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Critical Points for Random Boolean Networks
A model of cellular metabolism due to S. Kauffman is analyzed. It consists of
a network of Boolean gates randomly assembled according to a probability
distribution. It is shown that the behavior of the network depends very
critically on certain simple algebraic parameters of the distribution. In some
cases, the analytic results support conclusions based on simulations of random
Boolean networks, but in other cases, they do not.Comment: 19 page
Allan pierce and adiabatic normal modes
Author Posting. © Acoustical Society of America, 2021. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 149(3),(2021): R5-R6, https://doi.org/10.1121/10.0003595.One of the beautiful things about science is that many of its main equations and concepts appear in a multitude of different fields. Change the medium and the boundary conditions, make a change in variables, and you have gone from solid state physics to aeroacoustics or from nuclear physics to underwater acoustics, but within the same formal, theoretical framework. Rethink the Born–Oppenheimer approximation of molecular physics and you have the adiabatic approximation to coupled normal mode theory. This interplay is one of the hallmarks of Pierce's 1965 paper.2021-09-1
The voices of marine mammals-William E. Schevill and William A. Watkins: pioneers in bioacoustics
Author Posting. © Acoustical Society of America, 2020. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 148(1), (2020): 444, doi:10.1121/10.0001658.The Voices of Marine Mammals—William E. Schevill and William A. Watkins: Pioneers in Bioacoustics Brophy Christina New Bedford Whaling Museum, New Bedford, Massachusetts, 2019. 126 pp. ISBN 0997516178
One of the fond memories of my youth was a stop, en route to Cape Cod for a week's family vacation, at the New Bedford Whaling Museum. Back then, almost six decades ago (?!), the story I gleaned from the exhibits was that of an adventurous, exotic marine industry of yesteryear that helped make the Northeast an economic power. For one reason or another (“busy” being a good excuse), I never returned there again, despite working only 40 or so miles away at the Woods Hole Oceanographic Institution (WHOI). Eventually, the New Bedford Whaling Museum faded into being just another pleasant boyhood memory for me.2021-01-2
Report on the Office of Naval Research Shallow-Water Acoustic Workshop 1-3 October 1996
The results of an unclassified workshop on Shallow Water Acoustics, jointly sponsored by ONR and DARPA, are
presented. The workshop was held on October 1-3, 1996 at the Naval Research Laboratory, Stennis Space Center, and
included 83 participants specializing in ocean acoustics, geology and geophysics, physical oceanography, and other
disciplines relevant to shallow water research.
The goal of the workshop was to help determine the current status of and future directions for shallow water acoustics
research. The report summarizes the deliberations and recommendations of the workshop, and includes detailed report
from the three working groups (bottom, water column, and modeling and signal processing) as well as from the workshop
moderator (Dr. James Lynch, WHOI).Funding was provided by the Office of Naval Research through Contract No. N00014-96-1-1031.
Supported by ONR and DARPA
Antichaos in a Class of Random Boolean Cellular Automata
A variant of Kauffman's model of cellular metabolism is presented. It is a
randomly generated network of boolean gates, identical to Kauffman's except for
a small bias in favor of boolean gates that depend on at most one input. The
bias is asymptotic to 0 as the number of gates increases. Upper bounds on the
time until the network reaches a state cycle and the size of the state cycle,
as functions of the number of gates , are derived. If the bias approaches 0
slowly enough, the state cycles will be smaller than for some . This
lends support to Kauffman's claim that in his version of random network the
average size of the state cycles is approximately .Comment: 12 pages. A uuencoded, tar-compressed postscipt file containing
figures has been adde
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