pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying walkable accessibility to urban services: An application to Florence, Italy
Authors: Leonardo Boncinelli, Stefania Miricola, Eugenio Vicario
Date: 2025-04-17 00:00:00

The concept of quality of life in urban settings is increasingly associated to the accessibility of amenities within a short walking distance for residents. However, this narrative still requires thorough empirical investigation to evaluate the practical implications, benefits, and challenges. In this work, we propose a novel methodology for evaluating urban accessibility to services, with an application to the city of Florence, Italy. Our approach involves identifying the accessibility of essential services from residential buildings within a 10-minute walking distance, employing a rigorous spatial analysis process and open-source geospatial data. As a second contribution, we extend the concept of 10-minute accessibility within a network theory framework and apply a clustering algorithm to identify urban communities based on shared access to essential services. Finally, we explore the dimension of functional redundancy. Our proposed metrics represent a step forward towards an accurate assessment of the adherence to the 10-minute city model and offer a valuable tool for place-based policies aimed at addressing spatial disparities in urban development.
Energy Storage Autonomy in Renewable Energy Systems Through Hydrogen Salt Caverns
Authors: David Franzmann, Thora Schubert, Heidi Heinrichs, Peter A. Kukla, Detlef Stolten
Date: 2025-04-16 00:00:00

The expansion of renewable energy sources leads to volatility in electricity generation within energy systems. Subsurface storage of hydrogen in salt caverns can play an important role in long-term energy storage, but their global potential is not fully understood. This study investigates the global status quo and how much hydrogen salt caverns can contribute to stabilizing future renewable energy systems. A global geological suitability and land eligibility analysis for salt cavern placement is conducted and compared with the derived long-term storage needs of renewable energy systems. Results show that hydrogen salt caverns can balance between 43% and 66% of the global electricity demand and exist in North America, Europe, China, and Australia. By sharing the salt cavern potential with neighboring countries, up to 85% of the global electricity demand can be stabilized by salt caverns. Therefore, global hydrogen can play a significant role in stabilizing renewable energy systems.
The Paradox of Professional Input: How Expert Collaboration with AI Systems Shapes Their Future Value
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman
Date: 2025-04-17 00:00:00
Tasks: Navigate

This perspective paper examines a fundamental paradox in the relationship between professional expertise and artificial intelligence: as domain experts increasingly collaborate with AI systems by externalizing their implicit knowledge, they potentially accelerate the automation of their own expertise. Through analysis of multiple professional contexts, we identify emerging patterns in human-AI collaboration and propose frameworks for professionals to navigate this evolving landscape. Drawing on research in knowledge management, expertise studies, human-computer interaction, and labor economics, we develop a nuanced understanding of how professional value may be preserved and transformed in an era of increasingly capable AI systems. Our analysis suggests that while the externalization of tacit knowledge presents certain risks to traditional professional roles, it also creates opportunities for the evolution of expertise and the emergence of new forms of professional value. We conclude with implications for professional education, organizational design, and policy development that can help ensure the codification of expert knowledge enhances rather than diminishes the value of human expertise.
Digital Adoption and Cyber Security: An Analysis of Canadian Businesses
Authors: Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
Date: 2025-04-16 00:00:00
Tasks: Survey

This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when adopting digital technologies to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent impacts on efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the drivers of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises.
Climate-economy projections under shared socioeconomic pathways and net-zero scenarios
Authors: Daisuke Murakami, Pavel V. Shevchenko, Tomoko Matsui, Aleksandar Arandjelović, Tor A. Myrvoll
Date: 2025-04-16 00:00:00

We examine future trajectories of the social cost of carbon, global temperatures, and carbon concentrations using the cost-benefit Dynamic Integrated Climate-Economy (DICE) model calibrated to the five Shared Socioeconomic Pathways (SSPs) under two mitigation scenarios: achieving net-zero carbon emissions by 2050 and by 2100. The DICE model is calibrated to align industrial and land-use carbon emissions with projections from six leading process-based integrated assessment models (IAMs): IMAGE, MESSAGE--GLOBIOM, AIM/CGE, GCAM, REMIND--MAgPIE and WITCH--GLOBIOM. We find that even with aggressive mitigation (net-zero by 2050), global temperatures are projected to exceed $2^\circ\text{C}$ above preindustrial levels by 2100, with estimates ranging from $2.5^\circ\text{C}$ to $2.7^\circ\text{C}$ across all SSPs and IAMs considered. Under the more lenient mitigation scenario (net-zero by 2100), global temperatures are projected to rise to between $3^\circ\text{C}$ and $3.7^\circ\text{C}$ by 2100. Additionally, the social cost of carbon is estimated to increase from approximately USD 30--50 in 2025 to USD 250--400 in 2100.
Improvable Students in School Choice
Authors: Taylor Knipe, Josue Ortega
Date: 2025-04-17 00:00:00
Tasks: Blocking

The Deferred Acceptance algorithm (DA) frequently produces Pareto inefficient allocations in school choice problems. While a number of efficient mechanisms that Pareto-dominate DA are available, a normative question remains unexplored: which students should benefit from efficiency enhancements? We address it by introducing the concept of \emph{maximally improvable students}, who benefit in every improvement over DA that includes as many students as possible in set-inclusion terms. We prove that common mechanisms such as Efficiency-Adjusted DA (EADA) and Top Trading Cycles applied to DA (DA+TTC) can fall significantly short of this benchmark. These mechanisms may only improve two maximally-improvable students when up to $n-1$ could benefit. Addressing this limitation, we develop the Maximum Improvement over DA mechanism (MIDA), which generates an efficient allocation that maximises the number of students improved over DA. We show that MIDA can generate fewer blocking pairs than EADA and DA+TTC, demonstrating that its distributional improvements need not come at the cost of high justified envy.
Efficient Major Transition Exchange under Distributional and Dual Priority-respecting Constraints
Authors: Yao Cheng, Jingsheng Yu, Ling Zheng
Date: 2025-04-17 00:00:00
Tasks: Fairness

Many real matching markets encounter distributional and fairness constraints. Motivated by the Chinese Major Transition Program (CMT), this paper studies the design of exchange mechanisms within a fresh framework of both distributional and dual priority-respecting constraints. Specifically, each student has an initial assigned major and applies to transfer to a more desirable one. A student can successfully transfer majors only if they obtain eligibility from both their initial major and the applied major. Each major has a dual priority: a strict priority over current students who wish to transfer out and a strict priority over students from other majors who wish to transfer in. Additionally, each major faces a ceiling constraint and a floor constraint to regulate student distribution. We show that the existing mechanisms of CMT result in avoidable inefficiencies, and propose two mechanisms that can match students to majors in an efficient way as well as respecting each major's distributional and dual priority. The efficient mechanisms are based on a proposed solution concept: eligibility maximization (EM), and two processes for identifying improvement cycles--specifically, transfer-in exchangeable cycles and transfer-out exchangeable cycles.
The Social Learning Barrier
Authors: Florian Brandl
Date: 2025-04-16 00:00:00

We consider long-lived agents who interact repeatedly in a social network. In each period, each agent learns about an unknown state by observing a private signal and her neighbors' actions in the previous period before taking an action herself. Our main result shows that the learning rate of the slowest learning agent is bounded from above independently of the number of agents, the network structure, and the agents' strategies. Applying this result to equilibrium learning with rational agents shows that the learning rate of all agents in any equilibrium is bounded under general conditions. This extends recent findings on equilibrium learning and demonstrates that the limitation stems from an inherent tradeoff between optimal action choices and information revelation rather than strategic considerations.
The Coase Theorem and Ideal Exchanges
Authors: Daniel Lü
Date: 2025-04-14 00:00:00

This paper offers a proof of the Coase theorem by formalizing the notion of ideal exchanges.
CST-former: Multidimensional Attention-based Transformer for Sound Event Localization and Detection in Real Scenes
Authors: Yusun Shul, Dayun Choi, Jung-Woo Choi
Date: 2025-04-17 00:00:00
Tasks: Event Detection, Sound Event Localization and Detection

Sound event localization and detection (SELD) is a task for the classification of sound events and the identification of direction of arrival (DoA) utilizing multichannel acoustic signals. For effective classification and localization, a channel-spectro-temporal transformer (CST-former) was suggested. CST-former employs multidimensional attention mechanisms across the spatial, spectral, and temporal domains to enlarge the model's capacity to learn the domain information essential for event detection and DoA estimation over time. In this work, we present an enhanced version of CST-former with multiscale unfolded local embedding (MSULE) developed to capture and aggregate domain information over multiple time-frequency scales. Also, we propose finetuning and post-processing techniques beneficial for conducting the SELD task over limited training datasets. In-depth ablation studies of the proposed architecture and detailed analysis on the proposed modules are carried out to validate the efficacy of multidimensional attentions on the SELD task. Empirical validation through experimentation on STARSS22 and STARSS23 datasets demonstrates the remarkable performance of CST-former and post-processing techniques without using external data.
EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting
Authors: Guanrou Yang, Chen Yang, Qian Chen, Ziyang Ma, Wenxi Chen, Wen Wang, Tianrui Wang, Yifan Yang, Zhikang Niu, Wenrui Liu, Fan Yu, Zhihao Du, Zhifu Gao, Shiliang Zhang, Xie Chen
Date: 2025-04-17 00:00:00
Tasks: text-to-speech, Text to Speech

Human speech goes beyond the mere transfer of information; it is a profound exchange of emotions and a connection between individuals. While Text-to-Speech (TTS) models have made huge progress, they still face challenges in controlling the emotional expression in the generated speech. In this work, we propose EmoVoice, a novel emotion-controllable TTS model that exploits large language models (LLMs) to enable fine-grained freestyle natural language emotion control, and a phoneme boost variant design that makes the model output phoneme tokens and audio tokens in parallel to enhance content consistency, inspired by chain-of-thought (CoT) and chain-of-modality (CoM) techniques. Besides, we introduce EmoVoice-DB, a high-quality 40-hour English emotion dataset featuring expressive speech and fine-grained emotion labels with natural language descriptions. EmoVoice achieves state-of-the-art performance on the English EmoVoice-DB test set using only synthetic training data, and on the Chinese Secap test set using our in-house data. We further investigate the reliability of existing emotion evaluation metrics and their alignment with human perceptual preferences, and explore using SOTA multimodal LLMs GPT-4o-audio and Gemini to assess emotional speech. Demo samples are available at https://yanghaha0908.github.io/EmoVoice/. Dataset, code, and checkpoints will be released.
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios
Authors: Haohan Shi, Xiyu Shi, Safak Dogan, Saif Alzubi, Tianjin Huang, Yunxiao Zhang
Date: 2025-04-16 00:00:00
Tasks: Audio Deepfake Detection, Benchmarking, Data Augmentation, DeepFake Detection, Face Swapping

Existing Audio Deepfake Detection (ADD) systems often struggle to generalise effectively due to the significantly degraded audio quality caused by audio codec compression and channel transmission effects in real-world communication scenarios. To address this challenge, we developed a rigorous benchmark to evaluate the performance of the ADD system under such scenarios. We introduced ADD-C, a new test dataset to evaluate the robustness of ADD systems under diverse communication conditions, including different combinations of audio codecs for compression and packet loss rates. Benchmarking three baseline ADD models on the ADD-C dataset demonstrated a significant decline in robustness under such conditions. A novel Data Augmentation (DA) strategy was proposed to improve the robustness of ADD systems. Experimental results demonstrated that the proposed approach significantly enhances the performance of ADD systems on the proposed ADD-C dataset. Our benchmark can assist future efforts towards building practical and robustly generalisable ADD systems.
NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results
Authors: Xin Li, Kun Yuan, Bingchen Li, Fengbin Guan, Yizhen Shao, Zihao Yu, Xijun Wang, Yiting Lu, Wei Luo, Suhang Yao, Ming Sun, Chao Zhou, Zhibo Chen, Radu Timofte, Yabin Zhang, Ao-Xiang Zhang, Tianwu Zhi, Jianzhao Liu, Yang Li, Jingwen Xu, Yiting Liao, Yushen Zuo, Mingyang Wu, Renjie Li, Shengyun Zhong, Zhengzhong Tu, Yufan Liu, Xiangguang Chen, Zuowei Cao, Minhao Tang, Shan Liu, Kexin Zhang, Jingfen Xie, Yan Wang, Kai Chen, Shijie Zhao, Yunchen Zhang, Xiangkai Xu, Hong Gao, Ji Shi, Yiming Bao, Xiugang Dong, Xiangsheng Zhou, Yaofeng Tu, Ying Liang, Yiwen Wang, Xinning Chai, Yuxuan Zhang, Zhengxue Cheng, Yingsheng Qin, Yucai Yang, Rong Xie, Li Song, Wei Sun, Kang Fu, Linhan Cao, Dandan Zhu, Kaiwei Zhang, Yucheng Zhu, ZiCheng Zhang, Menghan Hu, Xiongkuo Min, Guangtao Zhai, Zhi Jin, Jiawei Wu, Wei Wang, Wenjian Zhang, Yuhai Lan, Gaoxiong Yi, Hengyuan Na, Wang Luo, Di wu, MingYin Bai, Jiawang Du, Zilong Lu, Zhenyu Jiang, Hui Zeng, Ziguan Cui, Zongliang Gan, Guijin Tang, Xinglin Xie, Kehuan Song, Xiaoqiang Lu, Licheng Jiao, Fang Liu, Xu Liu, Puhua Chen, Ha Thu Nguyen, Katrien De Moor, Seyed Ali Amirshahi, Mohamed-Chaker Larabi, Qi Tang, Linfeng He, Zhiyong Gao, Zixuan Gao, Guohua Zhang, Zhiye Huang, Yi Deng, Qingmiao Jiang, Lu Chen, Yi Yang, Xi Liao, Nourine Mohammed Nadir, YuXuan Jiang, Qiang Zhu, Siyue Teng, Fan Zhang, Shuyuan Zhu, Bing Zeng, David Bull, Meiqin Liu, Chao Yao, Yao Zhao
Date: 2025-04-17 00:00:00
Tasks: Form, Image Super-Resolution, Super-Resolution, valid, Video Quality Assessment, Visual Question Answering (VQA)

This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Quality Assessment (KVQ), and (ii) Diffusion-based Image Super-Resolution (KwaiSR). Track 1 aims to advance the development of lightweight and efficient video quality assessment (VQA) models, with an emphasis on eliminating reliance on model ensembles, redundant weights, and other computationally expensive components in the previous IQA/VQA competitions. Track 2 introduces a new short-form UGC dataset tailored for single image super-resolution, i.e., the KwaiSR dataset. It consists of 1,800 synthetically generated S-UGC image pairs and 1,900 real-world S-UGC images, which are split into training, validation, and test sets using a ratio of 8:1:1. The primary objective of the challenge is to drive research that benefits the user experience of short-form UGC platforms such as Kwai and TikTok. This challenge attracted 266 participants and received 18 valid final submissions with corresponding fact sheets, significantly contributing to the progress of short-form UGC VQA and image superresolution. The project is publicly available at https://github.com/lixinustc/KVQE- ChallengeCVPR-NTIRE2025.
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
Authors: Yundi Zhang, Paul Hager, Che Liu, Suprosanna Shit, Chen Chen, Daniel Rueckert, Jiazhen Pan
Date: 2025-04-17 00:00:00
Tasks: Anatomy

Cardiac magnetic resonance imaging is the gold standard for non-invasive cardiac assessment, offering rich spatio-temporal views of the cardiac anatomy and physiology. Patient-level health factors, such as demographics, metabolic, and lifestyle, are known to substantially influence cardiovascular health and disease risk, yet remain uncaptured by CMR alone. To holistically understand cardiac health and to enable the best possible interpretation of an individual's disease risk, CMR and patient-level factors must be jointly exploited within an integrated framework. Recent multi-modal approaches have begun to bridge this gap, yet they often rely on limited spatio-temporal data and focus on isolated clinical tasks, thereby hindering the development of a comprehensive representation for cardiac health evaluation. To overcome these limitations, we introduce ViTa, a step toward foundation models that delivers a comprehensive representation of the heart and a precise interpretation of individual disease risk. Leveraging data from 42,000 UK Biobank participants, ViTa integrates 3D+T cine stacks from short-axis and long-axis views, enabling a complete capture of the cardiac cycle. These imaging data are then fused with detailed tabular patient-level factors, enabling context-aware insights. This multi-modal paradigm supports a wide spectrum of downstream tasks, including cardiac phenotype and physiological feature prediction, segmentation, and classification of cardiac and metabolic diseases within a single unified framework. By learning a shared latent representation that bridges rich imaging features and patient context, ViTa moves beyond traditional, task-specific models toward a universal, patient-specific understanding of cardiac health, highlighting its potential to advance clinical utility and scalability in cardiac analysis.
TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology
Authors: Walid Rehamnia, Alexandra Getmanskaya, Evgeniy Vasilyev, Vadim Turlapov
Date: 2025-04-17 00:00:00
Tasks: Unsupervised nucleus segmentation, whole slide images

Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to identify the different tissue types within low-resolution training data. It selects representative patches from each identified group based on an uncertainty measure and then does unsupervised nuclei segmentation in their respective higher-resolution space without using any ML algorithms. Crucially, this solution integrates seamlessly into clinicians workflows, transforming the examination of a whole WSI into a review of concise, interpretable cross-level insights. This integration significantly enhances and accelerates the workflow while ensuring transparency. We evaluated our approach using the UPENN-GBM dataset, where the AE achieved a mean squared error (MSE) of 0.0016. Additionally, nucleus segmentation is assessed on the MoNuSeg dataset, outperforming all unsupervised approaches with an F1 score of 77.46% and a Jaccard score of 63.35%. These results demonstrate the efficacy of TUMLS in advancing the field of digital pathology.