pwc-archive/papers-with-abstracts huggingface dataset:
AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
Authors: Jinghua Piao, Yuwei Yan, Jun Zhang, Nian Li, Junbo Yan, Xiaochong Lan, Zhihong Lu, Zhiheng Zheng, Jing Yi Wang, Di Zhou, Chen Gao, Fengli Xu, Fang Zhang, Ke Rong, Jun Su, Yong Li
Date: 2025-02-12 00:00:00

Understanding human behavior and society is a central focus in social sciences, with the rise of generative social science marking a significant paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and logistically challenging traditional experiments with scalable, replicable, and systematic computational approaches for studying complex social dynamics. Recent advances in large language models (LLMs) have further transformed this research paradigm, enabling the creation of human-like generative social agents and realistic simulacra of society. In this paper, we propose AgentSociety, a large-scale social simulator that integrates LLM-driven agents, a realistic societal environment, and a powerful large-scale simulation engine. Based on the proposed simulator, we generate social lives for over 10k agents, simulating their 5 million interactions both among agents and between agents and their environment. Furthermore, we explore the potential of AgentSociety as a testbed for computational social experiments, focusing on four key social issues: polarization, the spread of inflammatory messages, the effects of universal basic income policies, and the impact of external shocks such as hurricanes. These four issues serve as valuable cases for assessing AgentSociety's support for typical research methods -- such as surveys, interviews, and interventions -- as well as for investigating the patterns, causes, and underlying mechanisms of social issues. The alignment between AgentSociety's outcomes and real-world experimental results not only demonstrates its ability to capture human behaviors and their underlying mechanisms, but also underscores its potential as an important platform for social scientists and policymakers.
ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion Rate Modeling
Authors: Wei Cheng, Yucheng Lu, Boyang xia, Jiangxia Cao, Kuan Xu, Mingxing Wen, Wei Jiang, Jiaming Zhang, Zhaojie Liu, Liyin Hong, Kun Gai, Guorui Zhou
Date: 2025-02-12 00:00:00
Tasks: counterfactual, Recommendation Systems, Selection bias

Post-click conversion rate (CVR) estimation is a vital task in many recommender systems of revenue businesses, e.g., e-commerce and advertising. In a perspective of sample, a typical CVR positive sample usually goes through a funnel of exposure to click to conversion. For lack of post-event labels for un-clicked samples, CVR learning task commonly only utilizes clicked samples, rather than all exposed samples as for click-through rate (CTR) learning task. However, during online inference, CVR and CTR are estimated on the same assumed exposure space, which leads to a inconsistency of sample space between training and inference, i.e., sample selection bias (SSB). To alleviate SSB, previous wisdom proposes to design novel auxiliary tasks to enable the CVR learning on un-click training samples, such as CTCVR and counterfactual CVR, etc. Although alleviating SSB to some extent, none of them pay attention to the discrimination between ambiguous negative samples (un-clicked) and factual negative samples (clicked but un-converted) during modelling, which makes CVR model lacks robustness. To full this gap, we propose a novel ChorusCVR model to realize debiased CVR learning in entire-space.
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification
Authors: Xuanze Chen, Jiajun Zhou, Jinsong Chen, Shanqing Yu, Qi Xuan
Date: 2025-02-12 00:00:00
Tasks: Mixture-of-Experts, Node Classification, Representation Learning

The varying degrees of homophily and heterophily in real-world graphs persistently constrain the universality of graph neural networks (GNNs) for node classification. Adopting a data-centric perspective, this work reveals an inherent preference of different graphs towards distinct message encoding schemes: homophilous graphs favor local propagation, while heterophilous graphs exhibit preference for flexible combinations of propagation and transformation. To address this, we propose GNNMoE, a universal node classification framework based on the Mixture-of-Experts (MoE) mechanism. The framework first constructs diverse message-passing experts through recombination of fine-grained encoding operators, then designs soft and hard gating layers to allocate the most suitable expert networks for each node's representation learning, thereby enhancing both model expressiveness and adaptability to diverse graphs. Furthermore, considering that soft gating might introduce encoding noise in homophilous scenarios, we introduce an entropy constraint to guide sharpening of soft gates, achieving organic integration of weighted combination and Top-K selection. Extensive experiments demonstrate that GNNMoE significantly outperforms mainstream GNNs, heterophilous GNNs, and graph transformers in both node classification performance and universality across diverse graph datasets.
A Data-Driven Method for Microgrid System Identification: Physically Consistent Sparse Identification of Nonlinear Dynamics
Authors: Mohan Du, Xiaozhe Wang
Date: 2025-02-13 00:00:00

Microgrids (MGs) play a crucial role in utilizing distributed energy resources (DERs) like solar and wind power, enhancing the sustainability and flexibility of modern power systems. However, the inherent variability in MG topology, power flow, and DER operating modes poses significant challenges to the accurate system identification of MGs, which is crucial for designing robust control strategies and ensuring MG stability. This paper proposes a Physically Consistent Sparse Identification of Nonlinear Dynamics (PC-SINDy) method for accurate MG system identification. By leveraging an analytically derived library of candidate functions, PC-SINDy extracts accurate dynamic models using only phasor measurement unit (PMU) data. Simulations on a 4-bus system demonstrate that PC-SINDy can reliably and accurately predict frequency trajectories under large disturbances, including scenarios not encountered during the identification/training phase, even when using noisy, low-sampled PMU data.
Enhancing Traffic Safety Analysis with Digital Twin Technology: Integrating Vehicle Dynamics and Environmental Factors into Microscopic Traffic Simulation
Authors: Guanhao Xu, Jianfei Chen, Zejiang Wang, Anye Zhou, Max Schrader, Joshua Bittle, Yunli Shao
Date: 2025-02-13 00:00:00

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles' movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.
Data-Enabled Predictive Control for Flexible Spacecraft
Authors: Huanqing Wang, Kaixiang Zhang, Amin Vahidi-Moghaddam, Haowei An, Nan Li, Daning Huang, Zhaojian Li
Date: 2025-02-13 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction, LEMMA

Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challenges for modeling and control due to their inherent nonlinearity. Data-driven control methods have gained traction to address such challenges. This paper introduces, to the best of the authors' knowledge, the first application of the data-enabled predictive control (DeePC) framework to boundary control for flexible spacecraft. Leveraging the fundamental lemma, DeePC constructs a non-parametric model by utilizing recorded past trajectories, eliminating the need for explicit model development. The developed method also incorporates dimension reduction techniques to enhance computational efficiency. Through comprehensive numerical simulations, this study compares the proposed method with Lyapunov-based control, demonstrating superior performance and offering a thorough evaluation of data-driven control for flexible spacecraft.
Coupled Rendezvous and Docking Maneuver control of satellite using Reinforcement learning-based Adaptive Fixed-Time Sliding Mode Controller
Authors: Rakesh Kumar Sahoo, Manoranjan Sinha
Date: 2025-02-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Satellite dynamics in unknown environments are inherently uncertain due to factors such as varying gravitational fields, atmospheric drag, and unpredictable interactions with space debris or other celestial bodies. Traditional sliding mode controllers with fixed parameters often struggle to maintain optimal performance under these fluctuating conditions. Therefore, an adaptive controller is essential to address these challenges by continuously tuning its gains in real-time. In this paper, we have tuned the slopes of the Fixed-time Sliding surface adaptively using reinforcement learning for coupled rendezvous and docking maneuver of chaser satellite with the target satellite in an unknown space environment. The neural network model is used to determine the optimal gains of reaching law of the fixed-time sliding surface. We have assumed that we don't have an accurate model of the system so we have added noise in the tangent space instead of directly on the manifold to preserve the geometric structure of the system while ensuring mathematically consistent uncertainty propagation. The reinforcement learning is used as an approximator to represent the value function of the agent to estimate the dynamical model of the system using the Actor-Critic method. The proposed control algorithm integrates a neural network and a sliding mode controller in a cascade loop architecture, where the tracking error dynamically tunes the sliding surface gains. Global fixed-time stability of the closed-loop feedback system is proved within the Lyapunov framework. This comprehensive approach of fixed-time sliding mode controller using a Reinforcement Learning based ensures the completion of the mission efficiently while addressing the critical challenges posed by the uncertain environment. The simulation results presented support the claims made.
Inverse Design with Dynamic Mode Decomposition
Authors: Yunpeng Zhu, Liangliang Cheng, Anping Jing, Hanyu Huo, Ziqiang Lang, Bo Zhang, J. Nathan Kutz
Date: 2025-02-13 00:00:00
Tasks: Uncertainty Quantification

We introduce a computationally efficient method for the automation of inverse design in science and engineering. Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace spanning multiple experiments in parameter space. The proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages the computed low-dimensional subspace to enable fast digital design and optimization on laptop-level computing, including the potential to prescribe the dynamics themselves. Moreover, the method is robust to noise, physically interpretable, and can provide uncertainty quantification metrics. The architecture can also efficiently scale to large-scale design problems using randomized algorithms in the ID-DMD. The simplicity of the method and its implementation are highly attractive in practice, and the ID-DMD has been demonstrated to be an order of magnitude more accurate than competing methods while simultaneously being 3-5 orders faster on challenging engineering design problems ranging from structural vibrations to fluid dynamics. Due to its speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with other leading machine learning methods represents a significant advancement in data-driven methods for inverse design and optimization, promising a paradigm shift in how to approach inverse design in practice.
Frequency Domain Stability and Convergence Analysis for General Reset Control Systems Architecture
Authors: S. Ali Hosseini, S. Hassan HosseinNia
Date: 2025-02-13 00:00:00

A key factor that generates significant interest in reset control systems, especially within industrial contexts, is their potential to be designed using a frequency-domain loop-shaping procedure. On the other hand, formulating and assessing stability analysis for these nonlinear elements often depends on access to parametric models and numerically solving linear matrix inequalities. These specific factors could present challenges to the successful implementation of reset control within industrial settings. Moreover, one of the most effective structures for implementing reset elements is to use them in parallel with a linear element. Therefore, this article presents the development of the frequency domain-based $H_\beta$ stability method from a series to a more general structure of reset control systems. Additionally, it investigates the behavior of different reset elements in terms of the feasibility of stability in the presence of time delay. To illustrate the research findings, two examples are provided, including one from an industrial application.
Dynamic Rolling Horizon Optimization for Network-Constrained V2X Value Stacking of Electric Vehicles Under Uncertainties
Authors: Canchen Jiang, Ariel Liebman, Bo Jie, Hao Wang
Date: 2025-02-13 00:00:00
Tasks: energy trading

Electric vehicle (EV) coordination can provide significant benefits through vehicle-to-everything (V2X) by interacting with the grid, buildings, and other EVs. This work aims to develop a V2X value-stacking framework, including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading, to maximize economic benefits for residential communities while maintaining distribution voltage. This work also seeks to quantify the impact of prediction errors related to building load, renewable energy, and EV arrivals. A dynamic rolling-horizon optimization (RHO) method is employed to leverage multiple revenue streams and maximize the potential of EV coordination. To address energy uncertainties, including hourly local building load, local photovoltaic (PV) generation, and EV arrivals, this work develops a Transformer-based forecasting model named Gated Recurrent Units-Encoder-Temporal Fusion Decoder (GRU-EN-TFD). The simulation results, using real data from Australia's National Electricity Market, and the Independent System Operators in New England and New York in the US, reveal that V2X value stacking can significantly reduce energy costs. The proposed GRU-EN-TFD model outperforms the benchmark forecast model. Uncertainties in EV arrivals have a more substantial impact on value-stacking performance, highlighting the significance of its accurate forecast. This work provides new insights into the dynamic interactions among residential communities, unlocking the full potential of EV batteries.
Adaptive Multi-Objective Bayesian Optimization for Capacity Planning of Hybrid Heat Sources in Electric-Heat Coupling Systems of Cold Regions
Authors: Ruizhe Yang, Zhongkai Yi, Ying Xu, Guiyu Chen, Haojie Yang, Rong Yi, Tongqing Li, Miaozhe ShenJin Li, Haoxiang Gao, Hongyu Duan
Date: 2025-02-13 00:00:00
Tasks: Bayesian Optimization

The traditional heat-load generation pattern of combined heat and power generators has become a problem leading to renewable energy source (RES) power curtailment in cold regions, motivating the proposal of a planning model for alternative heat sources. The model aims to identify non-dominant capacity allocation schemes for heat pumps, thermal energy storage, electric boilers, and combined storage heaters to construct a Pareto front, considering both economic and sustainable objectives. The integration of various heat sources from both generation and consumption sides enhances flexibility in utilization. The study introduces a novel optimization algorithm, the adaptive multi-objective Bayesian optimization (AMBO). Compared to other widely used multi-objective optimization algorithms, AMBO eliminates predefined parameters that may introduce subjectivity from planners. Beyond the algorithm, the proposed model incorporates a noise term to account for inevitable simulation deviations, enabling the identification of better-performing planning results that meet the unique requirements of cold regions. What's more, the characteristics of electric-thermal coupling scenarios are captured and reflected in the operation simulation model to make sure the simulation is close to reality. Numerical simulation verifies the superiority of the proposed approach in generating a more diverse and evenly distributed Pareto front in a sample-efficient manner, providing comprehensive and objective planning choices.
Array-Fed RIS: Validation of Friis-Based Modeling Using Full-Wave Simulations
Authors: Krishan Kumar Tiwari, Thomas Flisgen, Wolfgang Heinrich, Giuseppe Caire
Date: 2025-02-13 00:00:00

Space-fed large antenna arrays offer superior efficiency, simplicity, and reductions in size, weight, power, and cost (SWaP-C) compared to constrained-feed systems. Historically, horn antennas have been used for space feeding, but they suffer from limitations such as bulky designs, low aperture efficiency ($\approx 50\%$), and restricted degrees of freedom at the continuous aperture. In contrast, planar patch arrays achieve significantly higher aperture efficiency ($>90\%$) due to their more uniform aperture distribution, reduced weight, and increased degrees of freedom from the discretized aperture. Building on these advantages, we proposed an array-fed Reflective Intelligent Surface (RIS) system, where an active multi-antenna feeder (AMAF) optimizes power transfer by aligning with the principal eigenmode of the AMAF-RIS propagation matrix $\mathbf{T}$. While our previous studies relied on the Friis transmission formula for system modeling, we now validate this approach through full-wave simulations in CST Microwave Studio. By comparing the Friis-based matrix, $\mathbf{T}_{\rm Friis}$, with the full-wave solution, $\mathbf{T}_{\rm full$.$wave}$, we validate the relevance of the Friis-based modeling for top-level system design. Our findings confirm the feasibility of the proposed AMAF-RIS architecture for next-generation communication systems.
A Stochastic Fundamental Lemma with Reduced Disturbance Data Requirements
Authors: Ruchuan Ou, Guanru Pan, Timm Faulwasser
Date: 2025-02-13 00:00:00
Tasks: LEMMA

Recently, the fundamental lemma by Willems et. al has been extended towards stochastic LTI systems subject to process disturbances. Using this lemma requires previously recorded data of inputs, outputs, and disturbances. In this paper, we exploit causality concepts of stochastic control to propose a variant of the stochastic fundamental lemma that does not require past disturbance data in the Hankel matrices. Our developments rely on polynomial chaos expansions and on the knowledge of the disturbance distribution. Similar to our previous results, the proposed variant of the fundamental lemma allows to predict future input-output trajectories of stochastic LTI systems. We draw upon a numerical example to illustrate the proposed variant in data-driven control context.
Multi-user Visible Light Communications with Probabilistic Constellation Shaping and Precoding
Authors: Thang K. Nguyen, Thanh V. Pham, Hoang D. Le, Chuyen T. Nguyen, Anh T. Pham
Date: 2025-02-13 00:00:00
Tasks: Robust Design

This paper proposes a joint design of probabilistic constellation shaping (PCS) and precoding to enhance the sum-rate performance of multi-user visible light communications (VLC) broadcast channels subject to signal amplitude constraint. In the proposed design, the transmission probabilities of bipolar $M$-pulse amplitude modulation ($M$-PAM) symbols for each user and the transmit precoding matrix are jointly optimized to improve the sum-rate performance. The joint design problem is shown to be a complex multivariate non-convex problem due to the non-convexity of the objective function. To tackle the original non-convex optimization problem, the firefly algorithm (FA), a nature-inspired heuristic optimization approach, is employed to solve a local optima. The FA-based approach, however, suffers from high computational complexity. Thus, using zero-forcing (ZF) precoding, we propose a low-complexity design, which is solved using an alternating optimization approach. Additionally, considering the channel uncertainty, a robust design based on the concept of end-to-end learning with autoencoder (AE) is also presented. Simulation results reveal that the proposed joint design with PCS significantly improves the sum-rate performance compared to the conventional design with uniform signaling. For instance, the joint design achieves $\mathbf{17.5\%}$ and $\mathbf{19.2\%}$ higher sum-rate for 8-PAM and 16-PAM, respectively, at 60 dB peak amplitude-to-noise ratio. Some insights into the optimal symbol distributions of the two joint design approaches are also provided. Furthermore, our results show the advantage of the proposed robust design over the non-robust one under uncertain channel conditions.
Integrated Optimization and Game Theory Framework for Fair Cost Allocation in Community Microgrids
Authors: K. Victor Sam Moses Babu, Pratyush Chakraborty, Mayukha Pal
Date: 2025-02-13 00:00:00

Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and reduced participant satisfaction. This paper presents a novel framework integrating multi-objective optimization with cooperative game theory for fair and efficient microgrid operation and cost allocation. The proposed approach combines mixed-integer linear programming for optimal resource dispatch with Shapley value analysis for equitable benefit distribution, ensuring both system efficiency and participant satisfaction. The framework was validated using real-world data across six distinct operational scenarios, demonstrating significant improvements in both technical and economic performance. Results show peak demand reductions ranging from 7.8% to 62.6%, solar utilization rates reaching 114.8% through effective storage integration, and cooperative gains of up to $1,801.01 per day. The Shapley value-based allocation achieved balanced benefit-cost distributions, with net positions ranging from -16.0% to +14.2% across different load categories, ensuring sustainable participant cooperation.