Tootfinder

At the CTIF workshop in Brussels on copyright infrastructure, a cooperative project of the Finnish and the Estonian governments. I will talk about licensing linked data for trust in media

Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains
Yifu Gao, Runze Li, Efi Foufoula-Georgiou, Jasper A. Vrugt
https://arxiv.org/abs/2506.13939

Functional data decomposition reveals unexpectedly strong soil moisture-precipitation coupling over the Great Plains
Soil moisture-precipitation coupling (SMPC) plays a critical role in Earth's water and energy cycles but remains difficult to quantify due to synoptic-scale variability and the complex interplay of land-atmosphere processes. Here, we apply high-dimensional model representation (HDMR) to functionally decompose the structural, correlative, and cooperative contributions of key land-atmosphere variables to precipitation. Benchmark tests confirm that HDMR overcomes limitations of commonly used corre…

Confidence Boosts Trust-Based Resilience in Cooperative Multi-Robot Systems
Luca Ballotta, \'Aron V\'ek\'assy, Stephanie Gil, Michal Yemini
https://arxiv.org/abs/2506.08807

Confidence Boosts Trust-Based Resilience in Cooperative Multi-Robot Systems
Wireless communication-based multi-robot systems open the door to cyberattacks that can disrupt safety and performance of collaborative robots. The physical channel supporting inter-robot communication offers an attractive opportunity to decouple the detection of malicious robots from task-relevant data exchange between legitimate robots. Yet, trustworthiness indications coming from physical channels are uncertain and must be handled with this in mind. In this paper, we propose a resilient prot…

Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance
Zhiqing Luo, Yi Wang, Yingying He, Wei Wang
https://arxiv.org/abs/2506.00837

Improving Multi-Vehicle Perception Fusion with Millimeter-Wave Radar Assistance
Cooperative perception enables vehicles to share sensor readings and has become a new paradigm to improve driving safety, where the key enabling technology for realizing this vision is to real-time and accurately align and fuse the perceptions. Recent advances to align the views rely on high-density LiDAR data or fine-grained image feature representations, which however fail to meet the requirements of accuracy, real-time, and adaptability for autonomous driving. To this end, we present MMatch,…

Digital Twin-Empowered Cooperative Autonomous Car-sharing Services: Proof-of-Concept
Kazuma Nonomura, Kui Wang, Zongdian Li, Tao Yu, Kei Sakaguchi
https://arxiv.org/abs/2504.20542