Tootfinder

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
Martin J. O'Connor, Marcos Martinez-Romero, Attila L. Egyedi, Mete U. Akdogan, Michael V. Dorf, Mark A. Musen
https://arxiv.org/abs/2508.00859

Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor
High-quality, "rich" metadata are essential for making research data findable, interoperable, and reusable. The Center for Expanded Data Annotation and Retrieval (CEDAR) has long addressed this need by providing tools to design machine-actionable metadata templates that encode community standards in a computable form. To make these capabilities more accessible within real-world research workflows, we have developed the CEDAR Embeddable Editor (CEE)-a lightweight, interoperable Web Component tha…

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Luis Felipe Strano Moraes, Ignacio Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
https://arxiv.org/abs/2506.00294

Applying Vision Transformers on Spectral Analysis of Astronomical Objects
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key task…

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
Mohammed Baharoon, Luyang Luo, Michael Moritz, Abhinav Kumar, Sung Eun Kim, Xiaoman Zhang, Miao Zhu, Mahmoud Hussain Alabbad, Maha Sbayel Alhazmi, Neel P. Mistry, Kent Ryan Kleinschmidt, Brady Chrisler, Sathvik Suryadevara, Sri Sai Dinesh Jaliparthi, Noah Michael Prudlo, Mark David Marino, Jeremy Palacio, Rithvik Akula, Hong-Yu Zhou, Ibrahim Ethem Hamamci, Scott J. Adams, Hassan Rayhan AlOmaish, P…

ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports
We present ReXGroundingCT, the first publicly available dataset to link free-text radiology findings with pixel-level segmentations in 3D chest CT scans that is manually annotated. While prior datasets have relied on structured labels or predefined categories, ReXGroundingCT captures the full expressiveness of clinical language represented in free text and grounds it to spatially localized 3D segmentation annotations in volumetric imaging. This addresses a critical gap in medical AI: the abilit…

HD 143006: Interferometric Confirmation of Misaligned Protoplanetary Disc with CHARA/MIRCX and VLTI/PIONIER
I. Codron, S. Kraus, J. D. Monnier, S. Marino, C. L. Davies, N. Anugu, T. Gardner, N. Ibrahim, C. Lanthermann, J-B Le Bouquin
https://arxiv.org/abs/2506.19668

HD 143006: Interferometric Confirmation of Misaligned Protoplanetary Disc with CHARA/MIRCX and VLTI/PIONIER
The outer regions of the protoplanetary disc surrounding the T Tauri star HD 143006 show rings, dust asymmetries and shadows. Whilst rings and dust asymmetries can arise from companions and other mechanisms, shadows and misaligned discs in particular are typically attributed to the presence of misaligned planets or stellar-mass companions. To understand the mechanisms that drive these traits, the innermost regions of discs need to be studied. Using CHARA/MIRCX and VLTI/PIONIER, we observed the …

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Ding Wang, Mark D\'iaz, Charvi Rastogi, Aida Davani, Vinodkumar Prabhakaran, Pushkar Mishra, Roma Patel, Alicia Parrish, Zoe Ashwood, Michela Paganini, Tian Huey Teh, Verena Rieser, Lora Aroyo
https://

"Just a strange pic": Evaluating 'safety' in GenAI Image safety annotation tasks from diverse annotators' perspectives
Understanding what constitutes safety in AI-generated content is complex. While developers often rely on predefined taxonomies, real-world safety judgments also involve personal, social, and cultural perceptions of harm. This paper examines how annotators evaluate the safety of AI-generated images, focusing on the qualitative reasoning behind their judgments. Analyzing 5,372 open-ended comments, we find that annotators consistently invoke moral, emotional, and contextual reasoning that extends …

Probing Solar Polar Regions
Yuanyong Deng, Hui Tian, Jie Jiang, Shuhong Yang, Hao Li, Robert Cameron, Laurent Gizon, Louise Harra, Robert F. Wimmer-Schweingruber, Fr\'ed\'eric Auch\`ere, Xianyong Bai, Luis Bellot Rubio, Linjie Chen, Pengfei Chen, Lakshmi Pradeep Chitta, Jackie Davies, Fabio Favata, Li Feng, Xueshang Feng, Weiqun Gan, Don Hassler, Jiansen He, Junfeng Hou, Zhenyong Hou, Chunlan Jin, Wenya Li, Jiaben Lin, Dibyendu Nandy, Vaibhav Pant, Marco Romoli, Taro Sakao, Kri…

Probing Solar Polar Regions
The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind, ultimately being vital in controlling solar activities and driving space weather. Despite numerous efforts to explore these regions, to date no imaging observations of the Sun's poles have been achieved from vantage points out of the ecliptic plane, leaving their behavior and evolution poorly understood. This observation …

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
Benjamin Coriat, Eric Benhamou
https://arxiv.org/abs/2507.18560

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after t…

Multi-scale radiative-transfer model of the protoplanetary disc DoAr 44
Marco Souza de Joode, Miroslav Bro\v{z}
https://arxiv.org/abs/2507.18185 https://ar…

Multi-scale radiative-transfer model of the protoplanetary disc DoAr 44
We aim to construct a comprehensive global multi-scale kinematic equilibrium radiative-transfer model for the pre-transitional disc of DoAr 44 (Haro 1-16, V2062 Oph) in the Ophiuchus star-forming region. This model integrates diverse observational datasets to describe the system, spanning from the accretion region to the outer disc. Our analysis utilises a large set of observational data, including ALMA continuum complex visibilities, VLTI/GRAVITY continuum squared visibilities, closure phase…