pwc-archive/papers-with-abstracts huggingface dataset:
Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control
Authors: Kecheng Zhang, Ya-Feng Liu, Zhongbin Wang, Weijie Yuan, Musa Furkan Keskin, Henk Wymeersch, Shuqiang Xia
Date: 2025-03-19 00:00:00
Tasks: Computational Efficiency, Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm.
Disentangling Modes and Interference in the Spectrogram of Multicomponent Signals
Authors: Kévin Polisano, Sylvain Meignen, Nils Laurent, Hubert Leterme
Date: 2025-03-19 00:00:00

In this paper, we investigate how the spectrogram of multicomponent signals can be decomposed into a mode part and an interference part. We explore two approaches: (i) a variational method inspired by texture-geometry decomposition in image processing, and (ii) a supervised learning approach using a U-Net architecture, trained on a dataset encompassing diverse interference patterns and noise conditions. Once the interference component is identified, we explain how it enables us to define a criterion to locally adapt the window length used in the definition of the spectrogram, for the sake of improving ridge detection in the presence of close modes. Numerical experiments illustrate the advantages and limitations of both approaches for spectrogram decomposition, highlighting their potential for enhancing time-frequency analysis in the presence of strong interference.
A Cost Effective Deaf-mute Electronic Assistant System Using Myo Armband and Smartphone
Authors: Hussein Naeem Hasan
Date: 2025-03-19 00:00:00

Communication is essential feature in human communities. For some reasons, deaf-mute disabled people lose their ability to hear, speak, or both which makes them suffer to communicate and convey their ideas, especially with normal people. Sign language is the solution for communication in the deaf-mute societies, but it is difficult for the rest of people to understand. Therefore, in this work, a cost effective Deaf-Mute Electronic Assistant System (DMEAS) has been developed to help in solving the communication problem between the deaf-mute people and the normal people. The system hardware consists only of a Myo armband from \textit{Thalmic Labs} and a smartphone. The Myo armband reads the electromyographic signals of the muscles of the disabled person's forearm through non-invasive, surface mounted electrodes (sEMG) and sends the data directly to the smartphone via Bluetooth. The smartphone will recognize and interpret them to a predefined word depending on the hand gestures. The recognized gesture will be displayed on the smartphone screen as well as displaying the text of the word related to the gesture and its voice record. All the EMG signals are processed using discrete wavelet transform and classified by neural network classifier. The system was extensively tested through experiments by normal subjects to prove its functionality.
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties
Authors: Rafid Umayer Murshed, Md Shoaib Akhter Rafi, Sakib Reza, Mohammad Saquib, Ifana Mahbub
Date: 2025-03-19 00:00:00
Tasks: Meta-Learning, Property Prediction

Metasurfaces, and in particular reconfigurable intelligent surfaces (RIS), are revolutionizing wireless communications by dynamically controlling electromagnetic waves. Recent wireless communication advancements necessitate broadband and multi-band RIS, capable of supporting dynamic spectrum access and carrier aggregation from sub-6 GHz to mmWave and THz bands. The inherent frequency dependence of meta-atom resonances degrades performance as operating conditions change, making real-time, frequency-agnostic metasurface property prediction crucial for practical deployment. Yet, accurately predicting metasurface behavior across different frequencies remains challenging. Traditional simulations struggle with complexity, while standard deep learning models often overfit or generalize poorly. To address this, we introduce MetaFAP (Meta-Learning for Frequency-Agnostic Prediction), a novel framework built on the meta-learning paradigm for predicting metasurface properties. By training on diverse frequency tasks, MetaFAP learns broadly applicable patterns. This allows it to adapt quickly to new spectral conditions with minimal data, solving key limitations of existing methods. Experimental evaluations demonstrate that MetaFAP reduces prediction errors by an order of magnitude in MSE and MAE while maintaining high Pearson correlations. Remarkably, it achieves inference in less than a millisecond, bypassing the computational bottlenecks of traditional simulations, which take minutes per unit cell and scale poorly with array size. These improvements enable real-time RIS optimization in dynamic environments and support scalable, frequency-agnostic designs. MetaFAP thus bridges the gap between intelligent electromagnetic systems and practical deployment, offering a critical tool for next-generation wireless networks.
Robust Transmission of Punctured Text with Large Language Model-based Recovery
Authors: Sojeong Park, Hyeonho Noh, Hyun Jong Yang
Date: 2025-03-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Semantic Communication

With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust performance regardless of dataset or tasks. In this correspondence, we propose a novel text transmission model that selects and transmits only a few characters and recovers the missing characters at the receiver using a large language model (LLM). Additionally, we propose a novel importance character extractor (ICE), which selects transmitted characters to enhance LLM recovery performance. Simulations demonstrate that the proposed filter selection by ICE outperforms random filter selection, which selects transmitted characters randomly. Moreover, the proposed model exhibits robust performance across different datasets and tasks and outperforms traditional bit-based communication in low signal-to-noise ratio conditions.
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure
Authors: Fatemeh Dehrouyeh, Ibrahim Shaer, Soodeh Nikan, Firouz Badrkhani Ajaei, Abdallah Shami
Date: 2025-03-19 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, feature selection

With the growing need for real-time processing on IoT devices, optimizing machine learning (ML) models' size, latency, and computational efficiency is essential. This paper investigates a pruning method for anomaly detection in resource-constrained environments, specifically targeting Electric Vehicle Charging Infrastructure (EVCI). Using the CICEVSE2024 dataset, we trained and optimized three models-Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and XGBoost-through hyperparameter tuning with Optuna, further refining them using SHapley Additive exPlanations (SHAP)-based feature selection (FS) and unstructured pruning techniques. The optimized models achieved significant reductions in model size and inference times, with only a marginal impact on their performance. Notably, our findings indicate that, in the context of EVCI, pruning and FS can enhance computational efficiency while retaining critical anomaly detection capabilities.
From de Bruijn graphs to variation graphs-relationships between pangenome models
Authors: Adam Cicherski, Norbert Dojer
Date: 2025-03-14 00:00:00

Pangenomes serve as a framework for joint analysis of genomes of related organisms. Several pangenome models were proposed, offering different functionalities, applications provided by available tools, their efficiency etc. Among them, two graph-based models are particularly widely used: variation graphs and de Bruijn graphs. In the current paper we propose an axiomatization of the desirable properties of a graph representation of a collection of strings. We show the relationship between variation graphs satisfying these criteria and de Bruijn graphs. This relationship can be used to efficiently build a variation graph representing a given set of genomes, transfer annotations between both models, compare the results of analyzes based on each model etc.
Notes on Correlation Stress Tests
Authors: Piotr Chmielowski
Date: 2025-03-20 00:00:00
Tasks: Management, Time Series

This note outlines an approach to stress testing of covariance of financial time series, in the context of financial risk management. It discusses how the geodesic distance between covariance matrices implies a notion of plausibility of covariance stress tests. In this approach, correlation stress tests span a submanifold of constant determinant of the Fisher--Rao manifold of covariance matrices. A parsimonious geometrically invariant definition of arbitrarily large correlation stress tests is proposed, and a few examples are discussed.
Probing the topology of the space of tokens with structured prompts
Authors: Michael Robinson, Sourya Dey, Taisa Kushner
Date: 2025-03-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This article presents a general and flexible method for prompting a large language model (LLM) to reveal its (hidden) token input embedding up to homeomorphism. Moreover, this article provides strong theoretical justification -- a mathematical proof for generic LLMs -- for why this method should be expected to work. With this method in hand, we demonstrate its effectiveness by recovering the token subspace of Llemma-7B. The results of this paper apply not only to LLMs but also to general nonlinear autoregressive processes.
Big data comparison of quantum invariants
Authors: Daniel Tubbenhauer, Victor Zhang
Date: 2025-03-20 00:00:00
Tasks: Topological Data Analysis

We apply big data techniques, including exploratory and topological data analysis, to investigate quantum invariants. More precisely, our study explores the Jones polynomial's structural properties and contrasts its behavior under four principal methods of enhancement: coloring, rank increase, categorification, and leaving the realm of Lie algebras.
The global convergence time of stochastic gradient descent in non-convex landscapes: Sharp estimates via large deviations
Authors: Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos
Date: 2025-03-20 00:00:00

In this paper, we examine the time it takes for stochastic gradient descent (SGD) to reach the global minimum of a general, non-convex loss function. We approach this question through the lens of randomly perturbed dynamical systems and large deviations theory, and we provide a tight characterization of the global convergence time of SGD via matching upper and lower bounds. These bounds are dominated by the most "costly" set of obstacles that the algorithm may need to overcome in order to reach a global minimizer from a given initialization, coupling in this way the global geometry of the underlying loss landscape with the statistics of the noise entering the process. Finally, motivated by applications to the training of deep neural networks, we also provide a series of refinements and extensions of our analysis for loss functions with shallow local minima.
Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
Authors: Qiankun Shi, Jie Peng, Kun Yuan, Xiao Wang, Qing Ling
Date: 2025-03-20 00:00:00
Tasks: Stochastic Optimization

In this paper, we establish tight lower bounds for Byzantine-robust distributed first-order stochastic optimization methods in both strongly convex and non-convex stochastic optimization. We reveal that when the distributed nodes have heterogeneous data, the convergence error comprises two components: a non-vanishing Byzantine error and a vanishing optimization error. We establish the lower bounds on the Byzantine error and on the minimum number of queries to a stochastic gradient oracle required to achieve an arbitrarily small optimization error. Nevertheless, we identify significant discrepancies between our established lower bounds and the existing upper bounds. To fill this gap, we leverage the techniques of Nesterov's acceleration and variance reduction to develop novel Byzantine-robust distributed stochastic optimization methods that provably match these lower bounds, up to logarithmic factors, implying that our established lower bounds are tight.
Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time
Authors: Runze You, Shi Pu
Date: 2025-03-20 00:00:00

We study a distributed learning problem in which $n$ agents, each with potentially heterogeneous local data, collaboratively minimize the sum of their local cost functions via peer-to-peer communication. We propose a novel algorithm, Spanning Tree Push-Pull (STPP), which employs two spanning trees extracted from a general communication graph to distribute both model parameters and stochastic gradients. Unlike prior approaches that rely heavily on spectral gap properties, STPP leverages a more flexible topological characterization, enabling robust information flow and efficient updates. Theoretically, we prove that STPP achieves linear speedup and polynomial transient iteration complexity, up to $O(n^7)$ for smooth nonconvex objectives and $\tilde{O}(n^3)$ for smooth strongly convex objectives, under arbitrary network topologies. Moreover, compared with the existing methods, STPP achieves faster convergence rates on sparse and non-regular topologies (e.g., directed ring) and reduces communication overhead on dense networks (e.g., static exponential graph). These results significantly advance the state of the art, especially when $n$ is large. Numerical experiments further demonstrate the strong performance of STPP and confirm the practical relevance of its theoretical convergence rates across various common graph architectures. Our code is available at https://anonymous.4open.science/r/SpanningTreePushPull-5D3E.
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
Authors: Jie Liu, Yongqiang Wang
Date: 2025-03-20 00:00:00
Tasks: Distributed Optimization, Federated Learning

By letting local clients perform multiple local updates before communicating with a parameter server, modern federated learning algorithms such as FedAvg tackle the communication bottleneck problem in distributed learning and have found many successful applications. However, this asynchrony between local updates and communication also leads to a ''client-drift'' problem when the data is heterogeneous (not independent and identically distributed), resulting in errors in the final learning result. In this paper, we propose a federated learning algorithm, which is called FedCET, to ensure accurate convergence even under heterogeneous distributions of data across clients. Inspired by the distributed optimization algorithm NIDS, we use learning rates to weight information received from local clients to eliminate the ''client-drift''. We prove that under appropriate learning rates, FedCET can ensure linear convergence to the exact solution. Different from existing algorithms which have to share both gradients and a drift-correction term to ensure accurate convergence under heterogeneous data distributions, FedCET only shares one variable, which significantly reduces communication overhead. Numerical comparison with existing counterpart algorithms confirms the effectiveness of FedCET.
PARQ: Piecewise-Affine Regularized Quantization
Authors: Lisa Jin, Jianhao Ma, Zechun Liu, Andrey Gromov, Aaron Defazio, Lin Xiao
Date: 2025-03-19 00:00:00
Tasks: Quantization

We develop a principled method for quantization-aware training (QAT) of large-scale machine learning models. Specifically, we show that convex, piecewise-affine regularization (PAR) can effectively induce the model parameters to cluster towards discrete values. We minimize PAR-regularized loss functions using an aggregate proximal stochastic gradient method (AProx) and prove that it has last-iterate convergence. Our approach provides an interpretation of the straight-through estimator (STE), a widely used heuristic for QAT, as the asymptotic form of PARQ. We conduct experiments to demonstrate that PARQ obtains competitive performance on convolution- and transformer-based vision tasks.
Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net
Authors: Bowen Tan, Ying Cui
Date: 2025-03-19 00:00:00
Tasks: Action Detection, Activity Detection

Fast and accurate device activity detection is the critical challenge in grant-free access for supporting massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) in 5G and beyond. The state-of-the-art methods have unsatisfactory error rates or computation times. To address these outstanding issues, we propose new maximum likelihood estimation (MLE) and maximum a posterior estimation (MAPE) based device activity detection methods for known and unknown pathloss that achieve superior error rate and computation time tradeoffs using optimization and deep learning techniques. Specifically, we investigate four non-convex optimization problems for MLE and MAPE in the two pathloss cases, with one MAPE problem being formulated for the first time. For each non-convex problem, we develop an innovative parallel iterative algorithm using the parallel successive convex approximation (PSCA) method. Each PSCA-based algorithm allows parallel computations, uses up to the objective function's second-order information, converges to the problem's stationary points, and has a low per-iteration computational complexity compared to the state-of-the-art algorithms. Then, for each PSCA-based iterative algorithm, we present a deep unrolling neural network implementation, called PSCA-Net, to further reduce the computation time. Each PSCA-Net elegantly marries the underlying PSCA-based algorithm's parallel computation mechanism with the parallelizable neural network architecture and effectively optimizes its step sizes based on vast data samples to speed up the convergence. Numerical results demonstrate that the proposed methods can significantly reduce the error rate and computation time compared to the state-of-the-art methods, revealing their significant values for grant-free access.
Global Group Fairness in Federated Learning via Function Tracking
Authors: Yves Rychener, Daniel Kuhn, Yifan Hu
Date: 2025-03-19 00:00:00
Tasks: Fairness, Federated Learning

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probability metrics between distributions across all clients and are not naturally separable by client. To address this, we introduce a function-tracking scheme for the global fairness regularizer based on a Maximum Mean Discrepancy (MMD), which incurs a small communication overhead. This scheme seamlessly integrates into most federated learning algorithms while preserving rigorous convergence guarantees, as demonstrated in the context of FedAvg. Additionally, when enforcing differential privacy, the kernel-based MMD regularization enables straightforward analysis through a change of kernel, leveraging an intuitive interpretation of kernel convolution. Numerical experiments confirm our theoretical insights.
Nonlinear Bayesian Update via Ensemble Kernel Regression with Clustering and Subsampling
Authors: Yoonsang Lee
Date: 2025-03-19 00:00:00
Tasks: Clustering, Density Estimation, regression

Nonlinear Bayesian update for a prior ensemble is proposed to extend traditional ensemble Kalman filtering to settings characterized by non-Gaussian priors and nonlinear measurement operators. In this framework, the observed component is first denoised via a standard Kalman update, while the unobserved component is estimated using a nonlinear regression approach based on kernel density estimation. The method incorporates a subsampling strategy to ensure stability and, when necessary, employs unsupervised clustering to refine the conditional estimate. Numerical experiments on Lorenz systems and a PDE-constrained inverse problem illustrate that the proposed nonlinear update can reduce estimation errors compared to standard linear updates, especially in highly nonlinear scenarios.
General reproducing properties in RKHS with application to derivative and integral operators
Authors: Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier Roustant
Date: 2025-03-20 00:00:00

In this paper, we consider the reproducing property in Reproducing Kernel Hilbert Spaces (RKHS). We establish a reproducing property for the closure of the class of combinations of composition operators under minimal conditions. This allows to revisit the sufficient conditions for the reproducing property to hold for the derivative operator, as well as for the existence of the mean embedding function. These results provide a framework of application of the representer theorem for regularized learning algorithms that involve data for function values, gradients, or any other operator from the considered class.
On the Precise Asymptotics of Universal Inference
Authors: Kenta Takatsu
Date: 2025-03-18 00:00:00

In statistical inference, confidence set procedures are typically evaluated based on their validity and width properties. Even when procedures achieve rate-optimal widths, confidence sets can still be excessively wide in practice due to elusive constants, leading to extreme conservativeness, where the empirical coverage probability of nominal $1-\alpha$ level confidence sets approaches one. This manuscript studies this gap between validity and conservativeness, using universal inference (Wasserman et al., 2020) with a regular parametric model under model misspecification as a running example. We identify the source of asymptotic conservativeness and propose a general remedy based on studentization and bias correction. The resulting method attains exact asymptotic coverage at the nominal $1-\alpha$ level, even under model misspecification, provided that the product of the estimation errors of two unknowns is negligible, exhibiting an intriguing resemblance to double robustness in semiparametric theory.