The Experts below are selected from a list of 947190 Experts worldwide ranked by ideXlab platform
Carlos Roberto Barrios Hernandez - One of the best experts on this subject based on the ideXlab platform.
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thinking Parametric design introducing Parametric gaudi
Design Studies, 2006Co-Authors: Carlos Roberto Barrios HernandezAbstract:Abstract This paper presents an innovative methodology for Parametric design called Design Procedures (DP) and shows how it is applied to the columns of the Expiatory Temple of the Sagrada Familia. Design Procedures are actions that generate Parametric models where geometrical components are consider as variables. A brief introduction on Parametric design is followed by illustrated explanations of the traditional forms of Parametric models. Design Procedures is presented as an alternative to overcome the topological and geometrical limitations of traditional Parametric models. The DP is able to generate all original designs by Gaudi plus an infinite number of new designs.
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Thinking Parametric design: Introducing Parametric Gaudi
Design Studies, 2006Co-Authors: Carlos Roberto Barrios HernandezAbstract:This paper presents an innovative methodology for Parametric design called Design Procedures (DP) and shows how it is applied to the columns of the Expiatory Temple of the Sagrada Familia. Design Procedures are actions that generate Parametric models where geometrical components are consider as variables. A brief introduction on Parametric design is followed by illustrated explanations of the traditional forms of Parametric models. Design Procedures is presented as an alternative to overcome the topological and geometrical limitations of traditional Parametric models. The DP is able to generate all original designs by Gaudi plus an infinite number of new designs. ?? 2005 Elsevier Ltd. All rights reserved.
Stephen J Roberts - One of the best experts on this subject based on the ideXlab platform.
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Parametric and non-Parametric unsupervised cluster analysis
Pattern Recognition, 1997Co-Authors: Stephen J RobertsAbstract:Much work has been published on methods for assessing the probable number of clusters or structures within unknown data sets. This paper aims to look in more detail at two methods, a broad Parametric method, based around the assumption of Gaussian clusters and the other a non-Parametric method which utilises methods of scale-space filtering to extract robust structures within a data set. It is shown that, whilst both methods are capable of determining cluster validity for data sets in which clusters tend towards a multivariate Gaussian distribution, the Parametric method inevitably fails for clusters which have a non-Gaussian structure whilst the scale-space method is more robust.
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Parametric and non-Parametric unsupervised cluster analysis
Pattern Recognition, 1997Co-Authors: Stephen J RobertsAbstract:Much work has been published on methods for assessing the probable number of clusters or structures within unknown data sets. This paper aims to look in more detail at two methods, a broad Parametric method, based around the assumption of Gaussian clusters and the other a non-Parametric method which utilises methods of scale-space filtering to extract robust structures within a data set. It is shown that, whilst both methods are capable of determining cluster validity for data sets in which clusters tend towards a multivanate Gaussian distribution, the Parametric method inevitably fails for clusters which have a non-Gaussian structure whilst the scale-space method is more robust. Copyright @ 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.
Markus Reiher - One of the best experts on this subject based on the ideXlab platform.
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reliable estimation of prediction uncertainty for physicochemical property models
Journal of Chemical Theory and Computation, 2017Co-Authors: Jonny Proppe, Markus ReiherAbstract:One of the major challenges in computational science is to determine the uncertainty of a virtual measurement, that is the prediction of an observable based on calculations. As highly accurate first-principles calculations are in general unfeasible for most physical systems, one usually resorts to parameteric property models of observables, which require calibration by incorporating reference data. The resulting predictions and their uncertainties are sensitive to systematic errors such as inconsistent reference data, Parametric model assumptions, or inadequate computational methods. Here, we discuss the calibration of property models in the light of bootstrapping, a sampling method that can be employed for identifying systematic errors and for reliable estimation of the prediction uncertainty. We apply bootstrapping to assess a linear property model linking the 57Fe Mossbauer isomer shift to the contact electron density at the iron nucleus for a diverse set of 44 molecular iron compounds. The contact ele...
Vasilis Z. Marmarelis - One of the best experts on this subject based on the ideXlab platform.
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Parametric and non-Parametric modeling of short-term synaptic plasticity. Part II: Experimental study
Journal of Computational Neuroscience, 2009Co-Authors: Dong Song, Vasilis Z. Marmarelis, Zhuo Wang, Theodore W. BergerAbstract:This paper presents a synergistic Parametric and non-Parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-Parametric Poisson–Volterra models are obtained herein from broadband experimental input–output data. The non-Parametric model is shown to provide better prediction of the experimental output than a Parametric model with a single set of facilitation/depression ( FD ) process. The Parametric model is then validated in terms of its input–output transformational properties using the non-Parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-Parametric model and the equivalent non-Parametric model of the Parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the Parametric model makes it replicate better the characteristics of the experimentally-derived non-Parametric model. This improved Parametric model in turn provides the requisite biological interpretability that the non-Parametric model lacks.
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Parametric and non-Parametric modeling of short-term synaptic plasticity. Part I: computational study
Journal of Computational Neuroscience, 2008Co-Authors: Dong Song, Vasilis Z. Marmarelis, Theodore W. BergerAbstract:Parametric and non-Parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed Parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-Parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the Parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-Parametric models accurately and efficiently replicate the input–output transformations of the Parametric models. Volterra kernels provide a general and quantitative representation of the STP.
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A modeling paradigm incorporating Parametric and non-Parametric methods
The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004Co-Authors: D. Song, Vasilis Z. Marmarelis, Z. Wang, T.w. BergerAbstract:A novel Parametric/non-Parametric modeling paradigm was defined and used in characterization of synaptic transmission. In this paradigm, Parametric and nonParametric techniques were incorporated in a complementary manner. Non-Parametric method was used to generalize experimental data and extract system input/output properties. It provided a quantitative and intuitive way to validate a Parametric model with respect to general, complete input patterns. Biological processes or mechanisms missed by the conventional Parametric modeling approach were revealed and subsequently included into the modified Parametric model.
Abdelhak M. Zoubir - One of the best experts on this subject based on the ideXlab platform.
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A semi-Parametric approach for robust multiuser detection
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Ulrich Hammes, Abdelhak M. ZoubirAbstract:Robust parameter estimation in impulsive noise has risen a lot of attention in wireless communications. Previously, we proposed a non-Parametric estimator for multiuser detection based on non-Parametric density estimation. Here, we present a semi-Parametric estimator that outperforms its non-Parametric counterpart by combating multiple access interference and impulsive noise altogether. The approach is termed semi-Parametric since a nonlinear Parametric function is used to transform the noise data while non-Parametric estimation of the score function is performed using the transformed sample. This estimate is then used to determine the parameters of interest, i.e., the transmitted symbols. We also propose a Parametric function and an estimator for its parameter.
