<?xml version="1.0" encoding="UTF-8"?><rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcq="http://purl.org/dc/terms/"><records count="1" morepages="false" start="1" end="1"><record rownumber="1"><dc:product_type>Conference Paper</dc:product_type><dc:title>DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction</dc:title><dc:creator>Zhang, Xin; Li, Yanhua; Zhou, Xun; Mangoubi, Oren; Zhang, Ziming; Filardi, Vincent; Luo, Jun</dc:creator><dc:corporate_author/><dc:editor/><dc:description/><dc:publisher/><dc:date>2021-12-01</dc:date><dc:nsf_par_id>10317969</dc:nsf_par_id><dc:journal_name>IEEE International Conference on Data Mining (ICDM)</dc:journal_name><dc:journal_volume/><dc:journal_issue/><dc:page_range_or_elocation/><dc:issn/><dc:isbn/><dc:doi>https://doi.org/10.1109/ICDM51629.2021.00102</dc:doi><dcq:identifierAwardId>2104528; 1942680; 1952085; 2021871</dcq:identifierAwardId><dc:subject/><dc:version_number/><dc:location/><dc:rights/><dc:institution/><dc:sponsoring_org>National Science Foundation</dc:sponsoring_org></record></records></rdf:RDF>