668,705 research outputs found
Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information
[EN] The development of new measures and algorithms to quantify the entropy or related concepts of a data series is a continuous effort that has brought many innovations in this regard in recent years. The ultimate goal is usually to find new methods with a higher discriminating power, more efficient, more robust to noise and artifacts, less dependent on parameters or configurations, or any other possibly desirable feature. Among all these methods, Permutation Entropy (PE) is a complexity estimator for a time series that stands out due to its many strengths, with very few weaknesses. One of these weaknesses is the PE's disregarding of time series amplitude information. Some PE algorithm modifications have been proposed in order to introduce such information into the calculations. We propose in this paper a new method, Slope Entropy (SlopEn), that also addresses this flaw but in a different way, keeping the symbolic representation of subsequences using a novel encoding method based on the slope generated by two consecutive data samples. By means of a thorough and extensive set of comparative experiments with PE and Sample Entropy (SampEn), we demonstrate that SlopEn is a very promising method with clearly a better time series classification performance than those previous methods.Cuesta Frau, D. (2019). Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information. Entropy. 21(12):1-22. https://doi.org/10.3390/e21121167S1222112Kannathal, N., Choo, M. L., Acharya, U. R., & Sadasivan, P. K. (2005). Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine, 80(3), 187-194. doi:10.1016/j.cmpb.2005.06.012Abásolo, D., Hornero, R., Espino, P., Álvarez, D., & Poza, J. (2006). 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Safety Evaluation of Critical Applications Distributed on TDMA-Based Networks
Critical embedded systems have to provide a high level of dependability. In
automotive domain, for example, TDMA protocols are largely recommended because
of their deterministic behavior. Nevertheless, under the transient
environmental perturbations, the loss of communication cycles may occur with a
certain probability and, consequently, the system may fail. This paper analyzes
the impact of the transient perturbations (especially due to Electromagnetic
Interferences) on the dependability of systems distributed on TDMA-based
networks. The dependability of such system is modeled as that of
"consecutive-k-out-of-n:F" systems and we provide a efficient way for its
evaluation
Exact Failure Frequency Calculations for Extended Systems
This paper shows how the steady-state availability and failure frequency can
be calculated in a single pass for very large systems, when the availability is
expressed as a product of matrices. We apply the general procedure to
-out-of-:G and linear consecutive -out-of-:F systems, and to a
simple ladder network in which each edge and node may fail. We also give the
associated generating functions when the components have identical
availabilities and failure rates. For large systems, the failure rate of the
whole system is asymptotically proportional to its size. This paves the way to
ready-to-use formulae for various architectures, as well as proof that the
differential operator approach to failure frequency calculations is very useful
and straightforward
Forbidden patterns and shift systems
The scope of this paper is two-fold. First, to present to the researchers in
combinatorics an interesting implementation of permutations avoiding
generalized patterns in the framework of discrete-time dynamical systems.
Indeed, the orbits generated by piecewise monotone maps on one-dimensional
intervals have forbidden order patterns, i.e., order patterns that do not occur
in any orbit. The allowed patterns are then those patterns avoiding the
so-called forbidden root patterns and their shifted patterns. The second scope
is to study forbidden patterns in shift systems, which are universal models in
information theory, dynamical systems and stochastic processes. Due to its
simple structure, shift systems are accessible to a more detailed analysis and,
at the same time, exhibit all important properties of low-dimensional chaotic
dynamical systems (e.g., sensitivity to initial conditions, strong mixing and a
dense set of periodic points), allowing to export the results to other
dynamical systems via order-isomorphisms.Comment: 21 pages, expanded Section 5 and corrected Propositions 3 and
Identification of criticality in neuronal avalanches: II. A theoretical and empirical investigation of the Driven case
The observation of apparent power laws in neuronal systems has led to the suggestion that the brain is at, or close to, a critical state and may be a self-organised critical system. Within the framework of self-organised criticality a separation of timescales is thought to be crucial for the observation of power-law dynamics and computational models are often constructed with this property. However, this is not necessarily a characteristic of physiological neural networks—external input does not only occur when the network is at rest/a steady state. In this paper we study a simple neuronal network model driven by a continuous external input (i.e. the model does not have an explicit separation of timescales from seeding the system only when in the quiescent state) and analytically tuned to operate in the region of a critical state (it reaches the critical regime exactly in the absence of input—the case studied in the companion paper to this article). The system displays avalanche dynamics in the form of cascades of neuronal firing separated by periods of silence. We observe partial scale-free behaviour in the distribution of avalanche size for low levels of external input. We analytically derive the distributions of waiting times and investigate their temporal behaviour in relation to different levels of external input, showing that the system’s dynamics can exhibit partial long-range temporal correlations. We further show that as the system approaches the critical state by two alternative ‘routes’, different markers of criticality (partial scale-free behaviour and long-range temporal correlations) are displayed. This suggests that signatures of criticality exhibited by a particular system in close proximity to a critical state are dependent on the region in parameter space at which the system (currently) resides
Semantic Stability in Social Tagging Streams
One potential disadvantage of social tagging systems is that due to the lack
of a centralized vocabulary, a crowd of users may never manage to reach a
consensus on the description of resources (e.g., books, users or songs) on the
Web. Yet, previous research has provided interesting evidence that the tag
distributions of resources may become semantically stable over time as more and
more users tag them. At the same time, previous work has raised an array of new
questions such as: (i) How can we assess the semantic stability of social
tagging systems in a robust and methodical way? (ii) Does semantic
stabilization of tags vary across different social tagging systems and
ultimately, (iii) what are the factors that can explain semantic stabilization
in such systems? In this work we tackle these questions by (i) presenting a
novel and robust method which overcomes a number of limitations in existing
methods, (ii) empirically investigating semantic stabilization processes in a
wide range of social tagging systems with distinct domains and properties and
(iii) detecting potential causes for semantic stabilization, specifically
imitation behavior, shared background knowledge and intrinsic properties of
natural language. Our results show that tagging streams which are generated by
a combination of imitation dynamics and shared background knowledge exhibit
faster and higher semantic stability than tagging streams which are generated
via imitation dynamics or natural language streams alone
Planets Transiting Non-Eclipsing Binaries
The majority of binary stars do not eclipse. Current searches for transiting
circumbinary planets concentrate on eclipsing binaries, and are therefore
restricted to a small fraction of potential hosts. We investigate the concept
of finding planets transiting non-eclipsing binaries, whose geometry would
require mutually inclined planes. Using an N-body code we explore how the
number and sequence of transits vary as functions of observing time and orbital
parameters. The concept is then generalised thanks to a suite of simulated
circumbinary systems. Binaries are constructed from RV surveys of the solar
neighbourhood. They are then populated with orbiting gas giants, drawn from a
range of distributions. The binary population is shown to be compatible with
the Kepler eclipsing binary catalogue, indicating that the properties of
binaries may be as universal as the initial mass function. These synthetic
systems produce transiting circumbinary planets occurring on both eclipsing and
non-eclipsing binaries. Simulated planets transiting eclipsing binaries are
compared with published Kepler detections. We obtain 1) that planets transiting
non-eclipsing binaries probably exist in the Kepler data, 2) that observational
biases alone cannot account for the observed over-density of circumbinary
planets near the stability limit, implying a physical pile-up, and 3) that the
distributions of gas giants orbiting single and binary stars are likely
different. Estimating the frequency of circumbinary planets is degenerate with
the spread in mutual inclination. Only a minimum occurrence rate can be
produced, which we find to be compatible with 9%. Searching for inclined
circumbinary planets may significantly increase the population of known objects
and will test our conclusions. Their existence, or absence, will reveal the
true occurrence rate and help develop circumbinary planet formation theories.Comment: 19 pages, 14 figures, accepted August 2014 to A&A, minor changes to
previous arXiv versio
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