pwc-archive/papers-with-abstracts huggingface dataset:
GeLLMO: Generalizing Large Language Models for Multi-property Molecule Optimization
Authors: Vishal Dey, Xiao Hu, Xia Ning
Date: 2025-02-19 00:00:00
Tasks: Zero-shot Generalization

Despite recent advancements, most computational methods for molecule optimization are constrained to single- or double-property optimization tasks and suffer from poor scalability and generalizability to novel optimization tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable out-of-domain generalizability to novel tasks. To demonstrate LLMs' potential for molecule optimization, we introduce MuMOInstruct, the first high-quality instruction-tuning dataset specifically focused on complex multi-property molecule optimization tasks. Leveraging MuMOInstruct, we develop GeLLMOs, a series of instruction-tuned LLMs for molecule optimization. Extensive evaluations across 5 in-domain and 5 out-of-domain tasks demonstrate that GeLLMOs consistently outperform state-of-the-art baselines. GeLLMOs also exhibit outstanding zero-shot generalization to unseen tasks, significantly outperforming powerful closed-source LLMs. Such strong generalizability demonstrates the tremendous potential of GeLLMOs as foundational models for molecule optimization, thereby tackling novel optimization tasks without resource-intensive retraining. MuMOInstruct, models, and code are accessible through https://github.com/ninglab/GeLLMO.
The impact of conformer quality on learned representations of molecular conformer ensembles
Authors: Keir Adams, Connor W. Coley
Date: 2025-02-18 00:00:00
Tasks: Representation Learning

Training machine learning models to predict properties of molecular conformer ensembles is an increasingly popular strategy to accelerate the conformational analysis of drug-like small molecules, reactive organic substrates, and homogeneous catalysts. For high-throughput analyses especially, trained surrogate models can help circumvent traditional approaches to conformational analysis that rely on expensive conformer searches and geometry optimizations. Here, we question how the performance of surrogate models for predicting 3D conformer-dependent properties (of a single, active conformer) is affected by the quality of the 3D conformers used as their input. How well do lower-quality conformers inform the prediction of properties of higher-quality conformers? Does the fidelity of geometry optimization matter when encoding random conformers? For models that encode sets of conformers, how does the presence of the active conformer that induces the target property affect model accuracy? How do predictions from a surrogate model compare to estimating the properties from cheap ensembles themselves? We explore these questions in the context of predicting Sterimol parameters of conformer ensembles optimized with density functional theory. Although answers will be case-specific, our analyses provide a valuable perspective on 3D representation learning models and raise practical considerations regarding when conformer quality matters.
Application of machine learning algorithm in temperature field reconstruction
Authors: Qianyu He, Huaiwei Sun, Yubo Li, Zhiwen You, Qiming Zheng, Yinghan Huang, Sipeng Zhu, Fengyu Wang
Date: 2025-02-18 00:00:00

This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures, aiming to estimate and reconstruct the temperature field using limited and noisy local measurement data. Due to complex measurement environments and technical limitations, obtaining complete temperature information for reservoirs is highly challenging. Therefore, accurately reconstructing the temperature field from a small number of local data points has become a critical scientific issue. To address this, the study employs Proper Orthogonal Decomposition (POD) and sparse representation methods to reconstruct the temperature field based on temperature data from a limited number of local measurement points. The results indicate that satisfactory reconstruction can be achieved when the number of POD basis functions is set to 2 and the number of measurement points is 10. Under different water intake depths, the reconstruction errors of both POD and sparse representation methods remain stable at around 0.15, fully validating the effectiveness of these methods in reconstructing the temperature field based on limited local temperature data. Additionally, the study further explores the distribution characteristics of reconstruction errors for POD and sparse representation methods under different water level intervals, analyzing the optimal measurement point layout scheme and potential limitations of the reconstruction methods in this case. This research not only effectively reduces measurement costs and computational resource consumption but also provides a new technical approach for reservoir temperature analysis, holding significant theoretical and practical importance.
Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review
Authors: Zhang Ying, Wen Congcong, Sornette Didier, Zhan Chengxiang
Date: 2025-02-10 00:00:00
Tasks: Earthquake prediction

Earthquake forecasting remains a significant scientific challenge, with current methods falling short of achieving the performance necessary for meaningful societal benefits. Traditional models, primarily based on past seismicity and geomechanical data, struggle to capture the complexity of seismic patterns and often overlook valuable non-seismic precursors such as geophysical, geochemical, and atmospheric anomalies. The integration of such diverse data sources into forecasting models, combined with advancements in AI technologies, offers a promising path forward. AI methods, particularly deep learning, excel at processing complex, large-scale datasets, identifying subtle patterns, and handling multidimensional relationships, making them well-suited for overcoming the limitations of conventional approaches. This review highlights the importance of combining AI with geophysical knowledge to create robust, physics-informed forecasting models. It explores current AI methods, input data types, loss functions, and practical considerations for model development, offering guidance to both geophysicists and AI researchers. While many AI-based studies oversimplify earthquake prediction, neglecting critical features such as data imbalance and spatio-temporal clustering, the integration of specialized geophysical insights into AI models can address these shortcomings. We emphasize the importance of interdisciplinary collaboration, urging geophysicists to experiment with AI architectures thoughtfully and encouraging AI experts to deepen their understanding of seismology. By bridging these disciplines, we can develop more accurate, reliable, and societally impactful earthquake forecasting tools.
Pericoronary adipose tissue attenuation as a predictor of functional severity of coronary stenosis
Authors: Marta Pillitteri, Guido Nannini, Simone Saitta, Luca Mariani, Riccardo Maragna, Andrea Baggiano, Gianluca Pontone, Alberto Redaelli
Date: 2025-02-19 00:00:00

Objective: This study aims to evaluate the functional significance of coronary stenosis by analyzing low-level radiomic features of the pericoronary adipose tissue (PCAT) surrounding the lesions, which are indicative of its inflammation status. Methods: A dataset of 72 patients who underwent coronary computed tomography angiography (CCTA) was analyzed, with 3D segmentation and computational fluid dynamics (CFD) simulations from a prior study. Centerlines of the main epicardial branches were automatically extracted, and lesions identified using Gaussian kernel regression to estimate healthy branch caliber. PCAT features were computed per vessel following guideline recommendations and per lesion within a region extending radially for two vessel radii. Features like fat volume and mean attenuation (FAI) were analyzed for their relationship with CFD-derived hemodynamic biomarkers, such as fractional flow reserve (FFR) and wall shear stress (WSS). These features also informed a machine learning (ML) model for classifying potentially ischemic lesions. Results: PCAT exhibited, on average, higher attenuation in the presence of hemodynamically significant lesions (i.e., FFR < 0.80), although this difference was of limited statistical significance. The ML classifier, trained on PCAT features, successfully distinguished potentially ischemic lesions, yielding average accuracy of 0.84. Conclusion: PCAT attenuation is correlated with the functional status of coronary stenosis and can be used to inform ML models for predicting potential ischemia. Significance: PCAT features, readily available from CCTA, can be used to predict the hemodynamic characteristics of a lesion without the need for an invasive FFR examination.
Quantitative First-Pass Perfusion CMR: from technical principles to clinical practice
Authors: Catarina N Carvalho, Andreia Gaspar, Carlos Real, Carlos Galán-Arriola, Elisa Moya-Sáez, Rosa-María Menchón-Lara, Javier Sanchez, Carlos Alberola-López, Rita G Nunes, Borja Ibáñez, Teresa M Correia
Date: 2025-02-18 00:00:00
Tasks: Image Reconstruction

Myocardial perfusion cardiovascular magnetic resonance (pCMR) using first-pass contrast-enhanced imaging could play an important role in the detection of epicardial and microvascular coronary artery disease. Recently, the emergence of quantitative pCMR has provided a more reliable and observer-independent analysis compared to visual interpretation of dynamic images. This review aims to cover the basics of quantitative pCMR, from acquisition protocols, its use in preclinical and clinical studies, image reconstruction and motion handling, to automated quantitative pCMR pipelines. It also offers an overview of emerging tools in the field, including artificial intelligence-based methods.
Causal Interpretations in Observational Studies: The Role of Sociocultural Backgrounds and Team Dynamics
Authors: Jun Wang, Bei Yu
Date: 2025-02-04 00:00:00
Tasks: Causal Inference

The prevalence of drawing causal conclusions from observational studies has raised concerns about potential exaggeration in science communication. While some believe causal language should only apply to randomized controlled trials, others argue that rigorous methods can justify causal claims in observational studies. Ideally, causal language should align with the strength of the evidence. However, through the analysis of over 80,000 observational study abstracts using computational linguistic and regression methods, we found that causal language is more frequently used by less experienced authors, smaller research teams, male last authors, and authors from countries with higher uncertainty avoidance indices. These findings suggest that the use of causal language may be influenced by external factors such as the sociocultural backgrounds of authors and the dynamics of research collaboration. This newly identified link deepens our understanding of how such factors help shape scientific conclusions in causal inference and science communication.
GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits
Authors: Ahmad Alomari, Sathish A. P. Kumar
Date: 2025-02-19 00:00:00
Tasks: State Estimation

This study proposes a GPA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The GPA consists of two parts: the Quantum Policy Evaluation (QPE) and the Quantum Policy Improvement (QPI). The QPE performs phase estimation to generate the search space, while the QPI utilizes Grover search and amplitude amplification techniques to efficiently identify an optimal policy that generates optimal QSCs. The GPA generates QSCs by selecting sequences of gates that maximize the Quantum Fisher Information (QFI) while minimizing the number of gates. The QSCs generated by the GPA are capable of producing entangled quantum states, specifically the squeezed states. High QFI indicates increased sensitivity to parameter changes, making the circuit useful for quantum state estimation and control tasks. Evaluation of the GPA on a QSC that consists of two qubits and a sequence of R_x, R_y, and S gates demonstrates its efficiency in generating optimal QSCs with a QFI of 1. Compared to existing quantum agents, the GPA achieves higher QFI with fewer gates, demonstrating a more efficient and scalable approach to the design of QSCs. This work illustrates the potential computational power of quantum agents for solving quantum physics problems
Solving the encoding bottleneck: of the HHL algorithm, by the HHL algorithm
Authors: Guang Ping He
Date: 2025-02-19 00:00:00

The Harrow-Hassidim-Lloyd (HHL) algorithm offers exponential speedup for solving the quantum linear-system problem. But some caveats for the speedup could be hard to met. One of the difficulties is the encoding bottleneck, i.e., the efficient preparation of the initial quantum state. To prepare an arbitrary $N$-dimensional state exactly, existing state-preparation approaches generally require a runtime of $O(N)$, which will ruin the speedup of the HHL algorithm. Here we show that the states can be prepared approximately with a runtime of $O(poly(\log N))$ by employing a slightly modified version of the HHL algorithm itself. Thus, applying this approach to prepare the initial state of the original HHL algorithm can preserve the exponential speedup advantage. It can also serve as a standalone solution for other applications demanding fast state preparation.
Benchmarking MedMNIST dataset on real quantum hardware
Authors: Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr
Date: 2025-02-18 00:00:00
Tasks: Benchmarking, image-classification, Image Classification, Medical Image Classification, Quantum Machine Learning

Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. Our methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling, and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.
Large Language Models Can Help Mitigate Barren Plateaus
Authors: Jun Zhuang, Chaowen Guan
Date: 2025-02-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In the era of noisy intermediate-scale quantum (NISQ) computing, Quantum Neural Networks (QNNs) have emerged as a promising approach for various applications, yet their training is often hindered by barren plateaus (BPs), where gradient variance vanishes exponentially as the model size increases. To address this challenge, we propose a new Large Language Model (LLM)-driven search framework, AdaInit, that iteratively searches for optimal initial parameters of QNNs to maximize gradient variance and therefore mitigate BPs. Unlike conventional one-time initialization methods, AdaInit dynamically refines QNN's initialization using LLMs with adaptive prompting. Theoretical analysis of the Expected Improvement (EI) proves a supremum for the search, ensuring this process can eventually identify the optimal initial parameter of the QNN. Extensive experiments across four public datasets demonstrate that AdaInit significantly enhances QNN's trainability compared to classic initialization methods, validating its effectiveness in mitigating BPs.
Locally Robust Policy Learning: Inequality, Inequality of Opportunity and Intergenerational Mobility
Authors: Joël Terschuur
Date: 2025-02-19 00:00:00

Policy makers need to decide whether to treat or not to treat heterogeneous individuals. The optimal treatment choice depends on the welfare function that the policy maker has in mind and it is referred to as the policy learning problem. I study a general setting for policy learning with semiparametric Social Welfare Functions (SWFs) that can be estimated by locally robust/orthogonal moments based on U-statistics. This rich class of SWFs substantially expands the setting in Athey and Wager (2021) and accommodates a wider range of distributional preferences. Three main applications of the general theory motivate the paper: (i) Inequality aware SWFs, (ii) Inequality of Opportunity aware SWFs and (iii) Intergenerational Mobility SWFs. I use the Panel Study of Income Dynamics (PSID) to assess the effect of attending preschool on adult earnings and estimate optimal policy rules based on parental years of education and parental income.
The Risk-Neutral Equivalent Pricing of Model-Uncertainty
Authors: Ken Kangda Wren
Date: 2025-02-19 00:00:00
Tasks: model

Existing approaches to asset-pricing under model-uncertainty adapt classical utility-maximization frameworks and seek theoretical comprehensiveness. We move toward practice by considering binary model-risks and by emphasizing 'constraints' over 'preference'. This decomposes viable economic asset-pricing into that of model and non-model risks separately, leading to a unique and convenient model-risk pricing formula. Its parameter, a dynamically conserved constant of model-risk inference, allows an integrated representation of ex-ante risk-pricing and bias such that their ex-post impacts are disentangled via well-known anomalies, Momentum and Low-Risk, whose risk-reward patterns acquire a fresh significance: peak-reward reveals ex-ante risk-premia, and peak-location, bias.
Tensor dynamic conditional correlation model: A new way to pursuit "Holy Grail of investing"
Authors: Cheng Yu, Zhoufan Zhu, Ke Zhu
Date: 2025-02-19 00:00:00

Style investing creates asset classes (or the so-called "styles") with low correlations, aligning well with the principle of "Holy Grail of investing" in terms of portfolio selection. The returns of styles naturally form a tensor-valued time series, which requires new tools for studying the dynamics of the conditional correlation matrix to facilitate the aforementioned principle. Towards this goal, we introduce a new tensor dynamic conditional correlation (TDCC) model, which is based on two novel treatments: trace-normalization and dimension-normalization. These two normalizations adapt to the tensor nature of the data, and they are necessary except when the tensor data reduce to vector data. Moreover, we provide an easy-to-implement estimation procedure for the TDCC model, and examine its finite sample performance by simulations. Finally, we assess the usefulness of the TDCC model in international portfolio selection across ten global markets and in large portfolio selection for 1800 stocks from the Chinese stock market.
Imputation Strategies for Rightcensored Wages in Longitudinal Datasets
Authors: Jörg Drechsler, Johannes Ludsteck
Date: 2025-02-18 00:00:00
Tasks: Imputation, quantile regression

Censoring from above is a common problem with wage information as the reported wages are typically top-coded for confidentiality reasons. In administrative databases the information is often collected only up to a pre-specified threshold, for example, the contribution limit for the social security system. While directly accounting for the censoring is possible for some analyses, the most flexible solution is to impute the values above the censoring point. This strategy offers the advantage that future users of the data no longer need to implement possibly complicated censoring estimators. However, standard cross-sectional imputation routines relying on the classical Tobit model to impute right-censored data have a high risk of introducing bias from uncongeniality (Meng, 1994) as future analyses to be conducted on the imputed data are unknown to the imputer. Furthermore, as we show using a large-scale administrative database from the German Federal Employment agency, the classical Tobit model offers a poor fit to the data. In this paper, we present some strategies to address these problems. Specifically, we use leave-one-out means as suggested by Card et al. (2013) to avoid biases from uncongeniality and rely on quantile regression or left censoring to improve the model fit. We illustrate the benefits of these modeling adjustments using the German Structure of Earnings Survey, which is (almost) unaffected by censoring and can thus serve as a testbed to evaluate the imputation procedures.
Assortative Marriage and Geographic Sorting
Authors: Jiaming Mao, Jiayi Wen
Date: 2025-02-18 00:00:00

Between 1980 and 2000, the U.S. experienced a significant rise in geographic sorting and educational homogamy, with college graduates increasingly concentrating in high-skill cities and marrying similarly educated spouses. We develop and estimate a spatial equilibrium model with local labor, housing, and marriage markets, incorporating a marriage matching framework with transferable utility. Using the model, we estimate trends in assortative preferences, quantify the interplay between marital and geographic sorting, and assess their combined impact on household inequality. Welfare analyses show that after accounting for marriage, the college well-being gap grew substantially more than the college wage gap.
The Policy Paradox: Government Debt Servicing and Local Bank Risk Growth
Authors: Yan Li
Date: 2025-02-19 00:00:00

The issue of local government debt is widely recognized as one of the "gray rhinos" affecting the stable development of China's economy. Government debt can transmit risks to local banks, which are among the primary holders of local debt, thereby triggering systemic financial risks. Consequently, exploring debt resolution pathways and evaluating the systematic effects of debt servicing policies has become critically important. This study employs panel data from 348 local commercial banks across 29 provincial-level administrative regions in China from 2010 to 2023, and constructs a difference-in-differences (DID) model to investigate the impact of the State Council's special supervision of debt servicing on local bank risks. The findings indicate that the government's debt servicing policy essentially represents a shift of government debt from explicit to implicit forms, significantly increasing the risks faced by local banks and producing outcomes contrary to the policy's original intent. This effect is particularly pronounced for rural commercial banks and banks with high customer concentration and fewer branches. Mechanism analysis reveals two key insights. First, local banks are heavily influenced by local government control; the government's debt servicing requires banks to support the government by purchasing government bonds and other financial instruments, which leads to a deterioration in asset quality and an expansion of risk exposure. Second, government debt crowds out private credit from local banks, weakening the region's repayment capacity and ultimately increasing bank risk. Our research uncovers the counterintuitive effects of government debt servicing and offers corresponding policy recommendations.