Tootfinder

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Grounded GUI Understanding for Vision-Based Spatial Intelligent Agent: Exemplified by Extended Re...
Shuqing Li, Binchang Li, Yepang Liu, Cuiyun Gao, Jianping Zhang, Shing-Chi Cheung, Michael R. Lyu

Spatial Encoding of Flow Spaces for Intelligent SDN Applications
Abdur Rouf, Murat Yuksel
https://arxiv.org/abs/2509.16485 https://arxiv.org/pdf/2509.16485…

Spatial Encoding of Flow Spaces for Intelligent SDN Applications
Efficient encoding of network flow spaces while preserving spatial locality is essential for intelligent Software-Defined Networking (SDN) applications, particularly those employing reinforcement learning (RL) methods in a reactive manner. In this work, we introduce a spatially aware Bloom Filter-based approach to encode IP flow pairs, leveraging their inherent geographical locality. Through controlled experiments using IoT traffic data, we demonstrate that Bloom Filters effectively preserve sp…

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Rishab Parthasarathy
https://arxiv.org/abs/2509.11529 https://arxiv.org/pdf/2509.…

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Solving sparse systems of linear equations is a fundamental problem in the field of numerical methods, with applications spanning from circuit design to urban planning. These problems can have millions of constraints, such as when laying out transistors on a circuit, or trying to optimize traffic light timings, making fast sparse solvers extremely important. However, existing state-of-the-art software-level solutions for solving sparse linear systems, termed iterative solvers, are extremely ine…