7 citations · 9 across the 13 of their papers we have counts for
14 papers
Synergistic Learning in Domain Adaptation with a Known Link: A Minimax Understanding
Pengcheng Xiang, Ling Zhou, Yuhong Yang
When can combining source and target data yield much better performance than using either alone? From a minimax viewpoint, writing for the minimax risk from source a…
PhysX-CoT: Structured Physical Reasoning from a Single Image to Simulation-Ready 3D Assets
Jie Huang, Xiaohe Li, Jiahao Li +6
Simulation-ready 3D assets are central to robotics and embodied AI. Generating them from a single image is usually framed as a vision-language model that emits a serialized asset f…
Lingjing: A Simulation Testbed for Multi-Agent Embodied Tasks in Open-Ended Cities
Xiaohe Li, Yiru Wang, Junhao Fan +6
Urban embodied intelligence requires coordination among heterogeneous agents (e.g., UAVs, ground robots, and autonomous vehicles) in dynamic cities. Simulators therefore provide a…
Efficient Cloud-Edge-Device Query Execution Based on Collaborative Scan Operator
Chunyu Zhao, Hongzhi Wang, Kaixin Zhang +7
In cloud-edge-device (CED) collaborative query (CQ) processing, by leveraging CED collaboration, the advantages of both cloud computing and edge resources can be fully integrated.…
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception
Xiaohe Li, Haohua Wu, Jiahao Li +5
The rapid increase in remote sensing satellites has led to the emergence of distributed space-based observation systems. However, existing distributed remote sensing models often r…
DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
Kaixin Zhang, Hongzhi Wang, Ziqi Li +4
Research on learned cardinality estimation has made significant progress in recent years. However, existing methods still face distinct challenges that hinder their practical deplo…
CONCERTO: Complex Query Execution Mechanism-Aware Learned Cost Estimation
Kaixin Zhang, Hongzhi Wang, Kunkai Gu +4
With the growing demand for massive data analysis, many DBMSs have adopted complex underlying query execution mechanisms, including vectorized operators, parallel execution, and dy…
Updateable Data-Driven Cardinality Estimator with Bounded Q-error
Yingze Li, Xianglong Liu, Hongzhi Wang +2
Modern Cardinality Estimators struggle with data updates. This research tackles this challenge within single-table. We introduce ICE, an Index-based Cardinality Estimator, the firs…
QCFE: An efficient Feature engineering for query cost estimation
Yu Yan, Hongzhi Wang, Junfang Huang +5
Query cost estimation is a classical task for database management. Recently, researchers apply the AI-driven model to implement query cost estimation for achieving high accuracy. H…
Duet: efficient and scalable hybriD neUral rElation undersTanding
Kaixin Zhang, Hongzhi Wang, Yabin Lu +4
Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches have faced the workload drift pr…
TENSILE: A Tensor granularity dynamic GPU memory scheduling method toward multiple dynamic workloads system
Kaixin Zhang, Hongzhi Wang, Han Hu +4
Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usual…
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results
Chunnan Wang, Kaixin Zhang, Hongzhi Wang +1
In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN model…
ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence
Hongzhi Wang, Bozhou Chen, Yueyang Xu +2
In this demo, we present ConsciousControlFlow(CCF), a prototype system to demonstrate conscious Artificial Intelligence (AI). The system is based on the computational model for con…
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
Bozhou Chen, Kaixin Zhang, Longshen Ou +3
Machine learning algorithms have made remarkable achievements in the field of artificial intelligence. However, most machine learning algorithms are sensitive to the hyper-paramete…