141 citations
- Chinese Academy of SciencesCN5 papers
- George Mason UniversityUS3 papers
- Peking UniversityCN3 papers
- Wuhan UniversityCN3 papers
- Harbin Institute of TechnologyCN2 papers
- Southwest UniversityCN2 papers
- Tsinghua UniversityCN2 papers
- University of Chinese Academy of SciencesCN2 papers
- University of Hong KongHK2 papers
- Academy of Mathematics and Systems ScienceCN1 paper
- Aerospace Information Research InstituteCN1 paper
- Beijing Union UniversityCN1 paper
16 papers
Division of Labor and Collaboration Between Parents in Family Education
Ziyi Wang, Congrong Zhang, Jingying Deng +5
Homework tutoring work is a demanding and often conflict-prone practice in family life, and parents often lack targeted support for managing its cognitive and emotional burdens. Th…
A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings
Xuyuan Kang, Xiao Wang, Jingjing An +1
Thermal energy storage (TES) is an effective method for load shifting and demand response in buildings. Optimal TES control and management are essential to improve the performance…
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
Hao Guo, Han Wang, Di Zhu +3
Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, ne…
Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction
Hao Zhang, Yang Liu, Xiaoyan Liu +4
We introduce a novel graph-based framework for alleviating key challenges in distantly-supervised relation extraction and demonstrate its effectiveness in the challenging and impor…
Mega-cities dominate China's urban greening
Xiaoxin Zhang, Martin Brandt, Xiaoye Tong +10
Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being. China has initiated a range of urban gre…
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable
Cambridge Yang, Michael Littman, Michael Carbin
In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal p…