pwc-archive/papers-with-abstracts huggingface dataset:
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Authors: Yiling Wang, Elia Lombardo, Adrian Thummerer, Tom Blöcker, Yu Fan, Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H. N. Tijssen, Pia A. W. Görts, Shyama U. Tetar, Davide Cusumano, Martijn P. W. Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary Byrne, Lorenzo Placidi, Marco Fusella, Michael Jameson, Miguel Palacios, Paul Cobussen, Tobias Finazzi, Cornelis J. A. Haasbeek, Paul Keall, Christopher Kurz, Guillaume Landry, Matteo Maspero
Date: 2025-03-24 00:00:00
Tasks: Management

Purpose: Magnetic resonance imaging (MRI) to visualize anatomical motion is becoming increasingly important when treating cancer patients with radiotherapy. Hybrid MRI-linear accelerator (MRI-linac) systems allow real-time motion management during irradiation. This paper presents a multi-institutional real-time MRI time series dataset from different MRI-linac vendors. The dataset is designed to support developing and evaluating real-time tumor localization (tracking) algorithms for MRI-guided radiotherapy within the TrackRAD2025 challenge (https://trackrad2025.grand-challenge.org/). Acquisition and validation methods: The dataset consists of sagittal 2D cine MRIs in 585 patients from six centers (3 Dutch, 1 German, 1 Australian, and 1 Chinese). Tumors in the thorax, abdomen, and pelvis acquired on two commercially available MRI-linacs (0.35 T and 1.5 T) were included. For 108 cases, irradiation targets or tracking surrogates were manually segmented on each temporal frame. The dataset was randomly split into a public training set of 527 cases (477 unlabeled and 50 labeled) and a private testing set of 58 cases (all labeled). Data Format and Usage Notes: The data is publicly available under the TrackRAD2025 collection: https://doi.org/10.57967/hf/4539. Both the images and segmentations for each patient are available in metadata format. Potential Applications: This novel clinical dataset will enable the development and evaluation of real-time tumor localization algorithms for MRI-guided radiotherapy. By enabling more accurate motion management and adaptive treatment strategies, this dataset has the potential to advance the field of radiotherapy significantly.
Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach
Authors: Mengqi Lou, Kabir Aladin Verchand, Sara Fridovich-Keil, Ashwin Pananjady
Date: 2025-03-13 00:00:00
Tasks: Computed Tomography (CT)

We consider the problem of signal reconstruction for computed tomography (CT) under a nonlinear forward model that accounts for exponential signal attenuation, a polychromatic X-ray source, general measurement noise (e.g. Poisson shot noise), and observations acquired over multiple wavelength windows. We develop a simple iterative algorithm for single-material reconstruction, which we call EXACT (EXtragradient Algorithm for Computed Tomography), based on formulating our estimate as the fixed point of a monotone variational inequality. We prove guarantees on the statistical and computational performance of EXACT under practical assumptions on the measurement process. We also consider a recently introduced variant of this model with Gaussian measurements, and present sample and iteration complexity bounds for EXACT that improve upon those of existing algorithms. We apply our EXACT algorithm to a CT phantom image recovery task and show that it often requires fewer X-ray projection exposures, lower source intensity, and less computation time to achieve similar reconstruction quality to existing methods.
A decision-theoretic approach to dealing with uncertainty in quantum mechanics
Authors: Keano De Vos, Gert de Cooman, Alexander Erreygers, Jasper De Bock
Date: 2025-03-26 00:00:00

We provide a decision-theoretic framework for dealing with uncertainty in quantum mechanics. This uncertainty is two-fold: on the one hand there may be uncertainty about the state the quantum system is in, and on the other hand, as is essential to quantum mechanical uncertainty, even if the quantum state is known, measurements may still produce an uncertain outcome. In our framework, measurements therefore play the role of acts with an uncertain outcome and our simple decision-theoretic postulates ensure that Born's rule is encapsulated in the utility functions associated with such acts. This approach allows us to uncouple (precise) probability theory from quantum mechanics, in the sense that it leaves room for a more general, so-called imprecise probabilities approach. We discuss the mathematical implications of our findings, which allow us to give a decision-theoretic foundation to recent seminal work by Benavoli, Facchini and Zaffalon, and we compare our approach to earlier and different approaches by Deutsch and Wallace.
Geometric Meta-Learning via Coupled Ricci Flow: Unifying Knowledge Representation and Quantum Entanglement
Authors: Ming Lei, Christophe Baehr
Date: 2025-03-25 00:00:00
Tasks: LEMMA, Meta-Learning

This paper establishes a unified framework integrating geometric flows with deep learning through three fundamental innovations. First, we propose a thermodynamically coupled Ricci flow that dynamically adapts parameter space geometry to loss landscape topology, formally proved to preserve isometric knowledge embedding (Theorem~\ref{thm:isometric}). Second, we derive explicit phase transition thresholds and critical learning rates (Theorem~\ref{thm:critical}) through curvature blowup analysis, enabling automated singularity resolution via geometric surgery (Lemma~\ref{lem:surgery}). Third, we establish an AdS/CFT-type holographic duality (Theorem~\ref{thm:ads}) between neural networks and conformal field theories, providing entanglement entropy bounds for regularization design. Experiments demonstrate 2.1$\times$ convergence acceleration and 63\% topological simplification while maintaining $\mathcal{O}(N\log N)$ complexity, outperforming Riemannian baselines by 15.2\% in few-shot accuracy. Theoretically, we prove exponential stability (Theorem~\ref{thm:converge}) through a new Lyapunov function combining Perelman entropy with Wasserstein gradient flows, fundamentally advancing geometric deep learning.
Quantum Complex-Valued Self-Attention Model
Authors: Fu Chen, Qinglin Zhao, Li Feng, Longfei Tang, Yangbin Lin, Haitao Huang
Date: 2025-03-24 00:00:00
Tasks: model, Multi-class Classification, Quantum Machine Learning

Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, we introduce the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework to leverage complex-valued similarities, which captures amplitude and phase relationships between quantum states more comprehensively. To achieve this, QCSAM extends the Linear Combination of Unitaries (LCUs) into the Complex LCUs (CLCUs) framework, enabling precise complex-valued weighting of quantum states and supporting quantum multi-head attention. Experiments on MNIST and Fashion-MNIST show that QCSAM outperforms recent quantum self-attention models, including QKSAN, QSAN, and GQHAN. With only 4 qubits, QCSAM achieves 100% and 99.2% test accuracies on MNIST and Fashion-MNIST, respectively. Furthermore, we evaluate scalability across 3-8 qubits and 2-4 class tasks, while ablation studies validate the advantages of complex-valued attention weights over real-valued alternatives. This work advances quantum machine learning by enhancing the expressiveness and precision of quantum self-attention in a way that aligns with the inherent complexity of quantum mechanics.
Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities
Authors: Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
Date: 2025-03-26 00:00:00
Tasks: Causal Inference, counterfactual

The presence of unobserved confounders is one of the main challenges in identifying treatment effects. In this paper, we propose a new approach to causal inference using panel data with large large $N$ and $T$. Our approach imputes the untreated potential outcomes for treated units using the outcomes for untreated individuals with similar values of the latent confounders. In order to find units with similar latent characteristics, we utilize long pre-treatment histories of the outcomes. Our analysis is based on a nonparametric, nonlinear, and nonseparable factor model for untreated potential outcomes and treatments. The model satisfies minimal smoothness requirements. We impute both missing counterfactual outcomes and propensity scores using kernel smoothing based on the constructed measure of latent similarity between units, and demonstrate that our estimates can achieve the optimal nonparametric rate of convergence up to log terms. Using these estimates, we construct a doubly robust estimator of the period-specifc average treatment effect on the treated (ATT), and provide conditions, under which this estimator is $\sqrt{N}$-consistent, and asymptotically normal and unbiased. Our simulation study demonstrates that our method provides accurate inference for a wide range of data generating processes.
Large Structural VARs with Multiple Sign and Ranking Restrictions
Authors: Joshua Chan, Christian Matthes, Xuewen Yu
Date: 2025-03-26 00:00:00

Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and related measures in large structural VARs identified with a large number of structural restrictions on impulse responses. The methodology is illustrated using a 35-variable VAR with over 100 sign and ranking restrictions to identify 8 structural shocks.
Quasi-Bayesian Local Projections: Simultaneous Inference and Extension to the Instrumental Variable Method
Authors: Masahiro Tanaka
Date: 2025-03-26 00:00:00

While local projections (LPs) are widely used for impulse response analysis, existing Bayesian approaches face fundamental challenges because a set of LPs does not constitute a likelihood function. Prior studies address this issue by constructing a pseudo-likelihood, either by treating LPs as a system of seemingly unrelated regressions with a multivariate normal error structure or by applying a quasi-Bayesian approach with a sandwich estimator. However, these methods lead to posterior distributions that are not "well calibrated," preventing proper Bayesian belief updates and complicating the interpretation of posterior distributions. To resolve these issues, we propose a novel quasi-Bayesian approach for inferring LPs using the Laplace-type estimator. Specifically, we construct a quasi-likelihood based on a generalized method of moments criterion, which avoids restrictive distributional assumptions and provides well-calibrated inferences. The proposed framework enables the estimation of simultaneous credible bands and naturally extends to LPs with an instrumental variable, offering the first Bayesian treatment of this method. Furthermore, we introduce two posterior simulators capable of handling the high-dimensional parameter space of LPs with the Laplace-type estimator. We demonstrate the effectiveness of our approach through extensive Monte Carlo simulations and an empirical application to U.S. monetary policy.
Treatment Effects Inference with High-Dimensional Instruments and Control Variables
Authors: Xiduo Chen, Xingdong Feng, Antonio F. Galvao, Yeheng Ge
Date: 2025-03-26 00:00:00
Tasks: valid

Obtaining valid treatment effect inferences remains a challenging problem when dealing with numerous instruments and non-sparse control variables. In this paper, we propose a novel ridge regularization-based instrumental variables method for estimation and inference in the presence of both high-dimensional instrumental variables and high-dimensional control variables. These methods are applicable both with and without sparsity assumptions. To address the bias caused by high-dimensional instruments, we introduce a two-step procedure incorporating a data-splitting strategy. We establish statistical properties of the estimator, including consistency and asymptotic normality. Furthermore, we develop statistical inference procedures by providing a consistent estimator for the asymptotic variance of the estimator. The finite sample performance of the proposed method is evaluated through numerical simulations. Results indicate that the new estimator consistently outperforms existing sparsity-based approaches across various settings, offering valuable insights for more complex scenarios. Finally, we provide an empirical application estimating the causal effect of schooling on earnings by addressing potential endogeneity through the use of high-dimensional instrumental variables and high-dimensional covariates.
EASI Drugs in the Streets of Colombia: Modeling Heterogeneous and Endogenous Drug Preferences
Authors: Santiago Montoya-Blandón, Andrés Ramírez-Hassan
Date: 2025-03-25 00:00:00
Tasks: counterfactual

The response of illicit drug consumers to large-scale policy changes, such as legalization, is heavily mediated by their demand behavior. Since individual drug use is driven by many unobservable factors, accounting for unobserved heterogeneity is crucial for modeling demand and designing targeted public policies. This paper introduces a finite Gaussian mixture of Exact Affine Stone Index (EASI) demand systems to estimate the joint demand for marijuana, cocaine, and basuco (cocaine residual or "crack") in Colombia, accounting for corner solutions and endogenous price variation. Our results highlight the importance of unobserved heterogeneity in identifying reliable price elasticities. The method reveals two regular consumer subpopulations: "safe" (recreational) and "addict" users, with the majority falling into the first group. For the "safe" group, whose estimates are precise and nationally representative, all three drugs exhibit unitary price elasticities, with cocaine being complementary to marijuana and basuco an inferior substitute to cocaine. Given the low production cost of marijuana in Colombia, legalization is likely to drive prices down significantly. Our counterfactual analysis suggests that a 50% price decrease would result in a \$363 USD gain in utility-equivalent expenditure per representative consumer, \$120 million USD in government tax revenue, and a \$127 million USD revenue loss for drug dealers. Legalization, therefore, has the potential to reduce the incentive for drug-related criminal activity, the current largest source of violent crime in Colombia.
Identification of Average Treatment Effects in Nonparametric Panel Models
Authors: Susan Athey, Guido Imbens
Date: 2025-03-25 00:00:00

This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for example in problems of decompositions of group-level differences in outcomes, such as the much-studied gender wage gap.
Automatic Inference for Value-Added Regressions
Authors: Tian Xie
Date: 2025-03-24 00:00:00
Tasks: regression

It is common to use shrinkage methods such as empirical Bayes to improve estimates of teacher value-added. However, when the goal is to perform inference on coefficients in the regression of long-term outcomes on value-added, it's unclear whether shrinking the value-added estimators can help or hurt. In this paper, we consider a general class of value-added estimators and the properties of their corresponding regression coefficients. Our main finding is that regressing long-term outcomes on shrinkage estimates of value-added performs an automatic bias correction: the associated regression estimator is asymptotically unbiased, asymptotically normal, and efficient in the sense that it is asymptotically equivalent to regressing on the true (latent) value-added. Further, OLS standard errors from regressing on shrinkage estimates are consistent. As such, efficient inference is easy for practitioners to implement: simply regress outcomes on shrinkage estimates of value added.
Empirical Bayes shrinkage (mostly) does not correct the measurement error in regression
Authors: Jiafeng Chen, Jiaying Gu, Soonwoo Kwon
Date: 2025-03-24 00:00:00
Tasks: Attribute

In the value-added literature, it is often claimed that regressing on empirical Bayes shrinkage estimates corrects for the measurement error problem in linear regression. We clarify the conditions needed; we argue that these conditions are stronger than the those needed for classical measurement error correction, which we advocate for instead. Moreover, we show that the classical estimator cannot be improved without stronger assumptions. We extend these results to regressions on nonlinear transformations of the latent attribute and find generically slow minimax estimation rates.
Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model
Authors: KyungSu Kim
Date: 2025-03-24 00:00:00

This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies
Demand Estimation with Text and Image Data
Authors: Giovanni Compiani, Ilya Morozov, Stephan Seiler
Date: 2025-03-26 00:00:00
Tasks: Attribute, counterfactual

We propose a demand estimation method that leverages unstructured text and image data to infer substitution patterns. Using pre-trained deep learning models, we extract embeddings from product images and textual descriptions and incorporate them into a random coefficients logit model. This approach enables researchers to estimate demand even when they lack data on product attributes or when consumers value hard-to-quantify attributes, such as visual design or functional benefits. Using data from a choice experiment, we show that our approach outperforms standard attribute-based models in counterfactual predictions of consumers' second choices. We also apply it across 40 product categories on Amazon and consistently find that text and image data help identify close substitutes within each category.
General Training and Worker Motivation: Experimental Evidence on Discretionary Effort
Authors: Lawrence Choo, Senran Lin, Liangfo Zhao
Date: 2025-03-26 00:00:00

This study investigates the reaction of workers to employer-sponsored general training that provides skills useful not only in the incumbent employer but also in other firms in the industry. While previous research has focused primarily on workers' responses to wage renegotiation, our work extends this understanding by exploring an additional dimension -- workers' discretionary effort beyond their job duties, which is not verifiable. We conduct a laboratory experiment to observe workers' responses in such an effort to different training intensities. We find that workers generally increase their discretionary effort in response to general training, regardless of whether it is employer-sponsored or mandated. Moreover, the employer's intention behind offering training influences both effort and workers' renegotiation responses. Additionally, when workers can penalize employers, they do so, although higher employer-determined training intensities mitigate this behavior.
Economic impact of biomarker-based aging interventions on healthcare costs and individual value
Authors: Federico Felizzi
Date: 2025-03-26 00:00:00

We investigate the economic impact of controlling the pace of aging through biomarker monitoring and targeted interventions. Using the DunedinPACE epigenetic clock as a measure of biological aging rate, we model how different intervention scenarios affect frailty trajectories and their subsequent influence on healthcare costs, lifespan, and health quality. Our model demonstrates that controlling DunedinPACE from age 50 onwards can reduce frailty prevalence, resulting in cumulative healthcare savings of up to CHF 131,608 per person over 40 years in our most optimistic scenario. From an individual perspective, the willingness to pay for such interventions reaches CHF 6.7 million when accounting for both extended lifespan and improved health quality. These findings suggest substantial economic value in technologies that can monitor and modify biological aging rates, providing evidence for both healthcare systems and consumer-focused business models in longevity medicine.
The economics of global personality diversity
Authors: Paul X. McCarthy, Xian Gong, Marieth Coetzer, Marian-Andrei Rizoiu, Margaret L. Kern, John A. Johnson, Richard Holden, Fabian Braesemann
Date: 2025-03-25 00:00:00
Tasks: Diversity

This study explores the relationship between personality diversity and national economic performance, introducing the Global Personality Diversity Index (${\Psi}$-GPDI) as a novel metric. Leveraging a dataset of 760,242 individuals across 135 countries, we quantify within-country diversity based on the Big Five personality traits. Our findings reveal that personality diversity accounts for 19.9% of the variance in GDP per capita and provides an additional 2.8% explanatory power beyond institutional quality and immigration, underscoring its unique contribution to economic vitality. Through multi-factor analysis, we demonstrate how personality diversity complements existing economic frameworks, offering actionable insights for policymakers seeking to enhance innovation, productivity, and resilience. This research positions psychological diversity as a critical yet under explored factor in driving economic growth, bridging the fields of psychology and economics.