Jul 22, 2018
🔖 Proceedings for ALIFE 2018: The 2018 Conference on Artificial Life
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🔖 Proceedings for ALIFE 2018: The 2018 Conference on Artificial Life
Statistical Physics, Information Processing, and Biology Workshop at Santa Fe Institute
I just found out about this from John Carlos Baez and wish I could go! How have I not managed to have heard about it?
Stastical Physics, Information Processing, and Biology
Workshop
November 16, 2016 – November 18, 2016 9:00 AM Noyce Conference Room
Abstract. This workshop will address a fundamental question in theoretical biology: Does the relationship between statistical physics and the need of biological systems to process information underpin some of their deepest features? It recognizes that a core feature of biological systems is that they acquire, store and process information (i.e., perform computation). However to manipulate information in this way they require a steady flux of free energy from their environments. These two, inter-related attributes of biological systems are often taken for granted; they are not part of standard analyses of either the homeostasis or the evolution of biological systems. In this workshop we aim to fill in this major gap in our understanding of biological systems, by gaining deeper insight in the relation between the need for biological systems to process information and the free energy they need to pay for that processing.
The goal of this workshop is to address these issues by focusing on a set three specific question:
How has the fraction of free energy flux on earth that is used by biological computation changed with time?;
What is the free energy cost of biological computation / function?;
What is the free energy cost of the evolution of biological computation / function.
In all of these cases we are interested in the fundamental limits that the laws of physics impose on various aspects of living systems as expressed by these three questions.
Purpose: Research Collaboration SFI Host: David Krakauer, Michael Lachmann, Manfred Laubichler, Peter Stadler, and David Wolpert
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Quantum Information Science II
Quantum Information Science II (edX | MIT)
About this course
Already know something about quantum mechanics, quantum bits and quantum logic gates, but want to design new quantum algorithms, and explore multi-party quantum protocols? This is the course for you!
In this advanced graduate physics course on quantum computation and quantum information, we will cover:
The formalism of quantum errors (density matrices, operator sum representations)
Quantum error correction codes (stabilizers, graph states)
Fault-tolerant quantum computation (normalizers, Clifford group operations, the Gottesman-Knill Theorem)
Models of quantum computation (teleportation, cluster, measurement-based)
Quantum Fourier transform-based algorithms (factoring, simulation)
Quantum communication (noiseless and noisy coding)
Quantum protocols (games, communication complexity)
Research problem ideas are presented along the journey.
What you’ll learn
Formalisms for describing errors in quantum states and systems
Quantum error correction theory
Fault-tolerant quantum procedure constructions
Models of quantum computation beyond gates
Structures of exponentially-fast quantum algorithms
Multi-party quantum communication protocols
Meet the instructor
Isaac Chuang Professor of Electrical Engineering and Computer Science, and Professor of Physics MIT
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NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016
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.
Source: NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016
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Design and Control of Self-organizing Systems
UNAM Mexico City has an available free download of Carlos Gershenson’s 2007 text.
Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this book I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves.
Design and Control of Self-organizing Systems by Carlos Gershenson (2007)
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Penguin Revives Decades-Old Software for 30th Anniversary Edition of "The Blind Watchmaker" | The Digital Reader
Even in 2016, publishers and authors are still struggling when it comes to re-releasing decades-old books, but Penguin had a unique problem when it set out to publish a 30th anniversary edition of Richard Dawkin's The Blind Watchmaker. The Bookseller reports that Penguin decided to revive four programs Dawkins wrote in 1986. Written in Pascal for the Mac, The Watchmaker Suite was an experiment in algorithmic evolution. Users could run the programs and create a biomorph, and then watch it evolve across the generations. And now you can do the same in your web browser. A website, MountImprobable.com, was built by the publisher’s in-house Creative Technology team—comprising community manager Claudia Toia, creative developer Mathieu Triay and cover designer Matthew Young—who resuscitated and redeployed code Dawkins wrote in the 1980s and ’90s to enable users to create unique, “evolutionary” imprints. The images will be used as cover imagery on Dawkins’ trio to grant users an entirely individual, personalised print copy.
A New Thermodynamics Theory of the Origin of Life | Quanta Magazine
Hypothesis annotations
A New Physics Theory of Life | Quanta Magazine
From the standpoint of physics, there is one essential difference between living things and inanimate clumps of carbon atoms: The former tend to be much better at capturing energy from their environment and dissipating that energy as heat.
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
Chris Jarzynski, now at the University of Maryland, and Gavin Crooks, now at Lawrence Berkeley National Laboratory. Jarzynski and Crooks showed that the entropy produced by a thermodynamic process, such as the cooling of a cup of coffee, corresponds to a simple ratio: the probability that the atoms will undergo that process divided by their probability of undergoing the reverse process (that is, spontaneously interacting in such a way that the coffee warms up). As entropy production increases, so does this ratio: A system’s behavior becomes more and more “irreversible.”
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
in a paper appearing online this week in Proceedings of the National Academy of Sciences, Michael Brenner, a professor of applied mathematics and physics at Harvard, and his collaborators present theoretical models and simulations of microstructures that self-replicate.
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
the underlying principle driving the whole process is dissipation-driven adaptation of matter.
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
In a September paper in the Journal of Chemical Physics, he reported the theoretical minimum amount of dissipation that can occur during the self-replication of RNA molecules and bacterial cells, and showed that it is very close to the actual amounts these systems dissipate when replicating.
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
Ilya Prigogine, “Introduction to Thermodynamics of Irreversible Processes”, John Wiley Sons Inc., 1968
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
http://www.sciencedirect.com/science/article/pii/0895717794901880
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
http://arxiv.org/pdf/1412.1875v1.pdf
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
https://www.youtube.com/watch?v=e91D5UAz-f4
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
“Thermodynamic Dissipation Theory for the Origin of Life” (arXiv:0907.0042[physics.gen-ph]2009; Earth Syst. Dynam., 2, 37-51, 2011)
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
Brooks and Wiley, Evolution as Entropy, U Chicago Press (1986, 2nd edition 1988)
Get a copy to read through. Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
2009, K. Michaelian, arXiv:0907.0042 [physics.gen-ph] http://arxiv.org/abs/0907.0042 and again in 2011, K. Michaelian Earth Syst. Dynam., 2, 37-51, 2011 www.earth-syst-dynam.net/2/37/2011/doi:10.5194/esd-2-37-2011
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
If England’s approach stands up to more testing, it could further liberate biologists from seeking a Darwinian explanation for every adaptation and allow them to think more generally in terms of dissipation-driven organization. They might find, for example, that “the reason that an organism shows characteristic X rather than Y may not be because X is more fit than Y, but because physical constraints make it easier for X to evolve than for Y to evolve,”
Curated by chrisaldrich.
A New Physics Theory of Life | Quanta Magazine
Philip Marcus of the University of California, Berkeley, and reported in Physical Review Letters
Curated by chrisaldrich.
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Bits from Brains for Biologically Inspired Computing | Computational Intelligence
Bits from Brains for Biologically Inspired Computing by Michael Wibral, Joseph T. Lizier, and Viola Priesemann (Frontiers in Robotics and AI | Computational Intelligence journal.frontiersin.org)
Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification – locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.