pwc-archive/papers-with-abstracts huggingface dataset:
An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio
Authors: Chandrashekhar Rai, Debarati Sen
Date: 2025-04-19 00:00:00
Tasks: Denoising

Millimeter-wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems encounter both spatial wideband spreading and temporal wideband effects in the communication channels of individual users. Accurate estimation of a user's channel signature -- specifically, the direction of arrival and time of arrival -- is crucial for designing efficient beamforming transceivers, especially under noisy observations. In this work, we propose an Artificial Intelligence (AI)-enabled framework for estimating the channel signature of a user's location in mmWave massive MIMO systems. Our approach explicitly accounts for spatial wideband spreading, finite basis leakage effects, and significant unknown receiver noise. We demonstrate the effectiveness of a denoising convolutional neural network with residual learning for recovering channel responses, even when channel gains are of extremely low amplitude and embedded in ultra-high receiver noise environments. Notably, our method successfully recovers spatio-temporal diversity branches at signal-to-noise ratios as low as -20 dB. Furthermore, we introduce a local gravitation-based clustering algorithm to infer the number of physical propagation paths (unknown a priori) and to identify their respective support in the delay-angle domain of the denoised response. To complement our approach, we design tailored metrics for evaluating denoising and clustering performance within the context of wireless communications. We validate our framework through system-level simulations using Orthogonal Frequency Division Multiplexing (OFDM) with a Quadrature Phase Shift Keying (QPSK) modulation scheme over mmWave fading channels, highlighting the necessity and robustness of the proposed methods in ultra-low SNR scenarios.
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach
Authors: Ahmed O. Elmeligy, Ioannis Psaromiligkos, Au Minh
Date: 2025-04-18 00:00:00
Tasks: Multi-Armed Bandits

The use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the availability of the existing infrastructure. However, the massive number of user equipments (UEs) poses a significant challenge to the cellular network's random access channel (RACH) regarding congestion and overloading. To mitigate this problem, we first present a novel approach to model a two-priority RACH, which allows us to define access patterns that describe the random access behavior of UEs as observed by the base station (BS). A non-uniform preamble selection scheme is proposed, offering increased flexibility in resource allocation for different UE priority classes. Then, we formulate an allocation model that finds the optimal access probabilities to maximize the success rate of high-priority UEs while constraining low-priority UEs. Finally, we develop a reinforcement learning approach to solving the optimization problem using multi-armed bandits, which provides a near-optimal but scalable solution and does not require the BS to know the number of UEs in the network.
xLSTM-ECG: Multi-label ECG Classification via Feature Fusion with xLSTM
Authors: Lei Kang, Xuanshuo Fu, Javier Vazquez-Corral, Ernest Valveny, Dimosthenis Karatzas
Date: 2025-04-14 00:00:00
Tasks: Classification, Diagnostic, ECG Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic tools. Electrocardiograms (ECGs) are indispensable in diagnosing various heart conditions; however, their manual interpretation is time-consuming and error-prone. In this paper, we propose xLSTM-ECG, a novel approach that leverages an extended Long Short-Term Memory (xLSTM) network for multi-label classification of ECG signals, using the PTB-XL dataset. To the best of our knowledge, this work represents the first design and application of xLSTM modules specifically adapted for multi-label ECG classification. Our method employs a Short-Time Fourier Transform (STFT) to convert time-series ECG waveforms into the frequency domain, thereby enhancing feature extraction. The xLSTM architecture is specifically tailored to address the complexities of 12-lead ECG recordings by capturing both local and global signal features. Comprehensive experiments on the PTB-XL dataset reveal that our model achieves strong multi-label classification performance, while additional tests on the Georgia 12-Lead dataset underscore its robustness and efficiency. This approach significantly improves ECG classification accuracy, thereby advancing clinical diagnostics and patient care. The code will be publicly available upon acceptance.
Routing functions for parameter space decomposition to describe stability landscapes of ecological models
Authors: Joseph Cummings, Kyle J. -M. Dahlin, Elizabeth Gross, Jonathan D. Hauenstein
Date: 2025-04-30 00:00:00

Changes in environmental or system parameters often drive major biological transitions, including ecosystem collapse, disease outbreaks, and tumor development. Analyzing the stability of steady states in dynamical systems provides critical insight into these transitions. This paper introduces an algebraic framework for analyzing the stability landscapes of ecological models defined by systems of first-order autonomous ordinary differential equations with polynomial or rational rate functions. Using tools from real algebraic geometry, we characterize parameter regions associated with steady-state feasibility and stability via three key boundaries: singular, stability (Routh-Hurwitz), and coordinate boundaries. With these boundaries in mind, we employ routing functions to compute the connected components of parameter space in which the number and type of stable steady states remain constant, revealing the stability landscape of these ecological models. As case studies, we revisit the classical Levins-Culver competition-colonization model and a recent model of coral-bacteria symbioses. In the latter, our method uncovers complex stability regimes, including regions supporting limit cycles, that are inaccessible via traditional techniques. These results demonstrate the potential of our approach to inform ecological theory and intervention strategies in systems with nonlinear interactions and multiple stable states.
$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks
Authors: Júlia Vicens Figueres, Juliette Vanderhaeghen, Federica Bragone, Kateryna Morozovska, Khemraj Shukla
Date: 2025-04-26 00:00:00

Physics-Informed Neural Networks (PINNs) are a novel computational approach for solving partial differential equations (PDEs) with noisy and sparse initial and boundary data. Although, efficient quantification of epistemic and aleatoric uncertainties in big multi-scale problems remains challenging. We propose \$PINN a novel method of computing global uncertainty in PDEs using a Bayesian framework, by combining local Bayesian Physics-Informed Neural Networks (BPINN) with domain decomposition. The solution continuity across subdomains is obtained by imposing the flux continuity across the interface of neighboring subdomains. To demonstrate the effectiveness of \$PINN, we conduct a series of computational experiments on PDEs in 1D and 2D spatial domains. Although we have adopted conservative PINNs (cPINNs), the method can be seamlessly extended to other domain decomposition techniques. The results infer that the proposed method recovers the global uncertainty by computing the local uncertainty exactly more efficiently as the uncertainty in each subdomain can be computed concurrently. The robustness of \$PINN is verified by adding uncorrelated random noise to the training data up to 15% and testing for different domain sizes.
Topology across Scales on Heterogeneous Cell Data
Authors: Maria Torras-Pérez, Iris H. R. Yoon, Praveen Weeratunga, Ling-Pei Ho, Helen M. Byrne, Ulrike Tillmann, Heather A. Harrington
Date: 2025-05-05 00:00:00
Tasks: Topological Data Analysis

Multiplexed imaging allows multiple cell types to be simultaneously visualised in a single tissue sample, generating unprecedented amounts of spatially-resolved, biological data. In topological data analysis, persistent homology provides multiscale descriptors of ``shape" suitable for the analysis of such spatial data. Here we propose a novel visualisation of persistence homology (PH) and fine-tune vectorisations thereof (exploring the effect of different weightings for persistence images, a prominent vectorisation of PH). These approaches offer new biological interpretations and promising avenues for improving the analysis of complex spatial biological data especially in multiple cell type data. To illustrate our methods, we apply them to a lung data set from fatal cases of COVID-19 and a data set from lupus murine spleen.
Dynamical System Parameter Path Optimization using Persistent Homology
Authors: Max M. Chumley, Firas A. Khasawneh
Date: 2025-05-01 00:00:00
Tasks: Topological Data Analysis

Nonlinear dynamical systems are complex and typically only simple systems can be analytically studied. In applications, these systems are usually defined with a set of tunable parameters and as the parameters are varied the system response undergoes significant topological changes or bifurcations. In a high dimensional parameter space, it is difficult to determine which direction to vary the system parameters to achieve a desired system response or state. In this paper, we introduce a new approach for optimally navigating a dynamical system parameter space that is rooted in topological data analysis. Specifically we use the differentiability of persistence diagrams to define a topological language for intuitively promoting or deterring different topological features in the state space response of a dynamical system and use gradient descent to optimally move from one point in the parameter space to another. The end result is a path in this space that guides the system to a set of parameters that yield the desired topological features defined by the loss function. We show a number of examples by applying the methods to different dynamical systems and scenarios to demonstrate how to promote different features and how to choose the hyperparameters to achieve different outcomes.
Classification of Firn Data via Topological Features
Authors: Sarah Day, Jesse Dimino, Matt Jester, Kaitlin Keegan, Thomas Weighill
Date: 2025-04-22 00:00:00
Tasks: Classification, Robust classification, Topological Data Analysis

In this paper we evaluate the performance of topological features for generalizable and robust classification of firn image data, with the broader goal of understanding the advantages, pitfalls, and trade-offs in topological featurization. Firn refers to layers of granular snow within glaciers that haven't been compressed into ice. This compactification process imposes distinct topological and geometric structure on firn that varies with depth within the firn column, making topological data analysis (TDA) a natural choice for understanding the connection between depth and structure. We use two classes of topological features, sublevel set features and distance transform features, together with persistence curves, to predict sample depth from microCT images. A range of challenging training-test scenarios reveals that no one choice of method dominates in all categories, and uncoveres a web of trade-offs between accuracy, interpretability, and generalizability.
Mathematical Modeling of Protein Structures: A Cohomology-Based Approach to the Flagellar Motor
Authors: Zakaria Lamine, Abdelatif Hafid, Mohamed Rahouti
Date: 2025-04-08 00:00:00

This study presents a novel mathematical model derived from cohomology, leveraging the KEEL-proven theorem that establishes cohomology as tautological, generated by boundary classes of curves with fixed dual graphs. Simplicial complexes are constructed using skew-commutative graded algebra, and the structure theorem is applied to connect distinct homologies, enabling precise interpretations of the resulting geometric forms. The proposed model is utilized for protein structure analysis and prediction, with a specific application to the Flagellar Motor structure. This approach offers new insights into the geometric and algebraic foundations of biological macromolecular modeling, highlighting its potential for advancement in structural biology.
Geometry of Cells Sensible to Curvature and Their Receptive Profiles
Authors: Vasiliki Liontou
Date: 2025-04-23 00:00:00
Tasks: Position

We propose a model of the functional architecture of curvature sensible cells in the visual cortex that associates curvature with scale. The feature space of orientation and position is naturally enhanced via its oriented prolongation, yielding a 4-dimensional manifold endowed with a canonical Engel structure. This structure encodes position, orientation, signed curvature, and scale. We associate an open submanifold of the prolongation with the quasi-regular representation of the similitude group SIM (2), and find left-invariant generators for the Engel structure. Finally, we use the generators of the Engel structure to characterize curvature-sensitive receptive profiles .
Spatial modeling of forest-savanna bistability: Impacts of fire dynamics and timescale separation
Authors: Kimberly Shen, Simon Levin, Denis D. Patterson
Date: 2025-05-02 00:00:00

Forest-savanna bistability - the hypothesis that forests and savannas exist as alternative stable states in the tropics - and its implications are key challenges for mathematical modelers and ecologists in the context of ongoing climate change. To generate new insights into this problem, we present a spatial Markov jump process model of savanna forest fires that integrates key ecological processes, including seed dispersal, fire spread, and non-linear vegetation flammability. In contrast to many models of forest-savanna bistability, we explicitly model both fire dynamics and vegetation regrowth in a mathematically tractable framework. This approach bridges the gap between slow-timescale vegetation models and highly resolved fire dynamics, shedding light on the influence of short-term and transient processes on vegetation cover. In our spatial stochastic model, bistability arises from periodic fires that maintain low forest cover, whereas dense forest areas inhibit fire spread and preserve high tree density. The deterministic mean-field approximation of the model similarly predicts bistability, but deviates quantitatively from the fully spatial model, especially in terms of its transient dynamics. These results also underscore the critical role of timescale separation between fire and vegetation processes in shaping ecosystem structure and resilience.
New insights into population dynamics from the continuous McKendrick model
Authors: Dragos-Patru Covei
Date: 2025-04-29 00:00:00
Tasks: Epidemiology

The article covers the continuous McKendrick model, which is key in epidemiology and population dynamics, developed by Anderson Gray McKendrick. It describes population age structure changes via birth and death rates as continuous functions, shedding light on population dynamics through differential equations. The article delves into the equations, boundary conditions, and survival rates, offering insights into disease progression and population dynamics. It aims for an asymptotic analysis of population dynamics using previous studies and illustrative examples. The main finding is that the population nears extinction only if the expected offspring per individual ($R$) is less than 1.
Generalizing the Levins metapopulation model to time varying colonization and extinction rates
Authors: Gonzalo Robledo, Ramiro Bustamante
Date: 2025-04-29 00:00:00

The metapopulation theory explores the population persistence in fragmented habitats by considering a balance between the extinction of local populations and recolonization of empty sites. In general, the extinction and colonization rates have been considered as constant parameters and the novelty of this paper is to assume that they are subject to deterministic variations. We noticed that an averaging approach proposed by C. Puccia and R. Levins can be adapted to construct the upper and lower averages of the difference between the extinction and colonization rates, whose sign is useful to determine either the permanence or the extinction of the metapopulation. In fact, we use these averages to revisit the classical model introduced by R. Levins. From a mathematical perspective, these averages can be seen as Bohl exponents whereas the corresponding analysis is carried out by using tools of non autonomous dynamics. Last but not least, compared with the Levins model, the resulting dynamics of the time varying model shares the persistence/extinction scenario when the above stated upper and lower averages have the same sign but also raises open questions about metapopulation persistence in the case of the averages have different sign.
Generative Learning for Slow Manifolds and Bifurcation Diagrams
Authors: Ellis R. Crabtree, Dimitris G. Giovanis, Nikolaos Evangelou, Juan M. Bello-Rivas, Ioannis G. Kevrekidis
Date: 2025-04-29 00:00:00

In dynamical systems characterized by separation of time scales, the approximation of so called ``slow manifolds'', on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds are required parts of the ``traditional'' numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have demonstrated capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions conditioned on some quantity of interest (QoI). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a ``label'') on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately ``fill in'' missing segments of steady states in a bifurcation diagram.
Learning Stabilizing Policies via an Unstable Subspace Representation
Authors: Leonardo F. Toso, Lintao Ye, James Anderson
Date: 2025-05-02 00:00:00

We study the problem of learning to stabilize (LTS) a linear time-invariant (LTI) system. Policy gradient (PG) methods for control assume access to an initial stabilizing policy. However, designing such a policy for an unknown system is one of the most fundamental problems in control, and it may be as hard as learning the optimal policy itself. Existing work on the LTS problem requires large data as it scales quadratically with the ambient dimension. We propose a two-phase approach that first learns the left unstable subspace of the system and then solves a series of discounted linear quadratic regulator (LQR) problems on the learned unstable subspace, targeting to stabilize only the system's unstable dynamics and reduce the effective dimension of the control space. We provide non-asymptotic guarantees for both phases and demonstrate that operating on the unstable subspace reduces sample complexity. In particular, when the number of unstable modes is much smaller than the state dimension, our analysis reveals that LTS on the unstable subspace substantially speeds up the stabilization process. Numerical experiments are provided to support this sample complexity reduction achieved by our approach.
A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
Authors: Tianshu Chu, Dachuan Xu, Wei Yao, Chengming Yu, Jin Zhang
Date: 2025-05-02 00:00:00
Tasks: Bilevel Optimization

Bilevel optimization has recently attracted significant attention in machine learning due to its wide range of applications and advanced hierarchical optimization capabilities. In this paper, we propose a plug-and-play framework, named PnPBO, for developing and analyzing stochastic bilevel optimization methods. This framework integrates both modern unbiased and biased stochastic estimators into the single-loop bilevel optimization framework introduced in [9], with several improvements. In the implementation of PnPBO, all stochastic estimators for different variables can be independently incorporated, and an additional moving average technique is applied when using an unbiased estimator for the upper-level variable. In the theoretical analysis, we provide a unified convergence and complexity analysis for PnPBO, demonstrating that the adaptation of various stochastic estimators (including PAGE, ZeroSARAH, and mixed strategies) within the PnPBO framework achieves optimal sample complexity, comparable to that of single-level optimization. This resolves the open question of whether the optimal complexity bounds for solving bilevel optimization are identical to those for single-level optimization. Finally, we empirically validate our framework, demonstrating its effectiveness on several benchmark problems and confirming our theoretical findings.
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
Authors: Xin Chen, Yuze Chen, Yuan Zhou
Date: 2025-05-02 00:00:00
Tasks: Decision Making, Prediction Intervals, Sequential Decision Making

We study a class of sequential decision-making problems with augmented predictions, potentially provided by a machine learning algorithm. In this setting, the decision-maker receives prediction intervals for unknown parameters that become progressively refined over time, and seeks decisions that are competitive with the hindsight optimal under all possible realizations of both parameters and predictions. We propose a minimax Markov Decision Process (minimax-MDP) framework, where the system state consists of an adversarially evolving environment state and an internal state controlled by the decision-maker. We introduce a set of future-imposed conditions that characterize the feasibility of minimax-MDPs and enable the design of efficient, often closed-form, robustly competitive policies. We illustrate the framework through three applications: multi-period inventory ordering with refining demand predictions, resource allocation with uncertain utility functions, and a multi-phase extension of the minimax-MDP applied to the inventory problem with time-varying ordering costs. Our results provide a tractable and versatile approach to robust online decision-making under predictive uncertainty.