Cryo-electron microscopy (cryo-EM) is an emerging experimental method to characterize the structure of large biomolecular assemblies. Single particle cryo-EM records 2D images (so-called micrographs) of projections of the three-dimensional particle, which need to be processed to obtain the three-dimensional reconstruct…
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48 results for “single particle reconstruction”
Tempered Guided Diffusionstat.ML
TGD improves conditional sampling by concentrating computation on promising trajectories.
problem Efficiently training-free conditional sampling with diffusion priors.
method Tempered Guided Diffusion (TGD) using annealed sequential Monte Carlo.
result TGD yields a consistent particle approximation to the posterior as the number of particles grows.
Method recovers particle orientations from cryo-EM projections.
problem Unknown orientations in cryo-EM images.
method Two-step process: estimating distances and recovering orientations.
result Accurate orientation recovery from noisy projections.
A new method estimates target images directly from noisy observations in cryo-EM.
problem Estimating target images from noisy, rotated observations in cryo-EM.
method Estimates rotation-invariant features and then images from these features.
result Effectiveness demonstrated on synthetic cryo-EM datasets.
Machine Learning for Particle Flow Reconstruction at CMSphysics.data-an
CMS uses machine learning to improve particle flow reconstruction.
problem Improving particle flow reconstruction in CMS.
method Machine learning, graph neural network, heterogeneous computing.
result Machine-learned PF model outperforms standard algorithm.
Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorsphysics.data-an
Improved particle-flow event reconstruction for future colliders using scalable neural networks.
problem Efficient and accurate particle reconstruction in future particle detectors.
method Comparative study of scalable machine learning models (graph neural network and kernel-based transformer) for event reconstruction.
result Graph neural network model improves jet transverse momentum resolution by up to 50%.
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networksphysics.data-an
MLPF uses graph neural networks to improve particle-flow reconstruction in high-pileup conditions.
problem Improving particle-flow reconstruction in high-pileup conditions at high-luminosity LHC.
method End-to-end trainable machine-learned particle-flow algorithm based on graph neural networks.
result MLPF improves physics response and demonstrates scalable reconstruction in high-pileup environments.
KLT picker automates particle picking for cryo-EM, especially for low SNR images.
problem Challenges in particle picking, especially for low SNR micrographs.
method Data-driven optimal templates learned via Karhunen Loeve Transform.
result High-quality results with minimal manual effort on publicly available data sets.
Automatic post-picking using MAPPOS improves particle image detection from Cryo-EM micrographsstat.ML
Cryo-electron microscopy (cryo-EM) studies using single particle reconstruction are extensively used to reveal structural information on macromolecular complexes. Aiming at the highest achievable resolution, state of the art electron microscopes automatically acquire thousands of high-quality micrographs. Particles are…
EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstructionphysics.data-an
EggNet reconstructs particle tracks from hits using evolving graph attention networks.
problem Particle track reconstruction is computationally expensive and combinatorial.
method EggNet uses a one-shot object condensation approach with evolving graph attention networks.
result EggNet outperforms methods requiring fixed input graphs on TrackML dataset.
Learning representations of irregular particle-detector geometry with distance-weighted graph networksphysics.data-an
Graph networks improve particle reconstruction in irregular detectors.
problem Handling irregular particle-detector geometries in particle reconstruction.
method Introduce distance-weighted graph network architectures (GarNet, GravNet layers) for irregular geometry detectors.
result The proposed graph networks provide equally performing or less resource-demanding solutions compared to existing methods.
Geometric shape matching for recovering protein conformations from single-particle Cryo-EM dataq-bio.BM
Researchers use shape analysis to recover protein structures from Cryo-EM data.
problem Recovering the three-dimensional backbone structure of single polypeptide proteins from noisy tomographic projections.
method Shape analysis and matrix Lie group actions to deform point clouds to match 2D tomography data.
result Optimal deformations are computed to recover the three-dimensional backbone structure of proteins.
Towards a Computer Vision Particle Flowphysics.data-an
A computer vision approach improves neutral particle detection in particle flow algorithms.
problem Optimal reconstruction of particle content and kinematics in calorimeter images.
method Computer vision techniques applied to calorimeter images, using deep learning and super-resolution.
result Significantly improved reconstruction of neutral particle calorimeter energy deposits.
Jointly estimates flow fields and particle properties from Lagrangian data.
problem Estimating flow fields and particle properties from sparse, noisy Lagrangian data.
method Data assimilation framework coupling Eulerian and Lagrangian models.
result Joint estimation of flow fields and particle properties in various flow regimes.
3d-SMRnet speeds up MPI system matrix recovery to 1 minute with high quality.
problem Slow system matrix recovery in MPI due to recalibration.
method 3d-System Matrix Recovery Network using deep learning.
result 3d-SMRnet recovers 3d system matrix with 64x subsampling in 1 minute.
New method reconstructs moving parts of proteins in cryo-EM.
problem Reconstructing non-rigid molecules with moving parts in cryo-EM.
method Graph Laplacian construction from multiple projection images, followed by spectral volume expansion.
result High-resolution visualization of molecular dynamics using spectral volumes.
New deep learning methods improve track reconstruction in particle physics.
problem Reconstructing particle tracks from detector hits in GEM detectors.
method Two-stage approach combining hits preprocessing and deep neural networks.
result Deep neural networks can accurately reconstruct tracks without preprocessing.
Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically…
New method reconstructs 3D protein structures from cryo-EM images.
problem Reconstructing continuous protein structures from noisy cryo-EM projections.
method Neural network-based approach that models structural heterogeneity in Fourier space.
result Demonstrated successful ab initio reconstruction of 3D protein complexes.
Generative Adversarial Networks generate PXD background noise efficiently.
problem Efficiently generate statistically independent PXD background noise samples.
method Conditional Generative Adversarial Networks (GANs) with contrastive learning.
result On-demand PXD background generator reduces storage requirements.
VAE models simulate chaotic particle trajectories in flames.
problem Simulating chaotic particle trajectories in combustion systems.
method Variational autoencoder trained on 3D reconstructed particle trajectories.
result Generated trajectories match experimental data accurately.
Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographsq-bio.QM
Cryo-electron microscopy (cryoEM) is an increasingly popular method for protein structure determination. However, identifying a sufficient number of particles for analysis (often >100,000) can take months of manual effort. Current computational approaches are limited by high false positive rates and require significant…
Unified neural network model for astro-particle physics predictions with coverage, systematics, and goodness-of-fit.
problem Lack of statistical uncertainties, coverage, systematic uncertainties, and goodness-of-fit in neural network predictions.
method KL-divergence objective for joint distribution of data and labels, conditional normalizing flows, amortized with neural networks.
result Unified supervised learning and VAEs under stochastic variational inference for event property predictions.
Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particlesstat.ME
Estimates log-likelihood of interacting particle systems using virtual particles.
problem Inconsistent estimation of finite-particle log-likelihood in large particle systems.
method Stochastic gradient estimate using continuous trajectory and virtual particle systems.
result Convergence to stationary points of limiting mean-field system's log-likelihood.
Model reconstructs novel 3D shapes with a single prior image.
problem Generalizing single-view 3D reconstruction to new classes with limited data.
method Reframes reconstruction as refinement of a provided prior shape.
result Model reconstructs novel classes with limited training data.
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
Two supervised methods classify single-molecule patterns from X-ray imaging.
problem Classifying high-quality patterns from noisy, stochastic XFEL data.
method Supervised template-based learning methods: Eigen-Image and Log-Likelihood classifiers.
result Classifiers can find best-matched templates within milliseconds and parallelize for XFEL repetition rate.
Physics-informed Bayesian inference of external potentials in classical density-functional theorycond-mat.stat-mech
Bayesian inference reconstructs external potentials in DFT for many-particle systems.
problem Reconstructing external potentials in classical density-functional theory (DFT) for many-particle systems.
method Combines Bayesian inference with classical DFT to probabilistically reconstruct external potentials.
result Accurately infers external potentials and density profiles with uncertainty quantification.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising futu…
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
Improved track reconstruction using recurrent neural networks.
problem Reconstructing tracks from hits in detectors with many fake hits.
method Combining hit preprocessing and deep neural network in one stage.
result Proposed method is more accurate, faster, and does not require preprocessing.
Paper uses VAEs and GANs to estimate cryo-EM image orientation and camera parameters.
problem Estimating orientation and camera parameters from noisy cryo-EM images.
method Combines VAEs and GANs to learn latent representation, then designs estimation method.
result Geometric approach for fast cryo-EM biomolecule reconstruction.
Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clusteringphysics.ins-det
The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original points.…
Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into accou…
The Score Kalman Filtereess.SY
A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.
problem Nonlinear Bayesian filtering challenges in representing belief distributions.
method Combines score matching with Stein's identity to avoid partition function evaluation.
result The Score Kalman Filter (SKF) outperforms existing methods on nonlinear systems.
New approach reduces particle simulation complexity to linear time and space.
problem Challenges in learning dynamics from particle interactions, especially N-body problems.
method Transforms fully-connected interaction graphs into hierarchical ones, reducing complexity.
result Linear time and space complexity for large-scale simulations, retaining high accuracy.
Instantiation-Net reconstructs 3D mesh from single 2D image for right ventricle.
problem Reconstructing 3D shape from limited 2D images for surgical navigation.
method Combines DCNN for feature extraction and GCN for mesh reconstruction.
result Demonstrates practical strength and potential clinical use.
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
problem High-dimensional data assimilation challenges in ensemble filtering.
method Optimized Maximum Mean Discrepancy (MMD) for transport map construction.
result Significant improvement in robustness and posterior approximation.
Novel method uses Gaussian process to estimate particle sizes from scattering data.
problem Estimating particle size distributions from noisy optical scattering measurements.
method Constrained Gaussian process regression with normalization constraints.
result Accurately reconstructs particle size distributions from noisy data.
GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.
problem High-dimensional multimodal sampling problems in lattice field theory.
method GPU-accelerated particle Monte Carlo methods (Sequential Monte Carlo and nested sampling).
result These methods match or outperform neural samplers in sample quality and wall-clock time.
Impute missing events in continuous-time sequences using particle smoothing.
problem Missing events in continuous-time sequences.
method Particle smoothing with trainable bidirectional LSTM proposals.
result Imputed sequences have low Bayes risk compared to ground truth.
New model suggests universe emerges from single particle quantum mechanics.
problem Exploring how the universe might arise from a single particle in quantum mechanics.
method Novel spontaneous symmetry breaking acting on probability distributions of Hamiltonians.
result Evidence supports the hypothesis that nature seeks tensor decompositions.
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r…
Paper reconstructs training data from a single gradient query.
problem Privacy threats in federated learning due to model gradients.
method Provable attack using tensor decomposition.
result Training samples can be fully reconstructed from a single gradient query.
A method improves Cryo-EM 3D map refinement by regularizing rotation estimation.
problem Noise-robustness vs. data-consistency in Cryo-EM 3D map reconstruction.
method Ellipsoidal support lifting (ESL) for regularizing and approximating the global minimizer over Riemannian manifolds.
result The induced bias due to regularizing effect of ESL estimates better rotations than global optimisation.
Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)physics.space-ph
New model predicts particle precipitation from magnetosphere to ionosphere.
problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.
method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).
result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.
Multiple Wasserstein Gradient Descent Algorithm for Multi-Objective Distributional Optimizationcs.LG
MWGraD solves multi-objective distributional optimization using particle-based gradient descent.
problem Simultaneously minimize multiple objective functionals over probability distributions.
method Iterative particle-based algorithm MWGraD, estimating and aggregating Wasserstein gradients.
result Demonstrates effectiveness on synthetic and real-world datasets.
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Particle Gibbs Sampling method, which alternately samples coalescent times conditioned on coalescent tree structures, and tree…