pwc-archive/papers-with-abstracts huggingface dataset:
Further Exploration of Precise Binding Energies from Physics Informed Machine Learning and the Development of a Practical Ensemble Model
Authors: I. Bentley, J. Tedder, M. Gebran, A. Paul
Date: 2025-03-14 00:00:00
Tasks: Physics-informed machine learning

Sixteen new physics informed machine learning models have been trained on binding energy residuals from modern mass models that leverage shape parameters and other physical features. The models have been trained on a subset of AME 2012 data and have been verified with a subset of the AME 2020 data. Among the machine learning approaches tested in this work, the preferred approach is the least squares boosted ensemble of trees which appears to have a superior ability to both interpolate and extrapolate binding energy residuals. The machine learning models for four mass models created from the ensemble of trees approach have been combined to create a composite model called the Four Model Tree Ensemble (FMTE). The FMTE model predicts binding energy values from AME 2020 with a standard deviation of 76 keV and a mean deviation of 34 keV for all nuclei with N > 7 and Z > 7. A comparison with new mass measurements for 33 isotopes not included in AME 2012 or AME 2020 indicates that the FMTE performs better than all mass models that were tested.
FLASHμ: Fast Localizing And Sizing of Holographic Microparticles
Authors: Ayush Paliwal, Oliver Schlenczek, Birte Thiede, Manuel Santos Pereira, Katja Stieger, Eberhard Bodenschatz, Gholamhossein Bagheri, Alexander Ecker
Date: 2025-03-14 00:00:00
Tasks: object-detection, Object Detection

Reconstructing the 3D location and size of microparticles from diffraction images - holograms - is a computationally expensive inverse problem that has traditionally been solved using physics-based reconstruction methods. More recently, researchers have used machine learning methods to speed up the process. However, for small particles in large sample volumes the performance of these methods falls short of standard physics-based reconstruction methods. Here we designed a two-stage neural network architecture, FLASH$\mu$, to detect small particles (6-100$\mu$m) from holograms with large sample depths up to 20cm. Trained only on synthetic data with added physical noise, our method reliably detects particles of at least 9$\mu$m diameter in real holograms, comparable to the standard reconstruction-based approaches while operating on smaller crops, at quarter of the original resolution and providing roughly a 600-fold speedup. In addition to introducing a novel approach to a non-local object detection or signal demixing problem, our work could enable low-cost, real-time holographic imaging setups.
Configuration Design of Mechanical Assemblies using an Estimation of Distribution Algorithm and Constraint Programming
Authors: Hyunmin Cheong, Mehran Ebrahimi, Adrian Butscher, Francesco Iorio
Date: 2025-03-14 00:00:00

A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and black-box optimization problem. A novel method is developed to solve the problem by applying Bivariate Marginal Distribution Algorithm (BMDA) and constraint programming (CP). BMDA is a type of Estimation of Distribution Algorithm (EDA) that exploits the dependency knowledge learned between design variables without requiring too many fitness evaluations, which tend to be expensive for the current application. BMDA is extended with adaptive chi-square testing to identify dependencies and Gibbs sampling to generate new solutions. Also, repair operations based on CP are used to deal with infeasible solutions found during search. The method is applied to a vehicle suspension design problem and is found to be more effective in converging to good solutions than a genetic algorithm and other EDAs. These contributions are significant steps towards solving the difficult problem of configuration design in mechanical engineering with evolutionary computation.
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
Authors: Zhenyi Zhang, Yuhao Sun, Qiangwei Peng, Tiejun Li, Peijie Zhou
Date: 2025-03-14 00:00:00
Tasks: Time Series

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schr\"odinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.
DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks
Authors: Xingfei Wei, Qiankun Mo, Chi Chen, Mark Bathe, Rigoberto Hernandez
Date: 2025-03-13 00:00:00

Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that convolutional neural network (CNN) models can characterize DNA origami nanostructures employed in programmable self-assembling, which is important in many applications such as in biomedicine. Specifically, we benchmark the performance of 9 CNN models -- viz. AlexNet, GoogLeNet, VGG16, VGG19, ResNet18, ResNet34, ResNet50, ResNet101, and ResNet152 -- to characterize the ligation number of DNA origami nanostructures in transmission electron microscopy (TEM) images. We first pre-train CNN models using a large image dataset of 720 images from our coarse-grained (CG) molecular dynamics (MD) simulations. Then, we fine-tune the pre-trained CNN models, using a small experimental TEM dataset with 146 TEM images. All CNN models were found to have similar computational time requirements, while their model sizes and performances are different. We use 20 test MD images to demonstrate that among all of the pre-trained CNN models ResNet50 and VGG16 have the highest and second highest accuracies. Among the fine-tuned models, VGG16 was found to have the highest agreement on the test TEM images. Thus, we conclude that fine-tuned VGG16 models can quickly characterize the ligation number of nanostructures in large TEM images.
Fourier Neural Operator based surrogates for $CO_2$ storage in realistic geologies
Authors: Anirban Chandra, Marius Koch, Suraj Pawar, Aniruddha Panda, Kamyar Azizzadenesheli, Jeroen Snippe, Faruk O. Alpak, Farah Hariri, Clement Etienam, Pandu Devarakota, Anima Anandkumar, Detlef Hohl
Date: 2025-03-14 00:00:00
Tasks: Super-Resolution

This study aims to develop surrogate models for accelerating decision making processes associated with carbon capture and storage (CCS) technologies. Selection of sub-surface $CO_2$ storage sites often necessitates expensive and involved simulations of $CO_2$ flow fields. Here, we develop a Fourier Neural Operator (FNO) based model for real-time, high-resolution simulation of $CO_2$ plume migration. The model is trained on a comprehensive dataset generated from realistic subsurface parameters and offers $O(10^5)$ computational acceleration with minimal sacrifice in prediction accuracy. We also explore super-resolution experiments to improve the computational cost of training the FNO based models. Additionally, we present various strategies for improving the reliability of predictions from the model, which is crucial while assessing actual geological sites. This novel framework, based on NVIDIA's Modulus library, will allow rapid screening of sites for CCS. The discussed workflows and strategies can be applied to other energy solutions like geothermal reservoir modeling and hydrogen storage. Our work scales scientific machine learning models to realistic 3D systems that are more consistent with real-life subsurface aquifers/reservoirs, paving the way for next-generation digital twins for subsurface CCS applications.
Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
Authors: Anas Jnini, Harshinee Goordoyal, Sujal Dave, Flavio Vella, Katharine H. Fraser, Artem Korobenko
Date: 2025-03-14 00:00:00

The Physics-Constrained DeepONet (PC-DeepONet), an architecture that incorporates fundamental physics knowledge into the data-driven DeepONet model, is presented in this study. This methodology is exemplified through surrogate modeling of fluid dynamics over a curved backward-facing step, a benchmark problem in computational fluid dynamics. The model was trained on computational fluid dynamics data generated for a range of parameterized geometries. The PC-DeepONet was able to learn the mapping from the parameters describing the geometry to the velocity and pressure fields. While the DeepONet is solely data-driven, the PC-DeepONet imposes the divergence constraint from the continuity equation onto the network. The PC-DeepONet demonstrates higher accuracy than the data-driven baseline, especially when trained on sparse data. Both models attain convergence with a small dataset of 50 samples and require only 50 iterations for convergence, highlighting the efficiency of neural operators in learning the dynamics governed by partial differential equations.
Enhancing Deep Learning Based Structured Illumination Microscopy Reconstruction with Light Field Awareness
Authors: Long-Kun Shan, Ze-Hao Wang, Tong-Tian Weng, Xiang-Dong Chen, Fang-Wen Sun
Date: 2025-03-14 00:00:00

Structured illumination microscopy (SIM) is a pivotal technique for dynamic subcellular imaging in live cells. Conventional SIM reconstruction algorithms depend on accurately estimating the illumination pattern and can introduce artefacts when this estimation is imprecise. Although recent deep learning-based SIM reconstruction methods have improved speed, accuracy, and robustness, they often struggle with out-of-distribution data. To address this limitation, we propose an Awareness-of-Light-field SIM (AL-SIM) reconstruction approach that directly estimates the actual light field to correct for errors arising from data distribution shifts. Through comprehensive experiments on both simulated filament structures and live BSC1 cells, our method demonstrates a 7% reduction in the normalized root mean square error (NRMSE) and substantially lowers reconstruction artefacts. By minimizing these artefacts and improving overall accuracy, AL-SIM broadens the applicability of SIM for complex biological systems.
Financial Fraud Detection with Entropy Computing
Authors: Babak Emami, Wesley Dyk, David Haycraft, Carrie Spear, Lac Nguyen, Nicholas Chancellor
Date: 2025-03-14 00:00:00
Tasks: Fraud Detection

We introduce CVQBoost, a novel classification algorithm that leverages early hardware implementing Quantum Computing Inc's Entropy Quantum Computing (EQC) paradigm, Dirac-3 [Nguyen et. al. arXiv:2407.04512]. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely utilized ML method. Running on Dirac-3, CVQBoost demonstrates a significant runtime advantage over XGBoost, which we evaluate on high-performance hardware comprising up to 48 CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework. Our results show that CVQBoost maintains competitive accuracy (measured by AUC) while significantly reducing training time, particularly as dataset size and feature complexity increase. To assess scalability, we extend our study to large synthetic datasets ranging from 1M to 70M samples, demonstrating that CVQBoost on Dirac-3 is well-suited for large-scale classification tasks. These findings position CVQBoost as a promising alternative to gradient boosting methods, offering superior scalability and efficiency for high-dimensional ML applications such as fraud detection.
Nonlinear Forecast Error Variance Decompositions with Hermite Polynomials
Authors: Quinlan Lee
Date: 2025-03-14 00:00:00

A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial expansion to approximate the future trajectory of a nonlinear process. The orthogonality of Hermite polynomials under the Gaussian density facilitates the construction of the decomposition, providing a separation of shock effects by time horizon, by components of the structural innovation and by degree of nonlinearity. A link between the HFEVD and nonlinear Impulse Response Functions is established and distinguishes between marginal and interaction contributions of shocks. Simulation results from standard nonlinear models are provided as illustrations and an application to fiscal policy shocks is examined.
Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation
Authors: Yixiao Sun, Haitian Xie, Yuhang Zhang
Date: 2025-03-14 00:00:00
Tasks: Causal Inference

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with its own strengths and limitations. In this paper, we propose a novel methodology that integrates the advantages of both DiD and SC approaches. Our integrated approach provides a doubly robust identification strategy for causal effects in panel data with a group structure, identifying the average treatment effect on the treated (ATT) under either the parallel trends assumption or the group-level SC assumption. Building on this identification result, we develop a unified semiparametric framework for estimating the ATT. Notably, while the identification-robust moment function satisfies Neyman orthogonality under the parallel trends assumption, it does not under the SC assumption, leading to different asymptotic variances under these two identification strategies. To address this challenge, we propose a multiplier bootstrap method that consistently approximates the asymptotic distribution, regardless of which identification assumption holds. Furthermore, we extend our methodology to accommodate repeated cross-sectional data and staggered treatment designs. As an empirical application, we apply our method to evaluate the impact of the 2003 minimum wage increase in Alaska on family income.
On the numerical approximation of minimax regret rules via fictitious play
Authors: Patrik Guggenberger, Jiaqi Huang
Date: 2025-03-13 00:00:00

Finding numerical approximations to minimax regret treatment rules is of key interest. To do so when potential outcomes are in {0,1} we discretize the action space of nature and apply a variant of Robinson's (1951) algorithm for iterative solutions for finite two-person zero sum games. Our approach avoids the need to evaluate regret of each treatment rule in each iteration. When potential outcomes are in [0,1] we apply the so-called coarsening approach. We consider a policymaker choosing between two treatments after observing data with unequal sample sizes per treatment and the case of testing several innovations against the status quo.
Constructing an Instrument as a Function of Covariates
Authors: Moses Stewart
Date: 2025-03-13 00:00:00

Researchers often use instrumental variables (IV) models to investigate the causal relationship between an endogenous variable and an outcome while controlling for covariates. When an exogenous variable is unavailable to serve as the instrument for an endogenous treatment, a recurring empirical practice is to construct one from a nonlinear transformation of the covariates. We investigate how reliable these estimates are under mild forms of misspecification. Our main result shows that for instruments constructed from covariates, the IV estimand can be arbitrarily biased under mild forms of misspecification, even when imposing constant linear treatment effects. We perform a semi-synthetic exercise by calibrating data to alternative models proposed in the literature and estimating the average treatment effect. Our results show that IV specifications that use instruments constructed from covariates are non-robust to nonlinearity in the true structural function.
A New Design-Based Variance Estimator for Finely Stratified Experiments
Authors: Yuehao Bai, Xun Huang, Joseph P. Romano, Azeem M. Shaikh, Max Tabord-Meehan
Date: 2025-03-13 00:00:00

This paper considers the problem of design-based inference for the average treatment effect in finely stratified experiments. Here, by "design-based'' we mean that the only source of uncertainty stems from the randomness in treatment assignment; by "finely stratified'' we mean that units are stratified into groups of a fixed size according to baseline covariates and then, within each group, a fixed number of units are assigned uniformly at random to treatment and the remainder to control. In this setting we present a novel estimator of the variance of the difference-in-means based on pairing "adjacent" strata. Importantly, our estimator is well defined even in the challenging setting where there is exactly one treated or control unit per stratum. We prove that our estimator is upward-biased, and thus can be used for inference under mild restrictions on the finite population. We compare our estimator with some well-known estimators that have been proposed previously in this setting, and demonstrate that, while these estimators are also upward-biased, our estimator has smaller bias and therefore leads to more precise inferences whenever adjacent strata are sufficiently similar. To further understand when our estimator leads to more precise inferences, we introduce a framework motivated by a thought experiment in which the finite population is modeled as having been drawn once in an i.i.d. fashion from a well-behaved probability distribution. In this framework, we argue that our estimator dominates the others in terms of limiting bias and that these improvements are strict except under strong restrictions on the treatment effects. Finally, we illustrate the practical relevance of our theoretical results through a simulation study, which reveals that our estimator can in fact lead to substantially more precise inferences, especially when the quality of stratification is high.
Algorithms vs. Peers: Shaping Engagement with Novel Content
Authors: Shan Huang, Yi Ji, Leyu Lin
Date: 2025-03-14 00:00:00

The pervasive rise of digital platforms has reshaped how individuals engage with information, with algorithms and peer influence playing pivotal roles in these processes. This study investigates the effects of algorithmic curation and peer influence through social cues (e.g., peer endorsements) on engagement with novel content. Through a randomized field experiment on WeChat involving over 2.1 million users, we find that while peer-sharing exposes users to more novel content, algorithmic curation elicits significantly higher engagement with novel content than peer-sharing, even when social cues are present. Despite users' preference for redundant and less diverse content, both mechanisms mitigate this bias, with algorithms demonstrating a stronger positive effect than peer influence. These findings, though heterogeneous, are robust across demographic variations such as sex, age, and network size. Our results challenge concerns about "filter bubbles" and underscore the constructive role of algorithms in promoting engagement with non-redundant, diverse content.
Why do firms patent?
Authors: Gaetan de Rassenfosse
Date: 2025-03-14 00:00:00

Firms' decisions to patent innovations involve a complex evaluation of costs, benefits, and strategic considerations. This article explores the economic and practical factors that influence whether companies seek patent protection. It discusses explicit monetary costs--such as legal fees, filing, and international expenses--and non-monetary costs related to public disclosure. Benefits include the provision of exclusion rights that secure returns and protect competitive advantages. Additionally, firms use patents as strategic tools to block rivals, strengthen bargaining positions, signal innovation quality, and attract investment. The analysis also considers alternative protection strategies like secrecy, particularly for small enterprises or industries where disclosure is detrimental.
Scalable Global Solution Techniques for High-Dimensional Models in Dynare
Authors: Aryan Eftekhari, Michel Juillard, Normann Rion, Simon Scheidegger
Date: 2025-03-14 00:00:00
Tasks: Computational Efficiency

For over three decades, Dynare has been a cornerstone of dynamic stochastic modeling in economics, relying primarily on perturbation-based local solution methods. However, these techniques often falter in high-dimensional, non-linear models that demand more comprehensive approaches. This paper demonstrates that global solutions of economic models with substantial heterogeneity and frictions can be computed accurately and swiftly by augmenting Dynare with adaptive sparse grids (SGs) and high-dimensional model representation (HDMR). SGs mitigate the curse of dimensionality, as the number of grid points grows significantly slower than in traditional tensor-product Cartesian grids. Additionally, adaptivity focuses grid refinement on regions with steep gradients or non-differentiabilities, enhancing computational efficiency. Complementing SGs, HDMR tackles large state spaces by approximating policy functions with a hierarchical expansion of low-dimensional terms. Using a time iteration algorithm, we benchmark our approach on an international real business cycle model. Our results show that both SGs and HDMR alleviate the curse of dimensionality, enabling accurate solutions for at least 100-dimensional models on standard hardware in relatively short times. This advancement extends Dynare's capabilities beyond perturbation approaches, establishing a versatile platform for sophisticated non-linear models and paving the way for integrating the most recent global solution methods, such as those from machine learning.