@article{15407, abstract = {We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component (e.g., locality) and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We showcase the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow circuits of the quantum approximate optimization algorithm, solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a template for a broader class of hybrid quantum-classical algorithms for combinatorial optimization.}, author = {Finžgar, Jernej Rudi and Kerschbaumer, Aron and Schuetz, Martin J.A. and Mendl, Christian B. and Katzgraber, Helmut G.}, issn = {2691-3399}, journal = {PRX Quantum}, number = {2}, publisher = {American Physical Society}, title = {{Quantum-informed recursive optimization algorithms}}, doi = {10.1103/PRXQuantum.5.020327}, volume = {5}, year = {2024}, } @article{15360, abstract = {We investigate superradiant enhancements in the refrigeration performance of a set of N three-level systems that are collectively coupled to a hot and a cold thermal reservoir and are additionally subject to collective periodic (circular) driving. Assuming the system-reservoir coupling to be weak, we explore the regime of stronger periodic driving strengths by comparing collective weak driving, Floquet-Lindblad, and Floquet-Redfield master equations. We identify regimes where the power injected by the periodic driving is used to pump heat from the cold to the hot reservoir and derive analytic sufficient conditions for them based on a cycle analysis of the Floquet-Lindblad master equation. In those regimes, we also argue for which parameters collective enhancements like a quadratic scaling of the cooling current with N can be expected and support our arguments by numerical simulations.}, author = {Kolisnyk, Dmytro and Queißer, Friedemann and Schaller, Gernot and Schützhold, Ralf}, issn = {2331-7019}, journal = {Physical Review Applied}, number = {4}, publisher = {American Physical Society}, title = {{Floquet analysis of a superradiant many-qutrit refrigerator}}, doi = {10.1103/PhysRevApplied.21.044050}, volume = {21}, year = {2024}, } @article{18482, abstract = {This paper is dedicated to an optimization problem. Let A, B ⊂ Rn be compact convex sets. Consider the minimal number t0 > 0 such that t0B covers A after a shift to a vector x0 ∈ Rn. The goal is to find t0 and x0. In the special case of B being a unit ball centered at zero, x0 and t0 are known as the Chebyshev center and the Chebyshev radius of A. This paper focuses on the case in which A and B are defined with their black-box support functions. An algorithm for solving such problems efficiently is suggested. The algorithm has a superlinear convergence rate, and it can solve hundred-dimensional test problems in a reasonable time, but some additional conditions on A and B are required to guarantee the presence of convergence. Additionally, the behavior of the algorithm for a simple special case is investigated, which leads to a number of theoretical results. Perturbations of this special case are also studied.}, author = {Arkhipov, Pavel}, issn = {1608-3032}, journal = {Automation and Remote Control}, number = {6}, pages = {522--532}, publisher = {Springer Nature}, title = {{An algorithm for finding the generalized Chebyshev center of sets defined via their support functions}}, doi = {10.1134/S0005117924060031}, volume = {85}, year = {2024}, } @inproceedings{18450, abstract = {In this work we present the engineering of a portable platform for sensing SERS signals in droplets microfluidics. This system comprised not only the read-out platform but also a design for the microfluidic devices optimized to enhanced the obtained SERS signals.}, author = {Quintero, Sergio and Relvas, Maria and Aranda, Marta and Nodal, Fernando and Dieguez, Lorena and Abalde-Cela, Sara}, booktitle = {2024 International Conference on Optical MEMS and Nanophotonics}, isbn = {9798350384925}, issn = {2160-5041}, location = {San Sebastian, Spain}, publisher = {IEEE}, title = {{Portable Raman platform for SERS droplets microfludics}}, doi = {10.1109/OMN61224.2024.10685279}, year = {2024}, } @phdthesis{18588, abstract = {This thesis is an experimental work about two distinct research projects that evolved from a single project: non-equilibrium dynamics of an acoustically vibrated particle and microfabrication of particles with nano-scale 3D printing. The first project explores non equilibrium dynamics of a particle driven by ultrasonic vibrations. We design an experimental system consisting of an electromechanical vibration scheme to drive the particle’s vibrations and an imaging scheme to track its trajectories. We study the trajectories to determine how the particle’s dynamics evolve under the driven conditions, considering out of equilibrium systems in the context of equilibrium statistical mechanics. Using a Langevin framework and the Boltzmann factor, we characterize the particle’s dynamics as complex; the particle motion is not purely diffusive. We extract physical parameters like spring constant, effective temperature, damping coefficient and resonance frequency. In the second project, we explore and develop techniques in the design and microfabrication of particles across scales. Microfabrication involves building structures at the micron or submicron scale. These designed miniaturized patterns, objects, or devices are useful in biophysics, pharmacology, medical biology, and nanotechnology. We specifically apply two-photon polymerization, a form of 3D nano printing. We print millimetric particles, characterizing different designs to evaluate and showcase the resolution, aspect ratio integrity and print quality of the printing process. We also design and fabricate a microsensor to deflect under applicable force of order 0.1 pN. We present fundamental concepts needed to design the microsensor, showcasing 3D printing at considerably smaller scales down to the µm or below.}, author = {Mweka, Cecelia N}, issn = {2791-4585}, pages = {61}, publisher = {Institute of Science and Technology Austria}, title = {{Non equilibrium dynamics of driven individual particles and 3D printing across scales}}, doi = {10.15479/at:ista:18588}, year = {2024}, } @misc{14705, abstract = {Since the commercialization of brine shrimp (genus Artemia) in the 1950s, this lineage, and in particular the model species Artemia franciscana, has been the subject of extensive research. However, our understanding of the genetic mechanisms underlying various aspects of their reproductive biology, including sex determination, are still lacking. This is partly due to the scarcity of genomic resources for Artemia species and crustaceans in general. Here, we present a chromosome-level genome assembly of Artemia franciscana (Kellogg 1906), from the Great Salt Lake, USA. The genome is 1GB, and the majority of the genome (81%) is scaffolded into 21 linkage groups using a previously published high-density linkage map. We performed coverage and FST analyses using male and female genomic and transcriptomic reads to quantify the extent of differentiation between the Z and W chromosomes. Additionally, we quantified the expression levels in male and female heads and gonads and found further evidence for dosage compensation in this species.}, author = {Elkrewi, Marwan N}, keywords = {sex chromosome evolution, genome assembly, dosage compensation}, publisher = {Institute of Science and Technology Austria}, title = {{Data from "Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation"}}, doi = {10.15479/AT:ISTA:14705}, year = {2024}, } @inproceedings{17214, abstract = {Current numerical algorithms for simulating friction fall in one of two camps: smooth solvers sacrifice the stable treatment of static friction in exchange for fast convergence, and non-smooth solvers accurately compute friction at convergence rates that are often prohibitive for large graphics applications. We introduce a novel bridge between these two ideas that computes static and dynamic friction stably and efficiently. Our key idea is to convert the highly constrained non-smooth problem into an unconstrained smooth problem using logarithmic barriers that converges to the exact solution as accuracy increases. We phrase the problem as an interior point primal-dual problem that can be solved efficiently with Newton iteration. We observe quadratic convergence despite the non-smooth nature of the original problem, and our method is well-suited for large systems of tightly packed objects with many contact points. We demonstrate the efficacy of our method with stable piles of grains and stacks of objects, complex granular flows, and robust interlocking assemblies of rigid bodies.}, author = {Chen, Yi-Lu and Ly, Mickaël and Wojtan, Christopher J}, booktitle = {Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24}, isbn = {9798400705250}, keywords = {physical simulation, frictional contact, rigid body mechanics, non-smooth dynamics}, location = {Denver, United States}, publisher = {Association for Computing Machinery}, title = {{Primal-dual non-smooth friction for rigid body animation}}, doi = {10.1145/3641519.3657485}, year = {2024}, } @inproceedings{17426, abstract = {The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently, the scientific community has focused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz neural networks that leverage Lipschitz bounded dense and convolutional layers. Although different methods have been proposed in the literature to achieve this goal, understanding the performance of such methods is not straightforward, since different metrics can be relevant (e.g., training time, memory usage, accuracy, certifiable robustness) for different applications. For this reason, this work provides a thorough theoretical and empirical comparison between methods by evaluating them in terms of memory usage, speed, and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at https://github.com/berndprach/1LipschitzLayersCompared.}, author = {Prach, Bernd and Brau, Fabio and Buttazzo, Giorgio and Lampert, Christoph}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, location = {Seattle, WA, United States}, pages = {24574--24583}, publisher = {Computer Vision Foundation}, title = {{1-Lipschitz layers compared: Memory, speed, and certifiable robustness}}, doi = {10.1109/CVPR52733.2024.02320}, year = {2024}, } @unpublished{18874, 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. More precisely 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 explore how well robust classifiers generalize on datasets such as CIFAR-10. We come to the conclusion that on this datasets, the limitation of current robust models also lies in the generalization, and that they require a lot of data to do well on the test set. We also show that the problem is not in the expressiveness or generalization capabilities of current architectures, and that there are low magnitude features in the data which are useful for non-robust generalization but are not available for robust classifiers.}, author = {Prach, Bernd and Lampert, Christoph}, booktitle = {arXiv}, title = {{Intriguing properties of robust classification}}, doi = {10.48550/arXiv.2412.04245}, year = {2024}, } @unpublished{17361, abstract = {We present symplectic structures on the shape space of unparameterized space curves that generalize the classical Marsden-Weinstein structure. Our method integrates the Liouville 1-form of the Marsden-Weinstein structure with Riemannian structures that have been introduced in mathematical shape analysis. We also derive Hamiltonian vector fields for several classical Hamiltonian functions with respect to these new symplectic structures.}, author = {Bauer, Martin and Ishida, Sadashige and Michor, Peter W.}, booktitle = {arXiv}, keywords = {space of space curves, symplectic stuctures}, title = {{Symplectic structures on the space of space curves}}, doi = {10.48550/arXiv.2407.19908}, year = {2024}, } @article{17219, abstract = {We introduce a multi-material non-manifold mesh-based surface tracking algorithm that converts self-intersections 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 with thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting of millions of triangles.}, author = {Synak, Peter and Kalinov, Aleksei and Strugaru, Irina-Malina and Etemadihaghighi, Arian and Yang, Huidong and Wojtan, Christopher J}, issn = {1557-7368}, journal = {ACM Transactions on Graphics}, keywords = {surface tracking, topology change, non- manifold meshes, multi-material flows, solid modeling}, number = {4}, publisher = {Association for Computing Machinery}, title = {{Multi-material mesh-based surface tracking with implicit topology changes}}, doi = {10.1145/3658223}, volume = {43}, year = {2024}, } @phdthesis{18301, abstract = {Physics simulation in computer graphics can bring triangle meshes into topologically invalid states. The method in this thesis contributed to Heiss-Synak* and Kalinov* et al. [2024] who devised a non-manifold hybrid surface tracker—a surface tracker that repairs explicit non-manifold triangle meshes with the help of the implicit domain. Specifically, this thesis provides an algorithm for filling the holes that are left after removing problematic parts of the mesh.}, author = {Etemadihaghighi, Arian}, issn = {2791-4585}, keywords = {surface tracking, non-manifold, hole-filling, topology change, multi-material, solid-modeling}, pages = {39}, publisher = {Institute of Science and Technology Austria}, title = {{Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking}}, doi = {10.15479/at:ista:18301}, year = {2024}, } @article{12846, abstract = {We present a formula for the signed area of a spherical polygon via prequantization. In contrast to the traditional formula based on the Gauss-Bonnet theorem that requires measuring angles, the new formula mimics Green's theorem and is applicable to a wider range of degenerate spherical curves and polygons.}, author = {Chern, Albert and Ishida, Sadashige}, issn = {2470-6566}, journal = {SIAM Journal on Applied Algebra and Geometry}, number = {3}, pages = {782--796}, publisher = {Society for Industrial and Applied Mathematics}, title = {{Area formula for spherical polygons via prequantization}}, doi = {10.1137/23M1565255}, volume = {8}, year = {2024}, } @phdthesis{17485, abstract = {Large language models (LLMs) have made tremendous progress in the past few years, from being able to generate coherent text to matching or surpassing humans in a wide variety of creative, knowledge or reasoning tasks. Much of this can be attributed to massively increased scale, both in the size of the model as well as the amount of training data, from 100s of millions to 100s of billions, or even trillions. This trend is expected to continue, which, although exciting, also raises major practical concerns. Already today's 100+ billion parameter LLMs require top-of-the-line hardware just to run. Hence, it is clear that sustaining these developments will require significant efficiency advances. Historically, one of the most practical ways of improving model efficiency has been compression, especially in the form of sparsity or quantization. While this has been studied extensively in the past, existing accurate methods are all designed for models around 100 million parameters; scaling them up to ones literally 1000x larger is highly challenging. In this thesis, we introduce a new unified sparsification and quantization approach OBC, which through additional algorithmic enhancements leads to GPTQ and SparseGPT, the first techniques fast and accurate enough to compress 100+ billion parameter models to 4- or even 3-bit precision and 50% weight-sparsity, respectively. Additionally, we show how weight-only quantizion does not just bring space savings but also up to 4.5x faster generation speed, via custom GPU kernels. In fact, we show for the first time that it is possible to develop an FP16 times INT4 mixed-precision matrix multiplication kernel, called Marlin, which comes close to simultaneously maximizing both memory and compute utilization, making weight-only quantization highly practical even for multi-user serving. Further, we demonstrate that GPTQ can be scaled to widely overparametrized trillion-parameter models, where extreme sub-1-bit compression rates can be achieved without any inference slow-down, by co-designing a bespoke entropy coding scheme together with an efficient kernel. Finally, we also study compression from the perspective of someone with access to massive amounts of compute resources for training large models completely from scratch. Here the key questions evolve around the joint scaling behavior between compression, model size, and amount of training data used. Based on extensive experimental results for both vision and text models, we introduce the first scaling law which accurately captures the relationship between weight-sparsity, number of non-zero weights and data. This further allows us to characterize the optimal sparsity, which we find to increase the longer a fixed cost model is being trained. Overall, this thesis presents contributions to three different angles of large model efficiency: affordable but accurate algorithms, highly efficient systems implementations, and fundamental scaling laws for compressed training.}, author = {Frantar, Elias}, issn = {2663-337X}, pages = {129}, publisher = {Institute of Science and Technology Austria}, title = {{Compressing large neural networks: Algorithms, systems and scaling laws}}, doi = {10.15479/at:ista:17485}, year = {2024}, } @inproceedings{18062, abstract = {We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains. In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data, which we validate empirically across model and data scales; on ViT/JFT-4B and T5/C4. These results allow us to characterize the "optimal sparsity", the sparsity level which yields the best performance for a given effective model size and training budget. For a fixed number of non-zero parameters, we identify that the optimal sparsity increases with the amount of data used for training. We also extend our study to different sparsity structures (such as the hardware-friendly n:m pattern) and strategies (such as starting from a pretrained dense model). Our findings shed light on the power and limitations of weight sparsity across various parameter and computational settings, offering both theoretical understanding and practical implications for leveraging sparsity towards computational efficiency improvements. We provide pruning and scaling law fitting code at: github.com/google-research/jaxpruner/tree/main/jaxpruner/projects/bigsparse.}, author = {Frantar, Elias and Ruiz, Carlos Riquelme and Houlsby, Neil and Alistarh, Dan-Adrian and Evci, Utku}, booktitle = {The Twelfth International Conference on Learning Representations}, location = {Vienna, Austria}, title = {{Scaling laws for sparsely-connected foundation models}}, year = {2024}, } @inproceedings{18061, abstract = {Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently, which makes practical deployment challenging and expensive. In this paper, we present a solution to this memory problem, in form of a new compression and execution framework called QMoE. Specifically, QMoE consists of a scalable algorithm which accurately compresses trillion-parameter MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke GPU decoding kernels to facilitate efficient end-to-end compressed inference, with minor runtime overheads relative to uncompressed execution. Concretely, QMoE can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss, in less than a day on a single GPU. This enables, for the first time, the execution of a trillion-parameter model on affordable commodity hardware, like a single server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead relative to ideal uncompressed inference. The anonymized code is available at: github.com/mlsys24-qmoe/qmoe.}, author = {Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {Proceedings of Machine Learning and Systems}, location = {Santa Clara, CA, United States}, title = {{QMoE: Sub-1-bit compression of trillion parameter models}}, volume = {6}, year = {2024}, } @phdthesis{17490, abstract = {Deep learning is essential in numerous applications nowadays, with many recent advancements made possible by training very large models. Despite their broad applicability, training neural networks is often time-intensive, and it is usually impractical to manage large models and datasets on a single machine. To address these issues, distributed deep learning training has become increasingly important. However, distributed training requires synchronization among nodes, and the mini-batch stochastic gradient descent algorithm places a significant load on network connections. A possible solution to tackle the synchronization bottleneck is to reduce a message size by lossy compression. In this thesis, we investigate systems and algorithmic approaches to communication compression during training. From the systems perspective, we demonstrate that a common approach of expensive hardware overprovisioning can be replaced through a thorough system design. We introduce a framework that introduces efficient software support for compressed communication in machine learning applications, applicable to both multi-GPU single-node training and larger-scale multi-node training. Our framework integrates with popular ML frameworks, providing up to 3x speedups for multi-GPU nodes based on commodity hardware and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy. Also, we consider an application of our framework to different communication schemes, such as Fully Sharded Data Parallel. We provide strong convergence guarantees for the compression in such a setup. Empirical validation shows that our method preserves model accuracy for GPT-family models with up to 1.3 billion parameters, while completely removing the communication bottlenecks of non-compressed alternatives, providing up to 2.2x speedups end-to-end. From the algorithmic side, we propose a general framework that dynamically adjusts the degree of compression across a model's layers during training. This approach enhances overall compression and results in significant speedups without compromising accuracy. Our algorithm utilizes an adaptive algorithm that automatically selects the optimal compression parameters for model layers, ensuring the best compression ratio while adhering to an error constraint. Our method is effective across all existing families of compression methods. It achieves up to 2.5x faster training and up to a 5x improvement in compression compared to efficient implementations of current approaches. Additionally, LGreCo can complement existing adaptive algorithms. }, author = {Markov, Ilia}, issn = {2663-337X}, pages = {102}, publisher = {Institute of Science and Technology Austria}, title = {{Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective}}, doi = {10.15479/at:ista:17490}, year = {2024}, } @inproceedings{17456, abstract = {Data-parallel distributed training of deep neural networks (DNN) has gained very widespread adoption, but can still experience communication bottlenecks. To address this issue, entire families of compression mechanisms have been developed, including quantization, sparsification, and low-rank approximation, some of which are seeing significant practical adoption. Despite this progress, almost all known compression schemes apply compression uniformly across DNN layers, although layers are heterogeneous in terms of parameter count and their impact on model accuracy.In this work, we provide a general framework for adapting the degree of compression across the model's layers dynamically during training, improving the overall compression, while leading to substantial speedups, without sacrificing accuracy. Our framework, called L-GreCo, is based on an adaptive algorithm, which automatically picks the optimal compression parameters for model layers guaranteeing the best compression ratio while satisfying an error constraint. Extensive experiments over image classification and language modeling tasks shows that L-GreCo is effective across all existing families of compression methods, and achieves up to 2.5 × training speedup and up to 5 × compression improvement over efficient implementations of existing approaches, while recovering full accuracy. Moreover, L-GreCo is complementary to existing adaptive algorithms, improving their compression ratio by 50\% and practical throughput by 66\%. An anonymized implementation is available at https://github.com/LGrCo/L-GreCo.}, author = {Markov, Ilia and Alimohammadi, Kaveh and Frantar, Elias and Alistarh, Dan-Adrian}, booktitle = {Proceedings of Machine Learning and Systems }, editor = {Gibbons, P. and Pekhimenko, G. and De Sa, C.}, location = {Athens, Greece}, publisher = {Association for Computing Machinery}, title = {{L-GreCo: Layerwise-adaptive gradient compression for efficient data-parallel deep learning}}, volume = {6}, year = {2024}, } @phdthesis{18766, abstract = {Poxviruses are large pleomorphic double-stranded DNA viruses that include well known members such as variola virus, the causative agent of smallpox, Mpox virus, as well as Vaccinia virus (VACV), which serves as a vaccination strain for formerly mentioned viruses. VACV is a valuable model for studying large pleomorphic DNA viruses in general and poxviruses specifically, as many features, such as core morphology and structural proteins, are well conserved within this family. Despite decades of research, our understanding of the structural components and proteins that comprise the poxvirus core in mature virions remains limited. Although major core proteins were identified via indirect experimental evidence, the core's complexity, with its large size, structure and number of involved proteins, has hindered efforts to achieve high-resolution insights and to define the roles of the individual proteins. The specific protein composition of the core's individual layers, including the palisade layer and the inner core wall, has remained unclear. In this study, we have merged multiple approaches, including single particle cryo electron microscopy of purified virus cores, cryo-electron tomography and subtomogram averaging of mature virions and molecular modeling to elucidate the structural determinants of the VACV core. Due to the lack of experimentally derived structures, either in situ or reconstituted in vitro, we used Alphafold to predict models of the putative major core protein candidates, A10, 23k, A3, A4, and L4. Our results show that the VACV core is composed of several layers with varying local symmetries, forming more intricate interactions than observed previously. This allowed us to identify several molecular building blocks forming the viral core lattice. In particular, we identified trimers of protein A10 as a major core structure that forms the palisade layer of the viral core. Additionally, we revealed that six petals of a flower shaped core pore within the core wall are composed of A10 trimers. Furthermore, we obtained a cryo-EM density for the inner core wall that could potentially accommodate an A3 dimer. Integrating descriptions of protein interactions from previous studies enabled us to provide a detailed structural model of the poxvirus core wall, and our findings indicate that the interactions within A10 trimers are likely consistent across orthopox- and parapoxviruses. This combined application of cryo-SPA and cryo-ET can help overcome obstacles in studying complex virus structures in the future, including their key assembly proteins, interactions, and the formation into a core lattice. Our work provides important fundamental new insights into poxvirus core architecture, also considering the recent re-emergence of poxviruses.}, author = {Datler, Julia}, isbn = {978-3-99078-049-7}, issn = {2663-337X}, keywords = {cryo-EM, cryo-ET, cryo-SPA, Structural Virology, Poxvirus, Vaccinia Virus, Structural Biology}, pages = {106}, publisher = {Institute of Science and Technology Austria}, title = {{Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM}}, doi = {10.15479/at:ista:18766}, year = {2024}, } @article{14979, abstract = {Poxviruses are among the largest double-stranded DNA viruses, with members such as variola virus, monkeypox virus and the vaccination strain vaccinia virus (VACV). Knowledge about the structural proteins that form the viral core has remained sparse. While major core proteins have been annotated via indirect experimental evidence, their structures have remained elusive and they could not be assigned to individual core features. Hence, which proteins constitute which layers of the core, such as the palisade layer and the inner core wall, has remained enigmatic. Here we show, using a multi-modal cryo-electron microscopy (cryo-EM) approach in combination with AlphaFold molecular modeling, that trimers formed by the cleavage product of VACV protein A10 are the key component of the palisade layer. This allows us to place previously obtained descriptions of protein interactions within the core wall into perspective and to provide a detailed model of poxvirus core architecture. Importantly, we show that interactions within A10 trimers are likely generalizable over members of orthopox- and parapoxviruses.}, author = {Datler, Julia and Hansen, Jesse and Thader, Andreas and Schlögl, Alois and Bauer, Lukas W and Hodirnau, Victor-Valentin and Schur, Florian KM}, issn = {1545-9985}, journal = {Nature Structural & Molecular Biology}, keywords = {Molecular Biology, Structural Biology}, pages = {1114--1123}, publisher = {Springer Nature}, title = {{Multi-modal cryo-EM reveals trimers of protein A10 to form the palisade layer in poxvirus cores}}, doi = {10.1038/s41594-023-01201-6}, volume = {31}, year = {2024}, }