233 research outputs found
An Algorithmic Approach to Information and Meaning
I will survey some matters of relevance to a philosophical discussion of
information, taking into account developments in algorithmic information theory
(AIT). I will propose that meaning is deep in the sense of Bennett's logical
depth, and that algorithmic probability may provide the stability needed for a
robust algorithmic definition of meaning, one that takes into consideration the
interpretation and the recipient's own knowledge encoded in the story attached
to a message.Comment: preprint reviewed version closer to the version accepted by the
journa
Training-free Measures Based on Algorithmic Probability Identify High Nucleosome Occupancy in DNA Sequences
We introduce and study a set of training-free methods of
information-theoretic and algorithmic complexity nature applied to DNA
sequences to identify their potential capabilities to determine nucleosomal
binding sites. We test our measures on well-studied genomic sequences of
different sizes drawn from different sources. The measures reveal the known in
vivo versus in vitro predictive discrepancies and uncover their potential to
pinpoint (high) nucleosome occupancy. We explore different possible signals
within and beyond the nucleosome length and find that complexity indices are
informative of nucleosome occupancy. We compare against the gold standard
(Kaplan model) and find similar and complementary results with the main
difference that our sequence complexity approach. For example, for high
occupancy, complexity-based scores outperform the Kaplan model for predicting
binding representing a significant advancement in predicting the highest
nucleosome occupancy following a training-free approach.Comment: 8 pages main text (4 figures), 12 total with Supplementary (1 figure
On the possible Computational Power of the Human Mind
The aim of this paper is to address the question: Can an artificial neural
network (ANN) model be used as a possible characterization of the power of the
human mind? We will discuss what might be the relationship between such a model
and its natural counterpart. A possible characterization of the different power
capabilities of the mind is suggested in terms of the information contained (in
its computational complexity) or achievable by it. Such characterization takes
advantage of recent results based on natural neural networks (NNN) and the
computational power of arbitrary artificial neural networks (ANN). The possible
acceptance of neural networks as the model of the human mind's operation makes
the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of
Liverpool, UK. 23 page
On the Dynamic Qualitative Behaviour of Universal Computation
We explore the possible connections between the dynamic behaviour of a system
and Turing universality in terms of the system's ability to (effectively)
transmit and manipulate information. Some arguments will be provided using a
defined compression-based transition coefficient which quantifies the
sensitivity of a system to being programmed. In the same spirit, a list of
conjectures concerning the ability of Busy Beaver Turing machines to perform
universal computation will be formulated. The main working hypothesis is that
universality is deeply connected to the qualitative behaviour of a system,
particularly to its ability to react to external stimulus--as it needs to be
programmed--and to its capacity for transmitting this information.Comment: forthcoming in Complex Systems vol. 2
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