The Experts below are selected from a list of 12903 Experts worldwide ranked by ideXlab platform

Lourdes F. Vega - One of the best experts on this subject based on the ideXlab platform.

  • New Procedure for Enhancing the Transferability of Statistical Associating Fluid Theory (SAFT) Molecular Parameters: The Role of Derivative Properties
    Industrial & Engineering Chemistry Research, 2016
    Co-Authors: Mariana B. Oliveira, Fèlix Llovell, João A. P. Coutinho, Lourdes F. Vega
    Abstract:

    Here, we present a simple method for optimizing the fitting of molecular parameters involving vapor–liquid equilibria (VLE) and selected second-order thermodynamic properties and experimental data. The procedure is applied, as an example to the soft Statistical Associating Fluid Theory (soft-SAFT) equation of state. The method involves the introduction and testing of coupling factors ranging from 0 (only one selected Derivative Property) to 1 (only VLE), to change the weight of one set of properties over the other in the fitting procedure; this allows one to assess the role of Derivative properties in the robustness of the parameters and molecular model. The technique is illustrated by calculating a large number of thermodynamic properties of different compounds: the n-alkanes, n-perfluoroalkanes, and 1-alkanols families, and water, as representative of different types of molecular interactions, and in a wide range of thermodynamic conditions. The most relevant thermodynamic properties to be included in t...

Mariana B. Oliveira - One of the best experts on this subject based on the ideXlab platform.

  • New Procedure for Enhancing the Transferability of Statistical Associating Fluid Theory (SAFT) Molecular Parameters: The Role of Derivative Properties
    Industrial & Engineering Chemistry Research, 2016
    Co-Authors: Mariana B. Oliveira, Fèlix Llovell, João A. P. Coutinho, Lourdes F. Vega
    Abstract:

    Here, we present a simple method for optimizing the fitting of molecular parameters involving vapor–liquid equilibria (VLE) and selected second-order thermodynamic properties and experimental data. The procedure is applied, as an example to the soft Statistical Associating Fluid Theory (soft-SAFT) equation of state. The method involves the introduction and testing of coupling factors ranging from 0 (only one selected Derivative Property) to 1 (only VLE), to change the weight of one set of properties over the other in the fitting procedure; this allows one to assess the role of Derivative properties in the robustness of the parameters and molecular model. The technique is illustrated by calculating a large number of thermodynamic properties of different compounds: the n-alkanes, n-perfluoroalkanes, and 1-alkanols families, and water, as representative of different types of molecular interactions, and in a wide range of thermodynamic conditions. The most relevant thermodynamic properties to be included in t...

C. Seshadhri - One of the best experts on this subject based on the ideXlab platform.

  • SODA - Property Testing on Product Distributions: Optimal Testers for Bounded Derivative Properties
    2017
    Co-Authors: Deeparnab Chakrabarty, Kashyap Dixit, Madhav Jha, C. Seshadhri
    Abstract:

    The primary problem in Property testing is to decide whether a given function satisfies a certain Property or is far from any function satisfying it. This crucially requires a notion of distance between functions. The most prevalent notion is the Hamming distance over the uniform distribution on the domain. This restriction to uniformity is rather limiting, and it is important to investigate distances induced by more general distributions. In this article, we provide simple and optimal testers for bounded Derivative properties over arbitrary product distributions. Bounded Derivative properties include fundamental properties, such as monotonicity and Lipschitz continuity. Our results subsume almost all known results (upper and lower bounds) on monotonicity and Lipschitz testing over arbitrary ranges. We prove an intimate connection between bounded Derivative Property testing and binary search trees (BSTs). We exhibit a tester whose query complexity is the sum of expected depths of optimal BSTs for each marginal. Furthermore, we show that this sum-of-depths is also a lower bound. A technical contribution of our work is an optimal dimension reduction theorem for all bounded Derivative properties that relates the distance of a function from the Property to the distance of restrictions of the function to random lines. Such a theorem has been elusive even for monotonicity, and our theorem is an exponential improvement to the previous best-known result.

  • Property testing on product distributions optimal testers for bounded Derivative properties
    Symposium on Discrete Algorithms, 2017
    Co-Authors: Deeparnab Chakrabarty, Kashyap Dixit, Madhav Jha, C. Seshadhri
    Abstract:

    The primary problem in Property testing is to decide whether a given function satisfies a certain Property or is far from any function satisfying it. This crucially requires a notion of distance between functions. The most prevalent notion is the Hamming distance over the uniform distribution on the domain. This restriction to uniformity is rather limiting, and it is important to investigate distances induced by more general distributions. In this article, we provide simple and optimal testers for bounded Derivative properties over arbitrary product distributions. Bounded Derivative properties include fundamental properties, such as monotonicity and Lipschitz continuity. Our results subsume almost all known results (upper and lower bounds) on monotonicity and Lipschitz testing over arbitrary ranges. We prove an intimate connection between bounded Derivative Property testing and binary search trees (BSTs). We exhibit a tester whose query complexity is the sum of expected depths of optimal BSTs for each marginal. Furthermore, we show that this sum-of-depths is also a lower bound. A technical contribution of our work is an optimal dimension reduction theorem for all bounded Derivative properties that relates the distance of a function from the Property to the distance of restrictions of the function to random lines. Such a theorem has been elusive even for monotonicity, and our theorem is an exponential improvement to the previous best-known result.

  • Property Testing on Product Distributions: Optimal Testers for Bounded Derivative Properties
    arXiv: Discrete Mathematics, 2014
    Co-Authors: Deeparnab Chakrabarty, Kashyap Dixit, Madhav Jha, C. Seshadhri
    Abstract:

    The primary problem in Property testing is to decide whether a given function satisfies a certain Property, or is far from any function satisfying it. This crucially requires a notion of distance between functions. The most prevalent notion is the Hamming distance over the {\em uniform} distribution on the domain. This restriction to uniformity is more a matter of convenience than of necessity, and it is important to investigate distances induced by more general distributions. In this paper, we make significant strides in this direction. We give simple and optimal testers for {\em bounded Derivative properties} over {\em arbitrary product distributions}. Bounded Derivative properties include fundamental properties such as monotonicity and Lipschitz continuity. Our results subsume almost all known results (upper and lower bounds) on monotonicity and Lipschitz testing. We prove an intimate connection between bounded Derivative Property testing and binary search trees (BSTs). We exhibit a tester whose query complexity is the sum of expected depths of optimal BSTs for each marginal. Furthermore, we show this sum-of-depths is also a lower bound. A fundamental technical contribution of this work is an {\em optimal dimension reduction theorem} for all bounded Derivative properties, which relates the distance of a function from the Property to the distance of restrictions of the function to random lines. Such a theorem has been elusive even for monotonicity for the past 15 years, and our theorem is an exponential improvement to the previous best known result.

João A. P. Coutinho - One of the best experts on this subject based on the ideXlab platform.

  • New Procedure for Enhancing the Transferability of Statistical Associating Fluid Theory (SAFT) Molecular Parameters: The Role of Derivative Properties
    Industrial & Engineering Chemistry Research, 2016
    Co-Authors: Mariana B. Oliveira, Fèlix Llovell, João A. P. Coutinho, Lourdes F. Vega
    Abstract:

    Here, we present a simple method for optimizing the fitting of molecular parameters involving vapor–liquid equilibria (VLE) and selected second-order thermodynamic properties and experimental data. The procedure is applied, as an example to the soft Statistical Associating Fluid Theory (soft-SAFT) equation of state. The method involves the introduction and testing of coupling factors ranging from 0 (only one selected Derivative Property) to 1 (only VLE), to change the weight of one set of properties over the other in the fitting procedure; this allows one to assess the role of Derivative properties in the robustness of the parameters and molecular model. The technique is illustrated by calculating a large number of thermodynamic properties of different compounds: the n-alkanes, n-perfluoroalkanes, and 1-alkanols families, and water, as representative of different types of molecular interactions, and in a wide range of thermodynamic conditions. The most relevant thermodynamic properties to be included in t...

Fèlix Llovell - One of the best experts on this subject based on the ideXlab platform.

  • New Procedure for Enhancing the Transferability of Statistical Associating Fluid Theory (SAFT) Molecular Parameters: The Role of Derivative Properties
    Industrial & Engineering Chemistry Research, 2016
    Co-Authors: Mariana B. Oliveira, Fèlix Llovell, João A. P. Coutinho, Lourdes F. Vega
    Abstract:

    Here, we present a simple method for optimizing the fitting of molecular parameters involving vapor–liquid equilibria (VLE) and selected second-order thermodynamic properties and experimental data. The procedure is applied, as an example to the soft Statistical Associating Fluid Theory (soft-SAFT) equation of state. The method involves the introduction and testing of coupling factors ranging from 0 (only one selected Derivative Property) to 1 (only VLE), to change the weight of one set of properties over the other in the fitting procedure; this allows one to assess the role of Derivative properties in the robustness of the parameters and molecular model. The technique is illustrated by calculating a large number of thermodynamic properties of different compounds: the n-alkanes, n-perfluoroalkanes, and 1-alkanols families, and water, as representative of different types of molecular interactions, and in a wide range of thermodynamic conditions. The most relevant thermodynamic properties to be included in t...