161 research outputs found
A simple necessary decoherence condition for a set of histories
Within the decoherent histories formulation of quantum mechanics, we
investigate necessary conditions for decoherence of arbitrarily long histories.
We prove that fine-grained histories of arbitrary length decohere for all
classical initial states if and only if the unitary evolution preserves
classicality of states (using a natural formal definition of classicality). We
give a counterexample showing that this equivalence does not hold for
coarse-grained histories.Comment: 11 pages,LaTe
Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems
Most current methods for identifying coherent structures in
spatially-extended systems rely on prior information about the form which those
structures take. Here we present two new approaches to automatically filter the
changing configurations of spatial dynamical systems and extract coherent
structures. One, local sensitivity filtering, is a modification of the local
Lyapunov exponent approach suitable to cellular automata and other discrete
spatial systems. The other, local statistical complexity filtering, calculates
the amount of information needed for optimal prediction of the system's
behavior in the vicinity of a given point. By examining the changing
spatiotemporal distributions of these quantities, we can find the coherent
structures in a variety of pattern-forming cellular automata, without needing
to guess or postulate the form of that structure. We apply both filters to
elementary and cyclical cellular automata (ECA and CCA) and find that they
readily identify particles, domains and other more complicated structures. We
compare the results from ECA with earlier ones based upon the theory of formal
languages, and the results from CCA with a more traditional approach based on
an order parameter and free energy. While sensitivity and statistical
complexity are equally adept at uncovering structure, they are based on
different system properties (dynamical and probabilistic, respectively), and
provide complementary information.Comment: 16 pages, 21 figures. Figures considerably compressed to fit arxiv
requirements; write first author for higher-resolution version
Dimension of interaction dynamics
A method allowing to distinguish interacting from non-interacting systems
based on available time series is proposed and investigated. Some facts
concerning generalized Renyi dimensions that form the basis of our method are
proved. We show that one can find the dimension of the part of the attractor of
the system connected with interaction between its parts. We use our method to
distinguish interacting from non-interacting systems on the examples of
logistic and H\'enon maps. A classification of all possible interaction schemes
is given.Comment: 15 pages, 14 (36) figures, submitted to PR
A Roadmap for Privacy Preserving Speech Processing
This paper presents an overview of a strategy
for enabling speech recognition to be performed in the
cloud whilst preserving the privacy of users. The strategy
advocates a demarcation of responsibilities between the
client and server-side components for performing the
speech recognition task. On the client-side resides the
acoustic model, which symbolically encodes the audio and
encrypts the data before uploading to the server. The
server-side then employs searchable encryption-based
language modelling to perform the speech recognition
task. The paper details the proposed client-side acoustic
model components, and the proposed server-side
searchable encryption which will be the basis of the
language modelling. Some preliminary results are
presented, and potential problems and their solutions
regarding the encrypted communication between client
and server are discussed. Preliminary benchmarking
results with acceleration of the client and server
operations with GPGPU computing are also presented
Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integrate
disparate information sources to optimize their behavior in complex
environments. How this capability can be quantified and related to the
functional complexity of an organism remains a challenging problem, in
particular since organismal functional complexity is not well-defined. We
present here several candidate measures that quantify information and
integration, and study their dependence on fitness as an artificial agent
("animat") evolves over thousands of generations to solve a navigation task in
a simple, simulated environment. We compare the ability of these measures to
predict high fitness with more conventional information-theoretic processing
measures. As the animat adapts by increasing its "fit" to the world,
information integration and processing increase commensurately along the
evolutionary line of descent. We suggest that the correlation of fitness with
information integration and with processing measures implies that high fitness
requires both information processing as well as integration, but that
information integration may be a better measure when the task requires memory.
A correlation of measures of information integration (but also information
processing) and fitness strongly suggests that these measures reflect the
functional complexity of the animat, and that such measures can be used to
quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary
video files available on request. Version commensurate with published text in
PLoS Comput. Bio
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