@phdthesis{20276, abstract = {Complex 3D shapes can be created by morphing flat 2D configurations. Such deformations either preserve the intrinsic material geometry (e.g., folding paper) or modify it through localized contraction. Once transformed, the 3D shape can be further controlled to achieve a target functionality. A key challenge is to take the material specifications and the actuation process as input to automatically design the target 3D shape and its functionality. This thesis presents two novel computational pipelines for the design and control of shape-morphing structures used to create functional prototypes. The first pipeline borrows from the art of origami to fold paper into intricate shapes and applies this principle to make 3D lighting displays. We introduce, PCBend a computational design approach that covers a surface with individually addressable RGB LEDs, effectively forming a low-resolution surface by folding rigid printed circuit boards (PCBs). We optimize cut patterns on PCBs to act as hinges and co-design LED placement, circuit routing, and fabrication constraints to produce PCB blueprints. The PCBs are fabricated using automated standard manufacturing services with LEDs embedded on them. Finally, the fabricated PCBs are cut along the contour and folded onto a 3D-printed support. The 3D lighting display is then controlled to display complex surface light patterns. Creating 3D shapes through folding is only possible if their planar configuration, called ”unfolding” exists without any distortion or overlap. Existing methods often permit distortion or require multiple patches, which are unsuitable for fabrication pipelines that rely on folding non-stretchable materials. We reinforce such fabrication pipelines by providing a geometric relaxation to the problem, where the input shape is modified to admit overlap-free unfolding. The second fabrication pipeline extends shape morphing to soft robotics by emulating nature’s blueprint of distributed actuation. Inspired by vertebrates, we build musculoskeletal robots using modular active actuators, employing Liquid Crystal Elastomers (LCEs) as shrinkable artificial muscles integrated with 3D-printed bones. The chemical composition of LCEs is altered to enable untethered actuation through infrared radiation, allowing active control of individual muscles and their corresponding bones. The combined motion of individual bones defines the robot’s overall shape and functionality. Our proposed system significantly expands both the design and control spaces of soft robots, which we harness using our computational design tools. We build several physical robots that exhibit complex shape morphing and varied terrain navigation, showcasing the versatility of our pipeline. This thesis explores applications ranging from intricate light patterns displayed on 3D shapes formed by folding rigid PCBs to untethered robots that use contractile muscles to exhibit shape morphing and locomotion. Through these examples, the thesis highlights how computational design and distributed actuation, integrated with novel materials, can transform passive structures into functional prototypes.}, author = {Bhargava, Manas}, isbn = {978-3-99078-065-7}, issn = {2663-337X}, pages = {96}, publisher = {Institute of Science and Technology Austria}, title = {{Design and control of deformable structures: From PCB lighting displays to elastomer robots}}, doi = {10.15479/AT-ISTA-20276}, year = {2025}, } @article{18565, abstract = {We present a computational approach for unfolding 3D shapes isometrically into the plane as a single patch without overlapping triangles. This is a hard, sometimes impossible, problem, which existing methods are forced to soften by allowing for map distortions or multiple patches. Instead, we propose a geometric relaxation of the problem: We modify the input shape until it admits an overlap‐free unfolding. We achieve this by locally displacing vertices and collapsing edges, guided by the unfolding process. We validate our algorithm quantitatively and qualitatively on a large dataset of complex shapes and show its proficiency by fabricating real shapes from paper.}, author = {Bhargava, Manas and Schreck, Camille and Freire, M. and Hugron, P. A. and Lefebvre, S. and Sellán, S. and Bickel, Bernd}, issn = {1467-8659}, journal = {Computer Graphics Forum}, keywords = {fabrication, single patch unfolding, mesh simplification}, number = {1}, publisher = {Wiley}, title = {{Mesh simplification for unfolding}}, doi = {10.1111/cgf.15269}, volume = {44}, year = {2025}, } @phdthesis{19759, abstract = {Despite generating remarkable results in various computer vision tasks, deep learning comes with some surprising shortcomings. For example, tiny perturbations, often imperceptible to the human eye, can completely change the predictions of image classifiers. Despite a decade of research, the field has made limited progress in developing image classifiers that are both accurate and robust. This thesis aims to address this gap. As our first contribution, we aim to simplify the process of training certifiably robust image classifiers. We do this by designing a convolutional layer that does not require executing an iterative procedure in every forward pass, but relies on an explicit bound instead. We also propose a loss function that allows optimizing for a particular margin more precisely. Next, we provide an overview and comparison of various methods that create robust image classifiers by constraining the Lipschitz constant. This is important since generally longer training times and more parameters improve the performance of robust classifiers, making it challenging to determine the most practical and effective methods from existing literature. In 1-Lipschitz classification, the performance of current methods is still much worse than what we expect on the simple tasks we consider. Therefore, we next investigate potential causes of this shortcoming. We first consider the role of the activation function. We prove a theoretical shortcoming of the commonly used activation function, and provide an alternative without it. However this theoretical improvement does barely translate to the empirical performance of robust classifiers, suggesting a different bottleneck. Therefore, in the final chapter, we study how the performance depends on the amount of training data. We prove that in the worst case, we might require far more data to train a robust classifier compared to a normal one. We furthermore find that the amount of training data is a key determinant of the performance current methods achieve on popular datasets. Additionally, we show that linear subspaces exist with tiny data variance, and yet we can still train very accurate classifiers after projecting into those subspaces. This shows that on the datasets considered, enforcing robustness in classification makes the task strictly more challenging. }, author = {Prach, Bernd}, issn = {2663-337X}, pages = {84}, publisher = {Institute of Science and Technology Austria}, title = {{Robust image classification with 1-Lipschitz networks}}, doi = {10.15479/10.15479/at-ista-19759}, year = {2025}, } @inproceedings{20455, abstract = {Despite extensive research since the community learned about adversarial examples 10 years ago, we still do not know how to train high-accuracy classifiers that are guaranteed to be robust to small perturbations of their inputs. Previous works often argued that this might be because no classifier exists that is robust and accurate at the same time. However, in computer vision this assumption does not match reality where humans are usually accurate and robust on most tasks of interest. We offer an alternative explanation and show that in certain settings robust generalization is only possible with unrealistically large amounts of data. Specifically, we find a setting where a robust classifier exists, it is easy to learn an accurate classifier, yet it requires an exponential amount of data to learn a robust classifier. Based on this theoretical result, we evaluate the influence of the amount of training data on datasets such as CIFAR10. Our findings indicate that the the amount of training data is the main factor determining the robust performance. Furthermore we show that that there are low magnitude directions in the data which are useful for non-robust generalization but are not available for robust classifiers. This implies that robust classification is a strictly harder tasks than normal classification, thereby providing an explanation why robust classification requires more data.}, author = {Prach, Bernd and Lampert, Christoph}, booktitle = {2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops}, isbn = {9798331599942}, issn = {2160-7516}, location = {Nashville, TN, United States}, pages = {660--669}, publisher = {IEEE}, title = {{Intriguing properties of robust classification}}, doi = {10.1109/CVPRW67362.2025.00071}, year = {2025}, } @phdthesis{19630, abstract = {This thesis consists of three chapters, each corresponding to one publication. While each of these projects tackles a topic in a different area of research, they all share a common thread in the type of topological structure they handle - a partition of space into volumes separated by interfaces that meet in non-manifold junctions. In Chapter 2, we study clusters of soap bubbles from a simulation perspective. In particular, we develop a surface-only algorithm that couples large scale motion and shape deformation of soap bubble clusters with the small scale evolution of the thin film's thickness, which is responsible for visual phenomena like surface vortices, Newton's interference patterns, capillary waves, and deformation-dependent rupturing of films in a foam. We model film thickness as a reduced degree of freedom in the Navier-Stokes equations and from them derive three sets of equations governing normal and tangential motion of the soap film surface, as well as the evolution of the thin film thickness. We discretize these equations on a non-manifold triangle mesh, extending and adapting operators to handle complex topology. We also present an incompressible fluid solver for 2.5D films and an advection algorithm for convecting fields across non-manifold surface junctions. Our simulations enhance bubble solvers with additional effects caused by convection, rippling, draining, and evaporation of the thin film. In Chapter 3, we introduce a multi-material non-manifold mesh-based surface tracking algorithm that converts mesh defects, such as overlaps, self-intersections, and inversions into topological changes. Our algorithm generalizes prior work on manifold surface tracking with topological changes: it preserves surface features like mesh-based methods, and it robustly handles topological changes like level set methods. Our method also offers improved efficiency and robustness over the state of the art. We demonstrate the effectiveness of the approach on a range of examples, including complex soap film simulations, such as those presented in Chapter 2, but with an order of magnitude more interacting bubbles than what we could achieve before, and Boolean unions of non-manifold meshes consisting of millions of triangles. Lastly, in Chapter 4, we utilize developments in the theory of random geometric complexes facilitated by observations from Discrete Morse theory. We survey the methods and results obtained with this new approach, and discuss some of its shortcomings. We use simulations to illustrate the results and to form conjectures, getting numerical estimates for combinatorial, topological, and geometric properties of weighted and unweighted Delaunay mosaics, their dual Voronoi tessellations, and the Alpha and Wrap complexes contained in the mosaics.}, author = {Synak, Peter}, issn = {2663-337X}, pages = {106}, publisher = {Institute of Science and Technology Austria}, title = {{Methods for fluid simulation, surface tracking, and statistics of non-manifold structures}}, doi = {10.15479/AT-ISTA-19630}, year = {2025}, } @phdthesis{20551, abstract = {The space of codimension-2 shapes, such as curves in 3D and surfaces in 4D, is an infinite-dimensional manifold. This thesis explores geometric structures and dynamics on this space, with emphasis on their implications for physics, particularly hydrodynamics. Our investigation ranges from theoretical studies of infinite-dimensional symplectic and prequantum geometry to numerical computation of the time evolution of shapes. The thesis presents four main contributions. In the first part, we introduce implicit representations of codimension-2 shapes using a class of complex-valued functions, and prove that the space of these implicit representations forms a prequantum bundle over the codimension-2 shape space. This reveals a new geometric interpretation of the canonical symplectic structure on the codimension-2 shape space. In the second part, we use implicit representations to develop a simulation method for the dynamics of space curves. To handle chaotic systems such as vortex filaments in hydrodynamics, we exploit the infinite degrees of freedom, hidden in both the configuration and dynamics of implicit representations. In the third part, we introduce new symplectic structures on the space of space curves, which generalize the only previously known symplectic structure on this space, allowing for new Hamiltonian dynamics of space curves. In the fourth part, we apply a symplectic viewpoint to a differential geometric problem with practical applications. We derive a new area formula for spherical polygons via prequantization. }, author = {Ishida, Sadashige}, isbn = {978-3-99078-070-1}, issn = {2663-337X}, pages = {141}, publisher = {Institute of Science and Technology Austria}, title = {{Symplectic-prequantum structures and dynamics on the codimension-2 shape space}}, doi = {10.15479/AT-ISTA-20551}, year = {2025}, } @phdthesis{20234, abstract = {Game Theory is the mathematical formalization of social dynamics - systems where agents interact over time and the evolution of the state of the system depends on the decisions of every player. This thesis takes the perspective of a single player and focuses on what they can guarantee in the worst case over the behavior of other players. In other words, we consider that the objective of every other player in the game is exactly the opposite to the player. We focus on sustained interactions over time, where the players repeatedly obtain quantitative rewards over time, and they are interested in maximizing their long-term performance. Formally, this thesis focuses on zero-sum games with the liminf average objective. Two fundamental questions that Game Theory aims to answer are the following. 1. How much can a player guarantee to obtain after the interaction? 2. How to act in order to obtain the previously mentioned guarantee? These questions are formalized by the concepts of "value" and "optimal strategies". We study their properties on games that exhibit one or more of the following properties. 1. Partial Observation: the players can not perfectly observe the current state of the system during the game. We consider the model of (finite) Partially Observable Markov Decision Processes and prove that finite-memory strategies are sufficient to approximately guarantee the value. 2. Perturbed Description: the formal description of the game is perturbed by a small parameter. We consider the model of (finite) Perturbed Matrix Games, and provide algorithms to check various robustness properties and to compute the parameterized value and optimal strategies. 3. Stochastic Transitions: the actions of the players determine the behavior of the evolution of the system, described as a probability distribution over the next state. We consider the model of (finite) Perturbed Stochastic Games and provide formulas for the marginal value. 4. Infinite States: the system can be in infinitely many states. We consider the model of Random Dynamic Games on a class of infinite graphs, prove the existence of the value, and quantify the concentration of finite-horizon values.}, author = {Saona Urmeneta, Raimundo J}, issn = {2663-337X}, pages = {125}, publisher = {Institute of Science and Technology Austria}, title = {{Robustness of solutions in game theory: Values and strategies in partially observable, perturbed, stochastic, and infinite games}}, doi = {10.15479/AT-ISTA-20234}, year = {2025}, } @article{17037, abstract = {Zero-sum stochastic games are parameterized by payoffs, transitions, and possibly a discount rate. In this article, we study how the main solution concepts, the discounted and undiscounted values, vary when these parameters are perturbed. We focus on the marginal values, introduced by Mills in 1956 in the context of matrix games—that is, the directional derivatives of the value along any fixed perturbation. We provide a formula for the marginal values of a discounted stochastic game. Further, under mild assumptions on the perturbation, we provide a formula for their limit as the discount rate vanishes and for the marginal values of an undiscounted stochastic game. We also show, via an example, that the two latter differ in general.}, author = {Attia, Luc and Oliu-Barton, Miquel and Saona Urmeneta, Raimundo J}, issn = {1526-5471}, journal = {Mathematics of Operations Research}, number = {1}, pages = {482--505}, publisher = {Institute for Operations Research and the Management Sciences}, title = {{Marginal values of a stochastic game}}, doi = {10.1287/moor.2023.0297}, volume = {50}, year = {2025}, } @phdthesis{19048, abstract = {Rotations are found in physics problems at all scales: from spatial motion of celestial bodies, to transitions between quantum states of atoms and molecules. Mathematically, they represent a fundamental class of transformations and symmetries. Unlike spatial displacements, rotational transformations in three-dimensional space are non-commutative: the result of applying a sequence of rotations depends on the order of these operations. This feature makes the emergent physics that involves rotations rather intricate, but instrumental for studies of highly-interconnected many-body systems. In the presence of an environment, rotational properties of an object change, due to the interaction with particles of the environment. Owing to the complexity of this interaction, it can be engineered to exhibit certain properties of interest. In this Thesis, we examine several scenarios of how the rotational behavior of an impurity can be modified by interactions with its environment.}, author = {Maslov, Mikhail}, issn = {2663-337X}, pages = {86}, publisher = {Institute of Science and Technology Austria}, title = {{Emergent physics of rotating quantum impurities in many-body environments}}, doi = {10.15479/at:ista:19048}, year = {2025}, } @phdthesis{20147, abstract = {Quantitative properties offer a framework for specifying and verifying system behaviors beyond the traditional boolean perspective. For example, while a boolean property may specify whether a server eventually grants every request it receives, a quantitative one may map each server execution to its average response time. This quantitative view is relatively well-studied in the context of static verification. However, although such properties often appear in practice as performance or robustness measures in a dynamic verification context, a general theoretical framework for their analysis and classification from a monitoring perspective is still missing. In this thesis, we aim to develop such a framework that takes resource-precision tradeoffs of monitors as a central consideration. We present the first theory of monitorability for quantitative properties where monitors can be naturally approximate and compared regarding their precision and resource use. In particular, we show that additional monitor resources such as registers or states lead to strictly better approximations for some properties. To enable such analyses in a machine-model independent way, we describe an abstract notion of monitors that can be instantiated with concrete models of monitors. Within this framework, we study how abstract monitors behave and identify classes of properties amenable to approximate monitoring with resource-precision considerations. We then extend the boolean safety-liveness dichotomy and safety-progress hierarchy to the quantitative setting with a monitoring perspective. In particular, we prove that every property is the pointwise minimum of a safety property and a liveness property, and properties that are both safe and co-safe can be approximately monitored arbitrarily precisely using only finitely many states. We also study the classes of quantitative properties definable by finite-state quantitative automata and provide algorithms for deciding their safety or liveness as well as their safety-liveness decompositions. Finally, we present the first general-purpose tool for automating the analysis, verification, and monitoring of quantitative automata. }, author = {Sarac, Naci E}, issn = {2663-337X}, pages = {149}, publisher = {Institute of Science and Technology Austria}, title = {{A monitoring-oriented theory and classification of quantitative specifications}}, doi = {10.15479/AT-ISTA-20147}, year = {2025}, } @article{10045, abstract = {Given a fixed finite metric space (V,μ), the {\em minimum 0-extension problem}, denoted as 0-Ext[μ], is equivalent to the following optimization problem: minimize function of the form minx∈Vn∑ifi(xi)+∑ijcijμ(xi,xj) where cij,cvi are given nonnegative costs and fi:V→R are functions given by fi(xi)=∑v∈Vcviμ(xi,v). The computational complexity of 0-Ext[μ] has been recently established by Karzanov and by Hirai: if metric μ is {\em orientable modular} then 0-Ext[μ] can be solved in polynomial time, otherwise 0-Ext[μ] is NP-hard. To prove the tractability part, Hirai developed a theory of discrete convex functions on orientable modular graphs generalizing several known classes of functions in discrete convex analysis, such as L♮-convex functions. We consider a more general version of the problem in which unary functions fi(xi) can additionally have terms of the form cuv;iμ(xi,{u,v}) for {u,v}∈F, where set F⊆(V2) is fixed. We extend the complexity classification above by providing an explicit condition on (μ,F) for the problem to be tractable. In order to prove the tractability part, we generalize Hirai's theory and define a larger class of discrete convex functions. It covers, in particular, another well-known class of functions, namely submodular functions on an integer lattice. Finally, we improve the complexity of Hirai's algorithm for solving 0-Ext on orientable modular graphs. }, author = {Dvorak, Martin and Kolmogorov, Vladimir}, issn = {1436-4646}, journal = {Mathematical Programming}, keywords = {minimum 0-extension problem, metric labeling problem, discrete metric spaces, metric extensions, computational complexity, valued constraint satisfaction problems, discrete convex analysis, L-convex functions}, pages = {279--322}, publisher = {Springer Nature}, title = {{Generalized minimum 0-extension problem and discrete convexity}}, doi = {10.1007/s10107-024-02064-5}, volume = {209}, year = {2025}, } @article{17149, abstract = {The approximation of a circle with the edges of a fine square grid distorts the perimeter by a factor about 4/Pi. We prove that this factor is the same on average (in the ergodic sense) for approximations of any rectifiable curve by the edges of any non-exotic Delaunay mosaic (known as Voronoi path), and extend the results to all dimensions, generalizing Voronoi paths to Voronoi scapes.}, author = {Edelsbrunner, Herbert and Nikitenko, Anton}, issn = {1432-0444}, journal = {Discrete & Computational Geometry}, pages = {490--499}, publisher = {Springer Nature}, title = {{Average and expected distortion of Voronoi paths and scapes}}, doi = {10.1007/s00454-024-00660-y}, volume = {73}, year = {2025}, } @article{21253, abstract = {We solve a problem of Dujmović and Wood (2007) by showing that a complete convex geometric graph on n vertices cannot be decomposed into fewer than n - 1 star-forests, each consisting of noncrossing edges. This bound is clearly tight. We also discuss similar questions for abstract graphs.}, author = {Pach, János and Saghafian, Morteza and Schnider, Patrick}, issn = {0925-7721}, journal = {Computational Geometry}, publisher = {Elsevier}, title = {{Decomposition of geometric graphs into star-forests}}, doi = {10.1016/j.comgeo.2025.102186}, volume = {129}, year = {2025}, } @phdthesis{18979, abstract = {Topological Data Analysis (TDA) is a discipline utilizing the mathematical field of topology to study data, most prominently collections of point sets. This thesis summarizes three projects related to computations in TDA. The first one establishes a variant of TDA for chromatic point sets, where each point is given a color. For example, we are given positions of cells within a tumor microenvironment, and color the cancerous cells red, and the immune cells blue. The aim is then to give a quantitative description of how the two or more sets of points spatially interact. Building on image, kernel and cokernel variants of persistent homology, we suggest six-packs of persistent diagrams as such a descriptor. We describe a construction of a chromatic alpha complex, which enables efficient computation of several variants of the six-packs. We give topological descriptions of natural subcomplexes of the chromatic alpha complex, and show that the radii of the simplices form a discrete Morse function. Finally, we provide an implementation of the presented chromatic TDA pipeline. The second part aims to translate a powerful tool of sheaf theory to elementary terms using labeled matrices. The goal is to enable their use in computational settings. We show that derived categories of sheaves over finite posets have, up to isomorphism, unique objects---minimal injective resolutions---and give a concrete algorithm to compute them. We further describe simple algorithms to compute derived pushforwards and pullbacks for monotonic maps, and their proper variants for inclusions, and demonstrate their tractability by providing an implementation. Finally, we suggest a discrete definition of microsupport and show desirable properties inspired by discrete Morse theory. In the last part, we present a collection of observations about collapses. We give a characterization of collapsibility in terms of unitriangular submatrices of the boundary matrix, a cotree-tree decomposition, and the optimal solution to a variant of the Procrustes problem. We establish relation between dual collapses and relative Morse theory and pose several open questions. Finally, focusing on complexes embedded in the three-dimensional Euclidean space, we describe a relation between the collapsibility and the triviality of a polygonal knot.}, author = {Draganov, Ondrej}, issn = {2663-337X}, keywords = {topological data analysis, chromatic point set, alpha complex, persistent homology, six pack, sheaf, microlocal discrete Morse, injective resolution, collapse, knot, discrete Morse theory}, pages = {140}, publisher = {Institute of Science and Technology Austria}, title = {{Structures and computations in topological data analysis}}, doi = {10.15479/at:ista:18979}, year = {2025}, } @article{20323, abstract = {We establish several results combining discrete Morse theory and microlocal sheaf theory in the setting of finite posets and simplicial complexes. Our primary tool is a computationally tractable description of the bounded derived category of sheaves on a poset with the Alexandrov topology. We prove that each bounded complex of sheaves on a finite poset admits a unique (up to isomorphism of complexes) minimal injective resolution, and we provide algorithms for computing minimal injective resolution of an injective complex, as well as several useful functors between derived categories of sheaves. For the constant sheaf on a simplicial complex, we give asymptotically tight bounds on the complexity of computing the minimal injective resolution using those algorithms. Our main result is a novel definition of the discrete microsupport of a bounded complex of sheaves on a finite poset. We detail several foundational properties of the discrete microsupport, as well as a microlocal generalization of the discrete homological Morse theorem and Morse inequalities.}, author = {Brown, Adam and Draganov, Ondrej}, issn = {0022-4049}, journal = {Journal of Pure and Applied Algebra}, number = {10}, publisher = {Elsevier}, title = {{Discrete microlocal Morse theory}}, doi = {10.1016/j.jpaa.2025.108068}, volume = {229}, year = {2025}, } @article{20591, abstract = {In this paper we derive estimates for the Hessian of the logarithm (log-Hessian) for solutions to the heat equation. For initial data in the form of log-Lipschitz perturbation of strongly log-concave measures, the log-Hessian admits an explicit, uniform (in space) lower bound. This yields a new estimate for the Lipschitz constant of a transport map pushing forward the standard Gaussian to a measure in this class. On the other hand, we show that assuming only fast decay of the tails of the initial datum does not suffice to guarantee uniform log-Hessian upper bounds.}, author = {Brigati, Giovanni and Pedrotti, Francesco}, issn = {1083-589X}, journal = {Electronic Communications in Probability}, publisher = {Institute of Mathematical Statistics}, title = {{Heat flow, log-concavity, and Lipschitz transport maps}}, doi = {10.1214/25-ECP717}, volume = {30}, year = {2025}, } @article{20050, abstract = {We prove upper bounds on the L∞-Wasserstein distance from optimal transport between strongly log-concave probability densities and log-Lipschitz perturbations. In the simplest setting, such a bound amounts to a transport-information inequality involving the L∞-Wasserstein metric and the relative L∞-Fisher information. We show that this inequality can be sharpened significantly in situations where the involved densities are anisotropic. Our proof is based on probabilistic techniques using Langevin dynamics. As an application of these results, we obtain sharp exponential rates of convergence in Fisher’s infinitesimal model from quantitative genetics, generalising recent results by Calvez, Poyato, and Santambrogio in dimension 1 to arbitrary dimensions.}, author = {Khudiakova, Kseniia and Maas, Jan and Pedrotti, Francesco}, issn = {1050-5164}, journal = {The Annals of Applied Probability}, number = {3}, pages = {1913--1940}, publisher = {Institute of Mathematical Statistics}, title = {{L∞-optimal transport of anisotropic log-concave measures and exponential convergence in Fisher’s infinitesimal model}}, doi = {10.1214/25-aap2162}, volume = {35}, year = {2025}, } @unpublished{22807, abstract = {Cleavage - the series of rapid cell divisions that follow fertilization - marks the onset of metazoan development and represents a deeply conserved evolutionary process. Across animals, two principal modes exist: complete (holoblastic) and incomplete (meroblastic) cleavage. While holoblastic cleavage resembles conventional cytokinesis both in vitro and in vivo, the mechanisms underlying meroblastic cleavage have remained poorly understood. Using zebrafish embryos as a model, we show that meroblastic cleavage proceeds through a distinct two-step mechanism. The process begins with the assembly and contraction of a large, arc-shaped actomyosin cable. However, this contractile event alone is insufficient to complete division. A second phase, driven by cadherin-mediated membrane adhesion, is required to invaginate the furrow ridge. Strikingly, this transition depends on mechanical uncoupling of the contractile cable from the surrounding cortex. We demonstrate that such uncoupling arises from an active nematic instability, which both enhances contractility along the cable and generates actin depletion zones that relieve lateral connections. Together, these findings reveal that meroblastic cleavage is governed not by a single actomyosin-based event but by a sequential interplay between cytoskeletal contraction and cadherin-dependent adhesion, highlighting a mechanism fundamentally distinct from canonical cytokinesis.}, author = {Tong, Xin and Li, Yuting I and Schelle, Joséphine and Hannezo, Edouard B and Heisenberg, Carl-Philipp J}, booktitle = {bioRxiv}, title = {{Non-canonical cytokinesis driven by mechanical uncoupling via nematic flows and adhesion-based invagination}}, doi = {10.1101/2025.10.15.682552}, year = {2025}, } @article{20705, abstract = {Optical tweezers are widely used as a highly sensitive tool to measure forces on micron-scale particles. One such application is the measurement of the electric charge of a particle, which can be done with high precision in liquids, air, or vacuum. We experimentally investigate how the trapping laser itself can electrically charge such a particle, in our case a ∼1  μ⁢m SiO2 sphere in air. We model the charging mechanism as a two-photon process which reproduces the experimental data with high fidelity.}, author = {Stöllner, Andrea and Lenton, Isaac C and Volosniev, Artem and Millen, James and Shibuya, Renjiro and Ishii, Hisao and Rak, Dmytro and Alpichshev, Zhanybek and David, Grégory and Signorell, Ruth and Muller, Caroline J and Waitukaitis, Scott R}, issn = {1079-7114}, journal = {Physical Review Letters}, number = {21}, publisher = {American Physical Society}, title = {{Using optical tweezers to simultaneously trap, charge, and measure the charge of a microparticle in air}}, doi = {10.1103/5xd9-4tjj}, volume = {135}, year = {2025}, } @article{20662, abstract = {Task-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Here, we propose DeepTaskGen, a deep-learning approach that synthesizes non-acquired task-based contrast maps from resting-state (rs-) fMRI. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals from UK Biobank. DeepTaskGen outperforms several benchmarks in generating synthetic task-contrast maps, achieving superior reconstruction performance while retaining inter-individual variation essential for biomarker development. We further show comparable or superior predictive performance of synthetic maps relative to actual maps and rs-connectomes across diverse demographic, cognitive, and clinical variables. This approach facilitates the study of individual differences and the generation of task-related biomarkers by enabling the generation of arbitrary functional cognitive tasks from readily available rs-fMRI data.}, author = {Serin, Emin and Ritter, Kerstin and Schumann, Gunter and Banaschewski, Tobias and Marquand, Andre and Walter, Henrik and Ogoh, George and Stahl, Bernd Carsten and Brandlistuen, Ragnhild and Schikowski, Tamara and Young, Allan H. and Xinyang, Yu and Zhang, Zuo and Agunbiade, Kofoworola and Chen, Di and Desrivières, Sylvane and Clinton, Nicholas and Thompson, Paul and Köhler, Venessa and Schwalber, Ameli and Calhoun, Vince D. and Chang, Xiao and Zhang, Yanqing and Li, Yuzhu and Dai, Yuxiang and Yuan, Jiacan and Xia, Yunman and Jia, Tianye and Renner, Paul and Hese, Sören and Spanlang, Bernhard and Pearmund, Charlie and Athanasiadis, Anastasios Polykarpos and Petkoski, Spase and Jirsa, Viktor and Schmitt, Karen and Wilbertz, Johannes H. and Patraskaki, Myrto and Sommer, Peter and Heilmann-Heimbach, Stefanie and Mathey, Carina M. and Miller, Abigail J. and Claus, Isabelle and Nöthen, Markus M. and Hoffmann, Per and Forstner, Andreas J. and Pastor, Alvaro and Gallego, Jaime and Itatani, Reiya and Eiroa-Orosa, Francisco and Feixas, Guillem and Slater, Mel and Novarino, Gaia and Böttger, Sarah Jane and Tschorn, Mira and Rapp, Michael and Ask, Helga and Kjelkenes, Rikka and Fernandez, Sara and Van Der Meer, Dennis and Westlye, Lars T. and Andreassen, Ole A. and Aden, Rieke and Seefried, Beke and Nees, Frauke and Neidhart, Maja and Stringaris, Argyris and Schwarz, Emanuel and Holz, Nathalie and Tost, Heike and Meyer-Lindenberg, Andreas and Christmann, Nina and Janson, Karina and Schepanski, Kerstin and Schütz, Tatjana and Taron, Ulrike Helene and Eils, Roland and Roy, Jean Charles and Lett, Tristram A. and Kebir, Hedi and Polemiti, Elli and Hitchen, Esther and Jentsch, Marcel and Serin, Emin and Bernas, Antoine and Vaidya, Nilakshi and Twardziok, Sven and Ralser, Markus and Heinz, Andreas and Schumann, Gunter}, issn = {2399-3642}, journal = {Communications Biology}, publisher = {Springer Nature}, title = {{Generating synthetic task-based brain fingerprints for population neuroscience using deep learning}}, doi = {10.1038/s42003-025-09158-6}, volume = {8}, year = {2025}, }