pwc-archive/papers-with-abstracts huggingface dataset:
Backstepping Reach-avoid Controller Synthesis for Multi-input Multi-output Systems with Mixed Relative Degrees
Authors: Jianqiang Ding, Dingran Yuan, Shankar A. Deka
Date: 2025-05-06 00:00:00

Designing controllers with provable formal guarantees has become an urgent requirement for cyber-physical systems in safety-critical scenarios. Beyond addressing scalability in high-dimensional implementations, controller synthesis methodologies separating safety and reachability objectives may risk optimization infeasibility due to conflicting constraints, thereby significantly undermining their applicability in practical applications. In this paper, by leveraging feedback linearization and backstepping techniques, we present a novel framework for constructing provable reach-avoid formal certificates tailored to multi-input multi-output systems. Based on this, we developed a systematic synthesis approach for controllers with reach-avoid guarantees, which ensures that the outputs of the system eventually enter the predefined target set while staying within the required safe set. Finally, we demonstrate the effectiveness of our method through simulations.
Dynamic load balancing for cloud systems under heterogeneous setup delays
Authors: Fernando Paganini, Diego Goldsztajn
Date: 2025-05-06 00:00:00

We consider a distributed cloud service deployed at a set of distinct server pools. Arriving jobs are classified into heterogeneous types, in accordance with their setup times which are differentiated at each of the pools. A dispatcher for each job type controls the balance of load between pools, based on decentralized feedback. The system of rates and queues is modeled by a fluid differential equation system, and analyzed via convex optimization. A first, myopic policy is proposed, based on task delay-to-service. Under a simplified dynamic fluid queue model, we prove global convergence to an equilibrium point which minimizes the mean setup time; however queueing delays are incurred with this method. A second proposal is then developed based on proximal optimization, which explicitly models the setup queue and is proved to reach an optimal equilibrium, devoid of queueing delay. Results are demonstrated through a simulation example.
AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis
Authors: Haroui Ma, Francesco Quinzan, Theresa Willem, Stefan Bauer
Date: 2025-04-28 00:00:00
Tasks: counterfactual, Diagnostic, Fairness

Machine learning (ML) systems for medical imaging have demonstrated remarkable diagnostic capabilities, but their susceptibility to biases poses significant risks, since biases may negatively impact generalization performance. In this paper, we introduce a novel statistical framework to evaluate the dependency of medical imaging ML models on sensitive attributes, such as demographics. Our method leverages the concept of counterfactual invariance, measuring the extent to which a model's predictions remain unchanged under hypothetical changes to sensitive attributes. We present a practical algorithm that combines conditional latent diffusion models with statistical hypothesis testing to identify and quantify such biases without requiring direct access to counterfactual data. Through experiments on synthetic datasets and large-scale real-world medical imaging datasets, including \textsc{cheXpert} and MIMIC-CXR, we demonstrate that our approach aligns closely with counterfactual fairness principles and outperforms standard baselines. This work provides a robust tool to ensure that ML diagnostic systems generalize well, e.g., across demographic groups, offering a critical step towards AI safety in healthcare. Code: https://github.com/Neferpitou3871/AI-Alignment-Medical-Imaging.
Artificial Potential Field and Sliding Mode Control for Spacecraft Attitude Maneuver with Actuation and Pointing Constraints
Authors: Mauro Mancini, Dario Ruggiero
Date: 2025-05-06 00:00:00

This study investigates the combination of guidance and control strategies for rigid spacecraft attitude reorientation, while dealing with forbidden pointing constraints, actuator limitations, and system uncertainties. These constraints arise due to the presence of bright objects in space that may damage sensitive payloads onboard the spacecraft, and the risk that actuator saturations may compromise closed-loop system stability. Furthermore, spacecraft attitude dynamics are typically affected by parametric uncertainties, external disturbances, and system nonlinearities, which cannot be neglected. In this article, the problem of spacecraft reorientation under pointing and actuation constraints is addressed using a strategy that combines Artificial Potential Field (APF) and Sliding Mode Control (SMC). A rigorous Lyapunov-based analysis yields closed-form expressions for APF/SMC gains, providing explicit mathematical formulas for gain values without the need for iterative computations. These expressions account for angular velocity and control torque limitations, external disturbances, and inertia uncertainties. The robustness of the proposed control strategy is demonstrated through Monte Carlo simulations using a high-fidelity attitude dynamics simulator. Additionally, mu-analysis is employed to assess local stability properties and quantify robustness margins. The results confirm the practical feasibility of the proposed method in real-world space scenarios, highlighting its effectiveness in uncertain and constrained environments.
Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent
Authors: Yicheng Li, Xinghua Sun
Date: 2025-04-28 00:00:00
Tasks: LEMMA, Matrix Completion

This paper proposes and analyzes a gradient-type algorithm based on Burer-Monteiro factorization, called the Asymmetric Projected Gradient Descent (APGD), for reconstructing the point set configuration from partial Euclidean distance measurements, known as the Euclidean Distance Matrix Completion (EDMC) problem. By paralleling the incoherence matrix completion framework, we show for the first time that global convergence guarantee with exact recovery of this routine can be established given $\mathcal{O}(\mu^2 r^3 \kappa^2 n \log n)$ Bernoulli random observations without any sample splitting. Unlike leveraging the tangent space Restricted Isometry Property (RIP) and local curvature of the low-rank embedding manifold in some very recent works, our proof provides new upper bounds to replace the random graph lemma under EDMC setting. The APGD works surprisingly well and numerical experiments demonstrate exact linear convergence behavior in rich-sample regions yet deteriorates fast when compared with the performance obtained by optimizing the s-stress function, i.e., the standard but unexplained non-convex approach for EDMC, if the sample size is limited. While virtually matching our theoretical prediction, this unusual phenomenon might indicate that: (i) the power of implicit regularization is weakened when specified in the APGD case; (ii) the stabilization of such new gradient direction requires substantially more samples than the information-theoretic limit would suggest.
Event-Triggered GAT-LSTM Framework for Attack Detection in Heating, Ventilation, and Air Conditioning Systems
Authors: Zhenan Feng, Ehsan Nekouei
Date: 2025-05-06 00:00:00
Tasks: Binary Classification, Graph Attention

Heating, Ventilation, and Air Conditioning (HVAC) systems are essential for maintaining indoor environmental quality, but their interconnected nature and reliance on sensor networks make them vulnerable to cyber-physical attacks. Such attacks can interrupt system operations and risk leaking sensitive personal information through measurement data. In this paper, we propose a novel attack detection framework for HVAC systems, integrating an Event-Triggering Unit (ETU) for local monitoring and a cloud-based classification system using the Graph Attention Network (GAT) and the Long Short-Term Memory (LSTM) network. The ETU performs a binary classification to identify potential anomalies and selectively triggers encrypted data transmission to the cloud, significantly reducing communication cost. The cloud-side GAT module models the spatial relationships among HVAC components, while the LSTM module captures temporal dependencies across encrypted state sequences to classify the attack type. Our approach is evaluated on datasets that simulate diverse attack scenarios. Compared to GAT-only (94.2% accuracy) and LSTM-only (91.5%) ablations, our full GAT-LSTM model achieves 98.8% overall detection accuracy and reduces data transmission to 15%. These results demonstrate that the proposed framework achieves high detection accuracy while preserving data privacy by using the spatial-temporal characteristics of HVAC systems and minimizing transmission costs through event-triggered communication.
Identification and Estimation of Long-Term Treatment Effects with Monotone Missing
Authors: Qinwei Yang, Ruocheng Guo, Shasha Han, Peng Wu
Date: 2025-04-28 00:00:00
Tasks: Imputation

Estimating long-term treatment effects has a wide range of applications in various domains. A key feature in this context is that collecting long-term outcomes typically involves a multi-stage process and is subject to monotone missing, where individuals missing at an earlier stage remain missing at subsequent stages. Despite its prevalence, monotone missing has been rarely explored in previous studies on estimating long-term treatment effects. In this paper, we address this gap by introducing the sequential missingness assumption for identification. We propose three novel estimation methods, including inverse probability weighting, sequential regression imputation, and sequential marginal structural model (SeqMSM). Considering that the SeqMSM method may suffer from high variance due to severe data sparsity caused by monotone missing, we further propose a novel balancing-enhanced approach, BalanceNet, to improve the stability and accuracy of the estimation methods. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our proposed methods.
Sequentially learning regions of attraction from data
Authors: Oumayma Khattabi, Matteo Tacchi-Bénard, Sorin Olaru
Date: 2025-05-06 00:00:00

The paper is dedicated to data-driven analysis of dynamical systems. It deals with certifying the basin of attraction of a stable equilibrium for an unknown dynamical system. It is supposed that point-wise evaluation of the right-hand side of the ordinary differential equation governing the system is available for a set of points in the state space. Technically, a Piecewise Affine Lyapunov function will be constructed iteratively using an optimisation-based technique for the effective validation of the certificates. As a main contribution, whenever those certificates are violated locally, a refinement of the domain and the associated tessellation is produced, thus leading to an improvement in the description of the domain of attraction.
Power Loss and Temperature Distribution in Coil of PFC Inductor with Air Gap for Multimode Operation
Authors: Rafal Kasikowski
Date: 2025-05-06 00:00:00

Power converters inherently display non-linear load characteristics, resulting in a high level of mains harmonics, and hence the necessity of implementing Power Factor Correction (PFC). Active PFC circuitry typically comprises an inductor and a power switch to control and alter the input current so that it matches, in shape and phase, the input voltage. This modelling of the waveforms can be performed by means of distinct conduction modes of the PFC inductor. The digital controller implemented in the constructed and investigated boost-type PFC converter can be programmed to operate in discontinuous conduction mode (DCM), continuous conduction mode (CCM), or a combination of the two. The individual modes of operation, via distinct PFC inductor current waveforms, impact the overall efficiency of power conversion and, by extension, temperature distribution in the magnetic component. This paper investigates how the examined conduction modes bear on distinct power-loss mechanisms present in the PFC inductor, including high-frequency eddy-current-generating phenomena, and the fringing effect in particular. As demonstrated herein, the DCM operation, for the set output power level, exhibits exacerbated power dissipation in the winding of the inductor due to the somewhat increased RSM value of the current and the intensified fringing magnetic flux at an air gap. The latter assertion will undergo further, more quantitatively focused research. Finally, the construction of the coil was optimised to reduce power loss by diminishing eddy-current mechanisms.
Learning-based Homothetic Tube MPC
Authors: Yulong Gao, Shuhao Yan, Jian Zhou, Mark Cannon
Date: 2025-05-06 00:00:00
Tasks: Model Predictive Control

In this paper, we study homothetic tube model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input. Different from most existing work on robust MPC, we assume that the true disturbance set is unknown but a conservative surrogate is available a priori. Leveraging the real-time data, we develop an online learning algorithm to approximate the true disturbance set. This approximation and the corresponding constraints in the MPC optimisation are updated online using computationally convenient linear programs. We provide statistical gaps between the true and learned disturbance sets, based on which, probabilistic recursive feasibility of homothetic tube MPC problems is discussed. Numerical simulations are provided to demonstrate the efficacy of our proposed algorithm and compare with state-of-the-art MPC algorithms.
DISCO: learning to DISCover an evolution Operator for multi-physics-agnostic prediction
Authors: Rudy Morel, Jiequn Han, Edouard Oyallon
Date: 2025-04-28 00:00:00

We address the problem of predicting the next state of a dynamical system governed by unknown temporal partial differential equations (PDEs) using only a short trajectory. While standard transformers provide a natural black-box solution to this task, the presence of a well-structured evolution operator in the data suggests a more tailored and efficient approach. Specifically, when the PDE is fully known, classical numerical solvers can evolve the state accurately with only a few parameters. Building on this observation, we introduce DISCO, a model that uses a large hypernetwork to process a short trajectory and generate the parameters of a much smaller operator network, which then predicts the next state through time integration. Our framework decouples dynamics estimation (i.e., DISCovering an evolution operator from a short trajectory) from state prediction (i.e., evolving this operator). Experiments show that pretraining our model on diverse physics datasets achieves state-of-the-art performance while requiring significantly fewer epochs. Moreover, it generalizes well and remains competitive when fine-tuned on downstream tasks.
Multi-Class Stackelberg Games for the Co-Design of Networked Systems
Authors: Julian Barreiro-Gomez, Ye Wang
Date: 2025-05-06 00:00:00

We investigate a co-design problem, encompassing simultaneous design of system infrastructure and control, through a game-theoretical framework. To this end, we propose the co-design problem as a two-layer hierarchical strategic interaction. At the upper layer, a leader (or multiple leaders) determines system design parameters, while at the lower layer, a follower (or multiple followers) optimizes the control strategy. To capture this hierarchy, we propose four novel classes of Stackelberg games that integrate diverse strategic behaviors, including combinations of cooperative and non-cooperative interactions across two different layers. Notably, the leaders' interactions are represented using a normal-form game, whereas the followers' interactions are modeled by different games (dynamic games in discrete time). These distinct game structures result in a Stackelberg game that accommodates different game types per layer, and/or supports heterogeneous strategic behaviors involving cooperation and non-cooperation simultaneously. Learning algorithms using the best-response dynamics are used to solve the game problems when considering a discrete strategic space for the leaders. The efficacy of the proposed approach is demonstrated through an application to the co-design of the Barcelona drinking water network.
Non-linear dynamics of multibody systems: a system-based approach
Authors: Daniel Alazard, Francesco Sanfedino, Ervan Kassarian
Date: 2025-05-06 00:00:00

This paper presents causal block-diagram models to represent the equations of motion of multi-body systems in a very compact and simple closed form. Both the forward dynamics (from the forces and torques imposed at the various degrees-of-freedom to the motions of these degrees-of-freedom) or the inverse dynamics (from the motions imposed at the degrees-of-freedom to the resulting forces and torques) can be considered and described by a block diagram model. This work extends the Two-Input Two-Output Port (TITOP) theory by including all non-linear terms and uniform or gravitational acceleration fields. Connection among different blocks is possible through the definition of the motion vector. The model of a system composed of a floating base, rigid bodies, revolute and prismatic joints, working under gravity is developed to illustrate the methodology. The proposed model is validated by simulation and cross-checking with a model built using an alternative modeling tool on a scenario where the nonlinear terms are determining.
The ISAC systems aided by MIMO, RIS and with Beamforming Techniques
Authors: Rafael Augusto Pedriali, Isadora Martines Ferreira, Jose Carlos Marinello, Taufik Abrao
Date: 2025-05-06 00:00:00
Tasks: ISAC

This paper explores the integration of communication and sensing in modern wireless systems through the configuration of BS and RIS antenna elements. By leveraging time multiplexing for both communication and sensing, the proposed system optimizes spectral efficiency and operational performance. The use of static RIS configurations tailored to specific environments eliminates the need for dynamic reconfigurations, enhancing system agility, reducing processing complexity, and improving sensing accuracy. The system incorporates trilateration, angle of arrival, and time of arrival techniques to enable precise user localization by combining signals reflected along multiple paths. This method helps choose the best connections and lowers sensing costs while preventing interference with communication data, highlighting the need to bring together new technologies like passive and adaptive beamforming in one system.
Model uncertainty quantification using feature confidence sets for outcome excursions
Authors: Junting Ren, Armin Schwartzman
Date: 2025-04-28 00:00:00
Tasks: Prediction, Prediction Intervals, Uncertainty Quantification

When implementing prediction models for high-stakes real-world applications such as medicine, finance, and autonomous systems, quantifying prediction uncertainty is critical for effective risk management. Traditional approaches to uncertainty quantification, such as confidence and prediction intervals, provide probability coverage guarantees for the expected outcomes $f(\boldsymbol{x})$ or the realized outcomes $f(\boldsymbol{x})+\epsilon$. Instead, this paper introduces a novel, model-agnostic framework for quantifying uncertainty in continuous and binary outcomes using confidence sets for outcome excursions, where the goal is to identify a subset of the feature space where the expected or realized outcome exceeds a specific value. The proposed method constructs data-dependent inner and outer confidence sets that aim to contain the true feature subset for which the expected or realized outcomes of these features exceed a specified threshold. We establish theoretical guarantees for the probability that these confidence sets contain the true feature subset, both asymptotically and for finite sample sizes. The framework is validated through simulations and applied to real-world datasets, demonstrating its utility in contexts such as housing price prediction and time to sepsis diagnosis in healthcare. This approach provides a unified method for uncertainty quantification that is broadly applicable across various continuous and binary prediction models.
A Decision-Focused Predict-then-Bid Framework for Strategic Energy Storage
Authors: Ming Yi, Yiqian Wu, Saud Alghumayjan, James Anderson, Bolun Xu
Date: 2025-05-02 00:00:00

This paper introduces a novel decision-focused framework for energy storage arbitrage bidding. Inspired by the bidding process for energy storage in electricity markets, we propose a predict-then-bid end-to-end method incorporating the storage arbitrage optimization and market clearing models. This is achieved through a tri-layer framework that combines a price prediction layer with a two-stage optimization problem: an energy storage optimization layer and a market-clearing optimization layer. We leverage the implicit function theorem for gradient computation in the first optimization layer and incorporate a perturbation-based approach into the decision-focused loss function to ensure differentiability in the market-clearing layer. Numerical experiments using electricity market data from New York demonstrate that our bidding design substantially outperforms existing methods, achieving the highest profits and showcasing the effectiveness of the proposed approach.
Graph-based Semi-supervised and Unsupervised Methods for Local Clustering
Authors: Zhaiming Shen, Sung Ha Kang
Date: 2025-04-28 00:00:00
Tasks: Clustering

Local clustering aims to identify specific substructures within a large graph without requiring full knowledge of the entire graph. These substructures are typically small compared to the overall graph, enabling the problem to be approached by finding a sparse solution to a linear system associated with the graph Laplacian. In this work, we first propose a method for identifying specific local clusters when very few labeled data is given, which we term semi-supervised local clustering. We then extend this approach to the unsupervised setting when no prior information on labels is available. The proposed methods involve randomly sampling the graph, applying diffusion through local cluster extraction, then examining the overlap among the results to find each cluster. We establish the co-membership conditions for any pair of nodes and rigorously prove the correctness of our methods. Additionally, we conduct extensive experiments to demonstrate that the proposed methods achieve state-of-the-arts results in the low-label rates regime.
Ridge partial correlation screening for ultrahigh-dimensional data
Authors: Run Wang, An Nguyen, Somak Dutta, Vivekananda Roy
Date: 2025-04-27 00:00:00
Tasks: regression, Variable Selection

Variable selection in ultrahigh-dimensional linear regression is challenging due to its high computational cost. Therefore, a screening step is usually conducted before variable selection to significantly reduce the dimension. Here we propose a novel and simple screening method based on ordering the absolute sample ridge partial correlations. The proposed method takes into account not only the ridge regularized estimates of the regression coefficients but also the ridge regularized partial variances of the predictor variables providing sure screening property without strong assumptions on the marginal correlations. Simulation study and a real data analysis show that the proposed method has a competitive performance compared with the existing screening procedures. A publicly available software implementing the proposed screening accompanies the article.