2025 Fall
The seminar of this semester is organized by Shibo Zeng and Yongle Xie, and co-organized by the graduate student union in the School of Mathematical Sciences at Fudan. This section is partially sponsored by Shanghai Key Laboratory for Contemporary Applied Mathematics.
2025-09-18 16:10:00 - 17:00:00 @ Rm 1801, Guanghua East Tower
[poster]
- Title:
Sharp Estimates for Optimal Multistage Group Partition Testing
- Speaker: Guojiang Shao (Fudan University)
- Advisor: Qi Zhang (Fudan University)
Abstract: Click to expand
In multistage group testing, the tests within the same stage
are considered nonadaptive, while those conducted across
different stages are adaptive. Especially, when the pools
within the same stage are disjoint, meaning that
the entire set is divided into several disjoint subgroups,
it is referred to as a multistage group partition testing problem,
denoted as the $(n, d, s)$ problem, where $n$, $d$,
and $s$ represent the total number of items, defectives, and stages respectively.
This paper presents exact solutions for the $(n,1,s)$ and $(n,d,2)$ problems for the first time.
Furthermore, we develop a general dynamic programming framework for the $(n,d,s)$ problem,
which allows us to derive the sharp estimation of upper and lower bounds.
2025-09-25 16:10:00 - 17:00:00 @ Rm 1801, Guanghua East Tower
[poster]
- Title:
Reinforcement Learning for Heterogeneous DAG Scheduling with Weighted Cross-Attention
- Speaker: Ruisong Zhou (Peking University)
- Advisor: Zaiwen Wen (Peking University)
Abstract: Click to expand
Efficient scheduling of directed acyclic graphs (DAGs) in
heterogeneous environments is challenging due to diverse resource capacities
and intricate dependencies. In practice, scalability across environments
with varying resource pools, task types, and other settings,
alongside rapid schedule generation, complicates these challenges. We
propose WeCAN, an end-to-end reinforcement learning framework excelling
in heterogeneous DAG scheduling featuring task-resource compatibility.
WeCAN rapidly generates schedules through single-pass network
inference. Leveraging the weighted cross-attention layer, WeCAN
utilizes all available environment information while preserving scalability
across diverse heterogeneous environments. Moreover, we introduce
a criterion to analyze the optimality gap inherent in list scheduling based
methods, revealing barriers preventing these methods from consistently
finding optimal solutions. The skip action introduced in our framework
addresses this gap. Our approach delivers robust performance and scalability,
outperforming state-of-the-art methods across diverse datasets.
Past Presentations
