pwc-archive/papers-with-abstracts huggingface dataset:
A Practical Approach Towards Inertia Estimation Using Ambient Synchrophasor Data
Authors: Anushka Sharma, Anamitra Pal, Rajasekhar Anguluri, Tamojit Chakraborty
Date: 2025-05-05 00:00:00

Real-time tracking of inertia is important because it reflects the power system's ability to withstand contingencies and maintain frequency security. This paper proposes a practical approach to estimate inertia using ambient phasor measurement unit (PMU) data and a partitioned form of the swing equation. The approach accounts for (bounded) uncertainties in network parameters and PMU measurements, enabling precise estimation of inertia and damping constants, as well as mechanical power inputs. Instead of assuming constant mechanical power input throughout, the approach leverages knowledge of power system operations to determine intervals when it is actually constant to maintain estimation consistency. Simulation results on the IEEE 14-bus system and IEEE 39 bus system integrated with renewable energy sources affirm the method's accuracy and applicability.
Likelihood-Free Variational Autoencoders
Authors: Chen Xu, Qiang Wang, Lijun Sun
Date: 2025-04-24 00:00:00
Tasks: Decoder, scoring rule

Variational Autoencoders (VAEs) typically rely on a probabilistic decoder with a predefined likelihood, most commonly an isotropic Gaussian, to model the data conditional on latent variables. While convenient for optimization, this choice often leads to likelihood misspecification, resulting in blurry reconstructions and poor data fidelity, especially for high-dimensional data such as images. In this work, we propose EnVAE, a novel likelihood-free generative framework that has a deterministic decoder and employs the energy score--a proper scoring rule--to build the reconstruction loss. This enables likelihood-free inference without requiring explicit parametric density functions. To address the computational inefficiency of the energy score, we introduce a fast variant, FEnVAE, based on the local smoothness of the decoder and the sharpness of the posterior distribution of latent variables. This yields an efficient single-sample training objective that integrates seamlessly into existing VAE pipelines with minimal overhead. Empirical results on standard benchmarks demonstrate that EnVAE achieves superior reconstruction and generation quality compared to likelihood-based baselines. Our framework offers a general, scalable, and statistically principled alternative for flexible and nonparametric distribution learning in generative modeling.
Online Phase Estimation of Human Oscillatory Motions using Deep Learning
Authors: Antonio Grotta, Francesco De Lellis
Date: 2025-05-05 00:00:00
Tasks: Deep Learning, Position, Reinforcement Learning (RL)

Accurately estimating the phase of oscillatory systems is essential for analyzing cyclic activities such as repetitive gestures in human motion. In this work we introduce a learning-based approach for online phase estimation in three-dimensional motion trajectories, using a Long Short- Term Memory (LSTM) network. A calibration procedure is applied to standardize trajectory position and orientation, ensuring invariance to spatial variations. The proposed model is evaluated on motion capture data and further tested in a dynamical system, where the estimated phase is used as input to a reinforcement learning (RL)-based control to assess its impact on the synchronization of a network of Kuramoto oscillators.
$O(1/k)$ Finite-Time Bound for Non-Linear Two-Time-Scale Stochastic Approximation
Authors: Siddharth Chandak
Date: 2025-04-27 00:00:00

Two-time-scale stochastic approximation is an algorithm with coupled iterations which has found broad applications in reinforcement learning, optimization and game control. While several prior works have obtained a mean square error bound of $O(1/k)$ for linear two-time-scale iterations, the best known bound in the non-linear contractive setting has been $O(1/k^{2/3})$. In this work, we obtain an improved bound of $O(1/k)$ for non-linear two-time-scale stochastic approximation. Our result applies to algorithms such as gradient descent-ascent and two-time-scale Lagrangian optimization. The key step in our analysis involves rewriting the original iteration in terms of an averaged noise sequence which decays sufficiently fast. Additionally, we use an induction-based approach to show that the iterates are bounded in expectation.
Wise Goose Chase: A Predictive Path Planning Algorithm for Dynamic Rebalancing in Ride-Hailing Systems
Authors: Avalpreet Singh Brar, Rong Su, Christos G. Cassandras, Gioele Zardini
Date: 2025-05-05 00:00:00

Traditional rebalancing methods in ride-hailing systems direct idle drivers to fixed destinations, overlooking the fact that ride allocations frequently occur while cruising. This destination-centric view fails to exploit the path-dependent nature of modern platforms, where real-time matching depends on the entire trajectory rather than a static endpoint. We propose the Wise Goose Chase (WGC) algorithm, an event-triggered, driver-specific path planning framework that anticipates future matching opportunities by forecasting spatio-temporal supply and demand dynamics. WGC uses a system of Retarded Functional Differential Equations (RFDEs) to model the evolution of idle driver density and passenger queues at the road-segment level, incorporating both en-route matching and competition among drivers. Upon request, WGC computes personalized cruising paths that minimize each driver's expected time to allocation. Monte Carlo simulations on synthetic urban networks show that WGC consistently outperforms baseline strategies, highlighting the advantage of predictive, context-aware rebalancing in dynamic mobility systems.
Rethinking Label-specific Features for Label Distribution Learning
Authors: Suping Xu, Chuyi Dai, Lin Shang, Changbin Shao, XiBei Yang, Witold Pedrycz
Date: 2025-04-27 00:00:00

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms.
Maximal Compatibility Matching for Preference-Aware Ride-Hailing Systems
Authors: Avalpreet Singh Brar, Rong Su, Jaskaranveer Kaur, Xinling Li, Gioele Zardini
Date: 2025-05-05 00:00:00
Tasks: Unity

This paper presents the Maximal Compatibility Matching (MCM) framework, a novel assignment strategy for ride-hailing systems that explicitly incorporates passenger comfort into the matching process. Traditional assignment methods prioritize spatial efficiency, but often overlook behavioral alignment between passengers and drivers, which can significantly impact user satisfaction. MCM addresses this gap by learning personalized passenger comfort zones using gradient-boosted decision tree classifiers trained on labeled ride data, and by modeling driver behavior through empirical operating profiles constructed from time-series driving features. Compatibility between a passenger and a driver is computed as the closed-form volume of intersection between their respective feature-space regions. These compatibility scores are integrated into a utility-based matching algorithm that balances comfort and proximity through a tunable trade-off parameter. We validate the framework using a Unity-based driving simulator with real-time passenger feedback, demonstrating that MCM enables more personalized and socially acceptable matchings while maintaining high levels of operational performance.
Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization
Authors: Jean-Rémy Conti, Stéphan Clémençon
Date: 2025-04-27 00:00:00
Tasks: Fairness

The urging societal demand for fair AI systems has put pressure on the research community to develop predictive models that are not only globally accurate but also meet new fairness criteria, reflecting the lack of disparate mistreatment with respect to sensitive attributes ($\textit{e.g.}$ gender, ethnicity, age). In particular, the variability of the errors made by certain Facial Recognition (FR) systems across specific segments of the population compromises the deployment of the latter, and was judged unacceptable by regulatory authorities. Designing fair FR systems is a very challenging problem, mainly due to the complex and functional nature of the performance measure used in this domain ($\textit{i.e.}$ ROC curves) and because of the huge heterogeneity of the face image datasets usually available for training. In this paper, we propose a novel post-processing approach to improve the fairness of pre-trained FR models by optimizing a regression loss which acts on centroid-based scores. Beyond the computational advantages of the method, we present numerical experiments providing strong empirical evidence of the gain in fairness and of the ability to preserve global accuracy.
The Double Descent Behavior in Two Layer Neural Network for Binary Classification
Authors: Chathurika S Abeykoon, Aleksandr Beknazaryan, Hailin Sang
Date: 2025-04-27 00:00:00
Tasks: Binary Classification

Recent studies observed a surprising concept on model test error called the double descent phenomenon, where the increasing model complexity decreases the test error first and then the error increases and decreases again. To observe this, we work on a two layer neural network model with a ReLU activation function designed for binary classification under supervised learning. Our aim is to observe and investigate the mathematical theory behind the double descent behavior of model test error for varying model sizes. We quantify the model size by the ratio of number of training samples to the dimension of the model. Due to the complexity of the empirical risk minimization procedure, we use the Convex Gaussian Min Max Theorem to find a suitable candidate for the global training loss.
Observability conditions for neural state-space models with eigenvalues and their roots of unity
Authors: Andrew Gracyk
Date: 2025-04-22 00:00:00
Tasks: Computational Efficiency, Mamba, State Space Models, Unity

We operate through the lens of ordinary differential equations and control theory to study the concept of observability in the context of neural state-space models and the Mamba architecture. We develop strategies to enforce observability, which are tailored to a learning context, specifically where the hidden states are learnable at initial time, in conjunction to over its continuum, and high-dimensional. We also highlight our methods emphasize eigenvalues, roots of unity, or both. Our methods effectuate computational efficiency when enforcing observability, sometimes at great scale. We formulate observability conditions in machine learning based on classical control theory and discuss their computational complexity. Our nontrivial results are fivefold. We discuss observability through the use of permutations in neural applications with learnable matrices without high precision. We present two results built upon the Fourier transform that effect observability with high probability up to the randomness in the learning. These results are worked with the interplay of representations in Fourier space and their eigenstructure, nonlinear mappings, and the observability matrix. We present a result for Mamba that is similar to a Hautus-type condition, but instead employs an argument using a Vandermonde matrix instead of eigenvectors. Our final result is a shared-parameter construction of the Mamba system, which is computationally efficient in high exponentiation. We develop a training algorithm with this coupling, showing it satisfies a Robbins-Monro condition under certain orthogonality, while a more classical training procedure fails to satisfy a contraction with high Lipschitz constant.
Impact of Transceiver Selection on Synchronization Accuracy in White Rabbit Networks
Authors: Michal Špaček, Josef Vojtěch, Jaroslav Roztočil
Date: 2025-05-05 00:00:00

Achieving optimal synchronization accuracy between two White Rabbit devices hinges on the proper selection of transceivers, which act as electro-optical converters connecting WR devices to the optical network infrastructure. The correct choice of transceivers can significantly improve resilience to changes in the time offset between WR devices due to temperature fluctuations in the connecting optical fiber. To compare the performance of BiDi WDM and DWDM transceivers, an experimental setup was established under laboratory conditions to simulate a real optical network used for distributing precise time and frequency between two remote locations. The optical connection was emulated by integrating a 20 km G.652.D optical fiber into a climatic chamber, which provided variable environmental conditions similar to those experienced in real applications. The study compared BiDi WDM 1310/1550 nm transceivers with DWDM Ch33/Ch34 transceivers. Results showed that DWDM transceivers exhibited nearly thirteen times less sensitivity to temperature-induced changes in the optical connection, leading to a smaller time offset. Therefore, for achieving the highest accuracy in synchronizing WR devices in practical applications, DWDM transceiver technology is essential.
Test Set Sizing for the Ridge Regression
Authors: Alexander Dubbs
Date: 2025-04-27 00:00:00
Tasks: regression

We derive the ideal train/test split for the ridge regression to high accuracy in the limit that the number of training rows m becomes large. The split must depend on the ridge tuning parameter, alpha, but we find that the dependence is weak and can asymptotically be ignored; all parameters vanish except for m and the number of features, n. This is the first time that such a split is calculated mathematically for a machine learning model in the large data limit. The goal of the calculations is to maximize "integrity," so that the measured error in the trained model is as close as possible to what it theoretically should be. This paper's result for the ridge regression split matches prior art for the plain vanilla linear regression split to the first two terms asymptotically, and it appears that practically there is no difference.
On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing
Authors: Samantha Petti, Carlos Martí-Gómez, Justin B. Kinney, Juannan Zhou, David M. McCandlish
Date: 2025-04-26 00:00:00

Mappings from biological sequences (DNA, RNA, protein) to quantitative measures of sequence functionality play an important role in contemporary biology. We are interested in the related tasks of (i) inferring predictive sequence-to-function maps and (ii) decomposing sequence-function maps to elucidate the contributions of individual subsequences. Because each sequence-function map can be written as a weighted sum over subsequences in multiple ways, meaningfully interpreting these weights requires "gauge-fixing," i.e., defining a unique representation for each map. Recent work has established that most existing gauge-fixed representations arise as the unique solutions to $L_2$-regularized regression in an overparameterized "weight space" where the choice of regularizer defines the gauge. Here, we establish the relationship between regularized regression in overparameterized weight space and Gaussian process approaches that operate in "function space," i.e. the space of all real-valued functions on a finite set of sequences. We disentangle how weight space regularizers both impose an implicit prior on the learned function and restrict the optimal weights to a particular gauge. We also show how to construct regularizers that correspond to arbitrary explicit Gaussian process priors combined with a wide variety of gauges. Next, we derive the distribution of gauge-fixed weights implied by the Gaussian process posterior and demonstrate that even for long sequences this distribution can be efficiently computed for product-kernel priors using a kernel trick. Finally, we characterize the implicit function space priors associated with the most common weight space regularizers. Overall, our framework unifies and extends our ability to infer and interpret sequence-function relationships.
Deep Reinforcement Learning-Aided Frequency Control of LCC-S Resonant Converters for Wireless Power Transfer Systems
Authors: Reza Safari, Mohsen Hamzeh, Nima Mahdian Dehkordi
Date: 2025-05-03 00:00:00
Tasks: Deep Reinforcement Learning

This paper presents a novel deep reinforcement learning (DRL)-based control strategy for achieving precise and robust output voltage regulation in LCC-S resonant converters, specifically designed for wireless power transfer applications. Unlike conventional methods that rely on manually tuned PI controllers or heuristic tuning approaches, our method leverages the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to systematically optimize PI controller parameters. The complex converter dynamics are captured using the Direct Piecewise Affine (DPWA) modeling technique, providing a structured approach to handling its nonlinearities. This integration not only eliminates the need for manual tuning, but also enhances control adaptability under varying operating conditions. The simulation and experimental results confirm that the proposed DRL-based tuning approach significantly outperforms traditional methods in terms of stability, robustness, and response time. This work demonstrates the potential of DRL in power electronic control, offering a scalable and data-driven alternative to conventional controller design approaches.
Towards minimax optimal algorithms for Active Simple Hypothesis Testing
Authors: Sushant Vijayan
Date: 2025-04-26 00:00:00

We study the Active Simple Hypothesis Testing (ASHT) problem, a simpler variant of the Fixed Budget Best Arm Identification problem. In this work, we provide novel game theoretic formulation of the upper bounds of the ASHT problem. This formulation allows us to leverage tools of differential games and Partial Differential Equations (PDEs) to propose an approximately optimal algorithm that is computationally tractable compared to prior work. However, the optimal algorithm still suffers from a curse of dimensionality and instead we use a novel link to Blackwell Approachability to propose an algorithm that is far more efficient computationally. We show that this new algorithm, although not proven to be optimal, is always better than static algorithms in all instances of ASHT and is numerically observed to attain the optimal exponent in various instances.
Adaptive DRL for IRS Mirror Orientation in Dynamic OWC Networks
Authors: Ahrar N. Hamad, Ahmad Adnan Qidan, Taisir E. H. El-Gorashi, Jaafar M. H. Elmirghani
Date: 2025-05-03 00:00:00
Tasks: Deep Reinforcement Learning

Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm, specifically deep deterministic policy gradient (DDPG), is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that the proposed DDPG-based approach outperforms conventional DRL algorithms and achieves substantial improvements in sum rate compared to fixed-orientation IRS configurations.
Learning Stochastic Thermodynamics Directly from Correlation and Trajectory-Fluctuation Currents
Authors: Jinghao Lyu, Kyle J. Ray, James P. Crutchfield
Date: 2025-04-26 00:00:00

Markedly increased computational power and data acquisition have led to growing interest in data-driven inverse dynamics problems. These seek to answer a fundamental question: What can we learn from time series measurements of a complex dynamical system? For small systems interacting with external environments, the effective dynamics are inherently stochastic, making it crucial to properly manage noise in data. Here, we explore this for systems obeying Langevin dynamics and, using currents, we construct a learning framework for stochastic modeling. Currents have recently gained increased attention for their role in bounding entropy production (EP) from thermodynamic uncertainty relations (TURs). We introduce a fundamental relationship between the cumulant currents there and standard machine-learning loss functions. Using this, we derive loss functions for several key thermodynamic functions directly from the system dynamics without the (common) intermediate step of deriving a TUR. These loss functions reproduce results derived both from TURs and other methods. More significantly, they open a path to discover new loss functions for previously inaccessible quantities. Notably, this includes access to per-trajectory entropy production, even if the observed system is driven far from its steady-state. We also consider higher order estimation. Our method is straightforward and unifies dynamic inference with recent approaches to entropy production estimation. Taken altogether, this reveals a deep connection between diffusion models in machine learning and entropy production estimation in stochastic thermodynamics.
Evaluating an assembly- and disassembly-oriented expansion of Modular Function Deployment through a workshop-based assessment
Authors: Fabio Marco Monetti
Date: 2025-05-03 00:00:00

Modular product architectures are used to enhance flexibility, reduce production complexity, and support sustainability goals. However, traditional Modular Function Deployment (MFD) method does not fully integrate Design for Assembly (DFA) and Design for Disassembly (DFD) principles, leading to sub-optimal manufacturability and end-of-life strategies. This study introduces an expanded MFD method incorporating assembly and disassembly considerations into early-stage modularisation. A workshop-based evaluation assesses usability and applicability, involving participants using standard and expanded MFD. Results indicate that integrating DFA and DFD enhances assembly efficiency, ease of disassembly, and modular product strategy alignment. However, usability challenges were identified, necessitating refinements for industry application.