The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform

Wu Garidi - One of the best experts on this subject based on the ideXlab platform.

Tomohiko Konno - One of the best experts on this subject based on the ideXlab platform.

  • Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer
    Journal of the Physical Society of Japan, 2020
    Co-Authors: Tomohiko Konno
    Abstract:

    We verified that the deep learning method named reading Periodic table introduced by ref. Deep Learning Model for Finding New Superconductors [T. Konno et al., arXiv:1812.01995], which utilizes dee...

  • deep learning estimation of band gap with the reading Periodic table method and Periodic Convolution layer
    arXiv: Learning, 2019
    Co-Authors: Tomohiko Konno
    Abstract:

    In this study, the deep learning method named reading Periodic table, which utilizes deep learning to read the Periodic table and the laws of the elements, was extended. The method now also learns the Periodicity behind the Periodic table, that is, the left- and right-most columns are adjacent to one another behind the table with one row shifted at the learning representation level. While the original method handles the table as it is, the extended method treats the Periodic table as if its two edges are connected. This is achieved using novel layers named Periodic Convolution layers, which can handle inputs having Periodicity and may be applied to other problems related to computer vision, time series, and so on if the data possesses some Periodicity. In the reading Periodic table method, no input of any material feature or descriptor is required. We verified that the method is also applicable for estimating the band gap of materials other than superconductors, for which the method was originally applied. We demonstrated two types of deep learning estimation: methods to estimate the existence of a band gap and those to estimate the value of the band gap given that the materials were known to have one. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train--test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate.

  • Deep-learning estimation of band gap with the reading-Periodic-table method and Periodic Convolution layer.
    arXiv: Learning, 2019
    Co-Authors: Tomohiko Konno
    Abstract:

    In this study, the deep learning method named reading Periodic table, which utilizes deep learning to read the Periodic table and the laws of the elements, was extended. The method now also learns the Periodicity behind the Periodic table, that is, the left- and right-most columns are adjacent to one another behind the table at the learning representation level. While the original method handles the table as it is, the extended method treats the Periodic table as if its two edges are connected. This is achieved using novel layers named Periodic Convolution layers, which can handle inputs having Periodicity and may be applied to other problems related to computer vision, time series, and so on if the data possesses some Periodicity. In the reading Periodic table method, no input of any material feature or descriptor is required. We verified that the method is also applicable for estimating the band gap of materials other than superconductors, for which the method was originally applied. We demonstrated two types of deep learning estimation: methods to estimate the existence of a band gap and those to estimate the value of the band gap given that the materials were known to have one. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train--test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate.

Yongping Liu - One of the best experts on this subject based on the ideXlab platform.

R.k. Ward - One of the best experts on this subject based on the ideXlab platform.

  • Reduction of boundary artifacts in image restoration
    IEEE Transactions on Image Processing, 1996
    Co-Authors: Farhad Aghdasi, R.k. Ward
    Abstract:

    The abrupt boundary truncation of an image introduces artifacts in the restored image. The traditional solution is to smooth the image data using special window functions such as Hamming or trapezoidal windows. This is followed by zero-padding and linear Convolution with the restoration filter. This method improves the results but still distorts the image, especially at the margins. Instead of the above method, we propose a different procedure. This procedure is simple and exploits the natural property of "circular" or Periodic Convolution of the discrete Fourier transform (DFT). Instead of padding the image by zeros, it is padded by a reflected version of it. This is followed by "circular" Convolution with the restoration filter. This procedure is shown to lead to better restoration results than the windowing and linear Convolution techniques. The computational effort is also improved since our method requires half the number of computations required by the conventional linear deConvolution method.

Konno Tomohiko - One of the best experts on this subject based on the ideXlab platform.

  • Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer
    'Physical Society of Japan', 2020
    Co-Authors: Konno Tomohiko
    Abstract:

    We verified that the deep learning method named reading Periodic table introduced by ref. Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the Periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations. We then extended the method to learn the laws better by directly learning the cylindrical Periodicity between the right- and left-most columns in the Periodic table at the learning representation level, that is, by considering the left- and right-most columns to be adjacent to each other. Thus, while the original method handles the table as is, the extended method treats the Periodic table as if its two edges are connected. This is achieved using novel layers named Periodic Convolution layers, which can handle inputs exhibiting Periodicity and may be applied to other problems related to computer vision, time series, and so on for data that possess some Periodicity. In the reading Periodic table method, no material feature or descriptor is required as input. We demonstrated two types of deep learning estimation: methods to estimate the existence of a bandgap, and methods to estimate the value of the bandgap given when the existence of the bandgap in the materials is known. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train-test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate. The code and data are open.Comment: 11 pages for bod