The Experts below are selected from a list of 91695 Experts worldwide ranked by ideXlab platform

Riccardo Barbieri - One of the best experts on this subject based on the ideXlab platform.

  • inhomogeneous point process entropy an instantaneous Measure of Complexity in discrete systems
    Physical Review E, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    Measures of entropy have been widely used to characterize Complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these Measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed Measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system Complexity. The new Measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous Complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

  • inhomogeneous point process entropy an instantaneous Measure of Complexity in discrete systems
    Physical Review E, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    systems modeled in discrete time. Current approaches associate these Measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed Measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system Complexity. The new Measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous Complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

  • assessment of dynamic autonomic changes with posture using instantaneous entropy Measures
    Computing in Cardiology Conference, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    Dynamic analysis provides a powerful methodological framework for characterizing physiological systems. In particular, complex heartbeat dynamics related to autonomic control mechanisms are known to change at each moment in time, and Complexity Measures have been proven to have prognostic value in both health and disease. Nevertheless, an instantaneous Measure of Complexity for cardiovascular time series (or any other series of stochastic physiological “events”) is still missing. In this study we introduce a mathematical framework serving instantaneous complex estimates of heartbeat dynamics to characterize different activities, tasks, and/or pathological states. In particular we propose new definitions of inhomogeneous point-process approximate and sample entropy where the discrete events are modeled by probability density functions characterizing and predicting the time until the next event occurs as a function of past history. These definitions are built on our previous work employing Laguerre expansions of the Wiener-Volterra autoregressive terms to account for long-term memory. We demonstrate an exemplary study on heartbeat data gathered from healthy subjects undergoing postural changes such as stand-up, slow tilt, and fast tilt. Results show that instantaneous Complexity is able to effectively track the complex autonomic changes as they are affected by different postural changes.

Richard L Lewis - One of the best experts on this subject based on the ideXlab platform.

  • the dependence of effective planning horizon on model accuracy
    International Joint Conference on Artificial Intelligence, 2016
    Co-Authors: Nan Jiang, Alex Kulesza, Satinder Singh, Richard L Lewis
    Abstract:

    Because planning with a long horizon (i.e., looking far into the future) is computationally expensive, it is common in practice to save time by using reduced horizons. This is usually understood to come at the expense of computing suboptimal plans, which is the case when the planning model is exact. However, when the planning model is estimated from data, as is frequently true in the real world, the policy found using a shorter planning horizon can actually be better than a policy learned with the true horizon. In this paper we provide a precise explanation for this phenomenon based on principles of learning theory. We show formally that the planning horizon is a Complexity control parameter for the class of policies available to the planning algorithm, having an intuitive, monotonic relationship with a simple Measure of Complexity. We prove a planning loss bound predicting that shorter planning horizons can reduce overfitting and improve test performance, and we confirm these predictions empirically.

  • the dependence of effective planning horizon on model accuracy
    Adaptive Agents and Multi-Agents Systems, 2015
    Co-Authors: Nan Jiang, Alex Kulesza, Satinder Singh, Richard L Lewis
    Abstract:

    For Markov decision processes with long horizons (i.e., discount factors close to one), it is common in practice to use reduced horizons during planning to speed computation. However, perhaps surprisingly, when the model available to the agent is estimated from data, as will be the case in most real-world problems, the policy found using a shorter planning horizon can actually be better than a policy learned with the true horizon. In this paper we provide a precise explanation for this phenomenon based on principles of learning theory. We show formally that the planning horizon is a Complexity control parameter for the class of policies to be learned. In particular, it has an intuitive, monotonic relationship with a simple counting Measure of Complexity, and that a similar relationship can be observed empirically with a more general and data-dependent Rademacher Complexity Measure. Each Complexity Measure gives rise to a bound on the planning loss predicting that a planning horizon shorter than the true horizon can reduce overfitting and improve test performance, and we confirm these predictions empirically.

I Ahmad - One of the best experts on this subject based on the ideXlab platform.

  • the Measure of Complexity in charged celestial bodies in f r t rμνtμν gravity
    Physics of the Dark Universe, 2020
    Co-Authors: Z Yousaf, M Z Bhatti, T Naseer, I Ahmad
    Abstract:

    Abstract In this paper, we investigate irregularities in a cylindrical self-gravitating system which contains the properties of an imperfect matter and electromagnetic field. For f ( R , T , Q ) theory, in which R represents the Ricci scalar and T shows the trace of matter stress–energy tensor while Q ≡ R γ δ T γ δ , the field equations containing electric charge, mass functions and Darmois junction conditions at the hypersurface are examined. We have adopted new definition of Complexity introduced by Herrera (2018), generalized it for the static charged cylindrically symmetric case in f ( R , T , Q ) theory by performing a detailed analysis on the orthogonal splitting of the Riemann curvature tensor. One of the effective scalars, Y T F , has been recognized as a Complexity factor. This factor is comprised of certain physical components of the fluid such as irregularity in energy density, locally pressure anisotropy and electric charge (arranged in a specific way). In addition, the effects of extra curvature terms of modified gravity are examined by making the relations among the Complexity factor, Weyl scalar and Tolman mass.

  • the Measure of Complexity in charged celestial bodies in f r t r_ mu nu t mu nu gravity
    arXiv: General Physics, 2020
    Co-Authors: Z Yousaf, M Z Bhatti, T Naseer, I Ahmad
    Abstract:

    In this paper, we investigate irregularities in a cylindrical self-gravitating system which contains the properties of an imperfect matter and electromagnetic field. For $f(R,T,Q)$ theory, in which $R$ represents the Ricci scalar and $T$ shows the trace of matter stress-energy tensor while $Q\equiv R_{\gamma\delta}T^{\gamma\delta}$, the field equations containing electric charge, mass functions and Darmois junction conditions at the hypersurface are examined. We have adopted new definition of Complexity introduced by Herrera \cite{herrera2018new}, generalized it for the static charged cylindrically symmetric case in $f(R,T,Q)$ theory by performing a detailed analysis on the orthogonal splitting of the Riemann curvature tensor. One of the effective scalars, $Y_{TF}$, has been recognized as a Complexity factor. This factor is comprised of certain physical components of the fluid such as irregularity in energy density, locally pressure anisotropy and electric charge (arranged in a specific way). In addition, the effects of extra curvature terms of modified gravity are examined by making the relations among the Complexity factor, Weyl scalar and Tolman mass.

Gaetano Valenza - One of the best experts on this subject based on the ideXlab platform.

  • inhomogeneous point process entropy an instantaneous Measure of Complexity in discrete systems
    Physical Review E, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    Measures of entropy have been widely used to characterize Complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these Measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed Measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system Complexity. The new Measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous Complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

  • inhomogeneous point process entropy an instantaneous Measure of Complexity in discrete systems
    Physical Review E, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    systems modeled in discrete time. Current approaches associate these Measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed Measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system Complexity. The new Measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous Complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

  • assessment of dynamic autonomic changes with posture using instantaneous entropy Measures
    Computing in Cardiology Conference, 2014
    Co-Authors: Gaetano Valenza, Luca Citi, Enzo Pasquale Scilingo, Riccardo Barbieri
    Abstract:

    Dynamic analysis provides a powerful methodological framework for characterizing physiological systems. In particular, complex heartbeat dynamics related to autonomic control mechanisms are known to change at each moment in time, and Complexity Measures have been proven to have prognostic value in both health and disease. Nevertheless, an instantaneous Measure of Complexity for cardiovascular time series (or any other series of stochastic physiological “events”) is still missing. In this study we introduce a mathematical framework serving instantaneous complex estimates of heartbeat dynamics to characterize different activities, tasks, and/or pathological states. In particular we propose new definitions of inhomogeneous point-process approximate and sample entropy where the discrete events are modeled by probability density functions characterizing and predicting the time until the next event occurs as a function of past history. These definitions are built on our previous work employing Laguerre expansions of the Wiener-Volterra autoregressive terms to account for long-term memory. We demonstrate an exemplary study on heartbeat data gathered from healthy subjects undergoing postural changes such as stand-up, slow tilt, and fast tilt. Results show that instantaneous Complexity is able to effectively track the complex autonomic changes as they are affected by different postural changes.

Ricardo Lopezruiz - One of the best experts on this subject based on the ideXlab platform.