pwc-archive/papers-with-abstracts huggingface dataset:
Decoding Latent Spaces: Assessing the Interpretability of Time Series Foundation Models for Visual Analytics
Authors: Inmaculada Santamaria-Valenzuela, Victor Rodriguez-Fernandez, Javier Huertas-Tato, Jong Hyuk Park, David Camacho
Date: 2025-04-26 00:00:00
Tasks: Anomaly Detection, Imputation, Time Series

The present study explores the interpretability of latent spaces produced by time series foundation models, focusing on their potential for visual analysis tasks. Specifically, we evaluate the MOMENT family of models, a set of transformer-based, pre-trained architectures for multivariate time series tasks such as: imputation, prediction, classification, and anomaly detection. We evaluate the capacity of these models on five datasets to capture the underlying structures in time series data within their latent space projection and validate whether fine tuning improves the clarity of the resulting embedding spaces. Notable performance improvements in terms of loss reduction were observed after fine tuning. Visual analysis shows limited improvement in the interpretability of the embeddings, requiring further work. Results suggest that, although Time Series Foundation Models such as MOMENT are robust, their latent spaces may require additional methodological refinements to be adequately interpreted, such as alternative projection techniques, loss functions, or data preprocessing strategies. Despite the limitations of MOMENT, foundation models supose a big reduction in execution time and so a great advance for interactive visual analytics.
Multiple Receiver Over-the-Air Computation for Wireless Networked Control Systems
Authors: Seif Hussein, Chinwendu Enyioha, Carlo Fischione
Date: 2025-05-03 00:00:00

We propose a multi-sender, multi-receiver over-the-air computation (OAC) framework for wireless networked control systems (WNCS) with structural constraints. Our approach enables actuators to directly compute and apply control signals from sensor measurements, eliminating the need for a centralized controller. We use an iterative and convexifying procedure to obtain a control law that is structured with respect to the network topology and minimizes the overall system energy-to-energy gain. Furthermore, we solve a constrained matrix factorization problem to find the optimal OAC configuration with respect to power consumption, robustness, and stability of the WNCS. We prove the convergence of our proposed algorithms and present numerical results that validate our approach to preserve closed-loop stability with robust control performance and constrained power.
Factor Analysis with Correlated Topic Model for Multi-Modal Data
Authors: Małgorzata Łazęcka, Ewa Szczurek
Date: 2025-04-26 00:00:00
Tasks: Variational Inference

Integrating various data modalities brings valuable insights into underlying phenomena. Multimodal factor analysis (FA) uncovers shared axes of variation underlying different simple data modalities, where each sample is represented by a vector of features. However, FA is not suited for structured data modalities, such as text or single cell sequencing data, where multiple data points are measured per each sample and exhibit a clustering structure. To overcome this challenge, we introduce FACTM, a novel, multi-view and multi-structure Bayesian model that combines FA with correlated topic modeling and is optimized using variational inference. Additionally, we introduce a method for rotating latent factors to enhance interpretability with respect to binary features. On text and video benchmarks as well as real-world music and COVID-19 datasets, we demonstrate that FACTM outperforms other methods in identifying clusters in structured data, and integrating them with simple modalities via the inference of shared, interpretable factors.
Optimized Cascaded Position Control of BLDC Motors Considering Torque Ripple
Authors: Mohammad Vedadi
Date: 2025-05-03 00:00:00
Tasks: Position

Brushless DC (BLDC) motors are increasingly used in various industries due to their reliability, low noise, and extended lifespan compared to traditional DC motors. Their high torque-to-weight ratio and impressive starting torque make them ideal for automotive, robotics, and industrial applications. This paper explores the multi-objective tuning of BLDC motor controllers, focusing on position and torque ripple. A state-space model of the BLDC motor and the entire control system, including the power stage and control structure, is developed in the Simulink environment. Two common control mechanisms, trapezoidal and Field Oriented Control (FOC), are implemented and optimized. Both mechanisms utilize a cascaded closed-loop position control, providing fair disturbance rejection but requiring challenging tuning of the controllers. To address these challenges, the non-dominated sorting genetic algorithm II (NSGA-II) is used for optimization. This study demonstrates the effectiveness of optimization techniques in enhancing the performance of control systems.
Real-Time, Single-Ear, Wearable ECG Reconstruction, R-Peak Detection, and HR/HRV Monitoring
Authors: Carlos Santos, Sebastian Frey, Andrea Cossettini, Luca Benini, Victor Kartsch
Date: 2025-05-03 00:00:00
Tasks: Heart Rate Variability, parameter estimation

Biosignal monitoring, in particular heart activity through heart rate (HR) and heart rate variability (HRV) tracking, is vital in enabling continuous, non-invasive tracking of physiological and cognitive states. Recent studies have explored compact, head-worn devices for HR and HRV monitoring to improve usability and reduce stigma. However, this approach is challenged by the current reliance on wet electrodes, which limits usability, the weakness of ear-derived signals, making HR/HRV extraction more complex, and the incompatibility of current algorithms for embedded deployment. This work introduces a single-ear wearable system for real-time ECG (Electrocardiogram) parameter estimation, which directly runs on BioGAP, an energy-efficient device for biosignal acquisition and processing. By combining SoA in-ear electrode technology, an optimized DeepMF algorithm, and BioGAP, our proposed subject-independent approach allows for robust extraction of HR/HRV parameters directly on the device with just 36.7 uJ/inference at comparable performance with respect to the current state-of-the-art architecture, achieving 0.49 bpm and 25.82 ms for HR/HRV mean errors, respectively and an estimated battery life of 36h with a total system power consumption of 7.6 mW. Clinical relevance: The ability to reconstruct ECG signals and extract HR and HRV paves the way for continuous, unobtrusive cardiovascular monitoring with head-worn devices. In particular, the integration of cardiovascular measurements in everyday-use devices (such as earbuds) has potential in continuous at-home monitoring to enable early detection of cardiovascular irregularities.
A Langevin sampling algorithm inspired by the Adam optimizer
Authors: Benedict Leimkuhler, René Lohmann, Peter Whalley
Date: 2025-04-26 00:00:00

We present a framework for adaptive-stepsize MCMC sampling based on time-rescaled Langevin dynamics, in which the stepsize variation is dynamically driven by an additional degree of freedom. Our approach augments the phase space by an additional variable which in turn defines a time reparameterization. The use of an auxiliary relaxation equation allows accumulation of a moving average of a local monitor function and provides for precise control of the timestep while circumventing the need to modify the drift term in the physical system. Our algorithm is straightforward to implement and can be readily combined with any off-the-peg fixed-stepsize Langevin integrator. As a particular example, we consider control of the stepsize by monitoring the norm of the log-posterior gradient, which takes inspiration from the Adam optimizer, the stepsize being automatically reduced in regions of steep change of the log posterior and increased on plateaus, improving numerical stability and convergence speed. As in Adam, the stepsize variation depends on the recent history of the gradient norm, which enhances stability and improves accuracy compared to more immediate control approaches. We demonstrate the potential benefit of this method--both in accuracy and in stability--in numerical experiments including Neal's funnel and a Bayesian neural network for classification of MNIST data.
Enhanced Flexibility Aggregation Using LinDistFlow Model with Loss Compensation
Authors: Yanlin Jiang, Xinliang Dai, Frederik Zahn, Veit Hagenmeyer
Date: 2025-05-03 00:00:00

With the increasing integration of renewable energy resources and the growing need for data privacy between system operators, flexibility aggregation methods have emerged as a promising solution to coordinate integrated transmissiondistribution (ITD) systems with limited information exchange. However, existing methods face significant challenges due to the nonlinearity of AC power flow models, and therefore mostly rely on linearized models. This paper examines the inherent errors in the LinDistFlow model, a linearized approximation, and demonstrates their impact on flexibility aggregation. To address these issues, we propose an intuitive compensation approach to refine the LinDistFlow-based flexibility set. Simulation results demonstrate the effectiveness of the proposed method in efficiently coordinating ITD systems.
ReLU integral probability metric and its applications
Authors: Yuha Park, Kunwoong Kim, Insung Kong, Yongdai Kim
Date: 2025-04-26 00:00:00
Tasks: Causal Inference, Representation Learning

We propose a parametric integral probability metric (IPM) to measure the discrepancy between two probability measures. The proposed IPM leverages a specific parametric family of discriminators, such as single-node neural networks with ReLU activation, to effectively distinguish between distributions, making it applicable in high-dimensional settings. By optimizing over the parameters of the chosen discriminator class, the proposed IPM demonstrates that its estimators have good convergence rates and can serve as a surrogate for other IPMs that use smooth nonparametric discriminator classes. We present an efficient algorithm for practical computation, offering a simple implementation and requiring fewer hyperparameters. Furthermore, we explore its applications in various tasks, such as covariate balancing for causal inference and fair representation learning. Across such diverse applications, we demonstrate that the proposed IPM provides strong theoretical guarantees, and empirical experiments show that it achieves comparable or even superior performance to other methods.
Enhanced Prediction Model for Time Series Characterized by GARCH via Interval Type-2 Fuzzy Inference System
Authors: Hongpei Shao, Da-qing Zhang, Feilong Lu
Date: 2025-05-03 00:00:00
Tasks: Time Series, Time Series Forecasting

GARCH-type time series (characterized by Generalized Autoregressive Conditional Heteroskedasticity) exhibit pronounced volatility, autocorrelation, and heteroskedasticity. To address these challenges and enhance predictive accuracy, this study introduces a hybrid forecasting framework that integrates the Interval Type-2 Fuzzy Inference System (IT2FIS) with the GARCH model. Leveraging the interval-based uncertainty representation of IT2FIS and the volatility-capturing capability of GARCH, the proposed model effectively mitigates the adverse impact of heteroskedasticity on prediction reliability. Specifically, the GARCH component estimates conditional variance, which is subsequently incorporated into the Gaussian membership functions of IT2FIS. This integration transforms IT2FIS into an adaptive variable-parameter system, dynamically aligning with the time-varying volatility of the target series. Through systematic parameter optimization, the framework not only captures intricate volatility patterns but also accounts for heteroskedasticity and epistemic uncertainties during modeling, thereby improving both prediction precision and model robustness. Experimental validation employs diverse datasets, including air quality concentration, urban traffic flow, and energy consumption. Comparative analyses are conducted against models: the GARCH-Takagi-Sugeno-Kang (GARCH-TSK) model, fixed-variance time series models, the GARCH-Gated Recurrent Unit (GARCH-GRU), and Long Short-Term Memory (LSTM) networks. The results indicate that the proposed model achieves superior predictive performance across the majority of test scenarios in error metrics. These findings underscore the effectiveness of hybrid approaches in forecasting uncertainty for GARCH-type time series, highlighting their practical utility in real-world time series forecasting applications.
Nonconvex Linear System Identification with Minimal State Representation
Authors: Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, Ingvar Ziemann, René Vidal
Date: 2025-04-26 00:00:00
Tasks: Computational Efficiency

Low-order linear System IDentification (SysID) addresses the challenge of estimating the parameters of a linear dynamical system from finite samples of observations and control inputs with minimal state representation. Traditional approaches often utilize Hankel-rank minimization, which relies on convex relaxations that can require numerous, costly singular value decompositions (SVDs) to optimize. In this work, we propose two nonconvex reformulations to tackle low-order SysID (i) Burer-Monterio (BM) factorization of the Hankel matrix for efficient nuclear norm minimization, and (ii) optimizing directly over system parameters for real, diagonalizable systems with an atomic norm style decomposition. These reformulations circumvent the need for repeated heavy SVD computations, significantly improving computational efficiency. Moreover, we prove that optimizing directly over the system parameters yields lower statistical error rates, and lower sample complexities that do not scale linearly with trajectory length like in Hankel-nuclear norm minimization. Additionally, while our proposed formulations are nonconvex, we provide theoretical guarantees of achieving global optimality in polynomial time. Finally, we demonstrate algorithms that solve these nonconvex programs and validate our theoretical claims on synthetic data.
A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications
Authors: Huang Zhang, Xixi Liu, Faisal Altaf, Torsten Wik
Date: 2025-05-03 00:00:00
Tasks: Capacity Estimation, Gaussian Processes, Model Selection

Gaussian process (GP) models have been used in a wide range of battery applications, in which different kernels were manually selected with considerable expertise. However, to capture complex relationships in the ever-growing amount of real-world data, selecting a suitable kernel for the GP model in battery applications is increasingly challenging. In this work, we first review existing GP kernels used in battery applications and then extend an automatic kernel search method with a new base kernel and model selection criteria. The GP models with composite kernels outperform the baseline kernel in two numerical examples of battery applications, i.e., battery capacity estimation and residual load prediction. Particularly, the results indicate that the Bayesian Information Criterion may be the best model selection criterion as it achieves a good trade-off between kernel performance and computational complexity. This work should, therefore, be of value to practitioners wishing to automate their kernel search process in battery applications.
Non-Asymptotic Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes
Authors: Zaiwei Chen
Date: 2025-04-25 00:00:00
Tasks: Q-Learning

This work presents the first finite-time analysis for the last-iterate convergence of average-reward Q-learning with an asynchronous implementation. A key feature of the algorithm we study is the use of adaptive stepsizes, which serve as local clocks for each state-action pair. We show that the iterates generated by this Q-learning algorithm converge at a rate of $O(1/k)$ (in the mean-square sense) to the optimal relative Q-function in the span seminorm. Moreover, by adding a centering step to the algorithm, we further establish pointwise mean-square convergence to a centered optimal relative Q-function, also at a rate of $O(1/k)$. To prove these results, we show that adaptive stepsizes are necessary, as without them, the algorithm fails to converge to the correct target. In addition, adaptive stepsizes can be interpreted as a form of implicit importance sampling that counteracts the effects of asynchronous updates. Technically, the use of adaptive stepsizes makes each Q-learning update depend on the entire sample history, introducing strong correlations and making the algorithm a non-Markovian stochastic approximation (SA) scheme. Our approach to overcoming this challenge involves (1) a time-inhomogeneous Markovian reformulation of non-Markovian SA, and (2) a combination of almost-sure time-varying bounds, conditioning arguments, and Markov chain concentration inequalities to break the strong correlations between the adaptive stepsizes and the iterates. The tools developed in this work are likely to be broadly applicable to the analysis of general SA algorithms with adaptive stepsizes.
Explicit neural network classifiers for non-separable data
Authors: Patrícia Muñoz Ewald
Date: 2025-04-25 00:00:00

We fully characterize a large class of feedforward neural networks in terms of truncation maps. As an application, we show how a ReLU neural network can implement a feature map which separates concentric data.
Physics-informed Learning for Passivity-based Tracking Control
Authors: Thomas Beckers, Leonardo Colombo
Date: 2025-05-02 00:00:00

Passivity-based control ensures system stability by leveraging dissipative properties and is widely applied in electrical and mechanical systems. Port-Hamiltonian systems (PHS), in particular, are well-suited for interconnection and damping assignment passivity-based control (IDA-PBC) due to their structured, energy-centric modeling approach. However, current IDA-PBC faces two key challenges: (i) it requires precise system knowledge, which is often unavailable due to model uncertainties, and (ii) it is typically limited to set-point control. To address these limitations, we propose a data-driven tracking control approach based on a physics-informed model, namely Gaussian process Port-Hamiltonian systems, along with the modified matching equation. By leveraging the Bayesian nature of the model, we establish probabilistic stability and passivity guarantees. A simulation demonstrates the effectiveness of our approach.
Local Polynomial Lp-norm Regression
Authors: Ladan Tazik, James Stafford, John Braun
Date: 2025-04-25 00:00:00
Tasks: regression

The local least squares estimator for a regression curve cannot provide optimal results when non-Gaussian noise is present. Both theoretical and empirical evidence suggests that residuals often exhibit distributional properties different from those of a normal distribution, making it worthwhile to consider estimation based on other norms. It is suggested that $L_p$-norm estimators be used to minimize the residuals when these exhibit non-normal kurtosis. In this paper, we propose a local polynomial $L_p$-norm regression that replaces weighted least squares estimation with weighted $L_p$-norm estimation for fitting the polynomial locally. We also introduce a new method for estimating the parameter $p$ from the residuals, enhancing the adaptability of the approach. Through numerical and theoretical investigation, we demonstrate our method's superiority over local least squares in one-dimensional data and show promising outcomes for higher dimensions, specifically in 2D.
A Coordinated Routing Approach for Enhancing Bus Timeliness and Travel Efficiency in Mixed-Traffic Environment
Authors: Tanlu Liang, Ting Bai, Andreas A. Malikopoulos
Date: 2025-05-02 00:00:00

In this paper, we propose a coordinated routing strategy aimed at improving bus schedule adherence and enhancing travel efficiency for connected and automated vehicles (CAVs) operating within a mixed-traffic urban network. Our approach capitalizes on the existence of dedicated lanes for buses and CAVs, leveraging real-time traffic data to dynamically reroute CAVs in anticipation of congestion. By continuously monitoring traffic conditions on dedicated lanes and tracking the real-time positions of buses, we enable the system to proactively adjust CAV routes when potential interference with bus operations is detected. This coordination mitigates delays affecting transit services and reduces travel time for CAVs. We evaluate the proposed strategy through simulation studies conducted in the SUMO. The results demonstrate significant improvements in both transit reliability and CAV operational performance across a range of traffic conditions.
Deep Learning-Enabled System Diagnosis in Microgrids: A Feature-Feedback GAN Approach
Authors: Swetha Rani Kasimalla, Kuchan Park, Junho Hong, Young-Jin Kim, HyoJong Lee
Date: 2025-05-02 00:00:00
Tasks: Generative Adversarial Network

The increasing integration of inverter-based resources (IBRs) and communication networks has brought both modernization and new vulnerabilities to the power system infrastructure. These vulnerabilities expose the system to internal faults and cyber threats, particularly False Data Injection (FDI) attacks, which can closely mimic real fault scenarios. Hence, this work presents a two-stage fault and cyberattack detection framework tailored for inverter-based microgrids. Stage 1 introduces an unsupervised learning model Feature Feedback Generative Adversarial Network (F2GAN), to distinguish between genuine internal faults and cyber-induced anomalies in microgrids. Compared to conventional GAN architectures, F2GAN demonstrates improved system diagnosis and greater adaptability to zero-day attacks through its feature-feedback mechanism. In Stage 2, supervised machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Decision Trees (DT), and Artificial Neural Networks (ANN) are applied to localize and classify faults within inverter switches, distinguishing between single-switch and multi-switch faults. The proposed framework is validated on a simulated microgrid environment, illustrating robust performance in detecting and classifying both physical and cyber-related disturbances in power electronic-dominated systems.
Foundations of Safe Online Reinforcement Learning in the Linear Quadratic Regulator: $\sqrt{T}$-Regret
Authors: Benjamin Schiffer, Lucas Janson
Date: 2025-04-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Understanding how to efficiently learn while adhering to safety constraints is essential for using online reinforcement learning in practical applications. However, proving rigorous regret bounds for safety-constrained reinforcement learning is difficult due to the complex interaction between safety, exploration, and exploitation. In this work, we seek to establish foundations for safety-constrained reinforcement learning by studying the canonical problem of controlling a one-dimensional linear dynamical system with unknown dynamics. We study the safety-constrained version of this problem, where the state must with high probability stay within a safe region, and we provide the first safe algorithm that achieves regret of $\tilde{O}_T(\sqrt{T})$. Furthermore, the regret is with respect to the baseline of truncated linear controllers, a natural baseline of non-linear controllers that are well-suited for safety-constrained linear systems. In addition to introducing this new baseline, we also prove several desirable continuity properties of the optimal controller in this baseline. In showing our main result, we prove that whenever the constraints impact the optimal controller, the non-linearity of our controller class leads to a faster rate of learning than in the unconstrained setting.