The Experts below are selected from a list of 109056 Experts worldwide ranked by ideXlab platform

John Lafferty - One of the best experts on this subject based on the ideXlab platform.

  • AAAI - TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
    Proceedings of the AAAI Conference on Artificial Intelligence, 2019
    Co-Authors: Michihiro Yasunaga, John Lafferty
    Abstract:

    Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between Mathematical Equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates Mathematical Equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the Equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K Equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and Equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware Equation generation, Equation topic inference, and topic-aware alignment of Mathematical symbols and words.

  • TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
    arXiv: Information Retrieval, 2019
    Co-Authors: Michihiro Yasunaga, John Lafferty
    Abstract:

    Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between Mathematical Equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates Mathematical Equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the Equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K Equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and Equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware Equation generation, Equation topic inference, and topic-aware alignment of Mathematical symbols and words.

Josep R Tico - One of the best experts on this subject based on the ideXlab platform.

  • application of the sedem diagram and a new Mathematical Equation in the design of direct compression tablet formulation
    European Journal of Pharmaceutics and Biopharmaceutics, 2008
    Co-Authors: Josep M Sunenegre, Pilar Perezlozano, Montserrat Minarro, Manel Roig, Roser Fuster, Carmen Hernandez, Ramon Ruhi, Encarna Garciamontoya, Josep R Tico
    Abstract:

    Application of the new SeDeM Method is proposed for the study of the galenic properties of excipients in terms of the applicability of direct-compression technology. Through experimental studies of the parameters of the SeDeM Method and their subsequent Mathematical treatment and graphical expression (SeDeM Diagram), six different DC diluents were analysed to determine whether they were suitable for direct compression (DC). Based on the properties of these diluents, a Mathematical Equation was established to identify the best DC diluent and the optimum amount to be used when defining a suitable formula for direct compression, depending on the SeDeM properties of the active pharmaceutical ingredient (API) to be used. The results obtained confirm that the SeDeM Method is an appropriate system, effective tool for determining a viable formulation for tablets prepared by direct compression, and can thus be used as the basis for the relevant pharmaceutical development.

Michihiro Yasunaga - One of the best experts on this subject based on the ideXlab platform.

  • AAAI - TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
    Proceedings of the AAAI Conference on Artificial Intelligence, 2019
    Co-Authors: Michihiro Yasunaga, John Lafferty
    Abstract:

    Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between Mathematical Equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates Mathematical Equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the Equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K Equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and Equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware Equation generation, Equation topic inference, and topic-aware alignment of Mathematical symbols and words.

  • TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
    arXiv: Information Retrieval, 2019
    Co-Authors: Michihiro Yasunaga, John Lafferty
    Abstract:

    Scientific documents rely on both mathematics and text to communicate ideas. Inspired by the topical correspondence between Mathematical Equations and word contexts observed in scientific texts, we propose a novel topic model that jointly generates Mathematical Equations and their surrounding text (TopicEq). Using an extension of the correlated topic model, the context is generated from a mixture of latent topics, and the Equation is generated by an RNN that depends on the latent topic activations. To experiment with this model, we create a corpus of 400K Equation-context pairs extracted from a range of scientific articles from arXiv, and fit the model using a variational autoencoder approach. Experimental results show that this joint model significantly outperforms existing topic models and Equation models for scientific texts. Moreover, we qualitatively show that the model effectively captures the relationship between topics and mathematics, enabling novel applications such as topic-aware Equation generation, Equation topic inference, and topic-aware alignment of Mathematical symbols and words.

Hiroyuki Wakiwaka - One of the best experts on this subject based on the ideXlab platform.

  • consideration of theoretical Equation for output voltage of linear displacement sensor using meander coil and pattern guide
    Sensors and Actuators A-physical, 2008
    Co-Authors: M Norhisam, Rahman Wagiran, Roslina Mohd Sidek, Norman Mariun, A Norrimah, Hiroyuki Wakiwaka
    Abstract:

    This paper discusses the development and derivation of theoretical Equation for output voltage on a displacement sensor based on inductive concept. A linear displacement sensor is used to detect the displacement of moving part on linear machines. It consists of a sensor head and a pattern guide. The sensor head is made from copper meander coil while the pattern guide is made from a soft iron (SS400). The Mathematical Equation of the sensor output voltage is derived using magnetic coupling method. The effect of input frequency on the output voltage is analyzed and has been compared with the measurement data.

Josep M Sunenegre - One of the best experts on this subject based on the ideXlab platform.

  • application of the sedem diagram and a new Mathematical Equation in the design of direct compression tablet formulation
    European Journal of Pharmaceutics and Biopharmaceutics, 2008
    Co-Authors: Josep M Sunenegre, Pilar Perezlozano, Montserrat Minarro, Manel Roig, Roser Fuster, Carmen Hernandez, Ramon Ruhi, Encarna Garciamontoya, Josep R Tico
    Abstract:

    Application of the new SeDeM Method is proposed for the study of the galenic properties of excipients in terms of the applicability of direct-compression technology. Through experimental studies of the parameters of the SeDeM Method and their subsequent Mathematical treatment and graphical expression (SeDeM Diagram), six different DC diluents were analysed to determine whether they were suitable for direct compression (DC). Based on the properties of these diluents, a Mathematical Equation was established to identify the best DC diluent and the optimum amount to be used when defining a suitable formula for direct compression, depending on the SeDeM properties of the active pharmaceutical ingredient (API) to be used. The results obtained confirm that the SeDeM Method is an appropriate system, effective tool for determining a viable formulation for tablets prepared by direct compression, and can thus be used as the basis for the relevant pharmaceutical development.