pwc-archive/papers-with-abstracts huggingface dataset:
Toward Scalable Access to Neurodevelopmental Screening: Insights, Implementation, and Challenges
Authors: Andreas Bauer, William Bosl, Oliver Aalami, Paul Schmiedmayer
Date: 2025-03-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Children with neurodevelopmental disorders require timely intervention to improve long-term outcomes, yet early screening remains inaccessible in many regions. A scalable solution integrating standardized assessments with physiological data collection, such as electroencephalogram (EEG) recordings, could enable early detection in routine settings by non-specialists. To address this, we introduce NeuroNest, a mobile and cloud-based platform for large-scale EEG data collection, neurodevelopmental screening, and research. We provide a comprehensive review of existing behavioral and biomarker-based approaches, consumer-grade EEG devices, and emerging machine learning techniques. NeuroNest integrates low-cost EEG devices with digital screening tools, establishing a scalable, open-source infrastructure for non-invasive data collection, automated analysis, and interoperability across diverse hardware. Beyond the system architecture and reference implementation, we highlight key challenges in EEG data standardization, device interoperability, and bridging behavioral and physiological assessments. Our findings emphasize the need for future research on standardized data exchange, algorithm validation, and ecosystem development to expand screening accessibility. By providing an extensible, open-source system, NeuroNest advances machine learning-based early detection while fostering collaboration in screening technologies, clinical applications, and public health.
Medical Support System for Spontaneous Breathing Trial Prediction Using Nonuniform Discrete Fourier Transform
Authors: Hernando Gonzalez, Carlos Julio Arizmendi, Beatriz F. Giraldo
Date: 2025-03-03 00:00:00
Tasks: Decision Making

Spontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success or failure of SBT remains a significant challenge in clinical practice. This study proposes a healthcare system that employs machine learning techniques to predict the outcome of SBT. The model is trained on respiratory flow and electrocardiogram (ECG) signals, employing the non-uniform discrete Fourier transform (NUDFT) for frequency domain analysis. The SBT prediction model has the potential to significantly enhance clinical decision-making by enabling the early identification of patients at risk for SBT failure, achieving an accuracy of 84.4.
Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment
Authors: Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi, Wenrui Fan, Samer Alabed, Bishesh Khanal, Venet Osmani, Andrew J. Swift, Chen, Haiping Lu
Date: 2025-03-03 00:00:00
Tasks: Diagnostic

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagnostic tools. Several studies in PH have applied machine learning to low-cost diagnostic tools like 12-lead ECG (12L-ECG), but they mainly focus on areas with limited resources, overlooking areas with no diagnostic tools, such as rural primary healthcare in LMICs. Recent studies have shown the effectiveness of 6-lead ECG (6L-ECG), as a cheaper and portable alternative in detecting various cardiac conditions, but its clinical value for PH detection is not well proved. Furthermore, existing methods treat 12L-/6L-ECG as a single modality, capturing only shared features while overlooking lead-specific features essential for identifying complex cardiac hemodynamic changes. In this paper, we propose Lead-Specific Electrocardiogram Multimodal Variational Autoencoder (LS-EMVAE), a model pre-trained on large-population 12L-ECG data and fine-tuned on task-specific data (12L-ECG or 6L-ECG). LS-EMVAE models each 12L-ECG lead as a separate modality and introduces a hierarchical expert composition using Mixture and Product of Experts for adaptive latent feature fusion between lead-specific and shared features. Unlike existing approaches, LS-EMVAE makes better predictions on both 12L-ECG and 6L-ECG at inference, making it an equitable solution for areas with limited or no diagnostic tools. We pre-trained LS-EMVAE on 800,000 publicly available 12L-ECG samples and fine-tuned it for two tasks: 1) PH detection and 2) phenotyping pre-/post-capillary PH, on in-house datasets of 892 and 691 subjects across 12L-ECG and 6L-ECG settings. Extensive experiments show that LS-EMVAE outperforms existing baselines in both ECG settings, while 6L-ECG achieves performance comparable to 12L-ECG, unlocking its potential for global PH screening in areas without diagnostic tools.
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders
Authors: Ivan Sviridov, Konstantin Egorov
Date: 2025-03-03 00:00:00
Tasks: Transfer Learning

Cardiovascular diseases (CVDs) are disorders impacting the heart and circulatory system. These disorders are the foremost and continuously escalating cause of mortality worldwide. One of the main tasks when working with CVDs is analyzing and identifying pathologies on a 12-lead electrocardiogram (ECG) with a standard 10-second duration. Using machine learning (ML) in automatic ECG analysis increases CVD diagnostics' availability, speed, and accuracy. However, the most significant difficulty in developing ML models is obtaining a sufficient training dataset. Due to the limitations of medical data usage, such as expensiveness, errors, the ambiguity of labels, imbalance of classes, and privacy issues, utilizing synthetic samples depending on specific pathologies bypasses these restrictions and improves algorithm quality. Existing solutions for the conditional generation of ECG signals are mainly built on Generative Adversarial Networks (GANs), and only a few papers consider the architectures based on Variational Autoencoders (VAEs), showing comparable results in recent works. This paper proposes the publicly available conditional Nouveau VAE model for ECG signal generation (cNVAE-ECG), which produces high-resolution ECGs with multiple pathologies. We provide an extensive comparison of the proposed model on various practical downstream tasks, including transfer learning scenarios showing an area under the receiver operating characteristic (AUROC) increase up to 2% surpassing GAN-like competitors.
A CGAN-LSTM-Based Framework for Time-Varying Non-Stationary Channel Modeling
Authors: Keying Guo, Ruisi He, Mi Yang, Yuxin Zhang, Bo Ai, Haoxiang Zhang, Jiahui Han, Ruifeng Chen
Date: 2025-03-03 00:00:00

Time-varying non-stationary channels, with complex dynamic variations and temporal evolution characteristics, have significant challenges in channel modeling and communication system performance evaluation. Most existing methods of time-varying channel modeling focus on predicting channel state at a given moment or simulating short-term channel fluctuations, which are unable to capture the long-term evolution of the channel. This paper emphasizes the generation of long-term dynamic channel to fully capture evolution of non-stationary channel properties. The generated channel not only reflects temporal dynamics but also ensures consistent stationarity. We propose a hybrid deep learning framework that combines conditional generative adversarial networks (CGAN) with long short-term memory (LSTM) networks. A stationarity-constrained approach is designed to ensure temporal correlation of the generated time-series channel. This method can generate channel with required temporal non-stationarity. The model is validated by comparing channel statistical features, and the results show that the generated channel is in good agreement with raw channel and provides good performance in terms of non-stationarity.
A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
Authors: Jinfeng Wang, Yanhao Huang, Sifan Song, Boqian Wang, Jionglong Su, Jiaman Ding
Date: 2025-02-28 00:00:00
Tasks: EEG, EEG Emotion Recognition, Emotion Classification, Emotion Recognition

EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion classification. In this study, we present the Fourier Adjacency Transformer, a novel framework that seamlessly integrates Fourier-based periodic analysis with graph-driven structural modeling. Our method first leverages novel Fourier-inspired modules to extract periodic features from embedded EEG signals, effectively decoupling them from aperiodic components. Subsequently, we employ an adjacency attention scheme to reinforce universal inter-channel correlation patterns, coupling these patterns with their sample-based counterparts. Empirical evaluations on SEED and DEAP datasets demonstrate that our method surpasses existing state-of-the-art techniques, achieving an improvement of approximately 6.5% in recognition accuracy. By unifying periodicity and structural insights, this framework offers a promising direction for future research in EEG emotion analysis.
Recursive Self-Similarity in Deep Weight Spaces of Neural Architectures: A Fractal and Coarse Geometry Perspective
Authors: Ambarish Moharil, Indika Kumara, Damian Andrew Tamburri, Majid Mohammadi, Willem-Jan van den Heuvel
Date: 2025-03-18 00:00:00

This paper conceptualizes the Deep Weight Spaces (DWS) of neural architectures as hierarchical, fractal-like, coarse geometric structures observable at discrete integer scales through recursive dilation. We introduce a coarse group action termed the fractal transformation, $T_{r_k} $, acting under the symmetry group $G = (\mathbb{Z}, +) $, to analyze neural parameter matrices or tensors, by segmenting the underlying discrete grid $\Omega$ into $N(r_k)$ fractals across varying observation scales $ r_k $. This perspective adopts a box count technique, commonly used to assess the hierarchical and scale-related geometry of physical structures, which has been extensively formalized under the topic of fractal geometry. We assess the structural complexity of neural layers by estimating the Hausdorff-Besicovitch dimension of their layers and evaluating a degree of self-similarity. The fractal transformation features key algebraic properties such as linearity, identity, and asymptotic invertibility, which is a signature of coarse structures. We show that the coarse group action exhibits a set of symmetries such as Discrete Scale Invariance (DSI) under recursive dilation, strong invariance followed by weak equivariance to permutations, alongside respecting the scaling equivariance of activation functions, defined by the intertwiner group relations. Our framework targets large-scale structural properties of DWS, deliberately overlooking minor inconsistencies to focus on significant geometric characteristics of neural networks. Experiments on CIFAR-10 using ResNet-18, VGG-16, and a custom CNN validate our approach, demonstrating effective fractal segmentation and structural analysis.
The Relativity of Causal Knowledge
Authors: Gabriele D'Acunto, Claudio Battiloro
Date: 2025-03-13 00:00:00

Recent advances in artificial intelligence reveal the limits of purely predictive systems and call for a shift toward causal and collaborative reasoning. Drawing inspiration from the revolution of Grothendieck in mathematics, we introduce the relativity of causal knowledge, which posits structural causal models (SCMs) are inherently imperfect, subjective representations embedded within networks of relationships. By leveraging category theory, we arrange SCMs into a functor category and show that their observational and interventional probability measures naturally form convex structures. This result allows us to encode non-intervened SCMs with convex spaces of probability measures. Next, using sheaf theory, we construct the network sheaf and cosheaf of causal knowledge. These structures enable the transfer of causal knowledge across the network while incorporating interventional consistency and the perspective of the subjects, ultimately leading to the formal, mathematical definition of relative causal knowledge.
Cross-Modal Diffusion for Biomechanical Dynamical Systems Through Local Manifold Alignment
Authors: Sharmita Dey, Sarath Ravindran Nair
Date: 2025-03-15 00:00:00
Tasks: Motion Generation

We present a mutually aligned diffusion framework for cross-modal biomechanical motion generation, guided by a dynamical systems perspective. By treating each modality, e.g., observed joint angles ($X$) and ground reaction forces ($Y$), as complementary observations of a shared underlying locomotor dynamical system, our method aligns latent representations at each diffusion step, so that one modality can help denoise and disambiguate the other. Our alignment approach is motivated by the fact that local time windows of $X$ and $Y$ represent the same phase of an underlying dynamical system, thereby benefiting from a shared latent manifold. We introduce a simple local latent manifold alignment (LLMA) strategy that incorporates first-order and second-order alignment within the latent space for robust cross-modal biomechanical generation without bells and whistles. Through experiments on multimodal human biomechanics data, we show that aligning local latent dynamics across modalities improves generation fidelity and yields better representations.
Integrating Product Coefficients for Improved 3D LiDAR Data Classification
Authors: Patricia Medina
Date: 2025-03-15 00:00:00
Tasks: Classification

In this paper, we address the enhancement of classification accuracy for 3D point cloud Lidar data, an optical remote sensing technique that estimates the three-dimensional coordinates of a given terrain. Our approach introduces product coefficients, theoretical quantities derived from measure theory, as additional features in the classification process. We define and present the formulation of these product coefficients and conduct a comparative study, using them alongside principal component analysis (PCA) as feature inputs. Results demonstrate that incorporating product coefficients into the feature set significantly improves classification accuracy within this new framework.
Algebraic Adversarial Attacks on Explainability Models
Authors: Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew
Date: 2025-03-16 00:00:00

Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated. In this work, we propose an algebraic approach to adversarial attacks and study the conditions under which one can generate adversarial examples for post-hoc explainability models. Phrasing neural networks in the framework of geometric deep learning, algebraic adversarial attacks are constructed through analysis of the symmetry groups of neural networks. Algebraic adversarial examples provide a mathematically tractable approach to adversarial examples. We validate our approach of algebraic adversarial examples on two well-known and one real-world dataset.
Linear-Size Neural Network Representation of Piecewise Affine Functions in $\mathbb{R}^2$
Authors: Leo Zanotti
Date: 2025-03-17 00:00:00

It is shown that any continuous piecewise affine (CPA) function $\mathbb{R}^2\to\mathbb{R}$ with $p$ pieces can be represented by a ReLU neural network with two hidden layers and $O(p)$ neurons. Unlike prior work, which focused on convex pieces, this analysis considers CPA functions with connected but potentially non-convex pieces.
A Relaxed Wasserstein Distance Formulation for Mixtures of Radially Contoured Distributions
Authors: Keyu Chen, Zetian Wang, Yunxin Zhang
Date: 2025-03-18 00:00:00

Recently, a Wasserstein-type distance for Gaussian mixture models has been proposed. However, that framework can only be generalized to identifiable mixtures of general elliptically contoured distributions whose components come from the same family and satisfy marginal consistency. In this paper, we propose a simple relaxed Wasserstein distance for identifiable mixtures of radially contoured distributions whose components can come from different families. We show some properties of this distance and that its definition does not require marginal consistency. We apply this distance in color transfer tasks and compare its performance with the Wasserstein-type distance for Gaussian mixture models in an experiment. The error of our method is more stable and the color distribution of our output image is more desirable.
Statistical Inference for Weighted Sample Average Approximation in Contextual Stochastic Optimization
Authors: Yanyuan Wang, Xiaowei Zhang
Date: 2025-03-17 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Stochastic Optimization, valid

Contextual stochastic optimization provides a framework for decision-making under uncertainty incorporating observable contextual information through covariates. We analyze statistical inference for weighted sample average approximation (wSAA), a widely-used method for solving contextual stochastic optimization problems. We first establish central limit theorems for wSAA estimates of optimal values when problems can be solved exactly, characterizing how estimation uncertainty scales with covariate sample size. We then investigate practical scenarios with computational budget constraints, revealing a fundamental tradeoff between statistical accuracy and computational cost as sample sizes increase. Through central limit theorems for budget-constrained wSAA estimates, we precisely characterize this statistical-computational tradeoff. We also develop "over-optimizing" strategies for solving wSAA problems that ensure valid statistical inference. Extensive numerical experiments on both synthetic and real-world datasets validate our theoretical findings.
Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization
Authors: Dmitry Kovalev
Date: 2025-03-16 00:00:00

Optimization with matrix gradient orthogonalization has recently demonstrated impressive results in the training of deep neural networks (Jordan et al., 2024; Liu et al., 2025). In this paper, we provide a theoretical analysis of this approach. In particular, we show that the orthogonalized gradient method can be seen as a first-order trust-region optimization method, where the trust-region is defined in terms of the matrix spectral norm. Motivated by this observation, we develop the stochastic non-Euclidean trust-region gradient method with momentum, which recovers the Muon optimizer (Jordan et al., 2024) as a special case, along with normalized SGD and signSGD with momentum (Cutkosky and Mehta, 2020; Sun et al., 2023). In addition, we prove state-of-the-art convergence results for the proposed algorithm in a range of scenarios, which involve arbitrary non-Euclidean norms, constrained and composite problems, and non-convex, star-convex, first- and second-order smooth functions. Finally, our theoretical findings provide an explanation for several practical observations, including the practical superiority of Muon compared to the Orthogonal-SGDM algorithm of Tuddenham et al. (2022) and the importance of weight decay in the training of large-scale language models.
Fast filtering of non-Gaussian models using Amortized Optimal Transport Maps
Authors: Mohammad Al-jarrah, Bamdad Hosseini, Amirhossein Taghvaei
Date: 2025-03-16 00:00:00
Tasks: Mixture-of-Experts

In this paper, we present the amortized optimal transport filter (A-OTF) designed to mitigate the computational burden associated with the real-time training of optimal transport filters (OTFs). OTFs can perform accurate non-Gaussian Bayesian updates in the filtering procedure, but they require training at every time step, which makes them expensive. The proposed A-OTF framework exploits the similarity between OTF maps during an initial/offline training stage in order to reduce the cost of inference during online calculations. More precisely, we use clustering algorithms to select relevant subsets of pre-trained maps whose weighted average is used to compute the A-OTF model akin to a mixture of experts. A series of numerical experiments validate that A-OTF achieves substantial computational savings during online inference while preserving the inherent flexibility and accuracy of OTF.
Towards Optimal Offline Reinforcement Learning
Authors: Mengmeng Li, Daniel Kuhn, Tobias Sutter
Date: 2025-03-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

We study offline reinforcement learning problems with a long-run average reward objective. The state-action pairs generated by any fixed behavioral policy thus follow a Markov chain, and the {\em empirical} state-action-next-state distribution satisfies a large deviations principle. We use the rate function of this large deviations principle to construct an uncertainty set for the unknown {\em true} state-action-next-state distribution. We also construct a distribution shift transformation that maps any distribution in this uncertainty set to a state-action-next-state distribution of the Markov chain generated by a fixed evaluation policy, which may differ from the unknown behavioral policy. We prove that the worst-case average reward of the evaluation policy with respect to all distributions in the shifted uncertainty set provides, in a rigorous statistical sense, the least conservative estimator for the average reward under the unknown true distribution. This guarantee is available even if one has only access to one single trajectory of serially correlated state-action pairs. The emerging robust optimization problem can be viewed as a robust Markov decision process with a non-rectangular uncertainty set. We adapt an efficient policy gradient algorithm to solve this problem. Numerical experiments show that our methods compare favorably against state-of-the-art methods.
Mixed-feature Logistic Regression Robust to Distribution Shifts
Authors: Qingshi Sun, Nathan Justin, Andres Gomez, Phebe Vayanos
Date: 2025-03-15 00:00:00
Tasks: regression

Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same time, such domains are permeated by distribution shifts, where the distribution generating the data changes between training and deployment. In this paper, we study a distributionally robust logistic regression problem that seeks the model that will perform best against adversarial realizations of the data distribution drawn from a suitably constructed Wasserstein ambiguity set. Our model and solution approach differ from prior work in that we can capture settings where the likelihood of distribution shifts can vary across features, significantly broadening the applicability of our model relative to the state-of-the-art. We propose a graph-based solution approach that can be integrated into off-the-shelf optimization solvers. We evaluate the performance of our model and algorithms on numerous publicly available datasets. Our solution achieves a 408x speed-up relative to the state-of-the-art. Additionally, compared to the state-of-the-art, our model reduces average calibration error by up to 36.19% and worst-case calibration error by up to 41.70%, while increasing the average area under the ROC curve (AUC) by up to 18.02% and worst-case AUC by up to 48.37%.
Adaptive Stochastic Gradient Descents on Manifolds with an Application on Weighted Low-Rank Approximation
Authors: Peiqi Yang, Conglong Xu, Hao Wu
Date: 2025-03-14 00:00:00

We prove a convergence theorem for stochastic gradient descents on manifolds with adaptive learning rate and apply it to the weighted low-rank approximation problem.