pwc-archive/papers-with-abstracts huggingface dataset:
Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale
Authors: Mohammed Almutairi
Date: 2025-06-05 00:00:00
Tasks: feature selection, Management, Sentiment Analysis

During the wake of the Covid-19 pandemic, the educational paradigm has experienced a major change from in person learning traditional to online platforms. The change of learning convention has impacted the teacher-student especially in non-verbal communication. The absent of non-verbal communication has led to a reliance on verbal feedback which diminished the efficacy of the educational experience. This paper explores the integration of sentiment analysis into learning management systems (LMS) to bridge the student-teacher's gap by offering an alternative approach to interpreting student feedback beyond its verbal context. The research involves data preparation, feature selection, and the development of a deep neural network model encompassing word embedding, LSTM, and attention mechanisms. This model is compared against a logistic regression baseline to evaluate its efficacy in understanding student feedback. The study aims to bridge the communication gap between instructors and students in online learning environments, offering insights into the emotional context of student feedback and ultimately improving the quality of online education.
End-to-End Dynamic Metasurface Antenna Wireless System: Prototype, Opportunities, and Challenges
Authors: François Yven, Jean Tapie, Jérôme Sol, Philipp del Hougne
Date: 2025-06-11 00:00:00

Dynamic metasurface antennas (DMAs) are a promising hybrid analog/digital beamforming technology to realize next-generation wireless systems with low cost, footprint, and power consumption. The research on DMA-empowered wireless systems is still at an early stage, mostly limited to theoretical studies under simplifying assumptions on the one hand and a few antenna-level experiments on the other hand. Substantial knowledge gaps arise from the lack of complete end-to-end DMA-empowered wireless system prototypes. In addition, recently unveiled benefits of strong inter-element mutual coupling (MC) in DMAs remain untapped. Here, we demonstrate a K-band prototype of an end-to-end wireless system based on a DMA with strong inter-element MC. To showcase the flexible control over the DMA's radiation pattern, we present an experimental case study of simultaneously steering a beam to a desired transmitter and a null to an undesired jammer, achieving up to 43~dB discrimination. Using software-defined radios, we transmit and receive QPSK OFDM waveforms to evaluate the bit error rate. We also discuss algorithmic and technological challenges associated with envisioned future evolutions of our end-to-end testbed and real-life DMA-based wireless systems.
Markov Blanket Density and Free Energy Minimization
Authors: Luca M. Possati
Date: 2025-06-06 00:00:00

This paper presents a continuous, information-theoretic extension of the Free Energy Principle through the concept of Markov blanket density, i.e., a scalar field that quantifies the degree of conditional independence between internal and external states at each point in space (ranging from 0 for full coupling to 1 for full separation). It demonstrates that active inference dynamics (including the minimization of variational and expected free energy) naturally emerge from spatial gradients in this density, making Markov blanket density a necessary foundation for the definability and coherence of the Free Energy Principle. These ideas are developed through a mathematically framework that links density gradients to precise and testable dynamics, offering a foundation for novel predictions and simulation paradigms.
Modeling Probabilistic Reduction using Information Theory and Naive Discriminative Learning
Authors: Anna Stein, Kevin Tang
Date: 2025-06-11 00:00:00

This study compares probabilistic predictors based on information theory with Naive Discriminative Learning (NDL) predictors in modeling acoustic word duration, focusing on probabilistic reduction. We examine three models using the Buckeye corpus: one with NDL-derived predictors using information-theoretic formulas, one with traditional NDL predictors, and one with N-gram probabilistic predictors. Results show that the N-gram model outperforms both NDL models, challenging the assumption that NDL is more effective due to its cognitive motivation. However, incorporating information-theoretic formulas into NDL improves model performance over the traditional model. This research highlights a) the need to incorporate not only frequency and contextual predictability but also average contextual predictability, and b) the importance of combining information-theoretic metrics of predictability and information derived from discriminative learning in modeling acoustic reduction.
Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior
Authors: Ahmad Halimi Razlighi, Carsten Bockelmann, Armin Dekorsy
Date: 2025-06-10 00:00:00
Tasks: Semantic Communication

In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into a common unit (CU) and multiple specific units (SUs), to a more applicable design. Our proposed system model addresses real-world constraints by introducing a general design that operates over rate-limited wireless channels. Further, we aim to tackle the rate-limit constraint, represented through the Kullback-Leibler (KL) divergence, by employing the density ratio trick alongside the implicit optimal prior method (IoPm). By applying the IoPm to our multi-task processing framework, we propose a hybrid learning approach that combines deep neural networks with kernelized-parametric machine learning methods, enabling a robust solution for the CMT-SemCom. Our framework is grounded in information-theoretic principles and employs variational approximations to bridge theoretical foundations with practical implementations. Simulation results demonstrate the proposed system's effectiveness in rate-constrained multi-task SemCom scenarios, highlighting its potential for enabling intelligence in next-generation wireless networks.
Variational Inference Optimized Using the Curved Geometry of Coupled Free Energy
Authors: Kenric Nelson, Igor Oliveira, Amenah Al-Najafi, Fode Zhang, Hon Keung Tony Ng
Date: 2025-06-10 00:00:00
Tasks: Variational Inference

We introduce an optimization framework for variational inference based on the coupled free energy, extending variational inference techniques to account for the curved geometry of the coupled exponential family. This family includes important heavy-tailed distributions such as the generalized Pareto and the Student's t. By leveraging the coupled free energy, which is equal to the coupled evidence lower bound (ELBO) of the inverted probabilities, we improve the accuracy and robustness of the learned model. The coupled generalization of Fisher Information metric and the affine connection. The method is applied to the design of a coupled variational autoencoder (CVAE). By using the coupling for both the distributions and cost functions, the reconstruction metric is derived to still be the mean-square average loss with modified constants. The novelty comes from sampling the heavy-tailed latent distribution with its associated coupled probability, which has faster decaying tails. The result is the ability to train a model robust against severe outliers, while assuring that the training process is stable. The Wasserstein-2 or Fr\'echet Inception Distance of the reconstructed CelebA images shows the CVAE has a 3\% improvement over the VAE after 5 epochs of training.
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
Authors: Firas Laakom, Haobo Chen, Jürgen Schmidhuber, Yuheng Bu
Date: 2025-06-09 00:00:00
Tasks: Fairness, Generalization Bounds

Despite substantial progress in promoting fairness in high-stake applications using machine learning models, existing methods often modify the training process, such as through regularizers or other interventions, but lack formal guarantees that fairness achieved during training will generalize to unseen data. Although overfitting with respect to prediction performance has been extensively studied, overfitting in terms of fairness loss has received far less attention. This paper proposes a theoretical framework for analyzing fairness generalization error through an information-theoretic lens. Our novel bounding technique is based on Efron-Stein inequality, which allows us to derive tight information-theoretic fairness generalization bounds with both Mutual Information (MI) and Conditional Mutual Information (CMI). Our empirical results validate the tightness and practical relevance of these bounds across diverse fairness-aware learning algorithms. Our framework offers valuable insights to guide the design of algorithms improving fairness generalization.
Convergence of linear programming hierarchies for Gibbs states of spin systems
Authors: Hamza Fawzi, Omar Fawzi
Date: 2025-06-06 00:00:00

We consider the problem of computing expectation values of local functions under the Gibbs distribution of a spin system. In particular, we study two families of linear programming hierarchies for this problem. The first hierarchy imposes local spin flip equalities and has been considered in the bootstrap literature in high energy physics. For this hierarchy, we prove fast convergence under a spatial mixing (decay of correlations) condition. This condition is satisfied for example above the critical temperature for Ising models on a $d$-dimensional grid. The second hierarchy is based on a Markov chain having the Gibbs state as a fixed point and has been studied in the optimization literature and more recently in the bootstrap literature. For this hierarchy, we prove fast convergence provided the Markov chain mixes rapidly. Both hierarchies lead to an $\varepsilon$-approximation for local expectation values using a linear program of size quasi-polynomial in $n/\varepsilon$, where $n$ is the total number of sites, provided the interactions can be embedded in a $d$-dimensional grid with constant $d$. Compared to standard Monte Carlo methods, an advantage of this approach is that it always (i.e., for any system) outputs rigorous upper and lower bounds on the expectation value of interest, without needing an a priori analysis of the convergence speed.
On Inverse Problems, Parameter Estimation, and Domain Generalization
Authors: Deborah Pereg
Date: 2025-06-06 00:00:00
Tasks: Deblurring, Domain Generalization, Image Deblurring, parameter estimation

Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular step in almost all physical applications, which is normally executed prior to downstream tasks that often involve parameter estimation. In this work, we analyze the general problem of parameter estimation in an inverse problem setting. First, we address the domain-shift problem by re-formulating it in direct relation with the discrete parameter estimation analysis. We analyze a significant vulnerability in current attempts to enforce domain generalization, which we dubbed the Double Meaning Theorem. Our theoretical findings are experimentally illustrated for domain shift examples in image deblurring and speckle suppression in medical imaging. We then proceed to a theoretical analysis of parameter estimation given observed measurements before and after data processing involving an inversion of the observations. We compare this setting for invertible and non-invertible (degradation) processes. We distinguish between continuous and discrete parameter estimation, corresponding with regression and classification problems, respectively. Our theoretical findings align with the well-known information-theoretic data processing inequality, and to a certain degree question the common misconception that data-processing for inversion, based on modern generative models that may often produce outstanding perceptual quality, will necessarily improve the following parameter estimation objective. It is our hope that this paper will provide practitioners with deeper insights that may be leveraged in the future for the development of more efficient and informed strategic system planning, critical in safety-sensitive applications.
Dual Approach to Inverse Covariance Intersection Fusion
Authors: Jiří Ajgl, Ondřej Straka
Date: 2025-06-06 00:00:00

Linear fusion of estimates under the condition of no knowledge of correlation of estimation errors has reached maturity. On the other hand, various cases of partial knowledge are still active research areas. A frequent motivation is to deal with "common information" or "common noise", whatever it means. A fusion rule for a strict meaning of the former expression has already been elaborated. Despite the dual relationship, a strict meaning of the latter one has not been considered so far. The paper focuses on this area. The assumption of unknown "common noise" is formulated first, analysis of theoretical properties and illustrations follow. Although the results are disappointing from the perspective of a single upper bound of mean square error matrices, the partial knowledge demonstrates improvement over no knowledge in suboptimal cases and from the perspective of families of upper bounds.
RSMA-Enabled Covert Communications Against Multiple Spatially Random Wardens
Authors: Xinyue Pei, Jihao Liu, Xuewen Luo, Xingwei Wang, Yingyang Chen, Miaowen Wen, Theodoros A. Tsiftsis
Date: 2025-06-06 00:00:00

This work investigates covert communication in a rate-splitting multiple access (RSMA)-based multi-user multiple-input single-output system, where the random locations of the wardens follow a homogeneous Poisson point process. To demonstrate practical deployment scenarios, imperfect channel state information at the transmitter is considered. Closed-form expressions for the statistics of the received signal-to-interference-plus-noise ratio, along with the analytical formulations for the covertness constraint, outage probability, and effective covert throughput (ECT), are derived. Subsequently, an ECT maximization problem is formulated under covertness and power allocation constraints. This optimization problem is addressed using an alternating optimization-assisted genetic algorithm (AO-GA). Simulation results corroborate the theoretical analysis and demonstrate the superiority of RSMA over conventional multiple access schemes, as well as the effectiveness of the proposed AO-GA.
Latent Diffusion Model Based Denoising Receiver for 6G Semantic Communication: From Stochastic Differential Theory to Application
Authors: Xiucheng Wang, Honggang Jia, Nan Cheng, Dusit Niyato
Date: 2025-06-06 00:00:00
Tasks: Denoising, Semantic Communication, Semantic Compression, Zero-shot Generalization

In this paper, a novel semantic communication framework empowered by generative artificial intelligence (GAI) is proposed, to enhance the robustness against both channel noise and transmission data distribution shifts. A theoretical foundation is established using stochastic differential equations (SDEs), from which a closed-form mapping between any signal-to-noise ratio (SNR) and the optimal denoising timestep is derived. Moreover, to address distribution mismatch, a mathematical scaling method is introduced to align received semantic features with the training distribution of the GAI. Built on this theoretical foundation, a latent diffusion model (LDM)-based semantic communication framework is proposed that combines a variational autoencoder for semantic features extraction, where a pretrained diffusion model is used for denoising. The proposed system is a training-free framework that supports zero-shot generalization, and achieves superior performance under low-SNR and out-of-distribution conditions, offering a scalable and robust solution for future 6G semantic communication systems. Experimental results demonstrate that the proposed semantic communication framework achieves state-of-the-art performance in both pixel-level accuracy and semantic perceptual quality, consistently outperforming baselines across a wide range of SNRs and data distributions without any fine-tuning or post-training.
Model-based Neural Data Augmentation for sub-wavelength Radio Localization
Authors: Baptiste Chatelier, Vincent Corlay, Musa Furkan Keskin, Matthieu Crussière, Henk Wymeersch, Luc Le Magoarou
Date: 2025-06-05 00:00:00
Tasks: Data Augmentation

The increasing deployment of large antenna arrays at base stations has significantly improved the spatial resolution and localization accuracy of radio-localization methods. However, traditional signal processing techniques struggle in complex radio environments, particularly in scenarios dominated by non line of sight (NLoS) propagation paths, resulting in degraded localization accuracy. Recent developments in machine learning have facilitated the development of machine learning-assisted localization techniques, enhancing localization accuracy in complex radio environments. However, these methods often involve substantial computational complexity during both the training and inference phases. This work extends the well-established fingerprinting-based localization framework by simultaneously reducing its memory requirements and improving its accuracy. Specifically, a model-based neural network is used to learn the location-to-channel mapping, and then serves as a generative neural channel model. This generative model augments the fingerprinting comparison dictionary while reducing the memory requirements. The proposed method outperforms fingerprinting baselines by achieving sub-wavelength localization accuracy, even in NLoS environments. Remarkably, it offers an improvement by several orders of magnitude in localization accuracy, while simultaneously reducing memory requirements by an order of magnitude compared to classical fingerprinting methods.
Cascaded Multiwire-PLC/Multiple-VLC System: Characterization and Performance
Authors: Hugerles S. Silva, Higo T. P. Silva, Paulo V. B. Tomé, Felipe A. P. Figueiredo, Edson P. da Silva, Rausley A. A. de Souza
Date: 2025-06-03 00:00:00

This paper proposes a cascaded multiwire-power line communication (PLC)/multiple-visible light communication (VLC) system. This hybrid architecture offers low installation cost, enhanced performance, practical feasibility, and a wide range of applications. Novel analytical expressions are derived for key statistics and outage probability, bit error probability, and ergodic channel capacity metrics. Furthermore, the analytical results are validated through Monte Carlo simulations, with several performance curves presented under various channel and PLC/VLC system parameters. All expressions derived in this work are original and have not been previously published. Our proposed system proves feasible for smart environments, green communication systems, internet of things networks, industrial environments, and next-generation networks.
High-gain MIMO Beamforming Antenna System for DSRC and mmwave 5G Integration in Autonomous Vehicles
Authors: Mohammad Shahed Pervez, Amanpreet Kaur
Date: 2025-06-02 00:00:00
Tasks: Autonomous Vehicles

The evolution of autonomous vehicles necessitates robust, high-speed, and low-latency wireless communication systems. This paper presents a novel high-gain Multiple-Input Multiple-Output (MIMO) beamforming antenna system that concurrently supports Dedicated Short Range Communications (DSRC) at 5.9 GHz and millimeter-wave (mm Wave) 5G communications at 28 GHz. The proposed design addresses challenges such as compactness, dual-band operation, beam steering capability, and port-to-port isolation within dynamic vehicular environments.
BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
Authors: Taesoo Park, Mungwi Jeong, MinGyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, SoonChul Kwon
Date: 2025-06-11 00:00:00
Tasks: Audio Generation, FAD, SSIM

This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discriminator framework, we integrate the Multi-Envelope Discriminator (MED), a novel architecture we originally proposed, to extract rich temporal envelope features crucial for periodicity detection. Coupled with the Multi-Resolution Discriminator (MRD), this combination enables more accurate modeling of long-range dependencies in audio. We systematically evaluate various discriminator configurations, including MSD + MED, MSD + MRD, and MPD + MED + MRD, using objective metrics (FAD, SSIM, PLCC, MCD) and subjective evaluations (MOS, SMOS). This paper also provides a comprehensive tutorial on the model architecture, training methodology, and implementation to promote reproducibility. The code and pre-trained models are available at: https://github.com/dinhoitt/BemaGANv2.
Abstraction-Based Proof Production in Formal Verification of Neural Networks
Authors: Yizhak Yisrael Elboher, Omri Isac, Guy Katz, Tobias Ladner, Haoze Wu
Date: 2025-06-11 00:00:00

Modern verification tools for deep neural networks (DNNs) increasingly rely on abstraction to scale to realistic architectures. In parallel, proof production is becoming a critical requirement for increasing the reliability of DNN verification results. However, current proofproducing verifiers do not support abstraction-based reasoning, creating a gap between scalability and provable guarantees. We address this gap by introducing a novel framework for proof-producing abstraction-based DNN verification. Our approach modularly separates the verification task into two components: (i) proving the correctness of an abstract network, and (ii) proving the soundness of the abstraction with respect to the original DNN. The former can be handled by existing proof-producing verifiers, whereas we propose the first method for generating formal proofs for the latter. This preliminary work aims to enable scalable and trustworthy verification by supporting common abstraction techniques within a formal proof framework.
Premise Selection for a Lean Hammer
Authors: Thomas Zhu, Joshua Clune, Jeremy Avigad, Albert Qiaochu Jiang, Sean Welleck
Date: 2025-06-09 00:00:00

Neural methods are transforming automated reasoning for proof assistants, yet integrating these advances into practical verification workflows remains challenging. Hammers are tools that interface with external automatic theorem provers to automate tedious reasoning steps. They have dramatically improved productivity in proof assistants, but the Lean proof assistant still does not have a hammer despite its growing popularity. We present LeanHammer, the first end-to-end domain-general hammer for Lean, built on a novel neural premise selection system for a hammer in dependent type theory. Unlike existing Lean premise selectors, our approach dynamically adapts to user-specific contexts and combines with symbolic proof search and reconstruction to create a practical hammer. With comprehensive evaluations, we show that our premise selector enables LeanHammer to solve 21\% more goals relative to existing premise selectors, and generalize well to diverse domains. Our work bridges the gap between neural retrieval and symbolic reasoning, making formal verification more accessible to researchers and practitioners.