Tootfinder

Eliciting User Requirements for AI-Enhanced Learning Environments using a Participatory Approach
Bibeg Limbu, Irene-Angelica Chounta, Vilma Sukacke, Andromachi Filippidi, Chara Spyropoulou, Marianna Anagnostopoulou, Eleftheria Tsourlidaki, Nikos Karacapilidis
https://arxiv.org/abs/2507.21088

Eliciting User Requirements for AI-Enhanced Learning Environments using a Participatory Approach
This paper explores the needs \& expectations of educational stakeholders for AI (Artificial Intelligence)-enhanced learning environments. Data was collected following two-phased participatory workshops. The first workshop outlined stakeholders' profiles in terms of technical and pedagogical characteristics. The qualitative data collected was analysed using deductive thematic analysis with Activity Theory, explicating the user needs. The second workshop articulated expectations related to the i…

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
Yutong Du, Zicheng Liu, Bazargul Matkerim, Changyou Li, Yali Zong, Bo Qi, Jingwei Kou
https://arxiv.org/abs/2507.16321

Physics-Driven Neural Network for Solving Electromagnetic Inverse Scattering Problems
In recent years, deep learning-based methods have been proposed for solving inverse scattering problems (ISPs), but most of them heavily rely on data and suffer from limited generalization capabilities. In this paper, a new solving scheme is proposed where the solution is iteratively updated following the updating of the physics-driven neural network (PDNN), the hyperparameters of which are optimized by minimizing the loss function which incorporates the constraints from the collected scattered…

Congestion-Aware Path Selection for Load Balancing in AI Clusters
Erfan Nosrati, Majid Ghaderi
https://arxiv.org/abs/2506.08132 https://

Congestion-Aware Path Selection for Load Balancing in AI Clusters
Fast training of large machine learning models requires distributed training on AI clusters consisting of thousands of GPUs. The efficiency of distributed training crucially depends on the efficiency of the network interconnecting GPUs in the cluster. These networks are commonly built using RDMA following a Clos-like datacenter topology. To efficiently utilize the network bandwidth, load balancing is employed to distribute traffic across multiple redundant paths. While there exists numerous tec…