most citedTalos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.OS2026

Behind a Simple Read: Understanding Work and Waiting in the Linux I/O Stack

Yang Shen, Kai Lu, Yong Dong +5

A simple read interface provides uniform semantics but unpredictable performance. Using synchronous large-buffer Linux reads, we decompose the buffered-read path into work volume,…

quant-ph2026

Real-time decoding of quantum error correction codes using high-performance computing

Lingling Lao, Qiang Wang, Yuanqi Liu +10

Quantum error correction (QEC) is indispensable for building scalable fault-tolerant quantum computers. Effective QEC demands stringent real-time decoding: the decoder must process…

cs.OS2026

MARS: Multi-stage Accelerated Read Stack for Large-buffer Buffered Reads

Yang Shen, Kai Lu, Min Xie +3

Large-buffer reads increasingly connect data-intensive applications to high-speed storage. They amortize system-call overhead and create a larger in-kernel window for organizing pa…

cs.DC2026

Rethinking Burst Buffer Optimization: Enabling Layout Heterogeneity via Hybrid Analysis and LLM Guidance

Yuhan Cai, Huijun Wu, Zhuo Tang +6

Burst buffers (BBs) are essential for mitigating I/O bottlenecks in modern HPC systems. However, existing BB file systems often suffer from structural performance degradation due t…

cs.LG2024★ 1 cited

Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs

Duanyu Li, Huijun Wu, Min Xie +3

Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs a…