pwc-archive/papers-with-abstracts huggingface dataset:
Techno-Economic Analysis of Hydrogen Production: Costs, Policies, and Scalability in the Transition to Net-Zero
Authors: Eliseo Curcio
Date: 2025-02-17 00:00:00

This study presents a comprehensive techno-economic analysis of gray, blue, and green hydrogen production pathways, evaluating their cost structures, investment feasibility, infrastructure challenges, and policy-driven cost reductions. The findings confirm that gray hydrogen (1.50-2.50/kg) remains the most cost-effective today but is increasingly constrained by carbon pricing. Blue hydrogen (2.00-3.50/kg) offers a transitional pathway but depends on CCS costs, natural gas price volatility, and regulatory support. Green hydrogen (3.50-6.00/kg) is currently the most expensive but benefits from declining renewable electricity costs, electrolyzer efficiency improvements, and government incentives such as the Inflation Reduction Act (IRA), which provides tax credits of up to 3.00/kg. The analysis shows that renewable electricity costs below 20-30/MWh are essential for green hydrogen to achieve cost parity with fossil-based hydrogen. The DOE's Hydrogen Shot Initiative aims to lower green hydrogen costs to 1.00/kg by 2031, emphasizing the need for CAPEX reductions, economies of scale, and improved electrolyzer efficiency. Infrastructure remains a critical challenge, with pipeline retrofitting reducing transport costs by 50-70%, though liquefied hydrogen and chemical carriers remain costly due to energy losses and reconversion expenses. Investment trends indicate a shift toward green hydrogen, with over 250 billion projected by 2035, surpassing blue hydrogen's expected 100 billion. Carbon pricing above $100/ton CO2 will likely make gray hydrogen uncompetitive by 2030, accelerating the shift to low-carbon hydrogen. Hydrogen's long-term viability depends on continued cost reductions, policy incentives, and infrastructure expansion, with green hydrogen positioned as a cornerstone of the net-zero energy transition by 2035.
The probability of satisfying axioms: a non-binary perspective on economic design
Authors: Pierre Bardier
Date: 2025-02-19 00:00:00

We provide a formal framework accounting for a widespread idea in the theory of economic design: analytically established incompatibilities between given axioms should be qualified by the likelihood of their violation. We define the degree to which rules satisfy an axiom, as well as several axioms, on the basis of a probability measure over the inputs of the rules. Armed with this notion of degree, we propose and characterize i) a criterion to evaluate and compare rules given a set of axioms, allowing the importance of each combination of axioms to differ, and ii) a criterion to measure the compatibility between given axioms, building on a analogy with cooperative game theory.
Legal routes for accomplishing corporate environmental compliance against the "carbon peaking and carbon neutrality" goals
Authors: Yedong Zhang, Hua Han
Date: 2025-02-19 00:00:00
Tasks: Jurisprudence

Against the macro-background of "carbon peaking and carbon neutrality" goals, eco-environment protection regulations are increasingly stricter. Facing high government regulatory risks and frequent environment lawsuits, corporate environmental compliance starts to play a vital role in healthy corporate operation. Law fulfillment routes constitute a critical part in corporate environmental compliance. Few academic scholars have conducted a profound analysis or discussion of legal accomplishment routes for corporate environmental compliances. As a matter of fact, legal routes for accomplishing corporate environmental compliance should be based proper theories concerning corporate environmental rights and obligations as well as dual layer nested governance structure (government environmental power and corporate environmental liabilities). Under the guidance of environmental jurisprudence, enterprises are responsible for setting up practical legal fulfillment routes for their environmental compliance-related rights and obligations. A diversified environmental governance layout composed of government regulation, enterprise self-discipline and social participation should be established. Within enterprises, effective legal routes should be developed for dealing with government regulatory risks and environment lawsuit risks at the same time.
Collective Intelligence in Dynamic Networks
Authors: Florian Mudekereza
Date: 2025-02-18 00:00:00

We revisit DeGroot learning to examine the robustness of social learning in dynamic networks -- networks that evolve randomly over time. Dynamics have double-edged effects depending on social structure: while they can foster consensus and boost collective intelligence in "sparse" networks, they can have adverse effects such as slowing down the speed of learning and causing long-run disagreement in "well-connected" networks. Collective intelligence arises in dynamic networks when average influence and trust remain balanced as society grows. We also find that the initial social structure of a dynamic network plays a central role in shaping long-run beliefs. We then propose a robust measure of homophily based on the likelihood of the worst network fragmentation.
RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Authors: Ching-Hua Lee, Chouchang Yang, Jaejin Cho, Yashas Malur Saidutta, Rakshith Sharma Srinivasa, Yilin Shen, Hongxia Jin
Date: 2025-02-19 00:00:00
Tasks: Denoising, Image Restoration

Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful information about the target clean data distribution. However, existing adoption of the standard Gaussian as the prior distribution in turn discards such information when shaping the prior, resulting in sub-optimal performance. In this paper, we propose to improve conditional DDPMs for signal restoration by leveraging a more informative prior that is jointly learned with the diffusion model. The proposed framework, called RestoreGrad, seamlessly integrates DDPMs into the variational autoencoder (VAE) framework, taking advantage of the correlation between the degraded and clean signals to encode a better diffusion prior. On speech and image restoration tasks, we show that RestoreGrad demonstrates faster convergence (5-10 times fewer training steps) to achieve better quality of restored signals over existing DDPM baselines and improved robustness to using fewer sampling steps in inference time (2-2.5 times fewer), advocating the advantages of leveraging jointly learned prior for efficiency improvements in the diffusion process.
Adopting Whisper for Confidence Estimation
Authors: Vaibhav Aggarwal, Shabari S Nair, Yash Verma, Yash Jogi
Date: 2025-02-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recent research on word-level confidence estimation for speech recognition systems has primarily focused on lightweight models known as Confidence Estimation Modules (CEMs), which rely on hand-engineered features derived from Automatic Speech Recognition (ASR) outputs. In contrast, we propose a novel end-to-end approach that leverages the ASR model itself (Whisper) to generate word-level confidence scores. Specifically, we introduce a method in which the Whisper model is fine-tuned to produce scalar confidence scores given an audio input and its corresponding hypothesis transcript. Our experiments demonstrate that the fine-tuned Whisper-tiny model, comparable in size to a strong CEM baseline, achieves similar performance on the in-domain dataset and surpasses the CEM baseline on eight out-of-domain datasets, whereas the fine-tuned Whisper-large model consistently outperforms the CEM baseline by a substantial margin across all datasets.
MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced Medical Image Segmentation
Authors: Yucheng Zeng
Date: 2025-02-19 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation plays a crucial role in various clinical applications. A major challenge in medical image segmentation is achieving accurate delineation of regions of interest in the presence of noise, low contrast, or complex anatomical structures. Existing segmentation models often neglect the integration of multi-grained information and fail to preserve edge details, which are critical for precise segmentation. To address these challenges, we propose a novel image semantic segmentation model called the Multi-Grained Feature Integration Network (MGFI-Net). Our MGFI-Net is designed with two dedicated modules to tackle these issues. First, to enhance segmentation accuracy, we introduce a Multi-Grained Feature Extraction Module, which leverages hierarchical relationships between different feature scales to selectively focus on the most relevant information. Second, to preserve edge details, we incorporate an Edge Enhancement Module that effectively retains and integrates boundary information to refine segmentation results. Extensive experiments demonstrate that MGFI-Net not only outperforms state-of-the-art methods in terms of segmentation accuracy but also achieves superior time efficiency, establishing it as a leading solution for real-time medical image segmentation.
Denoising Designs-inherited Search Framework for Image Denoising
Authors: Zheyu Zhang, Yueyi Zhang, Xiaoyan Sun
Date: 2025-02-19 00:00:00
Tasks: Denoising, Image Denoising, Neural Architecture Search

How to benefit from plenty of existing denoising designs? Few methods via Neural Architecture Search (NAS) intend to answer this question. However, these NAS-based denoising methods explore limited search space and are hard to extend in terms of search space due to high computational burden. To tackle these limitations, we propose the first search framework to explore mainstream denoising designs. In our framework, the search space consists of the network-level, the cell-level and the kernel-level search space, which aims to inherit as many denoising designs as possible. Coordinating search strategies are proposed to facilitate the extension of various denoising designs. In such a giant search space, it is laborious to search for an optimal architecture. To solve this dilemma, we introduce the first regularization, i.e., denoising prior-based regularization, which reduces the search difficulty. To get an efficient architecture, we introduce the other regularization, i.e., inference time-based regularization, optimizes the search process on model complexity. Based on our framework, our searched architecture achieves state-of-the-art results for image denoising on multiple real-world and synthetic datasets. The parameters of our searched architecture are $1/3$ of Restormer's, and our method surpasses existing NAS-based denoising methods by $1.50$ dB in the real-world dataset. Moreover, we discuss the preferences of $\textbf{200}$ searched architectures, and provide directions for further work.
SpeHeatal: A Cluster-Enhanced Segmentation Method for Sperm Morphology Analysis
Authors: Yi Shi, Yunkai Wang, Xupeng Tian, Tieyi Zhang, Bing Yao, Hui Wang, Yong Shao, Cencen Wang, Rong Zeng
Date: 2025-02-18 00:00:00
Tasks: Segmentation

The accurate assessment of sperm morphology is crucial in andrological diagnostics, where the segmentation of sperm images presents significant challenges. Existing approaches frequently rely on large annotated datasets and often struggle with the segmentation of overlapping sperm and the presence of dye impurities. To address these challenges, this paper first analyzes the issue of overlapping sperm tails from a geometric perspective and introduces a novel clustering algorithm, Con2Dis, which effectively segments overlapping tails by considering three essential factors: CONnectivity, CONformity, and DIStance. Building on this foundation, we propose an unsupervised method, SpeHeatal, designed for the comprehensive segmentation of the SPErm HEAd and TAiL. SpeHeatal employs the Segment Anything Model(SAM) to generate masks for sperm heads while filtering out dye impurities, utilizes Con2Dis to segment tails, and then applies a tailored mask splicing technique to produce complete sperm masks. Experimental results underscore the superior performance of SpeHeatal, particularly in handling images with overlapping sperm.
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection
Authors: Zijian Cao, Hua Zhang, Le Liang, Haotian Wang, Shi Jin, Geoffrey Ye Li
Date: 2025-02-18 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Optical Flow Estimation, Semantic Communication

With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardware, the detection task is sometimes deployed on remote computing devices or the cloud to execute complex algorithms, which brings massive data transmission overhead. In response, this paper proposes an optical flow-driven semantic communication framework for the stereo-vision 3D object detection task. The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy. Specifically, we develop an optical flow-driven module to jointly extract and recover semantics from the left and right images to reduce the loss of the left-right photometric alignment semantic information and improve the accuracy of depth inference. Then, we design a 2D semantic extraction module to identify and extract semantic meaning around the objects to enhance the transmission of semantic information in the key areas. Finally, a fusion network is used to fuse the recovered semantics, and reconstruct the stereo-vision images for 3D detection. Simulation results show that the proposed method improves the detection accuracy by nearly 70% and outperforms the traditional method, especially for the low signal-to-noise ratio regime.
Synthetic generation of 2D data records based on Autoencoders
Authors: Darius Couchard, Oscar Olarte, Rob Haelterman
Date: 2025-02-18 00:00:00
Tasks: Component Classification

Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra, providing rich information but posing challenges for data-driven analysis due to limited labelled datasets. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders. Although applied here to GC-IMS data, the approach is broadly applicable to any two-dimensional spectral measurements where labelled data are scarce. While performing component classification over a labelled dataset of GC-IMS records, the addition of synthesized records significantly has improved the classification performance, demonstrating the method's potential for overcoming dataset limitations in machine learning frameworks.
Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized Myopia Management
Authors: Danli Shi, Bowen Liu, Zhen Tian, Yue Wu, Jiancheng Yang, Ruoyu Chen, Bo Yang, Ou Xiao, Mingguang He
Date: 2025-02-18 00:00:00
Tasks: Anatomy, Fairness, Management

Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss. Eyes with pathological myopia exhibit distinctive shape distributions, which are closely linked to the progression of vision-threatening complications. Recent understanding of eye-shape-based biomarkers requires magnetic resonance imaging (MRI), however, it is costly and unrealistic in routine ophthalmology clinics. We present Fundus2Globe, the first AI framework that synthesizes patient-specific 3D eye globes from ubiquitous 2D color fundus photographs (CFPs) and routine metadata (axial length, spherical equivalent), bypassing MRI dependency. By integrating a 3D morphable eye model (encoding biomechanical shape priors) with a latent diffusion model, our approach achieves submillimeter accuracy in reconstructing posterior ocular anatomy efficiently. Fundus2Globe uniquely quantifies how vision-threatening lesions (e.g., staphylomas) in CFPs correlate with MRI-validated 3D shape abnormalities, enabling clinicians to simulate posterior segment changes in response to refractive shifts. External validation demonstrates its robust generation performance, ensuring fairness across underrepresented groups. By transforming 2D fundus imaging into 3D digital replicas of ocular structures, Fundus2Globe is a gateway for precision ophthalmology, laying the foundation for AI-driven, personalized myopia management.
Local Flaw Detection with Adaptive Pyramid Image Fusion Across Spatial Sampling Resolution for SWRs
Authors: Siyu You, Huayi Gou, Leilei Yang, Zhiliang Liu, Mingjian Zuo
Date: 2025-02-18 00:00:00
Tasks: Defect Detection

The inspection of local flaws (LFs) in Steel Wire Ropes (SWRs) is crucial for ensuring safety and reliability in various industries. Magnetic Flux Leakage (MFL) imaging is commonly used for non-destructive testing, but its effectiveness is often hindered by the combined effects of inspection speed and sampling rate. To address this issue, the impacts of inspection speed and sampling rate on image quality are studied, as variations in these factors can cause stripe noise, axial compression of defect features, and increased interference, complicating accurate detection. We define the relationship between inspection speed and sampling rate as spatial sampling resolution (SSR) and propose an adaptive SSR target-feature-oriented (AS-TFO) method. This method incorporates adaptive adjustment and pyramid image fusion techniques to enhance defect detection under different SSR scenarios. Experimental results show that under high SSR scenarios, the method achieves a precision of 94.73% and a recall of 96.77%. It remains robust under low SSR scenarios with a precision of 94.30% and recall of 97.32%. The overall results show that the proposed method outperforms conventional approaches, achieving state-of-the-art performance. This improvement in detection accuracy and robustness is particularly valuable for handling complex inspection conditions, where inspection speed and sampling rate can vary significantly, making detection more robust and reliable in industrial settings.
Incomplete Graph Learning: A Comprehensive Survey
Authors: Riting Xia, Huibo Liu, Anchen Li, Xueyan Liu, Yan Zhang, Chunxu Zhang, Bo Yang
Date: 2025-02-18 00:00:00
Tasks: Attribute, Graph Learning, Survey

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.We developed an online resource to follow relevant research based on this review, available at https://github.com/cherry-a11y/Incomplete-graph-learning.git
3D ReX: Causal Explanations in 3D Neuroimaging Classification
Authors: Melane Navaratnarajah, Sophie A. Martin, David A. Kelly, Nathan Blake, Hana Chockler
Date: 2025-02-14 00:00:00
Tasks: Classification

Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first causality-based post-hoc explainability tool for 3D models. 3D ReX uses the theory of actual causality to generate responsibility maps which highlight the regions most crucial to the model's decision. We test 3D ReX on a stroke detection model, providing insight into the spatial distribution of features relevant to stroke.
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis
Authors: Xinpeng Wang, Rong Zhou, Han Xie, Xiaoying Tang, Lifang He, Carl Yang
Date: 2025-02-14 00:00:00
Tasks: Contrastive Learning, Federated Learning, Transfer Learning

Multimodal Federated Learning (MFL) has emerged as a promising approach for collaboratively training multimodal models across distributed clients, particularly in healthcare domains. In the context of brain imaging analysis, modality incompleteness presents a significant challenge, where some institutions may lack specific imaging modalities (e.g., PET, MRI, or CT) due to privacy concerns, device limitations, or data availability issues. While existing work typically assumes modality completeness or oversimplifies missing-modality scenarios, we simulate a more realistic setting by considering both client-level and instance-level modality incompleteness in this study. Building on this realistic simulation, we propose ClusMFL, a novel MFL framework that leverages feature clustering for cross-institutional brain imaging analysis under modality incompleteness. Specifically, ClusMFL utilizes the FINCH algorithm to construct a pool of cluster centers for the feature embeddings of each modality-label pair, effectively capturing fine-grained data distributions. These cluster centers are then used for feature alignment within each modality through supervised contrastive learning, while also acting as proxies for missing modalities, allowing cross-modal knowledge transfer. Furthermore, ClusMFL employs a modality-aware aggregation strategy, further enhancing the model's performance in scenarios with severe modality incompleteness. We evaluate the proposed framework on the ADNI dataset, utilizing structural MRI and PET scans. Extensive experimental results demonstrate that ClusMFL achieves state-of-the-art performance compared to various baseline methods across varying levels of modality incompleteness, providing a scalable solution for cross-institutional brain imaging analysis.