pwc-archive/papers-with-abstracts huggingface dataset:
A Pharmacy Benefit Manager Insurance Business Model
Authors: Lawrence W. Abrams
Date: 2025-03-13 00:00:00
Tasks: model

It is time to move on from attempts to make the pharmacy benefit manager (PBM) reseller business model more transparent. Time and time again the Big 3 PBMs have developed opaque alternatives to piece-meal 100% pass-through mandates. Time and time again PBMs have demonstrated expertise in finding loopholes in state government disclosure laws. The purpose of this paper is to provide quantitative estimates of two transparent insurance business models as a solution to the PBM agency issue. The key parameter used is an 8% gross profit margin figure disclosed by the Big 3 PBMs themselves. Based on reported drug trend delivered to plans, we use a $1,200 to $1,500 per member per year (PMPY) as the range for this key performance indicator (KPI). We propose that discussions of PBM insurance business models start with the following figures: (1) a fixed premium model with medical loss ratio ranging from 92% to 85%; (2) a fee-for-service model ranging from $96 to $180 PMPY with risk sharing of deviations from a contracted PMPY delivered drug spend.
Measuring the Sources of Taste-Based Discrimination Using List Experiments
Authors: Ariel Listo, Ercio A. Munoz, Dario Sansone
Date: 2025-03-12 00:00:00

This study examines how attitudes among supervisors, co-workers, and customers contribute to discrimination against sexual minorities in the workplace. A large, nationally representative sample in Chile was recruited in collaboration with a local firm. The survey employs a series of double list experiments designed to measure attitudes on sensitive issues while reducing social desirability bias, followed by direct questions on attitudes toward sexual minorities. Findings reveal a discrepancy between reported and actual comfort levels with gay individuals in the labor market. Respondents underreport their discomfort by 15 to 23 percentage points, with the largest bias and lowest comfort levels observed when asked about supervising a gay employee. Additionally, respondents consistently underestimate broader societal support for gay employees and co-workers. These differences are reflected in real-stakes donation behavior: respondents who choose not to donate any amount from a lottery to a local LGBTQ-related NGO also report lower comfort levels and exhibit greater misreporting.
The E-Rule: A Novel Composite Indicator for Predicting Economic Recessions
Authors: Esmaeil Ebadi
Date: 2025-03-12 00:00:00

This study develops the E-Rule, a novel composite recession indicator that integrates financial market and labor market signals to improve the precision of recession forecasting. Combining the yield curve and the Sahm rule, the E-Rule provides a holistic and early-warning measure of economic downturns. Using historical data from 1976 onward, we empirically evaluate the E-Rule's predictive power relative to traditional indicators. The analysis employs machine learning techniques, including logistic regression, support vector machines, gradient boosting, and random forests, to assess predictive accuracy. Our findings demonstrate that the E-Rule offers a superior lead time in forecasting recessions and improves stability over existing methods.
Pooling Liquidity Pools in AMMs
Authors: Marcelo Bagnulo, Angel Hernando-Veciana, Efthymios Smyrniotis
Date: 2025-03-12 00:00:00

Market fragmentation across multiple Automated Market Makers (AMMs) creates inefficiencies such as costly arbitrage, unnecessarily high slippage and delayed incorporation of new information into prices. These inefficiencies raise trading costs, reduce liquidity provider profits, and degrade overall market efficiency. To address these issues, we propose a modification of the Constant Product Market Maker (CPMM) pricing mechanism, called the Global Market Maker (GMM), which aggregates liquidity information from all AMMs to mitigate these inefficiencies. Through theoretical and numerical analyses, we demonstrate that the GMM enhances profits for both AMMs and traders by eliminating arbitrage opportunities. Additionally, it reduces the profitability of sandwich attacks and minimizes impermanent losses.
Shinohara Rock-Paper-Scissors
Authors: Takashi Ui
Date: 2025-03-13 00:00:00

This paper analyzes Shinohara Rock-Paper-Scissors (RPS), a variant of the classic RPS game introduced by board game designer Yoshiteru Shinohara. Players compete against a host who always plays rock, so players choose either rock or paper. The twist is that if two or more players choose paper, they are eliminated, and the last remaining player is the winner, creating strategic tension among the players. There exists a unique symmetric subgame perfect equilibrium, in which the probability of choosing paper satisfies the equation $(1-p)^{n-1} + p^{n-1}/n = 1/n$, where $n$ is the number of remaining players. The game also admits a continuum of asymmetric equilibria.
Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech
Authors: Moreno La Quatra, Juan Rafael Orozco-Arroyave, Marco Sabato Siniscalchi
Date: 2025-03-13 00:00:00
Tasks: Binary Classification, Contrastive Learning, Self-Supervised Learning

This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for type-based binary classification. One head is specialized for diadochokinetic patterns. The other head looks for natural speech patterns present in continuous spoken utterances. Only one of the two heads is operative accordingly to the nature of the input. Speech representations are extracted from self-supervised learning (SSL) models and wavelet transforms. Adaptive layers, convolutional bottlenecks, and contrastive learning are exploited to reduce variations across languages. Our solution is assessed against two distinct datasets, EWA-DB, and PC-GITA, which cover Slovak and Spanish languages, respectively. Results indicate that conventional models trained on a single language dataset struggle with cross-linguistic generalization, and naive combinations of datasets are suboptimal. In contrast, our model improves generalization on both languages, simultaneously.
Sound Field Estimation: Theories and Applications
Authors: Natsuki Ueno, Shoichi Koyama
Date: 2025-03-13 00:00:00
Tasks: Audio Signal Processing

The spatial information of sound plays a crucial role in various situations, ranging from daily activities to advanced engineering technologies. To fully utilize its potential, numerous research studies on spatial audio signal processing have been carried out in the literature. Sound field estimation is one of the key foundational technologies that can be applied to a wide range of acoustic signal processing techniques, including sound field reproduction using loudspeakers and binaural playback through headphones. The purpose of this paper is to present an overview of sound field estimation methods. After providing the necessary mathematical background, two different approaches to sound field estimation will be explained. This paper focuses on clarifying the essential theories of each approach, while also referencing state-of-the-art developments. Finally, several acoustic signal processing technologies will be discussed as examples of the application of sound field estimation.
Low Complexity Point Tracking of the Myocardium in 2D Echocardiography
Authors: Artem Chernyshov, John Nyberg, Vegard Holmstrøm, Md Abulkalam Azad, Bjørnar Grenne, Håvard Dalen, Svein Arne Aase, Lasse Lovstakken, Andreas Østvik
Date: 2025-03-13 00:00:00
Tasks: GPU, Point Tracking

Deep learning methods for point tracking are applicable in 2D echocardiography, but do not yet take advantage of domain specifics that enable extremely fast and efficient configurations. We developed MyoTracker, a low-complexity architecture (0.3M parameters) for point tracking in echocardiography. It builds on the CoTracker2 architecture by simplifying its components and extending the temporal context to provide point predictions for the entire sequence in a single step. We applied MyoTracker to the right ventricular (RV) myocardium in RV-focused recordings and compared the results with those of CoTracker2 and EchoTracker, another specialized point tracking architecture for echocardiography. MyoTracker achieved the lowest average point trajectory error at 2.00 $\pm$ 0.53 mm. Calculating RV Free Wall Strain (RV FWS) using MyoTracker's point predictions resulted in a -0.3$\%$ bias with 95$\%$ limits of agreement from -6.1$\%$ to 5.4$\%$ compared to reference values from commercial software. This range falls within the interobserver variability reported in previous studies. The limits of agreement were wider for both CoTracker2 and EchoTracker, worse than the interobserver variability. At inference, MyoTracker used 67$\%$ less GPU memory than CoTracker2 and 84$\%$ less than EchoTracker on large sequences (100 frames). MyoTracker was 74 times faster during inference than CoTracker2 and 11 times faster than EchoTracker with our setup. Maintaining the entire sequence in the temporal context was the greatest contributor to MyoTracker's accuracy. Slight additional gains can be made by re-enabling iterative refinement, at the cost of longer processing time.
An Ensemble-Based Two-Step Framework for Classification of Pap Smear Cell Images
Authors: Theo Di Piazza, Loic Boussel
Date: 2025-03-13 00:00:00

Early detection of cervical cancer is crucial for improving patient outcomes and reducing mortality by identifying precancerous lesions as soon as possible. As a result, the use of pap smear screening has significantly increased, leading to a growing demand for automated tools that can assist cytologists managing their rising workload. To address this, the Pap Smear Cell Classification Challenge (PS3C) has been organized in association with ISBI in 2025. This project aims to promote the development of automated tools for pap smear images classification. The analyzed images are grouped into four categories: healthy, unhealthy, both, and rubbish images which are considered as unsuitable for diagnosis. In this work, we propose a two-stage ensemble approach: first, a neural network determines whether an image is rubbish or not. If not, a second neural network classifies the image as containing a healthy cell, an unhealthy cell, or both.
Markerless Tracking-Based Registration for Medical Image Motion Correction
Authors: Luisa Neubig, Deirdre Larsen, Takeshi Ikuma, Markus Kopp, Melda Kunduk, Andreas M. Kist
Date: 2025-03-13 00:00:00
Tasks: Optical Flow Estimation, SSIM

Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swallowing a radiopaque bolus. These recordings capture multiple motion sources, including head movement, anatomical displacements, and bolus transit. To enable precise analysis of swallowing physiology, we aim to eliminate distracting motion, particularly head movement, while preserving essential swallowing-related dynamics. Optical flow methods fail due to artifacts like flickering and instability, making them unreliable for distinguishing different motion groups. We evaluated markerless tracking approaches (CoTracker, PIPs++, TAP-Net) and quantified tracking accuracy in key medical regions of interest. Our findings show that even sparse tracking points generate morphing displacement fields that outperform leading registration methods such as ANTs, LDDMM, and VoxelMorph. To compare all approaches, we assessed performance using MSE and SSIM metrics post-registration. We introduce a novel motion correction pipeline that effectively removes disruptive motion while preserving swallowing dynamics and surpassing competitive registration techniques.
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
Authors: Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov, Gerard Martí-Juan, Elisenda Eixarch, Andras Jakab, Vincent Dunet, Mériam Koob, Guillaume Auzias, Meritxell Bach Cuadra
Date: 2025-03-13 00:00:00
Tasks: Medical Image Analysis, Super-Resolution

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processing step where multiple stacks of thick 2D slices are registered together and combined to build a single, isotropic and artifact-free T2 weighted volume. We propose FetMRQC$_{SR}$, a machine-learning method that extracts more than 100 image quality metrics to predict image quality scores using a random forest model. This approach is well suited to a problem that is high dimensional, with highly heterogeneous data and small datasets. We validate FetMRQC$_{SR}$ in an out-of-domain (OOD) setting and report high performance (ROC AUC = 0.89), even when faced with data from an unknown site or SRR method. We also investigate failure cases and show that they occur in $45\%$ of the images due to ambiguous configurations for which the rating from the expert is arguable. These results are encouraging and illustrate how a non deep learning-based method like FetMRQC$_{SR}$ is well suited to this multifaceted problem. Our tool, along with all the code used to generate, train and evaluate the model are available at https://github.com/Medical-Image-Analysis-Laboratory/fetmrqc_sr/ .
Hybrid Agents for Image Restoration
Authors: Bingchen Li, Xin Li, Yiting Lu, Zhibo Chen
Date: 2025-03-13 00:00:00
Tasks: Image Restoration, In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Unified Image Restoration

Existing Image Restoration (IR) studies typically focus on task-specific or universal modes individually, relying on the mode selection of users and lacking the cooperation between multiple task-specific/universal restoration modes. This leads to insufficient interaction for unprofessional users and limits their restoration capability for complicated real-world applications. In this work, we present HybridAgent, intending to incorporate multiple restoration modes into a unified image restoration model and achieve intelligent and efficient user interaction through our proposed hybrid agents. Concretely, we propose the hybrid rule of fast, slow, and feedback restoration agents. Here, the slow restoration agent optimizes the powerful multimodal large language model (MLLM) with our proposed instruction-tuning dataset to identify degradations within images with ambiguous user prompts and invokes proper restoration tools accordingly. The fast restoration agent is designed based on a lightweight large language model (LLM) via in-context learning to understand the user prompts with simple and clear requirements, which can obviate the unnecessary time/resource costs of MLLM. Moreover, we introduce the mixed distortion removal mode for our HybridAgents, which is crucial but not concerned in previous agent-based works. It can effectively prevent the error propagation of step-by-step image restoration and largely improve the efficiency of the agent system. We validate the effectiveness of HybridAgent with both synthetic and real-world IR tasks.
G$^{2}$SF-MIAD: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection
Authors: Chengyu Tao, Xuanming Cao, Juan Du
Date: 2025-03-13 00:00:00
Tasks: Anomaly Detection

Industrial quality inspection plays a critical role in modern manufacturing by identifying defective products during production. While single-modality approaches using either 3D point clouds or 2D RGB images suffer from information incompleteness, multimodal anomaly detection offers promise through the complementary fusion of crossmodal data. However, existing methods face challenges in effectively integrating unimodal results and improving discriminative power. To address these limitations, we first reinterpret memory bank-based anomaly scores in single modalities as isotropic Euclidean distances in local feature spaces. Dynamically evolving from Eulidean metrics, we propose a novel \underline{G}eometry-\underline{G}uided \underline{S}core \underline{F}usion (G$^{2}$SF) framework that progressively learns an anisotropic local distance metric as a unified score for the fusion task. Through a geometric encoding operator, a novel Local Scale Prediction Network (LSPN) is proposed to predict direction-aware scaling factors that characterize first-order local feature distributions, thereby enhancing discrimination between normal and anomalous patterns. Additionally, we develop specialized loss functions and score aggregation strategy from geometric priors to ensure both metric generalization and efficacy. Comprehensive evaluations on the MVTec-3D AD dataset demonstrate the state-of-the-art detection performance of our method with low positive rate and better recall, which is essential in industrial application, and detailed ablation analysis validates each component's contribution.
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes
Authors: Donghyun Kim, Hyunah Ko, Chanyoung Kim, Seong Jae Hwang
Date: 2025-03-13 00:00:00
Tasks: Data Augmentation, Point Cloud Classification, Style Transfer

While 3D point clouds are widely utilized across various vision applications, their irregular and sparse nature make them challenging to handle. In response, numerous encoding approaches have been proposed to capture the rich semantic information of point clouds. Yet, a critical limitation persists: a lack of consideration for colored point clouds which are more capable 3D representations as they contain diverse attributes: color and geometry. While existing methods handle these attributes separately on a per-point basis, this leads to a limited receptive field and restricted ability to capture relationships across multiple points. To address this, we pioneer a point cloud encoding methodology that leverages 3D Fourier decomposition to disentangle color and geometric features while extending the receptive field through spectral-domain operations. Our analysis confirms that this encoding approach effectively separates feature components, where the amplitude uniquely captures color attributes and the phase encodes geometric structure, thereby enabling independent learning and utilization of both attributes. Furthermore, the spectral-domain properties of these components naturally aggregate local features while considering multiple points' information. We validate our point cloud encoding approach on point cloud classification and style transfer tasks, achieving state-of-the-art results on the DensePoint dataset with improvements via a proposed amplitude-based data augmentation strategy.
Dual-domain Modulation Network for Lightweight Image Super-Resolution
Authors: Wenjie Li, Heng Guo, Yuefeng Hou, Guangwei Gao, Zhanyu Ma
Date: 2025-03-13 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images with limited computational costs. We find existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts. Meanwhile, these methods are inefficient for handling frequency features and unsuitable for lightweight SR. In this paper, we show introducing both wavelet and Fourier information allows our model to consider both high-frequency features and overall SR structure reconstruction while reducing costs. Specifically, we propose a dual-domain modulation network that utilize wavelet-domain modulation self-Transformer (WMT) plus Fourier supervision to modulate frequency features in addition to spatial domain modulation. Compared to existing frequency-based SR modules, our WMT is more suitable for frequency learning in lightweight SR. Experimental results show that our method achieves a comparable PSNR of SRFormer and MambaIR while with less than 50% and 60% of their FLOPs and achieving inference speeds 15.4x and 5.4x faster, respectively, demonstrating the effectiveness of our method on SR quality and lightweight. Codes will be released upon acceptance.
CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning
Authors: Meng Wang, Zi Yang, Ruifeng Zhao, Yaoting Jiang
Date: 2025-03-13 00:00:00
Tasks: Lung Nodule Detection

The integration of Internet of Things (IoT) technology in pulmonary nodule detection significantly enhances the intelligence and real-time capabilities of the detection system. Currently, lung nodule detection primarily focuses on the identification of solid nodules, but different types of lung nodules correspond to various forms of lung cancer. Multi-type detection contributes to improving the overall lung cancer detection rate and enhancing the cure rate. To achieve high sensitivity in nodule detection, targeted improvements were made to the YOLOv8 model. Firstly, the C2f\_RepViTCAMF module was introduced to augment the C2f module in the backbone, thereby enhancing detection accuracy for small lung nodules and achieving a lightweight model design. Secondly, the MSCAF module was incorporated to reconstruct the feature fusion section of the model, improving detection accuracy for lung nodules of varying scales. Furthermore, the KAN network was integrated into the model. By leveraging the KAN network's powerful nonlinear feature learning capability, detection accuracy for small lung nodules was further improved, and the model's generalization ability was enhanced. Tests conducted on the LUNA16 dataset demonstrate that the improved model outperforms the original model as well as other mainstream models such as YOLOv9 and RT-DETR across various evaluation metrics.
RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging
Authors: Berk Iskender, Sushan Nakarmi, Nitin Daphalapurkar, Marc L. Klasky, Yoram Bresler
Date: 2025-03-13 00:00:00
Tasks: Denoising

Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only a single projection at one view angle is available at a time, making the inverse problem very challenging. Moreover, ground-truth dynamic data is usually either unavailable or too scarce to be used for supervised learning techniques. To tackle this problem, we propose RSR-NF, which uses a neural field (NF) to represent the dynamic object and, using the Regularization-by-Denoising (RED) framework, incorporates an additional static deep spatial prior into a variational formulation via a learned restoration operator. We use an ADMM-based algorithm with variable splitting to efficiently optimize the variational objective. We compare RSR-NF to three alternatives: NF with only temporal regularization; a recent method combining a partially-separable low-rank representation with RED using a denoiser pretrained on static data; and a deep-image prior-based model. The first comparison demonstrates the reconstruction improvements achieved by combining the NF representation with static restoration priors, whereas the other two demonstrate the improvement over state-of-the art techniques for dCT.