Tootfinder

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Hao Deng, Huijuan Zhan, Lijuan Hu, Hui-Hai Zhao, Ran Gao, Kannan Lu, Xizheng Ma, Zhijun Song, Fei Wang, Tenghui Wang, Feng Wu, Tian Xia, Gengyan Zhang, Xiaohang Zhang, Chunqing Deng
https://arxiv.org/abs/2508.15174

Identity and Quantify Various Dissipation Mechanisms of Josephson Junction in Superconducting Circuits
Pinpointing the dissipation mechanisms and evaluating their impacts to the performance of Josephson junction (JJ) are crucial for its application in superconducting circuits. In this work, we demonstrate the junction-embedded resonator (JER) as a platform which enables us to identify and quantify various dissipation mechanisms of JJ. JER is constructed by embedding JJ in the middle of an open-circuit, 1/2 λ transmission-line resonator. When the 1st and 2nd harmonics of JER are excited, JJ expe…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Javier Bisbal, Patrick Winter, Sebastian Jofre, Aaron Ponce, Sameer A. Ansari, Ramez Abdalla, Michael Markl, Oliver Welin Odeback, Sergio Uribe, Cristian Tejos, Julio Sotelo, Susanne Schnell, David Marlevi
https://arxiv.org/abs/2509.17726…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architecture…

System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework
Xiaoping Du
https://arxiv.org/abs/2509.16663 https://

System-Level Uncertainty Quantification with Multiple Machine Learning Models: A Theoretical Framework
ML models have errors when used for predictions. The errors are unknown but can be quantified by model uncertainty. When multiple ML models are trained using the same training points, their model uncertainties may be statistically dependent. In reality, model inputs are also random with input uncertainty. The effects of these types of uncertainty must be considered in decision-making and design. This study develops a theoretical framework that generates the joint distribution of multiple ML pre…

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Nishant Gadde
https://arxiv.org/abs/2507.15138

Adaptive Parameter Optimization in Gaussian Processes: A Comprehensive Study of Uncertainty Quantification and Dimensional Scaling
Gaussian Process (GP) models have also become extremely useful for optimization under uncertainty algorithms, especially where the objective functions are costly to compute. Yet, the more classical methods usually adopt strategies that, in certain circumstances, might be effective but not flexible to be applied to a wide range of problem terrains. This study aims to adapt parameter optimization in GP models and especially how uncertainty quantification can assist in the learning process. We inv…

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
Jun-Wei Zeng, Jerry Shen
https://arxiv.org/abs/2507.15862 https://

Quantifying Holistic Review: A Multi-Modal Approach to College Admissions Prediction
This paper introduces the Comprehensive Applicant Profile Score (CAPS), a novel multi-modal framework designed to quantitatively model and interpret holistic college admissions evaluations. CAPS decomposes applicant profiles into three interpretable components: academic performance (Standardized Academic Score, SAS), essay quality (Essay Quality Index, EQI), and extracurricular engagement (Extracurricular Impact Score, EIS). Leveraging transformer-based semantic embeddings, LLM scoring, and XGB…

Refining the Greisen Profile for Low-Energy Cosmic Gamma-Rays: Quantifying Deviations Across Altitudes and Zenith Angles
Constanza Valdivieso, B\'arbara Gutierrez, Nicol\'as Viaux M, Sebasti\'an Mendizabal, Raquel Pezoa R, Sebasti\'an Tapia
https://arxiv.org/abs/2509.16847

Refining the Greisen Profile for Low-Energy Cosmic Gamma-Rays: Quantifying Deviations Across Altitudes and Zenith Angles
This study refines the Greisen formalism by comparing the classical Greisen profile and a modified Greisen profile, which incorporates an empirical correction to the shower age parameter with zenith-angle dependence, aiming to better describe low-energy cascades against CORSIKA simulations of cosmic gamma-ray showers (20-800 GeV). Fittings across altitudes of 5000-5900 m and zenith angles from 0 to 40 degrees quantify deviations in particle numbers, showing that the modified profile yields devi…

Quantification of Information Flow by Dual Reporter System and Its Application to Bacterial Chemotaxis
Kento Nakamura, Hajime Fukuoka, Akihiko Ishijima, Tetsuya J. Kobayashi
https://arxiv.org/abs/2506.15957

Quantification of Information Flow by Dual Reporter System and Its Application to Bacterial Chemotaxis
Mutual information is a theoretically grounded metric for quantifying cellular signaling pathways. However, its measurement demands characterization of both input and output distributions, limiting practical applications. Here, we present alternative method that alleviates this requirement using dual reporter systems. By extending extrinsic-intrinsic noise analysis, we derive a mutual information estimator that eliminates the need to measure input distribution. We demonstrate our method by anal…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
G\'abor Lugosi, Marcos Matabuena
https://arxiv.org/abs/2507.15741 h…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample coverage guarantees and fast convergence rates of the oracle estimator. In heteroscedastic settings, we forgo these non-asymptotic guarantees to gain statistical efficiency, proposing a local $k$--nearest--neighbor method without conformal calibration that is ad…

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
Yijin Ni, Xiaoming Huo
https://arxiv.org/abs/2508.15084 https://

Kernel-based Equalized Odds: A Quantification of Accuracy-Fairness Trade-off in Fair Representation Learning
This paper introduces a novel kernel-based formulation of the Equalized Odds (EO) criterion, denoted as $EO_k$, for fair representation learning (FRL) in supervised settings. The central goal of FRL is to mitigate discrimination regarding a sensitive attribute $S$ while preserving prediction accuracy for the target variable $Y$. Our proposed criterion enables a rigorous and interpretable quantification of three core fairness objectives: independence (prediction $\hat{Y}$ is independent of $S$),…