activity
20172020
most citedDeep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems

367 citations · 423 across the 20 of their papers we have counts for

collaborators

23 papers

cs.DB2020

Tempura: A General Cost Based Optimizer Framework for Incremental Data Processing (Extended Version)

Zuozhi Wang, Kai Zeng, Botong Huang +10

Incremental processing is widely-adopted in many applications, ranging from incremental view maintenance, stream computing, to recently emerging progressive data warehouse and inte…

cs.IT2020

Reconfigurable Intelligent Surface Assisted Massive MIMO with Antenna Selection

Jinglian He, Kaiqiang Yu, Yuanming Shi +3

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigu…

eess.SY202024 cited

On Spectral Properties of Signed Laplacians with Connections to Eventual Positivity

Wei Chen, Dan Wang, Ji Liu +5

Signed graphs have appeared in a broad variety of applications, ranging from social networks to biological networks, from distributed control and computation to power systems. In t…

cs.GR2020

Exemplar-based Layout Fine-tuning for Node-link Diagrams

Jiacheng Pan, Wei Chen, Xiaodong Zhao +6

We design and evaluate a novel layout fine-tuning technique for node-link diagrams that facilitates exemplar-based adjustment of a group of substructures in batching mode. The key…

cs.HC20202 cited

PassVizor: Toward Better Understanding of the Dynamics of Soccer Passes

Xiao Xie, Jiachen Wang, Hongye Liang +5

In soccer, passing is the most frequent interaction between players and plays a significant role in creating scoring chances. Experts are interested in analyzing players' passing b…

stat.ML20201 cited

New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design

Kartikeya Bhardwaj, Wei Chen, Radu Marculescu

In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy…