pwc-archive/papers-with-abstracts huggingface dataset:
Cost-effectiveness of climate regulations depends on non-climate benefits
Authors: Nick Loris, Philip Rossetti, Chung-Yi See, Ashley Nunes
Date: 2025-03-14 00:00:00

The United States has long pursued regulations that aim to reduce fossil fuel use. However, while potential emission reduction motivates the introduction and enforcement of these regulations, realization of this potential does not obfuscate the need for prudent economic policy. Scrutinizing nearly two decades of climate regulations in the transportation, industrial, and electric power sector, we enumerate their associated cost effectiveness. Our findings are twofold. Firstly, we find that whereas there are instances where climate regulations carry net economic benefits, there are also cases where compliance costs substantially exceed the social cost of carbon (SCC). Aggregated across sectors and subject to the precise SCC leveraged, up to 65 percent of climate regulations have abatement costs that exceed the SCC. This exceedance is particularly profound in the transportation sector where up to 82 percent of regulations reviewed have abatement costs that exceed the SCC. Secondly, we find that whereas the economic benefits of climate regulations generally exceed the economic costs, this exceedance is largely contingent on the presence of non-climate benefits. Aggregated across sectors, climate benefits account for 33 percent of overall benefits associated with a regulation, compared to 18, 46, and 2 percent for public health, private, and other benefits respectively. Furthermore, we document instances where climate benefits account for as little as 1.6 percent of overall regulatory benefits. This finding hints at a regulatory inefficiency and raises the prospect that alternative, non-climate specific regulation may be a more appropriate mechanism for realizing the non-climate benefits associated with these regulations. Collectively, our findings challenge the economic pragmatism of some (but not all) climate regulations adopted by the United States over a period of nearly two decades.
Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning
Authors: Shidi Deng, Maximilian Schiffer, Martin Bichler
Date: 2025-03-14 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning (RL)

Nowadays, a significant share of the business-to-consumer sector is based on online platforms like Amazon and Alibaba and uses AI for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra-competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when Reinforcement Learning (RL) algorithms are used to decide on pricing strategies in competitive markets. Prior research in this field focused on Tabular Q-learning (TQL) and led to opposing views on whether learning-based algorithms can result in supra-competitive prices. Building on this, our work contributes to this ongoing discussion by providing a more nuanced numerical study that goes beyond TQL, additionally capturing off- and on- policy Deep Reinforcement Learning (DRL) algorithms, two distinct families of DRL algorithms that recently gained attention for algorithmic pricing. We study multiple Bertrand oligopoly variants and show that algorithmic collusion depends on the algorithm used. In our experiments, we observed that TQL tends to exhibit higher collusion and price dispersion. Moreover, it suffers from instability and disparity, as agents with higher learning rates consistently achieve higher profits, and it lacks robustness in state representation, with pricing dynamics varying significantly based on information access. In contrast, DRL algorithms, such as PPO and DQN, generally converge to lower prices closer to the Nash equilibrium. Additionally, we show that when pre-trained TQL agents interact with DRL agents, the latter quickly outperforms the former, highlighting the advantages of DRL in pricing competition. Lastly, we find that competition between heterogeneous DRL algorithms, such as PPO and DQN, tends to reduce the likelihood of supra-competitive pricing.
Public Support for Environmental Regulation: When Ideology Trumps Knowledge
Authors: Markus Dertwinkel-Kalt, Max R. P. Grossmann
Date: 2025-03-13 00:00:00
Tasks: Attribute

When environmental regulations are unpopular, policymakers often attribute resistance to information frictions and poor communication. We test this idea in the context of a major climate policy: Germany's Heating Law of 2023, which mandates the phase-out of fossil fuel heating. Through a survey experiment with property owners, we examine whether providing comprehensive information about the regulation's costs, requirements, and timeline affects adoption decisions and policy support. Despite successfully increasing factual knowledge, information provision has no significant effect on intended technology adoption, policy support, or incentivized measures of climate preferences. Instead, pre-existing environmental preferences and demographic characteristics emerge as the key predictors of responses to the regulation. A feeling that existing systems still work well and cost considerations dominate fossil fuel users' stated reasons for non-adoption, while independence from fossil fuels and perceived contributions to the common good drive adoption among switchers. Our findings suggest that opposition to climate policy stems from fundamental preference heterogeneity rather than information frictions. This has important implications for optimal policy design, highlighting potential limits of information provision in overcoming resistance to environmental regulation. The results also speak to broader questions in political economy about the relationship between knowledge, preferences, and support for policy reform.
Economic Impact of China's Retaliatory Soybean Tariff on U.S. Soybean Farmers
Authors: Xinyu Li
Date: 2025-03-13 00:00:00

This paper analyzes the economic impact of China's retaliatory soybean tariff on U.S. soybean farmers using advanced econometric methods and comprehensive datasets including USDA reports, trade data, and historical price movements. The analysis employs a Structural Vector Autoregression (SVAR), a Difference-in-Differences (DiD) estimation, and a Dynamic Stochastic General Equilibrium (DSGE) model, revealing the impacts of China's retaliatory tariff on soybean prices, exports, farm incomes, and acreage decisions. U.S. policy responses, including direct subsidies and market diversification strategies, are also evaluated.
EEG-Based Decoding of Sound Location: Comparing Free-Field to Headphone-Based Non-Individual HRTFs
Authors: Nils Marggraf-Turley, Martha Shiell, Niels Pontoppidan, Drew Cappotto, Lorenzo Picinali
Date: 2025-03-13 00:00:00
Tasks: EEG, Sound Source Localization

Sound source localization relies on spatial cues such as interaural time differences (ITD), interaural level differences (ILD), and monaural spectral cues. Individually measured Head-Related Transfer Functions (HRTFs) facilitate precise spatial hearing but are impractical to measure, necessitating non-individual HRTFs, which may compromise localization accuracy and externalization. To further investigate this phenomenon, the neurophysiological differences between free-field and non-individual HRTF listening are explored by decoding sound locations from EEG-derived Event-Related Potentials (ERPs). Twenty-two participants localized stimuli under both conditions with EEG responses recorded and logistic regression classifiers trained to distinguish sound source locations. Lower cortical response amplitudes were observed for KEMAR compared to free-field, especially in front-central and occipital-parietal regions. ANOVA identified significant main effects of auralization condition (F(1, 21) = 34.56, p < 0.0001) and location (F(3, 63) = 18.17, p < 0.0001) on decoding accuracy (DA), which was higher in free-field and interaural-cue-dominated locations. DA negatively correlated with front-back confusion rates (r = -0.57, p < 0.01), linking neural DA to perceptual confusion. These findings demonstrate that headphone-based non-individual HRTFs elicit lower amplitude cortical responses to static, azimuthally-varying locations than free-field conditions. The correlation between EEG-based DA and front-back confusion underscores neurophysiological markers' potential for assessing spatial auditory discrimination.
Pathology Image Compression with Pre-trained Autoencoders
Authors: Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis, Zilinghan Li, Tarak Nath Nandi, Ravi K Madduri, Prateek Prasanna, Joel Saltz, Dimitris Samaras
Date: 2025-03-14 00:00:00
Tasks: Computational Efficiency, Image Compression, Multiple Instance Learning, Quantization, whole slide images

The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compression methods, such as JPEG, reduce file sizes but often fail to preserve fine-grained phenotypic details critical for downstream tasks. In this work, we repurpose autoencoders (AEs) designed for Latent Diffusion Models as an efficient learned compression framework for pathology images. We systematically benchmark three AE models with varying compression levels and evaluate their reconstruction ability using pathology foundation models. We introduce a fine-tuning strategy to further enhance reconstruction fidelity that optimizes a pathology-specific learned perceptual metric. We validate our approach on downstream tasks, including segmentation, patch classification, and multiple instance learning, showing that replacing images with AE-compressed reconstructions leads to minimal performance degradation. Additionally, we propose a K-means clustering-based quantization method for AE latents, improving storage efficiency while maintaining reconstruction quality. We provide the weights of the fine-tuned autoencoders at https://huggingface.co/collections/StonyBrook-CVLab/pathology-fine-tuned-aes-67d45f223a659ff2e3402dd0.
Generalization performance of neural mapping schemes for the space-time interpolation of satellite-derived ocean colour datasets
Authors: Thi Thuy Nga Nguyen, Clément Dorffer, Frédéric Jourdin, Ronan Fablet
Date: 2025-03-14 00:00:00

Neural mapping schemes have become appealing approaches to deliver gap-free satellite-derived products for sea surface tracers. The generalization performance of these learning-based approaches naturally arises as a key challenge. This is particularly true for satellite-derived ocean colour products given the variety of bio-optical variables of interest, as well as the diversity of processes and scales involved. Considering region-specific and parameter-specific neural mapping schemes will result in substantial training costs. This study addresses generalization performance of neural mapping schemes to deliver gap-free satellite-derived ocean colour products. We develop a comprehensive experimental framework using real multi-sensor ocean colour datasets for two regions (the Mediterranean Sea and the North Sea) and a representative set of bio-optical parameters (Chlorophyll-a concentration, suspended particulate matter concentration, particulate backscattering coefficient). We consider several neural mapping schemes, and we report excellent generalization performance across regions and bio-optical parameters without any fine-tuning using appropriate dataset-specific normalization procedures. We discuss further how these results provide new insights towards the large-scale deployment of neural schemes for the processing of satellite-derived ocean colour datasets beyond case-study-specific demonstrations.
Observation-only learning of neural mapping schemes for gappy satellite-derived ocean colour parameters
Authors: Clément Dorffer, Frédéric Jourdin, Thi Thuy Nga Nguyen, Rodolphe Devillers, David Mouillot, Ronan Fablet
Date: 2025-03-14 00:00:00

Monitoring optical properties of coastal and open ocean waters is crucial to assessing the health of marine ecosystems. Deep learning offers a promising approach to address these ecosystem dynamics, especially in scenarios where gap-free ground-truth data is lacking, which poses a challenge for designing effective training frameworks. Using an advanced neural variational data assimilation scheme (called 4DVarNet), we introduce a comprehensive training framework designed to effectively train directly on gappy data sets. Using the Mediterranean Sea as a case study, our experiments not only highlight the high performance of the chosen neural network in reconstructing gap-free images from gappy datasets but also demonstrate its superior performance over state-of-the-art algorithms such as DInEOF and Direct Inversion, whether using CNN or UNet architectures.
Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models
Authors: Siva Manohar Reddy Kesu, Neelam Sinha, Hariharan Ramasangu, Thomas Gregor Issac
Date: 2025-03-14 00:00:00
Tasks: Deep Learning, Diagnostic

Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence. Early detection of Alzheimer's disease using retinal OCT is a primary challenging task. This work utilizes advanced deep learning techniques to classify retinal OCT images of subjects with Alzheimer's disease (AD) and healthy controls (CO). The goal is to enhance diagnostic capabilities through efficient image analysis. In the proposed model, Raw OCT images have been preprocessed with ImageJ and given to various deep-learning models to evaluate the accuracy. The best classification architecture is TransNetOCT, which has an average accuracy of 98.18% for input OCT images and 98.91% for segmented OCT images for five-fold cross-validation compared to other models, and the Swin Transformer model has achieved an accuracy of 93.54%. The evaluation accuracy metric demonstrated TransNetOCT and Swin transformer models capability to classify AD and CO subjects reliably, contributing to the potential for improved diagnostic processes in clinical settings.
Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Authors: Maximilian Fischer, Peter Neher, Peter Schüffler, Shuhan Xiao, Silvia Dias Almeida, Constantin Ulrich, Alexander Muckenhuber, Rickmer Braren, Michael Götz, Jens Kleesiek, Marco Nolden, Klaus Maier-Hein
Date: 2025-03-14 00:00:00
Tasks: Diagnostic, Image Compression, MS-SSIM, SSIM, whole slide images

Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. This paper investigates the limitations of the widely-used JPEG algorithm, the current clinical standard, and reveals severe image artifacts impacting diagnostic fidelity. To overcome these challenges, we introduce a novel deep-learning (DL)-based compression method tailored for pathology images. By enforcing feature similarity of deep features between the original and compressed images, our approach achieves superior Peak Signal-to-Noise Ratio (PSNR), Multi-Scale Structural Similarity Index (MS-SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) scores compared to JPEG-XL, Webp, and other DL compression methods.
FG-DFPN: Flow Guided Deformable Frame Prediction Network
Authors: M. Akin Yilmaz, Ahmet Bilican, A. Murat Tekalp
Date: 2025-03-14 00:00:00
Tasks: Optical Flow Estimation, Prediction, Video Compression

Video frame prediction remains a fundamental challenge in computer vision with direct implications for autonomous systems, video compression, and media synthesis. We present FG-DFPN, a novel architecture that harnesses the synergy between optical flow estimation and deformable convolutions to model complex spatio-temporal dynamics. By guiding deformable sampling with motion cues, our approach addresses the limitations of fixed-kernel networks when handling diverse motion patterns. The multi-scale design enables FG-DFPN to simultaneously capture global scene transformations and local object movements with remarkable precision. Our experiments demonstrate that FG-DFPN achieves state-of-the-art performance on eight diverse MPEG test sequences, outperforming existing methods by 1dB PSNR while maintaining competitive inference speeds. The integration of motion cues with adaptive geometric transformations makes FG-DFPN a promising solution for next-generation video processing systems that require high-fidelity temporal predictions. The model and instructions to reproduce our results will be released at: https://github.com/KUIS-AI-Tekalp-Research Group/frame-prediction
Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques
Authors: Leila Bagheriye, Johan Kwisthout
Date: 2025-03-14 00:00:00
Tasks: Decision Making

Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusion. It explores various real-time techniques, encompassing technical solutions, AI-based imaging, and image fusion diagnosis across multiple anatomical areas, and electromagnetic needle tracking. To provide a thorough overview, this paper discusses ultrasound image fusion, real-time in vivo cancer diagnosis with different spectroscopic techniques, different real-time optical imaging-based cancer diagnosis techniques, elastography-based cancer diagnosis, cervical cancer detection using neuromorphic architectures, different fluorescence image-based cancer diagnosis techniques, and hyperspectral imaging-based cancer diagnosis. We close by offering a more futuristic overview to solve existing problems in real-time image-based cancer diagnosis.
Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning
Authors: Lingyu Zhu, Xiangrui Zeng, Bolin Chen, Peilin Chen, Yung-Hui Li, Shiqi Wang
Date: 2025-03-14 00:00:00
Tasks: Image Compression, Image Reconstruction, Navigate

By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions produced by rate-distortion-optimized models. In this paper, we propose a novel deep learning-based generative image compression method injected with diffusion knowledge, obtaining the capacity to recover more realistic textures in practical scenarios. Efforts are made from three perspectives to navigate the rate-distortion-realism trade-off in the generative image compression task. First, recognizing the strong connection between image texture and frequency-domain characteristics, we design a Fractal Frequency-Aware Band Image Compression (FFAB-IC) network to effectively capture the directional frequency components inherent in natural images. This network integrates commonly used fractal band feature operations within a neural non-linear mapping design, enhancing its ability to retain essential given information and filter out unnecessary details. Then, to improve the visual quality of image reconstruction under limited bandwidth, we integrate diffusion knowledge into the encoder and implement diffusion iterations into the decoder process, thus effectively recovering lost texture details. Finally, to fully leverage the spatial and frequency intensity information, we incorporate frequency- and content-aware regularization terms to regularize the training of the generative image compression network. Extensive experiments in quantitative and qualitative evaluations demonstrate the superiority of the proposed method, advancing the boundaries of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before.
EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation
Authors: Qi Mao, Haobo Hu, Yujie He, Difei Gao, Haokun Chen, Libiao Jin
Date: 2025-03-14 00:00:00
Tasks: Decision Making, Image Manipulation

Affective Image Manipulation (AIM) aims to alter visual elements within an image to evoke specific emotional responses from viewers. However, existing AIM approaches rely on rigid \emph{one-to-one} mappings between emotions and visual cues, making them ill-suited for the inherently subjective and diverse ways in which humans perceive and express emotion.To address this, we introduce a novel task setting termed \emph{Diverse AIM (D-AIM)}, aiming to generate multiple visually distinct yet emotionally consistent image edits from a single source image and target emotion. We propose \emph{EmoAgent}, the first multi-agent framework tailored specifically for D-AIM. EmoAgent explicitly decomposes the manipulation process into three specialized phases executed by collaborative agents: a Planning Agent that generates diverse emotional editing strategies, an Editing Agent that precisely executes these strategies, and a Critic Agent that iteratively refines the results to ensure emotional accuracy. This collaborative design empowers EmoAgent to model \emph{one-to-many} emotion-to-visual mappings, enabling semantically diverse and emotionally faithful edits.Extensive quantitative and qualitative evaluations demonstrate that EmoAgent substantially outperforms state-of-the-art approaches in both emotional fidelity and semantic diversity, effectively generating multiple distinct visual edits that convey the same target emotion.
AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI
Authors: Praveen Shastry, Bhawana Sonawane, Kavya Mohan, Naveen Kumarasami, Raghotham Sripadraj, Anandakumar D, Keerthana R, Mounigasri M, Kaviya SP, Kishore Prasath Venkatesh, Bargava Subramanian, Kalyan Sivasailam
Date: 2025-03-14 00:00:00
Tasks: Diagnostic

Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation, and stenosis. Manual methods for measuring intervertebral disc height and spinal canal diameter are subjective and time-consuming. Automated solutions are needed to improve accuracy, efficiency, and reproducibility in clinical practice. Purpose: This study develops an autonomous AI system for segmenting and measuring key spinal structures in MRI scans, focusing on intervertebral disc height and spinal canal anteroposterior (AP) diameter in the cervical, lumbar, and thoracic regions. The goal is to reduce clinician workload, enhance diagnostic consistency, and improve assessments. Methods: The AI model leverages deep learning architectures, including UNet, nnU-Net, and CNNs. Trained on a large proprietary MRI dataset, it was validated against expert annotations. Performance was evaluated using Dice coefficients and segmentation accuracy. Results: The AI model achieved Dice coefficients of 0.94 for lumbar, 0.91 for cervical, and 0.90 for dorsal spine segmentation (D1-D12). It precisely measured spinal parameters like disc height and canal diameter, demonstrating robustness and clinical applicability. Conclusion: The AI system effectively automates MRI-based spinal measurements, improving accuracy and reducing clinician workload. Its consistent performance across spinal regions supports clinical decision-making, particularly in high-demand settings, enhancing spinal assessments and patient outcomes.