arXiv subjects

Solomon Asghar

Publications and source records attributed to Solomon Asghar.

3 recordsLinked to original sources

arXiv2025

U-Net 3+ for Anomalous Diffusion Analysis enhanced with Mixture Estimates (U-AnD-ME) in particle-tracking data

Biophysical processes within living systems rely on encounters and interactions between molecules in complex environments such as cells. They are often described by anomalous diffusion transport. Recent advances in single-molecule microscopy and particle-tracking techniques have yielded an abundance of data in the form of videos and trajectories that contain critical information about these biologically significant processes. However, standard approaches for characterizing anomalous diffusion from these measurements often struggle in cases of practical interest, e.g. due to short, noisy trajectories. Fully exploiting this data therefore requires the development of advanced analysis methods -- a core goal at the heart of the recent international Anomalous Diffusion Challenges. Here, we introduce a novel machine-learning framework, U-net 3+ for Anomalous Diffusion analysis enhanced with Mixture Estimates (U-AnD-ME), that applies a U-Net 3+ based neural network alongside Gaussian mixture models to enable highly accurate characterisation of single-particle tracking data. In the 2024 Anomalous Diffusion Challenge, U-AnD-ME outperformed all other participating methods for the analysis of two-dimensional anomalous diffusion trajectories at both single-trajectory and ensemble levels. Using a large dataset inspired by the Challenge, we further characterize the performance of U-AnD-ME in segmenting trajectories and inferring anomalous diffusion properties.

physics.data-an↗

arXiv2024

Efficient Rare Event Sampling with Unsupervised Normalising Flows

From Physics and Biology to Seismology and Economics, the behaviour of countless systems is determined by impactful yet unlikely transitions between metastable states known as \emph{rare events}, the study of which is essential for understanding and controlling these systems' properties. Classical computational methods to sample rare events remain prohibitively inefficient, and are a bottleneck for enhanced samplers requiring prior data. Here, we introduce a novel framework, FlowRES that uses unsupervised normalising flow neural networks to enhance Monte Carlo sampling of rare events by generating high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling the transition path ensembles of equilibrium and non-equilibrium systems of Brownian particles exploring increasingly complex potential surfaces. Beyond eliminating requirements for prior data, FlowRES features key advantages over established samplers: no collective variables need defining, its efficiency remains constant even as events become increasingly unlikely, and it can handle systems with multiple routes between states.

physics.comp-ph↗

arXiv2023

Quantitative evaluation of methods to analyze motion changes in single-particle experiments

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we report the results of a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we implemented a software library that simulates realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition constitutes the first assessment of these methods, providing insights into the current limitations of the field, fostering the development of new approaches, and guiding researchers to identify optimal tools for analyzing their experiments.

cond-mat.soft↗