pwc-archive/papers-with-abstracts huggingface dataset:
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
Authors: Kanghong Shi, Ruigang Wang, Ian R. Manchester
Date: 2025-04-28 00:00:00
Tasks: Position

We propose a neural control method to provide guaranteed stabilization for mechanical systems using a novel negative imaginary neural ordinary differential equation (NINODE) controller. Specifically, we employ neural networks with desired properties as state-space function matrices within a Hamiltonian framework to ensure the system possesses the NI property. This NINODE system can serve as a controller that asymptotically stabilizes an NI plant under certain conditions. For mechanical plants with colocated force actuators and position sensors, we demonstrate that all the conditions required for stability can be translated into regularity constraints on the neural networks used in the controller. We illustrate the utility, effectiveness, and stability guarantees of the NINODE controller through an example involving a nonlinear mass-spring system.
HyperController: A Hyperparameter Controller for Fast and Stable Training of Reinforcement Learning Neural Networks
Authors: Jonathan Gornet, Yiannis Kantaros, Bruno Sinopoli
Date: 2025-04-27 00:00:00
Tasks: Hyperparameter Optimization, reinforcement-learning, Reinforcement Learning

We introduce Hyperparameter Controller (HyperController), a computationally efficient algorithm for hyperparameter optimization during training of reinforcement learning neural networks. HyperController optimizes hyperparameters quickly while also maintaining improvement of the reinforcement learning neural network, resulting in faster training and deployment. It achieves this by modeling the hyperparameter optimization problem as an unknown Linear Gaussian Dynamical System, which is a system with a state that linearly changes. It then learns an efficient representation of the hyperparameter objective function using the Kalman filter, which is the optimal one-step predictor for a Linear Gaussian Dynamical System. To demonstrate the performance of HyperController, it is applied as a hyperparameter optimizer during training of reinforcement learning neural networks on a variety of OpenAI Gymnasium environments. In four out of the five Gymnasium environments, HyperController achieves highest median reward during evaluation compared to other algorithms. The results exhibit the potential of HyperController for efficient and stable training of reinforcement learning neural networks.
Evaluating Contrastive Feedback for Effective User Simulations
Authors: Andreas Konstantin Kruff, Timo Breuer, Philipp Schaer
Date: 2025-05-05 00:00:00
Tasks: Information Retrieval, Prompt Engineering, World Knowledge

The use of Large Language Models (LLMs) for simulating user behavior in the domain of Interactive Information Retrieval has recently gained significant popularity. However, their application and capabilities remain highly debated and understudied. This study explores whether the underlying principles of contrastive training techniques, which have been effective for fine-tuning LLMs, can also be applied beneficially in the area of prompt engineering for user simulations. Previous research has shown that LLMs possess comprehensive world knowledge, which can be leveraged to provide accurate estimates of relevant documents. This study attempts to simulate a knowledge state by enhancing the model with additional implicit contextual information gained during the simulation. This approach enables the model to refine the scope of desired documents further. The primary objective of this study is to analyze how different modalities of contextual information influence the effectiveness of user simulations. Various user configurations were tested, where models are provided with summaries of already judged relevant, irrelevant, or both types of documents in a contrastive manner. The focus of this study is the assessment of the impact of the prompting techniques on the simulated user agent performance. We hereby lay the foundations for leveraging LLMs as part of more realistic simulated users.
A Quadratic Programming Approach to Flight Envelope Protection Using Control Barrier Functions
Authors: Johannes Autenrieb
Date: 2025-04-26 00:00:00

Ensuring the safe operation of aerospace systems within their prescribed flight envelope is a fundamental requirement for modern flight control systems. Flight envelope protection prevents violations of aerodynamic, structural, and performance constraints, mitigating risks such as stall, excessive loads, and loss of control. Conventional FEP approaches, such as reference clipping via saturation functions and model-based command filtering, impose constraints at the reference input level but often fail to account for closed-loop system dynamics, potentially leading to constraint violations during transients. This paper introduces a new approach to the flight envelope protection problem by employing a quadratic programming-based safety filter using control barrier functions to dynamically enforce flight envelope constraints while preserving control performance. Unlike traditional reference filtering methods, the control barrier function-based safety filter actively ensures strict forward invariance of the safe flight envelope set, integrating seamlessly with existing control architectures. The proposed framework is implemented in a nonlinear missile flight control system and evaluated in a simulated environment. The results demonstrate its ability to prevent constraint violations while minimizing conservatism, offering a robust alternative to existing flight envelope protection methodologies.
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving
Authors: Gharbi Khamis Alshammari, Ahmad Abubakar, Nada M. O. Sid Ahmed, Naif Khalaf Alshammari
Date: 2025-04-26 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Federated Learning, object-detection, Object Detection, Privacy Preserving, Semantic Segmentation

Autonomous Vehicles (AVs) require precise lane and object detection to ensure safe navigation. However, centralized deep learning (DL) approaches for semantic segmentation raise privacy and scalability challenges, particularly when handling sensitive data. This research presents a new federated learning (FL) framework that integrates secure deep Convolutional Neural Networks (CNNs) and Differential Privacy (DP) to address these issues. The core contribution of this work involves: (1) developing a new hybrid UNet-ResNet34 architecture for centralized semantic segmentation to achieve high accuracy and tackle privacy concerns due to centralized training, and (2) implementing the privacy-preserving FL model, distributed across AVs to enhance performance through secure CNNs and DP mechanisms. In the proposed FL framework, the methodology distinguishes itself from the existing approach through the following: (a) ensuring data decentralization through FL to uphold user privacy by eliminating the need for centralized data aggregation, (b) integrating DP mechanisms to secure sensitive model updates against potential adversarial inference attacks, and (c) evaluating the frameworks performance and generalizability using RGB and semantic segmentation datasets derived from the CARLA simulator. Experimental results show significant improvements in accuracy, from 81.5% to 88.7% for the RGB dataset and from 79.3% to 86.9% for the SEG dataset over 20 to 70 Communication Rounds (CRs). Global loss was reduced by over 60%, and minor accuracy trade-offs from DP were observed. This study contributes by offering a scalable, privacy-preserving FL framework tailored for AVs, optimizing communication efficiency while balancing performance and data security.
Soft Reasoning Paths for Knowledge Graph Completion
Authors: Yanning Hou, Sihang Zhou, Ke Liang, Lingyuan Meng, Xiaoshu Chen, Ke Xu, Siwei Wang, Xinwang Liu, Jian Huang
Date: 2025-05-06 00:00:00
Tasks: Knowledge Graph Completion, Relation

Reasoning paths are reliable information in knowledge graph completion (KGC) in which algorithms can find strong clues of the actual relation between entities. However, in real-world applications, it is difficult to guarantee that computationally affordable paths exist toward all candidate entities. According to our observation, the prediction accuracy drops significantly when paths are absent. To make the proposed algorithm more stable against the missing path circumstances, we introduce soft reasoning paths. Concretely, a specific learnable latent path embedding is concatenated to each relation to help better model the characteristics of the corresponding paths. The combination of the relation and the corresponding learnable embedding is termed a soft path in our paper. By aligning the soft paths with the reasoning paths, a learnable embedding is guided to learn a generalized path representation of the corresponding relation. In addition, we introduce a hierarchical ranking strategy to make full use of information about the entity, relation, path, and soft path to help improve both the efficiency and accuracy of the model. Extensive experimental results illustrate that our algorithm outperforms the compared state-of-the-art algorithms by a notable margin. The code will be made publicly available after the paper is officially accepted.
State Reconstruction Under Malicious Sensor Attacks
Authors: Wei Liu
Date: 2025-04-26 00:00:00

This paper considers the state reconstruction problem for discrete-time cyber-physical systems when some of the sensors can be arbitrarily corrupted by malicious attacks where the attacked sensors belong to an unknown set. We first prove that the state is $s$-error correctable if the system under consideration is $s$-sparse observable where $s$ denotes the maximum number of attacked sensors. Then, two state reconstruction methods are presented where the first method is based on searching elements with the same value in a set and the second method is developed in terms of searching element satisfying a given condition. In addition, after establishing and analyzing the conditions that the proposed state reconstruction methods are not effective, we address that it is very hard to prevent the state reconstruction when either state reconstruction method proposed in this paper is used. The correctness and effectiveness of the proposed methods are examined via an example of four-dimensional dynamic systems and a real-world example of three-inertia systems.
Fairness-aware Dynamic Hosting Capacity and the Impacts of Strategic Solar PV Curtailment
Authors: Beyzanur Aydin, Rebecca Holt, Mads Almassalkhi
Date: 2025-04-26 00:00:00
Tasks: Fairness

Rapid deployment of distributed energy resources (DERs), such as solar photovoltaics (PV), poses a risk to the distribution grid under high penetration. Therefore, studying hosting capacity (HC) limits considering grid physics and demand variability is crucial. This paper introduces an improved framework for determining the HC of radial distribution networks by enhancing an existing convex inner approximation (CIA) approach. The proposed method achieves a more accurate and larger inner approximation, resulting in better HC limits. We also consider time-varying demand and the design of objective functions to ensure equitable access to grid resources. A case study with solar PV integration is conducted using a modified IEEE-37 radial network to examine the impact of increased PV capacity, demonstrating that with no more than 5% annual solar PV energy curtailed, it is possible to increase solar PV hosting capacity by at least 50% with no negative grid impacts and a net positive economic impact when accounted for the cost of carbon. Results show that fair allocation methods can lead to higher net profits and reduced PV curtailment and CO2.
A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems
Authors: Nachuan Yang, Yuzhe Li, Ling Shi, Tongwen Chen
Date: 2025-04-25 00:00:00

This paper fills a gap in the literature by considering a joint sensor and actuator configuration problem under the linear quadratic Gaussian (LQG) performance without assuming a predefined set of candidate components. Different from the existing research, which primarily focuses on selecting or placing sensors and actuators from a fixed group, we consider a more flexible formulation where these components must be designed from scratch, subject to general-form configuration costs and constraints. To address this challenge, we first analytically characterize the gradients of the LQG performance with respect to the sensor and actuator matrices using algebraic Riccati equations. Subsequently, we derive first-order optimality conditions based on the Karush-Kuhn-Tucker (KKT) analysis and develop a unified alternating direction method of multipliers (ADMM)-based alternating optimization framework to address the general-form sensor and actuator configuration problem. Furthermore, we investigate three representative scenarios: sparsity promoting configuration, low-rank promoting configuration, and structure-constrained configuration. For each scenario, we provide in-depth analysis and develop tailored computational schemes. The proposed framework ensures numerical efficiency and adaptability to various design constraints and configuration costs, making it well-suited for integration into numerical solvers.
Characterising Topic Familiarity and Query Specificity Using Eye-Tracking Data
Authors: Jiaman He, Zikang Leng, Dana McKay, Johanne R. Trippas, Damiano Spina
Date: 2025-05-06 00:00:00
Tasks: Pupil Dilation, Question Answering, Specificity

Eye-tracking data has been shown to correlate with a user's knowledge level and query formulation behaviour. While previous work has focused primarily on eye gaze fixations for attention analysis, often requiring additional contextual information, our study investigates the memory-related cognitive dimension by relying solely on pupil dilation and gaze velocity to infer users' topic familiarity and query specificity without needing any contextual information. Using eye-tracking data collected via a lab user study (N=18), we achieved a Macro F1 score of 71.25% for predicting topic familiarity with a Gradient Boosting classifier, and a Macro F1 score of 60.54% with a k-nearest neighbours (KNN) classifier for query specificity. Furthermore, we developed a novel annotation guideline -- specifically tailored for question answering -- to manually classify queries as Specific or Non-specific. This study demonstrates the feasibility of eye-tracking to better understand topic familiarity and query specificity in search.
A Hybrid Framework for Efficient Koopman Operator Learning
Authors: Alexander Estornell, Leonard Jung, Alenna Spiro, Mario Sznaier, Michael Everett
Date: 2025-04-25 00:00:00
Tasks: Operator learning

Koopman analysis of a general dynamics system provides a linear Koopman operator and an embedded eigenfunction space, enabling the application of standard techniques from linear analysis. However, in practice, deriving exact operators and mappings for the observable space is intractable, and deriving an approximation or expressive subset of these functions is challenging. Programmatic methods often rely on system-specific parameters and may scale poorly in both time and space, while learning-based approaches depend heavily on difficult-to-know hyperparameters, such as the dimension of the observable space. To address the limitations of both methods, we propose a hybrid framework that uses semidefinite programming to find a representation of the linear operator, then learns an approximate mapping into and out of the space that the operator propagates. This approach enables efficient learning of the operator and explicit mappings while reducing the need for specifying the unknown structure ahead of time.
Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
Authors: Zhengliang Shi, Lingyong Yan, Weiwei Sun, Yue Feng, Pengjie Ren, Xinyu Ma, Shuaiqiang Wang, Dawei Yin, Maarten de Rijke, Zhaochun Ren
Date: 2025-05-05 00:00:00
Tasks: Policy Gradient Methods, RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) integrates large language models ( LLM s) with retrievers to access external knowledge, improving the factuality of LLM generation in knowledge-grounded tasks. To optimize the RAG performance, most previous work independently fine-tunes the retriever to adapt to frozen LLM s or trains the LLMs to use documents retrieved by off-the-shelf retrievers, lacking end-to-end training supervision. Recent work addresses this limitation by jointly training these two components but relies on overly simplifying assumptions of document independence, which has been criticized for being far from real-world scenarios. Thus, effectively optimizing the overall RAG performance remains a critical challenge. We propose a direct retrieval-augmented optimization framework, named DRO, that enables end-to-end training of two key components: (i) a generative knowledge selection model and (ii) an LLM generator. DRO alternates between two phases: (i) document permutation estimation and (ii) re-weighted maximization, progressively improving RAG components through a variational approach. In the estimation step, we treat document permutation as a latent variable and directly estimate its distribution from the selection model by applying an importance sampling strategy. In the maximization step, we calibrate the optimization expectation using importance weights and jointly train the selection model and LLM generator. Our theoretical analysis reveals that DRO is analogous to policy-gradient methods in reinforcement learning. Extensive experiments conducted on five datasets illustrate that DRO outperforms the best baseline with 5%-15% improvements in EM and F1. We also provide in-depth experiments to qualitatively analyze the stability, convergence, and variance of DRO.
Voltage Stability and Control of Electrical Distribution Systems with High Penetration of Power Electronic Converters
Authors: Dionysios Moutevelis
Date: 2025-04-25 00:00:00

Power systems are currently undergoing a rapid paradigm change in their operation. Centralised energy production is being replaced by a number of Distributed Generation (DG) units that are placed at different locations and voltage levels in power networks. These distributed units are mostly based on renewable energy technologies, like wind turbines and photovoltaic cells and are commonly interfaced to the grid via power electronic converters. These sources reduce energy system dependency on conventional generation units based on fossil fuels. At the same time, this shift introduces technical challenges for the safe and reliable operation of electricity network since DG sources do not inherently provide the grid regulation services of conventional, centralised generation units. Moreover, the increased penetration of renewable energy sources and their converter-based interfaces is creating voltage deviation and voltage stability issues in distribution networks. These issues range from overvoltages during hours of peak renewable generation, reverse power flows and sudden voltage drops due to the variable nature of renewable energy production. All of the above jeopardise the reliable operation of the distribution networks that were not originally designed to accommodate for these effects. The objective of this thesis is to propose novel techniques for the accurate assessment of the DG impact on voltage stability in distribution net works and investigate how the control capabilities of converter-based interfaces of DG units can be harnessed to improve stability margins and overall system robustness and performance.
Improved Dwell-times for Switched Nonlinear Systems using Memory Regression Extension
Authors: Muzaffar Qureshi, Tochukwu Elijah Ogri, Humberto Ramos, Wanjiku A. Makumi, Zachary I. Bell, Rushikesh Kamalapurkar
Date: 2025-04-25 00:00:00
Tasks: State Estimation

This paper presents a switched systems approach for extending the dwell-time of an autonomous agent during GPS-denied operation by leveraging memory regressor extension (MRE) techniques. To maintain accurate trajectory tracking despite unknown dynamics and environmental disturbances, the agent periodically acquires access to GPS, allowing it to correct accumulated state estimation errors. The motivation for this work arises from the limitations of existing switched system approaches, where increasing estimation errors during GPS-denied intervals and overly conservative dwell-time conditions restrict the operational efficiency of the agent. By leveraging MRE techniques during GPS-available intervals, the developed method refines the estimates of unknown system parameters, thereby enabling longer and more reliable operation in GPS-denied environments. A Lyapunov-based switched-system stability analysis establishes that improved parameter estimates obtained through concurrent learning allow extended operation in GPS-denied intervals without compromising closed-loop system stability. Simulation results validate the theoretical findings, demonstrating dwell-time extensions and enhanced trajectory tracking performance.
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Authors: Bruno Sguerra, Viet-Anh Tran, Romain Hennequin, Manuel Moussallam
Date: 2025-05-05 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncertainty inherent in the feedback used to train these systems. Both implicit and explicit feedback are prone to noise due to the variability in human interactions, with implicit feedback being particularly challenging. In collaborative filtering, the reliability of interaction signals is critical, as these signals determine user and item similarities. Thus, deriving accurate confidence measures from implicit feedback is essential for ensuring the reliability of these signals. A common assumption in academia and industry is that repeated interactions indicate stronger user interest, increasing confidence in preference estimates. However, in domains such as music streaming, repeated consumption can shift user preferences over time due to factors like satiation and exposure. While literature on repeated consumption acknowledges these dynamics, they are often overlooked when deriving confidence scores for implicit feedback. This paper addresses this gap by focusing on music streaming, where repeated interactions are frequent and quantifiable. We analyze how repetition patterns intersect with key factors influencing user interest and develop methods to quantify the associated uncertainty. These uncertainty measures are then integrated as consistency metrics in a recommendation task. Our empirical results show that incorporating uncertainty into user preference models yields more accurate and relevant recommendations. Key contributions include a comprehensive analysis of uncertainty in repeated consumption patterns, the release of a novel dataset, and a Bayesian model for implicit listening feedback.