Bookmark | Chris Aldrich

🔖 Dynamics on expanding spaces: modeling the emergence of novelties

Bookmarked [1701.00994] Dynamics on expanding spaces: modeling the emergence of novelties (arxiv.org)

Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, experiment with new situations. Occasionally, we as individuals, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon's model tracing back to the 1950s, to the newest model of Polya's urn with triggering of one novelty by another. What seems to be key in the successful modelling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically it is very interesting to look at the consequences of the interplay between the "actual" and the "possible" and this is the aim of this short review.

Bookmarked after reading Mathematical Model Reveals the Patterns of How Innovations Arise.

🔖 Information theory, predictability, and the emergence of complex life

Bookmarked Information theory, predictability, and the emergence of complex life (arxiv.org)

Abstract: Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated to detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated to maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.

🔖 Foldscope – The Origami Paper Microscope | Kickstarter

Bookmarked Foldscope - The Origami Paper Microscope (Kickstarter)

See the invisible with a powerful yet affordable microscope that fits in your pocket. Curiosity, discovery, and science for everyone!

A microscope in every pocket is surely a great idea.

They also have a journal article on PLoS ONE. [1]

References

[1]

J. Cybulski S., J. Clements, and M. Prakash, “Foldscope: Origami-Based Paper Microscope,” PLoS ONE, vol. 9, no. 6, Jun. 2014 [Online]. Available: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098781 [Source]

🔖 AMS Open Math Notes

Bookmarked Open Math Notes (ams.org)

AMS Open Math Notes is a repository of freely downloadable mathematical works in progress hosted by the American Mathematical Society as a service to researchers, teachers and students. These draft works include course notes, textbooks, and research expositions in progress. They have not been published elsewhere, and, as works in progress, are subject to significant revision. Visitors are encouraged to download and use these materials as teaching and research aids, and to send constructive comments and suggestions to the authors.

h/t to Terry Tao for the notice.

Introducing Shortcut | This American Life

Bookmarked Introducing Shortcut (This American Life)

Have you ever heard a moment on the show that you wish you could share with your friends? Well, now you can! Shortcut is a new app we created that allows you to turn your favorite podcast moments into videos that you can post to social media. It’s kind of like making a gif, but for audio. Here’s how to use it.

I love the functionality that they’ve built into their podcast to clip and share audio snippets. It’s only a step or two further to highlight and annotate within an audio file. This is somewhat reminiscent of bits of SoundCloud’s user interface.

h/t Jon Udell via Twitter

@hypothes_is @judell s there a way to annotate mp3? I'd like to attach annotations to podcasts.

— Raymond Yee (@rdhyee) December 16, 2016

@rdhyee @hypothes_is @judell Something like this:https://t.co/2NEft9NbLY

— Michael Shook (@mshook) December 16, 2016

Yes, for any file-served mp3. https://t.co/1cm7OyXBfP did that, @dougkaye had a variant for ITConversations. @mshook @rdhyee

— Jon Udell (@judell) December 16, 2016

@judell @dougkaye @mshook @rdhyee check out @signlfm — they might work on an open spec with you for podcast annotation

— Boris Mann (@bmann) December 16, 2016

@signlfm @bmann @judell @dougkaye @mshook @rdhyee We're using WebVTT – already a standard. Could do some IndieWeb things with it.

— Jim Pick (@jimpick) December 16, 2016

@jimpick @signlfm hey @judell does @hypothes_is understand WebVTT? Maybe push tweets into Hypothesis as an annotation

— Boris Mann (@bmann) December 16, 2016

If text exists, cool. But bare MP3s are timecode-accessible, though we haven't leveraged that much. @bmann @jimpick @signlfm @hypothes_is

— Jon Udell (@judell) December 16, 2016

@judell @bmann @signlfm @hypothes_is Also the HTML5 Media Fragments URI, which I don't think many people know about https://t.co/Aok9KCYtn4

— Jim Pick (@jimpick) December 16, 2016

Basic Category Theory by Tom Leinster | Free Ebook Download

Bookmarked Basic Category Theory (arxiv.org)

This short introduction to category theory is for readers with relatively little mathematical background. At its heart is the concept of a universal property, important throughout mathematics. After a chapter introducing the basic definitions, separate chapters present three ways of expressing universal properties: via adjoint functors, representable functors, and limits. A final chapter ties the three together. For each new categorical concept, a generous supply of examples is provided, taken from different parts of mathematics. At points where the leap in abstraction is particularly great (such as the Yoneda lemma), the reader will find careful and extensive explanations.

Tom Leinster has released a digital e-book copy of his textbook Basic Category Theory on arXiv. [1]

h/t to John Carlos Baez for the notice:

My friend Tom Leinster has written a great introduction to that wonderful branch of math called category theory! It’s free:

https://arxiv.org/abs/1612.09375

It starts with the basics and it leads up to a trio of related concepts, which are all ways of talking about universal properties.

Huh? What’s a ‘universal property’?

In category theory, we try to describe things by saying what they do, not what they’re made of. The reason is that you can often make things out of different ingredients that still do the same thing! And then, even though they will not be strictly the same, they will be isomorphic: the same in what they do.

A universal property amounts to a precise description of what an object does.

Universal properties show up in three closely connected ways in category theory, and Tom’s book explains these in detail:

through representable functors (which are how you actually hand someone a universal property),

through limits (which are ways of building a new object out of a bunch of old ones),

through adjoint functors (which give ways to ‘freely’ build an object in one category starting from an object in another).

If you want to see this vague wordy mush here transformed into precise, crystalline beauty, read Tom’s book! It’s not easy to learn this stuff – but it’s good for your brain. It literally rewires your neurons.

Here’s what he wrote, over on the category theory mailing list:

…………………………………………………………………..

Dear all,

My introductory textbook “Basic Category Theory” was published by Cambridge University Press in 2014. By arrangement with them, it’s now also free online:

https://arxiv.org/abs/1612.09375

It’s also freely editable, under a Creative Commons licence. For instance, if you want to teach a class from it but some of the examples aren’t suitable, you can delete them or add your own. Or if you don’t like the notation (and when have two category theorists ever agreed on that?), you can easily change the Latex macros. Just go the arXiv, download, and edit to your heart’s content.

There are lots of good introductions to category theory out there. The particular features of this one are:
• It’s short.
• It doesn’t assume much.
• It sticks to the basics.

References

[1]

T. Leinster, Basic Category Theory, 1st ed. Cambridge University Press, 2014.

🔖 Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity

Bookmarked Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity (arxiv.org)

Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems, from micro- to macroscales. We present examples of how human brain imaging data is being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers, and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights critical to the neuroengineer's toolkit.

17 pages, 6 figures. Manuscript accepted to the journal Annual Review of Biomedical Engineering [1]

References

[1]

D. Bassett S., A. Khambhati N., and S. Grafton T., “Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity,” arXiv, 23-Dec-2016. [Online]. Available: https://arxiv.org/abs/1612.08059. [Accessed: 03-Jan-2017]

🔖 A Physical Basis for the Second Law of Thermodynamics: Quantum Nonunitarity

Bookmarked A Physical Basis for the Second Law of Thermodynamics: Quantum Nonunitarity (arxiv.org)

It is argued that if the non-unitary measurement transition, as codified by Von Neumann, is a real physical process, then the "probability assumption" needed to derive the Second Law of Thermodynamics naturally enters at that point. The existence of a real, indeterministic physical process underlying the measurement transition would therefore provide an ontological basis for Boltzmann's Stosszahlansatz and thereby explain the unidirectional increase of entropy against a backdrop of otherwise time-reversible laws. It is noted that the Transactional Interpretation (TI) of quantum mechanics provides such a physical account of the non-unitary measurement transition, and TI is brought to bear in finding a physically complete, non-ad hoc grounding for the Second Law.

Download .pdf copy

🔖 100 years after Smoluchowski: stochastic processes in cell biology

Bookmarked 100 years after Smoluchowski: stochastic processes in cell biology (arxiv.org)

100 years after Smoluchowski introduces his approach to stochastic processes, they are now at the basis of mathematical and physical modeling in cellular biology: they are used for example to analyse and to extract features from large number (tens of thousands) of single molecular trajectories or to study the diffusive motion of molecules, proteins or receptors. Stochastic modeling is a new step in large data analysis that serves extracting cell biology concepts. We review here the Smoluchowski's approach to stochastic processes and provide several applications for coarse-graining diffusion, studying polymer models for understanding nuclear organization and finally, we discuss the stochastic jump dynamics of telomeres across cell division and stochastic gene regulation.

65 pages, J. Phys A 2016 [1]

References

[1]

D. Holcman and Z. Schuss, “100 years after Smoluchowski: stochastic processes in cell biology,” arXiv, 26-Dec-2016. [Online]. Available: https://arxiv.org/abs/1612.08381. [Accessed: 03-Jan-2017]

1200 Free Online Courses from Top Universities | Open Culture

Bookmarked 1200 Free Online Courses from Top Universities (openculture.com)

Download 1200 free online courses from the world's top universities -- Stanford, Yale, MIT, & more. Over 30,000 hours of free audio & video lectures.

About the New Statesman

Bookmarked About the New Statesman (newstatesman.com)

Read an article and browsed through a few others; seems like an interesting publication to put into rotation?

Continue reading About the New Statesman

🔖 A First Step Toward Quantifying the Climate’s Information Production over the Last 68,000 Years

Bookmarked A First Step Toward Quantifying the Climate’s Information Production over the Last 68,000 Years (link.springer.com)

Paleoclimate records are extremely rich sources of information about the past history of the Earth system. We take an information-theoretic approach to analyzing data from the WAIS Divide ice core, the longest continuous and highest-resolution water isotope record yet recovered from Antarctica. We use weighted permutation entropy to calculate the Shannon entropy rate from these isotope measurements, which are proxies for a number of different climate variables, including the temperature at the time of deposition of the corresponding layer of the core. We find that the rate of information production in these measurements reveals issues with analysis instruments, even when those issues leave no visible traces in the raw data. These entropy calculations also allow us to identify a number of intervals in the data that may be of direct relevance to paleoclimate interpretation, and to form new conjectures about what is happening in those intervals—including periods of abrupt climate change.

Saw reference in Predicting unpredictability: Information theory offers new way to read ice cores [1]

References

[1]

“Predicting unpredictability: Information theory offers new way to read ice cores,” Phys.org. [Online]. Available: http://phys.org/news/2016-12-unpredictability-theory-ice-cores.html. [Accessed: 12-Dec-2016]