Tootfinder

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
Lizuo Liu, Lu Zhang, Anne Gelb
https://arxiv.org/abs/2507.01795

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this d…

Residual Income Valuation and Stock Returns: Evidence from a Value-to-Price Investment Strategy
Ahmad Haboub, Aris Kartsaklas, Vasilis Sarafidis
https://arxiv.org/abs/2506.00206

Residual Income Valuation and Stock Returns: Evidence from a Value-to-Price Investment Strategy
We hypothesize that portfolio sorts based on the V/P ratio generate excess returns and consist of companies that are undervalued for prolonged periods. Results, for the US market show that high V/P portfolios outperform low V/P portfolios across horizons extending from one to three years. The V/P ratio is positively correlated to future stock returns after controlling for firm characteristics, which are well known risk proxies. Findings also indicate that profitability and investment add explan…

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Qi Li, Shaheer U. Saeed, Yuliang Huang, Mingyuan Luo, Zhongnuo Yan, Jiongquan Chen, Xin Yang, Dong Ni, Nektarios Winter, Phuc Nguyen, Lucas Steinberger, Caelan Haney, Yuan Zhao, Mingjie Jiang, Bowen Ren, SiYeoul Lee, Seonho Kim, MinKyung Seo, MinWoo Kim, Yimeng Dou, Zhiwei Zhang, Yin Li, Tomy Varghese, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, Yipeng Hu

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems, offering a low-cost, portable, and widely deployable alternative for volumetric imaging. However, it presents significant challenges, including accurate inter-frame motion estimation, minimisation of drift accumulation over long sequences, and generalisability across scanning protocols. The TUS-REC2024 Challenge was established to ben…

A Fast and Accurate 3-D Reconstruction Algorithm for Near-Range Microwave Imaging with Handheld Synthetic Aperture Radar
Lei Wang, Xianxun Yao, Tiancheng Song, Guolin Sun
https://arxiv.org/abs/2506.23568

A Fast and Accurate 3-D Reconstruction Algorithm for Near-Range Microwave Imaging with Handheld Synthetic Aperture Radar
The design of image reconstruction algorithms for near-range handheld synthetic aperture radar (SAR) systems has gained increasing popularity due to the promising performance of portable millimeter-wave (MMW) imaging devices in various application fields. Time domain imaging algorithms including the backprojection algorithm (BPA) and the Kirchhoff migration algorithm (KMA) are widely adopted due to their direct applicability to arbitrary scan trajectories. However, they suffer from time complex…

Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Licai Sun, Xingxun Jiang, Haoyu Chen, Yante Li, Zheng Lian, Biu Liu, Yuan Zong, Wenming Zheng, Jukka M. Lepp\"anen, Guoying Zhao
https://arxiv.org/abs/2507.21015

Learning Transferable Facial Emotion Representations from Large-Scale Semantically Rich Captions
Current facial emotion recognition systems are predominately trained to predict a fixed set of predefined categories or abstract dimensional values. This constrained form of supervision hinders generalization and applicability, as it reduces the rich and nuanced spectrum of emotions into oversimplified labels or scales. In contrast, natural language provides a more flexible, expressive, and interpretable way to represent emotions, offering a much broader source of supervision. Yet, leveraging s…

Detailed Microwave Continuum Spectra from Bright Protoplanetary Disks in Taurus
Caleb Painter, Sean M. Andrews, Claire J. Chandler, Takahiro Ueda, David J. Wilner, Feng Long, Enrique Macias, Carlos Carrasco-Gonzalez, Chia-Ying Chung, Hauyu Baobab Liu, Tilman Birnstiel, A. Meredith Hughes
https://arxiv.org/abs/2507.21268

Detailed Microwave Continuum Spectra from Bright Protoplanetary Disks in Taurus
We present new observations that densely sample the microwave (4-360 GHz) continuum spectra from eight young systems in the Taurus region. Multi-component, empirical model prescriptions were used to disentangle the contributions from their dust disks and other emission mechanisms. We found partially optically thick, free-free emission in all these systems, with positive spectral indices (median $α_{\rm c} \approx 1$ at 10 GHz) and contributing 5-50% of the 43 GHz fluxes. There is no evidence f…

Quantum confinement effect in Sb thin films
Anuradha Wijesinghe, Yongxi Ou, Anjali Rathore, Chandima Edirisinghe, Pradip Adhikari, An-Hsi Chen, Dustin Gilbert, Anthony Richardella, Nitin Samarth, Joon Sue Lee
https://arxiv.org/abs/2507.23014

Quantum confinement effect in Sb thin films
Antimony (Sb), an element with strong spin-orbit coupling, is predicted to undergo a topological phase transition from a topological semimetal to a topological insulator as its dimensionality approaches the two-dimensional limit, driven by the quantum confinement effect. In this study, we investigate this transition in Sb thin films grown by molecular beam epitaxy, employing electrical transport measurements and angle-resolved photoemission spectroscopy (ARPES). Electrical transport measurement…

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
Chloe S. H. Hong, Keith J. Lee, Caitlin T. Mueller
https://arxiv.org/abs/2507.22804

Deep reinforcement learning for efficient exploration of combinatorial structural design spaces
This paper proposes a reinforcement learning framework for performance-driven structural design that combines bottom-up design generation with learned strategies to efficiently search large combinatorial design spaces. Motivated by the limitations of conventional top-down approaches such as optimization, the framework instead models structures as compositions of predefined elements, aligning form finding with practical constraints like constructability and component reuse. With the formulation …

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…

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 …