pwc-archive/papers-with-abstracts huggingface dataset:
Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
Authors: Siwei Tu, Ben Fei, Weidong Yang, Fenghua Ling, Hao Chen, Zili Liu, Kun Chen, Hang Fan, Wanli Ouyang, Lei Bai
Date: 2025-02-09 00:00:00
Proceeding: CVPR 2025 1
Tasks: Spatial Interpolation, Weather Forecasting

Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that meteorological states derived from satellite observations are often provided in the form of low-resolution grid fields, the direct application of spatial interpolation to obtain meteorological states for specific locations often results in significant discrepancies when compared to actual observations. Existing downscaling methods for acquiring meteorological state information at higher resolutions commonly overlook the correlation with satellite observations. To bridge the gap, we propose Satellite-observations Guided Diffusion Model (SGD), a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions, which is employed for sampling downscaled meteorological states through a zero-shot guided sampling strategy and patch-based methods. During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism, enabling SGD to generate atmospheric states that align more accurately with actual conditions. In the sampling, we employed optimizable convolutional kernels to simulate the upscale process, thereby generating high-resolution ERA5 maps using low-resolution ERA5 maps as well as observations from weather stations as guidance. Moreover, our devised patch-based method promotes SGD to generate meteorological states at arbitrary resolutions. Experiments demonstrate SGD fulfills accurate meteorological states downscaling to 6.25km.
MDCrow: Automating Molecular Dynamics Workflows with Large Language Models
Authors: Quintina Campbell, Sam Cox, Jorge Medina, Brittany Watterson, Andrew D. White
Date: 2025-02-13 00:00:00

Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 tasks of varying required subtasks and difficulty, and we evaluate the agent's robustness to both difficulty and prompt style. \texttt{gpt-4o} is able to complete complex tasks with low variance, followed closely by \texttt{llama3-405b}, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models.
Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows
Authors: Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
Date: 2025-02-11 00:00:00
Tasks: Dimensionality Reduction

Modeling and simulation of complex fluid flows with dynamics that span multiple spatio-temporal scales is a fundamental challenge in many scientific and engineering domains. Full-scale resolving simulations for systems such as highly turbulent flows are not feasible in the foreseeable future, and reduced-order models must capture dynamics that involve interactions across scales. In the present work, we propose a novel framework, Graph-based Learning of Effective Dynamics (Graph-LED), that leverages graph neural networks (GNNs), as well as an attention-based autoregressive model, to extract the effective dynamics from a small amount of simulation data. GNNs represent flow fields on unstructured meshes as graphs and effectively handle complex geometries and non-uniform grids. The proposed method combines a GNN based, dimensionality reduction for variable-size unstructured meshes with an autoregressive temporal attention model that can learn temporal dependencies automatically. We evaluated the proposed approach on a suite of fluid dynamics problems, including flow past a cylinder and flow over a backward-facing step over a range of Reynolds numbers. The results demonstrate robust and effective forecasting of spatio-temporal physics; in the case of the flow past a cylinder, both small-scale effects that occur close to the cylinder as well as its wake are accurately captured.
A Physics-Informed Deep Learning Model for MRI Brain Motion Correction
Authors: Mojtaba Safari, Shansong Wang, Zach Eidex, Richard Qiu, Chih-Wei Chang, David S. Yu, Xiaofeng Yang
Date: 2025-02-13 00:00:00
Tasks: Diagnostic, Motion Detection, parameter estimation, SSIM

Background: MRI is crucial for brain imaging but is highly susceptible to motion artifacts due to long acquisition times. This study introduces PI-MoCoNet, a physics-informed motion correction network that integrates spatial and k-space information to remove motion artifacts without explicit motion parameter estimation, enhancing image fidelity and diagnostic reliability. Materials and Methods: PI-MoCoNet consists of a motion detection network (U-net with spatial averaging) to identify corrupted k-space lines and a motion correction network (U-net with Swin Transformer blocks) to reconstruct motion-free images. The correction is guided by three loss functions: reconstruction (L1), perceptual (LPIPS), and data consistency (Ldc). Motion artifacts were simulated via rigid phase encoding perturbations and evaluated on IXI and MR-ART datasets against Pix2Pix, CycleGAN, and U-net using PSNR, SSIM, and NMSE. Results: PI-MoCoNet significantly improved image quality. On IXI, for minor artifacts, PSNR increased from 34.15 dB to 45.95 dB, SSIM from 0.87 to 1.00, and NMSE reduced from 0.55% to 0.04%. For moderate artifacts, PSNR improved from 30.23 dB to 42.16 dB, SSIM from 0.80 to 0.99, and NMSE from 1.32% to 0.09%. For heavy artifacts, PSNR rose from 27.99 dB to 36.01 dB, SSIM from 0.75 to 0.97, and NMSE decreased from 2.21% to 0.36%. On MR-ART, PI-MoCoNet achieved PSNR gains of ~10 dB and SSIM improvements of up to 0.20, with NMSE reductions of ~6%. Ablation studies confirmed the importance of data consistency and perceptual losses, yielding a 1 dB PSNR gain and 0.17% NMSE reduction. Conclusions: PI-MoCoNet effectively mitigates motion artifacts in brain MRI, outperforming existing methods. Its ability to integrate spatial and k-space information makes it a promising tool for clinical use in motion-prone settings. Code: https://github.com/mosaf/PI-MoCoNet.git.
The Observational Partial Order of Causal Structures with Latent Variables
Authors: Marina Maciel Ansanelli, Elie Wolfe, Robert W. Spekkens
Date: 2025-02-11 00:00:00
Tasks: Causal Discovery

For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible variables realizable by the second. Knowing such dominance relations is useful for adjudicating between these structures given observational data. We here consider the problem of determining the partial order of equivalence classes of causal structures with latent variables relative to observational dominance. We provide a complete characterization of the dominance order in the case of three visible variables, and a partial characterization in the case of four visible variables. Our techniques also help to identify which observational equivalence classes have a set of realizable distributions that is characterized by nontrivial inequality constraints, analogous to Bell inequalities and instrumental inequalities. We find evidence that as one increases the number of visible variables, the equivalence classes satisfying nontrivial inequality constraints become ubiquitous. (Because such classes are the ones for which there can be a difference in the distributions that are quantumly and classically realizable, this implies that the potential for quantum-classical gaps is also ubiquitous.) Furthermore, we find evidence that constraint-based causal discovery algorithms that rely solely on conditional independence constraints have a significantly weaker distinguishing power among observational equivalence classes than algorithms that go beyond these (i.e., algorithms that also leverage nested Markov constraints and inequality constraints).
A unifying account of warm start guarantees for patches of quantum landscapes
Authors: Hela Mhiri, Ricard Puig, Sacha Lerch, Manuel S. Rudolph, Thiparat Chotibut, Supanut Thanasilp, Zoë Holmes
Date: 2025-02-11 00:00:00

Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantial gradients. Previous work has studied certain classes of parameterized quantum circuits and found example regions where gradients vanish at worst polynomially in system size. Here we present a general bound that unifies all these previous cases and that can tackle physically-motivated ans\"atze that could not be analyzed previously. Concretely, we analytically prove a lower-bound on the variance of the loss that can be used to show that in a non-exponentially narrow region around a point with curvature the loss variance cannot decay exponentially fast. This result is complemented by numerics and an upper-bound that suggest that any loss function with a barren plateau will have exponentially vanishing gradients in any constant radius subregion. Our work thus suggests that while there are hopes to be able to warm-start variational quantum algorithms, any initialization strategy that cannot get increasingly close to the region of attraction with increasing problem size is likely inadequate.
Temporal Model On Quantum Logic
Authors: Francesco D'Agostino
Date: 2025-02-09 00:00:00
Tasks: model

This paper introduces a unified theoretical framework for modeling temporal memory dynamics, combining concepts from temporal logic, memory decay models, and hierarchical contexts. The framework formalizes the evolution of propositions over time using linear and branching temporal models, incorporating exponential decay (Ebbinghaus forgetting curve) and reactivation mechanisms via Bayesian updating. The hierarchical organization of memory is represented using directed acyclic graphs to model recall dependencies and interference. Novel insights include feedback dynamics, recursive influences in memory chains, and the integration of entropy-based recall efficiency. This approach provides a foundation for understanding memory processes across cognitive and computational domains.
Difference-in-Differences and Changes-in-Changes with Sample Selection
Authors: Javier Viviens
Date: 2025-02-12 00:00:00

Sample selection arises endogenously in causal research when the treatment affects whether certain units are observed. It is a common pitfall in longitudinal studies, particularly in settings where treatment assignment is confounded. In this paper, I highlight the drawbacks of one of the most popular identification strategies in such settings: Difference-in-Differences (DiD). Specifically, I employ principal stratification analysis to show that the conventional ATT estimand may not be well defined, and the DiD estimand cannot be interpreted causally without additional assumptions. To address these issues, I develop an identification strategy to partially identify causal effects on the subset of units with well-defined and observed outcomes under both treatment regimes. I adapt Lee bounds to the Changes-in-Changes (CiC) setting (Athey & Imbens, 2006), leveraging the time dimension of the data to relax the unconfoundedness assumption in the original trimming strategy of Lee (2009). This setting has the DiD identification strategy as a particular case, which I also implement in the paper. Additionally, I explore how to leverage multiple sources of sample selection to relax the monotonicity assumption in Lee (2009), which may be of independent interest. Alongside the identification strategy, I present estimators and inference results. I illustrate the relevance of the proposed methodology by analyzing a job training program in Colombia.
Scenario Analysis with Multivariate Bayesian Machine Learning Models
Authors: Michael Pfarrhofer, Anna Stelzer
Date: 2025-02-12 00:00:00

We present an econometric framework that adapts tools for scenario analysis, such as variants of conditional forecasts and impulse response functions, for use with dynamic nonparametric multivariate models. We demonstrate the utility of our approach with simulated data and three real-world applications: (1) scenario-based conditional forecasts aligned with Federal Reserve stress test assumptions, measuring (2) macroeconomic risk under varying financial conditions, and (3) asymmetric effects of US-based financial shocks and their international spillovers. Our results indicate the importance of nonlinearities and asymmetries in dynamic relationships between macroeconomic and financial variables.
Inference in dynamic models for panel data using the moving block bootstrap
Authors: Ayden Higgins, Koen Jochmans
Date: 2025-02-12 00:00:00
Tasks: valid

Inference in linear panel data models is complicated by the presence of fixed effects when (some of) the regressors are not strictly exogenous. Under asymptotics where the number of cross-sectional observations and time periods grow at the same rate, the within-group estimator is consistent but its limit distribution features a bias term. In this paper we show that a panel version of the moving block bootstrap, where blocks of adjacent cross-sections are resampled with replacement, replicates the limit distribution of the within-group estimator. Confidence ellipsoids and hypothesis tests based on the reverse-percentile bootstrap are thus asymptotically valid without the need to take the presence of bias into account.
Cracking the Code: Enhancing Development finance understanding with artificial intelligence
Authors: Pierre Beaucoral
Date: 2025-02-13 00:00:00

Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.
Assessing Generative AI value in a public sector context: evidence from a field experiment
Authors: Trevor Fitzpatrick, Seamus Kelly, Patrick Carey, David Walsh, Ruairi Nugent
Date: 2025-02-13 00:00:00
Tasks: document understanding

The emergence of Generative AI (Gen AI) has motivated an interest in understanding how it could be used to enhance productivity across various tasks. We add to research results for the performance impact of Gen AI on complex knowledge-based tasks in a public sector setting. In a pre-registered experiment, after establishing a baseline level of performance, we find mixed evidence for two types of composite tasks related to document understanding and data analysis. For the Documents task, the treatment group using Gen AI had a 17% improvement in answer quality scores (as judged by human evaluators) and a 34% improvement in task completion time compared to a control group. For the Data task, we find the Gen AI treatment group experienced a 12% reduction in quality scores and no significant difference in mean completion time compared to the control group. These results suggest that the benefits of Gen AI may be task and potentially respondent dependent. We also discuss field notes and lessons learned, as well as supplementary insights from a post-trial survey and feedback workshop with participants.
Package Bids in Combinatorial Electricity Auctions: Selection, Welfare Losses, and Alternatives
Authors: Thomas Hübner, Gabriela Hug
Date: 2025-02-13 00:00:00

A key challenge in combinatorial auctions is designing bid formats that accurately capture agents' preferences while remaining computationally feasible. This is especially true for electricity auctions, where complex preferences complicate straightforward solutions. In this context, we examine the XOR package bid, the default choice in combinatorial auctions and adopted in European day-ahead and intraday auctions under the name "exclusive group of block bids". Unlike parametric bid formats often employed in US power auctions, XOR package bids are technology-agnostic, making them particularly suitable for emerging demand-side participants. However, the challenge with package bids is that auctioneers must limit their number to maintain computational feasibility. As a result, agents are constrained in expressing their preferences, potentially lowering their surplus and reducing overall welfare. To address this issue, we propose decision support algorithms that optimize package bid selection, evaluate welfare losses resulting from bid limits, and explore alternative bid formats. In our analysis, we leverage the fact that electricity prices are often fairly predictable and, at least in European auctions, tend to approximate equilibrium prices reasonably well. Our findings offer actionable insights for both auctioneers and bidders.
Capitalizing on a Crisis: A Computational Analysis of all Five Million British Firms During the Covid-19 Pandemic
Authors: Naomi Muggleton, Charles Rahal, Aaron Reeves
Date: 2025-02-13 00:00:00
Tasks: All, Model Selection

The Covid-19 pandemic brought unprecedented changes to business ownership in the UK which affects a generation of entrepreneurs and their employees. Nonetheless, the impact remains poorly understood. This is because research on capital accumulation has typically lacked high-quality, individualized, population-level data. We overcome these barriers to examine who benefits from economic crises through a computationally orientated lens of firm creation. Leveraging a comprehensive cache of administrative data on every UK firm and all nine million people running them, combined with probabilistic algorithms, we conduct individual-level analyses to understand who became Covid entrepreneurs. Using these techniques, we explore characteristics of entrepreneurs--such as age, gender, region, business experience, and industry--which potentially predict Covid entrepreneurship. By employing an automated time series model selection procedure to generate counterfactuals, we show that Covid entrepreneurs were typically aged 35-49 (40.4%), men (73.1%), and had previously held roles in existing firms (59.4%). For most industries, growth was disproportionately concentrated around London. It was therefore existing corporate elites who were most able to capitalize on the Covid crisis and not, as some hypothesized, young entrepreneurs who were setting up their first businesses. In this respect, the pandemic will likely impact future wealth inequalities. Our work offers methodological guidance for future policymakers during economic crises and highlights the long-term consequences for capital and wealth inequality.
Trade and pollution: Evidence from India
Authors: Malin Niemi, Nicklas Nordfors, Anna Tompsett
Date: 2025-02-13 00:00:00

What happens to pollution when developing countries open their borders to trade? Theoretical predictions are ambiguous, and empirical evidence remains limited. We study the effects of the 1991 Indian trade liberalization reform on water pollution. The reform abruptly and unexpectedly lowered import tariffs, increasing exposure to trade. Larger tariff reductions are associated with relative increases in water pollution. The estimated effects imply a 0.12 standard deviation increase in water pollution for the median district exposed to the tariff reform.
Using Covid-19 Response Policy to Estimate Open Water Swim Drafting Effects in Triathlon
Authors: Felix Reichel
Date: 2025-02-13 00:00:00

This study investigates the causal effects of open-water swim drafting by leveraging a natural experiment induced by staggered race starts during the COVID-19 pandemic. Before 2020, athletes started in groups, enabling drafting benefits, while pandemic-related restrictions significantly reduced these opportunities. Using agglomerative hierarchical clustering of swim-out times, I analyze optimal drafting positions and estimate their impact on Swim-Out performance. Our empirical findings reveal that swim drafting benefits were statistically insignificant in 2020 but persisted post-pandemic at slightly reduced levels. I find that drafting becomes advantageous only from the third trailing position onward, with earlier positions primarily serving to minimize fatigue. To mitigate endogeneity, I employ athlete and event fixed effects. The seemingly inverse decaying nature of drafting benefits partially addresses some concerns of simultaneous reverse causality and omitted variable bias. This study provides the first largescale causal estimate of drafting effects in real-world triathlon race settings.
Blockchain-based Ecommerce It's an Evolution NOT a Revolution-Experimental Evidence from Users' Perspective
Authors: David Lee Kuo Chuen, Yang Li, Weibiao Xu, Willy Zhao
Date: 2025-02-13 00:00:00
Tasks: Survey

Proponents of blockchains believe that this technology will revolutionize e-commerce. To evaluate this belief, we invite several groups of students to transact on a decentralized peer-to-peer marketplace built on the platform provided by Origin Protocol Inc., and then we conduct a survey about their experience of usage. Based on our survey results, we find that 33% of respondents play tricks on others, which implies that this undesirable result may hinder the widespread adoption of blockchain technologies. We also attempt to propose a conceptual mechanism to mitigate fraudulent behaviors. In the event of disputation, a trusted authority is entitled to the right to downgrade the fraudulent side's credit record, which is stored by a permissioned blockchain accessed only by the authority. Such a punishment can effectively decrease agents' incentives to sell counterfeits and leave fake ratings. In sum, we must distinguish what we proposed blockchains will do and what blockchains can do before enabling this technology in e-commerce.