pwc-archive/papers-with-abstracts huggingface dataset:
DL-AMC: Deep Learning for Automatic Modulation Classification
Authors: Faheem Ur Rehman, Qamar Abbas, M. Karam Shehzad
Date: 2025-04-10 00:00:00
Tasks: Classification, Deep Learning

Automatic Modulation Classification (AMC) is a signal processing technique widely used at the physical layer of wireless systems to enhance spectrum utilization efficiency. In this work, we propose a fast and accurate AMC system, termed DL-AMC, which leverages deep learning techniques. Specifically, DL-AMC is built using convolutional neural network (CNN) architectures, including ResNet-18, ResNet-50, and MobileNetv2. To evaluate its performance, we curated a comprehensive dataset containing various modulation schemes. Each modulation type was transformed into an eye diagram, with signal-to-noise ratio (SNR) values ranging from -20 dB to 30 dB. We trained the CNN models on this dataset to enable them to learn the discriminative features of each modulation class effectively. Experimental results show that the proposed DL-AMC models achieve high classification accuracy, especially in low SNR conditions. These results highlight the robustness and efficacy of DL-AMC in accurately classifying modulations in challenging wireless environments
Estimation of Solar Spectral Irradiance Using Meteorological Data and Analysis of Optimal Conditions for Solar Power Generation
Authors: Jeonggyu Hwang
Date: 2025-04-10 00:00:00
Tasks: Anomaly Detection

This study proposes an approximate model to estimate the solar radiation spectrum intensity in Seoul, Republic of Korea, for the year 2024, aiming to analyze optimal conditions related to energy generation. Since the solar radiation spectrum varies with atmospheric conditions, accurately predicting it typically requires complex spectral radiation models. However, such models entail high computational costs, hindering real-time application. To address this, this study introduces a simplified approximation model using only direct normal irradiance (DNI) among real-time meteorological elements, employing linear scaling of the standard spectrum (ASTM G-173). This model first estimates DNI using global horizontal irradiance (GHI) and solar position information (such as zenith angle), then linearly adjusts the standard spectrum to compute real-time spectrum intensity. The model approximates realistic DNI values by correcting various meteorological parameters, including zenith angle, cloud cover, and visibility. The analysis shows that GHI exhibits stable seasonal patterns, peaking in summer and minimizing in winter. In contrast, DNI demonstrates significant temporal variability and frequent abnormal peaks (e.g., exceeding 9,000 W/m^2), highlighting the importance of data refinement and anomaly detection in predicting energy generation. In conclusion, GHI is suitable for general photovoltaic analyses, whereas DNI is crucial for direct-beam sensitive systems like concentrated solar power (CSP), requiring meticulous data quality management. Future research should focus on identifying the causes of DNI anomalies and developing real-time quality control algorithms.
Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation
Authors: Junlang Huang, Hao Chen, Li Luo, Yong Cai, Lexin Zhang, Tianhao Ma, Yitian Zhang, Zhong Guan
Date: 2025-04-08 00:00:00

This paper presents a hybrid model combining Transformer and CNN for predicting the current waveform in signal lines. Unlike traditional approaches such as current source models, driver linear representations, waveform functional fitting, or equivalent load capacitance methods, our model does not rely on fixed simplified models of standard-cell drivers or RC loads. Instead, it replaces the complex Newton iteration process used in traditional SPICE simulations, leveraging the powerful sequence modeling capabilities of the Transformer framework to directly predict current responses without iterative solving steps. The hybrid architecture effectively integrates the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs, significantly improving the accuracy of current waveform predictions. Experimental results demonstrate that, compared to traditional SPICE simulations, the proposed algorithm achieves an error of only 0.0098. These results highlight the algorithm's superior capabilities in predicting signal line current waveforms, timing analysis, and power evaluation, making it suitable for a wide range of technology nodes, from 40nm to 3nm.
Towards Simple Machine Learning Baselines for GNSS RFI Detection
Authors: Viktor Ivanov, Richard C. Wilson, Maurizio Scaramuzza
Date: 2025-04-08 00:00:00
Tasks: Deep Learning, Interpretable Machine Learning

Machine learning research in GNSS radio frequency interference (RFI) detection often lacks a clear empirical justification for the choice of deep learning architectures over simpler machine learning approaches. In this work, we argue for a change in research direction-from developing ever more complex deep learning models to carefully assessing their real-world effectiveness in comparison to interpretable and lightweight machine learning baselines. Our findings reveal that state-of-the-art deep learning models frequently fail to outperform simple, well-engineered machine learning methods in the context of GNSS RFI detection. Leveraging a unique large-scale dataset collected by the Swiss Air Force and Swiss Air-Rescue (Rega), and preprocessed by Swiss Air Navigation Services Ltd. (Skyguide), we demonstrate that a simple baseline model achieves 91\% accuracy in detecting GNSS RFI, outperforming more complex deep learning counterparts. These results highlight the effectiveness of pragmatic solutions and offer valuable insights to guide future research in this critical application domain.
Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks
Authors: Mohammed Mallik, Laurent Clavier, Davy P. Gaillot
Date: 2025-04-07 00:00:00

Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields (RF-EMF) is essential for conducting risk assessments. These assessments aim to explore potential connections between RF-EMF exposure and its effects on human health, as well as on wildlife and plant life. Existing research has used different machine learning tools for EMF exposure estimation; however, a comparative analysis of these techniques is required to better understand their performance for real-world datasets. In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels from 70 real-world sensors in Lille, France. A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy. To improve estimation accuracy for higher-resolution grids, we utilized a preconditioned gradient descent method for kernel estimation. Root Mean Square Error (RMSE) is used as the evaluation criterion for comparing the performance of these deep learning models.
Mathematical Approach in Hybrid Beamforming for ISAC Systems
Authors: Keivan Khosroshahi, Michael Suppa
Date: 2025-04-07 00:00:00
Tasks: Integrated sensing and communication, ISAC, Mathematical Proofs

This document serves as supplementary material for a journal paper submission, presenting detailed mathematical proofs and derivations that support the results outlined in the main manuscript. In this work, we formulate an Integrated sensing and communication (ISAC) optimization problem aimed at fairly maximizing beampattern gains across multiple directions of interest, while ensuring that the signal-to-interference-plus-noise ratio (SINR) requirements for communication users and the overall power constraint are satisfied. To solve this problem, we propose an optimization algorithm and provide a formal proof of its convergence.
Surrogate-based optimization of system architectures subject to hidden constraints
Authors: Jasper Bussemaker, Paul Saves, Nathalie Bartoli, Thierry Lefebvre, Björn Nagel
Date: 2025-04-11 00:00:00
Tasks: Bayesian Optimization

The exploration of novel architectures requires physics-based simulation due to a lack of prior experience to start from, which introduces two specific challenges for optimization algorithms: evaluations become more expensive (in time) and evaluations might fail. The former challenge is addressed by Surrogate-Based Optimization (SBO) algorithms, in particular Bayesian Optimization (BO) using Gaussian Process (GP) models. An overview is provided of how BO can deal with challenges specific to architecture optimization, such as design variable hierarchy and multiple objectives: specific measures include ensemble infills and a hierarchical sampling algorithm. Evaluations might fail due to non-convergence of underlying solvers or infeasible geometry in certain areas of the design space. Such failed evaluations, also known as hidden constraints, pose a particular challenge to SBO/BO, as the surrogate model cannot be trained on empty results. This work investigates various strategies for satisfying hidden constraints in BO algorithms. Three high-level strategies are identified: rejection of failed points from the training set, replacing failed points based on viable (non-failed) points, and predicting the failure region. Through investigations on a set of test problems including a jet engine architecture optimization problem, it is shown that best performance is achieved with a mixed-discrete GP to predict the Probability of Viability (PoV), and by ensuring selected infill points satisfy some minimum PoV threshold. This strategy is demonstrated by solving a jet engine architecture problem that features at 50% failure rate and could not previously be solved by a BO algorithm. The developed BO algorithm and used test problems are available in the open-source Python library SBArchOpt.
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design
Authors: Paul Saves, Nathalie Bartoli, Youssef Diouane, Thierry Lefebvre, Joseph Morlier, Christophe David, Eric Nguyen Van, Sébastien Defoort
Date: 2025-04-11 00:00:00
Tasks: Bayesian Optimization, Dimensionality Reduction

Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of design variables. Recently, there has been a growing interest in mixed variables constrained Bayesian optimization but most existing approaches severely increase the number of the hyperparameters related to the surrogate model. In this paper, we address this issue by constructing surrogate models using less hyperparameters. The reduction process is based on the partial least squares method. An adaptive procedure for choosing the number of hyperparameters is proposed. The performance of the proposed approach is confirmed on analytical tests as well as two real applications related to aircraft design. A significant improvement is obtained compared to genetic algorithms.
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization
Authors: Kyo Kuroki, Thiem Van Chu, Masato Motomura, Kazushi Kawamura
Date: 2025-04-11 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

In recent years, formulating various combinatorial optimization problems as Quadratic Unconstrained Binary Optimization (QUBO) has gained significant attention as a promising approach for efficiently obtaining optimal or near-optimal solutions. While QUBO offers a general-purpose framework, existing solvers often struggle with performance variability across different problems. This paper (i) theoretically analyzes Mean Field Annealing (MFA) and its variants--which are representative QUBO solvers, and reveals that their underlying self-consistent equations do not necessarily represent the minimum condition of the Kullback-Leibler divergence between the mean-field approximated distribution and the exact distribution, and (ii) proposes a novel method, the Annealed Mean Field Descent (AMFD), which is designed to address this limitation by directly minimizing the divergence. Through extensive experiments on five benchmark combinatorial optimization problems (Maximum Cut Problem, Maximum Independent Set Problem, Traveling Salesman Problem, Quadratic Assignment Problem, and Graph Coloring Problem), we demonstrate that AMFD exhibits superior performance in many cases and reduced problem dependence compared to state-of-the-art QUBO solvers and Gurobi--a state-of-the-art versatile mathematical optimization solver not limited to QUBO.
A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
Authors: Yixuan Zhang, Dongyan Huo, Yudong Chen, Qiaomin Xie
Date: 2025-04-11 00:00:00
Tasks: quantile regression, regression

Motivated by robust and quantile regression problems, we investigate the stochastic gradient descent (SGD) algorithm for minimizing an objective function $f$ that is locally strongly convex with a sub--quadratic tail. This setting covers many widely used online statistical methods. We introduce a novel piecewise Lyapunov function that enables us to handle functions $f$ with only first-order differentiability, which includes a wide range of popular loss functions such as Huber loss. Leveraging our proposed Lyapunov function, we derive finite-time moment bounds under general diminishing stepsizes, as well as constant stepsizes. We further establish the weak convergence, central limit theorem and bias characterization under constant stepsize, providing the first geometrical convergence result for sub--quadratic SGD. Our results have wide applications, especially in online statistical methods. In particular, we discuss two applications of our results. 1) Online robust regression: We consider a corrupted linear model with sub--exponential covariates and heavy--tailed noise. Our analysis provides convergence rates comparable to those for corrupted models with Gaussian covariates and noise. 2) Online quantile regression: Importantly, our results relax the common assumption in prior work that the conditional density is continuous and provide a more fine-grained analysis for the moment bounds.
Entropic bounds for conditionally Gaussian vectors and applications to neural networks
Authors: Lucia Celli, Giovanni Peccati
Date: 2025-04-11 00:00:00

Using entropic inequalities from information theory, we provide new bounds on the total variation and 2-Wasserstein distances between a conditionally Gaussian law and a Gaussian law with invertible covariance matrix. We apply our results to quantify the speed of convergence to Gaussian of a randomly initialized fully connected neural network and its derivatives - evaluated in a finite number of inputs - when the initialization is Gaussian and the sizes of the inner layers diverge to infinity. Our results require mild assumptions on the activation function, and allow one to recover optimal rates of convergence in a variety of distances, thus improving and extending the findings of Basteri and Trevisan (2023), Favaro et al. (2023), Trevisan (2024) and Apollonio et al. (2024). One of our main tools are the quantitative cumulant estimates established in Hanin (2024). As an illustration, we apply our results to bound the total variation distance between the Bayesian posterior law of the neural network and its derivatives, and the posterior law of the corresponding Gaussian limit: this yields quantitative versions of a posterior CLT by Hron et al. (2022), and extends several estimates by Trevisan (2024) to the total variation metric.
Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design
Authors: Robin Grapin, Youssef Diouane, Joseph Morlier, Nathalie Bartoli, Thierry Lefebvre, Paul Saves, Jasper Bussemaker
Date: 2025-04-11 00:00:00
Tasks: Bayesian Optimization, global-optimization, Mixture-of-Experts

Bayesian optimization is an advanced tool to perform ecient global optimization It consists on enriching iteratively surrogate Kriging models of the objective and the constraints both supposed to be computationally expensive of the targeted optimization problem Nowadays efficient extensions of Bayesian optimization to solve expensive multiobjective problems are of high interest The proposed method in this paper extends the super efficient global optimization with mixture of experts SEGOMOE to solve constrained multiobjective problems To cope with the illposedness of the multiobjective inll criteria different enrichment procedures using regularization techniques are proposed The merit of the proposed approaches are shown on known multiobjective benchmark problems with and without constraints The proposed methods are then used to solve a biobjective application related to conceptual aircraft design with ve unknown design variables and three nonlinear inequality constraints The preliminary results show a reduction of the total cost in terms of function evaluations by a factor of 20 compared to the evolutionary algorithm NSGA-II.
Statistically guided deep learning
Authors: Michael Kohler, Adam Krzyzak
Date: 2025-04-11 00:00:00
Tasks: Deep Learning

We present a theoretically well-founded deep learning algorithm for nonparametric regression. It uses over-parametrized deep neural networks with logistic activation function, which are fitted to the given data via gradient descent. We propose a special topology of these networks, a special random initialization of the weights, and a data-dependent choice of the learning rate and the number of gradient descent steps. We prove a theoretical bound on the expected $L_2$ error of this estimate, and illustrate its finite sample size performance by applying it to simulated data. Our results show that a theoretical analysis of deep learning which takes into account simultaneously optimization, generalization and approximation can result in a new deep learning estimate which has an improved finite sample performance.
Deep Distributional Learning with Non-crossing Quantile Network
Authors: Guohao Shen, Runpeng Dai, Guojun Wu, Shikai Luo, Chengchun Shi, Hongtu Zhu
Date: 2025-04-11 00:00:00
Tasks: Distributional Reinforcement Learning, quantile regression, Reinforcement Learning (RL)

In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that the learned distributions remain monotonic, effectively addressing the issue of quantile crossing. Furthermore, the NQ network-based deep distributional learning framework is highly adaptable, applicable to a wide range of applications, from classical non-parametric quantile regression to more advanced tasks such as causal effect estimation and distributional reinforcement learning (RL). We also develop a comprehensive theoretical foundation for the deep NQ estimator and its application to distributional RL, providing an in-depth analysis that demonstrates its effectiveness across these domains. Our experimental results further highlight the robustness and versatility of the NQ network.
Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap
Authors: Alice Driessen, Benedek Harsanyi, Marianna Rapsomaniki, Jannis Born
Date: 2025-04-11 00:00:00

Learning the response of single-cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport (OT) has emerged as a principled methodological framework because it inherently accommodates the challenges of unpaired data induced by cell destruction during data acquisition. However, most existing OT approaches are incapable of conditioning on different treatment contexts (e.g., time, drug treatment, drug dosage, or cell type) and we still lack methods that unanimously show promising generalization performance to unseen treatments. Here, we propose the Conditional Monge Gap which learns OT maps conditionally on arbitrary covariates. We demonstrate its value in predicting single-cell perturbation responses conditional to one or multiple drugs, a drug dosage, or combinations thereof. We find that our conditional models achieve results comparable and sometimes even superior to the condition-specific state-of-the-art on scRNA-seq as well as multiplexed protein imaging data. Notably, by aggregating data across conditions we perform cross-task learning which unlocks remarkable generalization abilities to unseen drugs or drug dosages, widely outperforming other conditional models in capturing heterogeneity (i.e., higher moments) in the perturbed population. Finally, by scaling to hundreds of conditions and testing on unseen drugs, we narrow the gap between structure-based and effect-based drug representations, suggesting a promising path to the successful prediction of perturbation effects for unseen treatments.
Information bounds on the accuracy of cell polarization
Authors: Tau-Mu Yi
Date: 2025-04-10 00:00:00

Here we characterized an information measure for cell polarity that applies to non-motile cells responding to a chemical gradient. The central idea is that polarization represents information about the direction of the gradient. We applied a theory of optimal gradient sensing and response in the presence of external noise based on the information capacity of a Gaussian channel. First, we formulated an information framework that describes spatial gradient sensing and polarization response. As part of this section, we modeled ligand diffusion and receptor-binding dynamics as a mixed Poisson distribution, confirming the single receptor accuracy limits derived by ten Wolde and colleagues. Second, we performed numerical calculations of stochastic ligand levels at the cell surface to estimate the information provided about the directional component of the gradient vector, which was close to the Gaussian channel bound for low signal-to-noise ratios. Third, we used the information framework to evaluate the noise-robustness of three generic models of cell polarity, demonstrating that a filter-amplifier architecture and time integration can attenuate the detrimental impact of noise on polarity so that the model can approach the theoretical limits. Fourth, we compared the theory to published experimental data on yeast mating projection growth in a pheromone gradient, identifying the ligand association rate and integration time as two key parameters affecting directional accuracy. By varying these parameters, we showed that for certain ranges the theory is roughly in agreement with the data, and that the slow binding rate constant is a key limiting factor. We concluded that temporal averaging can help overcome the slow binding rate to achieve greater accuracy, but with the drawback of a slow mating response.