pwc-archive/papers-with-abstracts huggingface dataset:
Multi-level Governance, Smart Meter Adoption, and Utilities' Energy Efficiency Savings in the U.S
Authors: Yue Gao, Jing Zhang
Date: 2025-05-08 00:00:00

Smart grids enable the alignment of energy supply and demand, enhance energy efficiency, and raise consumer awareness of energy conservation. Smart meter, a vital technological component of smart grids, enables bidirectional communication between consumers and utility companies. This paper employs the two-stage least squares panel data model to examine the effects of federal funding and state legislative actions on smart meter adoption and the resulting energy efficiency savings in the residential sector of the United States. Additionally, we use machine learning techniques to select the subset of control variables that are influential for smart meter adoption and energy efficiency savings. The findings suggest that both federal financial assistance and state legislative actions have positive effects on smart meter adoption, and the interaction of federal funding and state policy interventions have a significantly greater effect on adoption as compared to the sum of the individual policy instrument alone. Furthermore, there exists a positive association between the adoption rate and energy efficiency savings. This study presents empirical evidence that underscores the significance of multi-level governance as an effective means for policy integration regarding smart meter adoptions, and it discusses the policy implications for grid modernization and environmental policies.
Global Agricultural Competitiveness Index (Gaci) in the Context of Climate Change: A Holistic Approach
Authors: Bakhtmina Zia
Date: 2025-05-07 00:00:00

The impacts of climate change, conflicts, the spread of infectious diseases, and global economic downturns have greatly affected food production, disrupted supply chains, and hindered access to affordable, nutritious food. It poses risks to both local and global food security, in addition to agricultural market competitiveness. Given the increasing concerns about climate change and its implications for global agriculture and food security, evaluating agricultural competitiveness via a composite scale to measure the effects of climate change would be beneficial. This study examined a global agricultural competitiveness index (GACI) framework developed through a systematic review and an expert survey. The results show that most countries experienced a decline in their competitiveness scores with agricultural assessment in the context of the impact of climate change. This framework can serve as a global benchmark for assessing and comparing national and international standing. Furthermore, it can help policy development aimed at promoting sustainable and inclusive agriculture, ultimately contributing to improved global food security. Keywords: Agriculture, Competitiveness, Pillars, Global Agricultural Competitiveness, Index, Climate Change
1-Dimensional Normal Competitive Market Equilibrium
Authors: Thanawat Sornwanee
Date: 2025-05-13 00:00:00

We introduce a new microeconomics foundation of a specific type of competitive market equilibrium that can be used to study several markets with information asymmetry such as commodity market, credit market, and insurance market.
Utility Maximization Under Endogenous Uncertainty
Authors: Ayush Gupta
Date: 2025-05-11 00:00:00
Tasks: Decision Making

This paper establishes a general existence result for expected utility maximization in settings where the agent's decision affects the uncertainty faced by her. We introduce a continuity condition for choice-dependent probability measures which ensures the upper semi-continuity of expected utility. Our topological proof imposes minimal restrictions on the utility function and the random variable. In particular, we do not need common assumptions like the monotone likelihood ratio property (MLRP) or the convexity of distribution functions condition (CDFC). Additionally, we identify sufficient conditions - continuity of densities and stochastic dominance - which help verify our assumptions in most practical applications. These findings expand the applicability of expected utility theory in settings with endogenous uncertainty.
Investigating self-supervised features for expressive, multilingual voice conversion
Authors: Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Grzegorz Beringer, Iván Vallés-Pérez, Roberto Barra-Chicote, Biel Tura-Vecino, Adam Gabryś, Piotr Bilinski, Thomas Merritt, Jaime Lorenzo-Trueba
Date: 2025-05-13 00:00:00
Tasks: Self-Supervised Learning, Speech Synthesis, Voice Conversion

Voice conversion (VC) systems are widely used for several applications, from speaker anonymisation to personalised speech synthesis. Supervised approaches learn a mapping between different speakers using parallel data, which is expensive to produce. Unsupervised approaches are typically trained to reconstruct the input signal, which is composed of the content and the speaker information. Disentangling these components is a challenge and often leads to speaker leakage or prosodic information removal. In this paper, we explore voice conversion by leveraging the potential of self-supervised learning (SSL). A combination of the latent representations of SSL models, concatenated with speaker embeddings, is fed to a vocoder which is trained to reconstruct the input. Zero-shot voice conversion results show that this approach allows to keep the prosody and content of the source speaker while matching the speaker similarity of a VC system based on phonetic posteriorgrams (PPGs).
Is MixIT Really Unsuitable for Correlated Sources? Exploring MixIT for Unsupervised Pre-training in Music Source Separation
Authors: Kohei Saijo, Yoshiaki Bando
Date: 2025-05-12 00:00:00
Tasks: Music Source Separation, Unsupervised Pre-training

In music source separation (MSS), obtaining isolated sources or stems is highly costly, making pre-training on unlabeled data a promising approach. Although source-agnostic unsupervised learning like mixture-invariant training (MixIT) has been explored in general sound separation, they have been largely overlooked in MSS due to its implicit assumption of source independence. We hypothesize, however, that the difficulty of applying MixIT to MSS arises from the ill-posed nature of MSS itself, where stem definitions are application-dependent and models lack explicit knowledge of what should or should not be separated, rather than from high inter-source correlation. While MixIT does not assume any source model and struggles with such ambiguities, our preliminary experiments show that it can still separate instruments to some extent, suggesting its potential for unsupervised pre-training. Motivated by these insights, this study investigates MixIT-based pre-training for MSS. We first pre-train a model on in-the-wild, unlabeled data from the Free Music Archive using MixIT, and then fine-tune it on MUSDB18 with supervision. Using the band-split TF-Locoformer, one of the state-of-the-art MSS models, we demonstrate that MixIT-based pre-training improves the performance over training from scratch.
Collection: Datasets from AFAR Challenge
Authors: Saad Masrur, Ozgur Ozdemir, Anil Gurses, Ismail Guvenc, Mihail L. Sichitiu, Rudra Dutta, Magreth Mushi, homas Zajkowski, Cole Dickerson, Gautham Reddy, Sergio Vargas Villar, Chau-Wai Wong, Baisakhi Chatterjee, Sonali Chaudhari, zhizhen li, Yuchen Liu, Paul Kudyba, Haijian Sun, Jaya Sravani Mandapaka, Kamesh Namuduri, Weijie Wang, Fraida Fund
Date: 2025-05-11 00:00:00
Tasks: Autonomous Navigation

This paper presents a comprehensive real-world and Digital Twin (DT) dataset collected as part of the Find A Rover (AFAR) Challenge, organized by the NSF Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) testbed and hosted at the Lake Wheeler Field in Raleigh, North Carolina. The AFAR Challenge was a competition involving five finalist university teams, focused on promoting innovation in UAV-assisted radio frequency (RF) source localization. Participating teams were tasked with designing UAV flight trajectories and localization algorithms to detect the position of a hidden unmanned ground vehicle (UGV), also referred to as a rover, emitting wireless probe signals generated by GNU Radio. The competition was structured to evaluate solutions in a DT environment first, followed by deployment and testing in AERPAW's outdoor wireless testbed. For each team, the UGV was placed at three different positions, resulting in a total of 30 datasets, 15 collected in a DT simulation environment and 15 in a physical outdoor testbed. Each dataset contains time-synchronized measurements of received signal strength (RSS), received signal quality (RSQ), GPS coordinates, UAV velocity, and UAV orientation (roll, pitch, and yaw). Data is organized into structured folders by team, environment (DT and real-world), and UGV location. The dataset supports research in UAV-assisted RF source localization, air-to-ground (A2G) wireless propagation modeling, trajectory optimization, signal prediction, autonomous navigation, and DT validation. With approximately 300k time-synchronized samples collected from real-world experiments, the dataset provides a substantial foundation for training and evaluating deep learning (DL) models. Overall, the AFAR dataset serves as a valuable resource for advancing robust, real-world solutions in UAV-enabled wireless communications and sensing systems.
Towards Digital Twin in Flood Forecasting with Data Assimilation Satellite Earth Observations -- A Proof-of-Concept in the Alzette Catchment
Authors: Thanh Huy Nguyen, Sukriti Bhattacharya, Jefferson S. Wong, Yoanne Didry, Duc Long Phan, Thomas Tamisier, Patrick Matgen
Date: 2025-05-13 00:00:00

Floods pose significant risks to human lives, infrastructure, and the environment. Timely and accurate flood forecasting plays a pivotal role in mitigating these risks. This study presents a proof-of-concept for a Digital Twin framework aimed at improving flood forecasting in the Alzette Catchment, Luxembourg. The approach integrates satellite-based Earth observations, specifically Sentinel-1 flood probability maps, into a particle filter-based data assimilation (DA) process to enhance flood predictions. By combining the GloFAS global flood monitoring and GloFAS streamflow forecasts products with DA using a high-resolution LISFLOOD-FP hydrodynamic model, the Digital Twin can provide daily flood forecasts for up to 30 days with reduced prediction uncertainties. Using the 2021 flood event as a case study, we evaluate the performance of the Digital Twin in assimilating EO data to refine hydraulic model simulations and issue accurate forecasts. While some limitations, such as uncertainties in GloFAS discharge forecasts, remain large, the approach successfully improves forecast accuracy compared to open-loop simulations. Future developments will focus on constructing more adaptively the hazard catalog, and reducing inherent uncertainties related to GloFAS streamflow forecasts and Sentinel-1 flood maps, to further enhance predictive capability. The framework demonstrates potential for advancing real-time flood forecasting and strengthening flood resilience.
SAR-GTR: Attributed Scattering Information Guided SAR Graph Transformer Recognition Algorithm
Authors: Xuying Xiong, Xinyu Zhang, Weidong Jiang, Li Liu, Yongxiang Liu, Tianpeng Liu
Date: 2025-05-13 00:00:00

Utilizing electromagnetic scattering information for SAR data interpretation is currently a prominent research focus in the SAR interpretation domain. Graph Neural Networks (GNNs) can effectively integrate domain-specific physical knowledge and human prior knowledge, thereby alleviating challenges such as limited sample availability and poor generalization in SAR interpretation. In this study, we thoroughly investigate the electromagnetic inverse scattering information of single-channel SAR and re-examine the limitations of applying GNNs to SAR interpretation. We propose the SAR Graph Transformer Recognition Algorithm (SAR-GTR). SAR-GTR carefully considers the attributes and characteristics of different electromagnetic scattering parameters by distinguishing the mapping methods for discrete and continuous parameters, thereby avoiding information confusion and loss. Furthermore, the GTR combines GNNs with the Transformer mechanism and introduces an edge information enhancement channel to facilitate interactive learning of node and edge features, enabling the capture of robust and global structural characteristics of targets. Additionally, the GTR constructs a hierarchical topology-aware system through global node encoding and edge position encoding, fully exploiting the hierarchical structural information of targets. Finally, the effectiveness of the algorithm is validated using the ATRNet-STAR large-scale vehicle dataset.
Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
Authors: Jin Liu, Qing Lin, Zhuang Xiong, Shanshan Shan, Chunyi Liu, Min Li, Feng Liu, G. Bruce Pike, Hongfu Sun, Yang Gao
Date: 2025-05-13 00:00:00
Tasks: Denoising, MRI Reconstruction, SSIM

Incoherent k-space under-sampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8$\times$ or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model. Comprehensive experiments were conducted on both publicly available fastMRI images and an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (PSNR and SSIM), qualitative error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320*320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.
Towards a physically realistic computationally efficient DVS pixel model
Authors: Rui Graca, Tobi Delbruck
Date: 2025-05-12 00:00:00
Tasks: Computational Efficiency

Dynamic Vision Sensor (DVS) event camera models are important tools for predicting camera response, optimizing biases, and generating realistic simulated datasets. Existing DVS models have been useful, but have not demonstrated high realism for challenging HDR scenes combined with adequate computational efficiency for array-level scene simulation. This paper reports progress towards a physically realistic and computationally efficient DVS model based on large-signal differential equations derived from circuit analysis, with parameters fitted from pixel measurements and circuit simulation. These are combined with an efficient stochastic event generation mechanism based on first-passage-time theory, allowing accurate noise generation with timesteps greater than 1000x longer than previous methods
Smooth optimization algorithms for global and locally low-rank regularizers
Authors: Rodrigo A. Lobos, Javier Salazar Cavazos, Raj Rao Nadakuditi, Jeffrey A. Fessler
Date: 2025-05-09 00:00:00
Tasks: MRI Reconstruction

Many inverse problems and signal processing problems involve low-rank regularizers based on the nuclear norm. Commonly, proximal gradient methods (PGM) are adopted to solve this type of non-smooth problems as they can offer fast and guaranteed convergence. However, PGM methods cannot be simply applied in settings where low-rank models are imposed locally on overlapping patches; therefore, heuristic approaches have been proposed that lack convergence guarantees. In this work we propose to replace the nuclear norm with a smooth approximation in which a Huber-type function is applied to each singular value. By providing a theoretical framework based on singular value function theory, we show that important properties can be established for the proposed regularizer, such as: convexity, differentiability, and Lipschitz continuity of the gradient. Moreover, we provide a closed-form expression for the regularizer gradient, enabling the use of standard iterative gradient-based optimization algorithms (e.g., nonlinear conjugate gradient) that can easily address the case of overlapping patches and have well-known convergence guarantees. In addition, we provide a novel step-size selection strategy based on a quadratic majorizer of the line-search function that leverages the Huber characteristics of the proposed regularizer. Finally, we assess the proposed optimization framework by providing empirical results in dynamic magnetic resonance imaging (MRI) reconstruction in the context of locally low-rank models with overlapping patches.
Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
Authors: Abdullah, Tao Huang, Ickjai Lee, Euijoon Ahn
Date: 2025-05-09 00:00:00
Tasks: Denoising

The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models have been successfully applied across a range of applications. However, a significant challenge remains with the high computational cost associated with training and generating these models. This study focuses on the efficiency and inference time of diffusion-based generative models, highlighting their applications in both natural and medical imaging. We present the most recent advances in diffusion models by categorizing them into three key models: the Denoising Diffusion Probabilistic Model (DDPM), the Latent Diffusion Model (LDM), and the Wavelet Diffusion Model (WDM). These models play a crucial role in medical imaging, where producing fast, reliable, and high-quality medical images is essential for accurate analysis of abnormalities and disease diagnosis. We first investigate the general framework of DDPM, LDM, and WDM and discuss the computational complexity gap filled by these models in natural and medical imaging. We then discuss the current limitations of these models as well as the opportunities and future research directions in medical imaging.
ProTCT: Projection quantification and fidelity constraint integrated deep reconstruction for Tangential CT
Authors: Bingan Yuan, Bowei Liu, Zheng Fang
Date: 2025-05-09 00:00:00

Tangential computed tomography (TCT) is a useful tool for imaging the large-diameter samples, such as oil pipelines and rockets. However, TCT projections are truncated along the detector direction, resulting in degraded slices with radial artifacts. Meanwhile, existing methods fail to reconstruct decent images because of the ill-defined sampling condition in the projection domain and oversmoothing in the cross-section domain. In this paper, we propose a projection quantification and fidelity constraint integrated deep TCT reconstruction method (ProTCT) to improve the slice quality. Specifically, the sampling conditions for reconstruction are analysed, offering practical guidelines for TCT system design. Besides, a deep artifact-suppression network together with a fidelity-constraint module that operates across both projection and cross-section domains to remove artifacts and restore edge details. Demonstrated on simulated and real datasets, the ProTCT shows good performance in structure restoration and detail retention. This work contributes to exploring the sampling condition and improving the slice quality of TCT, further promoting the application of large view field CT imaging.
Evaluation of UAV-Based RGB and Multispectral Vegetation Indices for Precision Agriculture in Palm Tree Cultivation
Authors: Alavikunhu Panthakkan, S M Anzar, K. Sherin, Saeed Al Mansoori, Hussain Al-Ahmad
Date: 2025-05-06 00:00:00

Precision farming relies on accurate vegetation monitoring to enhance crop productivity and promote sustainable agricultural practices. This study presents a comprehensive evaluation of UAV-based imaging for vegetation health assessment in a palm tree cultivation region in Dubai. By comparing multispectral and RGB image data, we demonstrate that RGBbased vegetation indices offer performance comparable to more expensive multispectral indices, providing a cost-effective alternative for large-scale agricultural monitoring. Using UAVs equipped with multispectral sensors, indices such as NDVI and SAVI were computed to categorize vegetation into healthy, moderate, and stressed conditions. Simultaneously, RGB-based indices like VARI and MGRVI delivered similar results in vegetation classification and stress detection. Our findings highlight the practical benefits of integrating RGB imagery into precision farming, reducing operational costs while maintaining accuracy in plant health monitoring. This research underscores the potential of UAVbased RGB imaging as a powerful tool for precision agriculture, enabling broader adoption of data-driven decision-making in crop management. By leveraging the strengths of both multispectral and RGB imaging, this work advances the state of UAV applications in agriculture, paving the way for more efficient and scalable farming solutions.
Sub-diffraction terahertz backpropagation compressive imaging
Authors: Yongsheng Zhu, Shaojing Liu, Ximiao Wang, Runli Li, Haili Yang, Jiali Wang, Hongjia Zhu, Yanlin Ke, Ningsheng Xu, Huanjun Chen, Shaozhi Deng
Date: 2025-05-05 00:00:00

Terahertz single-pixel imaging (TSPI) has garnered significant attention due to its simplicity and cost-effectiveness. However, the relatively long wavelength of THz waves limits sub-diffraction-scale imaging resolution. Although TSPI technique can achieve sub-wavelength resolution, it requires harsh experimental conditions and time-consuming processes. Here, we propose a sub-diffraction THz backpropagation compressive imaging technique. We illuminate the object with monochromatic continuous-wave THz radiation. The transmitted THz wave is modulated by prearranged patterns generated on the back surface of a 500-{\mu}m-thick silicon wafer, realized through photoexcited carriers using a 532-nm laser. The modulated THz wave is then recorded by a single-element detector. An untrained neural network is employed to iteratively reconstruct the object image with an ultralow compression ratio of 1.5625% under a physical model constraint, thus reducing the long sampling times. To further suppress the diffraction-field effects, embedded with the angular spectrum propagation (ASP) theory to model the diffraction of THz waves during propagation, the network retrieves near-field information from the object, enabling sub-diffraction imaging with a spatial resolution of ~{\lambda}0/7 ({\lambda}0 = 833.3 {\mu}m at 0.36 THz) and eliminating the need for ultrathin photomodulators. This approach provides an efficient solution for advancing THz microscopic imaging and addressing other inverse imaging challenges.