pwc-archive/papers-with-abstracts huggingface dataset:
Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals
Authors: Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D. Robertson, Zhi-Qiang Zhang
Date: 2024-12-05 00:00:00
Tasks: Muscle Force Prediction

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.
A Note on Spectral Map
Authors: Tuğçe Gökdemir, Jakub Rydzewski
Date: 2024-12-05 00:00:00
Tasks: Physical Intuition

In molecular dynamics (MD) simulations, transitions between states are often rare events due to energy barriers that exceed the thermal temperature. Because of their infrequent occurrence and the huge number of degrees of freedom in molecular systems, understanding the physical properties that drive rare events is immensely difficult. A common approach to this problem is to propose a collective variable (CV) that describes this process by a simplified representation. However, choosing CVs is not easy, as it often relies on physical intuition. Machine learning (ML) techniques provide a promising approach for effectively extracting optimal CVs from MD data. Here, we provide a note on a recent unsupervised ML method called spectral map, which constructs CVs by maximizing the timescale separation between slow and fast variables in the system.
Nonlinear Optimal Control of Electron Dynamics within Hartree-Fock Theory
Authors: Harish S. Bhat, Hardeep Bassi, Christine M. Isborn
Date: 2024-12-04 00:00:00

Consider the problem of determining the optimal applied electric field to drive a molecule from an initial state to a desired target state. For even moderately sized molecules, solving this problem directly using the exact equations of motion -- the time-dependent Schr\"odinger equation (TDSE) -- is numerically intractable. We present a solution of this problem within time-dependent Hartree-Fock (TDHF) theory, a mean field approximation of the TDSE. Optimality is defined in terms of minimizing the total control effort while maximizing the overlap between desired and achieved target states. We frame this problem as an optimization problem constrained by the nonlinear TDHF equations; we solve it using trust region optimization with gradients computed via a custom-built adjoint state method. For three molecular systems, we show that with very small neural network parametrizations of the control, our method yields solutions that achieve desired targets within acceptable constraints and tolerances.
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex Systems
Authors: Xiangnan Yu, Hao Xu, Zhiping Mao, Hongguang Sun, Yong Zhang, Dongxiao Zhang, Yuntian Chen
Date: 2024-12-05 00:00:00
Tasks: Denoising, global-optimization

In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This study introduces a stepwise data-driven framework for discovering fractional differential equations (FDEs) directly from data. FDEs, known for their capacity to model non-local dynamics with fewer parameters than integer-order derivatives, can represent complex systems with long-range interactions. Our framework applies deep neural networks as surrogate models for denoising and reconstructing sparse and noisy observations while using Gaussian-Jacobi quadrature to handle the challenges posed by singularities in fractional derivatives. To optimize both the sparse coefficients and fractional order, we employ an alternating optimization approach that combines sparse regression with global optimization techniques. We validate the framework across various datasets, including synthetic anomalous diffusion data, experimental data on the creep behavior of frozen soils, and single-particle trajectories modeled by L\'{e}vy motion. Results demonstrate the framework's robustness in identifying the structure of FDEs across diverse noise levels and its capacity to capture integer-order dynamics, offering a flexible approach for modeling memory effects in complex systems.
Multi-Scale Node Embeddings for Graph Modeling and Generation
Authors: Riccardo Milocco, Fabian Jansen, Diego Garlaschelli
Date: 2024-12-05 00:00:00
Tasks: Community Detection, Data Compression, Link Prediction

Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector-based downstream tasks such as network modelling, data compression, link prediction, and community detection. Two apparently unrelated limitations affect these algorithms. On one hand, it is not clear what the basic operation defining vector spaces, i.e. the vector sum, corresponds to in terms of the original nodes in the network. On the other hand, while the same input network can be represented at multiple levels of resolution by coarse-graining the constituent nodes into arbitrary block-nodes, the relationship between node embeddings obtained at different hierarchical levels is not understood. Here, building on recent results in network renormalization theory, we address these two limitations at once and define a multiscale node embedding method that, upon arbitrary coarse-grainings, ensures statistical consistency of the embedding vector of a block-node with the sum of the embedding vectors of its constituent nodes. We illustrate the power of this approach on two economic networks that can be naturally represented at multiple resolution levels: namely, the international trade between (sets of) countries and the input-output flows among (sets of) industries in the Netherlands. We confirm the statistical consistency between networks retrieved from coarse-grained node vectors and networks retrieved from sums of fine-grained node vectors, a result that cannot be achieved by alternative methods. Several key network properties, including a large number of triangles, are successfully replicated already from embeddings of very low dimensionality, allowing for the generation of faithful replicas of the original networks at arbitrary resolution levels.
Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
Authors: Juan Sandubete-López, José L. Risco-Martín, Alexander H. McMillan, Eva Besada-Portas
Date: 2024-12-05 00:00:00
Tasks: Deep Learning

Microfluidic devices are increasingly used in biological and chemical experiments due to their cost-effectiveness for rheological estimation in fluids. However, these devices often face challenges in terms of accuracy, size, and cost. This study presents a methodology, integrating deep learning, modeling and simulation to enhance the design of microfluidic systems, used to develop an innovative approach for viscosity measurement of polymer melts. We use synthetic data generated from the simulations to train a deep learning model, which then identifies rheological parameters of polymer melts from pressure drop and flow rate measurements in a microfluidic circuit, enabling online estimation of fluid properties. By improving the accuracy and flexibility of microfluidic rheological estimation, our methodology accelerates the design and testing of microfluidic devices, reducing reliance on physical prototypes, and offering significant contributions to the field.
Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction
Authors: George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers, Andrew P. King, Andrew J. Reader
Date: 2024-12-05 00:00:00
Tasks: 3D Reconstruction, Image Reconstruction, SSIM

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing methods for SGM-based reconstruction from PET data suffer from slow reconstruction, burdensome hyperparameter tuning and slice inconsistency effects (in 3D). In this work, we propose a practical methodology for fully 3D reconstruction that accelerates reconstruction and reduces the number of critical hyperparameters by matching the likelihood of an SGM's reverse diffusion process to a current iterate of the maximum-likelihood expectation maximization algorithm. Using the example of low-count reconstruction from simulated [$^{18}$F]DPA-714 datasets, we show our methodology can match or improve on the NRMSE and SSIM of existing state-of-the-art SGM-based PET reconstruction while reducing reconstruction time and the need for hyperparameter tuning. We evaluate our methodology against state-of-the-art supervised and conventional reconstruction algorithms. Finally, we demonstrate a first-ever implementation of SGM-based reconstruction for real 3D PET data, specifically [$^{18}$F]DPA-714 data, where we integrate perpendicular pre-trained SGMs to eliminate slice inconsistency issues.
Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction
Authors: George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers, Andrew P. King, Andrew J. Reader
Date: 2024-12-05 00:00:00
Tasks: Image Generation, Image Reconstruction

Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We also show how our pseudo-PET images may be exploited as a generative prior for single-subject PET image reconstruction. Firstly, we perform deep-learned deformable registration of multi-subject magnetic resonance (MR) images paired to multi-subject PET images. We then use the anatomically-learned deformation fields to transform multiple PET images to the same reference space, before averaging random subsets of the transformed multi-subject data to form a large number of varying pseudo-PET images. We observe that using MR information for registration imbues the resulting pseudo-PET images with improved anatomical detail compared to the originals. We consider applications to PET image reconstruction, by generating pseudo-PET images in the same space as the intended single-subject reconstruction and using them as training data for a diffusion model-based reconstruction method. We show visual improvement and reduced background noise in our 2D reconstructions as compared to OSEM, MAP-EM and an existing state-of-the-art diffusion model-based approach. Our method shows the potential for utilising highly subject-specific prior information within a generative reconstruction framework. Future work may compare the benefits of our approach to explicitly MR-guided reconstruction methodologies.
Generative-Model-Based Fully 3D PET Image Reconstruction by Conditional Diffusion Sampling
Authors: George Webber, Yuya Mizuno, Oliver D. Howes, Alexander Hammers, Andrew P. King, Andrew J. Reader
Date: 2024-12-05 00:00:00
Tasks: Image Reconstruction

Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) datasets. In this work we have developed and implemented practical methodology for 3D image reconstruction with SGMs, and perform (to our knowledge) the first SGM-based reconstruction of real fully 3D PET data. We train an SGM on full-count reference brain images, and extend methodology to allow SGM-based reconstructions at very low counts (1% of original, to simulate low-dose or short-duration scanning). We then perform reconstructions for multiple independent realisations of 1% count data, allowing us to analyse the bias and variance characteristics of the method. We sample from the learned posterior distribution of the generative algorithm to calculate uncertainty images for our reconstructions. We evaluate the method's performance on real full- and low-count PET data and compare with conventional OSEM and MAP-EM baselines, showing that our SGM-based low-count reconstructions match full-dose reconstructions more closely and in a bias-variance trade-off comparison, our SGM-reconstructed images have lower variance than existing baselines. Future work will compare to supervised deep-learned methods, with other avenues for investigation including how data conditioning affects the SGM's posterior distribution and the algorithm's performance with different tracers.
Deep priors for satellite image restoration with accurate uncertainties
Authors: Biquard Maud, Marie Chabert, Florence Genin, Christophe Latry, Thomas Oberlin
Date: 2024-12-05 00:00:00
Tasks: Deblurring, Denoising, Hallucination, Image Restoration, Super-Resolution, Uncertainty Quantification

Satellite optical images, upon their on-ground receipt, offer a distorted view of the observed scene. Their restoration, classically including denoising, deblurring, and sometimes super-resolution, is required before their exploitation. Moreover, quantifying the uncertainty related to this restoration could be valuable by lowering the risk of hallucination and avoiding propagating these biases in downstream applications. Deep learning methods are now state-of-the-art for satellite image restoration. However, they require to train a specific network for each sensor and they do not provide the associated uncertainties. This paper proposes a generic method involving a single network to restore images from several sensors and a scalable way to derive the uncertainties. We focus on deep regularization (DR) methods, which learn a deep prior on target images before plugging it into a model-based optimization scheme. First, we introduce VBLE-xz, which solves the inverse problem in the latent space of a variational compressive autoencoder, estimating the uncertainty jointly in the latent and in the image spaces. It enables scalable posterior sampling with relevant and calibrated uncertainties. Second, we propose the denoiser-based method SatDPIR, adapted from DPIR, which efficiently computes accurate point estimates. We conduct a comprehensive set of experiments on very high resolution simulated and real Pleiades images, asserting both the performance and robustness of the proposed methods. VBLE-xz and SatDPIR achieve state-of-the-art results compared to direct inversion methods. In particular, VBLE-xz is a scalable method to get realistic posterior samples and accurate uncertainties, while SatDPIR represents a compelling alternative to direct inversion methods when uncertainty quantification is not required.
Inferring Leader-Follower Behavior from Presence Data in the Marine Environment: A Case Study on Reef Manta Rays
Authors: Juan Fernández-Gracia, Jorge P. Rodríguez, Lauren R. Peel, Konstantin Klemm, Mark G. Meekan, Víctor M. Eguíluz
Date: 2024-12-05 00:00:00

Social interactions are fundamental in animal groups, including humans, and can take various forms, such as competition, cooperation, or kinship. Understanding these interactions in marine environments has been historically challenging due to data collection difficulties. However, advancements in acoustic telemetry now enable remote analysis of such behaviors. This study proposes a method to derive leader-follower networks from presence data collected by a single acoustic receiver at a specific location. Using the Kolmogorov-Smirnov distance, the method analyzes lag times between consecutive presences of individuals to infer directed relationships. Tested on simulated data, it was then applied to detection data from acoustically tagged reef manta rays (\textit{Mobula~alfredi}) frequenting a known site. Results revealed temporal patterns, including circadian rhythms and burst-like behavior with power-law distributed time gaps between presences. The inferred leader-follower network highlighted key behavioral patterns: females followed males more often than expected, males showed stronger but fewer associations with specific females, and smaller individuals followed others less consistently than larger ones. These findings align with ecological insights, revealing structured social interactions and providing a novel framework for studying marine animal behavior through network theory.
Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions
Authors: Jebran Khan, Kashif Ahmad, Senthil Kumar Jagatheesaperumal, Nasir Ahmad, Kyung-Ah Sohn
Date: 2024-12-03 00:00:00
Tasks: Management

In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the need for more sustainable cities and societies. Addressing these challenges requires a multifaceted approach involving all the stakeholders, sustainable planning, efficient resource management, innovative solutions, and modern technologies. Like other modern technologies, social media informatics also plays its part in developing more sustainable and resilient cities and societies. Despite its limitations, social media informatics has proven very effective in various sustainable cities and society applications. In this paper, we review and analyze the role of social media informatics in sustainable cities and society by providing a detailed overview of its applications, associated challenges, and potential solutions. This work is expected to provide a baseline for future research in the domain.
Large Volatility Matrix Prediction using Tensor Factor Structure
Authors: Sung Hoon Choi, Donggyu Kim
Date: 2024-12-05 00:00:00
Tasks: Prediction

Several approaches for predicting large volatility matrices have been developed based on high-dimensional factor-based It\^o processes. These methods often impose restrictions to reduce the model complexity, such as constant eigenvectors or factor loadings over time. However, several studies indicate that eigenvector processes are also time-varying. To address this feature, this paper generalizes the factor structure by representing the integrated volatility matrix process as a cubic (order-3 tensor) form, which is decomposed into low-rank tensor and idiosyncratic tensor components. To predict conditional expected large volatility matrices, we propose the Projected Tensor Principal Orthogonal componEnt Thresholding (PT-POET) procedure and establish its asymptotic properties. The advantages of PT-POET are validated through a simulation study and demonstrated in an application to minimum variance portfolio allocation using high-frequency trading data.
On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
Authors: Yuta Okamoto, Yuuki Ozaki
Date: 2024-12-05 00:00:00
Tasks: Decision Making, regression

Regression discontinuity (RD) designs typically identify the treatment effect at a single cutoff point, which may not always be the primary interest. When and how, then, can we learn about treatment effects away from the cutoff? This paper addresses this question within a multiple-cutoff RD framework. We begin by examining the plausibility of the constant bias assumption proposed by Cattaneo, Keele, Titiunik, and Vazquez-Bare (2021) through the lens of rational decision-making behavior. We argue that this assumption is indeed plausible when the groups defined by different cutoffs consist of similar agents and the running variable is effort-invariant, that is, not influenced by the agents' effort (e.g., family income level). However, this positive conclusion no longer holds when the running variable is effort-contingent, meaning it depends on agents' effort (e.g., test scores). In such settings, extrapolated effects based on the constant bias assumption can be biased, even when all other features remain identical except for the cutoff position. To address this issue, we introduce an alternative set of assumptions grounded in empirical motivations and develop a novel partial identification strategy. We illustrate the usefulness and practical relevance of the proposed bounds through empirical examples. In this application, we also find that extrapolation under the constant bias assumption performs reasonably well when the running variable is effort-invariant and some similarity between groups is expected.
Endogenous Heteroskedasticity in Linear Models
Authors: Javier Alejo, Antonio F. Galvao, Julian Martinez-Iriarte, Gabriel Montes-Rojas
Date: 2024-12-03 00:00:00
Tasks: valid

Linear regressions with endogeneity are widely used to estimate causal effects. This paper studies a framework that involves two common issues: endogeneity of the regressors and heteroskedasticity that depends on endogenous regressors-i.e., endogenous heteroskedasticity. We show that the presence of endogenous heteroskedasticity in the structural regression renders the two-stage least squares estimator inconsistent. To address this issue, we propose sufficient conditions and a control function approach to identify and estimate the causal parameters of interest. We establish the limiting properties of the estimator--namely, consistency and asymptotic normality--and propose inference procedures. Monte Carlo simulations provide evidence on the finite-sample performance of the proposed methods and evaluate different implementation strategies. We revisit an empirical application on job training to illustrate the methods.