pwc-archive/papers-with-abstracts huggingface dataset:
Peer Disambiguation in Self-Reported Surveys using Graph Attention Networks
Authors: Ajitesh Srivastava, Aryan Shetty, Eric Rice
Date: 2025-03-25 00:00:00
Tasks: Graph Attention

Studying peer relationships is crucial in solving complex challenges underserved communities face and designing interventions. The effectiveness of such peer-based interventions relies on accurate network data regarding individual attributes and social influences. However, these datasets are often collected through self-reported surveys, introducing ambiguities in network construction. These ambiguities make it challenging to fully utilize the network data to understand the issues and to design the best interventions. We propose and solve two variations of link ambiguities in such network data -- (i) which among the two candidate links exists, and (ii) if a candidate link exists. We design a Graph Attention Network (GAT) that accounts for personal attributes and network relationships on real-world data with real and simulated ambiguities. We also demonstrate that by resolving these ambiguities, we improve network accuracy, and in turn, improve suicide risk prediction. We also uncover patterns using GNNExplainer to provide additional insights into vital features and relationships. This research demonstrates the potential of Graph Neural Networks (GNN) to advance real-world network data analysis facilitating more effective peer interventions across various fields.
Representative Ranking for Deliberation in the Public Sphere
Authors: Manon Revel, Smitha Milli, Tyler Lu, Jamelle Watson-Daniels, Max Nickel
Date: 2025-03-19 00:00:00

Online comment sections, such as those on news sites or social media, have the potential to foster informal public deliberation, However, this potential is often undermined by the frequency of toxic or low-quality exchanges that occur in these settings. To combat this, platforms increasingly leverage algorithmic ranking to facilitate higher-quality discussions, e.g., by using civility classifiers or forms of prosocial ranking. Yet, these interventions may also inadvertently reduce the visibility of legitimate viewpoints, undermining another key aspect of deliberation: representation of diverse views. We seek to remedy this problem by introducing guarantees of representation into these methods. In particular, we adopt the notion of justified representation (JR) from the social choice literature and incorporate a JR constraint into the comment ranking setting. We find that enforcing JR leads to greater inclusion of diverse viewpoints while still being compatible with optimizing for user engagement or other measures of conversational quality.
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Authors: Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu
Date: 2025-03-14 00:00:00
Tasks: Community Detection, Network Embedding, Node Classification

Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classification and community detection. Several embedding methods have been proposed to capture structural proximity among nodes in a network, where densely connected communities are preserved, while others have been proposed to preserve structural equivalence among nodes, capturing their structural roles regardless of their relative distance in the network. However, most existing methods that aim to preserve both network characteristics mainly focus on static networks and those designed for dynamic networks do not explicitly account for inter-snapshot structural properties. This paper proposes a novel unifying dynamic network embedding method that simultaneously preserves both structural proximity and equivalence while considering inter-snapshot structural relationships in a dynamic network. Specifically, to define structural equivalence in a dynamic network, we use temporal subgraphs, known as dynamic graphlets, to capture how a node's neighborhood structure evolves over time. We then introduce a temporal-structural random walk to flexibly sample time-respecting sequences of nodes, considering both their temporal proximity and similarity in evolving structures. The proposed method is evaluated using five real-world networks on node classification where it outperforms benchmark methods, showing its effectiveness and flexibility in capturing various aspects of a network.
Convergence Theory of Flexible ALADIN for Distributed Optimization
Authors: Xu Du, Xiaohua Zhou, Shijie Zhu
Date: 2025-03-26 00:00:00
Tasks: Distributed Optimization, Federated Learning

The Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method is a cutting-edge distributed optimization algorithm known for its superior numerical performance. It relies on each agent transmitting information to a central coordinator for data exchange. However, in practical network optimization and federated learning, unreliable information transmission often leads to packet loss, posing challenges for the convergence analysis of ALADIN. To address this issue, this paper proposes Flexible ALADIN, a random polling variant of ALADIN, and presents a rigorous convergence analysis, including global convergence for convex problems and local convergence for non-convex problems.
Insights from Game Theory into the Impact of Smart Balancing on Power System Stability
Authors: Johannes Lips, Hendrik Lens
Date: 2025-03-26 00:00:00

Smart balancing, also called passive balancing, is the intentional introduction of active power schedule deviations by balance responsible parties (BRPs) to receive a remuneration through the imbalance settlement mechanism. From a system perspective, smart balancing is meant to reduce the need for, and costs of, frequency restoration reserves (FRR), but it can also cause large oscillations in the FRR and jeopardize the system stability. Using a dynamic control area model, this work defines a 2x2 game in which two BRPs can choose to perform smart balancing. We study the impact of time delay, ramp rates, and pricing mechanisms on Nash equilibria and Experience-weighted Attraction (EWA) learning. It is found that, even in an idealized setting, a significant fraction of games in a learned equilibrium results in an overreaction relative to the baseline disturbance, creating an imbalance in the opposite direction. This suggests that the system stability risks are inherent to smart balancing and not a question of implementation. Recommendations are given for implementation choices that can reduce (but not eliminate) the risk of overreactions.
Model-free Vehicle Rollover Prevention: A Data-driven Predictive Control Approach
Authors: Mohammad R. Hajidavalloo, Kaixiang Zhang, Vaibhav Srivastava, Zhaojian Li
Date: 2025-03-26 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, Model Predictive Control

Vehicle rollovers pose a significant safety risk and account for a disproportionately high number of fatalities in road accidents. This paper addresses the challenge of rollover prevention using Data-EnablEd Predictive Control (DeePC), a data-driven control strategy that directly leverages raw input-output data to maintain vehicle stability without requiring explicit system modeling. To enhance computational efficiency, we employ a reduced-dimension DeePC that utilizes singular value decomposition-based dimension reduction to significantly lower computation complexity without compromising control performance. This optimization enables real-time application in scenarios with high-dimensional data, making the approach more practical for deployment in real-world vehicles. The proposed approach is validated through high-fidelity CarSim simulations in both sedan and utility truck scenarios, demonstrating its versatility and ability to maintain vehicle stability under challenging driving conditions. Comparative results with Linear Model Predictive Control (LMPC) highlight the superior performance of DeePC in preventing rollovers while preserving maneuverability. The findings suggest that DeePC offers a robust and adaptable solution for rollover prevention, capable of handling varying road and vehicle conditions.
Data-driven Distributionally Robust Control Based on Sinkhorn Ambiguity Sets
Authors: Riccardo Cescon, Andrea Martin, Giancarlo Ferrari-Trecate
Date: 2025-03-26 00:00:00

As the complexity of modern control systems increases, it becomes challenging to derive an accurate model of the uncertainty that affects their dynamics. Wasserstein Distributionally Robust Optimization (DRO) provides a powerful framework for decision-making under distributional uncertainty only using noise samples. However, while the resulting policies inherit strong probabilistic guarantees when the number of samples is sufficiently high, their performance may significantly degrade when only a few data are available. Inspired by recent results from the machine learning community, we introduce an entropic regularization to penalize deviations from a given reference distribution and study data-driven DR control over Sinkhorn ambiguity sets. We show that for finite-horizon control problems, the optimal DR linear policy can be computed via convex programming. By analyzing the relation between the ambiguity set defined in terms of Wasserstein and Sinkhorn discrepancies, we reveal that, as the regularization parameter increases, this optimal policy interpolates between the solution of the Wasserstein DR problem and that of the stochastic problem under the reference distribution. We validate our theoretical findings and the effectiveness of our approach when only scarce data are available on a numerical example.
Agent-Based Analysis of the Impact of Near Real-Time Data and Smart Balancing on the Frequency Stability of Power Systems
Authors: Johannes Lips, Boyana Georgieva, Dominik Schlipf, Hendrik Lens
Date: 2025-03-26 00:00:00

Single imbalance pricing provides an incentive to balance responsible parties (BRPs) to intentionally introduce power schedule deviations in order to reduce the control area imbalance and receive a remuneration through the imbalance settlement mechanism. This is called smart balancing or passive balancing and is actively encouraged in, e.g., the Netherlands and Belgium through the publication of near real-time (NRT) data on the control area imbalance by the transmission system operator. It is known that under certain conditions, smart balancing can deteriorate the frequency stability of the power system. This paper examines how the publication of different types of NRT data affects smart balancing and the frequency stability. A Monte-Carlo simulation of a dynamic multi-agent model is performed to analyse the effects of smart balancing with different parameters for the agents and the environment, using historical time series of the power imbalance of the German control block as a basis. It is found that smart balancing can significantly reduce the amount and cost of frequency restoration reserve activation, but leads to a general increase of the frequency variability. Depending on the type of NRT data and agent parameters, the frequency stability margins are also reduced. The negative effects on the frequency stability are stronger when NRT data is published using large bins and with long delays.
Problem-Structure-Informed Quantum Approximate Optimization Algorithm for Large-Scale Unit Commitment with Limited Qubits
Authors: Jingxian Zhou, Ziqing Zhu, Linghua Zhu, Siqi Bu
Date: 2025-03-26 00:00:00
Tasks: Computational Efficiency

As power systems expand, solving the Unit Commitment Problem (UCP) becomes increasingly challenging due to the dimensional catastrophe, and traditional methods often struggle to balance computational efficiency and solution quality. To tackle this issue, we propose a problem-structure-informed Quantum Approximate Optimization Algorithm (QAOA) framework that fully exploits the quantum advantage under extremely limited quantum resources. Specifically, we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems, each solvable in parallel by limited number of qubits. This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse. Consequently, our approach can be readily extended to future power systems that are larger and more complex.
Automated and Risk-Aware Engine Control Calibration Using Constrained Bayesian Optimization
Authors: Maarten Vlaswinkel, Duarte Antunes, Frank Willems
Date: 2025-03-26 00:00:00
Tasks: Bayesian Optimization, Self-Learning

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.
Model Predictive Control for Tracking Bounded References With Arbitrary Dynamics
Authors: Shibo Han, Bonan Hou, Yuhao Zhang, Xiaotong Shi, Xingwei Zhao
Date: 2025-03-26 00:00:00
Tasks: Model Predictive Control

In this article, a model predictive control (MPC) method is proposed for constrained linear systems to track bounded references with arbitrary dynamics. Besides control inputs to be determined, artificial reference is introduced as additional decision variable, which serves as an intermediate target to cope with sudden changes of reference and enlarges domain of attraction. Cost function penalizes both artificial state error and reference error, while terminal constraint is imposed on artificial state error and artificial reference. We specify the requirements for terminal constraint and cost function to guarantee recursive feasibility of the proposed method and asymptotic stability of tracking error. Then, periodic and non-periodic references are considered and the method to determine required cost function and terminal constraint is proposed. Finally, the efficiency of the proposed MPC controller is demonstrated with simulation examples.
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning
Authors: Georg Schäfer, Tatjana Krau, Jakob Rehrl, Stefan Huber, Simon Hirlaender
Date: 2025-03-26 00:00:00
Tasks: Reinforcement Learning (RL)

Reinforcement Learning (RL) offers promising solutions for control tasks in industrial cyber-physical systems (ICPSs), yet its real-world adoption remains limited. This paper demonstrates how seemingly small but well-designed modifications to the RL problem formulation can substantially improve performance, stability, and sample efficiency. We identify and investigate key elements of RL problem formulation and show that these enhance both learning speed and final policy quality. Our experiments use a one-degree-of-freedom (1-DoF) helicopter testbed, the Quanser Aero~2, which features non-linear dynamics representative of many industrial settings. In simulation, the proposed problem design principles yield more reliable and efficient training, and we further validate these results by training the agent directly on physical hardware. The encouraging real-world outcomes highlight the potential of RL for ICPS, especially when careful attention is paid to the design principles of problem formulation. Overall, our study underscores the crucial role of thoughtful problem formulation in bridging the gap between RL research and the demands of real-world industrial systems.
Structure Identification of NDS with Descriptor Subsystems under Asynchronous, Non-Uniform, and Slow-Rate Sampling
Authors: Yunxiang Ma, Tong Zhou
Date: 2025-03-26 00:00:00

Networked dynamic systems (NDS) exhibit collective behavior shaped by subsystem dynamics and complex interconnections, yet identifying these interconnections remains challenging due to irregularities in sampled data, including asynchronous, non-uniform, and low-rate sampling. This paper proposes a novel two-stage structure identification algorithm that leverages system zero-order moments, a concept traditionally used in model order reduction, to bridge system identification and model reduction. First, zero-order moments are estimated from steady-state time-domain outputs; second, subsystem interconnections are explicitly reconstructed from these moments. The method generalizes existing approaches by handling asynchronous, non-uniform, and slow sampling simultaneously, eliminating constraints on input signal periodicity and extending applicability to multi-input multi-output NDS with arbitrary interconnections. Unlike black-box identification techniques, our approach explicitly recovers subsystem interconnection structures. Validation on the IEEE 14-bus system demonstrates the algorithm's effectiveness in recovering subsystem interconnections from irregular sampling data.
Merits of Serving UAVs via Terrestrial Networks: A Vertical Antenna Radiation Study
Authors: Nesrine Cherif, Qurrat-Ul-Ain Nadeem
Date: 2025-03-26 00:00:00

Unmanned Aerial Vehicles (UAVs) are increasingly used in a plethora of applications such as shipping, surveillance, and search-and-rescue. For UAVs to operate safely, reliable cellular connectivity is essential. Utilizing the terrestrial networks for aerial connectivity has been proposed, but the 3D radiation pattern of base station antennas significantly affects the performance of aerial links.. To address this, we evaluate the coverage probability of cellular-connected UAVs, considering vertical antenna gain, by leveraging tools from stochastic geometry. We also analyze how the UAV hovering height, tilt angle and 3D antenna beamwidth influence the reliability of the communication link. Our results show that a down-tiled antenna does not only improve the connectivity of terrestrial users but also its cellularconnected UAVs counterpart. Moreover, the coverage probability of the UAV-UE becomes saturated at large down-tilt angles at the TBSs due to the antenna sidelobe gain at the serving and interfering TBSs. We also found that the significant increase of the vertical antenna beamwidth improves the UAV user coverage probability especially at relatively low hovering altitudes thanks to the increase of the desired signal strength compared to the interference power.
Small-Signal Stability Condition of Inverter-Integrated Power Systems: Closed-Form Expression by Stationary Power Flow Variables
Authors: Taku Nishino, Yoshiyuki Onishi, Takayuki Ishizaki
Date: 2025-03-26 00:00:00
Tasks: Form

This paper shows that a necessary and sufficient condition for the small-signal stability of an inverter-integrated power system can be expressed in terms of semidefinite matrix inequalities determined only by the synchronous reactance of the components, the susceptance matrix of the transmission network, and the stationary values of the power flow distribution. To derive the stability condition, we consider a class of grid-forming inverters corresponding to a singular perturbation of the synchronous generator. The resulting matrix inequality condition, which has twice as many dimensions as the number of buses and is independent of the dynamics of the connected components, is expressed in terms of each component compensating in a decentralized manner for the loss of frequency synchronization caused by the reactive power consumption in the transmission network. A simple numerical example using a 3-bus power system model shows that a grid-forming inverter load improves power system synchronization, while a grid-following inverter load disrupts it.
Unlocking Multi-Task Electric Energy System Intelligence: Data Scaling Laws and Performance with Limited Fine-Tuning
Authors: Shaohuai Liu, Lin Dong, Chao Tian, Le Xie
Date: 2025-03-25 00:00:00

Data scaling has revolutionized research fields like natural language processing, computer vision, and robotics control, providing foundation models with remarkable multi-task and generalization capabilities. In this paper, we investigate whether similar data scaling laws exist in developing foundation models for power systems, and whether appropriate data scaling can yield multi-task, cross-timescales capabilities that can be deployed in \textit{unseen} operational scenarios. To this end, we conducted a comprehensive empirical study on data scaling by fine-tuning open-source foundation models using labeled data collected from diverse operational tasks and scenarios. We study how a foundation model's scenario generalization performance evolves with the number of training tasks, scenarios, and demonstrations. Our study involved collecting more than 450k demonstrations and implementing independent tests under a rigorous evaluation framework. Our findings reveal several key insights: First, the generalization performance of a fine-tuned foundation model follows an approximate power-law relationship with the number of demonstrations and scenarios. Second, the fine-tuned model also demonstrates impressive multi-task capabilities, where multi-task training shares similar performance improvements with single-task training as the number of demonstrations increases, without interference among tasks. Lastly, models with small parameter sizes could have strong performance as well. Model performance does not scale significantly with parameter size. These findings underscore the feasibility of developing multi-task foundation models tailored for power systems, demonstrating that while larger datasets and models generally improve performance, extreme scaling is unnecessary to achieve satisfactory outcomes.
Control Barrier Functions for Shared Control and Vehicle Safety
Authors: James Dallas, John Talbot, Makoto Suminaka, Michael Thompson, Thomas Lew, Gabor Orosz, John Subosits
Date: 2025-03-25 00:00:00

This manuscript presents a control barrier function based approach to shared control for preventing a vehicle from entering the part of the state space where it is unrecoverable. The maximal phase recoverable ellipse is presented as a safe set in the sideslip angle--yaw rate phase plane where the vehicle's state can be maintained. An exponential control barrier function is then defined on the maximal phase recoverable ellipse to promote safety. Simulations demonstrate that this approach enables safe drifting, that is, driving at the handling limit without spinning out. Results are then validated for shared control drifting with an experimental vehicle in a closed course. The results show the ability of this shared control formulation to maintain the vehicle's state within a safe domain in a computationally efficient manner, even in extreme drifting maneuvers.