pwc-archive/papers-with-abstracts huggingface dataset:
Semiparametric Triple Difference Estimators
Authors: Sina Akbari, Negar Kiyavash, AmirEmad Ghassami
Date: 2025-02-27 00:00:00
Tasks: Causal Inference

The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-in-differences framework through leveraging data from an auxiliary domain. Despite being commonly applied in empirical research, the triple difference framework has received relatively limited attention in the statistics literature. Specifically, investigating the intricacies of identification and the design of robust and efficient estimators for this framework has remained largely unexplored. This work aims to address these gaps in the literature. From the identification standpoint, we present outcome regression and weighting methods to identify the average treatment effect on the treated in both panel data and repeated cross-section settings. For the latter, we relax the commonly made assumption of time-invariant covariates. From the estimation perspective, we consider semiparametric estimators for the triple difference framework in both panel data and repeated cross-sections settings. These estimators are based upon the cross-fitting technique, and flexible machine learning tools can be used to estimate the nuisance components. We demonstrate that our proposed estimators are doubly robust, and we characterize the conditions under which they are consistent and asymptotically normal.
Time-Varying Identification of Structural Vector Autoregressions
Authors: Annika Camehl, Tomasz Woźniak
Date: 2025-02-27 00:00:00

We propose a novel Bayesian heteroskedastic Markov-switching structural vector autoregression with data-driven time-varying identification. The model selects among alternative patterns of exclusion restrictions to identify structural shocks within the Markov process regimes. We implement the selection through a multinomial prior distribution over these patterns, which is a spike'n'slab prior for individual parameters. By combining a Markov-switching structural matrix with heteroskedastic structural shocks following a stochastic volatility process, the model enables shock identification through time-varying volatility within a regime. As a result, the exclusion restrictions become over-identifying, and their selection is driven by the signal from the data. Our empirical application shows that data support time variation in the US monetary policy shock identification. We also verify that time-varying volatility identifies the monetary policy shock within the regimes.
Empirical likelihood approach for high-dimensional moment restrictions with dependent data
Authors: Jinyuan Chang, Qiao Hu, Zhentao Shi, Jia Zhang
Date: 2025-02-26 00:00:00
Tasks: Dimensionality Reduction, Time Series

Economic and financial models -- such as vector autoregressions, local projections, and multivariate volatility models -- feature complex dynamic interactions and spillovers across many time series. These models can be integrated into a unified framework, with high-dimensional parameters identified by moment conditions. As the number of parameters and moment conditions may surpass the sample size, we propose adding a double penalty to the empirical likelihood criterion to induce sparsity and facilitate dimension reduction. Notably, we utilize a marginal empirical likelihood approach despite temporal dependence in the data. Under regularity conditions, we provide asymptotic guarantees for our method, making it an attractive option for estimating large-scale multivariate time series models. We demonstrate the versatility of our procedure through extensive Monte Carlo simulations and three empirical applications, including analyses of US sectoral inflation rates, fiscal multipliers, and volatility spillover in China's banking sector.
Mass Shootings, Community Mobility, and the Relocation of Economic Activity
Authors: Miguel Cuellar, Hyunseok Jung
Date: 2025-02-27 00:00:00

Using foot traffic data for over 150,000 points of interest (POIs) near the sites of 42 mass shootings (2018-2022, U.S.), we evaluate the spatial-temporal impact of the tragic events on community mobility and relocation of economic activities. Visits to nearby POIs decrease, while farther away POIs experience increased foot traffic, implying that communities shift their activities away from the shooting sites. The impact is stronger when stronger trauma responses are expected. Our results suggest that mass shootings drive significant displacements of economic activities and can consequently lead to welfare losses due to distortions in optimal choices of time and location.
Mobility and Mobility Measures
Authors: Frank A. Cowell, Emmanuel Flachaire
Date: 2025-02-26 00:00:00

We examine whether mobility measures appropriately represent changes in individual status, like income or ranks. We suggest three elementary principles for mobility comparisons and show that many commonly used indices violate one or more of them. These principles are used to characterise two classes of measures that have a natural interpretation in terms of distributional analysis. Class-1 measures are based on the summation of power functions of individual status levels and have connections with generalised-entropy and Kolm inequality measures. Class-2 measures are based on the weighted aggregation of individual status changes and have connections with (extended) Gini inequality measures.
Measuring trade costs and analyzing the determinants of trade growth between Cambodia and major trading partners: 1993 to 2019
Authors: Borin Keo, Bin Li, Waqas Younis
Date: 2025-02-26 00:00:00

High trade costs pose substantial barriers to the process of trade liberalization. This study aims to measure trade costs and explore the driving forces behind the growth of bilateral trade between Cambodia and its top 30 trading partners from 1993 to 2019. Using a micro-founded measure of trade costs derived from the gravity model, we find that Cambodia's average trade costs decreased by 35.43 percent between 1993 and 2019. Fluctuations in average trade costs persisted until 2014, despite Cambodia's accession to the World Trade Organization (WTO) in 2004. Since then, these costs have declined more rapidly. Cambodia's bilateral trade costs are lower with its major trading partners in Southeast Asia and East Asia than with those in South Asia, Oceania, Europe, and North America. Cambodia's average trade costs with developing and emerging economies are lower than those with developed economies. Between 2014 and 2019, Cambodia experienced a notable decline in average trade costs with trading partners along the Belt and Road Initiative (BRI) corridors by 34.78 percent, twice as fast as with non-BRI trading partners. Regarding the decomposition of trade growth, we find that the expansion of Cambodian trade over the period from 1993 to 2019 was driven by three factors: the rise in income (59.65 percent), the decline in trade costs (56.69 percent), and the decline in multilateral resistance (minus 16.34 percent). The findings of this study have significant implications for a better understanding of Cambodia's development process toward global trade integration over the past two decades. Our results suggest that Cambodia can optimize its trade expansion potential by focusing on its relations with trading partners exhibiting high economic growth potential and those showing substantial reductions in trade costs.
Incomplete Information Robustness
Authors: Stephen Morris, Takashi Ui
Date: 2025-02-26 00:00:00

Consider an analyst who models a strategic situation using an incomplete information game. The true game may involve correlated, duplicated belief hierarchies, but the analyst lacks knowledge of the correlation structure and can only approximate each belief hierarchy. To make predictions in this setting, the analyst uses belief-invariant Bayes correlated equilibria (BIBCE) and seeks to determine which one is justifiable. We address this question by introducing the notion of robustness: a BIBCE is robust if, for every nearby incomplete information game, there exists a BIBCE close to it. Our main result provides a sufficient condition for robustness using a generalized potential function. In a supermodular potential game, a robust BIBCE is a Bayes Nash equilibrium, whereas this need not hold in other classes of games.
Differentially Private Sequential Learning
Authors: Yuxin Liu, M. Amin Rahimian
Date: 2025-02-26 00:00:00
Tasks: Informativeness

In a differentially private sequential learning setting, agents introduce endogenous noise into their actions to maintain privacy. Applying this to a standard sequential learning model leads to different outcomes for continuous vs. binary signals. For continuous signals with a nonzero privacy budget, we introduce a novel smoothed randomized response mechanism that adapts noise based on distance to a threshold, unlike traditional randomized response, which applies uniform noise. This enables agents' actions to better reflect both private signals and observed history, accelerating asymptotic learning speed to $\Theta_{\epsilon}(\log(n))$, compared to $\Theta(\sqrt{\log(n)})$ in the non-private regime where privacy budget is infinite. Moreover, in the non-private setting, the expected stopping time for the first correct decision and the number of incorrect actions diverge, meaning early agents may make mistakes for an unreasonably long period. In contrast, under a finite privacy budget $\epsilon \in (0,1)$, both remain finite, highlighting a stark contrast between private and non-private learning. Learning with continuous signals in the private regime is more efficient, as smooth randomized response enhances the log-likelihood ratio over time, improving information aggregation. Conversely, for binary signals, differential privacy noise hinders learning, as agents tend to use a constant randomized response strategy before an information cascade forms, reducing action informativeness and hampering the overall process.
Multidimensional Monotonicity and Economic Applications
Authors: Frank Yang, Kai Hao Yang
Date: 2025-02-26 00:00:00

We characterize the extreme points of multidimensional monotone functions from $[0,1]^n$ to $[0,1]$, as well as the extreme points of the set of one-dimensional marginals of these functions. These characterizations lead to new results in various mechanism design and information design problems, including public good provision with interdependent values; interim efficient bilateral trade mechanisms; asymmetric reduced form auctions; and optimal private private information structure. As another application, we also present a mechanism anti-equivalence theorem for two-agent, two-alternative social choice problems: A mechanism is payoff-equivalent to a deterministic DIC mechanism if and only if they are ex-post equivalent.
Locational Energy Storage Bid Bounds for Facilitating Social Welfare Convergence
Authors: Ning Qi, Bolun Xu
Date: 2025-02-25 00:00:00

This paper proposes a novel method to generate bid bounds that can serve as offer caps for energy storage in electricity markets to help reduce system costs and regulate potential market power exercises. We derive the bid bounds based on a tractable multi-period economic dispatch chance-constrained formulation that systematically incorporates the uncertainty and risk preference of the system operator. The key analytical results verify that the bounds effectively cap storage bids across all uncertainty scenarios with a guaranteed confidence level. We show that bid bounds decrease as the state of charge increases but rise with greater netload uncertainty and risk preference. We test the effectiveness of the proposed pricing mechanism based on the 8-bus ISO-NE test system, including agent-based storage bidding models. Simulation results demonstrate that the proposed bid bounds effectively align storage bids with the social welfare objective and outperform existing deterministic bid bounds. Under 30% renewable capacity and 20% storage capacity, the bid bounds contribute to an average reduction of 0.17% in system cost, while increasing storage profit by an average of 10.16% across various system uncertainty scenarios and bidding strategies. These benefits scale up with increased storage economic withholding and storage capacity.
T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration
Authors: Tamir Shor, Moti Freiman, Chaim Baskin, Alex Bronstein
Date: 2025-02-27 00:00:00
Tasks: compressed sensing

Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema. However, the inherently dynamic nature of the heart imposes strict limits on acquisition times, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS) approaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling patterns with the reconstruction network can substantially improve performance. Still, most current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit the full acceleration and accuracy potential. In this work, we introduce T1-PILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model into the sampling-reconstruction framework to guide the learning of non-Cartesian trajectories, crossframe alignment, and T1 decay estimation. Through extensive experiments on the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes), achieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked improvements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both enhanced quantitative accuracy and reduced acquisition times. Code for reproducing all results will be made publicly available upon publication.
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering
Authors: Wei Yang, Yiran Zhu, Jiayu Shen, Yuhan Tang, Chengchang Pan, Hui He, Yan Su, Honggang Qi
Date: 2025-02-27 00:00:00
Tasks: Clustering, Computational Efficiency, Diagnostic, Dimensionality Reduction, Diversity, Medical Image Analysis

Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Although deep learning has achieved remarkable progress in medical image analysis, traditional DME diagnosis still relies on extensive annotated data and subjective ophthalmologist assessments, limiting practical applications. To address this, we present RURANET++, an unsupervised learning-based automated DME diagnostic system. This framework incorporates an optimized U-Net architecture with embedded Spatial and Channel Squeeze & Excitation (SCSE) attention mechanisms to enhance lesion feature extraction. During feature processing, a pre-trained GoogLeNet model extracts deep features from retinal images, followed by PCA-based dimensionality reduction to 50 dimensions for computational efficiency. Notably, we introduce a novel clustering algorithm employing multi-projection heads to explicitly control cluster diversity while dynamically adjusting similarity thresholds, thereby optimizing intra-class consistency and inter-class discrimination. Experimental results demonstrate superior performance across multiple metrics, achieving maximum accuracy (0.8411), precision (0.8593), recall (0.8411), and F1-score (0.8390), with exceptional clustering quality. This work provides an efficient unsupervised solution for DME diagnosis with significant clinical implications.
Few Shot Alternating GD and Minimization for Generalizable Real-Time MRI
Authors: Silpa Babu, Sajan Goud Lingala, Namrata Vaswani
Date: 2025-02-26 00:00:00

This work introduces a novel near real-time (real-time after an initial short delay) MRI solution that handles motion well and is generalizable. Here, real-time means the algorithm works well on a highly accelerated scan, is zero-latency (reconstructs a new frame as soon as MRI data for it arrives), and is fast enough, i.e., the time taken to process a frame is comparable to the scan time per frame or lesser. We demonstrate its generalizability through experiments on 6 prospective datasets and 17 retrospective datasets that span multiple different applications -- speech larynx imaging, brain, ungated cardiac perfusion, cardiac cine, cardiac OCMR, abdomen; sampling schemes -- Cartesian, pseudo-radial, radial, spiral; and sampling rates -- ranging from 6x to 4 radial lines per frame. Comparisons with a large number of existing real-time and batch methods, including unsupervised and supervised deep learning methods, show the power and speed of our approach.
Balanced Rate-Distortion Optimization in Learned Image Compression
Authors: Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu
Date: 2025-02-27 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Compression

Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced updates due to diverse gradients of the rate and distortion objectives. This imbalance can lead to suboptimal optimization, where one objective dominates, thereby reducing overall compression efficiency. To address this challenge, we reformulate R-D optimization as a multi-objective optimization (MOO) problem and introduce two balanced R-D optimization strategies that adaptively adjust gradient updates to achieve more equitable improvements in both rate and distortion. The first proposed strategy utilizes a coarse-to-fine gradient descent approach along standard R-D optimization trajectories, making it particularly suitable for training LIC models from scratch. The second proposed strategy analytically addresses the reformulated optimization as a quadratic programming problem with an equality constraint, which is ideal for fine-tuning existing models. Experimental results demonstrate that both proposed methods enhance the R-D performance of LIC models, achieving around a 2\% BD-Rate reduction with acceptable additional training cost, leading to a more balanced and efficient optimization process. Code will be available at https://gitlab.com/viper-purdue/Balanced-RD.
Generative augmentations for improved cardiac ultrasound segmentation using diffusion models
Authors: Gilles Van De Vyver, Aksel Try Lenz, Erik Smistad, Sindre Hellum Olaisen, Bjørnar Grenne, Espen Holte, Håavard Dalen, Lasse Løvstakken
Date: 2025-02-27 00:00:00
Tasks: Segmentation

One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differences in annotation conventions between datasets. This makes it difficult to design robust segmentation models that generalize well to external datasets. This work utilizes diffusion models to create generative augmentations that can significantly improve diversity of the dataset and thus the generalisability of segmentation models without the need for more annotated data. The augmentations are applied in addition to regular augmentations. A visual test survey showed that experts cannot clearly distinguish between real and fully generated images. Using the proposed generative augmentations, segmentation robustness was increased when training on an internal dataset and testing on an external dataset with an improvement of over 20 millimeters in Hausdorff distance. Additionally, the limits of agreement for automatic ejection fraction estimation improved by up to 20% of absolute ejection fraction value on out of distribution cases. These improvements come exclusively from the increased variation of the training data using the generative augmentations, without modifying the underlying machine learning model. The augmentation tool is available as an open source Python library at https://github.com/GillesVanDeVyver/EchoGAINS.
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
Authors: Guanting Liu, Yi Wang, Xi Chen, Baoyu Du, Penglei Li, Yuan Wu, Zhice Fang
Date: 2025-02-27 00:00:00
Tasks: Multi-Task Learning

Landslides are among the most common natural disasters globally, posing significant threats to human society. Deep learning (DL) has proven to be an effective method for rapidly generating landslide inventories in large-scale disaster areas. However, DL models rely heavily on high-quality labeled landslide data for strong feature extraction capabilities. And landslide detection using DL urgently needs a benchmark dataset to evaluate the generalization ability of the latest models. To solve the above problems, we construct a Large-scale Multi-source High-resolution Landslide Dataset (LMHLD) for Landslide Detection based on DL. LMHLD collects remote sensing images from five different satellite sensors across seven study areas worldwide: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023). The dataset includes a total of 25,365 patches, with different patch sizes to accommodate different landslide scales. Additionally, a training module, LMHLDpart, is designed to accommodate landslide detection tasks at varying scales and to alleviate the issue of catastrophic forgetting in multi-task learning. Furthermore, the models trained by LMHLD is applied in other datasets to highlight the robustness of LMHLD. Five dataset quality evaluation experiments designed by using seven DL models from the U-Net family demonstrate that LMHLD has the potential to become a benchmark dataset for landslide detection. LMHLD is open access and can be accessed through the link: https://doi.org/10.5281/zenodo.11424988. This dataset provides a strong foundation for DL models, accelerates the development of DL in landslide detection, and serves as a valuable resource for landslide prevention and mitigation efforts.
RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images
Authors: Yuhan Tang, Yudian Wang, Weizhen Li, Ye Yue, Chengchang Pan, Honggang Qi
Date: 2025-02-26 00:00:00
Tasks: Diagnostic, Image Restoration, SSIM

Fundus image quality is crucial for diagnosing eye diseases, but real-world conditions often result in blurred or unreadable images, increasing diagnostic uncertainty. To address these challenges, this study proposes RetinaRegen, a hybrid model for retinal image restoration that integrates a readability classifi-cation model, a Diffusion Model, and a Variational Autoencoder (VAE). Ex-periments on the SynFundus-1M dataset show that the proposed method achieves a PSNR of 27.4521, an SSIM of 0.9556, and an LPIPS of 0.1911 for the readability labels of the optic disc (RO) region. These results demonstrate superior performance in restoring key regions, offering an effective solution to enhance fundus image quality and support clinical diagnosis.