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Poster by M. Granfors at the Learning on graphs and geometry meetup in Uppsala, 11 February 2025

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.

Retina electronic paper with video-rate-tunable 45000 pixels per inch on ArXiv

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.

Ade Satria Saloka Santosa joins the Soft Matter Lab

(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.

Invited talk by J. Pineda at the CMCB Lab on 27 January 2025

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.

Reference
Pineda, Jesús, Sergi Masó-Orriols, Joan Bertran, Mattias Goksör, Giovanni Volpe, and Carlo Manzo. Spatial Clustering of Molecular Localizations with Graph Neural Networks.  arXiv: 2412.00173

Gan Wang defended his PhD thesis on 20 January 2025. Congrats!

Gan Wang, PhD defense.
(Photo by A. Ciarlo.)
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.

Thesis: https://hdl.handle.net/2077/84048

Supervisor: Giovanni Volpe
Examiner: Dag Hanstorp
Opponent: Peer Fischer
Committee: Heiner Linke, Anna Maciolek, Hao Zeng
Alternate board member: Francesco Ferranti

Mathilda Gustafsson joins the Soft Matter Lab

(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.

Brian Senabulya Joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Brian Senabulya joined the Soft Matter Lab on 20 January 2025.

Brian is a master student in Physics at the Physics Department of Gothenburg University.

During his time at the Soft Matter Lab, he will work on a project about deep learning based particle tracking and trajectory linking.

Diffusion models for super-resolution microscopy: a tutorial published in Journal of Physics: Photonics

Super-resolution by diffusion models: low-resolution images of microtubules (left) are transformed to high-resolution (right) by diffusion model. Dataset courtesy: BioSR Dataset. (Image by H. Bachimamchi.)
Diffusion models for super-resolution microscopy: a tutorial
Harshith Bachimanchi, Giovanni Volpe
Journal of Physics: Photonics 7, 013001 (2025)
doi: 10.1088/2515-7647/ada101
arXiv: 2409.16488

Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions in the context of super-resolution microscopy. We provide the necessary theoretical background, the essential mathematical derivations, and a detailed Python code implementation using PyTorch. We discuss the metrics to quantitatively evaluate the model, illustrate the model performance at different noise levels of the input low-resolution images, and briefly discuss how to adapt the tutorial for other applications. The code provided in this tutorial is also available as a Python notebook in the supplementary information.

Benjamin Midtvedt defended his PhD thesis on 9 January 2025. Congrats!

Benjamin Midtvedt, PhD defense. (Photo by H. P. Thanabalan.)
Benjamin Midtvedt defended his PhD thesis on 9 January 2025. Congrats!
The defense will take place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.

Title: Annotation-free deep learning for quantitative microscopy

Abstract: Quantitative microscopy is an essential tool for studying and understanding microscopic structures. However, analyzing the large and complex datasets generated by modern microscopes presents significant challenges. Manual analysis is time-intensive and subjective, rendering it impractical for large datasets. While automated algorithms offer faster and more consistent results, they often require careful parameter tuning to achieve acceptable performance, and struggle to interpret the more complex data produced by modern microscopes. As such, there is a pressing need to develop new, scalable analysis methods for quantitative microscopy. In recent years, deep learning has transformed the field of computer vision, achieving superhuman performance in tasks ranging from image classification to object detection. However, this success depends on large, annotated datasets, which are often unavailable in microscopy. As such, to successfully and efficiently apply deep learning to microscopy, new strategies that bypass the dependency on extensive annotations are required. In this dissertation, I aim to lower the barrier for applying deep learning in microscopy by developing methods that do not rely on manual annotations and by providing resources to assist researchers in using deep learning to analyze their own microscopy data. First, I present two cases where training annotations are generated through alternative means that bypass the need for human effort. Second, I introduce a deep learning method that leverages symmetries in both the data and the task structure to train a statistically optimal model for object detection without any annotations. Third, I propose a method based on contrastive learning to estimate nanoparticle sizes in diffraction-limited microscopy images, without requiring annotations or prior knowledge of the optical system. Finally, I deliver a suite of resources that empower researchers in applying deep learning to microscopy. Through these developments, I aim to demonstrate that deep learning is not merely a “black box” tool. Instead, effective deep learning models should be designed with careful consideration of the data, assumptions, task structure, and model architecture, encoding as much prior knowledge as possible. By structuring these interactions with care, we can develop models that are more efficient, interpretable, and generalizable, enabling them to tackle a wider range of microscopy tasks.

Thesis: https://hdl.handle.net/2077/84178

Supervisor: Giovanni Volpe
Examiner: Dag Hanstorp
Opponent: Ivo Sbalzarini
Committee: Susan Cox, Maria Arrate Munoz Barrutia, Ignacio Arganda-Carreras
Alternate board member: Måns Henningson

Ivo Sbalzarini (left) and Benjamin Midtvedt (right). (Photo by H. P. Thanabalan.)
Benjamin Midtvedt (left), Giovanni Volpe (right), announcement. (Photo by H. P. Thanabalan.)
From left to right: Ignacio Arganda, Arrate Muñoz Barrutia, Susan Cox, Benjamin Midtvedt, Giovanni Volpe, Ivo Sbalzarini. (Photo by H. P. Thanabalan.)

Talk by Ivo Sbalzarini, 9 January 2025

Ivo Sbalzarini, talk. (Photo by Y.-W. Chang.)
Content-adaptive deep learning for large-scale
fluorescence microscopy imaging

Ivo Sbalzarini
Max Planck Institute of Molecular Cell Biology and Genetics
Center for Systems Biology Dresden
https://sbalzarini-lab.org/

Date: 9 January 2025
Time: 11:00
Place: Nexus