pwc-archive/papers-with-abstracts huggingface dataset:
DRAFT-ing Architectural Design Decisions using LLMs
Authors: Rudra Dhar, Adyansh Kakran, Amey Karan, Karthik Vaidhyanathan, Vasudeva Varma
Date: 2025-04-11 00:00:00
Tasks: RAG, Retrieval-augmented Generation

Architectural Knowledge Management (AKM) is crucial for software development but remains challenging due to the lack of standardization and high manual effort. Architecture Decision Records (ADRs) provide a structured approach to capture Architecture Design Decisions (ADDs), but their adoption is limited due to the manual effort involved and insufficient tool support. Our previous work has shown that Large Language Models (LLMs) can assist in generating ADDs. However, simply prompting the LLM does not produce quality ADDs. Moreover, using third-party LLMs raises privacy concerns, while self-hosting them poses resource challenges. To this end, we experimented with different approaches like few-shot, retrieval-augmented generation (RAG) and fine-tuning to enhance LLM's ability to generate ADDs. Our results show that both techniques improve effectiveness. Building on this, we propose Domain Specific Retreival Augumented Few Shot Fine Tuninng, DRAFT, which combines the strengths of all these three approaches for more effective ADD generation. DRAFT operates in two phases: an offline phase that fine-tunes an LLM on generating ADDs augmented with retrieved examples and an online phase that generates ADDs by leveraging retrieved ADRs and the fine-tuned model. We evaluated DRAFT against existing approaches on a dataset of 4,911 ADRs and various LLMs and analyzed them using automated metrics and human evaluations. Results show DRAFT outperforms all other approaches in effectiveness while maintaining efficiency. Our findings indicate that DRAFT can aid architects in drafting ADDs while addressing privacy and resource constraints.
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Authors: Hsing-Huan Chung, Shravan Chaudhari, Xing Han, Yoav Wald, Suchi Saria, Joydeep Ghosh
Date: 2025-04-10 00:00:00
Tasks: Graph Learning

Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT and DyGFormer rely on sinusoidal time encoders to capture temporal relationships between edge events. In this paper, we study a simpler alternative: the linear time encoder, which avoids temporal information loss caused by sinusoidal functions and reduces the need for high dimensional time encoders. We show that the self-attention mechanism can effectively learn to compute time spans from linear time encodings and extract relevant temporal patterns. Through extensive experiments on six dynamic graph datasets, we demonstrate that the linear time encoder improves the performance of TGAT and DyGFormer in most cases. Moreover, the linear time encoder can lead to significant savings in model parameters with minimal performance loss. For example, compared to a 100-dimensional sinusoidal time encoder, TGAT with a 2-dimensional linear time encoder saves 43% of parameters and achieves higher average precision on five datasets. These results can be readily used to positively impact the design choices of a wide variety of dynamic graph learning architectures. The experimental code is available at: https://github.com/hsinghuan/dg-linear-time.git.
Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit
Authors: Tanmay Laud, Akadia Kacha-Ochana, Steven A. Sumner, Vikram Krishnasamy, Royal Law, Lyna Schieber, Munmun De Choudhury, Mai ElSherief
Date: 2025-04-10 00:00:00

Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reasons, including the stigma faced by people using opioids, online communities for recovery and support were formed on different social media platforms. In these communities, people share their experiences and solicit information by asking questions to learn about opioid use and recovery. However, these communities do not always contain clinically verified information. In this paper, we study natural language questions asked in the context of OUD-related discourse on Reddit. We adopt transformer-based question detection along with hierarchical clustering across 19 subreddits to identify six coarse-grained categories and 69 fine-grained categories of OUD-related questions. Our analysis uncovers ten areas of information seeking from Reddit users in the context of OUD: drug sales, specific drug-related questions, OUD treatment, drug uses, side effects, withdrawal, lifestyle, drug testing, pain management and others, during the study period of 2018-2021. Our work provides a major step in improving the understanding of OUD-related questions people ask unobtrusively on Reddit. We finally discuss technological interventions and public health harm reduction techniques based on the topics of these questions.
Reinforcement Learning-Driven Plant-Wide Refinery Planning Using Model Decomposition
Authors: Zhouchang Li, Runze Lin, Hongye Su, Lei Xie
Date: 2025-04-11 00:00:00
Tasks: Computational Efficiency, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In the era of smart manufacturing and Industry 4.0, the refining industry is evolving towards large-scale integration and flexible production systems. In response to these new demands, this paper presents a novel optimization framework for plant-wide refinery planning, integrating model decomposition with deep reinforcement learning. The approach decomposes the complex large scale refinery optimization problem into manageable submodels, improving computational efficiency while preserving accuracy. A reinforcement learning-based pricing mechanism is introduced to generate pricing strategies for intermediate products, facilitating better coordination between submodels and enabling rapid responses to market changes. Three industrial case studies, covering both single-period and multi-period planning, demonstrate significant improvements in computational efficiency while ensuring refinery profitability.
Analysis of the Unscented Transform Controller for Systems with Bounded Nonlinearities
Authors: Siddharth A. Dinkar, Ram Padmanabhan, Anna Clarke, Per-Olof Gutman, Melkior Ornik
Date: 2025-04-11 00:00:00

In this paper, we present an analysis of the Unscented Transform Controller (UTC), a technique to control nonlinear systems motivated as a dual to the Unscented Kalman Filter (UKF). We consider linear, discrete-time systems augmented by a bounded nonlinear function of the state. For such systems, we review 1-step and N-step versions of the UTC. Using a Lyapunov-based analysis, we prove that the states and inputs converge to a bounded ball around the origin, whose radius depends on the bound on the nonlinearity. Using examples of a fighter jet model and a quadcopter, we demonstrate that the UTC achieves satisfactory regulation and tracking performance on these nonlinear models.
Secondary Safety Control for Systems with Sector Bounded Nonlinearities [Extended Version]
Authors: Yankai Lin, Michelle S. Chong, Carlos Murguia
Date: 2025-04-11 00:00:00
Tasks: LEMMA

We consider the problem of safety verification and safety-aware controller synthesis for systems with sector bounded nonlinearities. We aim to keep the states of the system within a given safe set under potential actuator and sensor attacks. Specifically, we adopt the setup that a controller has already been designed to stabilize the plant. Using invariant sets and barrier certificate theory, we first give sufficient conditions to verify the safety of the closed-loop system under attacks. Furthermore, by using a subset of sensors that are assumed to be free of attacks, we provide a synthesis method for a secondary controller that enhances the safety of the system. The sufficient conditions to verify safety are derived using Lyapunov-based tools and the S-procedure. Using the projection lemma, the conditions are then formulated as linear matrix inequality (LMI) problems which can be solved efficiently. Lastly, our theoretical results are illustrated through numerical simulations.
POD-Based Sparse Stochastic Estimation of Wind Turbine Blade Vibrations
Authors: Lorenzo Schena, Wim Munters, Jan Helsen, Miguel A. Mendez
Date: 2025-04-11 00:00:00

This study presents a framework for estimating the full vibrational state of wind turbine blades from sparse deflection measurements. The identification is performed in a reduced-order space obtained from a Proper Orthogonal Decomposition (POD) of high-fidelity aeroelastic simulations based on Geometrically Exact Beam Theory (GEBT). In this space, a Reduced Order Model (ROM) is constructed using a linear stochastic estimator, and further enhanced through Kalman fusion with a quasi-steady model of azimuthal dynamics driven by measured wind speed. The performance of the proposed estimator is assessed in a synthetic environment replicating turbulent inflow and measurement noise over a wide range of operating conditions. Results demonstrate the method's ability to accurately reconstruct three-dimensional deformations and accelerations using noisy displacement and acceleration measurements at only four spatial locations. These findings highlight the potential of the proposed framework for real-time blade monitoring, optimal sensor placement, and active load control in wind turbine systems.
Physics-informed data-driven control without persistence of excitation
Authors: Martina Vanelli, Julien M. Hendrickx
Date: 2025-04-11 00:00:00

We show that data that is not sufficiently informative to allow for system re-identification can still provide meaningful information when combined with external or physical knowledge of the system, such as bounded system matrix norms. We then illustrate how this information can be leveraged for safety and energy minimization problems and to enhance predictions in unmodelled dynamics. This preliminary work outlines key ideas toward using limited data for effective control by integrating physical knowledge of the system and exploiting interpolation conditions.
Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks
Authors: Kundan Kumar, Muhammad Iqbal, Simo Särkkä
Date: 2025-04-11 00:00:00
Tasks: Blocking, State Estimation, State Space Models

Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversaries compromise the system by injecting false data or blocking measurement transmissions via denial-of-service attacks, distorting sensor readings. This paper develops a Kalman filter and Rauch--Tung--Striebel (RTS) smoother for linear stochastic state-space models subject to cyber-attacked measurements. We approximate the faulty measurement model via generalized statistical linear regression (GSLR). The GSLR-based approximated measurement model is then used to develop a Kalman filter and RTS smoother for the problem. The effectiveness of the proposed algorithms under cyber-attacks is demonstrated through a simulated aircraft tracking experiment.
Data-driven Estimator Synthesis with Instantaneous Noise
Authors: Felix Brändle, Frank Allgöwer
Date: 2025-04-11 00:00:00

Data-driven controller design based on data informativity has gained popularity due to its straightforward applicability, while providing rigorous guarantees. However, applying this framework to the estimator synthesis problem introduces technical challenges, which can only be solved so far by adding restrictive assumptions. In this work, we remove these restrictions to improve performance guarantees. Moreover, our parameterization allows the integration of additional structural knowledge, such as bounds on parameters. Our findings are validated using numerical examples.
Neural Network-assisted Interval Reachability for Systems with Control Barrier Function-Based Safe Controllers
Authors: Damola Ajeyemi, Saber Jafarpour, Emiliano Dall'Anese
Date: 2025-04-11 00:00:00

Control Barrier Functions (CBFs) have been widely utilized in the design of optimization-based controllers and filters for dynamical systems to ensure forward invariance of a given set of safe states. While CBF-based controllers offer safety guarantees, they can compromise the performance of the system, leading to undesirable behaviors such as unbounded trajectories and emergence of locally stable spurious equilibria. Computing reachable sets for systems with CBF-based controllers is an effective approach for runtime performance and stability verification, and can potentially serve as a tool for trajectory re-planning. In this paper, we propose a computationally efficient interval reachability method for performance verification of systems with optimization-based controllers by: (i) approximating the optimization-based controller by a pre-trained neural network to avoid solving optimization problems repeatedly, and (ii) using mixed monotone theory to construct an embedding system that leverages state-of-the-art neural network verification algorithms for bounding the output of the neural network. Results in terms of closeness of solutions of trajectories of the system with the optimization-based controller and the neural network are derived. Using a single trajectory of the embedding system along with our closeness of solutions result, we obtain an over-approximation of the reachable set of the system with optimization-based controllers. Numerical results are presented to corroborate the technical findings.
Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
Authors: Erica van der Sar, Alessandro Zocca, Sandjai Bhulai
Date: 2025-04-11 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL), Survey

Power grid operation is becoming increasingly complex due to the rising integration of renewable energy sources and the need for more adaptive control strategies. Reinforcement Learning (RL) has emerged as a promising approach to power network control (PNC), offering the potential to enhance decision-making in dynamic and uncertain environments. The Learning To Run a Power Network (L2RPN) competitions have played a key role in accelerating research by providing standardized benchmarks and problem formulations, leading to rapid advancements in RL-based methods. This survey provides a comprehensive and structured overview of RL applications for power grid topology optimization, categorizing existing techniques, highlighting key design choices, and identifying gaps in current research. Additionally, we present a comparative numerical study evaluating the impact of commonly applied RL-based methods, offering insights into their practical effectiveness. By consolidating existing research and outlining open challenges, this survey aims to provide a foundation for future advancements in RL-driven power grid optimization.
Advancing Autonomous Vehicle Safety: A Combined Fault Tree Analysis and Bayesian Network Approach
Authors: Lansu Dai, Burak Kantarci
Date: 2025-04-11 00:00:00
Tasks: Bayesian Inference, Decision Making

This paper integrates Fault Tree Analysis (FTA) and Bayesian Networks (BN) to assess collision risk and establish Automotive Safety Integrity Level (ASIL) B failure rate targets for critical autonomous vehicle (AV) components. The FTA-BN integration combines the systematic decomposition of failure events provided by FTA with the probabilistic reasoning capabilities of BN, which allow for dynamic updates in failure probabilities, enhancing the adaptability of risk assessment. A fault tree is constructed based on AV subsystem architecture, with collision as the top event, and failure rates are assigned while ensuring the total remains within 100 FIT. Bayesian inference is applied to update posterior probabilities, and the results indicate that perception system failures (46.06 FIT) are the most significant contributor, particularly failures to detect existing objects (PF5) and misclassification (PF6). Mitigation strategies are proposed for sensors, perception, decision-making, and motion control to reduce the collision risk. The FTA-BN integration approach provides dynamic risk quantification, offering system designers refined failure rate targets to improve AV safety.
Adaptive Control of Dubins Vehicle in the Presence of Loss of Effectiveness (Extended Version)
Authors: Daniel Maldonado Naranjo, Anuradha M. Annaswamy
Date: 2025-04-11 00:00:00

The control of a Dubins Vehicle when subjected to a loss of control effectiveness in the turning rate is considered. A complex state-space representation is used to model the vehicle dynamics. An adaptive control design is proposed, with the underlying stability analysis guaranteeing closed-loop boundedness and tracking of a desired path. It is shown that a path constructed by waypoints and a minimum turn radius can be specified using a reference model which can be followed by the closed loop system. The control design utilizes the complex state-space representation as well as a PID controller for the nominal closed-loop. How the design can be modified to ensure path following even in the presence input constraints is also discussed. Simulation studies are carried out to complement the theoretical derivations.
Safe Data-Driven Predictive Control
Authors: Amin Vahidi-Moghaddam, Kaian Chen, Kaixiang Zhang, Zhaojian Li, Yan Wang, Kai Wu
Date: 2025-04-11 00:00:00
Tasks: Model Predictive Control

In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential; however, its reliance on accurate models and substantial computational resources has hindered its broader application, especially within real-time nonlinear systems. This study presents an innovative control framework to enhance the practical viability of the MPC. The developed safe data-driven predictive control aims to eliminate the requirement for precise models and alleviate computational burdens in the nonlinear MPC (NMPC). This is achieved by learning both the system dynamics and the control policy, enabling efficient data-driven predictive control while ensuring system safety. The methodology involves a spatial temporal filter (STF)-based concurrent learning for system identification, a robust control barrier function (RCBF) to ensure the system safety amid model uncertainties, and a RCBF-based NMPC policy approximation. An online policy correction mechanism is also introduced to counteract performance degradation caused by the existing model uncertainties. Demonstrated through simulations on two applications, the proposed approach offers comparable performance to existing benchmarks with significantly reduced computational costs.
Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes
Authors: Xiaoyi Wu, Ravi Seshadri, Filipe Rodrigues, Carlos Lima Azevedo
Date: 2025-04-10 00:00:00
Tasks: Bayesian Optimization, Computational Efficiency, Deep Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Tradable credit schemes (TCS) are an increasingly studied alternative to congestion pricing, given their revenue neutrality and ability to address issues of equity through the initial credit allocation. Modeling TCS to aid future design and implementation is associated with challenges involving user and market behaviors, demand-supply dynamics, and control mechanisms. In this paper, we focus on the latter and address the day-to-day dynamic tolling problem under TCS, which is formulated as a discrete-time Markov Decision Process and solved using reinforcement learning (RL) algorithms. Our results indicate that RL algorithms achieve travel times and social welfare comparable to the Bayesian optimization benchmark, with generalization across varying capacities and demand levels. We further assess the robustness of RL under different hyperparameters and apply regularization techniques to mitigate action oscillation, which generates practical tolling strategies that are transferable under day-to-day demand and supply variability. Finally, we discuss potential challenges such as scaling to large networks, and show how transfer learning can be leveraged to improve computational efficiency and facilitate the practical deployment of RL-based TCS solutions.
Techno-economic environmental and social assessment framework for energy transition pathways in integrated energy communities: a case study in Alaska
Authors: Jayashree Yadav, Ingemar Mathiasson, Bindu Panikkar, Mads Almassalkhi
Date: 2025-04-10 00:00:00

The transition to low-carbon energy systems demands comprehensive evaluation tools that account for technical, economic, environmental, and social dimensions. While numerous studies address specific aspects of energy transition, few provide an integrated framework that captures the full spectrum of impacts. This study proposes a comprehensive techno-economic, environmental, and social (TEES) assessment framework to evaluate energy transition pathways. The framework provides a structured methodology for assessing infrastructure needs, cost implications, emissions reductions, and social equity impacts, offering a systematic approach for informed decision-making. To illustrate its applicability, a detailed case study of a remote community in Alaska is conducted, evaluating the TEES impacts of three distinct energy transition pathways including heat pumps (HPs) and battery integration, resource coordination and expanded community solar photovoltaic (PV). Findings show that coordination of HPs minimizes peak demand, enhances grid reliability, and reduces energy burdens among low-income households. Extensive simulation-based analysis reveals that strategically staging electric HPs with existing oil heating systems can lower overall energy costs by 19% and reduce emissions by 29% compared to the today's system and outperforms all-heat-pump strategy for economic savings. By combining a generalizable, community-centric assessment framework with data-driven case study insights, this work offers a practical tool for utilities, community stakeholders and policymakers to work toward equitable and sustainable energy transitions.
AstroLLaVA: towards the unification of astronomical data and natural language
Authors: Sharaf Zaman, Michael J. Smith, Pranav Khetarpal, Rishabh Chakrabarty, Michele Ginolfi, Marc Huertas-Company, Maja Jabłońska, Sandor Kruk, Matthieu Le Lain, Sergio José Rodríguez Méndez, Dimitrios Tanoglidis
Date: 2025-04-11 00:00:00
Tasks: Astronomy, Image Captioning, Language Modeling, Language Modelling, Question Answering, Visual Question Answering

We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue. By fine-tuning the LLaVA model on a diverse dataset of $\sim$30k images with captions and question-answer pairs sourced from NASA's `Astronomy Picture of the Day', the European Southern Observatory, and the NASA/ESA Hubble Space Telescope, we create a model capable of answering open-ended questions about astronomical concepts depicted visually. Our two-stage fine-tuning process adapts the model to both image captioning and visual question answering in the astronomy domain. We demonstrate AstroLLaVA's performance on an astronomical visual question answering benchmark and release the model weights, code, and training set to encourage further open source work in this space. Finally, we suggest a roadmap towards general astronomical data alignment with pre-trained language models, and provide an open space for collaboration towards this end for interested researchers.