pwc-archive/papers-with-abstracts huggingface dataset:
AH-UGC: Adaptive and Heterogeneous-Universal Graph Coarsening
Authors: Mohit Kataria, Shreyash Bhilwade, Sandeep Kumar, Jayadeva
Date: 2025-05-18 00:00:00

$\textbf{Graph Coarsening (GC)}$ is a prominent graph reduction technique that compresses large graphs to enable efficient learning and inference. However, existing GC methods generate only one coarsened graph per run and must recompute from scratch for each new coarsening ratio, resulting in unnecessary overhead. Moreover, most prior approaches are tailored to $\textit{homogeneous}$ graphs and fail to accommodate the semantic constraints of $\textit{heterogeneous}$ graphs, which comprise multiple node and edge types. To overcome these limitations, we introduce a novel framework that combines Locality Sensitive Hashing (LSH) with Consistent Hashing to enable $\textit{adaptive graph coarsening}$. Leveraging hashing techniques, our method is inherently fast and scalable. For heterogeneous graphs, we propose a $\textit{type isolated coarsening}$ strategy that ensures semantic consistency by restricting merges to nodes of the same type. Our approach is the first unified framework to support both adaptive and heterogeneous coarsening. Extensive evaluations on 23 real-world datasets including homophilic, heterophilic, homogeneous, and heterogeneous graphs demonstrate that our method achieves superior scalability while preserving the structural and semantic integrity of the original graph.
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Authors: Congyuan Zhao, Lingwei Wei, Ziming Qin, Wei Zhou, Yunya Song, Songlin Hu
Date: 2025-05-16 00:00:00
Tasks: Fake News Detection

Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake news. However, these approaches typically detect fake news based on specific platforms, ignoring differences in propagation characteristics across platforms. In this paper, we introduce the MPPFND dataset, which captures propagation structures across multiple platforms. We also describe the commenting and propagation characteristics of different platforms to show that their social contexts have distinct features. We propose a multi-platform fake news detection model (APSL) that uses graph neural networks to extract social context features from various platforms. Experiments show that accounting for cross-platform propagation differences improves fake news detection performance.
Delayed dynamic-feedback controller design for multi-frequency vibration suppression
Authors: Adrian Saldanha, Adam Peichl, Wim Michiels, Tomáš Vyhlídal
Date: 2025-05-22 00:00:00

We present a methodology for designing a dynamic controller with delayed output feedback for achieving non-collocated vibration suppression with a focus on the multi-frequency case. To synthesize the delay-based controller, we first remodel the system of equations as a delay-differential algebraic equation (DDAE) in such a way that existing tools for design of a static output feedback controller can be easily adapted. The problem of achieving non-collocated vibration suppression with sufficient damping is formulated as a constrained optimization problem of minimizing the spectral abscissa in the presence of zero-location constraints, with the constraints exhibiting polynomial dependence on its parameters. We transform the problem into an unconstrained one using elimination, following which we solve the resulting non-convex, non-smooth optimization problem.
Modeling and Constraint-Aware Control of Pressure Dynamics in Water Electrolysis Systems
Authors: Mostafaali Ayubirad, Madiha Akbar, Hamid R. Ossareh
Date: 2025-05-22 00:00:00

This paper addresses the challenge of pressure constraint violations in water electrolysis systems operating under dynamic power conditions, a problem common to both Proton Exchange Membrane and alkaline technologies. To investigate this issue, a control-oriented model of an alkaline electrolyzer is developed, capturing key pressure and flow dynamics. To manage rapid power fluctuations that may cause pressure to exceed manufacturer-defined operational boundaries, a model-based constraint-aware power governor based on the Reference Governor (RG) framework is proposed. Simulation results show that the strategy effectively maintains pressure within the specified operating range, outperforming conventional filtering methods while enhancing hydrogen production and reducing auxiliary energy consumption.
On the Deployment of RIS-mounted UAV Networks
Authors: Anupam Mondal, Priyadarshi Mukherjee, Sasthi C. Ghosh
Date: 2025-05-22 00:00:00
Tasks: Fairness

Reconfigurable intelligent surfaces (RIS) enable smart wireless environments by dynamically controlling signal propagation to enhance communication and localization. Unmanned aerial vehicles (UAVs) can act as flying base stations and thus, improve system performance by avoiding signal blockages. In this paper, we propose a gradient ascent and coordinate search based method to determine the optimal location for a system that consists of a UAV and a RIS, where the UAV serves cellular users (CUs) and the RIS serves device-to-device (D2D) pairs. In particular, by optimizing the net throughput for both the D2D pairs and the CUs, the suggested method establishes the ideal location for the RIS-mounted UAV. We consider both line of sight (LoS) and non-LoS paths for the RIS and UAV to calculate the throughput while accounting for blockages in the system. The numerical results show that the proposed method performs better than the existing approaches in terms of both the net throughput and the user fairness.
Data Center Model for Transient Stability Analysis of Power Systems
Authors: Alberto Jimenez-Ruiz, Federico Milano
Date: 2025-05-22 00:00:00

The rising demand of computing power leads to the installation of a large number of Data Centers (DCs). Their Fault-Ride-Through (FRT) behavior and their unique power characteristics, especially for DCs catered to Artificial Intelligence (AI) workloads, pose a threat to the stability of power systems. To ensure its stability, it is required accurate models of the loads involved. Here we propose a dynamic load model that properly captures the behaviour of DCs. Its three most defining features are the use of an Uninterrupted Power Supply (UPS) which sits between the server load and the grid, the cooling load represented by an induction motor, and a pulsing load that represents the transients caused by contemporary DCs with significant AI workloads. The features of the proposed model and its impact on the dynamic performance of transmission systems are illustrated through a model of the all-island Irish transmission system and real-world data of the DCs currently connected to this system.
Neural network based control of unknown nonlinear systems via contraction analysis
Authors: Hao Yin, Claudio De Persis, Bayu Jayawardhana, Santiago Sanchez Escalonilla Plaza
Date: 2025-05-22 00:00:00
Tasks: Computational Efficiency

This paper studies the design of neural network (NN)-based controllers for unknown nonlinear systems, using contraction analysis. A Neural Ordinary Differential Equation (NODE) system is constructed by approximating the unknown draft dynamics with a feedforward NN. Incremental sector bounds and contraction theory are applied to the activation functions and the weights of the NN, respectively. It is demonstrated that if the incremental sector bounds and the weights satisfy some non-convex conditions, the NODE system is contractive. To improve computational efficiency, these non-convex conditions are reformulated as convex LMI conditions. Additionally, it is proven that when the NODE system is contractive, the trajectories of the original autonomous system converge to a neighborhood of the unknown equilibrium, with the size of this neighborhood determined by the approximation error. For a single-layer NN, the NODE system is simplified to a continuous-time Hopfield NN. If the NODE system does not satisfy the contraction conditions, an NN-based controller is designed to enforce contractivity. This controller integrates a linear component, which ensures contraction through suitable control gains, and an NN component, which compensates for the NODE system's nonlinearities. This integrated controller guarantees that the trajectories of the original affine system converge to a neighborhood of the unknown equilibrium. The effectiveness of the proposed approach is demonstrated through two illustrative examples.
Robust Look-ahead Pursuit Control for Three-Dimensional Path Following within Finite-Time Stability Guarantee
Authors: Zimao Sheng, Hong an Yang, ZiRui Yu, Jiakang Wang
Date: 2025-05-22 00:00:00

This paper addresses the challenging problem of robust path following for fixed-wing unmanned aerial vehicles (UAVs) in complex environments with bounded external disturbances and non-smooth predefined paths. Due to the unique aerodynamic characteristics and flight constraints of fixed-wing UAVs, achieving accurate and stable path following remains difficult, especially in low-altitude mountainous terrains, urban landscapes, and under wind disturbances. Traditional path-following guidance laws often struggle with rapid stabilization and constrained input commands under unknown disturbances while maintaining robustness. To overcome these limitations, we propose a robust nonlinear path-following guidance law that considers the flight path angle and track angle, and dynamically adjusts controller parameters to achieve optimal compensation for acceleration increments. The proposed guidance law guarantees finite-time stability, reduced sensitivity to constrained uncertainties, and consistent behavior compared to traditional asymptotic convergence controllers. Additionally, it ensures that the UAV approaches mobile virtual target points in the shortest possible time while adhering to input constrained conditions. Our contributions include a thorough analysis of the conditions for robust stability, the derivation of the guidance law, and simulations demonstrating its effectiveness. The results show that the proposed guidance law significantly improves path-following performance under external disturbances, making it a promising solution for autonomous missions execution of fixed-wing UAVs.
Trajectory-Independent Flexibility Envelopes of Energy-Constrained Systems with State-Dependent Losses
Authors: Julie Rousseau, Carlo Tajoli, Hanmin Cai, Philipp Heer, Kristina Orehounig, Gabriela Hug
Date: 2025-05-22 00:00:00

As non-dispatchable renewable power units become prominent in electric power grids, demand-side flexibility appears as a key element of future power systems' operation. Power and energy bounds are intuitive metrics to describe the flexibility of energy-constrained loads. However, to be used in operation, any power consumption trajectory fulfilling the power and energy bounds must necessarily fulfill the load's constraints. In this paper, we demonstrate that energy bounds defined as the minimum and maximum energy consumption potential of a load with state-dependent losses are Trajectory-Dependent (TD), i.e., for any energy value in the bounds a feasible power trajectory exists, but not all power trajectories enclosed in the energy envelopes satisfy the load's constraints. To guarantee the satisfaction of load constraints for all trajectories, we define Trajectory-Independent (TI) energy bounds. We present TI envelope formulations for individual loads, as well as physically coupled loads and assess the proposed formulations in a building heating system, a system with state-dependent losses. We find that using a TD envelope as energy bounds in operation may yield room temperature up to 3.8{\deg}C higher and 3.4{\deg}C lower than admissible. Overall, poorly insulated buildings observe a TI energy envelope that differs significantly from their TD envelope.
Coverage Path Planning For Multi-view SAR-UAV Observation System Under Energy Constraint
Authors: Deyu Song, Xiangyin Zhang, Zipei Yu, Kaiyu Qin
Date: 2025-05-22 00:00:00
Tasks: Computational Efficiency

Multi-view Synthetic Aperture Radar (SAR) imaging can effectively enhance the performance of tasks such as automatic target recognition and image information fusion. Unmanned aerial vehicles (UAVs) have the advantages of flexible deployment and cost reduction. A swarm of UAVs equipped with synthetic aperture radar imaging equipment is well suited to meet the functional requirements of multi-view synthetic aperture radar imaging missions. However, to provide optimal paths for SAR-UAVs from the base station to cover target viewpoints in the mission area is of NP-hard computational complexity. In this work, the coverage path planning problem for multi-view SAR-UAV observation systems is studied. First, the coordinate of observation viewpoints is calculated based on the location of targets and base station under a brief geometric model. Then, the exact problem formulation is modeled in order to fully describe the solution space and search for optimal paths that provide maximum coverage rate for SAR-UAVs. Finally, an Adaptive Density Peak Clustering (ADPC) method is proposed to overcome the additional energy consumption due to the viewpoints being far away from the base station. The Particle Swarm Optimization (PSO) algorithm is introduced for optimal path generation. Experimental results demonstrate the effectiveness and computational efficiency of the proposed approach.
Scalable and Efficient Aggregation of Energy-Constrained Flexible Loads
Authors: Julie Rousseau, Philipp Heer, Kristina Orehounig, Gabriela Hug
Date: 2025-05-22 00:00:00

Loads represent a promising flexibility source to support the integration of renewable energy sources, as they may shift their energy consumption over time. By computing the aggregated flexibility of power and energy-constrained loads, aggregators can communicate the group's flexibility without sharing individual private information. However, this computation is, in practice, challenging. Some studies suggest different inner approximations of aggregated flexibility polytopes, but all suffer from large computational costs for realistic load numbers and horizon lengths. In this paper, we develop a novel approximation of the aggregated flexibility of loads based on the concept of worst-case energy dispatch, i.e., if aggregated energy consumptions are assumed to be dispatched in the worst manner possible. This leads to conservative piecewise linear bounds that restrict the aggregated energy consumption only based on the previous aggregated energy consumed. A comparative case study reveals that our method can compute an approximation of the aggregation of thousands of loads efficiently, while displaying an accuracy comparable to other approximation techniques.
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies
Authors: Runze Yan, Xun Shen, Akifumi Wachi, Sebastien Gros, Anni Zhao, Xiao Hu
Date: 2025-05-22 00:00:00
Tasks: Offline RL, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

When applying offline reinforcement learning (RL) in healthcare scenarios, the out-of-distribution (OOD) issues pose significant risks, as inappropriate generalization beyond clinical expertise can result in potentially harmful recommendations. While existing methods like conservative Q-learning (CQL) attempt to address the OOD issue, their effectiveness is limited by only constraining action selection by suppressing uncertain actions. This action-only regularization imitates clinician actions that prioritize short-term rewards, but it fails to regulate downstream state trajectories, thereby limiting the discovery of improved long-term treatment strategies. To safely improve policy beyond clinician recommendations while ensuring that state-action trajectories remain in-distribution, we propose \textit{Offline Guarded Safe Reinforcement Learning} ($\mathsf{OGSRL}$), a theoretically grounded model-based offline RL framework. $\mathsf{OGSRL}$ introduces a novel dual constraint mechanism for improving policy with reliability and safety. First, the OOD guardian is established to specify clinically validated regions for safe policy exploration. By constraining optimization within these regions, it enables the reliable exploration of treatment strategies that outperform clinician behavior by leveraging the full patient state history, without drifting into unsupported state-action trajectories. Second, we introduce a safety cost constraint that encodes medical knowledge about physiological safety boundaries, providing domain-specific safeguards even in areas where training data might contain potentially unsafe interventions. Notably, we provide theoretical guarantees on safety and near-optimality: policies that satisfy these constraints remain in safe and reliable regions and achieve performance close to the best possible policy supported by the data.
Partitioning and Observability in Linear Systems via Submodular Optimization
Authors: Mohamad H. Kazma, Ahmad F. Taha
Date: 2025-05-22 00:00:00

Network partitioning has gained recent attention as a pathway to enable decentralized operation and control in large-scale systems. This paper addresses the interplay between partitioning, observability, and sensor placement (SP) in dynamic networks. The problem, being computationally intractable at scale, is largely unexplored in the literature. To that end, the paper's objective is designing scalable partitioning of linear systems while maximizing observability metrics of the subsystems. We show that the partitioning problem can be posed as a submodular maximization problem -- and the SP problem can subsequently be solved over the partitioned network. Consequently, theoretical bounds are derived to compare observability metrics of the original network with those of the resulting partitions, highlighting the impact of partitioning on system observability. Case studies on networks of varying sizes corroborate the derived theoretical bounds.
A Distributed Local Energy Market Clearing Framework Using a Two-Loop ADMM Method
Authors: Milad Kabirifar, Biswarup Mukherjee, S. Gokul Krishnan, Charalambos Konstantinou, Subhash Lakshminarayana
Date: 2025-05-21 00:00:00
Tasks: Diversity

The diversity of prosumers' resources in energy communities can provide significant technical and economic benefits to both prosumers and the distribution system operator (DSO). To maximize these benefits, a coordination framework is required to address all techno-economic constraints as well as the objectives of all agents. This paper presents a fully distributed market-clearing scheme to coordinate the strategies of agents within a local energy community. In the proposed framework, prosumers, the DSO, and the local market operator (LMO) are the participating agents. The framework addresses the preferences and techno-economic constraints of all actors while preserving their privacy. The proposed model is based on a modified alternating direction method of multipliers (ADMM) method with two outer and inner loops; the outer loop models the interactions between the LMO and prosumers, while the inner loop addresses the interactions between the LMO and the DSO. The model is demonstrated on IEEE-69bus test network, showcasing its effectiveness from various perspectives.
A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation
Authors: Zeynab Kaseb, Matthias Moller, Peter Palensky, Pedro P. Vergara
Date: 2025-05-21 00:00:00
Tasks: Combinatorial Optimization

This study introduces the Adiabatic Quantum Power Flow (AQPF) and Adiabatic Quantum Optimal Power Flow (AQOPF) algorithms to solve power flow (PF) and optimal power flow (OPF) problems, respectively. These algorithms utilize a novel combinatorial optimization reformulation of classical PF and OPF problems, and hence, enable their implementation on Ising machines, e.g., quantum and quantum-inspired hardware. The experiments are conducted on standard test cases ranging from 4-bus to 1354-bus systems, using D-Wave's Advantage system (QA), D-Wave's quantum-classical hybrid solver (HA), Fujitsu's Digital Annealer V3 (DAv3), and Fujitsu's Quantum-Inspired Integrated Optimization software (QIIO). The annealers are systematically evaluated based on: (i) full and partitioned formulations, (ii) ability to handle ill-conditioned cases, and (iii) scalability. The results are benchmarked against the Newton-Raphson numerical method (NR) and suggest that AQPF and AQOPF can serve as effective solvers or complementary tools to classical methods to address unsolved challenges in large-scale modern power systems.
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works
Authors: Bendong Tan, Tong Su, Yu Weng, Ketian Ye, Parikshit Pareek, Petr Vorobev, Hung Nguyen, Junbo Zhao, Deepjyoti Deka
Date: 2025-05-21 00:00:00
Tasks: Decision Making, Gaussian Processes, Uncertainty Quantification

The increasing integration of renewable energy sources (RESs) and distributed energy resources (DERs) has significantly heightened operational complexity and uncertainty in modern power systems. Concurrently, the widespread deployment of smart meters, phasor measurement units (PMUs) and other sensors has generated vast spatiotemporal data streams, enabling advanced data-driven analytics and decision-making in grid operations. In this context, Gaussian processes (GPs) have emerged as a powerful probabilistic framework, offering uncertainty quantification, non-parametric modeling, and predictive capabilities to enhance power system analysis and control. This paper presents a comprehensive review of GP techniques and their applications in power system operation and control. GP applications are reviewed across three key domains: GP-based modeling, risk assessment, and optimization and control. These areas serve as representative examples of how GP can be utilized in power systems. Furthermore, critical challenges in GP applications are discussed, and potential research directions are outlined to facilitate future power system operations.
Path Planning Algorithm Comparison Analysis for Wireless AUVs Energy Sharing System
Authors: Zhengji Feng, Hengxiang Chen, Liqun Chen, Heyan Li, Xiaolin Mou
Date: 2025-05-21 00:00:00
Tasks: Autonomous Navigation

Autonomous underwater vehicles (AUVs) are increasingly used in marine research, military applications, and undersea exploration. However, their operational range is significantly affected by battery performance. In this paper, a framework for a wireless energy sharing system among AUVs is proposed, enabling rapid energy replenishment. Path planning plays a crucial role in the energy-sharing process and autonomous navigation, as it must generate feasible trajectories toward designated goals. This article focuses on efficient obstacle avoidance in complex underwater environments, including irregularly shaped obstacles and narrow passages. The proposed method combines Rapidly-exploring Random Trees Star (RRT*) with Particle Swarm Optimization (PSO) to improve path planning efficiency. Comparative analysis of the two algorithms is presented through simulation results in both random and irregular obstacle environments. Index Terms: Wireless charging, autonomous underwater vehicles (AUVs), path planning, irregular obstacles, narrow passages, RRT*, particle swarm optimization (PSO).