pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the Monty Hall Decision Problem with Reaction Kinetics
Authors: Oliver Steinbock, Wen Zhu
Date: 2025-05-07 00:00:00
Tasks: Decision Making

Using the Monty Hall probability problem as a model system, we ask whether simple chemical reaction mechanisms can implement optimal strategies for non-trivial decision making. In this puzzle, a contestant chooses one of three doors (only one hides a prize), the host, knowing the content, opens another door revealing no prize, and finally the contestant must decide whether to stay with the original choice or switch to the remaining closed door. Assigning distinct molecular species to the player, initial choice, reveal step, and final decision, we encode the problem in mass-action kinetics. For pseudo-first-order conditions, tuning a single rate constant shifts the network continuously between "always-stay" (1/3 success) and "always-switch" (2/3 success) regimes. We derive closed-form, time-dependent expressions for the success kinetics, concluding with a brief discussion of proposed DNA strand-displacement implementations and kinetically hard-wired molecular decision-making.
Using Echo-State Networks to Reproduce Rare Events in Chaotic Systems
Authors: Anton Erofeev, Balasubramanya T. Nadiga, Ilya Timofeyev
Date: 2025-05-22 00:00:00
Tasks: Time Series

We apply the Echo-State Networks to predict the time series and statistical properties of the competitive Lotka-Volterra model in the chaotic regime. In particular, we demonstrate that Echo-State Networks successfully learn the chaotic attractor of the competitive Lotka-Volterra model and reproduce histograms of dependent variables, including tails and rare events. We use the Generalized Extreme Value distribution to quantify the tail behavior.
Artificial Intelligence for Direct Prediction of Molecular Dynamics Across Chemical Space
Authors: Fuchun Ge, Pavlo O. Dral
Date: 2025-05-22 00:00:00
Tasks: Numerical Integration

Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present MDtrajNet-1, a foundational AI model that directly generates MD trajectories across chemical space, bypassing force calculations and integration. This approach accelerates simulations by up to two orders of magnitude compared to traditional MD, even those enhanced by machine-learning interatomic potentials. MDtrajNet-1 combines equivariant neural networks with a Transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories for both known and unseen systems. Remarkably, the errors of the trajectories generated by MDtrajNet-1 for various molecular systems are close to those of the conventional ab initio MD. The model's flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types. By overcoming the intrinsic speed barrier of conventional MD, MDtrajNet-1 opens new frontiers in efficient and scalable atomistic simulations.
PICT -- A Differentiable, GPU-Accelerated Multi-Block PISO Solver for Simulation-Coupled Learning Tasks in Fluid Dynamics
Authors: Aleksandra Franz, Hao Wei, Luca Guastoni, Nils Thuerey
Date: 2025-05-22 00:00:00
Tasks: GPU

Despite decades of advancements, the simulation of fluids remains one of the most challenging areas of in scientific computing. Supported by the necessity of gradient information in deep learning, differentiable simulators have emerged as an effective tool for optimization and learning in physics simulations. In this work, we present our fluid simulator PICT, a differentiable pressure-implicit solver coded in PyTorch with Graphics-processing-unit (GPU) support. We first verify the accuracy of both the forward simulation and our derived gradients in various established benchmarks like lid-driven cavities and turbulent channel flows before we show that the gradients provided by our solver can be used to learn complicated turbulence models in 2D and 3D. We apply both supervised and unsupervised training regimes using physical priors to match flow statistics. In particular, we learn a stable sub-grid scale (SGS) model for a 3D turbulent channel flow purely based on reference statistics. The low-resolution corrector trained with our solver runs substantially faster than the highly resolved references, while keeping or even surpassing their accuracy. Finally, we give additional insights into the physical interpretation of different solver gradients, and motivate a physically informed regularization technique. To ensure that the full potential of PICT can be leveraged, it is published as open source: https://github.com/tum-pbs/PICT.
A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules
Authors: Manuel Ruiz-Botella, Marta Sales-Pardo, Roger Guimerà
Date: 2025-05-22 00:00:00
Tasks: valid

Developing new molecular compounds is crucial to address pressing challenges, from health to environmental sustainability. However, exploring the molecular space to discover new molecules is difficult due to the vastness of the space. Here we introduce CoCoGraph, a collaborative and constrained graph diffusion model capable of generating molecules that are guaranteed to be chemically valid. Thanks to the constraints built into the model and to the collaborative mechanism, CoCoGraph outperforms state-of-the-art approaches on standard benchmarks while requiring up to an order of magnitude fewer parameters. Analysis of 36 chemical properties also demonstrates that CoCoGraph generates molecules with distributions more closely matching real molecules than current models. Leveraging the model's efficiency, we created a database of 8.2M million synthetically generated molecules and conducted a Turing-like test with organic chemistry experts to further assess the plausibility of the generated molecules, and potential biases and limitations of CoCoGraph.
Mesh-free sparse identification of nonlinear dynamics
Authors: Mars Liyao Gao, J. Nathan Kutz, Bernat Font
Date: 2025-05-21 00:00:00

Identifying the governing equations of a dynamical system is one of the most important tasks for scientific modeling. However, this procedure often requires high-quality spatio-temporal data uniformly sampled on structured grids. In this paper, we propose mesh-free SINDy, a novel algorithm which leverages the power of neural network approximation as well as auto-differentiation to identify governing equations from arbitrary sensor placements and non-uniform temporal data sampling. We show that mesh-free SINDy is robust to high noise levels and limited data while remaining computationally efficient. In our implementation, the training procedure is straight-forward and nearly free of hyperparameter tuning, making mesh-free SINDy widely applicable to many scientific and engineering problems. In the experiments, we demonstrate its effectiveness on a series of PDEs including the Burgers' equation, the heat equation, the Korteweg-De Vries equation and the 2D advection-diffusion equation. We conduct detailed numerical experiments on all datasets, varying the noise levels and number of samples, and we also compare our approach to previous state-of-the-art methods. It is noteworthy that, even in high-noise and low-data scenarios, mesh-free SINDy demonstrates robust PDE discovery, achieving successful identification with up to 75% noise for the Burgers' equation using 5,000 samples and with as few as 100 samples and 1% noise. All of this is achieved within a training time of under one minute.
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
Authors: Adib Bazgir, Amir Habibdoust Lafmajani, Yuwen Zhang
Date: 2025-05-22 00:00:00
Tasks: Causal Inference, Drug Discovery, Language Modeling, Language Modelling, Large Language Model

Large Language Models (LLMs) show promise in biomedicine but lack true causal understanding, relying instead on correlations. This paper envisions causal LLM agents that integrate multimodal data (text, images, genomics, etc.) and perform intervention-based reasoning to infer cause-and-effect. Addressing this requires overcoming key challenges: designing safe, controllable agentic frameworks; developing rigorous benchmarks for causal evaluation; integrating heterogeneous data sources; and synergistically combining LLMs with structured knowledge (KGs) and formal causal inference tools. Such agents could unlock transformative opportunities, including accelerating drug discovery through automated hypothesis generation and simulation, enabling personalized medicine through patient-specific causal models. This research agenda aims to foster interdisciplinary efforts, bridging causal concepts and foundation models to develop reliable AI partners for biomedical progress.
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI
Authors: Yamin Arefeen, Brett Levac, Bhairav Patel, Chang Ho, Jonathan I. Tamir
Date: 2025-05-21 00:00:00
Tasks: Denoising

Purpose: Magnetic Resonance Imaging (MRI) enables non-invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal intensive care unit (NICU) facilitate MRI of sick infants, but have long scan times due to lower signal-to-noise ratios (SNR) and limited receive coils. This work accelerates in-NICU MRI with diffusion probabilistic generative models by developing a training pipeline accounting for these challenges. Methods: We establish a novel training dataset of clinical, 1 Tesla neonatal MR images in collaboration with Aspect Imaging and Sha'are Zedek Medical Center. We propose a pipeline to handle the low quantity and SNR of our real-world dataset (1) modifying existing network architectures to support varying resolutions; (2) training a single model on all data with learned class embedding vectors; (3) applying self-supervised denoising before training; and (4) reconstructing by averaging posterior samples. Retrospective under-sampling experiments, accounting for signal decay, evaluated each item of our proposed methodology. A clinical reader study with practicing pediatric neuroradiologists evaluated our proposed images reconstructed from 1.5x under-sampled data. Results: Combining all data, denoising pre-training, and averaging posterior samples yields quantitative improvements in reconstruction. The generative model decouples the learned prior from the measurement model and functions at two acceleration rates without re-training. The reader study suggests that proposed images reconstructed from approximately 1.5x under-sampled data are adequate for clinical use. Conclusion: Diffusion probabilistic generative models applied with the proposed pipeline to handle challenging real-world datasets could reduce scan time of in-NICU neonatal MRI.
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing
Authors: Zhehui Wanga, Benjamin Chen Ming Choonga, Tian Huang, Daniel Gerlinghoffa, Rick Siow Mong Goh, Cheng Liu, Tao Luo
Date: 2025-05-22 00:00:00
Tasks: Model Compression, Neural Network Compression, Quantization

Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods such as adiabatic quantum computing (AQC) have been employed in recent years on important optimization problems across various domains. In deep learning, deep neural networks (DNN) have reached immense sizes to support new predictive capabilities. Optimization of large-scale models is critical for sustainable deployment, but becomes increasingly challenging with ever-growing model sizes and complexity. While quantum optimization is suitable for solving complex problems, its application to DNN optimization is not straightforward, requiring thorough reformulation for compatibility with commercially available quantum devices. In this work, we explore the potential of adopting AQC for fine-grained pruning-quantization of convolutional neural networks. We rework established heuristics to formulate model compression as a quadratic unconstrained binary optimization (QUBO) problem, and assess the solution space offered by commercial quantum annealing devices. Through our exploratory efforts of reformulation, we demonstrate that AQC can achieve effective compression of practical DNN models. Experiments demonstrate that adiabatic quantum computing (AQC) not only outperforms classical algorithms like genetic algorithms and reinforcement learning in terms of time efficiency but also excels at identifying global optima.
Analysis of Distributional Dynamics for Repeated Cross-Sectional and Intra-Period Observations
Authors: Bo Hu, Joon Y. Park, Junhui Qian
Date: 2025-05-21 00:00:00
Tasks: Time Series

This paper introduces a novel approach to investigate the dynamics of state distributions, which accommodate both cross-sectional distributions of repeated panels and intra-period distributions of a time series observed at high frequency. In our approach, densities of the state distributions are regarded as functional elements in a Hilbert space, and are assumed to follow a functional autoregressive model. We propose an estimator for the autoregressive operator, establish its consistency, and provide tools and asymptotics to analyze the forecast of state density and the moment dynamics of state distributions. We apply our methodology to study the time series of distributions of the GBP/USD exchange rate intra-month returns and the time series of cross-sectional distributions of the NYSE stocks monthly returns. Finally, we conduct simulations to evaluate the density forecasts based on our model.
Dynamic Decision-Making under Model Misspecification
Authors: Xinyu Dai
Date: 2025-05-20 00:00:00
Tasks: Decision Making, model, Thompson Sampling

In this study, I investigate the dynamic decision problem with a finite parameter space when the functional form of conditional expected rewards is misspecified. Traditional algorithms, such as Thompson Sampling, guarantee neither an $O(e^{-T})$ rate of posterior parameter concentration nor an $O(T^{-1})$ rate of average regret. However, under mild conditions, we can still achieve an exponential convergence rate of the parameter to a pseudo truth set, an extension of the pseudo truth parameter concept introduced by White (1982). I further characterize the necessary conditions for the convergence of the expected posterior within this pseudo-truth set. Simulations demonstrate that while the maximum a posteriori (MAP) estimate of the parameters fails to converge under misspecification, the algorithm's average regret remains relatively robust compared to the correctly specified case. These findings suggest opportunities to design simple yet robust algorithms that achieve desirable outcomes even in the presence of model misspecifications.
Bubble Detection with Application to Green Bubbles: A Noncausal Approach
Authors: Francesco Giancaterini, Alain Hecq, Joann Jasiak, Aryan Manafi Neyazi
Date: 2025-05-20 00:00:00

This paper introduces a new approach to detect bubbles based on mixed causal and noncausal processes and their tail process representation during explosive episodes. Departing from traditional definitions of bubbles as nonstationary and temporarily explosive processes, we adopt a perspective in which prices are viewed as following a strictly stationary process, with the bubble considered an intrinsic component of its non-linear dynamics. We illustrate our approach on the phenomenon referred to as the "green bubble" in the field of renewable energy investment.
Optimising the decision threshold in a weighted voting system: The case of the IMF's Board of Governors
Authors: Dóra Gréta Petróczy
Date: 2025-05-22 00:00:00

In a weighted majority voting game, the players' weights are determined based on the decision-maker's intentions. The weights are challenging to change in numerous cases, as they represent some desired disparity. However, the voting weights and the actual voting power do not necessarily coincide. Changing a decision threshold would offer some remedy. The International Monetary Fund (IMF) is one of the most important international organisations that uses a weighted voting system to make decisions. The voting weights in its Board of Governors depend on the quotas of the 191 member countries, which reflect their economic strengths to some extent. We analyse the connection between the decision threshold and the a priori voting power of the countries by calculating the Banzhaf indices for each threshold between 50% and 87\%. The difference between the quotas and voting powers is minimised if the decision threshold is 58% or 60%.
International Tourism and Global Biodiversity Risks
Authors: Yingtong Chen, Fei Wu, Dayong Zhang, Qiang Ji
Date: 2025-05-21 00:00:00

The impact of international tourism on biodiversity risks has received considerable attention, yet quantitative research in this field remains relatively limited. This study constructs a biodiversity risk index for 155 countries and regions spanning the years 2001 to 2019, analysing how international tourism influences biodiversity risks in destination countries. The results indicate that the growth of international tourism significantly elevates biodiversity risks, with these effects displaying both lagging and cumulative characteristics. Furthermore, spatial analysis shows that international tourism also intensifies biodiversity risks in neighbouring countries. The extent of its impact varies according to the tourism model and destination. In addition, government regulations and international financial assistance play a crucial role in mitigating the biodiversity risks associated with international tourism.
Following the herd: The influence of the bandwagon heuristic on e-commerce shoppers
Authors: Min Xiao, Paul Myers
Date: 2025-05-20 00:00:00

This research study examined how the number of reviews, review scores product involvement, and product review valence affect consumers' shopping decisions. Specifically, two online experiments were conducted to examine how product review scores interact with the number of reviews, product involvement, and review valence in affecting bandwagon perceptions and purchase intention. A mock website that displays products and product review metrics was constructed for the purpose of the research. The findings suggest that product review scores, and their interaction with product involvement and review valence, influenced consumers' shopping decisions.
Development of Railway Silk Road as a Platform for Promoting Georgias Economic Growth
Authors: Davit Gondauri, M. Moistsrapishvili
Date: 2025-05-20 00:00:00
Tasks: Sensitivity

The given paper emphasizes the importance of the Railway Silk Road for promoting Georgia's economic growth and development. The article notes that economic integration in the region increases cargo turnover in Central Asia and the Caucasus, thus boosting the volume of goods transported through Georgia and contributing to the sustainability of Georgia's macroeconomic and economic growth. The financial economic models aim to identify causal links between the sensitivity of railway cargo and the country's economic growth. The main task of the research was to use the Railway EVA and the Georgian economy to create a cargo sensitivity relationship between CAGR models. The paper analyzes key scientific problems regarding railway freight transportation studies. Calculations are provided for the share of the Railway System in the country's GDP for 2006-2017, as well as the average annual geometric (CAGR) growth of cargo volumes over a 16-year cycle, allowing Georgian Railway JSC to generate additional value in the country's overall GDP. The research shows that the added value to GDP comes in direct and indirect forms through the development and growth of various sectors of Georgia's economy, as some of the cargo shipped by railway remains in Georgia and is used in production, thereby adding value to the country's economic growth. The use of this model by foreign research centers also provides further opportunities for the economic growth of their countries.
Falling Birthrate and Rising C-section: Post-Pandemic Evidence from New York
Authors: Maysam Rabbani, Zahra Akbari
Date: 2025-05-20 00:00:00

The literature documents the effects of the pandemic on birthrate, birthweight, and pregnancy complications. This study contributes to this growing body of research by examining multiple facets of the phenomenon. Using the 2012-2022 hospital inpatient discharge data of New York, we implemented fixed-effects regression models and reported three key findings. First, birthrate was declining pre-pandemic by 1.11% annually. Second, we documented an additional 7.61% decline in birthrate with the onset of the pandemic in 2020. Notably, birthrate did not return to the pre-pandemic trajectory in subsequent years, indicating a persistent decline. Third, this post-pandemic decline was greater in vaginal delivery, with weak evidence of a drop in C-section. In our sample, C-section generates 61% more revenue than vaginal delivery. This raises the possibility that, in response to declining birthrate, healthcare providers have increased C-section rates to make up for lost revenues. While this hinted at upselling in the delivery room, further research is needed to draw definitive conclusions.
Birthweight Declined During the Pandemic and It Is Falling Further Post-pandemic
Authors: Maysam Rabbani, Elijah Gervais
Date: 2025-05-20 00:00:00

Recent literature reports mixed evidence on whether birthweight has decreased during the pandemic. In this paper, we use New York's hospital inpatient discharge data and contribute to this ongoing debate in multiple ways. First, we corroborate that birthweight has declined during the pandemic by 7g (grams). Second, we provide the first empirical evidence that, after the pandemic, not only birthweight has not reverted to the pre-pandemic levels, but it has fallen lower, 17g below the pre-pandemic levels. Third, in the post-pandemic years, mothers who are hospitalized to give birth are 27% more likely to be at a higher mortality risk and 8% more likely to have a higher severity of illness. Disruptions to birthweight could have far-reaching consequences for the health, longevity, and well-being of the population. Therefore, understanding the full scope of COVID-19's influence on birthweight is a vital and timely practice. Future research is needed to test whether our results are driven by true underlying changes in birthweight and complications or by healthcare providers being induced (financially or otherwise) to report birthweight differently.
The emerging AI 'revolution tranquille' in America
Authors: Omar R. Malik
Date: 2025-05-19 00:00:00

Using data from the U.S. Census Bureaus Business Trends and Outlook Survey (BTOS), I examine the adoption of AI among US firms at national, state, industry, and firm size levels. I find that adoption remains overall low (only around 7% of firms currently use AI), but is on a steady upward trajectory with a rising share of firms planning to implement AI. Adoption rates vary significantly across regions and sectors: some states are emerging as early adopters, while others lag, and knowledge-intensive industries (such as information technology and professional services) along with larger firms show higher openness to AI adoption compared to sectors like construction or small businesses. In general, these trends indicate that a quiet revolution in AI adoption is underway; a gradual but expanding diffusion of AI across the economy with important implications for future productivity and policy.