Tootfinder

Imprints of gravitational-wave polarizations on projected tidal tensor in three dimensions
Yusuke Mikura, Teppei Okumura, Misao Sasaki
https://arxiv.org/abs/2507.23302 https://

Imprints of gravitational-wave polarizations on projected tidal tensor in three dimensions
Gravitational waves (GWs) distort galaxy shapes through the tidal effect, offering a novel avenue to probe the nature of gravity. In this paper, we investigate how extra GW polarizations beyond those predicted by general relativity imprint observable signatures on galaxy shapes. Since galaxy shapes are measured as two-dimensional images projected onto the celestial sphere, we present three-dimensional statistical quantities of the projected tidal tensor sourced by the tensor perturbation. We sh…

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
Richard Sserujongi, Daniel Ogenrwot, Nicholas Niwamanya, Noah Nsimbe, Martin Bbaale, Benjamin Ssempala, Noble Mutabazi, Raja Fidel Wabinyai, Deo Okure, Engineer Bainomugisha
https://arxiv.org/abs/2508.14451

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings
The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper presents the design, implementation, and evaluation of the AirQo data pipeline: a modular, cloud-native Extract-Transform-Load (ETL) system engineered to support both real-time and batch processing of heterogeneous air quality data across urban deployments in Afri…

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara
https://arxiv.org/abs/2506.15468

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interacti…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
Ajay Shankar, Keisuke Okumura, Amanda Prorok
https://arxiv.org/abs/2507.11464 https:/…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots in…

Quantifying the Economic Impact of 2025 ICE Raids on California's Agricultural Industry: A Case Study of Oxnard
Xinyu Li
https://arxiv.org/abs/2508.03787 https://

Quantifying the Economic Impact of 2025 ICE Raids on California's Agricultural Industry: A Case Study of Oxnard
In 2025, intensified Immigration and Customs Enforcement (ICE) raids in Oxnard, California, disrupted the state's \$49 billion agricultural industry, a critical supplier of 75% of U.S. fruits and nuts and one-third of its vegetables. This paper quantifies the economic consequences of these raids on labor markets, crop production, and food prices using econometric modeling. We estimate a 20-40% reduction in the agricultural workforce, leading to \$3-7 billion in crop losses and a 5-12% increase …