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Technical Literature
====================
Steven K. Baum
v0.9, 2015-03-02
:doctype: book
:toc:
:icons:
:numbered!:
[preface]
Sites
-----
*Information Geometry* - John Baez - http://math.ucr.edu/home/baez/information/[+http://math.ucr.edu/home/baez/information/+]
*The RESTful Cookbook* - http://restcookbook.com/[+http://restcookbook.com/+]
*Seeing Circles, Sines and Signals: A Compact Primer on Digital Signal Processing* - http://jackschaedler.github.io/circles-sines-signals/[+http://jackschaedler.github.io/circles-sines-signals/+]
Periodicals and Archives
------------------------
arXiv - http://arxiv.org/[+http://arxiv.org/+]
* Atmospheric and Oceanic Physics -
http://arxiv.org/list/physics.ao-ph/recent[+http://arxiv.org/list/physics.ao-ph/recent+]
* Fluid Dynamics -
http://arxiv.org/list/physics.flu-dyn/recent[+http://arxiv.org/list/physics.flu-dyn/recent+]
* Geophysics -
http://arxiv.org/list/physics.geo-ph/recent[+http://arxiv.org/list/physics.geo-ph/recent+]
* Computational Physics -
http://arxiv.org/list/physics.comp-ph/recent[+http://arxiv.org/list/physics.comp-ph/recent+]
* Data Analysis, Statistics and Probability -
http://arxiv.org/list/physics.data-an/recent[+http://arxiv.org/list/physics.data-an/recent+]
* Numerical Analysis (Mathematics) -
http://arxiv.org/list/math.NA/recent[+http://arxiv.org/list/math.NA/recent+]
* Numerical Analysis (CS) -
http://arxiv.org/list/cs.NA/recent[+http://arxiv.org/list/cs.NA/recent+]
* Distributed, Parallel and Cluster Computing -
http://arxiv.org/list/cs.DC/recent[+http://arxiv.org/list/cs.DC/recent+]
* Emerging Technologies -
http://arxiv.org/list/cs.ET/recent[+http://arxiv.org/list/cs.ET/recent+]
* Mathematical Software -
http://arxiv.org/list/cs.MS/recent[+http://arxiv.org/list/cs.MS/recent+]
* Climate of the Past -
http://www.climate-of-the-past.net/home.html[+http://www.climate-of-the-past.net/home.html+]
* Computational Physics (Communications in) -
http://www.global-sci.com/[+http://www.global-sci.com/+]
* Open Access Section -
http://www.global-sci.com/main.php#oap[+http://www.global-sci.com/main.php#oap+]
* Computational Physics (Journal of) -
http://www.journals.elsevier.com/journal-of-computational-physics[+http://www.journals.elsevier.com/journal-of-computational-physics+]
* Computational Statistics and Data Analysis -
http://www.journals.elsevier.com/computational-statistics-and-data-analysis/[+http://www.journals.elsevier.com/computational-statistics-and-data-analysis/+]
* Current Climate Change Reports - https://link.springer.com/journal/40641[+https://link.springer.com/journal/40641+]
* European Journal of Physics -
http://iopscience.iop.org/0143-0807/[+http://iopscience.iop.org/0143-0807/+]
* Geometric Methods in Modern Physics (Int. J. of) -
http://www.worldscientific.com/worldscinet/ijgmmp[+http://www.worldscientific.com/worldscinet/ijgmmp+]
* Geoscience Data Journal -
http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292049-6060[+http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292049-6060+]
* Geoscientific Model Development -
http://www.geoscientific-model-development.net/home.html[+http://www.geoscientific-model-development.net/home.html+]
* JGR Oceans -
http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/%28ISSN%292169-9291/[+http://agupubs.onlinelibrary.wiley.com/agu/jgr/journal/10.1002/%28ISSN%292169-9291/+]
* Mathematical Modeling of Natural Phenomena -
http://journals.cambridge.org/action/displayJournal?jid=MNP[+http://journals.cambridge.org/action/displayJournal?jid=MNP+]
* Meteorological Applications -
http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291469-8080[+http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291469-8080+]
* Modeling Earth Systems (Journal of Advances in) -
http://agupubs.onlinelibrary.wiley.com/agu/journal/10.1002/%28ISSN%291942-2466/[+http://agupubs.onlinelibrary.wiley.com/agu/journal/10.1002/%28ISSN%291942-2466/+]
* Parallel and Distributed Systems (IEEE Transactions on)
- http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71[+http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=71+]
* Philosophical Transactions of the Royal Society A -
http://rsta.royalsocietypublishing.org/[+http://rsta.royalsocietypublishing.org/+]
* Royal Meteorological Society (Quarterly Journal of the) -
http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291477-870X[+http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291477-870X+]
* Scientific Computing (Journal of) -
http://epubs.siam.org/loi/sjoce3/[+http://epubs.siam.org/loi/sjoce3/+]
* Statistical Analysis and Data Mining: The ASA Data Science Journal -
http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291932-1872[+http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291932-1872+]
* Statistical Software (Journal of) -
http://www.jstatsoft.org/[+http://www.jstatsoft.org/+]
* Tellus -
http://www.tellusa.net/index.php/tellusa/index[+http://www.tellusa.net/index.php/tellusa/index+]
* Theoretical and Applied Climatology -
http://link.springer.com/journal/704[+http://link.springer.com/journal/704+]
Reviews
-------
* Aestimatio: Critical Reviews in the History of Science - https://ircps.org/aestimatio[+https://ircps.org/aestimatio+]
* American Historical Review - https://www.jstor.org/journal/amerhistrevi[+https://www.jstor.org/journal/amerhistrevi+] / https://academic.oup.com/ahr[+https://academic.oup.com/ahr+]
* Annual Review of Astronomy and Astrophysics - https://www.annualreviews.org/loi/astro[+https://www.annualreviews.org/loi/astro+]
* Annual Review of Control, Robotics, and Autonomous Systems - https://www.annualreviews.org/loi/control[+https://www.annualreviews.org/loi/control+]
* Annual Review of Earth and Planetary Sciences - https://www.annualreviews.org/loi/earth[+https://www.annualreviews.org/loi/earth+]
* Annual Review of Ecology, Evolution, and Systematics - https://www.annualreviews.org/loi/ecolsys[+https://www.annualreviews.org/loi/ecolsys+]
* Annual Review of Environment and Resources - https://www.annualreviews.org/loi/energy[+https://www.annualreviews.org/loi/energy+]
* Annual Review of Fluid Mechanics - https://www.annualreviews.org/loi/fluid[+https://www.annualreviews.org/loi/fluid+]
* Annual Review of Marine Science - https://www.annualreviews.org/loi/marine[+https://www.annualreviews.org/loi/marine+]
* Annual Review of Statistics and Its Application - https://www.annualreviews.org/loi/statistics[+https://www.annualreviews.org/loi/statistics+]
* Applied Computing Review - https://dl.acm.org/citation.cfm?id=J693[+https://dl.acm.org/citation.cfm?id=J693+]
* Applied Mechanics Reviews - http://appliedmechanicsreviews.asmedigitalcollection.asme.org/journal.aspx[+http://appliedmechanicsreviews.asmedigitalcollection.asme.org/journal.aspx+]
* Archives of Computational Methods in Engineering: State of the Art Reviews - https://link.springer.com/journal/volumesAndIssues/11831[+https://link.springer.com/journal/volumesAndIssues/11831+]
* Astronomy and Astrophysics Review - https://link.springer.com/journal/volumesAndIssues/159[+https://link.springer.com/journal/volumesAndIssues/159+]
* Computing Reviews (ACM) - http://www.computingreviews.com/index_dynamic.cfm[+http://www.computingreviews.com/index_dynamic.cfm+]
* Computing Surveys (ACM) - https://dl.acm.org/citation.cfm?id=J204[+https://dl.acm.org/citation.cfm?id=J204+]
* Earth-Science Reviews - https://www.sciencedirect.com/journal/earth-science-reviews[+https://www.sciencedirect.com/journal/earth-science-reviews+]
* Environmental Reviews - http://web.a.ebscohost.com/ehost/command/detail?vid=0&sid=b5b627a6-7d9d-4913-b4d3-6a1bf169ffcc%40sessionmgr4006&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#jid=35I&db=egs[+http://web.a.ebscohost.com/ehost/command/detail?vid=0&sid=b5b627a6-7d9d-4913-b4d3-6a1bf169ffcc%40sessionmgr4006&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#jid=35I&db=egs+]
* History of Science - https://journals.sagepub.com/loi/hos[+https://journals.sagepub.com/loi/hos+]
* London Review of Books - https://www.lrb.co.uk/[+https://www.lrb.co.uk/+]
* Physical Review: Fluids - https://journals.aps.org/prfluids/issues[+https://journals.aps.org/prfluids/issues+]
* Physics Reports -
http://www.sciencedirect.com/science/journal/03701573[+http://www.sciencedirect.com/science/journal/03701573+]
* Quaternary Science Reviews - https://www.sciencedirect.com/journal/quaternary-science-reviews[+https://www.sciencedirect.com/journal/quaternary-science-reviews+]
* Reviews of Geophysics -
http://agupubs.onlinelibrary.wiley.com/agu/journal/10.1002/%28ISSN%291944-9208/[+http://agupubs.onlinelibrary.wiley.com/agu/journal/10.1002/%+]28ISSN%291944-9208/+]
* Reviews of Modern Physics - https://journals.aps.org/rmp/issues[+https://journals.aps.org/rmp/issues+]
* Science Progress - https://search.proquest.com/publication/1586336[+https://search.proquest.com/publication/1586336+]
* SIAM Review -
http://epubs.siam.org/journal/siread[+http://epubs.siam.org/journal/siread+]
Parallel Computing
------------------
General
~~~~~~~
*There is No Now: Problems with simultaneity in distributed systems* (online,
2015) - Justin Sheehy -
http://queue.acm.org/detail.cfm?id=2745385[+http://queue.acm.org/detail.cfm?id=2745385+]
*Fallacies of Distributed Computing Explained* (article, PDF, ?, 11) - Arnon
Rotem-Gal-Oz -
http://www.rgoarchitects.com/Files/fallacies.pdf[+http://www.rgoarchitects.com/Files/fallacies.pdf+]
*The Network is Reliable: An informal survey of real-world communications
failures* (article, online, 2014) - Peter Bailis & Kyle Kingsburgy -
http://queue.acm.org/detail.cfm?id=2655736[+http://queue.acm.org/detail.cfm?id=2655736+]
*Beginners' Guide to Concurrency and Parallelism in Python* (article, online,
2015) - Marcus McMurdy -
http://www.toptal.com/python/beginners-guide-to-concurrency-and-parallelism-in-python[+http://www.toptal.com/python/beginners-guide-to-concurrency-and-parallelism-in-python+]
*Parallel and Distributed Computing with Julia* (slides, PDF, 2014, 87) - Marc
Maza -
http://www.csd.uwo.ca/~moreno/cs2101a_moreno/Parallel_computing_with_Julia.pdf[+http://www.csd.uwo.ca/~moreno/cs2101a_moreno/Parallel_computing_with_Julia.pdf+]
GPGPU, CUDA and OpenCL
~~~~~~~~~~~~~~~~~~~~~~
hpgu.org - http://newtheme.hgpu.org/[+http://newtheme.hgpu.org/+]
gpgpu.org - http://gpgpu.org/[+http://gpgpu.org/+]
*General Purpose Computing on GPUs* -
http://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units[+http://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units+]
*GPU Computing at BOINC* (2015-03-09) -
http://boinc.berkeley.edu/wiki/GPU_computing[+http://boinc.berkeley.edu/wiki/GPU_computing+]
*Code Optimization and Performance Analysis of Oceanographic Software Package
NEMO for GPGPU Systems* (2014) -
http://newtheme.hgpu.org/?p=12041[+http://newtheme.hgpu.org/?p=12041+]
*GPU Computing: Which Card Should You Get?* (2013-12-25) -
http://xcorr.net/2013/12/25/gpu-computing-which-card-should-you-get/[+http://xcorr.net/2013/12/25/gpu-computing-which-card-should-you-get/+]
*OpenCL vs CUDA Misconceptions* (2011-06-11) -
http://streamcomputing.eu/blog/2011-06-22/opencl-vs-cuda-misconceptions/[+http://streamcomputing.eu/blog/2011-06-22/opencl-vs-cuda-misconceptions/+]
*CUDA Tutorial from Colorado School of Mines* (2010?) -
http://geco.mines.edu/tesla/cuda_tutorial_mio/[+http://geco.mines.edu/tesla/cuda_tutorial_mio/+]
*Nvidia's CUDA: The End of the CPU?* (2008-06-18) -
http://www.tomshardware.com/reviews/nvidia-cuda-gpu,1954.html[+http://www.tomshardware.com/reviews/nvidia-cuda-gpu,1954.html+]
*Getting Started with OpenCL* -
http://developer.amd.com/tools-and-sdks/opencl-zone/opencl-resources/getting-started-with-opencl/[+http://developer.amd.com/tools-and-sdks/opencl-zone/opencl-resources/getting-started-with-opencl/+]
*Programming in OpenCL* -
http://developer.amd.com/tools-and-sdks/opencl-zone/opencl-resources/programming-in-opencl/[+http://developer.amd.com/tools-and-sdks/opencl-zone/opencl-resources/programming-in-opencl/+]
*OpenCL™ Tutorial: N-Body Simulation* -
http://www.browndeertechnology.com/docs/BDT_OpenCL_Tutorial_NBody-rev3.html[+http://www.browndeertechnology.com/docs/BDT_OpenCL_Tutorial_NBody-rev3.html+]
*A Gentle Introduction to OpenCL* -
http://www.drdobbs.com/parallel/a-gentle-introduction-to-opencl/231002854[+http://www.drdobbs.com/parallel/a-gentle-introduction-to-opencl/231002854+]
FPGA
~~~~
FPGA on Wikipedia -
http://en.wikipedia.org/wiki/Field-programmable_gate_array[+http://en.wikipedia.org/wiki/Field-programmable_gate_array+]
Enabling FPGAs for the Masses (2014) -
http://arxiv.org/abs/1408.5870[+http://arxiv.org/abs/1408.5870+]
Uncategorized
-------------
*Notation as Tool of Thought* (paper, PDF, 1979, 51) - Ken Iverson
*Atomic switch networks—nanoarchitectonic design of a complex system for natural computing* (paper, PDF, 2015, ?) - E. C. Dennis et al. - http://iopscience.iop.org/0957-4484/26/20/204003/article[+http://iopscience.iop.org/0957-4484/26/20/204003/article+]
*****
Self-organized complex systems are ubiquitous in nature, and the structural complexity of these natural systems can be used as a model to design new classes of functional nanotechnology based on highly interconnected networks of interacting units. Conventional fabrication methods for electronic computing devices are subject to known scaling limits, confining the diversity of possible architectures. This work explores methods of fabricating a self-organized complex device known as an atomic switch network and discusses its potential utility in computing. Through a merger of top-down and bottom-up techniques guided by mathematical and nanoarchitectonic design principles, we have produced functional devices comprising nanoscale elements whose intrinsic nonlinear dynamics and memorization capabilities produce robust patterns of distributed activity and a capacity for nonlinear transformation of input signals when configured in the appropriate network architecture. Their operational characteristics represent a unique potential for hardware implementation of natural computation, specifically in the area of reservoir computing—a burgeoning field that investigates the computational aptitude of complex biologically inspired systems.
*****
*Predictability: a way to characterize Complexity* (paper, PDF, 2001, 142) -
G. Boffetta et al. -
http://arxiv.org/abs/nlin/0101029[+http://arxiv.org/abs/nlin/0101029+]
*****
Different aspects of the predictability problem in dynamical systems are
reviewed. The deep relation among Lyapunov exponents, Kolmogorov-Sinai
entropy, Shannon entropy and algorithmic complexity is discussed. In
particular, we emphasize how a characterization of the unpredictability of a
system gives a measure of its complexity. Adopting this point of view, we
review some developments in the characterization of the predictability of
systems showing different kind of complexity: from low-dimensional systems to
high-dimensional ones with spatio-temporal chaos and to fully developed
turbulence. A special attention is devoted to finite-time and
finite-resolution effects on predictability, which can be accounted with
suitable generalization of the standard indicators. The problems involved in
systems with intrinsic randomness is discussed, with emphasis on the important
problems of distinguishing chaos from noise and of modeling the system. The
characterization of irregular behavior in systems with discrete phase space is
also considered.
*****
*The Ecology of Intentions: How to make Memes and Influence People:
Culturology* (online, 1994) - Adam Westoby -
http://ase.tufts.edu/cogstud/dennett/papers/ecointen.htm[+http://ase.tufts.edu/cogstud/dennett/papers/ecointen.htm+]
*****
Whether the meme meme deserves to flourish depends in part on how well if can
account for itself. Adam Westoby's "The Ecology of Intentions" makes a fine
double contribution to this reflexive task, both as analysis and example. If
you read it, you will see for yourself the variety of original and incisive
ideas about memes that prompted me to become its willing vector. And if you
read it, you will yourself provide another data point measuring the power of
memes--whatever their intrinsic virtue--to spread by harnessing human minds to
the task of their further replication. The draft before you is not just
unfinished and unpublished; it is full of blemishes, gaps, half-baked ideas
that distract us from the best ideas in it. Life is short, so why will you
read it? Perhaps to see how Westoby's memes got as far as this (the Zahavi
Principle at work in the memosphere).
*****
*SETI at Planck Energy: When Particle Physicists Become Cosmic Engineers*
(paper, PDF, 2015, 22) - Brian C. Lacki -
http://arxiv.org/abs/1503.01509[+http://arxiv.org/abs/1503.01509+]
*****
What is the meaning of the Fermi Paradox -- are we alone or is starfaring
rare? Can general relativity be united with quantum mechanics? The searches
for answers to these questions could intersect. It is known that an
accelerator capable of energizing particles to the Planck scale requires
cosmic proportions. The energy required to run a Planck accelerator is also
cosmic, of order 100 M_sun c^2 for a hadron collider, because the natural
cross section for Planck physics is so tiny. If aliens are interested in
fundamental physics, they could resort to cosmic engineering for their
experiments. These colliders are detectable through the vast amount of
"pollution" they produce, motivating a YeV SETI program. I investigate what
kinds of radiation they would emit in a fireball scenario, and the feasibility
of detecting YeV radiation at Earth, particularly YeV neutrinos. Although
current limits on YeV neutrinos are weak, Kardashev 3 YeV neutrino sources
appear to be at least 30--100 Mpc apart on average, if they are long-lived and
emit isotropically. I consider the feasibility of much larger YeV neutrino
detectors, including an acoustic detection experiment that spans all of
Earth's oceans, and instrumenting the entire Kuiper Belt. Any detection of YeV
neutrinos implies an extraordinary phenomenon at work, whether artificial and
natural. Searches for YeV neutrinos from any source are naturally commensal,
so a YeV neutrino SETI program has value beyond SETI itself, particularly in
limiting topological defects. I note that the Universe is very faint in all
kinds of nonthermal radiation, indicating that cosmic engineering is extremely
rare.
*****
*A moonshine dialogue in mathematical physics* (paper, PDF, 2015, 10) - Michel
Planat - http://arxiv.org/abs/1503.02677[+http://arxiv.org/abs/1503.02677+]
*****
Phys and Math are two colleagues at the University of Saccenbon (Crefan
Kingdom), dialoguing about the remarkable efficiency of mathematics for
physics. They talk about the notches on the Ishango bone, the various uses of
psi in maths and physics, they arrive at dessins d'enfants, moonshine
concepts, Rademacher sums and their significance in the quantum world. You
should not miss their eccentric proposal of relating Bell's theorem to the
Baby Monster group. Their hyperbolic polygons show a considerable
singularity/cusp structure that our modern age of computers is able to
capture. Henri Poincare would have been happy to see it.
*****
*Jerk and Hyperjerk in a Rotating Frame of Reference* (paper, PDF, 2015, 6) -
http://arxiv.org/find/physics/1/au:+Sparavigna_A/0/1/0/all/0/1[Amelia Carolina Sparavigna] -
http://arxiv.org/abs/1503.07051[+http://arxiv.org/abs/1503.07051+]
*****
Jerk is the derivative of acceleration with respect to time and then it is the
third order derivative of the position vector. Hyperjerks are the n-th order
derivatives with n>3. This paper describes the relations, for jerks and
hyperjerks, between the quantities measured in an inertial frame of reference
and those observed in a rotating frame. These relations can be interesting for
teaching purposes.
*****
*The symmetry and simplicity of the laws of physics and the Higgs boson*
(paper, PDF, 2014, 31) - Juan Maldacena -
http://arxiv.org/abs/1410.6753[+http://arxiv.org/abs/1410.6753+]
*****
We describe the theoretical ideas, developed between the 1950s-1970s, which
led to the prediction of the Higgs boson, the particle that was discovered in
2012. The forces of nature are based on symmetry principles. We explain the
nature of these symmetries through an economic analogy. We also discuss the
Higgs mechanism, which is necessary to avoid some of the naive consequences of
these symmetries, and to explain various features of elementary particles.
*****
*How to simulate the Universe in a Computer* (paper, PDF, 2004, 8) - Alexander
Knebe -
http://arxiv.org/abs/astro-ph/0412565[+http://arxiv.org/abs/astro-ph/0412565+]
*****
In this contribution a broad overview of the methodologies of cosmological
N-body simulations and a short introduction explaining the general idea behind
such simulations is presented. After explaining how to set up the initial
conditions using a set of N particles two (diverse) techniques are presented
for evolving these particles forward in time under the influence of their
self-gravity. One technique (tree codes) is solely based upon a sophistication
of the direct particle-particle summation whereas the other method relies on
the continuous (de-)construction of arbitrarily shaped grids and is realized
in adaptive mesh refinement codes.
*****
*In the beginning was game semantics* (paper, PDF, 2008, 73) - Giorgi
Japaridze - http://arxiv.org/abs/cs/0507045[+http://arxiv.org/abs/cs/0507045+]
*****
This article presents an overview of computability logic -- the
game-semantically constructed logic of interactive computational tasks and
resources. There is only one non-overview, technical section in it, devoted to
a proof of the soundness of affine logic with respect to the semantics of
computability logic.
Computation and computational problems in Computability Logic are understood
in their most general, interactive sense, and are precisely seen as games
played by a machine (computer, agent, robot) against its environment (user,
nature, or the devil himself). Computability of such problems means existence
of a machine that always wins the game. Logical operators stand for operations
on computational problems, and validity of a logical formula means being a
scheme of "always computable" problems.
*****
*Evolutionary games on graphs* (paper, PDF, 2007, 133) - Gyorgy Szabo & Gabor
Fath -
http://arxiv.org/abs/cond-mat/0607344[+http://arxiv.org/abs/cond-mat/0607344+]
*****
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.
*****
*Stochastic Models of Evolution in Genetics, Ecology and Linguistics* (paper,
PDF, 2007, 60) - R. A. Blythe & A. J. McKane -
http://arxiv.org/abs/cond-mat/0703478[+http://arxiv.org/abs/cond-mat/0703478+]
*****
We give a overview of stochastic models of evolution that have found
applications in genetics, ecology and linguistics for an audience of
nonspecialists, especially statistical physicists. In particular, we focus
mostly on neutral models in which no intrinsic advantage is ascribed to a
particular type of the variable unit, for example a gene, appearing in the
theory. In many cases these models are exactly solvable and furthermore go
some way to describing observed features of genetic, ecological and linguistic
systems.
*****
*Of `Cocktail Parties' and Exoplanets* (paper, PDF, 2011, 28) - Ingo P.
Waldmann - http://arxiv.org/abs/1106.1989[+http://arxiv.org/abs/1106.1989+]
*****
The characterisation of ever smaller and fainter extrasolar planets requires
an intricate understanding of one's data and the analysis techniques used.
Correcting the raw data at the 10^-4 level of accuracy in flux is one of the
central challenges. This can be difficult for instruments that do not feature
a calibration plan for such high precision measurements. Here, it is not
always obvious how to de-correlate the data using auxiliary information of the
instrument and it becomes paramount to know how well one can disentangle
instrument systematics from one's data, given nothing but the data itself. We
propose a non-parametric machine learning algorithm, based on the concept of
independent component analysis, to de-convolve the systematic noise and all
non-Gaussian signals from the desired astrophysical signal. Such a `blind'
signal de-mixing is commonly known as the `Cocktail Party problem' in
signal-processing. Given multiple simultaneous observations of the same
exoplanetary eclipse, as in the case of spectrophotometry, we show that we can
often disentangle systematic noise from the original light curve signal
without the use of any complementary information of the instrument. In this
paper, we explore these signal extraction techniques using simulated data and
two data sets observed with the Hubble-NICMOS instrument. Another important
application is the de-correlation of the exoplanetary signal from
time-correlated stellar variability. Using data obtained by the Kepler mission
we show that the desired signal can be de-convolved from the stellar noise
using a single time series spanning several eclipse events. Such
non-parametric techniques can provide important confirmations of the existent
parametric corrections reported in the literature, and their associated
results. Additionally they can substantially improve the precision
exoplanetary light curve analysis in the future.
*****
*One Decade of Universal Artificial Intelligence* (paper, PDF, 2012, 20) -
Marcus Hutter -
http://arxiv.org/abs/1202.6153[+http://arxiv.org/abs/1202.6153+]
*****
The first decade of this century has seen the nascency of the first
mathematical theory of general artificial intelligence. This theory of
Universal Artificial Intelligence (UAI) has made significant contributions to
many theoretical, philosophical, and practical AI questions. In a series of
papers culminating in book (Hutter, 2005), an exciting sound and complete
mathematical model for a super intelligent agent (AIXI) has been developed and
rigorously analyzed. While nowadays most AI researchers avoid discussing
intelligence, the award-winning PhD thesis (Legg, 2008) provided the
philosophical embedding and investigated the UAI-based universal measure of
rational intelligence, which is formal, objective and non-anthropocentric.
Recently, effective approximations of AIXI have been derived and
experimentally investigated in JAIR paper (Veness et al. 2011). This practical
breakthrough has resulted in some impressive applications, finally muting
earlier critique that UAI is only a theory. For the first time, without
providing any domain knowledge, the same agent is able to self-adapt to a
diverse range of interactive environments. For instance, AIXI is able to learn
from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial
and error, without even providing the rules of the games.
These achievements give new hope that the grand goal of Artificial General
Intelligence is not elusive.
This article provides an informal overview of UAI in context. It attempts to
gently introduce a very theoretical, formal, and mathematical subject, and
discusses philosophical and technical ingredients, traits of intelligence,
some social questions, and the past and future of UAI.
*****
* *Machine Super Intelligence* (thesis, PDF, 2008, 200) - Shane Legg -
http://www.vetta.org/documents/Machine_Super_Intelligence.pdf[+http://www.vetta.org/documents/Machine_Super_Intelligence.pdf+]
* *A Monte-Carlo AIXI Approximation* (paper, PDF, 2011, 48) - J. Veness et al.
- http://jair.org/papers/paper3125.html[+http://jair.org/papers/paper3125.html+]
*****
This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. Our approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto
open question in the affirmative, by providing the first computationally
feasible approximation to the AIXI agent. To develop our approximation, we
introduce a new Monte-Carlo Tree Search algorithm along with an agent-specific
extension to the Context Tree Weighting algorithm. Empirically, we present a
set of encouraging results on a variety of stochastic and partially observable
domains. We conclude by proposing a number of directions for future research.
*****
* *The Arcade Learning Environment: An Evaluation Platform for General Agents*
(paper, PDF, 2012, 27) - Marc G. Bellemare et al. -
http://arxiv.org/abs/1207.4708[+http://arxiv.org/abs/1207.4708+]
*Nonlinear Dynamics of Moving Curves and Surfaces: Applications to Physical
Systems* (paper, PDF, 2004, 38) - S. Murugesh & M. Lakshmanan -
http://arxiv.org/abs/nlin/0404005[+http://arxiv.org/abs/nlin/0404005+]
*****
The subject of moving curves (and surfaces) in three dimensional space (3-D)
is a fascinating topic not only because it represents typical nonlinear
dynamical systems in classical mechanics, but also finds important
applications in a variety of physical problems in different disciplines.
Making use of the underlying geometry, one can very often relate the
associated evolution equations to many interesting nonlinear evolution
equations, including soliton possessing nonlinear dynamical systems. Typical
examples include dynamics of filament vortices in ordinary and superfluids,
spin systems, phases in classical optics, various systems encountered in
physics of soft matter, etc. Such interrelations between geometric evolution
and physical systems have yielded considerable insight into the underlying
dynamics. We present a succinct tutorial analysis of these developments in
this article, and indicate further directions. We also point out how evolution
equations for moving surfaces are often intimately related to soliton
equations in higher dimensions.
*****
*Bluff your way in the second law of thermodynamics
Jos Uffink* (paper, PDF, 2001, 89) - Jos Uffink -
http://arxiv.org/abs/cond-mat/0005327[+http://arxiv.org/abs/cond-mat/0005327+]
*****
The aim of this article is to analyze the relation between the second law of
thermodynamics and the so-called arrow of time. For this purpose, a number of
different aspects in this arrow of time are distinguished, in particular those
of time-(a)symmetry and of (ir)reversibility. Next I review versions of the
second law in the work of Carnot, Clausius, Kelvin, Planck, Gibbs,
Carath\'eodory and Lieb and Yngvason, and investigate their connection with
these aspects of the arrow of time.
It is shown that this connection varies a great deal along with these
formulations of the second law. According to the famous formulation by Planck,
the second law expresses the irreversibility of natural processes. But in many
other formulations irreversibility or even time-asymmetry plays no role.
I therefore argue for the view that the second law has nothing to do with the
arrow of time.
*****
*Chaos, Complexity, and Entropy: A Physics Talk for
Non-Physicists* (paper, PDF, 2001, 17) - Michel Baranger -
http://www.necsi.edu/projects/baranger/cce.html[+http://www.necsi.edu/projects/baranger/cce.html+]
*****
The twenty-first century is starting with a huge bang. For the person in the
street, the bang is about a technical revolution that may eventually dwarf the
industrial revolution of the 18th and 19th centuries, having already produced
a drastic change in the rules of economics. For the scientifically minded, one
aspect of this bang is the complexity revolution, which is changing the focus
of research in all scientific disciplines, for instance human biology and
medicine. What role does physics, the oldest and simplest science, have to
play in this? Being a theoretical physicist to the core, I want to focus on
theoretical physics. Is it going to change also?
Twentieth-century theoretical physics came out of the relativistic revolution
and the quantum mechanical revolution. It was all about simplicity and
continuity (in spite of quantum jumps). Its principal tool was calculus. Its
final expression was field theory.
Twenty-first-century theoretical physics is coming out of the chaos
revolution. It will be about complexity and its principal tool will be the
computer. Its final expression remains to be found. Thermodynamics, as a vital
part of theoretical physics, will partake in the transformation.
*****
*Toward a Theory of Chaos* (paper, PDF, 2004, 103) - A. Sengupta -
http://arxiv.org/abs/nlin/0408044[+http://arxiv.org/abs/nlin/0408044+]
*****
This paper formulates a new approach to the study of chaos in discrete
dynamical systems based on the notions of inverse ill-posed problems,
set-valued mappings, generalized and multivalued inverses, graphical
convergence of a net of functions in an extended multifunction space, and the
topological theory of convergence. Order, chaos, and complexity are described
as distinct components of this unified mathematical structure that can be
viewed as an application of the theory of convergence in topological spaces to
increasingly nonlinear mappings, with the boundary between order and
complexity in the topology of graphical convergence being the region in
Multi(X) that is susceptible to chaos. The paper uses results from the
discretized spectral approximation in neutron transport theory and concludes
that the numerically exact results obtained by this approximation of the Case
singular eigenfunction solution is due to the graphical convergence of the
Poisson and conjugate Poisson kernels to the Dirac delta and the principal
value multifunctions respectively. In Multi(X), the continuous spectrum is
shown to reduce to a point spectrum, and we introduce a notion of latent
chaotic states to interpret superposition over generalized eigenfunctions.
Along with these latent states, spectral theory of nonlinear operators is used
to conclude that nature supports complexity to attain efficiently a
multiplicity of states that otherwise would remain unavailable to it.
*****
*From Classical to Quantum Shannon Theory* (book, PDF, 2013, 642) - Mark M.
Wilde - http://arxiv.org/abs/1106.1445[+http://arxiv.org/abs/1106.1445+]
http://www.theory.caltech.edu/\~preskill/ph219/index.html[+http://www.theory.caltech.edu/~preskill/ph219/index.html+]
*****
The aim of this book is to develop "from the ground up" many of the major,
exciting, pre- and post-millenium developments in the general area of study
known as quantum Shannon theory. As such, we spend a significant amount of
time on quantum mechanics for quantum information theory (Part II), we give a
careful study of the important unit protocols of teleportation, super-dense
coding, and entanglement distribution (Part III), and we develop many of the
tools necessary for understanding information transmission or compression
(Part IV). Parts V and VI are the culmination of this book, where all of the
tools developed come into play for understanding many of the important results
in quantum Shannon theory.
*****
*Quantum computing and the entanglement frontier* (paper, PDF, 2012, 18) -
John Preskill -
http://arxiv.org/abs/1203.5813[+http://arxiv.org/abs/1203.5813+]
*****
Quantum information science explores the frontier of highly complex quantum
states, the "entanglement frontier." This study is motivated by the
observation (widely believed but unproven) that classical systems cannot
simulate highly entangled quantum systems efficiently, and we hope to hasten
the day when well controlled quantum systems can perform tasks surpassing what
can be done in the classical world. One way to achieve such "quantum
supremacy" would be to run an algorithm on a quantum computer which solves a
problem with a super-polynomial speedup relative to classical computers, but
there may be other ways that can be achieved sooner, such as simulating exotic
quantum states of strongly correlated matter. To operate a large scale quantum
computer reliably we will need to overcome the debilitating effects of
decoherence, which might be done using "standard" quantum hardware protected
by quantum error-correcting codes, or by exploiting the nonabelian quantum
statistics of anyons realized in solid state systems, or by combining both
methods. Only by challenging the entanglement frontier will we learn whether
Nature provides extravagant resources far beyond what the classical world
would allow.
*****
*Introduction to Bionformatics* (paper, PDF, 2009, 155) - Sabu M. Thampi -
http://arxiv.org/abs/0911.4230[+http://arxiv.org/abs/0911.4230+]
*****
Bioinformatics is a new discipline that addresses the need to manage and
interpret the data that in the past decade was massively generated by genomic
research. This discipline represents the convergence of genomics,
biotechnology and information technology, and encompasses analysis and
interpretation of data, modeling of biological phenomena, and development of
algorithms and statistics. This article presents an introduction to
bioinformatics.
*****
*A Review on P2P Video Streaming* (paper, PDF, 2013, 47) - Sabu M. Thampi -
http://arxiv.org/abs/1304.1235[+http://arxiv.org/abs/1304.1235+]
*****
The main objective of this article is to provide an overview of P2P based
Video-on-Demand and live streaming services. The article starts with an
introduction to media streaming and its simplified architecture. Various
solutions offering video streaming in the context of widespread usage of
Internet are discussed. This is followed by a short introduction to P2P
networks and its applications. A broad discussion on various P2P streaming
schemes and P2P streaming applications are the main focus of this chapter.
Finally, the security issues and solutions for P2P video streaming are
discussed briefly.
*****
*Survey on Distributed Data Mining in P2P Networks* (paper, PDF, 2012, 23) -
Sunny T. Rekha & Sabu M. Thampi -
http://arxiv.org/abs/1205.3231[+http://arxiv.org/abs/1205.3231+]
*****
The exponential increase of availability of digital data and the necessity to
process it in business and scientific fields has literally forced upon us the
need to analyze and mine useful knowledge from it. Traditionally data mining
has used a data warehousing model of gathering all data into a central site,
and then running an algorithm upon that data. Such a centralized approach is
fundamentally inappropriate due to many reasons like huge amount of data,
infeasibility to centralize data stored at multiple sites, bandwidth
limitation and privacy concerns. To solve these problems, Distributed Data
Mining (DDM) has emerged as a hot research area. Careful attention in the
usage of distributed resources of data, computing, communication, and human
factors in a near optimal fashion are paid by distributed data mining. DDM is
gaining attention in peer-to-peer (P2P) systems which are emerging as a choice
of solution for applications such as file sharing, collaborative movie and
song scoring, electronic commerce, and surveillance using sensor networks. The
main intension of this draft paper is to provide an overview of DDM and P2P
Data Mining. The paper discusses the need for DDM, taxonomy of DDM
architectures, various DDM approaches, DDM related works in P2P systems and
issues and challenges in P2P data mining.
*****
*Information Hiding Techniques: A Tutorial Review* (paper, PDF, 2008, 19) -
Sabu M. Thampi -
http://arxiv.org/abs/0802.3746[+http://arxiv.org/abs/0802.3746+]
*****
The purpose of this tutorial is to present an overview of various information
hiding techniques. A brief history of steganography is provided along with
techniques that were used to hide information. Text, image and audio based
information hiding techniques are discussed. This paper also provides a basic
introduction to digital watermarking.
*****
*Artificial Immune Systems Tutorial* (paper, PDF, 2008, 29) - Uwe Aickelin &
Dispankar Dasgupta -
http://arxiv.org/abs/0803.3912[+http://arxiv.org/abs/0803.3912+]
*****
The biological immune system is a robust, complex, adaptive system that
defends the body from foreign pathogens. It is able to categorize all cells
(or molecules) within the body as self-cells or non-self cells. It does this
with the help of a distributed task force that has the intelligence to take
action from a local and also a global perspective using its network of
chemical messengers for communication. There are two major branches of the
immune system. The innate immune system is an unchanging mechanism that
detects and destroys certain invading organisms, whilst the adaptive immune
system responds to previously unknown foreign cells and builds a response to
them that can remain in the body over a long period of time. This remarkable
information processing biological system has caught the attention of computer
science in recent years. A novel computational intelligence technique,
inspired by immunology, has emerged, called Artificial Immune Systems. Several
concepts from the immune have been extracted and applied for solution to real
world science and engineering problems. In this tutorial, we briefly describe
the immune system metaphors that are relevant to existing Artificial Immune
Systems methods. We will then show illustrative real-world problems suitable
for Artificial Immune Systems and give a step-by-step algorithm walkthrough
for one such problem. A comparison of the Artificial Immune Systems to other
well-known algorithms, areas for future work, tips & tricks and a list of
resources will round this tutorial off. It should be noted that as Artificial
Immune Systems is still a young and evolving field, there is not yet a fixed
algorithm template and hence actual implementations might differ somewhat from
time to time and from those examples given here.
*****
*Notes on Convex Sets, Polytopes, Polyhedra, Combinatorial Topology, Voronoi
Diagrams and Delaunay Triangulations* (paper, PDF, 2008, 183) - Jean Gallier -
http://arxiv.org/abs/0805.0292[+http://arxiv.org/abs/0805.0292+]
*****
Some basic mathematical tools such as convex sets, polytopes and combinatorial
topology, are used quite heavily in applied fields such as geometric modeling,
meshing, computer vision, medical imaging and robotics. This report may be
viewed as a tutorial and a set of notes on convex sets, polytopes, polyhedra,
combinatorial topology, Voronoi Diagrams and Delaunay Triangulations. It is
intended for a broad audience of mathematically inclined readers. I have
included a rather thorough treatment of the equivalence of V-polytopes and
H-polytopes and also of the equivalence of V-polyhedra and H-polyhedra, which
is a bit harder. In particular, the Fourier-Motzkin elimination method (a
version of Gaussian elimination for inequalities) is discussed in some detail.
I also included some material on projective spaces, projective maps and polar
duality w.r.t. a nondegenerate quadric in order to define a suitable notion of
``projective polyhedron'' based on cones. To the best of our knowledge, this
notion of projective polyhedron is new. We also believe that some of our
proofs establishing the equivalence of V-polyhedra and H-polyhedra are new.
*****
*Normal and Anomalous Diffusion: A Tutorial* (paper, PDF, 2008, 39) -
http://arxiv.org/abs/0805.0419[+http://arxiv.org/abs/0805.0419+]
*****
The purpose of this tutorial is to introduce the main concepts behind normal
and anomalous diffusion. Starting from simple, but well known experiments, a
series of mathematical modeling tools are introduced, and the relation between
them is made clear. First, we show how Brownian motion can be understood in
terms of a simple random walk model. Normal diffusion is then treated (i)
through formalizing the random walk model and deriving a classical diffusion
equation, (ii) by using Fick's law that leads again to the same diffusion
equation, and (iii) by using a stochastic differential equation for the
particle dynamics (the Langevin equation), which allows to determine the mean
square displacement of particles. (iv) We discuss normal diffusion from the
point of view of probability theory, applying the Central Limit Theorem to the
random walk problem, and (v) we introduce the more general Fokker-Planck
equation for diffusion that includes also advection. We turn then to anomalous
diffusion, discussing first its formal characteristics, and proceeding to
Continuous Time Random Walk (CTRW) as a model for anomalous diffusion. It is
shown how CTRW can be treated formally, the importance of probability
distributions of the Levy type is explained, and we discuss the relation of
CTRW to fractional diffusion equations and show how the latter can be derived
from the CTRW equations. Last, we demonstrate how a general diffusion equation
can be derived for Hamiltonian systems, and we conclude this tutorial with a
few recent applications of the above theories in laboratory and astrophysical
plasmas.
*****
*A Shape Dynamics Tutorial* (paper, PDF, 2014, 71) - Flavio Mercati -
http://arxiv.org/abs/1409.0105[+http://arxiv.org/abs/1409.0105+]
*****
Shape Dynamics (SD) is a new theory of gravity that is based on fewer and more
fundamental first principles than General Relativity (GR). The most important
feature of SD is the replacement of GR's relativity of simultaneity with a
more tractable gauge symmetry, namely invariance under spatial conformal
transformations. This Tutorial contains both a quick introduction for readers
curious about SD and a detailed walk-through of the historical and conceptual
motivations for the theory, its logical development from first principles and
an in-depth description of its present status. The Tutorial is sufficiently
self-contained for an undergrad student with some basic background in General
Relativity and Lagrangian/Hamiltonian mechanics. It is intended both as a
reference text for students approaching the subject, and as a review article
for researchers interested in the theory.
*****
*Entropic Inference and the Foundations of Physics* (book, PDF, 2012, 292) -
Ariel Caticha -
http://www.albany.edu/physics/ACaticha-EIFP-book.pdf[+http://www.albany.edu/physics/ACaticha-EIFP-book.pdf+]
*Lectures on Probability, Entropy, and Statistical Physics* (lectures, PDF,
2008, 170) - Ariel Caticha -
http://arxiv.org/abs/0808.0012[+http://arxiv.org/abs/0808.0012+]
*****
These lectures deal with the problem of inductive inference, that is, the
problem of reasoning under conditions of incomplete information. Is there a
general method for handling uncertainty? Or, at least, are there rules that
could in principle be followed by an ideally rational mind when discussing
scientific matters? What makes one statement more plausible than another? How
much more plausible? And then, when new information is acquired how do we
change our minds? Or, to put it differently, are there rules for learning? Are
there rules for processing information that are objective and consistent? Are
they unique? And, come to think of it, what, after all, is information? It is
clear that data contains or conveys information, but what does this precisely
mean? Can information be conveyed in other ways? Is information physical? Can
we measure amounts of information? Do we need to? Our goal is to develop the
main tools for inductive inference--probability and entropy--from a thoroughly
Bayesian point of view and to illustrate their use in physics with examples
borrowed from the foundations of classical statistical physics.
*****
CFD Notes (various) - Hiroaki Nishikawa -
http://www.cfdnotes.com/[+http://www.cfdnotes.com/+]
I Do Like CFD, Vol. 1, 2nd Ed. (book, PDF, 2013, 303) - H. Nishikawa -
http://www.cfdbooks.com/cfdbooks.html[+http://www.cfdbooks.com/cfdbooks.html+]
The Science of Programming Matrix Computations (book, PDF, 2008, 143) - Robert
van de Geijn & Enrique Quintana-Orti -
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.2790[+http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.214.2790+]
A survey on parallel computing and its applications in data-parallel
problems using GPU architectures (paper, PDF, 2014, 45) - Cristobal Navarro et
al. -
http://www.global-sci.com/openaccess/v15_285.pdf[+http://www.global-sci.com/openaccess/v15_285.pdf+]
Python classes for numerical solution of PDEs (paper, PDF, 2015, 6) - Asif
Mushtaq et al. -
http://arxiv.org/abs/1503.04602[+http://arxiv.org/abs/1503.04602+]
A Python Class for Higher-Dimensional Schrödinger Equations (paper, PDF, 2015,
Amna Noreen & Kare Olaussen -
http://arxiv.org/abs/1503.04607[+http://arxiv.org/abs/1503.04607+]
Approaching complexity by stochastic methods: From biological systems to
turbulence (paper, PDF, 2011, 75) - R. Friedrich et al. -
http://www.sciencedirect.com/science/article/pii/S0370157311001530[+http://www.sciencedirect.com/science/article/pii/S0370157311001530+]
*****
This review addresses a central question in the field of complex systems:
given a fluctuating (in time or space), sequentially measured set of
experimental data, how should one analyze the data, assess their underlying
trends, and discover the characteristics of the fluctuations that generate the
experimental traces? In recent years, significant progress has been made in
addressing this question for a class of stochastic processes that can be
modeled by Langevin equations, including additive as well as multiplicative
fluctuations or noise. Important results have emerged from the analysis of
temporal data for such diverse fields as neuroscience, cardiology, finance,
economy, surface science, turbulence, seismic time series and epileptic brain
dynamics, to name but a few. Furthermore, it has been recognized that a
similar approach can be applied to the data that depend on a length scale,
such as velocity increments in fully developed turbulent flow, or height
increments that characterize rough surfaces. A basic ingredient of the
approach to the analysis of fluctuating data is the presence of a Markovian
property, which can be detected in real systems above a certain time or length
scale. This scale is referred to as the Markov–Einstein (ME) scale, and has
turned out to be a useful characteristic of complex systems. We provide a
review of the operational methods that have been developed for analyzing
stochastic data in time and scale. We address in detail the following issues:
(i) reconstruction of stochastic evolution equations from data in terms of the
Langevin equations or the corresponding Fokker–Planck equations and (ii)
intermittency, cascades, and multiscale correlation functions.
*****
Comments on "Opportunities and Challenges in 21st Century Experimental
Computation: ICERM Workshop Report" (TR, PDF, 2014, 10) - Nelson Beebe -
http://www.davidhbailey.com/dhbpapers/ICERM-2014-NOTES.pdf[+http://www.davidhbailey.com/dhbpapers/ICERM-2014-NOTES.pdf+]
Mahler measures, short walks and log-sine integrals: A case study in hybrid
computation (slides, PDF, 2012, 160) - J. Borwein -
https://www.carma.newcastle.edu.au/jon/alfcon.pdf[+https://www.carma.newcastle.edu.au/jon/alfcon.pdf+]
*****
I intend to show off the interplay between numeric and symbolic computing
while exploring the three mathematical topics in my title.
*****
The life of pi: from Archimedes to ENIAC and beyond (article, PDF, 2012, 30) -
J. Borwein -
https://carma.newcastle.edu.au/jon/pi-2012.pdf[+https://carma.newcastle.edu.au/jon/pi-2012.pdf+]
*****
The desire to understand pi, the challenge, and originally the need,
to calculate ever more accurate values of pi, the ratio of the
circumference of a circle to its diameter, has challenged mathematicians -
great and less great - for many centuries. It has also, especially
recently, provided compelling examples of computational mathematics.
Pi, uniquely in mathematics, is pervasive in popular culture and
the popular imagination.
I shall intersperse this largely chronological account of pi's mathematical
status with examples of its ubiquity.
*****
Walking on real numbers (paper, PDF, 2012, 37) - F. J. A. Artacho et al. -
https://carma.newcastle.edu.au/jon/numtools.pdf[+https://carma.newcastle.edu.au/jon/numtools.pdf+]
*****
Motivated by the desire to visualize large mathematical data sets, especially
in
number theory, we offer various tools for representing floating point numbers as
planar
(or three dimensional) walks and for quantitatively measuring their
randomness."
*****
Exploratory experimentation and computation (article, PDF, 2012, 10) - D. H.
Bailey & J. M. Borwein -
http://www.ams.org/notices/201110/rtx111001410p.pdf[+http://www.ams.org/notices/201110/rtx111001410p.pdf+]
*****
Many,
if not most, research mathematicians now
use the computer in a variety of ways to
draw pictures, inspect numerical data, manipulate
expressions symbolically, and run simulations.
However, it seems to us that there has not yet been
substantial and intellectually rigorous progress
in the way mathematics is presented in research
papers, textbooks, and classroom instruction or
in how the mathematical discovery process is
organized.
*****
High-precision arithmetic in mathematical physics (article, PDF, 2015, 29) -
D. Bailey & J. Borwein -
http://www.davidhbailey.com/dhbpapers/hp-math-physics.pdf[+http://www.davidhbailey.com/dhbpapers/hp-math-physics.pdf+]
*****
For many scientific calculations, particularly those involving empirical data,
IEEE 32-bit floating-point arithmetic produces results of sufficient accuracy,
while for other
applications IEEE 64-bit floating-point is more appropriate. But for some very
demanding
applications, even higher levels of precision are often required. This article
discusses
the challenge of high-precision computation, in the context of mathematical
physics, and
highlights what facilities are required to support future computation, in
light of emerging
developments in computer architecture.
*****
High-precision computation: Mathematical physics and dynamics (TR, PDF, 2012,
29) - D. H. Bailey et al. -
http://crd-legacy.lbl.gov/\~dhbailey/dhbpapers/hpmpd.pdf[+http://crd-legacy.lbl.gov/~dhbailey/dhbpapers/hpmpd.pdf+]
*****
At the present time, IEEE 64-bit floating-point arithmetic is sufficiently
accurate for most
scientific applications. However, for a rapidly growing body of important
scientific computing
applications, a higher level of numeric precision is required. Such
calculations are facilitated
by high-precision software packages that include high-level language
translation modules to
minimize the conversion effort. This paper presents an overview of recent
applications of
these techniques and provides some analysis of their numerical requirements.
We conclude
that high-precision arithmetic facilities are now an indispensable component
of a modern
large-scale scientific computing environment.
*****
A catalogue of software for matrix functions, version 1.0 (TR, PDF, 2014, 20)
- http://eprints.ma.man.ac.uk/2102/01/covered/MIMS_ep2014_8.pdf[+http://eprints.ma.man.ac.uk/2102/01/covered/MIMS_ep2014_8.pdf+]
*****
A catalogue of software for computing matrix functions and their Frechet
derivatives
is presented. For a wide variety of languages and for software ranging from
commercial
products to open source packages we describe what matrix function codes are
available and
which algorithms they implement.
*****
Functions of matrices (slides, PDF, 2013, 236) - Nick Higham -
http://www.maths.manchester.ac.uk/\~higham/misc/ggsss13/fun.pdf[+http://www.maths.manchester.ac.uk/~higham/misc/ggsss13/fun.pdf+]
Survey of stochastic computing (paper, PDF, 2013, 19) - A. Alaghi & J. P.
Hayes -
http://web.eecs.umich.edu/\~jhayes/Alaghi_Survey_TECS_2013.pdf[+http://web.eecs.umich.edu/~jhayes/Alaghi_Survey_TECS_2013.pdf+]
*****
Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative
to conventional binary computing.
It is unique in that it represents and processes information in the
form of digitized probabilities. SC
employs very low-complexity arithmetic units which was a primary design
concern in the past. Despite this
advantage and also its inherent error tolerance, SC was seen as impractical
because of very long
computation times and relatively low accuracy. However, current technology trends
tend to increase uncertainty
in circuit behavior and imply a need to better understand, and perhaps
exploit, probability in computation.
This article surveys SC from a modern perspective where the small size, error
resilience, and probabilistic
features of SC may compete successfully with conventional methodologies in
certain applications. First, we
survey the literature and review the key concepts of stochastic number