Tootfinder

Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems
Akshay Thakur, Sawan Kumar, Matthew Zahr, Souvik Chakraborty
https://arxiv.org/abs/2508.09623

Scalable h-adaptive probabilistic solver for time-independent and time-dependent systems
Solving partial differential equations (PDEs) within the framework of probabilistic numerics offers a principled approach to quantifying epistemic uncertainty arising from discretization. By leveraging Gaussian process regression and imposing the governing PDE as a constraint at a finite set of collocation points, probabilistic numerics delivers mesh-free solutions at arbitrary locations. However, the high computational cost, which scales cubically with the number of collocation points, remains…

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Stavros Orfanoudakis, Frans Oliehoek, Peter Palesnky, Pedro P. Vergara
https://arxiv.org/abs/2510.12335

Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging often disregard physical grid constraints or have limited performance for complex large-scale tasks, limiting their scalability and real-world applicability. This paper introduces a physics-informed (PI) RL algorithm that integrates a differentiable power flow m…

Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Jeffrey N. Clark, Elena Fillola, Nawid Keshtmand, Raul Santos-Rodriguez, Matthew Rigby
https://arxiv.org/abs/2510.05751

Uncertainty assessment in satellite-based greenhouse gas emissions estimates using emulated atmospheric transport
Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advances in satellite observations, provide new opportunities to evaluate emissions at continental and global scales. However, transport models used in these methods remain a key source of uncertainty: they are computationally expensive to run at scale, and their uncertainty is difficult to characterise. Artificial intelli…

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Marzieh Ajirak, Anand Ravishankar, Petar M. Djuric
https://arxiv.org/abs/2509.05877

Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights
Uncertainty Quantification (UQ) is essential in probabilistic machine learning models, particularly for assessing the reliability of predictions. In this paper, we present a systematic framework for estimating both epistemic and aleatoric uncertainty in probabilistic models. We focus on Gaussian Process Latent Variable Models and employ scalable Random Fourier Features-based Gaussian Processes to approximate predictive distributions efficiently. We derive a theoretical formulation for UQ, propo…

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Jyotirmoy V. Deshmukh, Lars Lindemann, Bistra Dilkina
https://arxiv.org/abs/2508.07515

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Autonomous systems must solve motion planning problems subject to increasingly complex, time-sensitive, and uncertain missions. These problems often involve high-level task specifications, such as temporal logic or chance constraints, which require solving large-scale Mixed-Integer Linear Programs (MILPs). However, existing MILP-based planning methods suffer from high computational cost and limited scalability, hindering their real-time applicability. We propose to use a neuro-symbolic approach…

Scaling Truth: The Confidence Paradox in AI Fact-Checking
Ihsan A. Qazi, Zohaib Khan, Abdullah Ghani, Agha A. Raza, Zafar A. Qazi, Wassay Sajjad, Ayesha Ali, Asher Javaid, Muhammad Abdullah Sohail, Abdul H. Azeemi
https://arxiv.org/abs/2509.08803

Scaling Truth: The Confidence Paradox in AI Fact-Checking
The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet their effectiveness across global contexts remains uncertain. We systematically evaluate nine established LLMs across multiple categories (open/closed-source, multiple sizes, diverse architectures, reasoning-based) using 5,000 claims previously assessed by 174 professional fact-checking organizations across 47 languages…

Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
Fabio Morreale, Wiebke Hutiri, Joan Serr\`a, Alice Xiang, Yuki Mitsufuji
https://arxiv.org/abs/2510.08062

Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems
The rise of AI-generated music is diluting royalty pools and revealing structural flaws in existing remuneration frameworks, challenging the well-established artist compensation systems in the music industry. Existing compensation solutions, such as piecemeal licensing agreements, lack scalability and technical rigour, while current data attribution mechanisms provide only uncertain estimates and are rarely implemented in practice. This paper introduces a framework for a generative music infras…

A Cryogenic Dielectric Antenna for Wireless Sensing and Interfacing Outside the 10 K Environment
Ingrid Torres, Alex Krasnok
https://arxiv.org/abs/2509.06199 https://

A Cryogenic Dielectric Antenna for Wireless Sensing and Interfacing Outside the 10 K Environment
The performance and scalability of cryogenic microwave systems, particularly for quantum processors, are fundamentally limited by the thermal stability and loss of their constituent dielectric materials. While mixed titanate ceramics like MgTiO3-CaTiO3 (MCT) and (Zr,Sn)TiO4 (ZST) are primary candidates, their comparative performance as radiative antennas in the deep-cryogenic regime has remained uncharacterized. Here we present a side-by-side comparison of MCT and ZST operated as dielectric res…

Fast and Scalable Game-Theoretic Trajectory Planning with Intentional Uncertainties
Zhenmin Huang, Yusen Xie, Benshan Ma, Shaojie Shen, Jun Ma
https://arxiv.org/abs/2507.12174

Fast and Scalable Game-Theoretic Trajectory Planning with Intentional Uncertainties
Trajectory planning involving multi-agent interactions has been a long-standing challenge in the field of robotics, primarily burdened by the inherent yet intricate interactions among agents. While game-theoretic methods are widely acknowledged for their effectiveness in managing multi-agent interactions, significant impediments persist when it comes to accommodating the intentional uncertainties of agents. In the context of intentional uncertainties, the heavy computational burdens associated …

DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction
Kyle Naddeo, Nikolas Koutsoubis, Rahul Krish, Ghulam Rasool, Nidhal Bouaynaya, Tony OSullivan, Raj Krish
https://arxiv.org/abs/2507.23736

DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction
Access to medical imaging and associated text data has the potential to drive major advances in healthcare research and patient outcomes. However, the presence of Protected Health Information (PHI) and Personally Identifiable Information (PII) in Digital Imaging and Communications in Medicine (DICOM) files presents a significant barrier to the ethical and secure sharing of imaging datasets. This paper presents a hybrid de-identification framework developed by Impact Business Information Solutio…