5,500 research outputs found

    How Do Program Recruiters at One Ontario University Interpret the Internationalization Strategic Plan?

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    This study examines how program recruiters interpret a university’s internationalization strategic plan in Ontario, and how they translate this policy initiative into Chinese international students’ recruiting at the graduate level. In order to gain a deeper understanding of the processes related to the internationalization of higher education in Canada, this study explores recruitment as one key aspect of the university’s internationalization efforts. A qualitative research approach was used in designing this study. The data was gathered from three Ontario universities’ internationalization policy initiatives and semi-structured interviews with recruiters from a research-intensive university in Ontario. The findings demonstrate significant implications for internal and external recruitment networks and for the recruitment strategies in Canadian higher education

    Migrating Knowledge between Physical Scenarios based on Artificial Neural Networks

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    Deep learning is known to be data-hungry, which hinders its application in many areas of science when datasets are small. Here, we propose to use transfer learning methods to migrate knowledge between different physical scenarios and significantly improve the prediction accuracy of artificial neural networks trained on a small dataset. This method can help reduce the demand for expensive data by making use of additional inexpensive data. First, we demonstrate that in predicting the transmission from multilayer photonic film, the relative error rate is reduced by 46.8% (26.5%) when the source data comes from 10-layer (8-layer) films and the target data comes from 8-layer (10-layer) films. Second, we show that the relative error rate is decreased by 22% when knowledge is transferred between two very different physical scenarios: transmission from multilayer films and scattering from multilayer nanoparticles. Finally, we propose a multi-task learning method to improve the performance of different physical scenarios simultaneously in which each task only has a small dataset

    Blue House In Sisimiut, Greenland:Initial investigation of the Blue House

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    Poly[aqua­[μ5-5-(isonicotinamido)­isophthalato][μ4-5-(isonicotinamido)­isophthalato]cerium(III)silver(I)]

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    The 4d–4f heteronuclear title complex, [AgCe(C14H8N2O5)2(H2O)]n, has a three-dimensional framework structure, generated by the carboxyl­ate and pyridyl groups of the 5-(isonicotinamido)­isophthalate (INAIP) ligands bridging the metal ions. The CeIII atom is coordinated by eight O atoms from six INAIP ligands and a water mol­ecule in a distorted tricapped trigonal–prismatic geometry, while the AgI atom has a distorted trigonal–planar AgN2O geometry. O—H⋯O and N—H⋯O hydrogen bonds and π–π inter­actions between the pyridine and benzene rings [centroid–centroid distances = 3.642 (4) and 3.624 (3) Å] stabilize the crystal structure
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