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This argument certainly applies to reading advanced mathematics texts. (Cross reference: http://boffosocko.com/2015/03/16/why-arent-math-textbooks-more-straightforward/) While most advanced mathematicians can read somewhat rapidly through advanced texts very easily following the trail of breadcrumbs the author has laid, out, the author usually takes for granted that the reader has some level of prerequisites to make jumping from one idea to the next possible without including every single stepping stone along the way. Those found without the sometimes unstated required background find themselves more quickly lost and need to spend far more time to be able to jump easily to the next thought.

In Pinker's framework, advanced mathematics texts do have the requisite coherence relations, it's just that the distance between the relations are less coherent to some than others (who have more background.)

 

John, your excellent work certainly isn't going unnoticed!

I thank you not only for originally hosting the workshop, but for continuing to follow up to help it continue it's life online long after the fact. My hypothesis is that because the work entailed here is so cutting edge (and does require some reasonable work on the part of the viewer to watch, read, and cogitate) that your work today is really for the long-tail. I'm sure that there are others, like me, who weren't able to trek to Tennessee for the original, but appreciate that they can relive it online.

I'm sorry some of the videos didn't manage to come out. I'm still distraught over Harte's not being available, though I suppose that his text is a possible stand in.

Sadly, scholarly communication isn't a more sexy profession, but fortunately we have people like you who manage to do it well. I always find myself wishing that I knew 20 more people like you who had the depth and breadth of experience in physics, mathematics, and biology.

If it helps, your influence (both through this workshop and your prior posts) has prompted me to begin delving heavily into <a href="http://cat.boffosocko.com">category theory</a> in the last week, and as a result there are about 20 others who are joining me in studying it over the coming months. I know that Andrew Eckford is also in the same camp.[<a href="https://twitter.com/andreweckford/status/593870544224833538">1</a>][<a href="https://twitter.com/andreweckford/status/593870774655651840">2</a>][<a href="https://twitter.com/andreweckford/status/603705613466673152">3</a>] (Psst.. you might be missing some of the conversation which is taking place on Twitter.)

Thanks, as always, for the effort you've made into these (and many other) fields, which helps pave the way for those following you!

 

In a Search for a Structure, Part 1: On Entropy. Category theory is the appropriate language for describing entropy.

Mathematics is about ”interesting structures”. What make a structure interesting is an abundance of interesting problems; we study a structure by solving these problems.
The worlds of science, as well as of mathematics itself, is abundant with gems (germs?) of simple beautiful ideas. When and how may such an idea direct you toward beautiful mathematics?
I try to present in this talk a few suggestive examples.

 

For all the "sad" mathematicians out there,
Combinatorics is the mathematics of counting.

 

Free textbook: Abstract and Concrete Categories: The Joy of Cats

Abstract and Concrete Categories was published by John Wiley and Sons, Inc, in 1990, and after several reprints, the book has been sold out and unavailable for several years. We now present an improved and corrected version as an open access file. This was made possible due to the return of copyright to the authors, and due to many hours of hard work and the exceptional skill of Christoph Schubert, to whom we wish to express our profound gratitude. The illustrations of Edward Gorey are unfortunately missing in the current version (for copyright reasons), but fortunately additional original illustrations by Marcel Erné, to whom additional special thanks of the authors belong, counterbalance the loss.
Open access includes the right of any reader to copy, store or distribute the book or parts of it freely. (See the GNU Free Documentation License at the end of the text.)

 

I find it hard to believe, particularly if you're in financial need, that you'd have a full ride at UT and not have been offered anything at JHU, so double check with the JHU office of financial aid first to see what, if anything, you'd be offered.

More than anything I'd recommend at least visiting both campuses in person and taking a tour and speaking to some students and professors. Asking a question here like you have with only the three data points (two schools and one financial aid package) is not nearly enough to go on for anyone to presume to help you make one of the most influential decisions you're going to make in your life - particularly when the advice is likely to be a terrifically biased and spurious and you're unlikely to delve into the nature of the responses you're getting.

In some sense, you're comparing apples and oranges. A couple of questions you should ask, however, above and beyond the simple ranking portion of the question which most are focusing on in their answers (while the remainder seem to be a bit more biased based on their personal experiences) include:
What are the campus environments like?
How big are the schools and what kind of attention and resources will I have access to?
How big are the cities they're in and what do they offer as part of the undergraduate experience?
What happens after one or two years if you decide to change your major?
Other than the end result of the degree, what do you want out of a college experience?
Are you looking for more diversity or less in terms of culture, religion, etc.?
What are your other interests outside of academics and are those interests actively represented or possible at the school you want to attend? (As an example, are you a musician and want to take classes or have practice time at a college's sister conservatory - perhaps you could get a dual degree? or are there a variety of other music outlets available like bands, symphonies, orchestras, music groups, etc.?)

UT is a humongous place, while Hopkins is significantly smaller, so at UT you're much more likely to be treated like just another number, even within the electrical engineering department. At Hopkins your classes are guaranteed to be much smaller and more intimate which gives you far more access to your professors - particularly when you may need recommendations down the road.

Statistically, if you're planning on going to graduate school, you'll have far more resources for doing research as an undergraduate at Hopkins, which will give you more preparation and a stronger case when you apply. Hopkins excels in undergraduate research experience in part because of its philosophy but also because it gets almost twice the government funding than even the next closest competitor. Additional resources like the proximity of the Applied Physics Lab and the Space Telescope Science Institute (which managed projects like the Hubble Telescope) will give you additional opportunities related to your field while you're in school.

If you do want to play the simple ranking game, keep in mind that only about a third of your course work as an undergraduate will be in the EE department. How highly ranked are all the other programs and departments like mathematics, physics, chemistry, biology, English, and others from which the remainder of your coursework will come? Again, what will things look like if you decide to change major part way through?

[Disclosure: I'm an alum of Johns Hopkins class of 1996 with degrees in both biomedical engineering and electrical engineering. I've been both very active on the Hopkins Society of Engineering Alumni board as well as the board that oversees the University's larger Alumni Association. I'm happy to give you more information about Hopkins, and can be easily found via a variety of social media outlets.]

 

Mathematics and Category Theory for Big History?! @davidgchristian @bighistory @BigHistoryInst

5 min read

The more I read of the introductory chapter of Category Theory for the Sciences by David Spivak, the more I'm sure there is a firm and solid foundation for potentially making mathematics a major pillar of Big History.  

The structural scheme for category theory within mathematics sounds almost like that of the structure of Big History as a whole. If nothing else it screams convergence while making very complex structures much simpler. (Big History in reverse perhaps?)

Cover of Complexity Theory for Scientists

I can't wait to delve further in. Spivak's text, though very mathematical in nature seems to have all the basic set theory (at a high school level), and the introduction indicates that he expects readers to have some facility with linear algebra, but there are no other highly daunting prerequisites.

A few excerpts:

This book extols the virtues of a new branch of mathematics, category theory, which was invented for powerful communication of ideas between different fields and subfields within mathematics. By powerful communication of ideas I mean something precise. Different branches of mathematics can be formalized into categories. These categories can then be connected by functors. And the sense in which these functors provide powerful communication of ideas is that facts and theorems proven in one category can be transferred through a connecting functor to yield proofs of analogous theorems in another category. A functor is like a conductor of mathematical truth.

We build scientific understanding by developing models, and category theory is the study of basic conceptual building blocks and how they cleanly fit together to make such models. Certain structures and conceptual frameworks show up again and again in our understanding of reality. No one would dispute that vector spaces are ubiquitous throughout the sciences. But so are hierarchies, symmetries, actions of agents on objects, data models, global behavior emerging as the aggregate of local behavior, self-similarity, and the effect of methodological context.

Hierarchies are partial orders, symmetries are group elements, data models are categories, agent actions are monoid actions, local-to-global principles are sheaves, self-similarity is modeled by operads, context can be modeled by monads.

The paradigm shift brought on by Einstein’s theory of relativity led to a widespread realization that there is no single perspective from which to view the world. There is no background framework that we need to find; there are infinitely many different frameworks and perspectives, and the real power lies in being able to translate between them. It is in this historical context that category theory got its start.

However, in 1957 Alexander Grothendieck used category theory to build new mathematical machinery (new cohomology theories) that granted unprecedented insight into the behavior of algebraic equations. Since that time, categories have been built specifically to zoom in on particular features of mathematical subjects and study them with a level of acuity that is unavailable elsewhere.

Bill Lawvere saw category theory as a new foundation for all mathematical thought. Mathematicians had been searching for foundations in the nineteenth century and were reasonably satisfied with set theory as the foundation. But Lawvere showed that the category of sets is simply one category with certain nice properties, not necessarily the center of the mathematical universe. He explained how whole algebraic theories can be viewed as examples of a single system. He and others went on to show that higherorder logic was beautifully captured in the setting of category theory... 

In 1980, Joachim Lambek showed that the types and programs used in computer science form a specific kind of category. This provided a new semantics for talking about programs, allowing people to investigate how programs combine and compose to create other programs, without caring about the specifics of implementation. Eugenio Moggi brought the category-theoretic notion of monads into computer science to encapsulate ideas that up to that point were considered outside the realm of such theory.

It is difficult to explain the clarity and beauty brought to category theory by people like Daniel Kan and Andr´e Joyal. They have each repeatedly extracted the essence of a whole mathematical subject to reveal and formalize a stunningly simple yet extremely powerful pattern of thinking, revolutionizing how mathematics is done.

All this time, however, category theory was consistently seen by much of the mathematical community as ridiculously abstract. But in the twenty-first century it has finally come to find healthy respect within the larger community of pure mathematics. It is the language of choice for graduate-level algebra and topology courses, and in my opinion will continue to establish itself as the basic framework in which to think about and express mathematical ideas.

As mentioned, category theory has branched out into certain areas of science as well. Baez and Dolan [6] have shown its value in making sense of quantum physics, it is well established in computer science, and it has found proponents in several other fields as well. 

But to my mind, we are at the very beginning of its venture into scientific methodology. Category theory was invented as a bridge, and it will continue to serve in that role.

 

This brings up, not surpisingly, an old trope in mathematics: "what use is abstract mathematics?" Some of the philosophy is disccussed well in Michael Harris's new book Mathematics without Apologies: Portrait of a Problematic Vocation [http://www.amazon.com/gp/product/0691154236/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=0691154236&linkCode=as2&tag=math01-20&linkId=OD6GPNGZWT4PJ6XG] which many in the mathematics community have been reading in the past few months.

Often courses like analysis and topology seem awfully abstract, but become much more useful when combined with other topics like linear algebra and group theory to build up other abstract topics like Lie groups and Lie algebras which have more specific problem applications. A few years ago I was shocked to see a Birkhauser/Springer text entitled Stochastic Models, Information Theory, and Lie Groups [http://www.amazon.com/gp/product/081764802X/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=081764802X&linkCode=as2&tag=math01-20&linkId=UTUGVB2S56AMUEGH] by Gregory Chirikjian which applies these seemingly disparate areas of mathematics directly to robotics. None of it is possible without analysis as a mathematical base.

I might suggest you sit back and enjoy the abstract portions of the ride for a bit while keeping your eyes out for applied areas for application. You'll find enough applied math as you go along. In general, you may find that many applied areas will become more comprehensible to you because you better understand the abstract math which underpins them.

One bright light at the end of the tunnel for analysis is that it builds up things for measure theory which underpins probability theory.

For resources, you might find Francis Su's (he's the current president of the MAA) online lectures to be interesting/useful. They can be found via http://rudinium.herokuapp.com/#/help or his related blog http://analysisyawp.blogspot.com/p/faq.html.

There are also a wealth of materials via India's IIT http://nptel.ac.in/courses.php?disciplineId=111, which covers many areas of math including analysis. A little bit of searching will also uncover their engineering, physics, and other departments which have applied courses as well.

 

Math should be taught and approached from a creative, reasearch-like perspective -- which is how it organically developed throughout history anyway. This will help children (and even adults) to know how to better approach problems and figure them out when they get to higher levels. Some of the "rote" memorization of forms and formulas is just silly, and in part is what is keeping anyone from learning any math created after the 18th century until they're a junior in college, by which point most have given up on mathematics entirely.

I maintain that part of the reason that 'geniuses' like Euler and Gauss 'discovered' so much of math is that there was a truly large amount of easy low-hanging fruit for them to find. There's no reason that students couldn't be 'led' to rediscover some of these types of areas for themselves.

 

I came to your article in a terrifically circuitous fashion (via a serendipitous Google Image search), but am so glad that I did. Now it appears I'll end up spending the rest of my day reading all your links and references. I work in areas of information theory and molecular biology and have been filling in my knowledge of mathematics over time. Having just finished up six months of work in Lie groups and algebras, it would appear that category theory is the next sandbox in which I should play. Thanks especially for the textbook references as well as the journal articles!

 

First, let's unpack your question a bit. What we're really doing is taking mathematical form, function, and theory and apply it in some sort of unifying manner to unify the areas of information theory and statistical thermodynamics.

You can also ask your question in the reverse direction to apply the mathematics of information to thermodynamics as well as there should be a mathematical duality of sorts, if in fact the two are directly related.

First, let's unpack your question a bit. What we're really doing is taking mathematical form, function, and theory and apply it in some sort of unifying manner to unify the areas of information theory and statistical mechanics and thermodynamics.

You can also ask your question in the reverse direction to apply the mathematics of information to thermodynamics as well as there should be a mathematical duality of sorts, if in fact the two are directly related.

The original link between the two areas stems from a paper by E.T. Jaynes entitled "Information Theory and Statistical Mechanics" (downloadable from http://bayes.wustl.edu/etj/articles/theory.1.pdf). Since then there has been a lot of uninformed back and forth in the literature from agreement to flat out denial, primarily as a result of the multitude of areas from which a researcher can approach the topics and rarely are many/any experts in more than one of these areas. (I specifically recall an article in 2011-2012 in the esteemed journal Science from a senior researcher in information theory that flatly stated there was no link between the two fields!)

Since then, in my opinion, the best source tying the fields together mathematically is A Farewell To Entropy: Statistical Thermodynamics Based on Information by Arieh Ben-Naim [http://www.amazon.com/gp/product/9812707077/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=9812707077&linkCode=as2&tag=itbio-20&linkId=WDUVNEIJG5BIO242]. In it he closely and coherently ties the two fields directly together and does a reasonable job of pointing out problems with some of the more popular misconceptions and conflicts in the literature. (For those without the physics, engineering, or math backgrounds in these subjects, I can also highly recommend Arieh Ben-Naim's forthcoming book Information, Entropy, Life and the Universe: What We Know and What We Do Not Know [http://www.amazon.com/gp/product/9814651672/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=9814651672&linkCode=as2&tag=itbio-20&linkId=WKTUT4I7WDIYS5MG] which is due out at the end of May.)

As for other connections, I've not seen anything as direct, primarily as there aren't any other major concepts with the exact same name. However, because of the underlying mathematics, I strongly suspect that there are many similar 'concepts'. A few in particular, I have a feeling will come out of the more rigorous mathematical frameworks based on Lieb and Yngvason’s concptualization of thermodynamics (see Resource Convertibility (Part 1)[https://johncarlosbaez.wordpress.com/2015/04/07/resource-convertibility-part-1/] and The Entropy Principle: Thermodynamics for the Unsatisfied: Andre Thess [http://www.amazon.com/gp/product/3642133487/ref=as_li_tl?ie=UTF8&camp=1789&creative=390957&creativeASIN=3642133487&linkCode=as2&tag=itbio-20&linkId=3XLV4PT33UDZ2XLW].) Given the power of the mathematics and the more abstract nature of information theory, I suspect that thermodynamics and statistical mechanics will eventually be viewed as a sub-area or application of information theory.

Another fruitful area of conceptual similarities is slowly grown out of quantum information theory and the statistical mechanics of black holes, though there is some heavily rarified air surrounding these given the required background needed to operate with them both simultaneously.

 

I'll turn this question around to suggest that instead of taking notes from your math/physics textbooks, that you're FAR better off PUTTING notes INTO them! Those margins are meant for writing down the parts of problems and examples that the author implicitly leaves out.

One typically wouldn't take notes from a Spanish, French, or Latin textbook would they? Like most languages, mathematics should be read and written to practice it (and maybe even spoken).

Knowing math or physics is best demonstrated by actually doing problems - and the majority of the time, this is what is going to be on the test too, so just pick up a pencil or pen and start working out the answers.

These subjects aren't like history, philosophy, or psychology with multiple choice or essay type questions that might benefit from note-taking, so just jump right in. Give the book a short read and start plugging away at problems.

If you have problems getting started, take a look at some of the examples provided by the author (or in other books), cover up the answer, and try to recreate the solution.

Drafting off of the Quora question "Why aren't math textbooks more straightforward?" (https://www.quora.com/Why-arent-math-textbooks-more-straightforward) I'd suggest reading some of my extended answer here: http://boffosocko.com/2015/03/16/why-arent-math-textbooks-more-straightforward/

 

The Math Major Who Never Reads Math http://mathwithbaddrawings.com/2015/03/17/the-math-major-who-never-reads-math

Certainly an interesting perspective on a very pervasive problem. I saw the provocative question "Why Aren’t Math Textbooks More Straightforward?" on Quora just the other day. I take much the same view as you (though I religiously read not only my assigned math texts for classes, but also read their competitors in search of increased clarity and beauty.) I spent a few minutes writing up a few thoughts on what is going on in math textbooks taking a broader view of the forest instead of complaining about the trees.

Perhaps some of the tentative will gain a better appreciation for mathematics: http://boffosocko.com/2015/03/16/why-arent-math-textbooks-more-straightforward/

The repetition that a good mathematician (or really any good thinker) reads books with a pencil in hand needs to be drilled down to younger learners.

A parting math textbook joke:
Q. Why did Nicolas Bourbaki quit writing mathematics textbooks?
A. "He" realized that Serge Lang was just one person.