pwc-archive/papers-with-abstracts huggingface dataset:
The OpenLAM Challenges
Authors: Anyang Peng, Xinzijian Liu, Ming-Yu Guo, Linfeng Zhang, Han Wang
Date: 2025-01-20 00:00:00
Tasks: valid

Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep Potential team has been actively pretraining LAMs and launched the OpenLAM Initiative to develop an open-source foundation model spanning the periodic table. A core objective is establishing comprehensive benchmarks for reliable LAM evaluation, addressing limitations in existing datasets. As a first step, the LAM Crystal Philately competition has collected over 19.8 million valid structures, including 1 million on the OpenLAM convex hull, driving advancements in generative modeling and materials science applications.
Reduced-order modeling and classification of hydrodynamic pattern formation in gravure printing
Authors: Pauline Rothmann-Brumm, Steven L. Brunton, Isabel Scherl
Date: 2025-01-24 00:00:00

Hydrodynamic pattern formation phenomena in printing and coating processes are still not fully understood. However, fundamental understanding is essential to achieve high-quality printed products and to tune printed patterns according to the needs of a specific application like printed electronics, graphical printing, or biomedical printing. The aim of the paper is to develop an automated pattern classification algorithm based on methods from supervised machine learning and reduced-order modeling. We use the HYPA-p dataset, a large image dataset of gravure-printed images, which shows various types of hydrodynamic pattern formation phenomena. It enables the correlation of printing process parameters and resulting printed patterns for the first time. 26880 images of the HYPA-p dataset have been labeled by a human observer as dot patterns, mixed patterns, or finger patterns; 864000 images (97%) are unlabeled. A singular value decomposition (SVD) is used to find the modes of the labeled images and to reduce the dimensionality of the full dataset by truncation and projection. Selected machine learning classification techniques are trained on the reduced-order data. We investigate the effect of several factors, including classifier choice, whether or not fast Fourier transform (FFT) is used to preprocess the labeled images, data balancing, and data normalization. The best performing model is a k-nearest neighbor (kNN) classifier trained on unbalanced, FFT-transformed data with a test error of 3%, which outperforms a human observer by 7%. Data balancing slightly increases the test error of the kNN-model to 5%, but also increases the recall of the mixed class from 90% to 94%. Finally, we demonstrate how the trained models can be used to predict the pattern class of unlabeled images and how the predictions can be correlated to the printing process parameters, in the form of regime maps.
A novel Trunk Branch-net PINN for flow and heat transfer prediction in porous medium
Authors: Haoyun Xing, Kaiyan Jin, Guice Yao, Jin Zhao, Dichu Xu, Dongsheng Wen
Date: 2025-01-21 00:00:00
Tasks: Transfer Learning

A novel Trunk-Branch (TB)-net physics-informed neural network (PINN) architecture is developed, which is a PINN-based method incorporating trunk and branch nets to capture both global and local features. The aim is to solve four main classes of problems: forward flow problem, forward heat transfer problem, inverse heat transfer problem, and transfer learning problem within the porous medium, which are notoriously complex that could not be handled by origin PINN. In the proposed TB-net PINN architecture, a Fully-connected Neural Network (FNN) is used as the trunk net, followed by separated FNNs as the branch nets with respect to outputs, and automatic differentiation is performed for partial derivatives of outputs with respect to inputs by considering various physical loss. The effectiveness and flexibility of the novel TB-net PINN architecture is demonstrated through a collection of forward problems, and transfer learning validates the feasibility of resource reuse. Combining with the superiority over traditional numerical methods in solving inverse problems, the proposed TB-net PINN shows its great potential for practical engineering applications.
5G Channel Models for Railway Use Cases at mmWave Band and the Path Towards Terahertz
Authors: Ke Guan, Juan Moreno Garcia-Lloygorri, Bo Ai, Cesar Briso-Rodriguez, Bile Peng, Danping He, Andrej Hrovat, Zhangdui Zhong, Thomas Kurner
Date: 2025-01-28 00:00:00

High-speed trains are one of the most relevant scenarios for the fifth-generation (5G) mobile communications and the "smart rail mobility" vision, where a high-data-rate wireless connectivity with up to several GHz bandwidths will be required. This is a strong motivation for the exploration of millimeter wave (mmWave) band. In this article, we identify the main challenges and make progress towards realistic 5G mmWave channel models for railway use cases. In order to cope with the challenge of including the railway features in the channel models, we define reference scenarios to help the parameterization of channel models for railway use at mmWave band. Simulations and the subsequent measurements used to validate the model reflect the detailed influence of railway objects and the accuracy of the simulations. Finally, we point out the future directions towards the full version of the smart rail mobility which will be powered by terahertz (THz) communications.
Image-Space Gridding for Nonrigid Motion-Corrected MR Image Reconstruction
Authors: Kwang Eun Jang, Mario O. Malavé, Dwight G. Nishimura
Date: 2025-01-28 00:00:00
Tasks: Image Reconstruction, Motion Estimation

Motion remains a major challenge in magnetic resonance (MR) imaging, particularly in free-breathing cardiac MR imaging, where data are acquired over multiple heartbeats at varying respiratory phases. We adopt a model-based approach for nonrigid motion correction, addressing two challenges: (a) motion representation and (b) motion estimation. For motion representation, we derive image-space gridding by adapting the nonuniform fast Fourier transform (NUFFT) to represent and compute nonrigid motion, which provides an exact forward-adjoint pair of linear operators. We then introduce nonrigid SENSE operators that incorporate nonrigid motion into the multi-coil MR acquisition model. For motion estimation, we employ both low-resolution 3D image-based navigators (iNAVs) and high-resolution 3D self-navigating image-based navigators (self-iNAVs). During each heartbeat, data are acquired along two types of non-Cartesian trajectories: a subset of a high-resolution trajectory that sparsely covers 3D k-space, followed by a full low-resolution trajectory. We reconstruct 3D iNAVs for each heartbeat using the full low-resolution data, which are then used to estimate bulk motion and identify the respiratory phase of each heartbeat. By combining data from multiple heartbeats within the same respiratory phase, we reconstruct high-resolution 3D self-iNAVs, allowing estimation of nonrigid respiratory motion. For each respiratory phase, we construct the nonrigid SENSE operator, reformulating the nonrigid motion-corrected reconstruction as a standard regularized inverse problem. In a preliminary study, the proposed method enhanced sharpness of the coronary arteries and improved image quality in non-cardiac regions, outperforming translational motion-corrected reconstruction.
CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting
Authors: Yin Shen, Zhaojie Fang, Ke Zhuang, Guanyu Zhou, Xiao Yu, Yucheng Zhao, Yuan Tian, Ruiquan Ge, Changmiao Wang, Xiaopeng Fan, Ahmed Elazab
Date: 2025-01-27 00:00:00
Tasks: Computed Tomography (CT)

Pulmonary nodules are an early sign of lung cancer, and detecting them early is vital for improving patient survival rates. Most current methods use only single Computed Tomography (CT) images to assess nodule malignancy. However, doctors typically make a comprehensive assessment in clinical practice by integrating follow-up CT scans with clinical data. To enhance this process, our study introduces a Cross-Modal Spatiotemporal Fusion Network, named CSF-Net, designed to predict the malignancy of pulmonary nodules using follow-up CT scans. This approach simulates the decision-making process of clinicians who combine follow-up imaging with clinical information. CSF-Net comprises three key components: spatial feature extraction module, temporal residual fusion module, and cross-modal attention fusion module. Together, these modules enable precise predictions of nodule malignancy. Additionally, we utilized the publicly available NLST dataset to screen and annotate the specific locations of pulmonary nodules and created a new dataset named NLST-cmst. Our experimental results on the NLST-cmst dataset demonstrate significant performance improvements, with an accuracy of 0.8974, a precision of 0.8235, an F1 score of 0.8750, an AUC of 0.9389, and a recall of 0.9333. These findings indicate that our multimodal spatiotemporal fusion approach, which combines follow-up data with clinical information, surpasses existing methods, underscoring its effectiveness in predicting nodule malignancy.
Towards reconstruction of Pulsed-wave Doppler signals from Non-invasive fetal ECG
Authors: Aman Verma, Deva Satya Sriram Chintapenta, Saikat Mujumder
Date: 2025-01-12 00:00:00

Fetal cardiac health monitoring with invasive methods have a limited viability because they can only be utilized during labor and are uncomfortable. On the other hand non-invasive fECG are adulterated with maternal ECG, and hence resulting in poor analysis. In contrast, Pulsed-wave Doppler (PwD) echocardiography generates high-quality signals representing fetal blood volume inflow-outflow. It also follows non-invasive signal acquisition. The only drawback is that it requires highly expensive setup. To address this aspect, we put forward a challenging research question - can we reconstruct PwD signals using non-invasive fetal ECG?
Comparative Analysis of Stochastic and Predictable Models in the HIV Epidemic Across Genders
Authors: Nuzhat Nuari Khan Rivu, Md Kamrujjaman, Shohel Ahmed
Date: 2025-01-28 00:00:00

This study conducts a comparative analysis of stochastic and deterministic models to better understand the dynamics of the HIV epidemic across genders. By incorporating gender-specific transmission probabilities and treatment uptake rates, the research addresses gaps in existing models that often overlook these critical factors. The introduction of gender-specific treatment, where only one gender receives treatment, allows for a detailed examination of its effects on both male and female populations. Two compartmental models, divided by gender, are analyzed in parallel to identify the parameters that most significantly impact the control of infected populations and the number of treated females. Stochastic methods, including the Euler, Runge-Kutta, and Non-Standard Finite Difference (SNSFD) approaches, demonstrate that stochastic models provide a more accurate and realistic portrayal of HIV transmission and progression compared to deterministic models. Key findings reveal that the stochastic Runge-Kutta method is particularly effective in capturing the epidemic's complex dynamics, such as subtle fluctuations in transmission and population changes. The study also emphasizes the crucial role of transmission probabilities and treatment rates in shaping the epidemic's trajectory, highlighting their importance for optimizing public health interventions. The research concludes that advanced stochastic modeling is essential for improving public health policies and responses, especially in resource-constrained settings.
Exploring the economic, social and environmental prospects for commercial natural annual grasslands by performing a sensitivity analysis on a multidisciplinary integrated model
Authors: Javier Ibáñez, Jaime Martínez-Valderrama, Joaquín Francisco Lavado Contador, Manuel Pulido Fernández
Date: 2025-01-28 00:00:00

This paper presents an integrated modelling assessment that estimated the sensitivities of five endogenous factors in commercial rangelands, i.e. number of active farmers, profits, stocking rate, standing herbage biomass, and soil erosion, to the same percentage variation in 70 factors, including economic and climate drivers. The assessment utilised a system dynamics model (107 equations) which represents an area of extensive private farms, its farmers, the main local markets on which they trade, and key ecosystem services involved. The assessment procedure consisted in analysing the behaviours of 288,000 variants of this system during 300 years, each under a different economic and climate scenario. Our key findings were as follows: 1) It is likely that at least annual grasslands will suffer environmental degradation in the future, and that such degradation will be primarily caused by climate change, not by the increasing demand for livestock products; 2) Private farming systems provide social and economic security to farmers against the effects of climate change, especially in a scenario of rising prices of animal products. However, this research will remain incomplete until its methods and results can be contrasted with other similar assessments.
Uniform Confidence Band for Marginal Treatment Effect Function
Authors: Toshiki Tsuda, Yanchun Jin, Ryo Okui
Date: 2025-01-29 00:00:00

This paper presents a method for constructing uniform confidence bands for the marginal treatment effect function. Our approach visualizes statistical uncertainty, facilitating inferences about the function's shape. We derive a Gaussian approximation for a local quadratic estimator, enabling computationally inexpensive construction of these bands. Monte Carlo simulations demonstrate that our bands provide the desired coverage and are less conservative than those based on the Gumbel approximation. An empirical illustration is included.
Demand Analysis under Price Rigidity and Endogenous Assortment: An Application to China's Tobacco Industry
Authors: Hui Liu, Yao Luo
Date: 2025-01-28 00:00:00

We observe nominal price rigidity in tobacco markets across China. The monopolistic seller responds by adjusting product assortments, which remain unobserved by the analyst. We develop and estimate a logit demand model that incorporates assortment discrimination and nominal price rigidity. We find that consumers are significantly more responsive to price changes than conventional models predict. Simulated tax increases reveal that neglecting the role of endogenous assortments results in underestimations of the decline in higher-tier product sales, incorrect directional predictions of lower-tier product sales, and overestimation of tax revenue by more than 50%. Finally, we extend our methodology to settings with competition and random coefficient models.
Bayesian Analyses of Structural Vector Autoregressions with Sign, Zero, and Narrative Restrictions Using the R Package bsvarSIGNs
Authors: Xiaolei Wang, Tomasz Woźniak
Date: 2025-01-28 00:00:00

The R package bsvarSIGNs implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions identified by sign, zero, and narrative restrictions. It offers fast and efficient estimation thanks to the deployment of frontier econometric and numerical techniques and algorithms written in C++. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors. The structural model can be identified by sign, zero, and narrative restrictions, including a novel solution, making it possible to use the three types of restrictions at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation. The package was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.
Philip G. Wright, directed acyclic graphs, and instrumental variables
Authors: Jaap H. Abbring, Victor Chernozhukov, Iván Fernández-Val
Date: 2025-01-26 00:00:00
Tasks: Causal Inference, counterfactual, Econometrics

Wright (1928) deals with demand and supply of oils and butter. In Appendix B of this book, Philip Wright made several fundamental contributions to causal inference. He introduced a structural equation model of supply and demand, established the identification of supply and demand elasticities via the method of moments and directed acyclical graphs, developed empirical methods for estimating demand elasticities using weather conditions as instruments, and proposed methods for counterfactual analysis of the welfare effect of imposing tariffs and taxes. Moreover, he took all of these methods to data. These ideas were far ahead, and much more profound than, any contemporary theoretical and empirical developments on causal inference in statistics or econometrics. This editorial aims to present P. Wright's work in a more modern framework, in a lecture note format that can be useful for teaching and linking to contemporary research.
Towards post-growth policymaking: Barriers and enablers for wellbeing economy and Doughnut economics government initiatives
Authors: Laura Angresius, Milena Buchs, Alessia Greselin, Daniel W. O'Neill
Date: 2025-01-29 00:00:00

Providing wellbeing for all while safeguarding planetary boundaries may require governments to pursue post-growth policies. To understand how post-growth policymaking can be fostered, we examine wellbeing economy and Doughnut economics government initiatives across governance scales in Europe, New Zealand, and Canada. To identify political dimensions of barriers and enablers as well as priorities for future action, we apply a framework that distinguishes polity, politics, and policy to analyze the data. We find that the main barriers are polity-related contextual factors while the main enablers are the political agency of key individuals and positive framings of post-growth visions. Despite the focus of the post-growth literature on policies, our results suggest they have limited transformative potential in the current system. The overarching economic growth paradigm severely limits the initiatives' scope of action. Highly motivated individuals and high-level political support are more essential in driving initiatives than pressure from civil society. Practitioners who promote growth-critical perspectives often face tensions: they need to appeal to broad stakeholder groups while avoiding cooptation. Overall, our findings suggest that further structural changes are required to support post-growth initiatives: a greater presence of post-growth approaches in economics education and the media is required for post-growth discourses to become more accepted, and to build understandings of how growth dependencies can be addressed.
Monetary-Fiscal Interaction and the Liquidity of Government Debt
Authors: Cristiano Cantore, Edoardo Leonardi
Date: 2025-01-29 00:00:00

How does the monetary and fiscal policy mix alter households' saving incentives? To answer these questions, we build a heterogenous agents New Keynesian model where three different types of agents can save in assets with different liquidity profiles to insure against idiosyncratic risk. Policy mixes affect saving incentives differently according to their effect on the liquidity premium -- the return difference between less liquid assets and public debt. We derive an intuitive analytical expression linking the liquidity premium with consumption differentials amongst different types of agents. This underscores the presence of a transmission mechanism through which the interaction of monetary and fiscal policy shapes economic stability via its effect on the portfolio choice of private agents. We call it the 'self-insurance demand channel', which moves the liquidity premium in the opposite direction to the standard 'policy-driven supply channel'. Our analysis thus reveals the presence of two competing forces driving the liquidity premium. We show that the relative strength of the two is tightly linked to the policy mix in place and the type of business cycle shock hitting the economy. This implies that to stabilize the economy, monetary policy should consider the impact of the 'self-insurance' on the liquidity premium.
Will artificial intelligence accelerate or delay the race between nuclear energy technology budgeting and net-zero emissions?
Authors: Danish, Adnan Khan
Date: 2025-01-29 00:00:00
Tasks: quantile regression

This study explores the impact of nuclear energy technology budgeting and artificial intelligence on carbon dioxide (CO2) emissions in 20 OECD economies. Unlike previous research that relied on conventional panel techniques, we utilize the Method of Moment Quantile Regression panel data estimation techniques. This approach provides quantile-specific insights while addressing issues of endogeneity and heteroscedasticity, resulting in a more nuanced and robust understanding of complex relationships. A novel aspect of this research work is introducing the moderating effect of artificial intelligence on the relationship between nuclear energy and CO2 emissions. The results found that the direct impact of artificial intelligence on CO2 emissions is significant, while the effect of nuclear energy technology budgeting is not. Additionally, artificial intelligence moderates the relationship between nuclear energy technology budgeting and CO2 emissions, aiding nuclear energy in reducing carbon emissions across OECD countries. Our findings indicate that transitioning to a low-carbon future is achievable by replacing fossil fuel energy sources with increased integration of artificial intelligence to promote nuclear energy technologies. This study demonstrates that energy innovations can serve as effective climate-resilience strategies to mitigate the impacts of climate change.
Seasonal Influenza Vaccination Hesitancy and Digital Literacy: Evidence from the European countries
Authors: Martina Celidoni, Nita Handastya, Guglielmo Weber, Nancy Zambon
Date: 2025-01-28 00:00:00

This study documents the relationship between computer skills/digital literacy and influenza vaccination take-up among older adults in Europe during and after the COVID-19 pandemic. Using data from the Survey of Health, Aging and Retirement in Europe, we find a positive partial association between influenza vaccination take-up and two indicators of computer skills/digital literacy, self-assessed pre-pandemic computer skills and having used a computer at work in any pre-pandemic job. We do not estimate significant behavioural changes for individuals with better computer skills that may have been driven by spillover effects from the pandemic experience.