pwc-archive/papers-with-abstracts huggingface dataset:
Noise equals endogenous control
Authors: Eric De Giuli
Date: 2025-03-19 00:00:00

Stochastic systems have a control-theoretic interpretation in which noise plays the role of endogenous control. In the weak-noise limit, relevant at low temperatures or in large populations, control is optimal and an exact mathematical mapping from noise to control is described, where the maximizing the probability of a state becomes the control objective. In Langevin dynamics noise is identified directly with control, while in general Markov jump processes, which include chemical reaction networks and electronic circuits, we use the Doi-Zel'dovich-Grassberger-Goldenfeld-Peliti path integral to identify the `response' or `tilt' field $\pi$ as control, which is proportional to the noise in the semiclassical limit. This solves the longstanding problem of interpreting $\pi$. We illustrate the mapping on multistable chemical reaction networks and systems with unstable fixed points. The noise-control mapping builds intuition for otherwise puzzling phenomena of stochastic systems: why the probability is generically a non-smooth function of state out of thermal equilibrium; why biological mechanisms can work better in the presence of noise; and how agentic behavior emerges naturally without recourse to mysticism.
Machine learning identifies nullclines in oscillatory dynamical systems
Authors: Bartosz Prokop, Jimmy Billen, Nikita Frolov, Lendert Gelens
Date: 2025-03-20 00:00:00
Tasks: Time Series

We introduce CLINE (Computational Learning and Identification of Nullclines), a neural network-based method that uncovers the hidden structure of nullclines from oscillatory time series data. Unlike traditional approaches aiming at direct prediction of system dynamics, CLINE identifies static geometric features of the phase space that encode the (non)linear relationships between state variables. It overcomes challenges such as multiple time scales and strong nonlinearities while producing interpretable results convertible into symbolic differential equations. We validate CLINE on various oscillatory systems, showcasing its effectiveness.
Low Dimensional Dynamics of Globally Coupled Complex Riccati Equations: Exact Firing-rate Equations for Spiking Neurons with Clustered Substructure
Authors: Diego Pazó, Rok Cestnik
Date: 2025-03-05 00:00:00
Tasks: Dimensionality Reduction

We report on an exact theory for ensembles of globally coupled, heterogeneous complex Riccati equations. A drastic dimensionality reduction to a few ordinary differential equations is achieved for Lorentzian heterogeneity. By applying this technique, we obtain low-dimensional firing-rate equations for populations of spiking neurons with a clustered substructure.
Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings
Authors: Jonathan D. Schultz, Kelsey A. Parker, Bashir Sbaiti, David N. Beratan
Date: 2025-03-19 00:00:00
Tasks: Experimental Design

Two-dimensional electronic spectroscopy (2DES) has enabled significant discoveries in both biological and synthetic energy-transducing systems. Although deriving chemical information from 2DES is a complex task, machine learning (ML) offers exciting opportunities to translate complicated spectroscopic data into physical insight. Recent studies have found that neural networks (NNs) can map simulated multidimensional spectra to molecular-scale properties with high accuracy. However, simulations often do not capture experimental factors that influence real spectra, including noise and suboptimal pulse resonance conditions, bringing into question the experimental utility of NNs trained on simulated data. Here, we show how factors associated with experimental 2D spectral data influence the ability of NNs to map simulated 2DES spectra onto underlying intermolecular electronic couplings. By systematically introducing multisourced noise into a library of 356000 simulated 2D spectra, we show that noise does not hamper NN performance for spectra exceeding threshold signal-to-noise ratios (SNR) (> 6.6 if background noise dominates vs. > 2.5 for intensity-dependent noise). In stark contrast to human-based analyses of 2DES data, we find that the NN accuracy improves significantly (ca. 84% $\rightarrow$ 96%) when the data are constrained by the bandwidth and center frequency of the pump pulses. This result is consistent with the NN learning the optical trends described by Kasha's theory of molecular excitons. Our findings convey positive prospects for adapting simulation-trained NNs to extract molecular properties from inherently imperfect experimental 2DES data. More broadly, we propose that machine-learned perspectives of nonlinear spectroscopic data may produce unique and, perhaps, counterintuitive guidelines for experimental design.
Machine learning surrogate models of many-body dispersion interactions in polymer melts
Authors: Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz, Alexandre Tkatchenko, Stéphane P. A. Bordas
Date: 2025-03-19 00:00:00
Tasks: Computational Efficiency

Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations.
Hyperspectral Unmixing using Iterative, Sparse and Ensambling Approaches for Large Spectral Libraries Applied to Soils and Minerals
Authors: Jade Preston, William Basener
Date: 2025-03-20 00:00:00
Tasks: feature selection, Hyperspectral Unmixing, regression

Unmixing is a fundamental process in hyperspectral image processing in which the materials present in a mixed pixel are determined based on the spectra of candidate materials and the pixel spectrum. Practical and general utility requires a large spectral library with sample measurements covering the full variation in each candidate material as well as a sufficiently varied collection of potential materials. However, any spectral library with more spectra than bands will lead to an ill-posed inversion problem when using classical least-squares regression-based unmixing methods. Moreover, for numerical and dimensionality reasons, libraries with over 10 or 20 spectra behave computationally as though they are ill-posed. In current practice, unmixing is often applied to imagery using manually-selected materials or image endmembers. General unmixing of a spectrum from an unknown material with a large spectral library requires some form of sparse regression; regression where only a small number of coefficients are nonzero. This requires a trade-off between goodness-of-fit and model size. In this study we compare variations of two sparse regression techniques, focusing on the relationship between structure and chemistry of materials and the accuracy of the various models for identifying the correct mixture of materials present. Specifically, we examine LASSO regression and ElasticNet in contrast with variations of iterative feature selection, Bayesian Model Averaging (BMA), and quadratic BMA (BMA-Q) -- incorporating LASSO regression and ElasticNet as their base model. To evaluate the the effectiveness of these methods, we consider the molecular composition similarities and differences of substances selected in the models compared to the ground truth.
Using machine learning to measure evidence of students' sensemaking in physics courses
Authors: Kaitlin Gili, Kyle Heuton, Astha Shah, Michael C. Hughes
Date: 2025-03-19 00:00:00

In the education system, problem-solving correctness is often inappropriately conflated with student learning. Advances in both Physics Education Research (PER) and Machine Learning (ML) provide the initial tools to develop a more meaningful and efficient measurement scheme for whether physics students are engaging in sensemaking: a learning process of figuring out the how and why for a particular phenomena. In this work, we contribute such a measurement scheme, which quantifies the evidence of students' physical sensemaking given their written explanations for their solutions to physics problems. We outline how the proposed human annotation scheme can be automated into a deployable ML model using language encoders and shared probabilistic classifiers. The procedure is scalable for a large number of problems and students. We implement three unique language encoders with logistic regression, and provide a deployability analysis on 385 real student explanations from the 2023 Introduction to Physics course at Tufts University. Furthermore, we compute sensemaking scores for all students, and analyze these measurements alongside their corresponding problem-solving accuracies. We find no linear relationship between these two variables, supporting the hypothesis that one is not a reliable proxy for the other. We discuss how sensemaking scores can be used alongside problem-solving accuracies to provide a more nuanced snapshot of student performance in physics class.
Accelerating Transient CFD through Machine Learning-Based Flow Initialization
Authors: Peter Sharpe, Rishikesh Ranade, Kaustubh Tangsali, Mohammad Amin Nabian, Ram Cherukuri, Sanjay Choudhry
Date: 2025-03-20 00:00:00

Transient computational fluid dynamics (CFD) simulations are essential for many industrial applications, but suffer from high compute costs relative to steady-state simulations. This is due to the need to: (a) reach statistical steadiness by physically advecting errors in the initial field sufficiently far downstream, and (b) gather a sufficient sample of fluctuating flow data to estimate time-averaged quantities of interest. We present a machine learning-based initialization method that aims to reduce the cost of transient solve by providing more accurate initial fields. Through a case study in automotive aerodynamics on a 17M-cell unsteady incompressible RANS simulation, we evaluate three proposed ML-based initialization strategies against existing methods. Here, we demonstrate 50% reductions in time-to-convergence compared to traditional uniform and potential flow-based initializations. Two ML-based initialization strategies are recommended for general use: (1) a hybrid method combining ML predictions with potential flow solutions, and (2) an approach integrating ML predictions with uniform flow. Both strategies enable CFD solvers to achieve convergence times comparable to computationally-expensive steady RANS initializations, while requiring far less wall-clock time to compute the initialization field. Notably, these improvements are achieved using an ML model trained on a different dataset of diverse automotive geometries, demonstrating generalization capabilities relevant to specific industrial application areas. Because this Hybrid-ML workflow only modifies the inputs to an existing CFD solver, rather than modifying the solver itself, it can be applied to existing CFD workflows with relatively minimal changes; this provides a practical approach to accelerating industrial CFD simulations using existing ML surrogate models.
Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings
Authors: Rahul Sundar, Didier Lucor, Sunetra Sarkar
Date: 2025-03-19 00:00:00
Tasks: Transfer Learning

For a data-driven and physics combined modelling of unsteady flow systems with moving immersed boundaries, Sundar {\it et al.} introduced an immersed boundary-aware (IBA) framework, combining Physics-Informed Neural Networks (PINNs) and the immersed boundary method (IBM). This approach was beneficial because it avoided case-specific transformations to a body-attached reference frame. Building on this, we now address the challenges of long time integration in velocity reconstruction and pressure recovery by extending this IBA framework with sequential learning strategies. Key difficulties for PINNs in long time integration include temporal sparsity, long temporal domains and rich spectral content. To tackle these, a moving boundary-enabled PINN is developed, proposing two sequential learning strategies: - a time marching with gradual increase in time domain size, however, this approach struggles with error accumulation over long time domains; and - a time decomposition which divides the temporal domain into smaller segments, combined with transfer learning it effectively reduces error propagation and computational complexity. The key findings for modelling of incompressible unsteady flows past a flapping airfoil include: - for quasi-periodic flows, the time decomposition approach with preferential spatio-temporal sampling improves accuracy and efficiency for pressure recovery and aerodynamic load reconstruction, and, - for long time domains, decomposing it into smaller temporal segments and employing multiple sub-networks, simplifies the problem ensuring stability and reduced network sizes. This study highlights the limitations of traditional PINNs for long time integration of flow-structure interaction problems and demonstrates the benefits of decomposition-based strategies for addressing error accumulation, computational cost, and complex dynamics.
Ambient Noise Full Waveform Inversion with Neural Operators
Authors: Caifeng Zou, Zachary E. Ross, Robert W. Clayton, Fan-Chi Lin, Kamyar Azizzadenesheli
Date: 2025-03-19 00:00:00

Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end-to-end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint-state method. Since neural operators do not involve the Born approximation, when used for full waveform inversion they have the potential to include additional phases and alleviate cycle-skipping problems present in traditional adjoint-state formulations. In this study, we demonstrate the first application of neural operators for full waveform inversion on a real seismic dataset, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.
Implantable CMOS probes for high resolution Electrical Imaging of Local Field Potentials Across the Rat Barrel Cortex in vivo
Authors: Claudia Cecchetto, Mufti Mahmud, Vincenzo Sorrenti, Marta Maschietto, Enrico Chiarello, Mathias Schulz, Roland Thewes, Stefano Vassanelli
Date: 2025-03-19 00:00:00

High-resolution recordings of extracellular potentials are fundamental for the study of neuronal networks, the basis of neuronal coding and transmission. Here we present an innovative method for an in vivo electrical imaging of Local Field Potentials using novel implantable neural interfaces with a high-density array of 256 recording sites and a spatial resolution of 15 {\mu}m. Thanks to this technology, we analyze the propagation of whisker-evoked activity in a single barrel-column of the rat somatosensory cortex.
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Authors: Vivek Gopalakrishnan, Neel Dey, David-Dimitris Chlorogiannis, Andrew Abumoussa, Anna M. Larson, Darren B. Orbach, Sarah Frisken, Polina Golland
Date: 2025-03-20 00:00:00

The integration of artificial intelligence in image-guided interventions holds transformative potential, promising to extract 3D geometric and quantitative information from conventional 2D imaging modalities during complex procedures. Achieving this requires the rapid and precise alignment of 2D intraoperative images (e.g., X-ray) with 3D preoperative volumes (e.g., CT, MRI). However, current 2D/3D registration methods fail across the broad spectrum of procedures dependent on X-ray guidance: traditional optimization techniques require custom parameter tuning for each subject, whereas neural networks trained on small datasets do not generalize to new patients or require labor-intensive manual annotations, increasing clinical burden and precluding application to new anatomical targets. To address these challenges, we present xvr, a fully automated framework for training patient-specific neural networks for 2D/3D registration. xvr uses physics-based simulation to generate abundant high-quality training data from a patient's own preoperative volumetric imaging, thereby overcoming the inherently limited ability of supervised models to generalize to new patients and procedures. Furthermore, xvr requires only 5 minutes of training per patient, making it suitable for emergency interventions as well as planned procedures. We perform the largest evaluation of a 2D/3D registration algorithm on real X-ray data to date and find that xvr robustly generalizes across a diverse dataset comprising multiple anatomical structures, imaging modalities, and hospitals. Across surgical tasks, xvr achieves submillimeter-accurate registration at intraoperative speeds, improving upon existing methods by an order of magnitude. xvr is released as open-source software freely available at https://github.com/eigenvivek/xvr.
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Authors: Sam Narimani, Solveig Roth Hoff, Kathinka Dahli Kurz, Kjell-Inge Gjesdal, Jurgen Geisler, Endre Grovik
Date: 2025-03-19 00:00:00
Tasks: Lesion Segmentation

Purpose: Segmentation of the breast lesion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an essential step to accurately diagnose and plan treatment and monitor progress. This study aims to highlight the impact of breast region segmentation (BRS) on deep learning-based breast lesion segmentation (BLS) in breast DCE-MRI. Methods Using the Stavanger Dataset containing primarily 59 DCE-MRI scans and UNet++ as deep learning models, four different process were conducted to compare effect of BRS on BLS. These four approaches included the whole volume without BRS and with BRS, BRS with the selected lesion slices and lastly optimal volume with BRS. Preprocessing methods like augmentation and oversampling were used to enhance the small dataset, data shape uniformity and improve model performance. Optimal volume size were investigated by a precise process to ensure that all lesions existed in slices. To evaluate the model, a hybrid loss function including dice, focal and cross entropy along with 5-fold cross validation method were used and lastly a test dataset which was randomly split used to evaluate the model performance on unseen data for each of four mentioned approaches. Results Results demonstrate that using BRS considerably improved model performance and validation. Significant improvement in last approach -- optimal volume with BRS -- compared to the approach without BRS counting around 50 percent demonstrating how effective BRS has been in BLS. Moreover, huge improvement in energy consumption, decreasing up to 450 percent, introduces a green solution toward a more environmentally sustainable approach for future work on large dataset.
A Language Vision Model Approach for Automated Tumor Contouring in Radiation Oncology
Authors: Yi Luo, Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, Xiaojian Chen, Rui Zhang, Quan Chen, Wil Ngwa, Kai Ding
Date: 2025-03-19 00:00:00
Tasks: Descriptive

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence(AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: Deployments of the OCC system resulted in a significant reduction in the false discovery rate by 35.0%, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs to improve contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation, reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVMs hallucinations with ablation study, and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges.
SpecReX: Explainable AI for Raman Spectroscopy
Authors: Nathan Blake, David A. Kelly, Akchunya Chanchal, Sarah Kapllani-Mucaj, Geraint Thomas, Hana Chockler
Date: 2025-03-18 00:00:00
Tasks: Medical Diagnosis

Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted to explaining Raman spectra. SpecReX uses the theory of actual causality to rank causal responsibility in a spectrum, quantified by iteratively refining mutated versions of the spectrum and testing if it retains the original classification. The explanations provided by SpecReX take the form of a responsibility map, highlighting spectral regions most responsible for the model to make a correct classification. To assess the validity of SpecReX, we create increasingly complex simulated spectra, in which a "ground truth" signal is seeded, to train a classifier. We then obtain SpecReX explanations and compare the results with another explainability tool. By using simulated spectra we establish that SpecReX localizes to the known differences between classes, under a number of conditions. This provides a foundation on which we can find the spectral features which differentiate disease classes. This is an important first step in proving the validity of SpecReX.