93 research outputs found
Phase Transition and Strong Predictability
The statistical mechanical interpretation of algorithmic information theory
(AIT, for short) was introduced and developed in our former work [K. Tadaki,
Local Proceedings of CiE 2008, pp.425-434, 2008], where we introduced the
notion of thermodynamic quantities into AIT. These quantities are real
functions of temperature T>0. The values of all the thermodynamic quantities
diverge when T exceeds 1. This phenomenon corresponds to phase transition in
statistical mechanics. In this paper we introduce the notion of strong
predictability for an infinite binary sequence and then apply it to the
partition function Z(T), which is one of the thermodynamic quantities in AIT.
We then reveal a new computational aspect of the phase transition in AIT by
showing the critical difference of the behavior of Z(T) between T=1 and T<1 in
terms of the strong predictability for the base-two expansion of Z(T).Comment: 5 pages, LaTeX2e, no figure
Numerical Investigation of Graph Spectra and Information Interpretability of Eigenvalues
We undertake an extensive numerical investigation of the graph spectra of
thousands regular graphs, a set of random Erd\"os-R\'enyi graphs, the two most
popular types of complex networks and an evolving genetic network by using
novel conceptual and experimental tools. Our objective in so doing is to
contribute to an understanding of the meaning of the Eigenvalues of a graph
relative to its topological and information-theoretic properties. We introduce
a technique for identifying the most informative Eigenvalues of evolving
networks by comparing graph spectra behavior to their algorithmic complexity.
We suggest that extending techniques can be used to further investigate the
behavior of evolving biological networks. In the extended version of this paper
we apply these techniques to seven tissue specific regulatory networks as
static example and network of a na\"ive pluripotent immune cell in the process
of differentiating towards a Th17 cell as evolving example, finding the most
and least informative Eigenvalues at every stage.Comment: Forthcoming in 3rd International Work-Conference on Bioinformatics
and Biomedical Engineering (IWBBIO), Lecture Notes in Bioinformatics, 201
Universal fluctuations in subdiffusive transport
Subdiffusive transport in tilted washboard potentials is studied within the
fractional Fokker-Planck equation approach, using the associated continuous
time random walk (CTRW) framework. The scaled subvelocity is shown to obey a
universal law, assuming the form of a stationary Levy-stable distribution. The
latter is defined by the index of subdiffusion alpha and the mean subvelocity
only, but interestingly depends neither on the bias strength nor on the
specific form of the potential. These scaled, universal subvelocity
fluctuations emerge due to the weak ergodicity breaking and are vanishing in
the limit of normal diffusion. The results of the analytical heuristic theory
are corroborated by Monte Carlo simulations of the underlying CTRW
Computational and Biological Analogies for Understanding Fine-Tuned Parameters in Physics
In this philosophical paper, we explore computational and biological
analogies to address the fine-tuning problem in cosmology. We first clarify
what it means for physical constants or initial conditions to be fine-tuned. We
review important distinctions such as the dimensionless and dimensional
physical constants, and the classification of constants proposed by
Levy-Leblond. Then we explore how two great analogies, computational and
biological, can give new insights into our problem. This paper includes a
preliminary study to examine the two analogies. Importantly, analogies are both
useful and fundamental cognitive tools, but can also be misused or
misinterpreted. The idea that our universe might be modelled as a computational
entity is analysed, and we discuss the distinction between physical laws and
initial conditions using algorithmic information theory. Smolin introduced the
theory of "Cosmological Natural Selection" with a biological analogy in mind.
We examine an extension of this analogy involving intelligent life. We discuss
if and how this extension could be legitimated.
Keywords: origin of the universe, fine-tuning, physical constants, initial
conditions, computational universe, biological universe, role of intelligent
life, cosmological natural selection, cosmological artificial selection,
artificial cosmogenesis.Comment: 25 pages, Foundations of Science, in pres
Polynomial iterative algorithms for coloring and analyzing random graphs
We study the graph coloring problem over random graphs of finite average
connectivity . Given a number of available colors, we find that graphs
with low connectivity admit almost always a proper coloring whereas graphs with
high connectivity are uncolorable. Depending on , we find the precise value
of the critical average connectivity . Moreover, we show that below
there exist a clustering phase in which ground states
spontaneously divide into an exponential number of clusters. Furthermore, we
extended our considerations to the case of single instances showing consistent
results. This lead us to propose a new algorithm able to color in polynomial
time random graphs in the hard but colorable region, i.e when .Comment: 23 pages, 10 eps figure
A Behavioural Foundation for Natural Computing and a Programmability Test
What does it mean to claim that a physical or natural system computes? One
answer, endorsed here, is that computing is about programming a system to
behave in different ways. This paper offers an account of what it means for a
physical system to compute based on this notion. It proposes a behavioural
characterisation of computing in terms of a measure of programmability, which
reflects a system's ability to react to external stimuli. The proposed measure
of programmability is useful for classifying computers in terms of the apparent
algorithmic complexity of their evolution in time. I make some specific
proposals in this connection and discuss this approach in the context of other
behavioural approaches, notably Turing's test of machine intelligence. I also
anticipate possible objections and consider the applicability of these
proposals to the task of relating abstract computation to nature-like
computation.Comment: 37 pages, 4 figures. Based on an invited Talk at the Symposium on
Natural/Unconventional Computing and its Philosophical Significance, Alan
Turing World Congress 2012, Birmingham, UK.
http://link.springer.com/article/10.1007/s13347-012-0095-2 Ref. glitch fixed
in 2nd. version; Philosophy & Technology (special issue on History and
Philosophy of Computing), Springer, 201
G\"odel Incompleteness and the Black Hole Information Paradox
Semiclassical reasoning suggests that the process by which an object
collapses into a black hole and then evaporates by emitting Hawking radiation
may destroy information, a problem often referred to as the black hole
information paradox. Further, there seems to be no unique prediction of where
the information about the collapsing body is localized. We propose that the
latter aspect of the paradox may be a manifestation of an inconsistent
self-reference in the semiclassical theory of black hole evolution. This
suggests the inadequacy of the semiclassical approach or, at worst, that
standard quantum mechanics and general relavity are fundamentally incompatible.
One option for the resolution for the paradox in the localization is to
identify the G\"odel-like incompleteness that corresponds to an imposition of
consistency, and introduce possibly new physics that supplies this
incompleteness. Another option is to modify the theory in such a way as to
prohibit self-reference. We discuss various possible scenarios to implement
these options, including eternally collapsing objects, black hole remnants,
black hole final states, and simple variants of semiclassical quantum gravity.Comment: 14 pages, 2 figures; revised according to journal requirement
Entropy and Quantum Kolmogorov Complexity: A Quantum Brudno's Theorem
In classical information theory, entropy rate and Kolmogorov complexity per
symbol are related by a theorem of Brudno. In this paper, we prove a quantum
version of this theorem, connecting the von Neumann entropy rate and two
notions of quantum Kolmogorov complexity, both based on the shortest qubit
descriptions of qubit strings that, run by a universal quantum Turing machine,
reproduce them as outputs.Comment: 26 pages, no figures. Reference to publication added: published in
the Communications in Mathematical Physics
(http://www.springerlink.com/content/1432-0916/
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