pwc-archive/papers-with-abstracts huggingface dataset:
Fiscal Policy and Household Savings in Central Europe (Poland, Croatia, and Slovak Republic) -- A Markov Switching VAR with Covid Shock
Authors: Tuhin G M Al Mamun, Ehsanullah, Md Sharif Hassan, Mohd Faizal Yusof, Md Aminul Islam, Naharin Binte Rab
Date: 2025-02-19 00:00:00

This study investigates the effectiveness of fiscal policies on household consumption, disposable income, and the propensity to consume during the COVID-19 pandemic across Croatia, Slovakia, and Poland. The purpose is to assess how variations in government debt, expenditures, revenue, and subsidies influenced household financial behaviors in response to economic shocks. Using a Markov Switching VAR model across three regimes: initial impact, peak crisis, and recovery.This analysis captures changes in household consumption, disposable income, and consumption propensities under different fiscal policy measures. The findings reveal that the Slovak Republic exhibited the highest fiscal effectiveness, demonstrating effective government policies that stimulated consumer spending and supported household income during the pandemic. Croatia also showed positive outcomes, particularly in terms of income, although rising government debt posed challenges to overall effectiveness. Conversely, Poland faced significant obstacles, with its fiscal measures leading to lower consumption and income outcomes, indicating limited policy efficacy. Conclusions emphasize the importance of tailored fiscal measures, as their effectiveness varied across countries and economic contexts. Recommendations include reinforcing consumption-supportive policies, particularly during crisis periods, to stabilize income and consumption expectations. This study underscores the significance of targeted fiscal actions in promoting household resilience and economic stability, as exemplified by the successful approach taken by the Slovak Republic.
Does Ideological Polarization Lead to Policy Polarization?
Authors: Philipp Denter
Date: 2025-02-20 00:00:00

I analyze an election involving two parties who are both office- and policy-motivated and who are ideologically polarized. One party may possess a valence advantage. The parties compete by proposing policies on a second policy issue. The analysis reveals a subtle relationship between ideological polarization and policy polarization. If ideologies are highly dispersed, there is a U-shaped relationship between ideological polarization and platform polarization. In contrast, if ideological dispersion is limited, increasing ideological polarization generally results in policy moderation. In both cases, valence plays no role in policy polarization. Finally, as in Buisseret and van Weelden (2022), adding ideological polarization adds nuance on the effects of increasing valence: both high- and low-valence candidates may adopt more extreme positions, depending on the electorate's degree of ideological polarization.
Ordinality in Random Allocation
Authors: Eun Jeong Heo, Vikram Manjunath
Date: 2025-02-19 00:00:00

In allocating objects via lotteries, it is common to consider ordinal rules that rely solely on how agents rank degenerate lotteries. While ordinality is often imposed due to cognitive or informational constraints, we provide another justification from an axiomatic perspective: for three-agent problems, the combination of efficiency, strategy-proofness, non-bossiness, and a weak form of continuity collectively implies ordinality.
Role of the Pretraining and the Adaptation data sizes for low-resource real-time MRI video segmentation
Authors: Masoud Thajudeen Tholan, Vinayaka Hegde, Chetan Sharma, Prasanta Kumar Ghosh
Date: 2025-02-20 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

Real-time Magnetic Resonance Imaging (rtMRI) is frequently used in speech production studies as it provides a complete view of the vocal tract during articulation. This study investigates the effectiveness of rtMRI in analyzing vocal tract movements by employing the SegNet and UNet models for Air-Tissue Boundary (ATB)segmentation tasks. We conducted pretraining of a few base models using increasing numbers of subjects and videos, to assess performance on two datasets. First, consisting of unseen subjects with unseen videos from the same data source, achieving 0.33% and 0.91% (Pixel-wise Classification Accuracy (PCA) and Dice Coefficient respectively) better than its matched condition. Second, comprising unseen videos from a new data source, where we obtained an accuracy of 99.63% and 98.09% (PCA and Dice Coefficient respectively) of its matched condition performance. Here, matched condition performance refers to the performance of a model trained only on the test subjects which was set as a benchmark for the other models. Our findings highlight the significance of fine-tuning and adapting models with limited data. Notably, we demonstrated that effective model adaptation can be achieved with as few as 15 rtMRI frames from any new dataset.
LLM-Enhanced Dialogue Management for Full-Duplex Spoken Dialogue Systems
Authors: Hao Zhang, Weiwei Li, Rilin Chen, Vinay Kothapally, Meng Yu, Dong Yu
Date: 2025-02-19 00:00:00
Tasks: Action Detection, Activity Detection, Decision Making, Dialogue Management, Management, Response Generation, Spoken Dialogue Systems

Achieving full-duplex communication in spoken dialogue systems (SDS) requires real-time coordination between listening, speaking, and thinking. This paper proposes a semantic voice activity detection (VAD) module as a dialogue manager (DM) to efficiently manage turn-taking in full-duplex SDS. Implemented as a lightweight (0.5B) LLM fine-tuned on full-duplex conversation data, the semantic VAD predicts four control tokens to regulate turn-switching and turn-keeping, distinguishing between intentional and unintentional barge-ins while detecting query completion for handling user pauses and hesitations. By processing input speech in short intervals, the semantic VAD enables real-time decision-making, while the core dialogue engine (CDE) is only activated for response generation, reducing computational overhead. This design allows independent DM optimization without retraining the CDE, balancing interaction accuracy and inference efficiency for scalable, next-generation full-duplex SDS.
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders
Authors: Seungbae Kim, Daeun Lee, Brielle Stark, Jinyoung Han
Date: 2025-02-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, speech-recognition, Speech Recognition, Zero-Shot Learning

Individuals with language disorders often face significant communication challenges due to their limited language processing and comprehension abilities, which also affect their interactions with voice-assisted systems that mostly rely on Automatic Speech Recognition (ASR). Despite advancements in ASR that address disfluencies, there has been little attention on integrating non-verbal communication methods, such as gestures, which individuals with language disorders substantially rely on to supplement their communication. Recognizing the need to interpret the latent meanings of visual information not captured by speech alone, we propose a gesture-aware ASR system utilizing a multimodal large language model with zero-shot learning for individuals with speech impairments. Our experiment results and analyses show that including gesture information significantly enhances semantic understanding. This study can help develop effective communication technologies, specifically designed to meet the unique needs of individuals with language impairments.
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics
Authors: Kabir Kumar
Date: 2025-02-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, Language Modeling, Language Modelling, Large Language Model, Medical Diagnosis, speech-recognition, Speech Recognition

Natural Language Processing (NLP) and Voice Recognition agents are rapidly evolving healthcare by enabling efficient, accessible, and professional patient support while automating grunt work. This report serves as my self project wherein models finetuned on medical call recordings are analysed through a two-stage system: Automatic Speech Recognition (ASR) for speech transcription and a Large Language Model (LLM) for context-aware, professional responses. ASR, finetuned on phone call recordings provides generalised transcription of diverse patient speech over call, while the LLM matches transcribed text to medical diagnosis. A novel audio preprocessing strategy, is deployed to provide invariance to incoming recording/call data, laden with sufficient augmentation with noise/clipping to make the pipeline robust to the type of microphone and ambient conditions the patient might have while calling/recording.
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Authors: Fadillah Maani, Numan Saeed, Tausifa Saleem, Zaid Farooq, Hussain Alasmawi, Werner Diehl, Ameera Mohammad, Gareth Waring, Saudabi Valappi, Leanne Bricker, Mohammad Yaqub
Date: 2025-02-20 00:00:00
Tasks: Age Estimation, Benchmarking

Foundation models are becoming increasingly effective in the medical domain, offering pre-trained models on large datasets that can be readily adapted for downstream tasks. Despite progress, fetal ultrasound images remain a challenging domain for foundation models due to their inherent complexity, often requiring substantial additional training and facing limitations due to the scarcity of paired multimodal data. To overcome these challenges, here we introduce FetalCLIP, a vision-language foundation model capable of generating universal representation of fetal ultrasound images. FetalCLIP was pre-trained using a multimodal learning approach on a diverse dataset of 210,035 fetal ultrasound images paired with text. This represents the largest paired dataset of its kind used for foundation model development to date. This unique training approach allows FetalCLIP to effectively learn the intricate anatomical features present in fetal ultrasound images, resulting in robust representations that can be used for a variety of downstream applications. In extensive benchmarking across a range of key fetal ultrasound applications, including classification, gestational age estimation, congenital heart defect (CHD) detection, and fetal structure segmentation, FetalCLIP outperformed all baselines while demonstrating remarkable generalizability and strong performance even with limited labeled data. We plan to release the FetalCLIP model publicly for the benefit of the broader scientific community.
MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
Authors: Maya Varma, Ashwin Kumar, Rogier van der Sluijs, Sophie Ostmeier, Louis Blankemeier, Pierre Chambon, Christian Bluethgen, Jip Prince, Curtis Langlotz, Akshay Chaudhari
Date: 2025-02-20 00:00:00
Tasks: Computational Efficiency

Medical images are acquired at high resolutions with large fields of view in order to capture fine-grained features necessary for clinical decision-making. Consequently, training deep learning models on medical images can incur large computational costs. In this work, we address the challenge of downsizing medical images in order to improve downstream computational efficiency while preserving clinically-relevant features. We introduce MedVAE, a family of six large-scale 2D and 3D autoencoders capable of encoding medical images as downsized latent representations and decoding latent representations back to high-resolution images. We train MedVAE autoencoders using a novel two-stage training approach with 1,052,730 medical images. Across diverse tasks obtained from 20 medical image datasets, we demonstrate that (1) utilizing MedVAE latent representations in place of high-resolution images when training downstream models can lead to efficiency benefits (up to 70x improvement in throughput) while simultaneously preserving clinically-relevant features and (2) MedVAE can decode latent representations back to high-resolution images with high fidelity. Our work demonstrates that large-scale, generalizable autoencoders can help address critical efficiency challenges in the medical domain. Our code is available at https://github.com/StanfordMIMI/MedVAE.
TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound
Authors: Mohamed Harmanani, Paul F. R. Wilson, Minh Nguyen Nhat To, Mahdi Gilany, Amoon Jamzad, Fahimeh Fooladgar, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
Date: 2025-02-20 00:00:00
Tasks: Deep Learning, Multiple Instance Learning, Self-Supervised Learning

While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. There is high heterogeneity in tissue appearance, significant class imbalance in favor of benign examples, and scarcity in the number and quality of ground truth annotations available to train models. Failure to address even a single one of these problems can result in unacceptable clinical outcomes.We propose TRUSWorthy, a carefully designed, tuned, and integrated system for reliable PCa detection. Our pipeline integrates self-supervised learning, multiple-instance learning aggregation using transformers, random-undersampled boosting and ensembling: these address label scarcity, weak labels, class imbalance, and overconfidence, respectively. We train and rigorously evaluate our method using a large, multi-center dataset of micro-ultrasound data. Our method outperforms previous state-of-the-art deep learning methods in terms of accuracy and uncertainty calibration, with AUROC and balanced accuracy scores of 79.9% and 71.5%, respectively. On the top 20% of predictions with the highest confidence, we can achieve a balanced accuracy of up to 91%. The success of TRUSWorthy demonstrates the potential of integrated deep learning solutions to meet clinical needs in a highly challenging deployment setting, and is a significant step towards creating a trustworthy system for computer-assisted PCa diagnosis.
Vision Foundation Models in Medical Image Analysis: Advances and Challenges
Authors: Pengchen Liang, Bin Pu, Haishan Huang, Yiwei Li, Hualiang Wang, Weibo Ma, Qing Chang
Date: 2025-02-20 00:00:00
Tasks: Domain Adaptation, Federated Learning, Image Segmentation, Knowledge Distillation, Medical Image Analysis, Medical Image Segmentation, Model Compression, Segmentation, Semantic Segmentation

The rapid development of Vision Foundation Models (VFMs), particularly Vision Transformers (ViT) and Segment Anything Model (SAM), has sparked significant advances in the field of medical image analysis. These models have demonstrated exceptional capabilities in capturing long-range dependencies and achieving high generalization in segmentation tasks. However, adapting these large models to medical image analysis presents several challenges, including domain differences between medical and natural images, the need for efficient model adaptation strategies, and the limitations of small-scale medical datasets. This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation, focusing on the challenges of domain adaptation, model compression, and federated learning. We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling, and propose future directions to overcome these bottlenecks. Our analysis highlights the potential of VFMs, along with emerging methodologies such as federated learning and model compression, to revolutionize medical image analysis and enhance clinical applications. The goal of this work is to provide a comprehensive overview of current approaches and suggest key areas for future research that can drive the next wave of innovation in medical image segmentation.
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving
Authors: Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
Date: 2025-02-20 00:00:00
Tasks: Autonomous Driving, image-classification, Image Classification, Semantic Segmentation

Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic segmentation in autonomous driving. To advance research in such safety-critical systems, determining the precise contribution of spectral information to complex DNNs' output is needed. To address this, several saliency methods, such as class activation maps (CAM), have been proposed primarily for image classification. However, recent studies have raised concerns regarding their reliability. In this paper, we address their limitations and propose an alternative approach by leveraging the data provided by activations and weights from relevant DNN layers to better capture the relationship between input features and predictions. The study aims to assess the superior performance of HSI compared to 3-channel and single-channel DNNs. We also address the influence of spectral signature normalization for enhancing DNN robustness in real-world driving conditions.
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
Authors: Haishan Huang, Pengchen Liang, Naier Lin, Luxi Wang, Bin Pu, Jianguo Chen, Qing Chang, Xia Shen, Guo Ran
Date: 2025-02-20 00:00:00
Tasks: Mamba, Segmentation

Nasopharyngeal carcinoma (NPC) patients often undergo radiotherapy and chemotherapy, which can lead to postoperative complications such as limited mouth opening and joint stiffness, particularly in recurrent cases that require re-surgery. These complications can affect airway function, making accurate postoperative airway risk assessment essential for managing patient care. Accurate segmentation of airway-related structures in postoperative CT scans is crucial for assessing these risks. This study introduces TopoWMamba (Topology-aware Wavelet Mamba), a novel segmentation model specifically designed to address the challenges of postoperative airway risk evaluation in recurrent NPC patients. TopoWMamba combines wavelet-based multi-scale feature extraction, state-space sequence modeling, and topology-aware modules to segment airway-related structures in CT scans robustly. By leveraging the Wavelet-based Mamba Block (WMB) for hierarchical frequency decomposition and the Snake Conv VSS (SCVSS) module to preserve anatomical continuity, TopoWMamba effectively captures both fine-grained boundaries and global structural context, crucial for accurate segmentation in complex postoperative scenarios. Through extensive testing on the NPCSegCT dataset, TopoWMamba achieves an average Dice score of 88.02%, outperforming existing models such as UNet, Attention UNet, and SwinUNet. Additionally, TopoWMamba is tested on the SegRap 2023 Challenge dataset, where it shows a significant improvement in trachea segmentation with a Dice score of 95.26%. The proposed model provides a strong foundation for automated segmentation, enabling more accurate postoperative airway risk evaluation.
EyeBench: A Call for More Rigorous Evaluation of Retinal Image Enhancement
Authors: Wenhui Zhu, Xuanzhao Dong, Xin Li, Yujian Xiong, Xiwen Chen, Peijie Qiu, Vamsi Krishna Vasa, Zhangsihao Yang, Yi Su, Oana Dumitrascu, Yalin Wang
Date: 2025-02-20 00:00:00
Tasks: Denoising, Image Enhancement, Lesion Segmentation, SSIM

Over the past decade, generative models have achieved significant success in enhancement fundus images.However, the evaluation of these models still presents a considerable challenge. A comprehensive evaluation benchmark for fundus image enhancement is indispensable for three main reasons: 1) The existing denoising metrics (e.g., PSNR, SSIM) are hardly to extend to downstream real-world clinical research (e.g., Vessel morphology consistency). 2) There is a lack of comprehensive evaluation for both paired and unpaired enhancement methods, along with the need for expert protocols to accurately assess clinical value. 3) An ideal evaluation system should provide insights to inform future developments of fundus image enhancement. To this end, we propose a novel comprehensive benchmark, EyeBench, to provide insights that align enhancement models with clinical needs, offering a foundation for future work to improve the clinical relevance and applicability of generative models for fundus image enhancement. EyeBench has three appealing properties: 1) multi-dimensional clinical alignment downstream evaluation: In addition to evaluating the enhancement task, we provide several clinically significant downstream tasks for fundus images, including vessel segmentation, DR grading, denoising generalization, and lesion segmentation. 2) Medical expert-guided evaluation design: We introduce a novel dataset that promote comprehensive and fair comparisons between paired and unpaired methods and includes a manual evaluation protocol by medical experts. 3) Valuable insights: Our benchmark study provides a comprehensive and rigorous evaluation of existing methods across different downstream tasks, assisting medical experts in making informed choices. Additionally, we offer further analysis of the challenges faced by existing methods. The code is available at \url{https://github.com/Retinal-Research/EyeBench}
Designing Parameter and Compute Efficient Diffusion Transformers using Distillation
Authors: Vignesh Sundaresha
Date: 2025-02-20 00:00:00
Tasks: Knowledge Distillation, NVIDIA Jetson Orin Nano, Video Generation

Diffusion Transformers (DiTs) with billions of model parameters form the backbone of popular image and video generation models like DALL.E, Stable-Diffusion and SORA. Though these models are necessary in many low-latency applications like Augmented/Virtual Reality, they cannot be deployed on resource-constrained Edge devices (like Apple Vision Pro or Meta Ray-Ban glasses) due to their huge computational complexity. To overcome this, we turn to knowledge distillation and perform a thorough design-space exploration to achieve the best DiT for a given parameter size. In particular, we provide principles for how to choose design knobs such as depth, width, attention heads and distillation setup for a DiT. During the process, a three-way trade-off emerges between model performance, size and speed that is crucial for Edge implementation of diffusion. We also propose two distillation approaches - Teaching Assistant (TA) method and Multi-In-One (MI1) method - to perform feature distillation in the DiT context. Unlike existing solutions, we demonstrate and benchmark the efficacy of our approaches on practical Edge devices such as NVIDIA Jetson Orin Nano.