pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers
Authors: Sompote Youwai, Tipok Kitkobsin, Sutat Leelataviwat, Pornkasem Jongpradist
Date: 2025-02-11 00:00:00

This study presents an explainable parallel transformer architecture for soil liquefaction prediction that integrates three distinct data streams: spectral seismic encoding, soil stratigraphy tokenization, and site-specific features. The architecture processes data from 165 case histories across 11 major earthquakes, employing Fast Fourier Transform for seismic waveform encoding and principles from large language models for soil layer tokenization. Interpretability is achieved through SHapley Additive exPlanations (SHAP), which decompose predictions into individual contributions from seismic characteristics, soil properties, and site conditions. The model achieves 93.75% prediction accuracy on cross-regional validation sets and demonstrates robust performance through sensitivity analysis of ground motion intensity and soil resistance parameters. Notably, validation against previously unseen ground motion data from the 2024 Noto Peninsula earthquake confirms the model's generalization capabilities and practical utility. Implementation as a publicly accessible web application enables rapid assessment of multiple sites simultaneously. This approach establishes a new framework in geotechnical deep learning where sophisticated multi-modal analysis meets practical engineering requirements through quantitative interpretation and accessible deployment.
Towards Automatic Identification of Missing Tissues using a Geometric-Learning Correspondence Model
Authors: Eliana M. Vasquez Osorio, Edward Henderson
Date: 2025-02-16 00:00:00
Tasks: Anatomy

Missing tissue presents a big challenge for dose mapping, e.g., in the reirradiation setting. We propose a pipeline to identify missing tissue on intra-patient structure meshes using a previously trained geometric-learning correspondence model. For our application, we relied on the prediction discrepancies between forward and backward correspondences of the input meshes, quantified using a correspondence-based Inverse Consistency Error (cICE). We optimised the threshold applied to cICE to identify missing points in a dataset of 35 simulated mandible resections. Our identified threshold, 5.5 mm, produced a balanced accuracy score of 0.883 in the training data, using an ensemble approach. This pipeline produced plausible results for a real case where ~25% of the mandible was removed after a surgical intervention. The pipeline, however, failed on a more extreme case where ~50% of the mandible was removed. This is the first time geometric-learning modelling is proposed to identify missing points in corresponding anatomy.
Exploiting network optimization stability for enhanced PET image denoising using deep image prior
Authors: Fumio Hashimoto, Kibo Ote, Yuya Onishi, Hideaki Tashima, Go Akamatsu, Yuma Iwao, Miwako Takahashi, Taiga Yamaya
Date: 2025-02-16 00:00:00
Tasks: Denoising, Diagnostic, Image Denoising

PET is affected by statistical noise due to constraints on tracer dose and scan duration, impacting both diagnostic performance and quantitative accuracy. While deep learning (DL)-based PET denoising methods have been used to improve image quality, they may introduce over-smoothing, compromising quantitative accuracy. We propose a method for making a DL solution more reliable and apply it to the conditional deep image prior (DIP). We introduce the idea of stability information in the optimization process of conditional DIP, enabling the identification of unstable regions within the network's optimization trajectory. Our method incorporates a stability map, which is derived from multiple intermediate outputs of moderate network at different optimization steps. The final denoised image is then obtained by computing linear combination of the DIP output and the original reconstructed image, weighted by the stability map. Our method effectively reduces noise while preserving small structure details in brain FDG images. Results demonstrated that our approach outperformed existing methods in peak-to-valley ratio and noise suppression across various low-dose levels. Region-of-interest analysis confirmed that the proposed method maintains quantitative accuracy without introducing under- or over-estimation. We applied our method to full-dose PET data to assess its impact on image quality. The results revealed that the proposed method significantly reduced background noise while preserving the peak-to-valley ratio at a level comparable to that of unfiltered full-dose PET images. The proposed method introduces a robust approach to DL-based PET denoising, enhancing its reliability and preserving quantitative accuracy. This strategy has the potential to advance performance in high-sensitivity PET scanners, demonstrating that DL can extend PET imaging capabilities beyond low-dose applications.
Accelerating Quantitative MRI using Subspace Multiscale Energy Model (SS-MuSE)
Authors: Yan Chen, Jyothi Rikhab Chand, Steven R. Kecskemeti, James H. Holmes, Mathews Jacob
Date: 2025-02-14 00:00:00
Tasks: Quantitative MRI

Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.
Dynamic-Computed Tomography Angiography for Cerebral Vessel Templates and Segmentation
Authors: Shrikanth Yadav, Jisoo Kim, Geoffrey Young, Lei Qin
Date: 2025-02-14 00:00:00
Tasks: Segmentation

Background: Computed Tomography Angiography (CTA) is crucial for cerebrovascular disease diagnosis. Dynamic CTA is a type of imaging that captures temporal information about the We aim to develop and evaluate two segmentation techniques to segment vessels directly on CTA images: (1) creating and registering population-averaged vessel atlases and (2) using deep learning (DL). Methods: We retrieved 4D-CT of the head from our institutional research database, with bone and soft tissue subtracted from post-contrast images. An Advanced Normalization Tools pipeline was used to create angiographic atlases from 25 patients. Then, atlas-driven ROIs were identified by a CT attenuation threshold to generate segmentation of the arteries and veins using non-linear registration. To create DL vessel segmentations, arterial and venous structures were segmented using the MRA vessel segmentation tool, iCafe, in 29 patients. These were then used to train a DL model, with bone-in CT images as input. Multiple phase images in the 4D-CT were used to increase the training and validation dataset. Both segmentation approaches were evaluated on a test 4D-CT dataset of 11 patients which were also processed by iCafe and validated by a neuroradiologist. Specifically, branch-wise segmentation accuracy was quantified with 20 labels for arteries and one for veins. DL outperformed the atlas-based segmentation models for arteries (average modified dice coefficient (amDC) 0.856 vs. 0.324) and veins (amDC 0.743 vs. 0.495) overall. For ICAs, vertebral and basilar arteries, DL and atlas -based segmentation had an amDC of 0.913 and 0.402, respectively. The amDC for MCA-M1, PCA-P1, and ACA-A1 segments were 0.932 and 0.474, respectively. Conclusion: Angiographic CT templates are developed for the first time in literature. Using 4D-CTA enables the use of tools like iCafe, lessening the burden of manual annotation.
How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning
Authors: Matt Landreman, Jong Youl Choi, Caio Alves, Prasanna Balaprakash, R. Michael Churchill, Rory Conlin, Gareth Roberg-Clark
Date: 2025-02-17 00:00:00
Tasks: Feature Importance, feature selection

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimized and randomly generated stellarator equilibria. At fixed gradients, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection, and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find many previously published proxies do correlate well with both the heat flux and stability boundary.
Multiscale autonomous forecasting of plasma systems' dynamics using neural networks
Authors: Farbod Faraji, Maryam Reza
Date: 2025-02-16 00:00:00

Plasma systems exhibit complex multiscale dynamics, resolving which poses significant challenges for conventional numerical simulations. Machine learning (ML) offers an alternative by learning data-driven representations of these dynamics. Yet existing ML time-stepping models suffer from error accumulation, instability, and limited long-term forecasting horizons. This paper demonstrates the application of a hierarchical multiscale neural network architecture for autonomous plasma forecasting. The framework integrates multiple neural networks trained across different temporal scales to capture both fine-scale and large-scale behaviors while mitigating compounding error in recursive evaluation. Fine-scale networks accurately resolve fast-evolving features, while coarse-scale networks provide broader temporal context, reducing the frequency of recursive updates and limiting the accumulation of small prediction errors over time. We first evaluate the method using canonical nonlinear dynamical systems and compare its performance against classical single-scale neural networks. The results demonstrate that single-scale neural networks experience rapid divergence due to recursive error accumulation, whereas the multiscale approach improves stability and extends prediction horizons. Next, our ML model is applied to two plasma configurations of high scientific and applied significance, demonstrating its ability to preserve spatial structures and capture multiscale plasma dynamics. By leveraging multiple time-stepping resolutions, the applied framework is shown to outperform conventional single-scale networks for the studied plasma test cases. The results of this work position the hierarchical multiscale neural network as a promising tool for efficient plasma forecasting and digital twin applications.
Towards identifying possible fault-tolerant advantage of quantum linear system algorithms in terms of space, time and energy
Authors: Yue Tu, Mark Dubynskyi, Mohammadhossein Mohammadisiahroudi, Ekaterina Riashchentceva, Jinglei Cheng, Dmitry Ryashchentsev, Tamás Terlaky, Junyu Liu
Date: 2025-02-16 00:00:00

Quantum computing, a prominent non-Von Neumann paradigm beyond Moore's law, can offer superpolynomial speedups for certain problems. Yet its advantages in efficiency for tasks like machine learning remain under investigation, and quantum noise complicates resource estimations and classical comparisons. We provide a detailed estimation of space, time, and energy resources for fault-tolerant superconducting devices running the Harrow-Hassidim-Lloyd (HHL) algorithm, a quantum linear system solver relevant to linear algebra and machine learning. Excluding memory and data transfer, possible quantum advantages over the classical conjugate gradient method could emerge at $N \approx 2^{33} \sim 2^{48}$ or even lower, requiring ${O}(10^5)$ physical qubits, ${O}(10^{12}\sim10^{13})$ Joules, and ${O}(10^6)$ seconds under surface code fault-tolerance with three types of magic state distillation (15-1, 116-12, 225-1). Key parameters include condition number, sparsity, and precision $\kappa, s\approx{O}(10\sim100)$, $\epsilon\sim0.01$, and physical error $10^{-5}$. Our resource estimator adjusts $N, \kappa, s, \epsilon$, providing a map of quantum-classical boundaries and revealing where a practical quantum advantage may arise. Our work quantitatively determine how advanced a fault-tolerant quantum computer should be to achieve possible, significant benefits on problems related to real-world.
A Fast Quantum Image Compression Algorithm based on Taylor Expansion
Authors: Vu Tuan Hai, Huynh Ho Thi Mong Trinh, Pham Hoai Luan
Date: 2025-02-15 00:00:00
Tasks: Image Compression

With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakness by using the advantage of qubits. In this study, we upgrade a quantum image compression algorithm within parameterized quantum circuits. Our approach encodes image data as unitary operator parameters and applies the quantum compilation algorithm to emulate the encryption process. By utilizing first-order Taylor expansion, we significantly reduce both the computational cost and loss, better than the previous version. Experimental results on benchmark images, including Lenna and Cameraman, show that our method achieves up to 86\% reduction in the number of iterations while maintaining a lower compression loss, better for high-resolution images. The results confirm that the proposed algorithm provides an efficient and scalable image compression mechanism, making it a promising candidate for future image processing applications.
Machine Learning for Phase Estimation in Satellite-to-Earth Quantum Communication
Authors: Nathan K Long, Robert Malaney, Kenneth J Grant
Date: 2025-02-14 00:00:00

A global continuous-variable quantum key distribution (CV-QKD) network can be established using a series of satellite-to-Earth channels. Increased performance in such a network is provided by performing coherent measurement of the optical quantum signals using a real local oscillator, calibrated locally by encoding known information on transmitted reference pulses and using signal phase error estimation algorithms. The speed and accuracy of the signal phase error estimation algorithm are vital to practical CV-QKD implementation. Our work provides a framework to analyze long short-term memory neural network (NN) architecture parameterization, with respect to the quantum Cram\'er-Rao uncertainty bound of the signal phase error estimation, with a focus on reducing the model complexity. More specifically, we demonstrate that signal phase error estimation can be achieved using a low-complexity NN architecture, without significantly sacrificing accuracy. Our results significantly improve the real-time performance of practical CV-QKD systems deployed over satellite-to-Earth channels, thereby contributing to the ongoing development of the Quantum Internet.
Causal Inference for Qualitative Outcomes
Authors: Riccardo Di Francesco, Giovanni Mellace
Date: 2025-02-17 00:00:00
Tasks: Causal Inference

Causal inference methods such as instrumental variables, regression discontinuity, and difference-in-differences are widely used to estimate treatment effects. However, their application to qualitative outcomes poses fundamental challenges, as standard causal estimands are ill-defined in this context. This paper highlights these issues and introduces an alternative framework that focuses on well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. We establish that standard identification assumptions are sufficient for identification and propose simple, intuitive estimation strategies that remain fully compatible with conventional econometric methods. To facilitate implementation, we provide an open-source R package, $\texttt{causalQual}$, which is publicly available on GitHub.
Maximal Inequalities for Separately Exchangeable Empirical Processes
Authors: Harold D. Chiang
Date: 2025-02-17 00:00:00

This paper derives new maximal inequalities for empirical processes associated with separately exchangeable random arrays. For fixed index dimension $K\ge 1$, we establish a global maximal inequality bounding the $q$-th moment ($q\in[1,\infty)$) of the supremum of these processes. We also obtain a refined local maximal inequality controlling the first absolute moment of the supremum. Both results are proved for a general pointwise measurable function class. Our approach uses a new technique partitioning the index set into transversal groups, decoupling dependencies and enabling more sophisticated higher moment bounds.
Policy Learning with Confidence
Authors: Victor Chernozhukov, Sokbae Lee, Adam M. Rosen, Liyang Sun
Date: 2025-02-15 00:00:00

This paper proposes a framework for selecting policies that maximize expected benefit in the presence of estimation uncertainty, by controlling for estimation risk and incorporating risk aversion. The proposed method explicitly balances the size of the estimated benefit against the uncertainty inherent in its estimation, ensuring that chosen policies meet a reporting guarantee, namely that the actual benefit of the implemented policy is guaranteed not to fall below the reported estimate with a pre-specified confidence level. This approach applies to a variety of settings, including the selection of policy rules that allocate individuals to treatments based on observed characteristics, using both experimental and non-experimental data; and the allocation of limited budgets among competing social programs; as well as many others. Across these applications, the framework offers a principled and robust method for making data-driven policy choices under uncertainty. In broader terms, it focuses on policies that are on the efficient decision frontier, describing policies that offer maximum estimated benefit for a given acceptable level of estimation risk.
Residualised Treatment Intensity and the Estimation of Average Partial Effects
Authors: Julius Schäper
Date: 2025-02-14 00:00:00
Tasks: LEMMA

This paper introduces R-OLS, an estimator for the average partial effect (APE) of a continuous treatment variable on an outcome variable in the presence of non-linear and non-additively separable confounding of unknown form. Identification of the APE is achieved by generalising Stein's Lemma (Stein, 1981), leveraging an exogenous error component in the treatment along with a flexible functional relationship between the treatment and the confounders. The identification results for R-OLS are used to characterize the properties of Double/Debiased Machine Learning (Chernozhukov et al., 2018), specifying the conditions under which the APE is estimated consistently. A novel decomposition of the ordinary least squares estimand provides intuition for these results. Monte Carlo simulations demonstrate that the proposed estimator outperforms existing methods, delivering accurate estimates of the true APE and exhibiting robustness to moderate violations of its underlying assumptions. The methodology is further illustrated through an empirical application to Fetzer (2019).
Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series
Authors: Yannick Hoga, Christian Schulz
Date: 2025-02-14 00:00:00
Tasks: quantile regression, regression, Time Series, valid

This paper proposes valid inference tools, based on self-normalization, in time series expected shortfall regressions and, as a corollary, also in quantile regressions. Extant methods for such time series regressions, based on a bootstrap or direct estimation of the long-run variance, are computationally more involved, require the choice of tuning parameters and have serious size distortions when the regression errors are strongly serially dependent. In contrast, our inference tools only require estimates of the (quantile, expected shortfall) regression parameters that are computed on an expanding window, and are correctly sized as we show in simulations. Two empirical applications to stock return predictability and to Growth-at-Risk demonstrate the practical usefulness of the developed inference tools.
High-dimensional censored MIDAS logistic regression for corporate survival forecasting
Authors: Wei Miao, Jad Beyhum, Jonas Striaukas, Ingrid Van Keilegom
Date: 2025-02-13 00:00:00
Tasks: regression

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors by employing a sparse-group penalty. We establish finite-sample bounds for the estimation error, accounting for censoring, the MIDAS approximation error, and heavy tails. The superior performance of the method is demonstrated through Monte Carlo simulations. Finally, we present an extensive application of our methodology to predict the financial distress of Chinese-listed firms. Our novel procedure is implemented in the R package 'Survivalml'.
Potato Potahto in the FAO-GAEZ Productivity Measures? Nonclassical Measurement Error with Multiple Proxies
Authors: Rafael Araujo, Vitor Possebom
Date: 2025-02-17 00:00:00

The FAO-GAEZ productivity data are widely used in Economics. However, the empirical literature rarely discusses measurement error. We use two proxies to derive novel bounds around the effect of agricultural productivity in a setting with nonclassical measurement error. These bounds rely on assumptions that are weaker than the ones imposed in empirical studies and exhaust the information contained in the first two moments of the data. We reevaluate three influential studies, documenting that measurement error matters and that the impact of agricultural productivity may be smaller than previously reported. Our methodology has broad applications in empirical research involving mismeasured variables.