*(Quanta Magazine)*

In his rapid ascent to the top of his field, James Maynard has cut a path through simple-sounding questions about prime numbers that have stumped mathematicians for centuries.

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# Tag: mathematics

Read A Number Theorist Who Solves the Hardest Easy Problems by Erica Klarreich *(Quanta Magazine)*
Followed Dan MacKinlay *(danmackinlay.name)*
Bookmarked Introduction to Statistical Learning with Applications in R by *(faculty.marshall.usc.edu)*
## Followed Xinli Wang

Followed Xinli Wang *(mathrojak.ca)*
## 🔖 [1809.05923] What is Applied Category Theory? by Tai-Danae Bradley

Bookmarked [1809.05923] What is Applied Category Theory? by Tai-Danae Bradley *(arxiv.org)*
## 🔖 OpenStax

Bookmarked OpenStax *(OpenStax)*
## Checkin Mathematical Sciences Building, UCLA

## 👓 Physicists Uncover Geometric ‘Theory Space’ | Quanta Magazine

Read Physicists Uncover Geometric ‘Theory Space’ *(Quanta Magazine)*
## Chris Aldrich is reading “Clinton’s Substantial Popular-Vote Win” | New York Times

Read Clinton’s Substantial Popular-Vote Win *(nytimes.com)*
## 🔖 H-theorem in quantum physics by G. B. Lesovik, et al.

Bookmarked H-theorem in quantum physics *(Nature.com)*
### Abstract

### Footnotes

## Warren Weaver Bot!

Liked Someone has built a Warren Weaver Bot! by Weaverbot *(Twitter)*
## NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016

Bookmarked NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016 by *(nimbios.org)*
## Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science

Bookmarked Disconnected, Fragmented, or United? A Trans-disciplinary Review of Network Science by César A. Hidalgo *(Applied Network Science | SpringerLink)*
### Abstract

## Weekly Recap: Interesting Articles 7/24-7/31 2016

## Introduction to Complex Analysis | UCLA Extension

In his rapid ascent to the top of his field, James Maynard has cut a path through simple-sounding questions about prime numbers that have stumped mathematicians for centuries.

A paragraph or two on some of the more technical aspects would have been better than some of the hero-worship and seeming lone-genius narrative that was developed here. Use the particulars about the person to develop some interest in the mathematics and its history.

Hi, I’m Dan MacKinlay.

I’m a statistician and musician from Australia.

Musicianshould be clear.Statistician, though, what’s that? A statistician is the exact same thing as adata scientistormachine learning researcherwith the differences that there are qualifications needed to be a statistician, and that we are snarkier.These days I work across data analysis, music, generative design and machine learning, especially for time series. I’ve been based at various times at such locations as

- Zürich, Switzerland, where I did my MSc in statistics under Professors Sara van de Geer and Didier Sornette at the Swiss Federal Institute of Technology
- Sydney, Australia, where I visualised data for the Powerhouse Museum under Seb Chan
- Bandung, Indonesia, where I worked on interactive music with Common Room and Gustaff Harriman Iskandar
Currently I am doing a PhD in statistics with Zdravko Botev at the University of New South Wales Sydney.

My current methods of interest are point process inference, compressive sensing, design grammars, sequential Monte Carlo methods, concatenative synthesis, differentiable learning, branching processes, Hilbert-space methods in high dimensional inference, stochastic differential equations and the application of all of these to economic modelling, reliability engineering and sick breakbeats.

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

For a more advanced treatment of these topics:

The Elements of Statistical Learning.Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

I’ll note that the author has a downloadable .pdf copy of his text on his site.

I teach mathematics courses at University of Toronto Mississauga and Seneca College since 2016 when my family moved to Canada from Singapore. I taught full-time at Singapore Polytechnic as a math lecturer from 2012 to 2016. This is the space where I explore and share my journey of teaching mathematics, conducting education research projects, and learning about OER.

This is a collection of introductory, expository notes on applied category theory, inspired by the 2018 Applied Category Theory Workshop, and in these notes we take a leisurely stroll through two themes (functorial semantics and compositionality), two constructions (monoidal categories and decorated cospans) and two examples (chemical reaction networks and natural language processing) within the field. [PDF]

hat tip:

Friends! I am so happy to share that my little booklet “What is Applied Category Theory?” is now available on the arXiv. It’s a collection of introductory, expository notes inspired by the ACT workshop that took place earlier this year. Enjoy! https://t.co/EPYP19z14x pic.twitter.com/O4uVhj401s

— Tai-Danae Bradley (@math3ma) September 18, 2018

See also Notes on Applied Category Theory

Access our free college textbooks and low-cost learning materials.

Complex Analysis II

A decades-old method called the “bootstrap” is enabling new discoveries about the geometry underlying all quantum theories.

In the 1960s, the charismatic physicist Geoffrey Chew espoused a radical vision of the universe, and with it, a new way of doing physics. Theorists of the era were struggling to find order in an unruly zoo of newfound particles. They wanted to know which ones were the fundamental building blocks of nature and which were composites. But Chew, a professor at the University of California, Berkeley, argued against such a distinction. “Nature is as it is because this is the only possible nature consistent with itself,” he wrote at the time. He believed he could deduce nature’s laws solely from the demand that they be self-consistent. Continue reading 👓 Physicists Uncover Geometric ‘Theory Space’ | Quanta Magazine

She won by more than Gore in 2000, Nixon in 1968 or Kennedy in 1960, it seems. And therein lies a dilemma.

When are people going to come around and fix the electoral college? There’s lots of math to support different methods.

Remarkable progress of quantum information theory (QIT) allowed to formulate mathematical theorems for conditions that data-transmitting or data-processing occurs with a non-negative entropy gain. However, relation of these results formulated in terms of entropy gain in quantum channels to temporal evolution of real physical systems is not thoroughly understood. Here we build on the mathematical formalism provided by QIT to formulate the quantum H-theorem in terms of physical observables. We discuss the manifestation of the second law of thermodynamics in quantum physics and uncover special situations where the second law can be violated. We further demonstrate that the typical evolution of energy-isolated quantum systems occurs with non-diminishing entropy. [1]

[1]

G. B. Lesovik, A. V. Lebedev, I. A. Sadovskyy, M. V. Suslov, and V. M. Vinokur, “H-theorem in quantum physics,” *Scientific Reports*, vol. 6. Springer Nature, p. 32815, 12-Sep-2016 [Online]. Available: http://dx.doi.org/10.1038/srep32815

This is the signal for the second.

How can you **not** follow this twitter account?!

Now I’m waiting for a Shannon bot and a Weiner bot. Maybe a John McCarthy bot would be apropos too?!

This tutorial will review the basics of theory in the field of evolutionary quantitative genetics and its connections to evolution observed at various time scales. Quantitative genetics deals with the inheritance of measurements of traits that are affected by many genes. Quantitative genetic theory for natural populations was developed considerably in the period from 1970 to 1990 and up to the present, and it has been applied to a wide range of phenomena including the evolution of differences between the sexes, sexual preferences, life history traits, plasticity of traits, as well as the evolution of body size and other morphological measurements. Textbooks have not kept pace with these developments, and currently few universities offer courses in this subject aimed at evolutionary biologists. There is a need for evolutionary biologists to understand this field because of the ability to collect large amounts of data by computer, the development of statistical methods for changes of traits on evolutionary trees and for changes in a single species through time, and the realization that quantitative characters will not soon be fully explained by genomics. This tutorial aims to fill this need by reviewing basic aspects of theory and illustrating how that theory can be tested with data, both from single species and with multiple-species phylogenies. Participants will learn to use R, an open-source statistical programming language, to build and test evolutionary models. The intended participants for this tutorial are graduate students, postdocs, and junior faculty members in evolutionary biology.

During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified.

Went on vacation or fell asleep at the internet wheel this week? Here’s some of the interesting stuff you missed.
## Science & Math

## Publishing

## Indieweb, Internet, Identity, Blogging, Social Media

## General

- Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering | PLOS Computational Biology
- The Competing Incentives of Academic Research in Mathematics
- [1607.08473] Quantum circuits and low-degree polynomials over F_2
- This Physics Pioneer Walked Away from it All | Nautilus
- Monumental proof to torment mathematicians for years to come: Conference on Shinichi Mochizuki’s work inspires cautious optimism. | Nature
- What Your Brain Looks Like When It Solves a Math Problem | New York Times
- Habits of Highly Mathematical People
- Why You Should Care About High-Dimensional Sphere Packing | Roots of Unity
- Initial steps toward reproducible research
- Bridging the Curation Gap between Research and Libraries – A Case Study
- Quantum steampunk: Quantum information applied to thermodynamics
- How Vector Space Mathematics Reveals the Hidden Sexism in Language
- How Sound Can Make Food Taste Better | Nautilus
- Top 10 algorithms of 20th century numerical analysis, from a talk by Alex Townsend
- UK vs. US: Who’s got the right way to teach math(s)? | Math with Bad Drawin
- Physics & Caffeine: Stop Motion Film Uses a Cup of Coffee to Explain Key Co
- The Water Kingdom: A Secret History of China by Philip Ball (review)
- The master of them all: Book review for”Leonhard Euler: Mathematical Genius in the Enlightenment” | The Economist
- Biologists Search for New Model Organisms: The bulk of biological research is centered on a handful of species. Are we missing a huge chunk of life’s secrets?
- One-sentence proof of Fermat’s theorem on sums of two squares | Fermat’s Library
- This protein designer aims to revolutionize medicines and materials
- Our last common ancestor inhaled hydrogen from underwater volcanoes
- Meet Luca, The Ancestor of All Living Things | New York Times
- *Disconnected, fragmented, or united? a trans-disciplinary review of network
- What’s Behind A Science vs. Philosophy Fight? | Big Think
- What is a “Neutral Network” Anyway? An Exploration and Rediscovery of the Aims of Net Neutrality in Theory and Practice
- The Brachistochrone Curve: The Problem of Quickest Descent | Fermat’s Library
- In what sense is Quantum Mechanics a theory of information? | Quora
- Major transitions in information technology | Philosophical Transactions of
- Human brain mapped in unprecedented detail: Nearly 100 previously unidentified brain areas revealed by examination of the cerebral cortex. | Nature
- Cell biologists should specialize, not hybridize: Dry cell biologists, who bridge computer science and cell biology, should have a pivotal role in driving effective team science, says Assaf Zaritsky | Nature
- Internet 3.0: How we take back control from the giants | New Scientist
- How a Guy From a Montana Trailer Park Overturned 150 Years of Biology | The Atlantic
- People can sense single photons | Nature News & Comment
- Defining synergy thermodynamically using quantitative measurements of entropy and free energy
- A Prime Case of Chaos | AMS.org
- Murray Gell-Mann (video interviews) – YouTube
- Mathematics & Chalk: A teary goodbye to Hagomoro | Jeremy Kun

- Want to Change Academic Publishing? Just Say No | Chronicle
- Textbooks Show Aging Signs: Curated Guides Are Next – 10+ Disruptive Factors Transforming the World of Education and Learning — Consequences, Opportunities, Tools
- Simon & Schuster, Penguin, Random House Don’t Want to Talk About Their Ebook Sales
- Amazon Sales Rank: Taming the Algorithm | Self-Publishing Author Advice
- What Authors Should Know About Advance Review Copies
- Ingram Launches Ingram Academic Services
- How a Publishing House Designs a Book Cover
- How Indie Bookstores Help Drive Book Discoverability
- How to Grow Your Email List
- 3 Ways Indie Publishers Sell Books | Digital Book World
- 10 Self-Publishing Trends to Watch
- Ingram Launches Academic Services for University Presses and Academic Publishers
- Indigo Goes Where Amazon, B&N, Goodreads, and a Dozen Publishers and Startus Have Dared to Tread
- How To Make An Ebook Feel More Like A Real Book
- Looking for open digital collections – Wynken de Worde

- What is Open Source?
- Microformats with Tantek Çelik | tlks.io https://www.youtube.com/watch?v=kDQigkxyiqE
- My Text Editor is Absolutely Sublime | Devon Zuegel
- My zsh aliases | Devon Zuegel
- XOXO Festival
- Web Design in 4 minutes
- Custom Elements
- Design Principles
- Infographic: The Optimal Length for Every Social Media Update
- Notes For New (and Potential) Twitter Followers | Whatever
- How Blogs Work Today – Whatever
- My reply to: How Blogs Work Today | Whatever
- Unicode Character ‘ZERO WIDTH SPACE’ (U 200B)
- A Book Apart, Practical SVG
- Gillmor Gang Trumpathon
- The best news aggregation service – The Sweet Setup
- Social Startup Sprinklr Is Now Valued At $1.8 Billion After $105 Million Raise | Forbes
- Epeus’ epigone: Digital publics, Conversations and Twitter

- The New Meaning of Success
- 7 Lessons from the Future of Content: Part One — Tools Are Cheap, Time Is Expensive
- 7 Lessons from the Future of Content: Part Two — Let’s Play Risk
- Aron Pilhofer Joining Temple University School of Media and Communication
- Secrets and agents: George Akerlof’s 1970 paper, “The Market for Lemons”, is a foundation stone of information economics. The first in our series on seminal economic ideas | The Economist
- John Oliver has the takedown of Donald Trump’s Republican convention
- Reference: New Interactive Map Of 100,000 Photos and Videos Reveal “Lost London in the Victorian Era”
- “better modifiers than “insane(ly)” (Grammar and Usage)
- A lesson in the errors of statistical thinking: Nate Silver on Trump
- Trump & Putin. Yes, It’s Really a Thing
- Charlie Parker Plays with Dizzy Gillespie in Only Footage Capturing the “Bird” in True Live Performance
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Dr. Michael Miller has announced his Autumn mathematics course, and it is…
## Introduction to Complex Analysis

#### Update 9/1/16

### Textbook

### Alternate textbooks

#### Undergraduate

#### More advanced

### References

## Course Description

Complex analysis is one of the most beautiful and useful disciplines of mathematics, with applications in engineering, physics, and astronomy, as well as other branches of mathematics. This introductory course reviews the basic algebra and geometry of complex numbers; develops the theory of complex differential and integral calculus; and concludes by discussing a number of elegant theorems, including many–the fundamental theorem of algebra is one example–that are consequences of Cauchy’s integral formula. Other topics include De Moivre’s theorem, Euler’s formula, Riemann surfaces, Cauchy-Riemann equations, harmonic functions, residues, and meromorphic functions. The course should appeal to those whose work involves the application of mathematics to engineering problems as well as individuals who are interested in how complex analysis helps explain the structure and behavior of the more familiar real number system and real-variable calculus.

## Prerequisites

Basic calculus or familiarity with differentiation and integration of real-valued functions.

## Details

MATH X 451.37 – 268651 Introduction to Complex Analysis

Fall 2016

Time 7:00PM to 10:00PM

Dates Tuesdays, Sep 20, 2016 to Dec 06, 2016

Contact Hours 33.00

Location: UCLA, Math Sciences Building

Standard credit (3.9 units) $453.00

Instructor: Michael Miller

Register Now at UCLA

For many who will register, this certainly won’t be their first course with Dr. Miller — yes, he’s that good! But for the newcomers, I’ve written some thoughts and tips to help them more easily and quickly settle in and adjust:

Dr. Michael Miller Math Class Hints and Tips | UCLA Extension

I often recommend people to join in Mike’s classes and more often hear the refrain: “I’ve been away from math too long”, or “I don’t have the prerequisites to even begin to think about taking that course.” For people in those categories, you’re in luck! If you’ve even had a soupcon of calculus, you’ll be able to keep up here. In fact, it was a similar class exactly a decade ago by Mike Miller that got me back into mathematics. (Happy 10th math anniversary to me!)

I look forward to seeing everyone in the Fall!

Dr. Miller is back from summer vacation and emailed me this morning to say that he’s chosen the textbook for the class. We’ll be using *Complex Analysis with Applications* by Richard A. Silverman. [1]

(Note that there’s another introductory complex analysis textbook from Silverman that’s offered through Dover, so be sure to choose the correct one.)

As always in Dr. Miller’s classes, the text is just *recommended* (read: not required) and in-class notes are more than adequate. To quote him directly, “We will be using as a basic guide, but, as always, supplemented by additional material and alternate ways of looking at things.”

The bonus surprise of his email: He’s doing two quarters of Complex Analysis! So we’ll be doing both the Fall and Winter Quarters to really get some depth in the subject!

If you’re like me, you’ll probably take a look at some of the other common (and some more advanced) textbooks in the area. Since I’ve already compiled a list, I’ll share it:

*Complex Analysis*by Joseph Bak and Donald J. Newman [2]*Complex Analysis*by Theodore Gamelin [3]*Complex Variables and Applications*by James Brown and Ruel Churchill [4]*Fundamentals of Complex Analysis with Applications to Engineering, Science, and Mathematics*by Edward Saff and Arthur D. Snider (Pearson, 2014, 3rd edition) [5]

*Complex Analysis*by Lars Ahlfors [6]*Complex Analysis*by Serge Lang [7]*Functions of One Complex Variable*(Graduate Texts in Mathematics by John B. Conway (Springer, 1978) [8]*Complex Analysis*(Princeton Lectures in Analysis, No. 2) by Elias M. Stein and Rami Shakarchi (Princeton University Press, 2003) [9]

[1]

R. A. Silverman, *Complex Analysis with Applications*, 1st ed. Dover Publications, Inc., 2010, pp. 304–304 [Online]. Available: http://amzn.to/2c7KaQy

[2]

J. Bak and D. J. Newman, *Complex Analysis*, 3rd ed. Springer, 2010, pp. 328–328 [Online]. Available: http://amzn.to/2bLPW89

[3]

T. Gamelin, *Complex Analysis*. Springer, 2003, pp. 478–478 [Online]. Available: http://amzn.to/2bGNQct

[4]

J. Brown and R. V. Churchill, *Complex Variables and Applications*, 8th ed. McGraw-Hill, 2008, pp. 468–468 [Online]. Available: http://amzn.to/2bLQWcu

[5]

E. B. Saff and A. D. Snider, *Fundamentals of Complex Analysis with Applications to Engineering, Science, and Mathematics*, 3rd ed. Pearson, 2003, pp. 563–563 [Online]. Available: http://amzn.to/2f3Nyj6

[6]

L. V. Ahlfors, *Complex Analysis*, 3rd ed. McGraw-Hill, 1979, pp. 336–336 [Online]. Available: http://amzn.to/2bMXrxm

[7]

S. Lang, *Complex Analysis*, 4th ed. Springer, 2003, pp. 489–489 [Online]. Available: http://amzn.to/2c7OaR0

[8]

J. B. Conway, *Functions of One Complex Variable*, 2nd ed. Springer, 1978, pp. 330–330 [Online]. Available: http://amzn.to/2cggbF1

[9]

El. M. Stein and R. Shakarchi, *Complex Analysis*. Princeton University Press, 2003, pp. 400–400 [Online]. Available: http://amzn.to/2bGOG9c