
Doors open at 4:00. Please plan to arrive early to check in and find a seat. We expect a large audience.
Okay math nerds, this looks like an interesting lecture if you’re in Los Angeles next Wednesday. I remember reading and mostly liking Singh’s book The Simpsons and Their Mathematical Secrets a few years back.
The hard core math crowd may be disappointed in the level, but it could be an interesting group to get out and be social with.
My review of The Simpsons and Their Mathematical Secretsfrom Goodreads:
I’m both a math junkie and fan of the Simpsons. Singh’s book is generally excellent and well written and covers a broad range of mathematical areas. I’m a major fan of his book Big Bang: The Origin of the Universe, but find myself wanting much more from this effort. Much of my problem stems from my very deep knowledge of math and its history as well as having read most of the vignettes covered here in other general popular texts multiple times. Fortunately most readers won’t suffer from this and will hopefully find some interesting tidbits both about the Simpsons and math here to whet their appetites.
There were several spots at which I felt that Singh stretched a bit too far in attempting to tie the Simpsons to “mathematics” and possibly worse, several spots where he took deliberate detours into tangential subjects that had absolutely no relation to the Simpsons, but these are ultimately good for the broader public reading what may be the only math-related book they pick up this decade.
This could be considered a modern-day version of E.T. Bell‘s Men of Mathematics but with an overly healthy dose of side-entertainment via the Simpsons and Futurama to help the medicine go down.
A qualitatively more diverse range of possible behaviors emerge in many-particle systems once external drives are allowed to push the system far from equilibrium; nonetheless, general thermodynamic principles governing nonequilibrium pattern formation and self-assembly have remained elusive, despite intense interest from researchers across disciplines. Here, we use the example of a randomly wired driven chemical reaction network to identify a key thermodynamic feature of a complex, driven system that characterizes the “specialness” of its dynamical attractor behavior. We show that the network’s fixed points are biased toward the extremization of external forcing, causing them to become kinetically stabilized in rare corners of chemical space that are either atypically weakly or strongly coupled to external environmental drives.
A chemical mixture that continually absorbs work from its environment may exhibit steady-state chemical concentrations that deviate from their equilibrium values. Such behavior is particularly interesting in a scenario where the environmental work sources are relatively difficult to access, so that only the proper orchestration of many distinct catalytic actors can power the dissipative flux required to maintain a stable, far-from-equilibrium steady state. In this article, we study the dynamics of an in silico chemical network with random connectivity in an environment that makes strong thermodynamic forcing available only to rare combinations of chemical concentrations. We find that the long-time dynamics of such systems are biased toward states that exhibit a fine-tuned extremization of environmental forcing.