pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying Changes to Healthcare Utilization After a Reduction in Cost-sharing Among Deductible Plan Enrollees
Authors: Kris Wain, Debra P Ritzwoller, Marcelo Coca Perraillon
Date: 2025-03-25 00:00:00

Health plan deductibles are a form of cost-sharing that require patients to pay out-of-pocket before insurance pays for benefits. Deductible plans have become increasingly common in the United Sates to mitigate escalating healthcare costs. Quantifying the impact of increased cost-sharing from deductibles on utilization is a challenging empirical question because individuals and employers self-select into plans with deductibles. We evaluated the impact of cost-sharing in plans with deductibles by leveraging an accidental injury to a family member as an instrumental variable that strongly predicted the non-injured family member reaching their deductible maximum, which resulted in a reduction in cost-sharing. Our outcome measures examined utilization subject to cost-sharing as compared to utilization exempt from cost-sharing. Using data from the same healthcare system to control for quality and provider network, we found that reaching the deductible increased emergency department (ED) utilization by 10.0 percentage points (pp). Nearly one-quarter of the increased ED utilization was potentially avoidable. Wellness visits not subject to cost-sharing decreased by 5.7 pp. Results were similar for high-deductible plans and for families meeting their maximum out-of-pocket amount. These findings provide causal evidence that individuals enrolled in plans with deductibles change utilization patterns after an exogenous reduction in cost-sharing.
The Nonlinear Impact of Minimum Wage on Labor Employment in China
Authors: Junhan Lyu, Tianle Zhai, Zicheng Peng, Xuhang Huang
Date: 2025-03-25 00:00:00
Tasks: regression, Time Series

This paper examines the impact of increasing minimum wages, focusing primarily on their effect on employment. Our research involved analyzing the statistics of panel data, testing fixed effects and stationary, conducting linear regression, and integrating the linear regression model with nonlinear model analysis. The results indicate that fluctuations in the employment rate are almost entirely explained by the selected explanatory variables, and there is a significant negative correlation between minimum wages and the employment rate. This paper contributes to current research by providing more comprehensive analyses, particularly through the use of nonlinear models, resulting in better-fitting models. We employed multiple fitting methods for time series data and their differentials, combining these results with nonlinear analysis.
Augmenting or Automating Labor? The Effect of AI Development on New Work, Employment, and Wages
Authors: David Marguerit
Date: 2025-03-24 00:00:00

Artificial intelligence (AI) is reshaping the labor market by changing the task content of occupations. This study investigates the impact of AI development on the emergence of new work, employment, and wages in the United States from 2015 to 2022. I develop innovative methods to measure occupational and industry exposure to AI technologies that substitute labor (automation AI ) or enhance workers' output (augmentation AI), and to identify new work (i.e., new job titles). To address endogeneity, I use instrumental variable estimators, leveraging AI development in countries with limited economic ties to the United States. The findings indicate that automation AI negatively impacts new work, employment, and wages in low-skilled occupations, while augmentation AI fosters the emergence of new work and raises wages for high-skilled occupations. These results suggest that AI may contribute to rising wage inequality.
Cursed Job Market Signaling
Authors: Po-Hsuan Lin, Yen Ling Tan
Date: 2025-03-24 00:00:00

We study how cursedness, the tendency to neglect how other people's strategies depend on their private information, affects information transmission in Spence's job market signaling game. We characterize the Cursed Sequential Equilibrium and show that as players become more cursed, the worker obtains less education -- a costly signal that does not enhance productivity -- suggesting that cursedness improves the efficiency of information transmission. However, this efficiency improvement depends on the richness of the message space. Revisiting the job market signaling experiment by K\"ubler, M\"uller, and Normann (2008), we find supportive evidence for our theory.
Flexible Learning via Noise Reduction
Authors: Peter Achim, Kemal Ozbek
Date: 2025-03-26 00:00:00

We develop a novel framework for costly information acquisition in which a decision-maker learns about an unobserved state by choosing a signal distribution, with the cost of information determined by the distribution of noise in the signal. We show that a natural set of axioms admits a unique integral representation of the cost function, and we establish the uniform dominance principle: there always exists an optimal experiment that generates signals with uniform noise. The uniform dominance principle allows us to reduce the infinite-dimensional optimization problem of finding an optimal information structure to finding a single parameter that measures the level of noise. We show that an optimal experiment exists under natural conditions, and we characterize it using generalized first-order conditions that accommodate non-smooth payoff functions and decision rules. Finally, we demonstrate the tractability of our framework in a bilateral trade setting in which a buyer learns about product quality.
Entry and disclosure in group contests
Authors: Luke Boosey, Philip Brookins, Dmitry Ryvkin
Date: 2025-03-25 00:00:00

We study information disclosure policies for contests among groups. Each player endogenously decides whether or not to participate in competition as a member of their group. Within-group aggregation of effort is best-shot, i.e., each group's performance is determined by the highest investment among its members. We consider a generalized all-pay auction setting, in which the group with the highest performance wins the contest with certainty. Players' values for winning are private information at the entry stage, but may be disclosed at the competition stage. We compare three disclosure policies: (i) no disclosure, when the number of entrants remains unknown and their values private; (ii) within-group disclosure, when this information is disclosed within each group but not across groups; and (iii) full disclosure, when the information about entrants is disclosed across groups. For the benchmark case of contests between individuals, information disclosure always reduces expected aggregate investment. However, this is no longer true in group contests: Within-group disclosure unambiguously raises aggregate investment, while the effect of full disclosure is ambiguous.
The Design of Monopoly Information Broker
Authors: Junjie Chen, Takuro Yamashita
Date: 2025-03-25 00:00:00

An information broker incentivizes consumers to share their information, while designing an information structure to shape the market segmentation. The information broker is a metaphor for an Internet platform that matches consumers with retailers. We are interested in a market with heterogeneous retailers and heterogeneous consumers. The optimal broking mechanism consists of a simple threshold-based structure where consumers with strong preferences are assigned to the efficient retailer while consumers with weaker preferences are assigned to the inefficient retailer stochastically. Our analysis suggests that the privacy protection policy may have a stronger impact on less competitive retail markets.
A theory of anticipated surprise for understanding risky intertemporal choices
Authors: Ho Ka Chan, Taro Toyoizumi
Date: 2025-03-25 00:00:00
Tasks: Decision Making

People often deviate from expected utility theory when making risky and intertemporal choices. While the effects of probabilistic risk and time delay have been extensively studied in isolation, their interplay and underlying theoretical basis are still under debate. In this work, we applied our previously proposed anticipated surprise framework to intertemporal choices with and without explicit probabilistic risk, assuming that delayed reward may fail to materialize at a fixed hazard rate. The model prediction is consistent with key empirical findings: time inconsistency and aversion to timing risk stem from the avoidance of large negative surprises, while differences in mental representations of outcome resolution explain the conflicting effects of probabilistic risk on temporal discounting. This framework is applicable to a broad range of decision-making problems and offers a new perspective over how various types of risk may interact.
A Characterization of Sequential Equilibrium through $\varepsilon$-Perfect $γ$-Sequential Equilibrium with Local Sequential Rationality and Its Computation
Authors: Yiyin Cao, Chuangyin Dang
Date: 2025-03-25 00:00:00

Sequential equilibrium requires a consistent assessment and sequential rationality, where the consistent assessment emerges from a convergent sequence of totally mixed behavioral strategies and associated beliefs. However, the original definition lacks explicit guidance on constructing such convergent sequences. To overcome this difficulty, this paper presents a characterization of sequential equilibrium by introducing $\varepsilon$-perfect $\gamma$-sequential equilibrium with local sequential rationality. For any $\gamma>0$, we establish a perfect $\gamma$-sequential equilibrium as a limit point of a sequence of $\varepsilon_k$-perfect $\gamma$-sequential equilibrium with $\varepsilon_k\to 0$. A sequential equilibrium is then derived from a limit point of a sequence of perfect $\gamma_q$-sequential equilibrium with $\gamma_q\to 0$. This characterization systematizes the construction of convergent sequences and enables the analytical determination of sequential equilibria and the development of a polynomial system serving as a necessary and sufficient condition for $\varepsilon$-perfect $\gamma$-sequential equilibrium. Exploiting the characterization, we develop a differentiable path-following method to compute a sequential equilibrium.
Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages
Authors: Yangyang Meng, Jinpeng Li, Guodong Lin, Yu Pu, Guanbo Wang, Hu Du, Zhiming Shao, YuKai Huang, Ke Li, Wei-Qiang Zhang
Date: 2025-03-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This report introduces Dolphin, a large-scale multilingual automatic speech recognition (ASR) model that extends the Whisper architecture to support a wider range of languages. Our approach integrates in-house proprietary and open-source datasets to refine and optimize Dolphin's performance. The model is specifically designed to achieve notable recognition accuracy for 40 Eastern languages across East Asia, South Asia, Southeast Asia, and the Middle East, while also supporting 22 Chinese dialects. Experimental evaluations show that Dolphin significantly outperforms current state-of-the-art open-source models across various languages. To promote reproducibility and community-driven innovation, we are making our trained models and inference source code publicly available.
AutoRad-Lung: A Radiomic-Guided Prompting Autoregressive Vision-Language Model for Lung Nodule Malignancy Prediction
Authors: Sadaf Khademi, Mehran Shabanpour, Reza Taleei, Anastasia Oikonomou, Arash Mohammadi
Date: 2025-03-26 00:00:00
Tasks: Computed Tomography (CT), cross-modal alignment, Language Modeling, Language Modelling, Lung Cancer Diagnosis

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. A crucial challenge for early diagnosis is differentiating uncertain cases with similar visual characteristics and closely annotation scores. In clinical practice, radiologists rely on quantitative, hand-crafted Radiomic features extracted from Computed Tomography (CT) images, while recent research has primarily focused on deep learning solutions. More recently, Vision-Language Models (VLMs), particularly Contrastive Language-Image Pre-Training (CLIP)-based models, have gained attention for their ability to integrate textual knowledge into lung cancer diagnosis. While CLIP-Lung models have shown promising results, we identified the following potential limitations: (a) dependence on radiologists' annotated attributes, which are inherently subjective and error-prone, (b) use of textual information only during training, limiting direct applicability at inference, and (c) Convolutional-based vision encoder with randomly initialized weights, which disregards prior knowledge. To address these limitations, we introduce AutoRad-Lung, which couples an autoregressively pre-trained VLM, with prompts generated from hand-crafted Radiomics. AutoRad-Lung uses the vision encoder of the Large-Scale Autoregressive Image Model (AIMv2), pre-trained using a multi-modal autoregressive objective. Given that lung tumors are typically small, irregularly shaped, and visually similar to healthy tissue, AutoRad-Lung offers significant advantages over its CLIP-based counterparts by capturing pixel-level differences. Additionally, we introduce conditional context optimization, which dynamically generates context-specific prompts based on input Radiomics, improving cross-modal alignment.
UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift
Authors: Antoine Schieb, Bilal Hadjadji, Daniel Tshokola Mweze, Natalia Fernanda Valderrama, Valentin Derangère, Laurent Arnould, Sylvain Ladoire, Alain Lalande, Louis-Oscar Morel, Nathan Vinçon
Date: 2025-03-26 00:00:00
Tasks: Domain Adaptation, Image Registration, whole slide images

Histopathology slide digitization introduces scanner-induced domain shift that can significantly impact computational pathology models based on deep learning methods. In the state-of-the-art, this shift is often characterized at a broad scale (slide-level or dataset-level) but not patch-level, which limits our comprehension of the impact of localized tissue characteristics on the accuracy of the deep learning models. To address this challenge, we present a domain shift analysis framework based on UWarp, a novel registration tool designed to accurately align histological slides scanned under varying conditions. UWarp employs a hierarchical registration approach, combining global affine transformations with fine-grained local corrections to achieve robust tissue patch alignment. We evaluate UWarp using two private datasets, CypathLung and BosomShieldBreast, containing whole slide images scanned by multiple devices. Our experiments demonstrate that UWarp outperforms existing open-source registration methods, achieving a median target registration error (TRE) of less than 4 pixels (<1 micrometer at 40x magnification) while significantly reducing computational time. Additionally, we apply UWarp to characterize scanner-induced local domain shift in the predictions of Breast-NEOprAIdict, a deep learning model for breast cancer pathological response prediction. We find that prediction variability is strongly correlated with tissue density on a given patch. Our findings highlight the importance of localized domain shift analysis and suggest that UWarp can serve as a valuable tool for improving model robustness and domain adaptation strategies in computational pathology.
SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging Environments
Authors: Tanmoy Dam, Sanjay Bhargav Dharavath, Sameer Alam, Nimrod Lilith, Aniruddha Maiti, Supriyo Chakraborty, Mir Feroskhan
Date: 2025-03-26 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection, Sensor Fusion

The fusion of LiDAR and camera sensors has demonstrated significant effectiveness in achieving accurate detection for short-range tasks in autonomous driving. However, this fusion approach could face challenges when dealing with long-range detection scenarios due to disparity between sparsity of LiDAR and high-resolution camera data. Moreover, sensor corruption introduces complexities that affect the ability to maintain robustness, despite the growing adoption of sensor fusion in this domain. We present SaViD, a novel framework comprised of a three-stage fusion alignment mechanism designed to address long-range detection challenges in the presence of natural corruption. The SaViD framework consists of three key elements: the Global Memory Attention Network (GMAN), which enhances the extraction of image features through offering a deeper understanding of global patterns; the Attentional Sparse Memory Network (ASMN), which enhances the integration of LiDAR and image features; and the KNNnectivity Graph Fusion (KGF), which enables the entire fusion of spatial information. SaViD achieves superior performance on the long-range detection Argoverse-2 (AV2) dataset with a performance improvement of 9.87% in AP value and an improvement of 2.39% in mAPH for L2 difficulties on the Waymo Open dataset (WOD). Comprehensive experiments are carried out to showcase its robustness against 14 natural sensor corruptions. SaViD exhibits a robust performance improvement of 31.43% for AV2 and 16.13% for WOD in RCE value compared to other existing fusion-based methods while considering all the corruptions for both datasets. Our code is available at \href{https://github.com/sanjay-810/SAVID}
Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling
Authors: Vinzenz Uhr, Ivan Diaz, Christian Rummel, Richard McKinley
Date: 2025-03-26 00:00:00
Tasks: Brain Segmentation, Deep Learning, Denoising, Segmentation

Cortical thickness measurements from magnetic resonance imaging, an important biomarker in many neurodegenerative and neurological disorders, are derived by many tools from an initial voxel-wise tissue segmentation. White matter (WM) hypointensities in T1-weighted imaging, such as those arising from multiple sclerosis or small vessel disease, are known to affect the output of brain segmentation methods and therefore bias cortical thickness measurements. These effects are well-documented among traditional brain segmentation tools but have not been studied extensively in tools based on deep-learning segmentations, which promise to be more robust. In this paper, we explore the potential of deep learning to enhance the accuracy and efficiency of cortical thickness measurement in the presence of WM lesions, using a high-quality lesion filling algorithm leveraging denoising diffusion networks. A pseudo-3D U-Net architecture trained on the OASIS dataset to generate synthetic healthy tissue, conditioned on binary lesion masks derived from the MSSEG dataset, allows realistic removal of white matter lesions in multiple sclerosis patients. By applying morphometry methods to patient images before and after lesion filling, we analysed robustness of global and regional cortical thickness measurements in the presence of white matter lesions. Methods based on a deep learning-based segmentation of the brain (Fastsurfer, DL+DiReCT, ANTsPyNet) exhibited greater robustness than those using classical segmentation methods (Freesurfer, ANTs).
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation
Authors: Farzan Moodi, Fereshteh Khodadadi Shoushtari, Gelareh Valizadeh, Dornaz Mazinani, Hanieh Mobarak Salari, Hamidreza Saligheh Rad
Date: 2025-03-26 00:00:00
Tasks: Brain Tumor Segmentation, Tumor Segmentation

Accurate segmentation of glioma brain tumors is crucial for diagnosis and treatment planning. Deep learning techniques offer promising solutions, but optimal model architectures remain under investigation. We used the BraTS 2021 dataset, selecting T1 with contrast enhancement (T1CE), T2, and Fluid-Attenuated Inversion Recovery (FLAIR) sequences for model development. The proposed Attention Xception UNet (AXUNet) architecture integrates an Xception backbone with dot-product self-attention modules, inspired by state-of-the-art (SOTA) large language models such as Google Bard and OpenAI ChatGPT, within a UNet-shaped model. We compared AXUNet with SOTA models. Comparative evaluation on the test set demonstrated improved results over baseline models. Inception-UNet and Xception-UNet achieved mean Dice scores of 90.88 and 93.24, respectively. Attention ResUNet (AResUNet) attained a mean Dice score of 92.80, with the highest score of 84.92 for enhancing tumor (ET) among all models. Attention Gate UNet (AGUNet) yielded a mean Dice score of 90.38. AXUNet outperformed all models with a mean Dice score of 93.73. It demonstrated superior Dice scores across whole tumor (WT) and tumor core (TC) regions, achieving 92.59 for WT, 86.81 for TC, and 84.89 for ET. The integration of the Xception backbone and dot-product self-attention mechanisms in AXUNet showcases enhanced performance in capturing spatial and contextual information. The findings underscore the potential utility of AXUNet in facilitating precise tumor delineation.
Euclidean Distance to Convex Polyhedra and Application to Class Representation in Spectral Images
Authors: Antoine Bottenmuller, Florent Magaud, Arnaud Demortière, Etienne Decencière, Petr Dokladal
Date: 2025-03-26 00:00:00

With the aim of estimating the abundance map from observations only, linear unmixing approaches are not always suitable to spectral images, especially when the number of bands is too small or when the spectra of the observed data are too correlated. To address this issue in the general case, we present a novel approach which provides an adapted spatial density function based on any arbitrary linear classifier. A robust mathematical formulation for computing the Euclidean distance to polyhedral sets is presented, along with an efficient algorithm that provides the exact minimum-norm point in a polyhedron. An empirical evaluation on the widely-used Samson hyperspectral dataset demonstrates that the proposed method surpasses state-of-the-art approaches in reconstructing abundance maps. Furthermore, its application to spectral images of a Lithium-ion battery, incompatible with linear unmixing models, validates the method's generality and effectiveness.
Modality-Independent Brain Lesion Segmentation with Privacy-aware Continual Learning
Authors: Yousef Sadegheih, Pratibha Kumari, Dorit Merhof
Date: 2025-03-26 00:00:00
Tasks: Continual Learning, Knowledge Distillation, Lesion Segmentation, Mixture-of-Experts, Segmentation

Traditional brain lesion segmentation models for multi-modal MRI are typically tailored to specific pathologies, relying on datasets with predefined modalities. Adapting to new MRI modalities or pathologies often requires training separate models, which contrasts with how medical professionals incrementally expand their expertise by learning from diverse datasets over time. Inspired by this human learning process, we propose a unified segmentation model capable of sequentially learning from multiple datasets with varying modalities and pathologies. Our approach leverages a privacy-aware continual learning framework that integrates a mixture-of-experts mechanism and dual knowledge distillation to mitigate catastrophic forgetting while not compromising performance on newly encountered datasets. Extensive experiments across five diverse brain MRI datasets and four dataset sequences demonstrate the effectiveness of our framework in maintaining a single adaptable model, capable of handling varying hospital protocols, imaging modalities, and disease types. Compared to widely used privacy-aware continual learning methods such as LwF, SI, EWC, and MiB, our method achieves an average Dice score improvement of approximately 11%. Our framework represents a significant step toward more versatile and practical brain lesion segmentation models, with implementation available at \href{https://github.com/xmindflow/BrainCL}{GitHub}.