Adaptive power allocation for many-core systems inspired from multiagent auction model. - Xiaohang Wang 001; Baoxin Zhao; Terrence S. T. Mak; Mei Yang 001; Yingtao Jiang; Masoud Daneshtalab; Maurizio Palesi (DATE, 2014)

An efficient runtime power allocation scheme for many-core systems inspired from auction theory. - Xiaohang Wang 001; Baoxin Zhao; Terrence S. T. Mak; Mei Yang 001; Yingtao Jiang; Masoud Daneshtalab (Integr., 2015)

A pareto-optimal runtime power budgeting scheme for many-core systems. - Xiaohang Wang 001; Baoxin Zhao; Ling Wang 005; Terrence S. T. Mak; Mei Yang 001; Yingtao Jiang; Masoud Daneshtalab (Microprocess. Microsystems, 2016)

On Fine-Grained Runtime Power Budgeting for Networks-on-Chip Systems. - Xiaohang Wang 001; Baoxin Zhao; Terrence S. T. Mak; Mei Yang 001; Yingtao Jiang; Masoud Daneshtalab (IEEE Trans. Computers, 2016)

A data-driven congestion diffusion model for characterizing traffic in metrocity scales. - Baoxin Zhao; Chengzhong Xu 001; Siyuan Liu (IEEE BigData, 2017)

SecureGBM: Secure Multi-Party Gradient Boosting. - Zhi Feng; Jun Huan; Haoyi Xiong; Chuanyuan Song; Sijia Yang; Baoxin Zhao; Licheng Wang; Zeyu Chen; Shengwen Yang; Liping Liu (IEEE BigData, 2019)

A Congestion Diffusion Model with Influence Maximization for Traffic Bottlenecks Identification in Metrocity Scales. - Baoxin Zhao; Chengzhong Xu 001; Siyuan Liu; Juanjuan Zhao; Li Li 064 (IEEE BigData, 2019)

Spatiotemporal Data Fusion in Graph Convolutional Networks for Traffic Prediction. - Baoxin Zhao; Xitong Gao; Jianqi Liu; Juanjuan Zhao; Cheng-Zhong Xu 001 (IEEE Access, 2020)

Dynamic traffic bottlenecks identification based on congestion diffusion model by influence maximization in metro-city scales. - Baoxin Zhao; Cheng-Zhong Xu 001; Siyuan Liu; Juanjuan Zhao; Li Li 064 (Concurr. Comput. Pract. Exp., 2021)

COMO: Efficient Deep Neural Networks Expansion With COnvolutional MaxOut. - Baoxin Zhao; Haoyi Xiong; Jiang Bian 003; Zhishan Guo; Cheng-Zhong Xu 001; Dejing Dou (IEEE Trans. Multim., 2021)