GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)Global graph features unveiled by unsupervised geometric deep learning Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
arXiv: 2503.05560
Graphs provide a powerful framework for modeling complex systems, but their structural variability makes analysis and classification challenging. To address this, we introduce GAUDI (Graph Autoencoder Uncovering Descriptive Information), a novel unsupervised geometric deep learning framework that captures both local details and global structure. GAUDI employs an innovative hourglass architecture with hierarchical pooling and upsampling layers, linked through skip connections to preserve essential connectivity information throughout the encoding-decoding process. By mapping different realizations of a system – generated from the same underlying parameters – into a continuous, structured latent space, GAUDI disentangles invariant process-level features from stochastic noise. We demonstrate its power across multiple applications, including modeling small-world networks, characterizing protein assemblies from super-resolution microscopy, analyzing collective motion in the Vicsek model, and capturing age-related changes in brain connectivity. This approach not only improves the analysis of complex graphs but also provides new insights into emergent phenomena across diverse scientific domains.
Recent advances in nonequilibrium physics allow extracting thermodynamic quantities, such as entropy production, directly from dynamical information in microscopic movies. (Image by S. Manikandan.)Localizing entropy production in non-equilibrium processes
Sreekanth Manikandan Date: 5 Mar 2025 Time: 14:45 Place: Nordita
Part of the 14th Nordic Workshop on Statistical Physics
Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.
Propagation of scattered light through a scattering microscope, illustrating typical nanoparticles studied. (Image by B. García Rodriguez.)Optical Label-Free Microscopy Characterization of Dielectric Nanoparticles
Berenice Garcia Rodriguez, Erik Olsén, Fredrik Skärberg, Giovanni Volpe, Fredrik Höök, Daniel Sundås Midtvedt
Nanoscale, 17, 8336-8362 (2025)
arXiv: 2409.11810
doi: 10.1039/D4NR03860F
In order to relate nanoparticle properties to function, fast and detailed particle characterization, is needed. The ability to characterize nanoparticle samples using optical microscopy techniques has drastically improved over the past few decades; consequently, there are now numerous microscopy methods available for detailed characterization of particles with nanometric size. However, there is currently no “one size fits all” solution to the problem of nanoparticle characterization. Instead, since the available techniques have different detection limits and deliver related but different quantitative information, the measurement and analysis approaches need to be selected and adapted for the sample at hand. In this tutorial, we review the optical theory of single particle scattering and how it relates to the differences and similarities in the quantitative particle information obtained from commonly used microscopy techniques, with an emphasis on nanometric (submicron) sized dielectric particles. Particular emphasis is placed on how the optical signal relates to mass, size, structure, and material properties of the detected particles and to its combination with diffusivity-based particle sizing. We also discuss emerging opportunities in the wake of new technology development, with the ambition to guide the choice of measurement strategy based on various challenges related to different types of nanoparticle samples and associated analytical demands.
MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)Relational Inductive Biases as a Key to Smarter Deep Learning Microscopy Jesús Pineda Learning on graphs and geometry meetup at Uppsala University Date: 12 February 2025 Time: 11:15 Place: Lecture hall 4101, Ångströmlaboratoriet, Uppsala, Sweden
Geometric deep learning has revolutionized fields like social network analysis, molecular chemistry, and neuroscience, but its application to microscopy data analysis remains a significant challenge. The hurdles stem not only from the scarcity of high-quality data but also from the intrinsic complexity and variability of microscopy datasets. This presentation introduces two groundbreaking geometric deep-learning frameworks designed to overcome these barriers, advancing the integration of graph neural networks (GNNs) into microscopy and unlocking their full potential. First, we present MAGIK, a cutting-edge framework for analyzing biological system dynamics through time-lapse microscopy. Leveraging a graph neural network augmented with attention-based mechanisms, MAGIK processes object features using geometric priors. This enables it to perform a range of tasks, from linking coordinates into trajectories to uncovering local and global dynamic properties with unprecedented precision. Remarkably, MAGIK excels under minimal data conditions, maintaining exceptional performance and robust generalization across diverse scenarios. Next, we introduce MIRO, a novel algorithm powered by recurrent graph neural networks. MIRO pre-processes Single Molecule Localization (SML) datasets to enhance the efficiency of conventional clustering methods. Its ability to handle clusters of varying shapes and scales enables more accurate and consistent analyses across complex datasets. Furthermore, MIRO’s single- and few-shot learning capabilities allow it to generalize effortlessly across scenarios, making it an efficient, scalable, and versatile tool for microscopy data analysis. Together, MAGIK and MIRO address critical limitations in microscopy data analysis, offering innovative solutions for multi-scale data analysis and advancing the boundaries of what is currently achievable with geometric deep learning in the field.
GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)Global graph features unveiled by unsupervised geometric deep learning
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Daniel Vereb, Joana B. Pereira, Carlo Manzo, and Giovanni Volpe Learning on graphs and geometry meetup at Uppsala University Date: 11 February 2025 Place: Uppsala university
Graphs are used to model complex relationships, such as interactions between particles or connections between brain regions. The structural complexity and variability of graphs pose challenges to their efficient analysis and classification. Here, we propose GAUDI (Graph Autoencoder Uncovering Descriptive Information), a graph autoencoder that addresses these challenges. GAUDI’s encoder progressively reduces the size of the graph using multi-step hierarchical pooling, while its decoder incrementally increases the graph size until the original dimensions are restored, focusing on the node and edge features while preserving the graph structure through skip-connections. By training GAUDI to minimize the difference between the node and edge features of the input graph and those of the output graph, it is compelled to capture the most critical parameters describing these features in the latent space, thereby enabling the extraction of essential parameters characterizing the graphs. We demonstrate the performance of GAUDI across diverse graph data originating from complex systems, including the estimation of the parameters of Watts-Strogatz graphs, the classification of protein assembly structures from single-molecule localization microscopy data, the analysis of collective behaviors, and correlations between brain connections and age. This approach offers a robust framework for efficiently analyzing and interpreting complex graph data, facilitating the extraction of meaningful patterns and insights across a wide range of applications.
High-resolution display of “The Kiss” on Retina E-Paper vs. iPhone 15: Photographs comparing the display of “The Kiss” on an iPhone 15 and Retina E-paper. The surface area of the Retina E-paper is ~ 1/4000 times smaller than the iPhone 15. (Image by the Authors of the manuscript.)Retina electronic paper with video-rate-tunable 45000 pixels per inch
Ade Satria Saloka Santosa, Yu-Wei Chang, Andreas B. Dahlin, Lars Osterlund, Giovanni Volpe, Kunli Xiong
arXiv: 2502.03580
As demand for immersive experiences grows, displays are moving closer to the eye with smaller sizes and higher resolutions. However, shrinking pixel emitters reduce intensity, making them harder to perceive. Electronic Papers utilize ambient light for visibility, maintaining optical contrast regardless of pixel size, but cannot achieve high resolution. We show electrically tunable meta-pixels down to ~560 nm in size (>45,000 PPI) consisting of WO3 nanodiscs, allowing one-to-one pixel-photodetector mapping on the retina when the display size matches the pupil diameter, which we call Retina Electronic Paper. Our technology also supports video display (25 Hz), high reflectance (~80%), and optical contrast (~50%), which will help create the ultimate virtual reality display.
(Photo by A. Ciarlo.)Ade Satria Saloka Santosa joins the Physics Department of the University of Gothenburg as a visiting PhD student from Uppsala University on 1 February 2025.
Ade holds a Master of Science degree in Industrial Chemistry from Pukyong National University, South Korea, and has research experience at the Korea Institute of Materials Science (KIMS).
During his PhD, he will focus on nanofabrication and e-paper technology.
MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)Spatial clustering of molecular localizations with graph neural networks
Jesús Pineda Date: 27 January 2025 Time: 10:00 Place: SciLifeLab Campus Solna, Sweden
Single-molecule localization microscopy (SMLM) generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multimodal Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.
Geared mechanism. (Image by G. Wang)Gan Wang defended his PhD thesis on 20 January 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 10:00.
Title: Microfabrication technique applications: from passive particle manipulation to active microswimmers, micromachines, and fluidic control
Abstract: Overcoming Brownian motion at the micro- and nanoscale to achieve precise control of objects is crucial for fields such as materials science and biology. Significant progress has been made in trapping and manipulating micro- and nanoscale objects, either by generating gradients through external physical fields or by engineering systems that can harvest energy from their environment for autonomous motion. These techniques rely on the precise application of forces, such as optical and electromagnetic forces, and have found extensive applications across various scientific disciplines. Recent advances in micro- and nanofabrication technologies have greatly enhanced the generation and regulation of these forces, offering new possibilities for manipulating micro- and nanoscale objects.
This thesis applies traditional micro- and nanofabrication techniques, typically used in semiconductor manufacturing, to construct micro- and nanostructures for manipulating forces, primarily critical Casimir forces and optical forces, to achieve precise control over microscale object movement.
I first show the fabrication of periodic micropatterns on a substrate, followed by chemical functionalization to impart hydrophilic and hydrophobic properties. Near the critical temperature of a binary liquid, attractive and repulsive critical Casimir forces are generated between the micropatterns and microparticles. These forces allow the stable trapping of the microparticles on the substrate and the manipulation of their configuration and movement.
Then, my research transitions from passive control to active motion by fabricating metasurfaces capable of modulating optical fields and embedding them within micro-particles (microswimmers). This enables light-momentum exchange under planar laser illumination, resulting in autonomous movement of the microswimmers. By varying the metasurface design as well as the intensity and polarization of the light, complex behaviors can emerge within these microswimmers. Subsequently, My research focused on using these microfabrication techniques to build micromotors integrated on a chip surface. These micromotors couple with other objects through gear structures, creating miniature machines that can execute functional tasks. Finally, by altering the configuration of these machines and the distances between them, I acheived precise, multifunctional control over fluid dynamics, facilitating the transport of micro- and nanoscale objects.
Insights gained from this research suggest innovative manufacturing approaches for scalable manipulation of particles, more intelligent microrobots, and powerful miniaturized on-chip machines, with applications across various fields.
(Photo by A. Ciarlo.)Mathilda Gustafsson joined the Soft Matter Lab on 20 January 2025.
Mathilda is a master student in Complex Adaptive Systems at Chalmers University of Technology.
During her time at the Soft Matter Lab, she will work on a project about tracking bacteria in sequences of microscopic images. In particular she will try to solve problems with overlapping bacteria using recurrent neural networks.
