2,265,745 research outputs found

    Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application

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    A new comprehensive approach to nonlinear time series analysis and modeling is developed in the present paper. We introduce novel data-specific mid-distribution based Legendre Polynomial (LP) like nonlinear transformations of the original time series Y(t) that enables us to adapt all the existing stationary linear Gaussian time series modeling strategy and made it applicable for non-Gaussian and nonlinear processes in a robust fashion. The emphasis of the present paper is on empirical time series modeling via the algorithm LPTime. We demonstrate the effectiveness of our theoretical framework using daily S&P 500 return data between Jan/2/1963 - Dec/31/2009. Our proposed LPTime algorithm systematically discovers all the `stylized facts' of the financial time series automatically all at once, which were previously noted by many researchers one at a time.Comment: Major restructuring has been don

    The Hubble constant inferred from 18 time-delay lenses

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    We present a simultaneous analysis of 18 galaxy lenses with time delay measurements. For each lens we derive mass maps using pixelated simultaneous modeling with shared Hubble constant. We estimate the Hubble constant to be 66_{-4}^{+6} km/s/Mpc (for a flat Universe with \Omega_m=0.3, \Omega_\Lambda=0.7). We have also selected a subsample of five relatively isolated early type galaxies and by simultaneous modeling with an additional constraint on isothermality of their mass profiles we get H_0=76 +/-3 km/s/Mpc.Comment: 11 page, 4 figures, Accepted for publication in Ap

    Time Dependent Radiative Transfer Calculations for Supernovae

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    In previous papers we discussed results from fully time-dependent radiative transfer models for core-collapse supernova (SN) ejecta, including the Type II-peculiar SN 1987A, the more "generic" SN II-Plateau, and more recently Type IIb/Ib/Ic SNe. Here we describe the modifications to our radiative modeling code, CMFGEN, which allowed those studies to be undertaken. The changes allow for time-dependent radiative transfer of SN ejecta in homologous expansion. In the modeling we treat the entire SN ejecta, from the innermost layer that does not fall back on the compact remnant out to the progenitor surface layers. From our non-LTE time-dependent line-blanketed synthetic spectra, we compute the bolometric and multi-band light curves: light curves and spectra are thus calculated simultaneously using the same physical processes and numerics. These upgrades, in conjunction with our previous modifications which allow the solution of the time dependent rate equations, will improve the modeling of SN spectra and light curves, and hence facilitate new insights into SN ejecta properties, the SN progenitors and the explosion mechanism(s). CMFGEN can now be applied to the modeling of all SN typesComment: 20 pages, 10 figures, to appear in MNRA

    Modeling Time(s)

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    The original publication is available at www.springerlink.com (http://dx.doi.org/10.1007/978-3-540-75209-7_38)International audienceTime and timing features are an important aspect of modern electronic systems, often of embedded nature. We argue here that in early design phases, time is often of logical (rather than physical) nature, even possibly multiform. The compilation/synthesis of heterogeneous applications onto architecture platforms then largely amounts to adjusting the former logical time(s) demands onto the latter physical time abilities. Many distributed scheduling techniques pertain to this approach of “time refinement”. We provide extensive Time and Allocation metamodels that open the possibility to cast this approach in a Model-Driven Engineering light. We give a UML representation of these concepts through two subprofiles, parts of the foundations of the forthcoming OMG UML Profile for Modeling and Analysis of Real-Time and Embedded systems (MARTE). Time modeling also allows for a precise description of time-related entities and their associated timed properties

    A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

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    We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their co-firing (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1's (spike) and 0's (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a Gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows: the nonparametric component (i.e., the Gaussian process model) provides a flexible framework for modeling the underlying firing rates; the parametric component (i.e., the copula model) allows us to make inference regarding both contemporaneous and lagged relationships among neurons; using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of dependence structure among variables; our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas in rat's brain

    The utility of a digital simulation language for ecological modeling

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    Dynamic modeling of ecological phenomena has been greatly facilitated by the recent development of continuous system simulator programs. This paper illustrates the application of one of these programs, S/360 Continuous System Modeling Program (S/360 CSMP), to four systems of graduated complexity. The first is a two species system, with one feeding on the other, using differential equations with constant coefficients. The second and third systems involve two competing plant species in which the coefficients of the differential equations are varying with time. The final example considers the management of a postulated buffalo herd in which the dynamics of the herd population and composition by sex and age is combined with various strategies to control its size and to optimize buffalo production
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