Chris Aldrich Blog: BoffoSocko.com

Checkin Mathematical Sciences Building, UCLA

It’s true what they say, “Complex Analysis IS for lovers.” #theoremoncanonicalproducts #HappyValentinesDay

The Tuesday night meeting of the lonely hearts club aka Math Lovers Anonymous.

📖 5.27% done with American Amnesia by Jacob S. Hacker and Paul Pierson

📖 Read loc 1-682 of 12932 (5.27%) of American Amnesia by Jacob S. Hacker and Paul Pierson

This portends to be very interesting in that they plan to show what has changed over much of the 1900’s to indicate the drastic evolution in American politics, life, and philosophy over the recent decades. In light of the political battles between the left and the right over the past several years, this could provide some much needed help and guidance.

Their basic thesis seems to be that a shift away from a mixed economy has slowed American growth and general prosperity. While they do seem to have a pointed (political) view, so far it’s incredibly well documented and footnoted for those who would like to make the counter-argument. They’ve definitely got some serious evidence to indicate how drastic the situation is, but I’m curious if they can directly tie their proposed cause to the effect. If nothing else, they’ve created a laundry list of problems in America which need to be addressed by some serious leadership soon.

In some sense I’m torn about what to think of a broader issue this touches upon and which I mentioned briefly while reading At Home in the Universe. Should we continue on the general path we’ve struck out upon (the mixed economy with government regulation/oversight), or should we continue evolving away? While we can’t see the complexity effects seven levels further in, they may be more valuable than what we’ve got now. For example Cesar Hidalgo looks at the evolution along a continuum of personbyte to larger groups: firms (firmbyte), governments, and mega-corporations in Why Information Grows, so I can easily see larger governments and corporations like Google drastically changing the world in which we live (operating at a level above what most humans can imagine presently), but the complexity of why and how they operate above (and potentially against) the good of the individual should certainly be called into question and considered as we move forward.

🔖 The Epidemic Spreading Model and the Direction of Information Flow in Brain Networks

Bookmarked The Epidemic Spreading Model and the Direction of Information Flow in Brain Networks (NeuroImage, February 5, 2017)

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.

Part of the alphabet in Palmer Park

Instagram filter used: Normal

Photo taken at: City of Glendale, CA

Annie (Sony, 2014)

Watched Annie from Sony Pictures, 2014

Directed by Will Gluck. With Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, Rose Byrne. A foster kid, who lives with her mean foster mom, sees her life change when business tycoon and New York mayoral candidate Will Stacks makes a thinly-veiled campaign move and takes her in.

Finishing out the Adewale Akinnuoye-Agbaje 2014 mini-movie retrospective after watching Pompeii yesterday. What polar opposite characters in many senses. He didn’t seem as big or imposing here while he was positively brutal in the other.

The opening with the alternate red headed Annie would have played better if it was a tad more respectful to its own source material.

This incarnation of Annie was alright, but not as solid as the 1982 version. The update to a modern setting was relatively good, but New York didn’t feel much like New York. A part of the magic was missing and I’d suggest that it was the music that killed it for me. None of the singing felt live and all of it was auto-tuned even for actors for whom it probably wasn’t necessary. On top of this none of the classic songs from the book were understandable from a lyrics point of view, and this is sad since that’s where half of the story is hiding.

The other piece that was missing was the general chemistry between Annie and her benefactor. The ebullient joy of the 1982 Annie with the curmudgeon played by Albert Finney just overwhelmed the poorly developed relationship in this version.

This might have been better timed from a zeitgeist perspective if it had been released in Fall 2016 with more veiled references to Trump, though no one would have bought the rich-guy-with-a-heart ending.

IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease, March 1-3

Bookmarked IPAM Workshop on Regulatory and Epigenetic Stochasticity in Development and Disease (Institute for Pure and Applied Mathematics, UCLA | March 1-3, 2017)

Epigenetics refers to information transmitted during cell division other than the DNA sequence per se, and it is the language that distinguishes stem cells from somatic cells, one organ from another, and even identical twins from each other. In contrast to the DNA sequence, the epigenome is relatively susceptible to modification by the environment as well as stochastic perturbations over time, adding to phenotypic diversity in the population. Despite its strong ties to the environment, epigenetics has never been well reconciled to evolutionary thinking, and in fact there is now strong evidence against the transmission of so-called “epi-alleles,” i.e. epigenetic modifications that pass through the germline.

However, genetic variants that regulate stochastic fluctuation of gene expression and phenotypes in the offspring appear to be transmitted as an epigenetic or even Lamarckian trait. Furthermore, even the normal process of cellular differentiation from a single cell to a complex organism is not understood well from a mathematical point of view. There is increasingly strong evidence that stem cells are highly heterogeneous and in fact stochasticity is necessary for pluripotency. This process appears to be tightly regulated through the epigenome in development. Moreover, in these biological contexts, “stochasticity” is hardly synonymous with “noise”, which often refers to variation which obscures a “true signal” (e.g., measurement error) or which is structural, as in physics (e.g., quantum noise). In contrast, “stochastic regulation” refers to purposeful, programmed variation; the fluctuations are random but there is no true signal to mask.

This workshop will serve as a forum for scientists and engineers with an interest in computational biology to explore the role of stochasticity in regulation, development and evolution, and its epigenetic basis. Just as thinking about stochasticity was transformative in physics and in some areas of biology, it promises to fundamentally transform modern genetics and help to explain phase transitions such as differentiation and cancer.

This workshop will include a poster session; a request for poster titles will be sent to registered participants in advance of the workshop.

Speaker List:
Adam Arkin (Lawrence Berkeley Laboratory)
Gábor Balázsi (SUNY Stony Brook)
Domitilla Del Vecchio (Massachusetts Institute of Technology)
Michael Elowitz (California Institute of Technology)
Andrew Feinberg (Johns Hopkins University)
Don Geman (Johns Hopkins University)
Anita Göndör (Karolinska Institutet)
John Goutsias (Johns Hopkins University)
Garrett Jenkinson (Johns Hopkins University)
Andre Levchenko (Yale University)
Olgica Milenkovic (University of Illinois)
Johan Paulsson (Harvard University)
Leor Weinberger (University of California, San Francisco (UCSF))

🔖 IPAM Workshop on Gauge Theory and Categorification, March 6-10

Bookmarked IPAM Workshop on Gauge Theory and Categorification (Institute of Pure and Applied Mathematics at UCLA - March 6-10, 2017)

The equations of gauge theory lie at the heart of our understanding of particle physics. The Standard Model, which describes the electromagnetic, weak, and strong forces, is based on the Yang-Mills equations. Starting with the work of Donaldson in the 1980s, gauge theory has also been successfully applied in other areas of pure mathematics, such as low dimensional topology, symplectic geometry, and algebraic geometry.

More recently, Witten proposed a gauge-theoretic interpretation of Khovanov homology, a knot invariant whose origins lie in representation theory. Khovanov homology is a “categorification” of the celebrated Jones polynomial, in the sense that its Euler characteristic recovers this polynomial. At the moment, Khovanov homology is only defined for knots in the three-sphere, but Witten’s proposal holds the promise of generalizations to other three-manifolds, and perhaps of producing new invariants of four-manifolds.

This workshop will bring together researchers from several different fields (theoretical physics, mathematical gauge theory, topology, analysis / PDE, representation theory, symplectic geometry, and algebraic geometry), and thus help facilitate connections between these areas. The common focus will be to understand Khovanov homology and related invariants through the lens of gauge theory.

This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.

Edward Witten will be giving two public lectures as part of the Green Family Lecture series:

March 6, 2017
From Gauge Theory to Khovanov Homology Via Floer Theory
The goal of the lecture is to describe a gauge theory approach to Khovanov homology of knots, in particular, to motivate the relevant gauge theory equations in a way that does not require too much physics background. I will give a gauge theory perspective on the construction of singly-graded Khovanov homology by Abouzaid and Smith.

March 8, 2017
An Introduction to the SYK Model
The Sachdev-Ye model was originally a model of quantum spin liquids that was introduced in the mid-1990′s. In recent years, it has been reinterpreted by Kitaev as a model of quantum chaos and black holes. This lecture will be primarily a gentle introduction to the SYK model, though I will also describe a few more recent results.

📖 On page 215 of 321 of At Home in the Universe by Stuart Kauffman

📖 Read pages 191 – 215 of At Home in the Universe by Stuart Kauffman

In chapter 9 Kauffman applies his NK landscape model to explain the evolution seen in the Cambrian explosion and the re-population following the Permian extinction. He then follows it up with some interesting discussion which applies it to technological innovation, learning curves, and growth in areas of economics. The chapter has given me a few thoughts on the shape and structure (or “landscape”) of mathematics. I’ll come back to this section to see if I can’t extend the analogy to come up with something unique in math.

The beginning of Chapter 10 he begins discussing power laws and covering the concept of emergence from ecosystems, coevolution, and the evolution of coevolution. In one part he evokes Adam Smith’s invisible hand which seemingly benefits everyone acting for its own selfishness. Though this seems to be the case since it was written, I do wonder what timescales and conditions it works under. As an example, selfishness on the individual, corporate, nation, and other higher levels may not necessarily be so positive with respect to potential issues like climate change which may drastically affect the landscape on and in which we live.