141,103 research outputs found
Quantum correlations require multipartite information principles
Identifying which correlations among distant observers are possible within
our current description of Nature, based on quantum mechanics, is a fundamental
problem in Physics. Recently, information concepts have been proposed as the
key ingredient to characterize the set of quantum correlations. Novel
information principles, such as, information causality or non-trivial
communication complexity, have been introduced in this context and successfully
applied to some concrete scenarios. We show in this work a fundamental
limitation of this approach: no principle based on bipartite information
concepts is able to single out the set of quantum correlations for an arbitrary
number of parties. Our results reflect the intricate structure of quantum
correlations and imply that new and intrinsically multipartite information
concepts are needed for their full understanding.Comment: Appendix adde
Causality - Complexity - Consistency: Can Space-Time Be Based on Logic and Computation?
The difficulty of explaining non-local correlations in a fixed causal
structure sheds new light on the old debate on whether space and time are to be
seen as fundamental. Refraining from assuming space-time as given a priori has
a number of consequences. First, the usual definitions of randomness depend on
a causal structure and turn meaningless. So motivated, we propose an intrinsic,
physically motivated measure for the randomness of a string of bits: its length
minus its normalized work value, a quantity we closely relate to its Kolmogorov
complexity (the length of the shortest program making a universal Turing
machine output this string). We test this alternative concept of randomness for
the example of non-local correlations, and we end up with a reasoning that
leads to similar conclusions as in, but is conceptually more direct than, the
probabilistic view since only the outcomes of measurements that can actually
all be carried out together are put into relation to each other. In the same
context-free spirit, we connect the logical reversibility of an evolution to
the second law of thermodynamics and the arrow of time. Refining this, we end
up with a speculation on the emergence of a space-time structure on bit strings
in terms of data-compressibility relations. Finally, we show that logical
consistency, by which we replace the abandoned causality, it strictly weaker a
constraint than the latter in the multi-party case.Comment: 17 pages, 16 figures, small correction
Causal connectivity of evolved neural networks during behavior
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics
Characterization of Vehicle Behavior with Information Theory
This work proposes the use of Information Theory for the characterization of
vehicles behavior through their velocities. Three public data sets were used:
i.Mobile Century data set collected on Highway I-880, near Union City,
California; ii.Borl\"ange GPS data set collected in the Swedish city of
Borl\"ange; and iii.Beijing taxicabs data set collected in Beijing, China,
where each vehicle speed is stored as a time series. The Bandt-Pompe
methodology combined with the Complexity-Entropy plane were used to identify
different regimes and behaviors. The global velocity is compatible with a
correlated noise with f^{-k} Power Spectrum with k >= 0. With this we identify
traffic behaviors as, for instance, random velocities (k aprox. 0) when there
is congestion, and more correlated velocities (k aprox. 3) in the presence of
free traffic flow
Libor at crossroads: stochastic switching detection using information theory quantifiers
This paper studies the 28 time series of Libor rates, classified in seven
maturities and four currencies), during the last 14 years. The analysis was
performed using a novel technique in financial economics: the
Complexity-Entropy Causality Plane. This planar representation allows the
discrimination of different stochastic and chaotic regimes. Using a temporal
analysis based on moving windows, this paper unveals an abnormal movement of
Libor time series arround the period of the 2007 financial crisis. This
alteration in the stochastic dynamics of Libor is contemporary of what press
called "Libor scandal", i.e. the manipulation of interest rates carried out by
several prime banks. We argue that our methodology is suitable as a market
watch mechanism, as it makes visible the temporal redution in informational
efficiency of the market.Comment: 17 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1508.04748, arXiv:1509.0021
Causality and the semantics of provenance
Provenance, or information about the sources, derivation, custody or history
of data, has been studied recently in a number of contexts, including
databases, scientific workflows and the Semantic Web. Many provenance
mechanisms have been developed, motivated by informal notions such as
influence, dependence, explanation and causality. However, there has been
little study of whether these mechanisms formally satisfy appropriate policies
or even how to formalize relevant motivating concepts such as causality. We
contend that mathematical models of these concepts are needed to justify and
compare provenance techniques. In this paper we review a theory of causality
based on structural models that has been developed in artificial intelligence,
and describe work in progress on a causal semantics for provenance graphs.Comment: Workshop submissio
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