Tootfinder

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Rafael Ferreira da Silva, Milad Abolhasani, Dionysios A. Antonopoulos, Laura Biven, Ryan Coffee, Ian T. Foster, Leslie Hamilton, Shantenu Jha, Theresa Mayer, Benjamin Mintz, Robert G. Moore, Salahudin Nimer, Noah Paulson, Woong Shin, Frederic Suter, Mitra Taheri, Michela Taufer, Newell R. Washburn

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery
Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institu…

Secure API-Driven Research Automation to Accelerate Scientific Discovery
Tyler J. Skluzacek, Paul Bryant, A. J. Ruckman, Daniel Rosendo, Suzanne Prentice, Michael J. Brim, Ryan Adamson, Sarp Oral, Mallikarjun Shankar, Rafael Ferreira da Silva
https://arxiv.org/abs/2506.11950

Secure API-Driven Research Automation to Accelerate Scientific Discovery
The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M revolutionizes programmatic access to high performance computing (HPC) while maintaining uncompromising security. This framework allows intelligent agents and experimental facilities to dyn…

QISCIT: A validated concept inventory assessment for quantum information science
Kelley Durkin, Manshuo Lin, Michael H. Kolodrubetz, Ryan P. McMahan
https://arxiv.org/abs/2506.17122

QISCIT: A validated concept inventory assessment for quantum information science
Quantum information science (QIS) is a critical interdisciplinary field that requires a well-educated workforce in the near future. Numerous researchers and educators have been actively investigating how to best educate and prepare such a workforce. An open issue has been the lack of a validated tool to asses QIS understanding without requiring college-level math. In this paper, we present the systematic development and content validation of a new assessment instrument called the Quantum Inform…

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Ryan Barker
https://arxiv.org/abs/2506.09878 https://

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Virtualising the Radio Access Network (RAN) is widely touted as the corner-stone of affordable 5G and a prerequisite for AI-native 6G. Yet current discourse often isolates spectrum policy, cloud engineering and organisational readiness into silos. This paper delivers an integrated analysis that spans science, technology, business strategy and culture. I first review spectrum-auction economics and show-via a comparative study of T-Mobile US and Verizon-that mid-band contiguity leveraged through …

Machine Learning in Acoustics: A Review and Open-Source Repository
Ryan A. McCarthy, You Zhang, Samuel A. Verburg, William F. Jenkins, Peter Gerstoft
https://arxiv.org/abs/2507.04419

Machine Learning in Acoustics: A Review and Open-Source Repository
Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics, including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We h…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Ryan DeWolfe, Fran\c{c}ois Th\'eberge
https://arxiv.org/abs/2506.03105 ht…

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering
Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph…