The Experts below are selected from a list of 61209 Experts worldwide ranked by ideXlab platform
Benedikt Diemer - One of the best experts on this subject based on the ideXlab platform.
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colossus a python toolkit for cosmology large scale structure and dark matter halos
Astrophysical Journal Supplement Series, 2018Co-Authors: Benedikt DiemerAbstract:This paper introduces Colossus, a public, open-source python package for calculations related to cosmology, the large-scale structure (LSS) of matter in the universe, and the properties of dark matter halos. The code is designed to be fast and easy to use, with a coherent, well-documented user interface. The cosmology module implements Friedman–Lemaitre–Robertson–Walker cosmologies including curvature, relativistic species, and different dark energy equations of state, and provides fast computations of the linear matter power spectrum, variance, and correlation function. The LSS module is concerned with the properties of peaks in Gaussian random fields and halos in a Statistical Sense, including their peak height, peak curvature, halo bias, and mass function. The halo module deals with spherical overdensity radii and masses, density profiles, concentration, and the splashback radius. To facilitate the rapid exploration of these quantities, Colossus implements more than 40 different fitting functions from the literature. I discuss the core routines in detail, with particular emphasis on their accuracy. Colossus is available at bitbucket.org/bdiemer/colossus.
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colossus a python toolkit for cosmology large scale structure and dark matter halos
arXiv: Cosmology and Nongalactic Astrophysics, 2017Co-Authors: Benedikt DiemerAbstract:This paper introduces Colossus, a public, open-source python package for calculations related to cosmology, the large-scale structure of matter in the universe, and the properties of dark matter halos. The code is designed to be fast and easy to use, with a coherent, well-documented user interface. The cosmology module implements FLRW cosmologies including curvature, relativistic species, and different dark energy equations of state, and provides fast computations of the linear matter power spectrum, variance, and correlation function. The large-scale structure module is concerned with the properties of peaks in Gaussian random fields and halos in a Statistical Sense, including their peak height, peak curvature, halo bias, and mass function. The halo module deals with spherical overdensity radii and masses, density profiles, concentration, and the splashback radius. To facilitate the rapid exploration of these quantities, Colossus implements about 40 different fitting functions from the literature. I discuss the core routines in detail, with a particular emphasis on their accuracy. Colossus is available at bitbucket.org/bdiemer/colossus.
Robert D. Palmer - One of the best experts on this subject based on the ideXlab platform.
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ARTICLE Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation
2016Co-Authors: Evgeni Fedorovich, Robert D. PalmerAbstract:Abstract Small-scale turbulent fluctuations of temperature are known to affect the propaga-tion of both electromagnetic and acoustic waves. Within the inertial-subrange scale, where the turbulence is locally homogeneous and isotropic, these temperature perturbations can be described, in a Statistical Sense, using the structure-function parameter for temperature, C2T. Here we investigate different methods of evaluating C2T, using data from a numerical large-eddy simulation together with atmospheric observations collected by an unmanned aerial system and a sodar. An example case using data from a late afternoon unmanned aerial system flight on April 24 2013 and corresponding large-eddy simulation data is presented and discussed
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Methods for Evaluating the Temperature Structure-Function Parameter Using Unmanned Aerial Systems and Large-Eddy Simulation
Boundary-Layer Meteorology, 2015Co-Authors: Charlotte E. Wainwright, Timothy A. Bonin, Phillip B. Chilson, Jeremy A. Gibbs, Evgeni Fedorovich, Robert D. PalmerAbstract:Small-scale turbulent fluctuations of temperature are known to affect the propagation of both electromagnetic and acoustic waves. Within the inertial-subrange scale, where the turbulence is locally homogeneous and isotropic, these temperature perturbations can be described, in a Statistical Sense, using the structure-function parameter for temperature, $$C_{T}^2$$ C T 2 . Here we investigate different methods of evaluating $$C_{T}^2$$ C T 2 , using data from a numerical large-eddy simulation together with atmospheric observations collected by an unmanned aerial system and a sodar. An example case using data from a late afternoon unmanned aerial system flight on April 24 2013 and corresponding large-eddy simulation data is presented and discussed.
D H Mackay - One of the best experts on this subject based on the ideXlab platform.
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modelling the global solar corona iii origin of the hemispheric pattern of filaments
Solar Physics, 2009Co-Authors: A R Yeates, D H MackayAbstract:We consider the physical origin of the hemispheric pattern of filament chirality on the Sun. Our 3D simulations of the coronal field evolution over a period of six months, based on photospheric magnetic measurements, were previously shown to be highly successful at reproducing observed filament chiralities. In this paper we identify and describe the physical mechanisms responsible for this success. The key mechanisms are found to be (1) differential rotation of north – south polarity inversion lines, (2) the shape of bipolar active regions, and (3) evolution of skew over a period of many days. As on the real Sun, the hemispheric pattern in our simulations holds in a Statistical Sense. Exceptions arise naturally for filaments in certain locations relative to bipolar active regions or from interactions among a number of active regions.
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modelling the global solar corona iii origin of the hemispheric pattern of filaments
arXiv: Astrophysics, 2008Co-Authors: A R Yeates, D H MackayAbstract:We consider the physical origin of the hemispheric pattern of filament chirality on the Sun. Our 3D simulations of the coronal field evolution over a period of 6 months, based on photospheric magnetic measurements, were previously shown to be highly successful at reproducing observed filament chiralities. In this paper we identify and describe the physical mechanisms responsible for this success. The key mechanisms are found to be (1) differential rotation of north-south polarity inversion lines, (2) the shape of bipolar active regions, and (3) evolution of skew over a period of many days. As on the real Sun, the hemispheric pattern in our simulations holds in a Statistical Sense. Exceptions arise naturally for filaments in certain locations relative to bipolar active regions, or from interactions between a number of active regions.
T V Lakshman - One of the best experts on this subject based on the ideXlab platform.
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Statistical analysis and simulation study of video teleconference traffic in atm networks
IEEE Transactions on Circuits and Systems for Video Technology, 1992Co-Authors: Daniel P Heyman, Ali Tabatabai, T V LakshmanAbstract:Source modeling and performance issues are studied using a long (30 min) sequence of real video teleconference data. It is found that traffic periodicity can cause different sources with identical Statistical characteristics to experience differing cell-loss rates. For a single-stage multiplexer model, some of this source-periodicity effect can be mitigated by appropriate buffer scheduling and one effective scheduling policy is presented. For the sequence analyzed, the number of cells per frame follows a gamma (or negative binomial) distribution. The number of cells per frame is a stationary stochastic process. For traffic studies, neither an autoregressive model of order two nor a two-state Markov chain model is good because they do not model correctly the occurrence of frames with a large number of cells, which are a primary factor in determining cell-loss rates. The order two autoregressive model, however, fits the data well in a Statistical Sense. A multistate Markov chain model that can be derived from three traffic parameters is sufficiently accurate for use in traffic studies. >
Richard T Monopoli - One of the best experts on this subject based on the ideXlab platform.
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public versus private real estate equities a more refined long term comparison
Real Estate Economics, 2005Co-Authors: Joseph L Pagliari, Kevin A Scherer, Richard T MonopoliAbstract:In this article we compare public and private real estate equities. In so doing, we control for three of the main differences between these investment alternatives: property-type mix, leverage and appraisal smoothing. With these two restated indices, we then run tests to determine in a Statistical Sense whether the restated means and volatilities of the two series were different from one another. The clear answer is that they were not. The results of the Statistical tests combined with the fact that the average difference between the two (restated) return series has substantially narrowed (to approximately 60 basis points) in the more recent (1993–2001) period jointly suggest a seamless real estate market in which public- and private-market vehicles display a long-run synchronicity. This has important implications for portfolio management. First, public- and private-market vehicles ought to be viewed as offering investors a risk/return continuum of real estate investment opportunities. Second, while the “platform” did not matter in terms of observed return characteristics, the platform may matter with regard to liquidity, governance, transparency, control, executive compensation and so forth; an apparent clientele effect hints at these issues being valued differently by large and small investors.
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public versus private real estate equities
The Journal of Portfolio Management, 2003Co-Authors: Joseph L Pagliari, Kevin A Scherer, Richard T MonopoliAbstract:Comparisons of the performance of public and private real estate equities are provided in this article. In so doing, the authors control for three of the main differences between these investment alternatives: property-type mix, leverage and appraisal smoothing. They then ran tests to determine in a Statistical Sense whether the restated means and volatilities of the two series were in fact different from one another. The clear answer is that they were not, suggesting a fairly seamless real estate market in which public- and private-market vehicles display a long-run synchronicity. This has two important implications for portfolio management. First, public- and private-market vehicles ought to be viewed as (somewhat interchangeably) offering investors a risk/return continuum of real estate investment opportunities. Second, while the “platform” did not matter in terms of observed return characteristics, the platform may matter with regard to liquidity, governance, transparency, control, executive compensation, and so on, an apparent clientele effect hints that these issues may be valued differently by large and small investors.
