pwc-archive/papers-with-abstracts huggingface dataset:
Local damage detection in rolling element bearings based on a Single Ensemble Empirical Mode Decomposition
Authors: Yaakoub Berrouche, Govind Vashishtha, Sumika Chauhan, Radoslaw Zimroz
Date: 2025-02-12 00:00:00

A Single Ensemble Empirical Mode Decomposition (SEEMD) is proposed for locating the damage in rolling element bearings. The SEEMD does not require a number of ensembles from the addition or subtraction of noise every time while processing the signals. The SEEMD requires just a single sifting process of a modified raw signal to reduce the computation time significantly. The other advantage of the SEEMD method is its success in dealing with non-Gaussian or non-stationary perturbing signals. In SEEMD, initially, a fractional Gaussian noise (FGN) is added to the raw signal to emphasize on high frequencies of the signal. Then, a convoluted white Gaussian noise is multiplied to the resulting signal which changes the spectral content of the signal which helps in extraction of the weak periodic signal. Finally, the obtained signal is decomposed by using a single sifting process. The proposed methodology is applied to the raw signals obtained from the mining industry. These signals are difficult to analyze since cyclic impulsive components are obscured by noise and other interference. Based on the results, the proposed method can effectively detect the fault where the signal of interest (SOI) has been extracted with good quality.
A crayfish-optimized wavelet filter and its application to fault diagnosis
Authors: Sumika Chauhan, Govind Vashishtha, Radoslaw Zimroz, Rajesh Kumar
Date: 2025-02-12 00:00:00
Tasks: Fault Diagnosis

Industrial machine fault diagnosis ensures the reliability and functionality of the system, but identifying informative frequency bands in vibration signals can be challenging due to low signal-to-noise ratio (SNR), background noise, and random interferences. The wavelet filter is commonly used for this purpose, but its parameters are crucial for locating the informative frequency band to extract repetitive transients. This study utilizes a crayfish optimization algorithm (COA) to optimize the wavelet filter adaptively for extracting fault characteristics. COA uses correlated kurtosis (CK) as a fitness function while addressing issues related to inaccurate CK period through an updation process. The proposed methodology is applied to different industrial cases and compared with existing methods, demonstrating its superiority in extracting informative frequencies.
Exploiting Non-uniform Quantization for Enhanced ILC in Wideband Digital Pre-distortion
Authors: Jinfei Wang, Yi Ma, Fei Tong, Ziming He
Date: 2025-02-12 00:00:00
Tasks: Quantization

In this paper, it is identified that lowering the reference level at the vector signal analyzer can significantly improve the performance of iterative learning control (ILC). We present a mathematical explanation for this phenomenon, where the signals experience logarithmic transform prior to analogue-to-digital conversion, resulting in non-uniform quantization. This process reduces the quantization noise of low-amplitude signals that constitute a substantial portion of orthogonal frequency division multiplexing (OFDM) signals, thereby improving ILC performance. Measurement results show that compared to setting the reference level to the peak amplitude, lowering the reference level achieves 3 dB improvement on error vector magnitude (EVM) and 15 dB improvement on normalized mean square error (NMSE) for 320 MHz WiFi OFDM signals.
MovISAC: Coherent Imaging of Moving Targets with Distributed Asynchronous ISAC Devices
Authors: Jacopo Pegoraro, Dario Tagliaferri, Joerg Widmer
Date: 2025-02-12 00:00:00
Tasks: Integrated sensing and communication, ISAC

Distributed integrated sensing and communication (ISAC) devices can overcome the traditional resolution limitations imposed by the signal bandwidth, cooperating to produce high-resolution images of the environment. However, existing phase-coherent imaging approaches are not suited to imaging multiple moving targets, since the Doppler effect causes a phase rotation that degrades the image focus and biases the targets' locations. In this paper, we propose MovISAC, the first coherent imaging method for moving targets using distributed asynchronous ISAC devices. Our approach obtains a set of high-resolution images of the environment, in which each image represents only targets moving with a selected velocity. To achieve this, MovISAC performs over-the-air (OTA) synchronization to compensate for timing, frequency, and phase offsets among distributed ISAC devices. Then, to solve the prohibitive complexity of an exhaustive search over the targets' velocities, we introduce a new association algorithm to match the Doppler shifts observed by each ISAC device pair to the corresponding spatial peaks obtained by standard imaging methods. This gives MovISAC the unique capability of pre-compensating the Doppler shift for each target before forming the images, thus recasting it from an undesired impairment to image formation to an additional means for resolving the targets. We perform extensive numerical simulations showing that our approach achieves extremely high resolution, superior robustness to clutter thanks to the additional target separation in the Doppler domain, and obtains cm- and cm/s-level target localization and velocity estimation errors. MovISAC significantly outperforms existing imaging methods that do not compensate for the Doppler shift, demonstrating up to 18 times lower localization error and enabling velocity estimation.
Can TDD Be Employed in LEO SatCom Systems? Challenges and Potential Approaches
Authors: Hyunwoo Lee, Ian P. Roberts, Jehyun Heo, Joohyun Son, Hanwoong Kim, Yunseo Lee, Daesik Hong
Date: 2025-02-12 00:00:00

Frequency-division duplexing (FDD) remains the de facto standard in modern low Earth orbit (LEO) satellite communication (SatCom) systems, such as SpaceX's Starlink, OneWeb, and Amazon's Project Kuiper. While time-division duplexing (TDD) is often regarded as superior in today's terrestrial networks, its viability in future LEO SatCom systems remains unclear. This article details how the long propagation delays and high orbital velocities exhibited by LEO SatCom systems impedes the adoption of TDD, due to challenges involving the frame structure and synchronization. We then present potential approaches to overcome these challenges, which vary in terms of resource efficiency and operational/device complexity and thus would likely be application-specific. We conclude by assessing the performance of these proposed approaches, putting into perspective the tradeoff between complexity and performance gains over FDD. Overall, this article aims to motivate future investigation into the prospects of TDD in LEO SatCom systems and solutions to enable such, with the goal of enhancing future systems and unifying them with terrestrial networks.
Continuous-Aperture Array Based OAM High-Capacity Communication For Metaverse
Authors: Hongyun Jin, Wenchi Cheng, Jingqing Wang, Wei zhang
Date: 2025-02-12 00:00:00
Tasks: Data Interaction

The extensive data interaction demands of an immersive metaverse necessitate the adoption of emerging technologies to enable high-capacity communication. Vortex electromagnetic waves with different orbital angular momentum (OAM) modes are spatially orthogonal, providing a novel spatial multiplexing dimension to achieve high-capacity communication. However, the number of orthogonal OAM modes based on a discrete uniform circular array (UCA) is limited by the number of array elements in the UCA, and traditional discrete channel models are unable to accurately capture the physical properties of vortex electromagnetic wave propagation. The continuous-aperture array (CAPA) is composed of densely packed electromagnetic excitation elements, capable of flexibly and efficiently generating the desired surface currents to produce an arbitrary number of mutually orthogonal OAM modes. From the perspective of electromagnetic information theory (EIT), we propose a CAPA-based OAM orthogonal transmission scheme to realize high-capacity communication. We design the surface currents of the CAPA using Fourier basis functions, derive the electromagnetic channel for vortex electromagnetic waves, and investigate the upper bound of the spectrum efficiency for CAPA-based OAM orthogonal transmission. This paper establishes a theoretical foundation for applying EIT to the orthogonal transmission of vortex electromagnetic waves, offering a novel solution for achieving CAPA-based efficient and high-capacity communication.
Mathematical Data Science
Authors: Michael R. Douglas, Kyu-Hwan Lee
Date: 2025-02-12 00:00:00

Can machine learning help discover new mathematical structures? In this article we discuss an approach to doing this which one can call "mathematical data science". In this paradigm, one studies mathematical objects collectively rather than individually, by creating datasets and doing machine learning experiments and interpretations. After an overview, we present two case studies: murmurations in number theory and loadings of partitions related to Kronecker coefficients in representation theory and combinatorics.
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
Authors: Jiachang Liu, Soroosh Shafiee, Andrea Lodi
Date: 2025-02-13 00:00:00

This paper investigates the problem of certifying optimality for sparse generalized linear models (GLMs), where sparsity is enforced through an $\ell_0$ cardinality constraint. While branch-and-bound (BnB) frameworks can certify optimality by pruning nodes using dual bounds, existing methods for computing these bounds are either computationally intensive or exhibit slow convergence, limiting their scalability to large-scale problems. To address this challenge, we propose a first-order proximal gradient algorithm designed to solve the perspective relaxation of the problem within a BnB framework. Specifically, we formulate the relaxed problem as a composite optimization problem and demonstrate that the proximal operator of the non-smooth component can be computed exactly in log-linear time complexity, eliminating the need to solve a computationally expensive second-order cone program. Furthermore, we introduce a simple restart strategy that enhances convergence speed while maintaining low per-iteration complexity. Extensive experiments on synthetic and real-world datasets show that our approach significantly accelerates dual bound computations and is highly effective in providing optimality certificates for large-scale problems.
Wasserstein distributional adversarial training for deep neural networks
Authors: Xingjian Bai, Guangyi He, Yifan Jiang, Jan Obloj
Date: 2025-02-13 00:00:00

Design of adversarial attacks for deep neural networks, as well as methods of adversarial training against them, are subject of intense research. In this paper, we propose methods to train against distributional attack threats, extending the TRADES method used for pointwise attacks. Our approach leverages recent contributions and relies on sensitivity analysis for Wasserstein distributionally robust optimization problems. We introduce an efficient fine-tuning method which can be deployed on a previously trained model. We test our methods on a range of pre-trained models on RobustBench. These experimental results demonstrate the additional training enhances Wasserstein distributional robustness, while maintaining original levels of pointwise robustness, even for already very successful networks. The improvements are less marked for models pre-trained using huge synthetic datasets of 20-100M images. However, remarkably, sometimes our methods are still able to improve their performance even when trained using only the original training dataset (50k images).
A First-order Generative Bilevel Optimization Framework for Diffusion Models
Authors: Quan Xiao, Hui Yuan, A F M Saif, Gaowen Liu, Ramana Kompella, Mengdi Wang, Tianyi Chen
Date: 2025-02-12 00:00:00
Tasks: Bilevel Optimization

Diffusion models, which iteratively denoise data samples to synthesize high-quality outputs, have achieved empirical success across domains. However, optimizing these models for downstream tasks often involves nested bilevel structures, such as tuning hyperparameters for fine-tuning tasks or noise schedules in training dynamics, where traditional bilevel methods fail due to the infinite-dimensional probability space and prohibitive sampling costs. We formalize this challenge as a generative bilevel optimization problem and address two key scenarios: (1) fine-tuning pre-trained models via an inference-only lower-level solver paired with a sample-efficient gradient estimator for the upper level, and (2) training diffusion models from scratch with noise schedule optimization by reparameterizing the lower-level problem and designing a computationally tractable gradient estimator. Our first-order bilevel framework overcomes the incompatibility of conventional bilevel methods with diffusion processes, offering theoretical grounding and computational practicality. Experiments demonstrate that our method outperforms existing fine-tuning and hyperparameter search baselines.
Robustly Learning Monotone Generalized Linear Models via Data Augmentation
Authors: Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas
Date: 2025-02-12 00:00:00
Tasks: Data Augmentation

We study the task of learning Generalized Linear models (GLMs) in the agnostic model under the Gaussian distribution. We give the first polynomial-time algorithm that achieves a constant-factor approximation for \textit{any} monotone Lipschitz activation. Prior constant-factor GLM learners succeed for a substantially smaller class of activations. Our work resolves a well-known open problem, by developing a robust counterpart to the classical GLMtron algorithm (Kakade et al., 2011). Our robust learner applies more generally, encompassing all monotone activations with bounded $(2+\zeta)$-moments, for any fixed $\zeta>0$ -- a condition that is essentially necessary. To obtain our results, we leverage a novel data augmentation technique with decreasing Gaussian noise injection and prove a number of structural results that may be useful in other settings.
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach
Authors: Yao Wang, Yiyang Yang, Kaidong Wang, Shanxing Gao, Xiuwu Liao
Date: 2025-02-12 00:00:00
Tasks: Matrix Completion

We consider the problem of matrix completion with graphs as side information depicting the interrelations between variables. The key challenge lies in leveraging the similarity structure of the graph to enhance matrix recovery. Existing approaches, primarily based on graph Laplacian regularization, suffer from several limitations: (1) they focus only on the similarity between neighboring variables, while overlooking long-range correlations; (2) they are highly sensitive to false edges in the graphs and (3) they lack theoretical guarantees regarding statistical and computational complexities. To address these issues, we propose in this paper a novel graph regularized matrix completion algorithm called GSGD, based on preconditioned projected gradient descent approach. We demonstrate that GSGD effectively captures the higher-order correlation information behind the graphs, and achieves superior robustness and stability against the false edges. Theoretically, we prove that GSGD achieves linear convergence to the global optimum with near-optimal sample complexity, providing the first theoretical guarantees for both recovery accuracy and efficacy in the perspective of nonconvex optimization. Our numerical experiments on both synthetic and real-world data further validate that GSGD achieves superior recovery accuracy and scalability compared with several popular alternatives.
Strong bounds for large-scale Minimum Sum-of-Squares Clustering
Authors: Anna Livia Croella, Veronica Piccialli, Antonio M. Sudoso
Date: 2025-02-12 00:00:00
Tasks: Clustering

Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used. MSSC aims to minimize the total squared Euclidean distance between data points and their corresponding cluster centroids. Due to the unsupervised nature of clustering, achieving global optimality is crucial, yet computationally challenging. The complexity of finding the global solution increases exponentially with the number of data points, making exact methods impractical for large-scale datasets. Even obtaining strong lower bounds on the optimal MSSC objective value is computationally prohibitive, making it difficult to assess the quality of heuristic solutions. We address this challenge by introducing a novel method to validate heuristic MSSC solutions through optimality gaps. Our approach employs a divide-and-conquer strategy, decomposing the problem into smaller instances that can be handled by an exact solver. The decomposition is guided by an auxiliary optimization problem, the "anticlustering problem", for which we design an efficient heuristic. Computational experiments demonstrate the effectiveness of the method for large-scale instances, achieving optimality gaps below 3% in most cases while maintaining reasonable computational times. These results highlight the practicality of our approach in assessing feasible clustering solutions for large datasets, bridging a critical gap in MSSC evaluation.
Convergence of Distributed Adaptive Optimization with Local Updates
Authors: Ziheng Cheng, Margalit Glasgow
Date: 2024-09-20 00:00:00

We study distributed adaptive algorithms with local updates (intermittent communication). Despite the great empirical success of adaptive methods in distributed training of modern machine learning models, the theoretical benefits of local updates within adaptive methods, particularly in terms of reducing communication complexity, have not been fully understood yet. In this paper, for the first time, we prove that \em Local SGD \em with momentum (\em Local \em SGDM) and \em Local \em Adam can outperform their minibatch counterparts in convex and weakly convex settings in certain regimes, respectively. Our analysis relies on a novel technique to prove contraction during local iterations, which is a crucial yet challenging step to show the advantages of local updates, under generalized smoothness assumption and gradient clipping strategy.
RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
Authors: Cheng Fang, Rishabh Dixit, Waheed U. Bajwa, Mert Gurbuzbalaban
Date: 2025-02-11 00:00:00

Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algorithmic convergence rates, which measure the speed at which optimization algorithms approach a solution, and statistical learning rates, which characterize how well the solution generalizes to unseen data. Privacy, memory, computational, and communications constraints increasingly necessitate data collection, processing, and storage across network-connected devices. In many applications, these networks operate in decentralized settings where a central server cannot be assumed, requiring decentralized ML algorithms that are both efficient and resilient. Decentralized learning, however, faces significant challenges, including an increased attack surface for adversarial interference during decentralized learning processes. This paper focuses on the man-in-the-middle (MITM) attack, which can cause models to deviate significantly from their intended ERM solutions. To address this challenge, we propose RESIST (Resilient dEcentralized learning using conSensus gradIent deScenT), an optimization algorithm designed to be robust against adversarially compromised communication links. RESIST achieves algorithmic and statistical convergence for strongly convex, Polyak-Lojasiewicz, and nonconvex ERM problems. Experimental results demonstrate the robustness and scalability of RESIST for real-world decentralized learning in adversarial environments.
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
Authors: Nikita Kornilov, Philip Zmushko, Andrei Semenov, Mark Ikonnikov, Alexander Gasnikov, Alexander Beznosikov
Date: 2025-02-11 00:00:00
Tasks: Distributed Optimization

In recent years, non-convex optimization problems are more often described by generalized $(L_0, L_1)$-smoothness assumption rather than standard one. Meanwhile, severely corrupted data used in these problems has increased the demand for methods capable of handling heavy-tailed noises, i.e., noises with bounded $\kappa$-th moment. Motivated by these real-world trends and challenges, we explore sign-based methods in this setup and demonstrate their effectiveness in comparison with other popular solutions like clipping or normalization. In theory, we prove the first-known high probability convergence bounds under $(L_0, L_1)$-smoothness and heavy-tailed noises with mild parameter dependencies. In the case of standard smoothness, these bounds are novel for sign-based methods as well. In particular, SignSGD with batching achieves sample complexity $\tilde{O}\left(\left(\frac{\Delta L_0d}{\varepsilon^2} + \frac{\Delta L_1d^\frac{3}{2}}{\varepsilon}\right)\left[1 + \left(\frac{\sigma}{\varepsilon}\right)^\frac{\kappa}{\kappa-1}\right]\right), \kappa \in (1,2]$. Under the assumption of symmetric noises, SignSGD with Majority Voting can robustly work on the whole range of $\kappa \in (0,2]$ with complexity $\tilde{O}\left(\left(\frac{\Delta L_0d}{\varepsilon^2} + \frac{\Delta L_1d^\frac{3}{2}}{\varepsilon}\right)\left[\frac{1}{\kappa^2} + \frac{\sigma^2}{\varepsilon^2}\right]\right)$. We also obtain results for parameter-agnostic setups, Polyak-Lojasiewicz functions and momentum-based methods (in expectation). Our theoretical findings are supported by the superior performance of sign-based methods in training Large Language Models compared to clipping and normalization.
Minimal Shortfall Strategies for Liquidation of a Basket of Stocks using Reinforcement Learning
Authors: Moustapha Pemy, Na Zhang
Date: 2025-02-11 00:00:00

This paper studies the ubiquitous problem of liquidating large quantities of highly correlated stocks, a task frequently encountered by institutional investors and proprietary trading firms. Traditional methods in this setting suffer from the curse of dimensionality, making them impractical for high-dimensional problems. In this work, we propose a novel method based on stochastic optimal control to optimally tackle this complex multidimensional problem. The proposed method minimizes the overall execution shortfall of highly correlated stocks using a reinforcement learning approach. We rigorously establish the convergence of our optimal trading strategy and present an implementation of our algorithm using intra-day market data.