pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Interest-aware Graph Learning for Group Identification
Authors: Rui Zhao, Beihong Jin, Beibei Li, Yiyuan Zheng
Date: 2025-06-13 00:00:00
Tasks: Graph Learning

With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI), which is to recommend groups to users. We reveal that users are influenced by both group-level and item-level interests, and these dual-level interests have a collaborative evolution relationship: joining a group expands the user's item interests, further prompting the user to join new groups. Ultimately, the two interests tend to align dynamically. However, existing GI methods fail to fully model this collaborative evolution relationship, ignoring the enhancement of group-level interests on item-level interests, and suffering from false-negative samples when aligning cross-level interests. In order to fully model the collaborative evolution relationship between dual-level user interests, we propose CI4GI, a Collaborative Interest-aware model for Group Identification. Specifically, we design an interest enhancement strategy that identifies additional interests of users from the items interacted with by the groups they have joined as a supplement to item-level interests. In addition, we adopt the distance between interest distributions of two users to optimize the identification of negative samples for a user, mitigating the interference of false-negative samples during cross-level interests alignment. The results of experiments on three real-world datasets demonstrate that CI4GI significantly outperforms state-of-the-art models.
A Gamified Evaluation and Recruitment Platform for Low Resource Language Machine Translation Systems
Authors: Carlos Rafael Catalan
Date: 2025-06-13 00:00:00
Tasks: Machine Translation

Human evaluators provide necessary contributions in evaluating large language models. In the context of Machine Translation (MT) systems for low-resource languages (LRLs), this is made even more apparent since popular automated metrics tend to be string-based, and therefore do not provide a full picture of the nuances of the behavior of the system. Human evaluators, when equipped with the necessary expertise of the language, will be able to test for adequacy, fluency, and other important metrics. However, the low resource nature of the language means that both datasets and evaluators are in short supply. This presents the following conundrum: How can developers of MT systems for these LRLs find adequate human evaluators and datasets? This paper first presents a comprehensive review of existing evaluation procedures, with the objective of producing a design proposal for a platform that addresses the resource gap in terms of datasets and evaluators in developing MT systems. The result is a design for a recruitment and gamified evaluation platform for developers of MT systems. Challenges are also discussed in terms of evaluating this platform, as well as its possible applications in the wider scope of Natural Language Processing (NLP) research.
Joint Scheduling of DER under Demand Charges: Structure and Approximation
Authors: Ruixiao Yang, Gulai Shen, Ahmed S. Alahmed, Chuchu Fan
Date: 2025-06-26 00:00:00
Tasks: Scheduling

We study the joint scheduling of behind-the-meter distributed energy resources (DERs), including flexible loads, renewable generation, and battery energy storage systems, under net energy metering frameworks with demand charges. The problem is formulated as a stochastic dynamic program aimed at maximizing expected operational surplus while accounting for renewable generation uncertainty. We analytically characterize the structure of the optimal control policy and show that it admits a threshold-based form. However, due to the strong temporal coupling of the storage and demand charge constraints, the number of conditional branches in the policy scales combinatorially with the scheduling horizon, as it requires a look-ahead over future states. To overcome the high computational complexity in the general formulation, an efficient approximation algorithm is proposed, which searches for the peak demand under a mildly relaxed problem. We show that the algorithm scales linearly with the scheduling horizon. Extensive simulations using two open-source datasets validate the proposed algorithm and compare its performance against different DER control strategies, including a reinforcement learning-based one. Under varying storage and tariff parameters, the results show that the proposed algorithm outperforms various benchmarks in achieving a relatively small solution gap compared to the theoretical upper bound.
Towards an Optimal Control Perspective of ResNet Training
Authors: Jens Püttschneider, Simon Heilig, Asja Fischer, Timm Faulwasser
Date: 2025-06-26 00:00:00

We propose a training formulation for ResNets reflecting an optimal control problem that is applicable for standard architectures and general loss functions. We suggest bridging both worlds via penalizing intermediate outputs of hidden states corresponding to stage cost terms in optimal control. For standard ResNets, we obtain intermediate outputs by propagating the state through the subsequent skip connections and the output layer. We demonstrate that our training dynamic biases the weights of the unnecessary deeper residual layers to vanish. This indicates the potential for a theory-grounded layer pruning strategy.
Estimating Technical Loss without Power Flows: A Practical, Data-Driven Approach for Loss Estimation in Distribution Grids
Authors: Mohini Bariya, Genevieve Flaspohler
Date: 2025-06-26 00:00:00

Electric grids in low- and middle-income countries (LMICs) across the world face an acute challenge. To support global decarbonisation efforts and raise millions from energy poverty, these grids must shoulder substantial load growth while integrating distributed renewable generation. However, decades of rapid and poorly funded infrastructure expansions have led to national grids in many LMICs that are strained and weak, composed of aging, faulty, and undersized infrastructure. A cause and symptom of this weakness is excessive technical loss within the grid infrastructure during energy delivery, particularly at the distribution level; network losses are regularly estimated to be well over 20 percent, compared to a baseline of 5 percent in higher-income nations. Addressing technical loss through targeted interventions is essential for bolstering grids' physical and economic strength. Unfortunately, current approaches for estimating and localizing technical loss require expensive, extensive power flow sensing, which is essentially absent in LMIC distribution systems. We present a novel approach to technical loss estimation without power flows, which leverages more readily available voltage magnitude measurements at sparse locations in the grid. This estimator puts loss estimation and localization within reach for LMIC grids globally, and provides a critical tool for the effective design, implementation, and evaluation of loss-reduction interventions.
Coordinated Control of Autonomous Vehicles for Traffic Density Reduction at a Signalized Junction: An MPC Approach
Authors: Rudra Sen, Subashish Datta
Date: 2025-06-26 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Management, Model Predictive Control

The effective and safe management of traffic is a key issue due to the rapid advancement of the urban transportation system. Connected autonomous vehicles (CAVs) possess the capability to connect with each other and adjacent infrastructure, presenting novel opportunities for enhancing traffic flow and coordination. This work proposes a dual-mode model predictive control (MPC) architecture that tackles two interrelated issues: mitigating traffic density at signalized junctions and facilitating seamless, cooperative lane changes in high-density traffic conditions. The objective of this work is to facilitate responsive decision-making for CAVs, thereby enhancing the efficiency and safety of urban mobility. Moreover, we ensure recursive feasibility and convergence of the proposed MPC scheme by the integration of an online-calculated maximal control invariant terminal set. Finally, the efficacy of the proposed approach is validated through numerical simulation.
Optimal Parameter Design for Power Electronic Converters Using a Probabilistic Learning-Based Stochastic Surrogate Model
Authors: Akash Mahajan, Shivam Chaturvedi, Srijita Das, Wencong Su, Van-Hai Bui
Date: 2025-06-26 00:00:00

The selection of optimal design for power electronic converter parameters involves balancing efficiency and thermal constraints to ensure high performance without compromising safety. This paper introduces a probabilistic-learning-based stochastic surrogate modeling framework to address this challenge and significantly reduce the time required during the design phase. The approach begins with a neural network classifier that evaluates the feasibility of parameter configurations, effectively filtering out unsafe and/or impractical inputs. Subsequently, a probabilistic prediction model estimates the converter's efficiency and temperature while quantifying prediction uncertainty, providing both performance insights and reliability metrics. Finally, a heuristic optimization-based model is employed to optimize a multi-objective function that maximizes efficiency while adhering to thermal constraints. The optimization process incorporates penalty terms to discourage solutions that violate practical thresholds, ensuring actionable and realistic recommendations. An advanced heuristic optimization method is used to find the optimal solution and is compared with several well-known search algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Tabu-Search (TS), and Stochastic Hill Climbing (SHC). The results demonstrate significant improvements in predictive accuracy and optimization outcomes, offering a robust solution for advancing power electronics design.
Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response
Authors: Anouar Boumeftah, Sarah McKenzie-Picot, Peter Klimas, Gunes Karabulut Kurt
Date: 2025-06-25 00:00:00

Satellite systems increasingly face operational risks from jamming, cyberattacks, and electromagnetic disruptions. Traditional redundancy strategies often fail against dynamic, multi-vector threats. This paper introduces a resilience-by-design framework grounded in the PACE (Primary, Alternate, Contingency, Emergency) methodology, originally developed for tactical communications in military operations, adapting it to satellite systems through a layered state-transition model informed by threat scoring frameworks such as CVSS, DREAD, and NASA's risk matrix. We define a dynamic resilience index to quantify system adaptability and implement three PACE variants: static, adaptive, and softmax-based decision models, to evaluate resilience under diverse disruption scenarios. The proposed approach highlights the effectiveness of lightweight, decision-aware fallback mechanisms in improving survivability and operational continuity for next-generation space assets.
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
Authors: Milad Hasanzadeh, Amin Kargarian
Date: 2025-06-25 00:00:00
Tasks: Distributed Optimization

\textit{DPLib} is an open-source MATLAB-based benchmark library created to support research and development in distributed and decentralized power system analysis and optimization. Distributed and decentralized methods offer scalability, privacy preservation, and resilience to single points of failure, making them increasingly important for modern power systems. However, unlike centralized tools such as MATPOWER, no general-purpose, reproducible data library package currently exists for distributed power system studies. DPLib fills this gap by providing a standard power system library featuring over 20 multi-region benchmark test cases of varying sizes, along with a graph-based partitioning toolkit that decomposes any MATPOWER test system into multiple electrically coherent regions. The partitioning toolkit, an easy-to-use MATLAB code, generates standardized \texttt{.mat} and \texttt{.m} files, along with region visualizations for intuitive understanding. We also provide modular, easy-to-use distributed optimal power flow (OPF) solvers: an alternating direction method of multipliers(ADMM)-based DC-OPF solver implemented in YALMIP, and an ADMM-based AC-OPF solver leveraging IPOPT. These solvers validate the generated test systems for distributed optimization applications. Numerical results validate the generated test cases, establishing DPLib as a foundation for reproducible distributed power system research.
Structural System Identification via Validation and Adaptation
Authors: Cristian López, Keegan J. Moore
Date: 2025-06-25 00:00:00
Tasks: parameter estimation, Uncertainty Quantification

Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equation of motion to obtain fake accelerations, which are compared to real training data via a mean square error loss. To simultaneously validate the learned parameters, we use independent validation datasets. The generated accelerations from these datasets are evaluated by a discriminator network, which determines whether the output is real or fake, and guides the parameter-generator network. Analytical and real experiments show the parameter estimation accuracy and model validation for different nonlinear structural systems.
Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control
Authors: Mahmood Mazare, Hossein Ramezani
Date: 2025-06-25 00:00:00
Tasks: Sensitivity

This paper focuses on a key challenge in hybrid data-driven predictive control: the effect of measurement noise on Hankel matrices. While noise is handled in direct and indirect methods, hybrid approaches often overlook its impact during trajectory estimation. We propose a Noise-Tolerant Data-Driven Predictive Control (NTDPC) framework that integrates singular value decomposition to separate system dynamics from noise within reduced-order Hankel matrices. This enables accurate prediction with shorter data horizons and lower computational effort. A sensitivity index is introduced to support horizon selection under different noise levels. Simulation results indicate improved robustness and efficiency compared to existing hybrid methods.
Distributed Lyapunov Functions for Nonlinear Networks
Authors: Yiming Wang, Arthur N. Montanari, Adilson E. Motter
Date: 2025-06-25 00:00:00
Tasks: Dimensionality Reduction, LEMMA

Nonlinear networks are often multistable, exhibiting coexisting stable states with competing regions of attraction (ROAs). As a result, ROAs can have complex "tentacle-like" morphologies that are challenging to characterize analytically or computationally. In addition, the high dimensionality of the state space prohibits the automated construction of Lyapunov functions using state-of-the-art optimization methods, such as sum-of-squares (SOS) programming. In this letter, we propose a distributed approach for the construction of Lyapunov functions based solely on local information. To this end, we establish an augmented comparison lemma that characterizes the existence conditions of partial Lyapunov functions, while also accounting for residual effects caused by the associated dimensionality reduction. These theoretical results allow us to formulate an SOS optimization that iteratively constructs such partial functions, whose aggregation forms a composite Lyapunov function. The resulting composite function provides accurate convex approximations of both the volumes and shapes of the ROAs. We validate our method on networks of van der Pol and Ising oscillators, demonstrating its effectiveness in characterizing high-dimensional systems with non-convex ROAs.
Identifiability and Maximum Likelihood Estimation for System Identification of Networks of Dynamical Systems
Authors: Anders Hansson, João Victor Galvão da Mata, Martin S. Andersen
Date: 2025-06-25 00:00:00

In this paper we investigate identifiability and maximum likelihood estimation for direct system identification of networks of dynamical systems. We provide necessary and sufficient conditions for network identifiability in terms of Gr\"obner bases. We show that the maximum likelihood approach is both consistent and efficient, which is in contrast to existing prediction error approaches. Moreover, our approach has wider applicability, i.e., it is applicable whenever network identifiability holds. Finally, we show that we can formulate the maximum likelihood problem without the use of a predictor, which is the key to numerically being able to solve it efficiently.
Learning-based safety lifting monitoring system for cranes on construction sites
Authors: Hao Chen, Yu Hin Ng, Ching-Wei Chang, Haobo Liang, Yanke Wang
Date: 2025-06-25 00:00:00

Lifting on construction sites, as a frequent operation, works still with safety risks, especially for modular integrated construction (MiC) lifting due to its large weight and size, probably leading to accidents, causing damage to the modules, or more critically, posing safety hazards to on-site workers. Aiming to reduce the safety risks in lifting scenarios, we design an automated safe lifting monitoring algorithm pipeline based on learning-based methods, and deploy it on construction sites. This work is potentially to increase the safety and efficiency of MiC lifting process via automation technologies. A dataset is created consisting of 1007 image-point cloud pairs (37 MiC liftings). Advanced object detection models are trained for automated two-dimensional (2D) detection of MiCs and humans. Fusing the 2D detection results with the point cloud information allows accurate determination of the three-dimensional (3D) positions of MiCs and humans. The system is designed to automatically trigger alarms that notify individuals in the MiC lifting danger zone, while providing the crane operator with real-time lifting information and early warnings. The monitoring process minimizes the human intervention and no or less signal men are required on real sites assisted by our system. A quantitative analysis is conducted to evaluate the effectiveness of the algorithmic pipeline. The pipeline shows promising results in MiC and human perception with the mean distance error of 1.5640 m and 0.7824 m respectively. Furthermore, the developed system successfully executes safety risk monitoring and alarm functionalities during the MiC lifting process with limited manual work on real construction sites.
Cooperative Sensing and Communication Beamforming Design for Low-Altitude Economy
Authors: Fangzhi Li, Zhichu Ren, Cunhua Pan, Hong Ren, Jing Jin, Qixing Wang, Jiangzhou Wang
Date: 2025-06-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

To empower the low-altitude economy with high-accuracy sensing and high-rate communication, this paper proposes a cooperative integrated sensing and communication (ISAC) framework for aerial-ground networks. In the proposed system, the ground base stations (BSs) cooperatively serve the unmanned aerial vehicles (UAVs), which are equipped for either joint communication and sensing or sensing-only operations. The BSs employ coordinated beamforming to simultaneously transmit communication and sensing signals, while the UAVs execute their missions. To maximize the weighted sum rate under the sensing signal-to-interference-plus-noise ratio (SINR) constraints, we jointly optimize the transmit beamforming, receive filtering, and UAV trajectory. The resulting non-convex problem is solved using an alternating optimization framework incorporating semidefinite relaxation (SDR) and successive convex approximation (SCA). Simulation results demonstrate that the proposed joint design achieves higher communication throughput while ensuring required sensing robustness. Additionally, the sensing SINR threshold and the UAV altitude have a significant impact on the trajectory design, highlighting the necessity of adaptive deployment strategies in practical applications.
A Data-Driven Approach for Topology Correction in Low Voltage Networks with DERs
Authors: Dong Liu, Sander Timmerman, Yu Xiang, Peter Palensky, Pedro P. Vergara
Date: 2025-06-25 00:00:00

This paper introduces a data-driven topology identification and correction approach for low-voltage distribution networks (LVDNs) combined with a time-based smart meter data selection strategy, aiming to correct outdated recordings and identify the missed recordings. The proposed approach solely relies on voltage magnitude measurements, releasing privacy concerns and measurement burdens. It enables the distribution system operators to identify switch states through supervised learning algorithms, as well as determine user-feeder connections and phase labels of customers by a modified Hierarchical Clustering algorithm. To address the similarity among smart meter (SM) data caused by distributed photovoltaic (PV) systems, a time-based SM data selection strategy is combined with the proposed correlation analysis. The feasibility and robustness of the proposed approach are validated using modified real-world LVDNs and multiple incomplete SM datasets collected from customers in the Netherlands. The results demonstrate that the time-based SM data selection strategy effectively mitigates their impact on phase identification, and the corrected topology not only improves network observability but also supports network operators in load balancing and PV consumption.
Recursive-ARX for Grid-Edge Fault Detection
Authors: Soufiane El Yaagoubi, Keith Moffat, Eduardo Prieto Araujo, Florian Dörfler
Date: 2025-06-24 00:00:00
Tasks: Fault Detection

Future electrical grids will require new ways to identify faults as inverters are not capable of supplying large fault currents to support existing fault detection methods and because distributed resources may feed faults from the edge of the grid. This paper proposes the use of real-time system identification for online power-system fault detection. Specifically, we implement Recursive ARX (rARX) system identification on a grid-connected inverter. Experiments demonstrate that the proposed rARX method is able to both detect large faults quickly, and distinguish between high-impedance faults and large load increases. These results indicate that rARX grid-edge fault detection is a promising research direction for improving the reliability and safety of modern electric grids.
Thermodynamic free energy map for the non-oxidative glycolysis pathways
Authors: Adittya Pal
Date: 2025-06-24 00:00:00

Designing reaction pathways that maximize the production of a target compound in a given metabolic network is a fundamental problem in systems biology. In this study, we systematically explore the non-oxidative glycolysis metabolic network, guided by the principle that reactions with negative Gibbs free energy differences are thermodynamically favored. We enumerate alternative pathways that implement the net non-oxidative glycolysis reaction, categorized by their length. Our analysis reveals several alternative thermodynamically favorable pathways beyond those reported in experiments. In addition, we identify molecules within the network, such as 3-hydroxypropionic acid, that may have significant potential for further investigation.