--- OA_place: publisher _id: '18766' abstract: - lang: eng text: 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. acknowledged_ssus: - _id: EM-Fac - _id: LifeSc - _id: ScienComp acknowledgement: "This work was funded by the Austrian Science Fund (FWF) grant P31445 and ISTA. I\r\nwould like to express my gratitude to the Scientific Service Units, particularly the Lab\r\nSupport Facility, the Scientific Computing Facility and the Electron Microscopy Facility\r\nfor their tremendous support. I want to especially thank Alois for assisting me with the\r\ninstallation of countless new software and for troubleshooting cluster issues. A special\r\nthanks goes to Valentin for his outstanding support in cryo-EM data acquisition and\r\nhis ongoing help in improving the process to ensure that I obtained the best possible\r\ndata from my sample." alternative_title: - ISTA thesis article_processing_charge: No author: - first_name: Julia full_name: Datler, Julia id: 3B12E2E6-F248-11E8-B48F-1D18A9856A87 last_name: Datler orcid: 0000-0002-3616-8580 citation: ama: Datler J. Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM. 2024. doi:10.15479/at:ista:18766 apa: Datler, J. (2024). Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18766 chicago: Datler, Julia. “Elucidating the Structural Determinants of the Poxvirus Core Using Multi-Modal Cryo-EM.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18766. ieee: J. Datler, “Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM,” Institute of Science and Technology Austria, 2024. ista: Datler J. 2024. Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM. Institute of Science and Technology Austria. mla: Datler, Julia. Elucidating the Structural Determinants of the Poxvirus Core Using Multi-Modal Cryo-EM. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18766. short: J. Datler, Elucidating the Structural Determinants of the Poxvirus Core Using Multi-Modal Cryo-EM, Institute of Science and Technology Austria, 2024. corr_author: '1' date_created: 2025-01-07T10:23:12Z date_published: 2024-12-30T00:00:00Z date_updated: 2026-04-07T12:59:44Z day: '30' ddc: - '570' degree_awarded: PhD department: - _id: GradSch - _id: FlSc doi: 10.15479/at:ista:18766 file: - access_level: closed checksum: 3e51cab327c754045c3d29c1a50cc9a9 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: jstanger date_created: 2025-01-07T12:15:11Z date_updated: 2025-01-07T12:15:11Z file_id: '18769' file_name: PhD_thesis_Julia_Datler.docx file_size: 38814932 relation: source_file - access_level: open_access checksum: 22fabe5b97950bf852212f6edb555173 content_type: application/pdf creator: jstanger date_created: 2025-01-07T12:15:14Z date_updated: 2025-01-07T12:15:14Z file_id: '18770' file_name: PhD_thesis_Julia_Datler.pdf file_size: 12044865 relation: main_file success: 1 file_date_updated: 2025-01-07T12:15:14Z has_accepted_license: '1' keyword: - cryo-EM - cryo-ET - cryo-SPA - Structural Virology - Poxvirus - Vaccinia Virus - Structural Biology language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '12' oa: 1 oa_version: Published Version page: '106' project: - _id: 26736D6A-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P31445 name: Structural conservation and diversity in retroviral capsid publication_identifier: isbn: - 978-3-99078-049-7 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '12334' relation: part_of_dissertation status: public - id: '14979' relation: part_of_dissertation status: public status: public supervisor: - first_name: Florian KM full_name: Schur, Florian KM id: 48AD8942-F248-11E8-B48F-1D18A9856A87 last_name: Schur orcid: 0000-0003-4790-8078 title: Elucidating the structural determinants of the poxvirus core using multi-modal cryo-EM tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: dissertation user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18847' abstract: - lang: eng text: "Machine Learning and AI have the potential to transform data-driven\r\nscientific discovery, enabling accurate predictions for several scientific\r\nphenomena. As many scientific questions are inherently causal, this paper looks\r\nat the causal inference task of treatment effect estimation, where the outcome\r\nof interest is recorded in high-dimensional observations in a Randomized\r\nControlled Trial (RCT). Despite being the simplest possible causal setting and\r\na perfect fit for deep learning, we theoretically find that many common choices\r\nin the literature may lead to biased estimates. To test the practical impact of\r\nthese considerations, we recorded ISTAnt, the first real-world benchmark for\r\ncausal inference downstream tasks on high-dimensional observations as an RCT\r\nstudying how garden ants (Lasius neglectus) respond to microparticles applied\r\nonto their colony members by hygienic grooming. Comparing 6 480 models\r\nfine-tuned from state-of-the-art visual backbones, we find that the sampling\r\nand modeling choices significantly affect the accuracy of the causal estimate,\r\nand that classification accuracy is not a proxy thereof. We further validated\r\nthe analysis, repeating it on a synthetically generated visual data set\r\ncontrolling the causal model. Our results suggest that future benchmarks should\r\ncarefully consider real downstream scientific questions, especially causal\r\nones. Further, we highlight guidelines for representation learning methods to\r\nhelp answer causal questions in the sciences." acknowledgement: We thank Piersilvio De Bartolomeis, and the full Causal Learning and Artificial Intelligence (CLAI) group at ISTA for the extremely helpful discussions. Riccardo Cadei was supported by a Google Research Scholar Award and a Google Initiated Gift to Francesco Locatello. We thank the Social Immunity team at ISTA particularly Michaela Hönigsberger and Wilfrid Jean Louis, for supporting the ecological experiment and Farnaz Beikzadeh Abbasi, Luisa Fiebig and Martin Estermann for annotating ant behavior in ISTAnt. article_processing_charge: No arxiv: 1 author: - first_name: Riccardo full_name: Cadei, Riccardo id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b last_name: Cadei - first_name: Lukas full_name: Lindorfer, Lukas id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455 last_name: Lindorfer - first_name: Sylvia full_name: Cremer, Sylvia id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87 last_name: Cremer orcid: 0000-0002-2193-3868 - first_name: Cordelia full_name: Schmid, Cordelia last_name: Schmid - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 citation: ama: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. Smoke and mirrors in causal downstream tasks. In: ICML 2024 Workshop AI4Science. Vol 38. Curran Associates; 2024.' apa: Cadei, R., Lindorfer, L., Cremer, S., Schmid, C., & Locatello, F. (2024). Smoke and mirrors in causal downstream tasks. In ICML 2024 Workshop AI4Science (Vol. 38). Curran Associates. chicago: Cadei, Riccardo, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Smoke and Mirrors in Causal Downstream Tasks.” In ICML 2024 Workshop AI4Science, Vol. 38. Curran Associates, 2024. ieee: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, and F. Locatello, “Smoke and mirrors in causal downstream tasks,” in ICML 2024 Workshop AI4Science, 2024, vol. 38. ista: 'Cadei R, Lindorfer L, Cremer S, Schmid C, Locatello F. 2024. Smoke and mirrors in causal downstream tasks. ICML 2024 Workshop AI4Science. ICML: International Conference on Machine Learning vol. 38.' mla: Cadei, Riccardo, et al. “Smoke and Mirrors in Causal Downstream Tasks.” ICML 2024 Workshop AI4Science, vol. 38, Curran Associates, 2024. short: R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, ICML 2024 Workshop AI4Science, Curran Associates, 2024. conference: end_date: 2024-07-26 name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-26 corr_author: '1' date_created: 2025-01-14T07:27:26Z date_published: 2024-09-25T00:00:00Z date_updated: 2025-07-10T11:51:50Z day: '25' ddc: - '000' - '570' department: - _id: SyCr - _id: FrLo - _id: GradSch external_id: arxiv: - '2405.17151' file: - access_level: open_access checksum: beedf05388bbdb7ddda81ec3d5ec7026 content_type: application/pdf creator: dernst date_created: 2025-01-27T11:42:24Z date_updated: 2025-01-27T11:42:24Z file_id: '18896' file_name: 2024_ICML_Cadei.pdf file_size: 4453014 relation: main_file success: 1 file_date_updated: 2025-01-27T11:42:24Z has_accepted_license: '1' intvolume: ' 38' language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication: ICML 2024 Workshop AI4Science publication_status: published publisher: Curran Associates quality_controlled: '1' related_material: link: - relation: software url: https://github.com/CausalLearningAI/ISTAnt record: - id: '18895' relation: research_data status: public - id: '19509' relation: is_continued_by status: for_moderation scopus_import: '1' status: public title: Smoke and mirrors in causal downstream tasks tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 38 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18875' abstract: - lang: eng text: Current state-of-the-art methods for differentially private model training are based on matrix factorization techniques. However, these methods suffer from high computational overhead because they require numerically solving a demanding optimization problem to determine an approximately optimal factorization prior to the actual model training. In this work, we present a new matrix factorization approach, BSR, which overcomes this computational bottleneck. By exploiting properties of the standard matrix square root, BSR allows to efficiently handle also large-scale problems. For the key scenario of stochastic gradient descent with momentum and weight decay, we even derive analytical expressions for BSR that render the computational overhead negligible. We prove bounds on the approximation quality that hold both in the centralized and in the federated learning setting. Our numerical experiments demonstrate that models trained using BSR perform on par with the best existing methods, while completely avoiding their computational overhead. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Nikita full_name: Kalinin, Nikita id: 4b14526e-14d2-11ed-ba64-c14c9553d137 last_name: Kalinin - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Kalinin N, Lampert C. Banded square root matrix factorization for differentially private model training. In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Kalinin, N., & Lampert, C. (2024). Banded square root matrix factorization for differentially private model training. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization for Differentially Private Model Training.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: N. Kalinin and C. Lampert, “Banded square root matrix factorization for differentially private model training,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Kalinin N, Lampert C. 2024. Banded square root matrix factorization for differentially private model training. 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Kalinin, Nikita, and Christoph Lampert. “Banded Square Root Matrix Factorization for Differentially Private Model Training.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: N. Kalinin, C. Lampert, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-24T17:58:16Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-05-14T11:34:20Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ChLa external_id: arxiv: - '2405.13763' file: - access_level: open_access checksum: a216cab8eddc1fe7840aede0e2c0d41e content_type: application/pdf creator: dernst date_created: 2025-01-27T09:52:15Z date_updated: 2025-01-27T09:52:15Z file_id: '18888' file_name: 2024_NeurIPS_Nikita.pdf file_size: 1144656 relation: main_file success: 1 file_date_updated: 2025-01-27T09:52:15Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: 38th Annual Conference on Neural Information Processing Systems publication_identifier: eissn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Banded square root matrix factorization for differentially private model training tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '18890' abstract: - lang: eng text: Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been measured in a variety of settings, its emergence is typically explained via data-agnostic approaches, such as the unconstrained features model. In this work, we introduce a data-dependent setting where DNC forms due to feature learning through the average gradient outer product (AGOP). The AGOP is defined with respect to a learned predictor and is equal to the uncentered covariance matrix of its input-output gradients averaged over the training dataset. The Deep Recursive Feature Machine (Deep RFM) is a method that constructs a neural network by iteratively mapping the data with the AGOP and applying an untrained random feature map. We demonstrate empirically that DNC occurs in Deep RFM across standard settings as a consequence of the projection with the AGOP matrix computed at each layer. Further, we theoretically explain DNC in Deep RFM in an asymptotic setting and as a result of kernel learning. We then provide evidence that this mechanism holds for neural networks more generally. In particular, we show that the right singular vectors and values of the weights can be responsible for the majority of within-class variability collapse for DNNs trained in the feature learning regime. As observed in recent work, this singular structure is highly correlated with that of the AGOP. acknowledgement: 'We acknowledge support from the National Science Foundation (NSF) and the Simons Foundation for the Collaboration on the Theoretical Foundations of Deep Learning through awards DMS-2031883 and #814639 as well as the TILOS institute (NSF CCF-2112665). This work used the programs (1) XSEDE (Extreme science and engineering discovery environment) which is supported by NSF grant numbers ACI-1548562, and (2) ACCESS (Advanced cyberinfrastructure coordination ecosystem: services & support) which is supported by NSF grants numbers #2138259, #2138286, #2138307, #2137603, and #2138296. Specifically, we used the resources from SDSC Expanse GPU compute nodes, and NCSA Delta system, via allocations TG-CIS220009. Marco Mondelli is supported by the 2019 Lopez-Loreta prize. We also acknowledge useful feedback from anonymous reviewers. ' alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Daniel full_name: Beaglehole, Daniel last_name: Beaglehole - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Mikhail full_name: Belkin, Mikhail last_name: Belkin citation: ama: 'Beaglehole D, Súkeník P, Mondelli M, Belkin M. Average gradient outer product as a mechanism for deep neural collapse. In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Beaglehole, D., Súkeník, P., Mondelli, M., & Belkin, M. (2024). Average gradient outer product as a mechanism for deep neural collapse. In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Beaglehole, Daniel, Peter Súkeník, Marco Mondelli, and Mikhail Belkin. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: D. Beaglehole, P. Súkeník, M. Mondelli, and M. Belkin, “Average gradient outer product as a mechanism for deep neural collapse,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Beaglehole D, Súkeník P, Mondelli M, Belkin M. 2024. Average gradient outer product as a mechanism for deep neural collapse. 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Beaglehole, Daniel, et al. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: D. Beaglehole, P. Súkeník, M. Mondelli, M. Belkin, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-27T11:11:40Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-05-14T11:29:45Z day: '01' department: - _id: GradSch - _id: MaMo external_id: arxiv: - '2402.13728' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/forum?id=lJ1jdl2K9k month: '12' oa: 1 oa_version: Preprint project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 38th Annual Conference on Neural Information Processing Systems publication_identifier: eissn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Average gradient outer product as a mechanism for deep neural collapse type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18891' abstract: - lang: eng text: "Deep neural networks (DNNs) exhibit a surprising structure in their final layer\r\nknown as neural collapse (NC), and a growing body of works has currently investigated the propagation of neural collapse to earlier layers of DNNs – a phenomenon\r\ncalled deep neural collapse (DNC). However, existing theoretical results are restricted to special cases: linear models, only two layers or binary classification.\r\nIn contrast, we focus on non-linear models of arbitrary depth in multi-class classification and reveal a surprising qualitative shift. As soon as we go beyond two\r\nlayers or two classes, DNC stops being optimal for the deep unconstrained features\r\nmodel (DUFM) – the standard theoretical framework for the analysis of collapse.\r\nThe main culprit is a low-rank bias of multi-layer regularization schemes: this bias\r\nleads to optimal solutions of even lower rank than the neural collapse. We support\r\nour theoretical findings with experiments on both DUFM and real data, which show\r\nthe emergence of the low-rank structure in the solution found by gradient descent." acknowledged_ssus: - _id: ScienComp acknowledgement: Marco Mondelli is partially supported by the 2019 Lopez-Loreta prize. This research was supported by the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp). alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Peter full_name: Súkeník, Peter id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c last_name: Súkeník - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: ama: 'Súkeník P, Lampert C, Mondelli M. Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In: 38th Annual Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Súkeník, P., Lampert, C., & Mondelli, M. (2024). Neural collapse versus low-rank bias: Is deep neural collapse really optimal? In 38th Annual Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Súkeník, Peter, Christoph Lampert, and Marco Mondelli. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'P. Súkeník, C. Lampert, and M. Mondelli, “Neural collapse versus low-rank bias: Is deep neural collapse really optimal?,” in 38th Annual Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Súkeník P, Lampert C, Mondelli M. 2024. Neural collapse versus low-rank bias: Is deep neural collapse really optimal? 38th Annual Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Súkeník, Peter, et al. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” 38th Annual Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-16 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-16 corr_author: '1' date_created: 2025-01-27T11:15:18Z date_published: 2024-12-01T00:00:00Z date_updated: 2025-06-04T07:19:21Z day: '01' ddc: - '000' department: - _id: GradSch - _id: MaMo - _id: ChLa external_id: arxiv: - '2405.14468' file: - access_level: open_access checksum: b7b79f1ea3ac1e9e11b3d91faaeb0780 content_type: application/pdf creator: dernst date_created: 2025-02-04T08:11:25Z date_updated: 2025-02-04T08:11:25Z file_id: '18989' file_name: 2024_NeurIPS_Sukenik.pdf file_size: 1784118 relation: main_file success: 1 file_date_updated: 2025-02-04T08:11:25Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: 38th Annual Conference on Neural Information Processing Systems publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' status: public title: 'Neural collapse versus low-rank bias: Is deep neural collapse really optimal?' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: gold _id: '18895' abstract: - lang: eng text: 'ISTAnt is a new ecological dataset for social immunity and represents the first real-world benchmark for causal inference downstream tasks on high-dimensional observations. It analyzes grooming behavior in the ant Lasius neglectus in groups of three worker ants. The workers for the experiment were obtained from their laboratory stock colony, which had been collected from the field in 2022 in the Botanical Garden Jena, Germany. Ant collection and all experimental work were performed in compliance with international, national and institutional regulations and ethical guidelines. For the experiment, the body surface of one of the three ants was treated with a suspension of either of two microparticle types (diameter ~5 µm) to induce grooming by the two nestmates, which were individually color-coded by application of a dot of blue or orange paint, respectively. The three ants were housed in small plastic containers (diameter 28mm, height 30mm) with moistened, plastered ground and the interior walls covered with PTFE (polytetrafluoroethane) to hamper climbing by the ants. Filming occurred in a temperature- and humidity-controlled room at 23°C within a custom-made filming box with controlled lighting and ventilation conditions. We set up nine ant groups at a time (always containing both treatments) and placed them randomly on positions 1-9 marked on the floor in a 3x3 grid, about 3mm from each other. The experiment was performed on two consecutive days. Videos were acquired using a USB camera (FLIR blackfly S BFS-U3-120S4C, Teledyne FLIR) with a high-performance lens (HP Series 25mm Focal Length, Edmund optics 86-572) in OBS studio 29.0.0 \citep{bailey2017obs} at a framerate of 30 FPS and a resolution of 2500x2500 pixels. From each original video (105x105 mm), we generated nine individual videos .mkv (each ~32x32 mm, 770x770 pixels) by determining exact coordinates per container from one frame in GIMP 2.10.36 and cropping of the videos with FFmpeg 6.1.1. Annotation was performed over two consecutive days by three observers who had not been involved in the experimental setup or recording and were unaware of the treatment assignments to ensure bias-free behavioral annotation. They annotated the behavior of the ants during video observations, using custom-made software that saves the start and end frames of behaviors marked in a .csv file (see ''annotations'' folder). In one of the videos, one of the nestmates'' legs got inadvertently stuck to its body surface during the color-coding, interfering with its behavior, so the video was discarded. This left 44 videos from 5 independent setups (n=24 of treatment 1 and n=20 of treatment 2) of 10 minutes each for a total of 792 000 annotated frames (see ''video'' folder). For each video, we provide the following information: the number of the set to which it belongs (1-5); the number of the position within the set reflecting the position of the ant group under the camera (1-9), for which we also provide ‘coordinates’ in the 3x3 grid (taking values -1/0/1 for both X and Y axis); treatment (1 or 2); the hour of the day when the recording was started (in 24h CEST); experimental day (A or B); the top left coordinate of the cropping square from the original video (CropX/CropY); the person annotating the video (given as A, B, C); the date of annotation (1: first day, 2: second day) and in which order the videos were annotated by each person, both reflecting a possible training effect of the person (see ''experiments_settings.csv'' file).' article_processing_charge: No author: - first_name: Riccardo full_name: Cadei, Riccardo id: 0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b last_name: Cadei - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Sylvia M full_name: Cremer, Sylvia M id: 2F64EC8C-F248-11E8-B48F-1D18A9856A87 last_name: Cremer orcid: 0000-0002-2193-3868 - first_name: Lukas full_name: Lindorfer, Lukas id: 85f0e6d3-06b3-11ec-8982-8c5049fa4455 last_name: Lindorfer - first_name: Cordelia full_name: Schmid, Cordelia last_name: Schmid citation: ama: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. ISTAnt. 2024. doi:10.6084/M9.FIGSHARE.26484934.V2 apa: Cadei, R., Locatello, F., Cremer, S., Lindorfer, L., & Schmid, C. (2024). ISTAnt. Institute of Science and Technology Austria. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2 chicago: Cadei, Riccardo, Francesco Locatello, Sylvia Cremer, Lukas Lindorfer, and Cordelia Schmid. “ISTAnt.” Institute of Science and Technology Austria, 2024. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2. ieee: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, and C. Schmid, “ISTAnt.” Institute of Science and Technology Austria, 2024. ista: Cadei R, Locatello F, Cremer S, Lindorfer L, Schmid C. 2024. ISTAnt, Institute of Science and Technology Austria, 10.6084/M9.FIGSHARE.26484934.V2. mla: Cadei, Riccardo, et al. ISTAnt. Institute of Science and Technology Austria, 2024, doi:10.6084/M9.FIGSHARE.26484934.V2. short: R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, C. Schmid, (2024). corr_author: '1' date_created: 2025-01-27T11:45:43Z date_published: 2024-10-23T00:00:00Z date_updated: 2025-01-27T11:58:38Z day: '23' ddc: - '570' department: - _id: SyCr - _id: FrLo - _id: GradSch doi: 10.6084/M9.FIGSHARE.26484934.V2 main_file_link: - open_access: '1' url: https://10.6084/M9.FIGSHARE.26484934.V2 month: '10' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria related_material: record: - id: '18847' relation: used_in_publication status: public status: public title: ISTAnt type: research_data_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18961' abstract: - lang: eng text: "Automated contact tracing (ACT) emerged as a promising measure to curb the spread of Covid-19. Users enable ACT on their smartphones to automatically record contacts with other users. If a user tests positive for the disease, they report their diagnosis to alert their contacts.\r\nDesigning effective ACT protocols is challenging since they need to be efficient and secure while also ensuring users' privacy. As ACT protocols necessarily leak some information by design, defining privacy is difficult. For example, a user cannot deny having met another user. Ideally, however, the user can plausibly deny everything else, in particular, when they met. We call this privacy property contact-time deniability.\r\nWhile some early works discussed contact-time deniability informally, it has received little attention since then. We investigate deniability from a rigorous, theoretical point of view and arrive at the following impossibility result:\r\nA decentralized protocol with unidirectional communication cannot be contact-time deniable and replay-secure. This holds even if malicious users treat smartphones as black-boxes.\r\n Unidirectional protocols are usually very efficient and many proposals are unidirectional, e.g., the widely-deployed Google-Apple Exposure Notifications. So the impossibility result considerably constrains the design space of efficient, secure, and private ACT protocols. However, it can also be used as a guide; we discuss several possibilities to achieve contact-time deniability in practice." acknowledgement: "We thank Raluca-Georgia Diugan for her initial contributions and support afterward.\r\nThis research was funded in whole or in part by the Austrian Science Fund (FWF) 10.55776/F85." article_processing_charge: No article_type: original author: - first_name: Christoph Ullrich full_name: Günther, Christoph Ullrich id: ec98511c-eb8e-11eb-b029-edd25d7271a1 last_name: Günther - first_name: Krzysztof Z full_name: Pietrzak, Krzysztof Z id: 3E04A7AA-F248-11E8-B48F-1D18A9856A87 last_name: Pietrzak orcid: 0000-0002-9139-1654 citation: ama: 'Günther CU, Pietrzak KZ. Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. 2024;2024(4):636-648. doi:10.56553/popets-2024-0134' apa: 'Günther, C. U., & Pietrzak, K. Z. (2024). Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. Bristol, UK/Virtual: Privacy Enhancing Technologies Symposium Advisory Board. https://doi.org/10.56553/popets-2024-0134' chicago: 'Günther, Christoph Ullrich, and Krzysztof Z Pietrzak. “Deniability in Automated Contact Tracing: Impossibilities and Possibilities.” Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium Advisory Board, 2024. https://doi.org/10.56553/popets-2024-0134.' ieee: 'C. U. Günther and K. Z. Pietrzak, “Deniability in automated contact tracing: Impossibilities and possibilities,” Proceedings on Privacy Enhancing Technologies, vol. 2024, no. 4. Privacy Enhancing Technologies Symposium Advisory Board, pp. 636–648, 2024.' ista: 'Günther CU, Pietrzak KZ. 2024. Deniability in automated contact tracing: Impossibilities and possibilities. Proceedings on Privacy Enhancing Technologies. 2024(4), 636–648.' mla: 'Günther, Christoph Ullrich, and Krzysztof Z. Pietrzak. “Deniability in Automated Contact Tracing: Impossibilities and Possibilities.” Proceedings on Privacy Enhancing Technologies, vol. 2024, no. 4, Privacy Enhancing Technologies Symposium Advisory Board, 2024, pp. 636–48, doi:10.56553/popets-2024-0134.' short: C.U. Günther, K.Z. Pietrzak, Proceedings on Privacy Enhancing Technologies 2024 (2024) 636–648. conference: end_date: 2024-07-20 location: Bristol, UK/Virtual name: 'PETs: Privacy Enhancing Technologies Symposium ' start_date: 2024-07-15 corr_author: '1' date_created: 2025-01-29T13:39:34Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-04-15T08:16:04Z day: '01' ddc: - '000' department: - _id: KrPi - _id: GradSch doi: 10.56553/popets-2024-0134 file: - access_level: open_access checksum: 348ed6adcf6ad2f925227bde1758cae6 content_type: application/pdf creator: dernst date_created: 2025-01-29T13:44:47Z date_updated: 2025-01-29T13:44:47Z file_id: '18962' file_name: 2024_ProcPrivacyEnhTech_Guenther.pdf file_size: 611567 relation: main_file success: 1 file_date_updated: 2025-01-29T13:44:47Z has_accepted_license: '1' intvolume: ' 2024' issue: '4' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 636-648 project: - _id: 34a34d57-11ca-11ed-8bc3-a2688a8724e1 grant_number: F8509 name: Security and Privacy by Design for Complex Systems publication: Proceedings on Privacy Enhancing Technologies publication_identifier: issn: - 2299-0984 publication_status: published publisher: Privacy Enhancing Technologies Symposium Advisory Board quality_controlled: '1' status: public title: 'Deniability in automated contact tracing: Impossibilities and possibilities' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2024 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '18998' abstract: - lang: eng text: Word embeddings represent language vocabularies as clouds of d-dimensional points. We investigate how information is conveyed by the general shape of these clouds, instead of representing the semantic meaning of each token. Specifically, we use the notion of persistent homology from topological data analysis (TDA) to measure the distances between language pairs from the shape of their unlabeled embeddings. These distances quantify the degree of non-isometry of the embeddings. To distinguish whether these differences are random training errors or capture real information about the languages, we use the computed distance matrices to construct language phylogenetic trees over 81 Indo-European languages. Careful evaluation shows that our reconstructed trees exhibit strong and statistically-significant similarities to the reference. article_processing_charge: No arxiv: 1 author: - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 - first_name: Steven full_name: Skiena, Steven last_name: Skiena citation: ama: 'Draganov O, Skiena S. The shape of word embeddings: Quantifying non-isometry with topological data analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics; 2024:12080-12099. doi:10.18653/v1/2024.findings-emnlp.705' apa: 'Draganov, O., & Skiena, S. (2024). The shape of word embeddings: Quantifying non-isometry with topological data analysis. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 12080–12099). Miami, FL, United States: Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.705' chicago: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying Non-Isometry with Topological Data Analysis.” In Findings of the Association for Computational Linguistics: EMNLP 2024, 12080–99. Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.findings-emnlp.705.' ieee: 'O. Draganov and S. Skiena, “The shape of word embeddings: Quantifying non-isometry with topological data analysis,” in Findings of the Association for Computational Linguistics: EMNLP 2024, Miami, FL, United States, 2024, pp. 12080–12099.' ista: 'Draganov O, Skiena S. 2024. The shape of word embeddings: Quantifying non-isometry with topological data analysis. Findings of the Association for Computational Linguistics: EMNLP 2024. EMNLP: Conference on Empirical Methods in Natural Language Processing, 12080–12099.' mla: 'Draganov, Ondrej, and Steven Skiena. “The Shape of Word Embeddings: Quantifying Non-Isometry with Topological Data Analysis.” Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp. 12080–99, doi:10.18653/v1/2024.findings-emnlp.705.' short: 'O. Draganov, S. Skiena, in:, Findings of the Association for Computational Linguistics: EMNLP 2024, Association for Computational Linguistics, 2024, pp. 12080–12099.' conference: end_date: 2024-11-16 location: Miami, FL, United States name: 'EMNLP: Conference on Empirical Methods in Natural Language Processing' start_date: 2024-11-12 corr_author: '1' date_created: 2025-02-04T16:19:28Z date_published: 2024-11-01T00:00:00Z date_updated: 2025-02-10T08:21:37Z day: '01' ddc: - '500' department: - _id: GradSch - _id: HeEd doi: 10.18653/v1/2024.findings-emnlp.705 external_id: arxiv: - '2404.00500' file: - access_level: open_access checksum: f4416a5962194f0181ab0dc7f9ef93c0 content_type: application/pdf creator: dernst date_created: 2025-02-10T08:20:34Z date_updated: 2025-02-10T08:20:34Z file_id: '19016' file_name: 2024_EMNLP_Draganov.pdf file_size: 1312638 relation: main_file success: 1 file_date_updated: 2025-02-10T08:20:34Z has_accepted_license: '1' language: - iso: eng month: '11' oa: 1 oa_version: Published Version page: 12080-12099 publication: 'Findings of the Association for Computational Linguistics: EMNLP 2024' publication_status: published publisher: Association for Computational Linguistics quality_controlled: '1' scopus_import: '1' status: public title: 'The shape of word embeddings: Quantifying non-isometry with topological data analysis' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '18999' abstract: - lang: eng text: Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry. article_number: '2406.04102' article_processing_charge: No arxiv: 1 author: - first_name: Sebastiano full_name: Cultrera di Montesano, Sebastiano id: 34D2A09C-F248-11E8-B48F-1D18A9856A87 last_name: Cultrera di Montesano orcid: 0000-0001-6249-0832 - first_name: Ondrej full_name: Draganov, Ondrej id: 2B23F01E-F248-11E8-B48F-1D18A9856A87 last_name: Draganov orcid: 0000-0003-0464-3823 - first_name: Herbert full_name: Edelsbrunner, Herbert id: 3FB178DA-F248-11E8-B48F-1D18A9856A87 last_name: Edelsbrunner orcid: 0000-0002-9823-6833 - first_name: Morteza full_name: Saghafian, Morteza id: f86f7148-b140-11ec-9577-95435b8df824 last_name: Saghafian citation: ama: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic topological data analysis. arXiv. doi:10.48550/ARXIV.2406.04102 apa: Cultrera di Montesano, S., Draganov, O., Edelsbrunner, H., & Saghafian, M. (n.d.). Chromatic topological data analysis. arXiv. https://doi.org/10.48550/ARXIV.2406.04102 chicago: Cultrera di Montesano, Sebastiano, Ondrej Draganov, Herbert Edelsbrunner, and Morteza Saghafian. “Chromatic Topological Data Analysis.” ArXiv, n.d. https://doi.org/10.48550/ARXIV.2406.04102. ieee: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, and M. Saghafian, “Chromatic topological data analysis,” arXiv. . ista: Cultrera di Montesano S, Draganov O, Edelsbrunner H, Saghafian M. Chromatic topological data analysis. arXiv, 2406.04102. mla: Cultrera di Montesano, Sebastiano, et al. “Chromatic Topological Data Analysis.” ArXiv, 2406.04102, doi:10.48550/ARXIV.2406.04102. short: S. Cultrera di Montesano, O. Draganov, H. Edelsbrunner, M. Saghafian, ArXiv (n.d.). corr_author: '1' date_created: 2025-02-04T16:21:21Z date_published: 2024-06-06T00:00:00Z date_updated: 2025-02-10T08:14:27Z day: '06' ddc: - '510' department: - _id: GradSch - _id: HeEd doi: 10.48550/ARXIV.2406.04102 external_id: arxiv: - '2406.04102' has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.04102 month: '06' oa: 1 oa_version: Preprint publication: arXiv publication_status: submitted status: public title: Chromatic topological data analysis tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '19063' abstract: - lang: eng text: "Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data. This makes them vulnerable to manipulations such as indirect prompt injections and generally unsuitable for safety-critical tasks. Surprisingly, there is currently no established definition or benchmark to quantify this phenomenon. In this work, we close this gap by introducing a formal measure for instruction-data separation and an empirical variant that is calculable from a model's outputs. We also present a new dataset, SEP, that allows estimating the measure for real-world models. Our results on various LLMs show that the problem of instruction-data separation is real: all models fail to achieve high separation, and canonical mitigation techniques, such as prompt engineering and fine-tuning, either fail to substantially improve separation or reduce model utility. The source code and SEP dataset are openly accessible at https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed.\r\n" acknowledged_ssus: - _id: ScienComp acknowledgement: The authors would like to sincerely thank Juan Rocamonde for valuable feedback to our manuscript. We acknowledge the support from the Scientific Service Units (SSU) of ISTA through resources provided by Scientific Computing (SciComp). We thank Dan Alistarh for providing us with computational resources. This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under the grant AIgenCY (16KIS2012) and ELSA – European Lighthouse on Secure and Safe AI funded by the European Union under grant agreement No. 101070617. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the European Commission can be held responsible for them. article_number: '2403.06833' article_processing_charge: No arxiv: 1 author: - first_name: Egor full_name: Zverev, Egor id: 05162b19-1340-11ed-8f02-fa94e0e8c3bc last_name: Zverev - first_name: Sahar full_name: Abdelnabi, Sahar last_name: Abdelnabi - first_name: Soroush full_name: Tabesh, Soroush id: 06000900-6068-11ef-8d61-c2472ef2e752 last_name: Tabesh orcid: 0009-0003-4119-6281 - first_name: Mario full_name: Fritz, Mario last_name: Fritz - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Zverev E, Abdelnabi S, Tabesh S, Fritz M, Lampert C. Can LLMs separate instructions from data? And what do we even mean by that? arXiv. 2024. doi:10.48550/arXiv.2403.06833 apa: Zverev, E., Abdelnabi, S., Tabesh, S., Fritz, M., & Lampert, C. (2024). Can LLMs separate instructions from data? And what do we even mean by that? arXiv. https://doi.org/10.48550/arXiv.2403.06833 chicago: Zverev, Egor, Sahar Abdelnabi, Soroush Tabesh, Mario Fritz, and Christoph Lampert. “Can LLMs Separate Instructions from Data? And What Do We Even Mean by That?” ArXiv, 2024. https://doi.org/10.48550/arXiv.2403.06833. ieee: E. Zverev, S. Abdelnabi, S. Tabesh, M. Fritz, and C. Lampert, “Can LLMs separate instructions from data? And what do we even mean by that?,” arXiv. 2024. ista: Zverev E, Abdelnabi S, Tabesh S, Fritz M, Lampert C. 2024. Can LLMs separate instructions from data? And what do we even mean by that? arXiv, 2403.06833. mla: Zverev, Egor, et al. “Can LLMs Separate Instructions from Data? And What Do We Even Mean by That?” ArXiv, 2403.06833, 2024, doi:10.48550/arXiv.2403.06833. short: E. Zverev, S. Abdelnabi, S. Tabesh, M. Fritz, C. Lampert, ArXiv (2024). corr_author: '1' date_created: 2025-02-20T10:13:42Z date_published: 2024-03-01T00:00:00Z date_updated: 2025-02-24T12:52:23Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ChLa doi: 10.48550/arXiv.2403.06833 external_id: arxiv: - '2403.06833' file: - access_level: open_access checksum: 35eb43968684b87be59144603ef10af0 content_type: application/pdf creator: ezverev date_created: 2025-02-20T10:11:45Z date_updated: 2025-02-20T10:11:45Z file_id: '19064' file_name: 2403.06833v3.pdf file_size: 530972 relation: main_file success: 1 file_date_updated: 2025-02-20T10:11:45Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2403.06833 month: '03' oa: 1 oa_version: Preprint publication: arXiv publication_status: published related_material: link: - relation: software url: ' https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed' status: public title: Can LLMs separate instructions from data? And what do we even mean by that? tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '20071' abstract: - lang: eng text: Farkas established that a system of linear inequalities has a solution if and only if we cannot obtain a contradiction by taking a linear combination of the inequalities. We state and formally prove several Farkas-like theorems over linearly ordered fields in Lean 4. Furthermore, we extend duality theory to the case when some coefficients are allowed to take "infinite values". acknowledgement: We would like to thank David Bartl and Jasmin Blanchette for frequent consultations. We would also like to express gratitude to Andrew Yang for the proof of Finset.univ sum of zero when not and to Henrik B¨oving for a help with generalization from extended rationals to extended linearly ordered fields. We would also like to acknowledge Antoine Chambert-Loir, Apurva Nakade, Ya¨el Dillies, Richard Copley, Edward van de Meent, Markus Himmel, Mario Carneiro, and Kevin Buzzard. article_number: '2409.08119' article_processing_charge: No arxiv: 1 author: - first_name: Martin full_name: Dvorak, Martin id: 40ED02A8-C8B4-11E9-A9C0-453BE6697425 last_name: Dvorak orcid: 0000-0001-5293-214X - first_name: Vladimir full_name: Kolmogorov, Vladimir id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87 last_name: Kolmogorov citation: ama: Dvorak M, Kolmogorov V. Duality theory in linear optimization and its extensions -- formally  verified. arXiv. doi:10.48550/arXiv.2409.08119 apa: Dvorak, M., & Kolmogorov, V. (n.d.). Duality theory in linear optimization and its extensions -- formally  verified. arXiv. https://doi.org/10.48550/arXiv.2409.08119 chicago: Dvorak, Martin, and Vladimir Kolmogorov. “Duality Theory in Linear Optimization and Its Extensions -- Formally  Verified.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2409.08119. ieee: M. Dvorak and V. Kolmogorov, “Duality theory in linear optimization and its extensions -- formally  verified,” arXiv. . ista: Dvorak M, Kolmogorov V. Duality theory in linear optimization and its extensions -- formally  verified. arXiv, 2409.08119. mla: Dvorak, Martin, and Vladimir Kolmogorov. “Duality Theory in Linear Optimization and Its Extensions -- Formally  Verified.” ArXiv, 2409.08119, doi:10.48550/arXiv.2409.08119. short: M. Dvorak, V. Kolmogorov, ArXiv (n.d.). corr_author: '1' date_created: 2025-07-23T11:21:52Z date_published: 2024-09-12T00:00:00Z date_updated: 2026-03-27T12:36:59Z day: '12' department: - _id: GradSch - _id: VlKo doi: 10.48550/arXiv.2409.08119 external_id: arxiv: - '2409.08119' keyword: - Farkas lemma - linear programming - extended reals - calculus of inductive constructions language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2409.08119 month: '09' oa: 1 oa_version: Preprint publication: arXiv publication_status: draft related_material: link: - description: full version of all definitions, statement, and proofs relation: software url: https://github.com/madvorak/duality/tree/v3.2 record: - id: '21393' relation: dissertation_contains status: public status: public title: Duality theory in linear optimization and its extensions -- formally verified type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ... --- APC_amount: 6828 EUR DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '17202' abstract: - lang: eng text: Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions have recently emerged as building blocks for hybrid quantum circuits. In this study, we present a gatemon fabricated in planar Germanium. We induce superconductivity in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which separates the superconductor from the Ge quantum well. The Josephson junction is then integrated into an Xmon circuit and capacitively coupled to a transmission line resonator. We showcase the qubit tunability in a broad frequency range with resonator and two-tone spectroscopy. Time-domain characterizations reveal energy relaxation and coherence times up to 75 ns. Our results, combined with the recent advances in the spin qubit field, pave the way towards novel hybrid and protected qubits in a group IV, CMOS-compatible material. acknowledged_ssus: - _id: ScienComp - _id: M-Shop - _id: NanoFab acknowledgement: "We acknowledge Lucas Casparis, Jeroen Danon, Valla Fatemi, Morten Kjaergard and Javad Shabani for their valuable insights and comments. This research was supported by the Scientific Service Units of ISTA through resources provided by the MIBA Machine Shop\r\nand the Nanofabrication facility. This research and related results were made possible with the support of the NOMIS Foundation and the FWF Projects with DOI:10.55776/I5060 and DOI:10.55776/P36507. We also acknowledge the NextGenerationEU PRIN project\r\n2022A8CJP3 (GAMESQUAD) for partial financial support." article_number: '6400' article_processing_charge: Yes article_type: original arxiv: 1 author: - first_name: Oliver full_name: Sagi, Oliver id: 71616374-A8E9-11E9-A7CA-09ECE5697425 last_name: Sagi - first_name: Alessandro full_name: Crippa, Alessandro id: 1F2B21A2-F6E7-11E9-9B82-F7DBE5697425 last_name: Crippa orcid: 0000-0002-2968-611X - first_name: Marco full_name: Valentini, Marco id: C0BB2FAC-D767-11E9-B658-BC13E6697425 last_name: Valentini - first_name: Marian full_name: Janik, Marian id: 396A1950-F248-11E8-B48F-1D18A9856A87 last_name: Janik orcid: 0009-0003-9037-8831 - first_name: Levon full_name: Baghumyan, Levon id: 7aa1f788-b527-11ee-aa9e-e6111a79e0c7 last_name: Baghumyan - first_name: Giorgio full_name: Fabris, Giorgio id: 298cf6f3-1ff6-11ee-9fa6-d94cfa0b3352 last_name: Fabris - first_name: Lucky full_name: Kapoor, Lucky id: 84b9700b-15b2-11ec-abd3-831089e67615 last_name: Kapoor orcid: 0000-0001-8319-2148 - first_name: Farid full_name: Hassani, Farid id: 2AED110C-F248-11E8-B48F-1D18A9856A87 last_name: Hassani orcid: 0000-0001-6937-5773 - first_name: Johannes M full_name: Fink, Johannes M id: 4B591CBA-F248-11E8-B48F-1D18A9856A87 last_name: Fink orcid: 0000-0001-8112-028X - first_name: Stefano full_name: Calcaterra, Stefano last_name: Calcaterra - first_name: Daniel full_name: Chrastina, Daniel last_name: Chrastina - first_name: Giovanni full_name: Isella, Giovanni last_name: Isella - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X citation: ama: Sagi O, Crippa A, Valentini M, et al. A gate tunable transmon qubit in planar Ge. Nature Communications. 2024;15. doi:10.1038/s41467-024-50763-6 apa: Sagi, O., Crippa, A., Valentini, M., Janik, M., Baghumyan, L., Fabris, G., … Katsaros, G. (2024). A gate tunable transmon qubit in planar Ge. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-024-50763-6 chicago: Sagi, Oliver, Alessandro Crippa, Marco Valentini, Marian Janik, Levon Baghumyan, Giorgio Fabris, Lucky Kapoor, et al. “A Gate Tunable Transmon Qubit in Planar Ge.” Nature Communications. Springer Nature, 2024. https://doi.org/10.1038/s41467-024-50763-6. ieee: O. Sagi et al., “A gate tunable transmon qubit in planar Ge,” Nature Communications, vol. 15. Springer Nature, 2024. ista: Sagi O, Crippa A, Valentini M, Janik M, Baghumyan L, Fabris G, Kapoor L, Hassani F, Fink JM, Calcaterra S, Chrastina D, Isella G, Katsaros G. 2024. A gate tunable transmon qubit in planar Ge. Nature Communications. 15, 6400. mla: Sagi, Oliver, et al. “A Gate Tunable Transmon Qubit in Planar Ge.” Nature Communications, vol. 15, 6400, Springer Nature, 2024, doi:10.1038/s41467-024-50763-6. short: O. Sagi, A. Crippa, M. Valentini, M. Janik, L. Baghumyan, G. Fabris, L. Kapoor, F. Hassani, J.M. Fink, S. Calcaterra, D. Chrastina, G. Isella, G. Katsaros, Nature Communications 15 (2024). corr_author: '1' date_created: 2024-07-04T11:40:45Z date_published: 2024-07-30T00:00:00Z date_updated: 2026-04-07T13:01:55Z day: '30' ddc: - '530' department: - _id: GeKa - _id: JoFi - _id: GradSch doi: 10.1038/s41467-024-50763-6 external_id: arxiv: - '2403.16774' isi: - '001281271000022' pmid: - '39080279' file: - access_level: open_access checksum: ddf5361dcb6c543e2cea818501c09910 content_type: application/pdf creator: dernst date_created: 2024-08-05T08:38:01Z date_updated: 2024-08-05T08:38:01Z file_id: '17388' file_name: 2024_NatureComm_Sagi.pdf file_size: 1928001 relation: main_file success: 1 file_date_updated: 2024-08-05T08:38:01Z has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 project: - _id: bd8bd29e-d553-11ed-ba76-f0070d4b237a grant_number: P36507 name: Merging spin and superconducting qubits in planar Ge - _id: c0977eea-5a5b-11eb-8a69-a862db0cf4d1 grant_number: I05060 name: High impedance circuit quantum electrodynamics with hole spins - _id: 262116AA-B435-11E9-9278-68D0E5697425 name: Hybrid Semiconductor - Superconductor Quantum Devices - _id: 3AC91DDA-15DF-11EA-824D-93A3E7B544D1 call_identifier: FWF name: FWF Open Access Fund publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: link: - relation: erratum url: https://doi.org/10.1038/s41467-024-53910-1 record: - id: '17196' relation: research_data status: public - id: '18076' relation: dissertation_contains status: public scopus_import: '1' status: public title: A gate tunable transmon qubit in planar Ge tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15 year: '2024' ... --- OA_place: publisher _id: '17208' abstract: - lang: eng text: "Can current quantum computers provide a speedup over their classical counterparts for some kinds of problems? In this thesis, with a focus on ground state search/preparation, we address some of the challenges that both quantum annealing and variational quantum algorithms suffer from, hindering any possible practical speedup in comparison to the best classical counterparts. \r\n\r\nIn the first part of the thesis, we study the performance of quantum annealing for solving a particular combinatorial optimization problem called 3-XOR satisfability (3-XORSAT). The classical problem is mapped into a ground state search of a 3-local classical Hamiltonian $H_C$. We consider how modifying the initial problem, by adding more interaction terms to the corresponding Hamiltonian, leads to the emergence of a first-order phase transition during the annealing process. This phenomenon causes the total annealing duration, $T$, required to prepare the ground state of $H_C$ with a high probability to increase exponentially with the size of the problem. Our findings indicate that with the growing complexity of problem instances, the likelihood of encountering first-order phase transitions also increases, making quantum annealing an impractical solution for these types of combinatorial optimization problems.\r\n\r\nIn the second part, we focus on the problem of barren plateaus in generic variational quantum algorithms. Barren plateaus correspond to flat regions in the parameter space where the gradient of the cost function is zero in expectation, and with the variance decaying exponentially with the system size, thus obstructing an efficient parameter optimization. We propose an algorithm to circumvent Barren Plateaus by monitoring the entanglement entropy of k-local reduced density matrices, alongside a method for estimating entanglement entropy via classical shadow tomography. We illustrate the approach with the paradigmatic example of the variational quantum eigensolver, and show that our algorithm effectively avoids barren plateaus in the initialization as well as during the optimization stage. \r\n\r\nLastly, in the last two Chapters of this thesis, we focus on the quantum approximate optimization algorithm (QAOA), originally introduced as an algorithm for solving generic combinatorial optimization problems in near-term quantum devices. Specifically, we focus on how to develop rigorous initialization strategies with guarantee improvement. Our motivation for this study lies in that for random initialization, the optimization typically leads to local minima with poor performance. Our main result corresponds to the analytical construction of index-1 saddle points or transition states, stationary points with a single direction of descent, as a tool for systematically exploring the QAOA optimization landscape. This leads us to propose a novel greedy parameter initialization strategy that guarantees for the energy to decrease with an increasing number of circuit layers. Furthermore, with precise estimates for the negative Hessian eigenvalue and its eigenvector, we establish a lower bound for energy improvement following a QAOA iteration." acknowledged_ssus: - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Raimel A full_name: Medina Ramos, Raimel A id: CE680B90-D85A-11E9-B684-C920E6697425 last_name: Medina Ramos orcid: 0000-0002-5383-2869 citation: ama: Medina Ramos RA. Exploring the optimization landscape of variational quantum algorithms. 2024. doi:10.15479/at:ista:17208 apa: Medina Ramos, R. A. (2024). Exploring the optimization landscape of variational quantum algorithms. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17208 chicago: Medina Ramos, Raimel A. “Exploring the Optimization Landscape of Variational Quantum Algorithms.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17208. ieee: R. A. Medina Ramos, “Exploring the optimization landscape of variational quantum algorithms,” Institute of Science and Technology Austria, 2024. ista: Medina Ramos RA. 2024. Exploring the optimization landscape of variational quantum algorithms. Institute of Science and Technology Austria. mla: Medina Ramos, Raimel A. Exploring the Optimization Landscape of Variational Quantum Algorithms. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17208. short: R.A. Medina Ramos, Exploring the Optimization Landscape of Variational Quantum Algorithms, Institute of Science and Technology Austria, 2024. corr_author: '1' date_created: 2024-07-09T09:14:24Z date_published: 2024-07-09T00:00:00Z date_updated: 2026-04-07T12:43:22Z day: '09' ddc: - '539' degree_awarded: PhD department: - _id: GradSch - _id: MaSe doi: 10.15479/at:ista:17208 ec_funded: 1 file: - access_level: closed checksum: 6f45273d04f4418bc2adc018baed0525 content_type: application/zip creator: rmedinar date_created: 2024-07-09T09:21:44Z date_updated: 2024-07-10T11:34:09Z file_id: '17212' file_name: Raimel_Thesis-Final.zip file_size: '14218691' relation: source_file - access_level: open_access checksum: 6724a95bec772dbabc0111b9f08a805e content_type: application/pdf creator: rmedinar date_created: 2024-07-17T09:23:24Z date_updated: 2024-07-17T09:23:24Z file_id: '17275' file_name: Raimel_Thesis-20_pdfa.pdf file_size: 11253627 relation: main_file success: 1 file_date_updated: 2024-07-17T09:23:24Z has_accepted_license: '1' keyword: - Quantum computing - Variational Quantum Algorithms - Optimization language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '133' project: - _id: 23841C26-32DE-11EA-91FC-C7463DDC885E call_identifier: H2020 grant_number: '850899' name: 'Non-Ergodic Quantum Matter: Universality, Dynamics and Control' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '10545' relation: part_of_dissertation status: public - id: '10067' relation: part_of_dissertation status: public - id: '17222' relation: part_of_dissertation status: public - id: '13125' relation: part_of_dissertation status: public - id: '11471' relation: part_of_dissertation status: public status: public supervisor: - first_name: Maksym full_name: Serbyn, Maksym id: 47809E7E-F248-11E8-B48F-1D18A9856A87 last_name: Serbyn orcid: 0000-0002-2399-5827 title: Exploring the optimization landscape of variational quantum algorithms tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: dissertation user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd year: '2024' ... --- _id: '17214' abstract: - lang: eng text: '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.' acknowledgement: We thank Vincent Acary for his help with Siconos, as well as the anonymous reviewers and the members of the Visual Computing Group at ISTA for their helpful comments. This research was funded in part by the European Union (ERC-2021-COG 101045083 CoDiNA). article_processing_charge: Yes (via OA deal) author: - first_name: Yi-Lu full_name: Chen, Yi-Lu id: 0b467602-dbcd-11ea-9d1d-ed480aa46b70 last_name: Chen - first_name: Mickaël full_name: Ly, Mickaël id: 6340d7f0-b48d-11eb-b10d-b7487e71d9f1 last_name: Ly - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: 'Chen Y-L, Ly M, Wojtan C. Primal-dual non-smooth friction for rigid body animation. In: Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Association for Computing Machinery; 2024. doi:10.1145/3641519.3657485' apa: 'Chen, Y.-L., Ly, M., & Wojtan, C. (2024). Primal-dual non-smooth friction for rigid body animation. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Denver, United States: Association for Computing Machinery. https://doi.org/10.1145/3641519.3657485' chicago: Chen, Yi-Lu, Mickaël Ly, and Chris Wojtan. “Primal-Dual Non-Smooth Friction for Rigid Body Animation.” In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Association for Computing Machinery, 2024. https://doi.org/10.1145/3641519.3657485. ieee: Y.-L. Chen, M. Ly, and C. Wojtan, “Primal-dual non-smooth friction for rigid body animation,” in Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Denver, United States, 2024. ista: 'Chen Y-L, Ly M, Wojtan C. 2024. Primal-dual non-smooth friction for rigid body animation. Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. SIGGRAPH: Computer Graphics and Interactive Techniques Conference.' mla: Chen, Yi-Lu, et al. “Primal-Dual Non-Smooth Friction for Rigid Body Animation.” Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Association for Computing Machinery, 2024, doi:10.1145/3641519.3657485. short: Y.-L. Chen, M. Ly, C. Wojtan, in:, Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Association for Computing Machinery, 2024. conference: end_date: 2024-08-01 location: Denver, United States name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference' start_date: 2024-07-28 corr_author: '1' date_created: 2024-07-10T11:06:20Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-09-08T08:54:38Z day: '01' ddc: - '621' - '531' - '006' department: - _id: GradSch - _id: ChWo doi: 10.1145/3641519.3657485 external_id: isi: - '001282218200091' file: - access_level: open_access checksum: b8b203ed09e3995ba0d7e6a76288663a content_type: application/pdf creator: yichen date_created: 2024-07-10T11:03:14Z date_updated: 2024-07-10T11:03:14Z file_id: '17215' file_name: sig24_friction_authors.pdf file_size: 47309472 relation: main_file success: 1 - access_level: open_access checksum: 89d81b397b4b6469d828808a68b70820 content_type: application/pdf creator: yichen date_created: 2024-07-10T11:03:12Z date_updated: 2024-07-10T11:03:12Z file_id: '17216' file_name: sig24_friction_supplementary.pdf file_size: 10518286 relation: main_file success: 1 - access_level: open_access checksum: 7123deed34a5456810e7b5336a31c657 content_type: video/mp4 creator: yichen date_created: 2024-07-10T11:03:51Z date_updated: 2024-07-10T11:03:51Z file_id: '17217' file_name: friction_paper_extra_video_finished.mp4 file_size: 71789192 relation: main_file success: 1 - access_level: open_access checksum: e606fc1ae8f2610ce3b4421566800b45 content_type: video/mp4 creator: yichen date_created: 2024-07-10T11:03:58Z date_updated: 2024-07-10T11:03:58Z file_id: '17218' file_name: friction_paper_video_finished.mp4 file_size: 280610763 relation: main_file success: 1 file_date_updated: 2024-07-10T11:03:58Z has_accepted_license: '1' isi: 1 keyword: - physical simulation - frictional contact - rigid body mechanics - non-smooth dynamics language: - iso: eng month: '07' oa: 1 oa_version: Published Version project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24 publication_identifier: isbn: - '9798400705250' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: Primal-dual non-smooth friction for rigid body animation type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '17219' abstract: - lang: eng text: '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.' acknowledgement: Peter Heiss-Synak helped conceive the project, helped formulate the algorithm structure, contributed ideas and code to Sections 6 & 8, the mesh data structure, algorithm robustness and benchmarks, helped write the paper, and provided supervision and conceptual solutions throughout the project. Aleksei Kalinov contributed ideas and code to Sections 7, 8.5, and 5, the sparse grid data structure, algorithm robustness and benchmarks, optimized the performance, produced all results, most figures, and the supplementary video, helped write the text, and provided conceptual solutions throughout the project. Malina Strugaru helped implement the mesh data structure and designed re-meshing operations for non-manifold triangle meshes. Arian Etemadi developed early prototypes for ideas in Sections 8.1 and 8.3 and helped write the paper. Huidong Yang developed early prototypes for isosurface extraction and visualization. Chris Wojtan helped conceive the project, helped write the paper, and provided supervision, prototype grid data structure code, and conceptual solutions throughout the project. We thank the anonymous reviewers for their helpful comments, the members of the Visual Computing Group at ISTA for their feedback, Christopher Batty for discussions about LosTopos, and SideFX for the Houdini Education software licenses. This research was funded in part by the European Union (ERC-2021-COG 101045083 CoDiNA). article_number: '54' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Peter full_name: Synak, Peter id: 331776E2-F248-11E8-B48F-1D18A9856A87 last_name: Synak - first_name: Aleksei full_name: Kalinov, Aleksei id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02 last_name: Kalinov orcid: 0000-0003-2189-3904 - first_name: Irina-Malina full_name: Strugaru, Irina-Malina id: 2afc607f-f128-11eb-9611-8f2a0dfcf074 last_name: Strugaru - first_name: Arian full_name: Etemadihaghighi, Arian id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f last_name: Etemadihaghighi - first_name: Huidong full_name: Yang, Huidong last_name: Yang - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. 2024;43(4). doi:10.1145/3658223 apa: Synak, P., Kalinov, A., Strugaru, I.-M., Etemadi, A., Yang, H., & Wojtan, C. (2024). Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3658223 chicago: Synak, Peter, Aleksei Kalinov, Irina-Malina Strugaru, Arian Etemadi, Huidong Yang, and Chris Wojtan. “Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes.” ACM Transactions on Graphics. Association for Computing Machinery, 2024. https://doi.org/10.1145/3658223. ieee: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, and C. Wojtan, “Multi-material mesh-based surface tracking with implicit topology changes,” ACM Transactions on Graphics, vol. 43, no. 4. Association for Computing Machinery, 2024. ista: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. 2024. Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. 43(4), 54. mla: Synak, Peter, et al. “Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes.” ACM Transactions on Graphics, vol. 43, no. 4, 54, Association for Computing Machinery, 2024, doi:10.1145/3658223. short: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, C. Wojtan, ACM Transactions on Graphics 43 (2024). corr_author: '1' date_created: 2024-07-10T12:24:00Z date_published: 2024-07-01T00:00:00Z date_updated: 2026-04-07T13:02:36Z day: '01' ddc: - '004' department: - _id: GradSch - _id: ChWo doi: 10.1145/3658223 external_id: isi: - '001289270900021' file: - access_level: open_access checksum: 1917067d4b52d7729019b03560004e43 content_type: application/pdf creator: dernst date_created: 2024-07-23T06:35:15Z date_updated: 2024-07-23T06:35:15Z file_id: '17317' file_name: 2024_ACMToG_HeissSynak.pdf file_size: 48763368 relation: main_file success: 1 - access_level: open_access checksum: a4f0e293184bfa034c0c585848806b17 content_type: video/mp4 creator: akalinov date_created: 2024-07-10T12:23:44Z date_updated: 2024-07-10T12:23:44Z file_id: '17221' file_name: sdtopofixer_final.mp4 file_size: 48021463 relation: main_file success: 1 - access_level: open_access checksum: 18fc310a78ec91651148c45a8b89fa44 content_type: application/pdf creator: akalinov date_created: 2025-11-11T09:50:52Z date_updated: 2025-11-11T09:50:52Z file_id: '20633' file_name: SuperDuperTopoFixer.pdf file_size: 48639581 relation: preprint title: Authors' version of the text file_date_updated: 2025-11-11T09:50:52Z has_accepted_license: '1' intvolume: ' 43' isi: 1 issue: '4' keyword: - surface tracking - topology change - non- manifold meshes - multi-material flows - solid modeling language: - iso: eng license: https://creativecommons.org/licenses/by-nc-sa/4.0/ month: '07' oa: 1 oa_version: Published Version project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: ACM Transactions on Graphics publication_identifier: eissn: - 1557-7368 issn: - 0730-0301 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: record: - id: '19630' relation: dissertation_contains status: public - id: '18301' relation: dissertation_contains status: public scopus_import: '1' status: public title: Multi-material mesh-based surface tracking with implicit topology changes tmp: image: /images/cc_by_nc_sa.png legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) short: CC BY-NC-SA (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 43 year: '2024' ... --- _id: '17278' abstract: - lang: eng text: An azeotrope is a constant boiling point mixture, and its behavior is important for fluid separation processes. Predicting azeotropes from atomistic simulations is difficult due to the complexities and convergence problems of Monte Carlo and free-energy perturbation techniques. Here, we present a methodology for predicting the azeotropes of binary mixtures, which computes the compositional dependence of chemical potentials from molecular dynamics simulations using the S0 method and employs experimental boiling point and vaporization enthalpy data. Using this methodology, we reproduce the azeotropes, or lack thereof, in five case studies, including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and acetone/chloroform mixtures. We find that it is crucial to use the experimental boiling point and vaporization enthalpy for reliable azeotrope predictions, as empirical force fields are not accurate enough for these quantities. Finally, we use regular solution models to rationalize the azeotropes and reveal that they tend to form when the mixture components have similar boiling points and strong interactions. acknowledgement: B.C. thanks Alessandro Laio, who introduced the phenomenon of azeotrope and suggested using the S0 method to compute it. B.C. and X.W. thank Felix Wodaczek for the insightful comments and suggestions on the manuscript. B.C. and X.W. acknowledge the resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service, funded by EPSRC Tier-2 capital (Grant No. EP/P020259/1). article_number: '034111' article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Xiaoyu full_name: Wang, Xiaoyu id: 8dff9c62-32b0-11ee-9fa8-fc73025e10f3 last_name: Wang - first_name: Bingqing full_name: Cheng, Bingqing id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9 last_name: Cheng orcid: 0000-0002-3584-9632 citation: ama: Wang X, Cheng B. Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. Journal of Chemical Physics. 2024;161(3). doi:10.1063/5.0217232 apa: Wang, X., & Cheng, B. (2024). Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0217232 chicago: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal of Chemical Physics. AIP Publishing, 2024. https://doi.org/10.1063/5.0217232. ieee: X. Wang and B. Cheng, “Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures,” Journal of Chemical Physics, vol. 161, no. 3. AIP Publishing, 2024. ista: Wang X, Cheng B. 2024. Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. Journal of Chemical Physics. 161(3), 034111. mla: Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations and Experimental Data for Azeotrope Predictions in Binary Mixtures.” Journal of Chemical Physics, vol. 161, no. 3, 034111, AIP Publishing, 2024, doi:10.1063/5.0217232. short: X. Wang, B. Cheng, Journal of Chemical Physics 161 (2024). corr_author: '1' date_created: 2024-07-21T22:01:00Z date_published: 2024-07-14T00:00:00Z date_updated: 2025-09-08T08:26:09Z day: '14' department: - _id: BiCh - _id: GradSch doi: 10.1063/5.0217232 external_id: arxiv: - '2405.02216' isi: - '001281819100016' pmid: - '39007379' intvolume: ' 161' isi: 1 issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2405.02216 month: '07' oa: 1 oa_version: Preprint pmid: 1 publication: Journal of Chemical Physics publication_identifier: eissn: - 1089-7690 issn: - 0021-9606 publication_status: published publisher: AIP Publishing quality_controlled: '1' related_material: link: - relation: software url: https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0 scopus_import: '1' status: public title: Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 161 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '17283' abstract: - lang: eng text: 'We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are essential for their correctness both in implementation and in compilation. In this work, we present a new method for static equivalence and similarity refutation. Our method refutes equivalence and similarity by computing a function over program outputs whose expected value with respect to the output distributions of two programs is different. The function is computed simultaneously with an upper expectation supermartingale and a lower expectation submartingale for the two programs, which we show to together provide a formal certificate for refuting equivalence and similarity. To the best of our knowledge, our method is the first approach to relational program analysis to offer the combination of the following desirable features: (1) it is fully automated, (2) it is applicable to infinite-state probabilistic programs, and (3) it provides formal guarantees on the correctness of its results. We implement a prototype of our method and our experiments demonstrate the effectiveness of our method to refute equivalence and similarity for a number of examples collected from the literature.' acknowledgement: "This research was partially supported by the ERC CoG 863818 (ForM-SMArt) grant. Petr Novotný\r\nis supported by the Czech Science Foundation grant no. GA23-06963S.\r\n" article_number: '232' article_processing_charge: Yes (via OA deal) article_type: original arxiv: 1 author: - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Ehsan full_name: Kafshdar Goharshadi, Ehsan id: 103b4fa0-896a-11ed-bdf8-87b697bef40d last_name: Kafshdar Goharshadi orcid: 0000-0002-8595-0587 - first_name: Petr full_name: Novotný, Petr id: 3CC3B868-F248-11E8-B48F-1D18A9856A87 last_name: Novotný - first_name: Dorde full_name: Zikelic, Dorde id: 294AA7A6-F248-11E8-B48F-1D18A9856A87 last_name: Zikelic orcid: 0000-0002-4681-1699 citation: ama: Chatterjee K, Goharshady E, Novotný P, Zikelic D. Equivalence and similarity refutation for probabilistic programs. Proceedings of the ACM on Programming Languages. 2024;8. doi:10.1145/3656462 apa: Chatterjee, K., Goharshady, E., Novotný, P., & Zikelic, D. (2024). Equivalence and similarity refutation for probabilistic programs. Proceedings of the ACM on Programming Languages. Association for Computing Machinery. https://doi.org/10.1145/3656462 chicago: Chatterjee, Krishnendu, Ehsan Goharshady, Petr Novotný, and Dorde Zikelic. “Equivalence and Similarity Refutation for Probabilistic Programs.” Proceedings of the ACM on Programming Languages. Association for Computing Machinery, 2024. https://doi.org/10.1145/3656462. ieee: K. Chatterjee, E. Goharshady, P. Novotný, and D. Zikelic, “Equivalence and similarity refutation for probabilistic programs,” Proceedings of the ACM on Programming Languages, vol. 8. Association for Computing Machinery, 2024. ista: Chatterjee K, Goharshady E, Novotný P, Zikelic D. 2024. Equivalence and similarity refutation for probabilistic programs. Proceedings of the ACM on Programming Languages. 8, 232. mla: Chatterjee, Krishnendu, et al. “Equivalence and Similarity Refutation for Probabilistic Programs.” Proceedings of the ACM on Programming Languages, vol. 8, 232, Association for Computing Machinery, 2024, doi:10.1145/3656462. short: K. Chatterjee, E. Goharshady, P. Novotný, D. Zikelic, Proceedings of the ACM on Programming Languages 8 (2024). corr_author: '1' date_created: 2024-07-21T22:01:01Z date_published: 2024-06-20T00:00:00Z date_updated: 2025-04-14T07:52:47Z day: '20' ddc: - '000' department: - _id: KrCh - _id: GradSch doi: 10.1145/3656462 ec_funded: 1 external_id: arxiv: - '2404.03430' file: - access_level: open_access checksum: 8cbf220f284a4a87d093db5320c5afdd content_type: application/pdf creator: dernst date_created: 2024-07-22T07:17:14Z date_updated: 2024-07-22T07:17:14Z file_id: '17290' file_name: 2024_ACMProgLang_Chatterjee.pdf file_size: 355421 relation: main_file success: 1 file_date_updated: 2024-07-22T07:17:14Z has_accepted_license: '1' intvolume: ' 8' language: - iso: eng month: '06' oa: 1 oa_version: Published Version project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: Proceedings of the ACM on Programming Languages publication_identifier: eissn: - 2475-1421 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: Equivalence and similarity refutation for probabilistic programs tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 8 year: '2024' ... --- OA_place: publisher _id: '17336' abstract: - lang: eng text: "This thesis deals with the study of stochastic processes and their ergodicity properties. The\r\nvariety of problems encountered calls for a set of different approaches, ranging from classical to\r\nmodern ones: a special place is held by probabilistic methods based on couplings, by functional\r\ninequalities, and by the theory of gradient flows in the space of measures.\r\n\r\nThe material is organized as follows. Chapter 1 contains the introduction to this thesis, starting\r\nwith a general presentation of some of the relevant topics. Section 1.1 is dedicated to the\r\ntheory of gradient flows in metric spaces, and introduces the first contribution of this thesis\r\n[DSMP24], which is presented in detail in Chapter 2. Section 1.2 moves to the topic of\r\ncurvature of Markov chains, concluding with a brief description of our second contribution\r\n[Ped23], which is included in Chapter 3. Section 1.3 discusses applications of stochastic\r\nprocesses to the theory of sampling, in particular the recent framework of score-based diffusion\r\nmodels, and our contribution [PMM24], which is contained in Chapter 4. Section 1.4 discusses\r\nsome related problems, concerning the regularization properties of the heat flow. It serves\r\nas a motivation for the work [BP24], which we report in Chapter 5. Finally, Section 1.5\r\ndiscusses the last contribution of this thesis, which can be found in Chapter 6. It deals with\r\nthe convergence to equilibrium of a particular stochastic model from quantitative genetics:\r\nthis is established via some functional inequalities, which we prove with probabilistic arguments\r\nbased on couplings.\r\n" alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Francesco full_name: Pedrotti, Francesco id: d3ac8ac6-dc8d-11ea-abe3-e2a9628c4c3c last_name: Pedrotti citation: ama: Pedrotti F. Functional inequalities and convergence of stochastic processes. 2024. doi:10.15479/at:ista:17336 apa: Pedrotti, F. (2024). Functional inequalities and convergence of stochastic processes. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17336 chicago: Pedrotti, Francesco. “Functional Inequalities and Convergence of Stochastic Processes.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17336. ieee: F. Pedrotti, “Functional inequalities and convergence of stochastic processes,” Institute of Science and Technology Austria, 2024. ista: Pedrotti F. 2024. Functional inequalities and convergence of stochastic processes. Institute of Science and Technology Austria. mla: Pedrotti, Francesco. Functional Inequalities and Convergence of Stochastic Processes. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17336. short: F. Pedrotti, Functional Inequalities and Convergence of Stochastic Processes, Institute of Science and Technology Austria, 2024. corr_author: '1' date_created: 2024-07-29T09:14:14Z date_published: 2024-07-31T00:00:00Z date_updated: 2026-04-07T13:00:03Z day: '31' ddc: - '500' - '510' - '515' - '519' degree_awarded: PhD department: - _id: GradSch - _id: JaMa doi: 10.15479/at:ista:17336 ec_funded: 1 file: - access_level: open_access checksum: 11650bab714ef85ad43a287060850523 content_type: application/pdf creator: fpedrott date_created: 2024-08-02T09:23:26Z date_updated: 2024-08-02T09:23:26Z file_id: '17366' file_name: thesis_final.pdf file_size: 2941599 relation: main_file success: 1 - access_level: closed checksum: c30ba5611941226cf1bfc867c25b1e80 content_type: application/x-zip-compressed creator: fpedrott date_created: 2024-08-02T09:27:15Z date_updated: 2024-08-02T09:27:15Z file_id: '17367' file_name: thesis_final_source.zip file_size: 6293375 relation: source_file file_date_updated: 2024-08-02T09:27:15Z has_accepted_license: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: '183' project: - _id: 256E75B8-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '716117' name: Optimal Transport and Stochastic Dynamics - _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2 grant_number: F6504 name: Taming Complexity in Partial Differential Systems publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '17351' relation: part_of_dissertation status: public - id: '17353' relation: part_of_dissertation status: public - id: '17350' relation: part_of_dissertation status: public - id: '17352' relation: part_of_dissertation status: public - id: '17143' relation: part_of_dissertation status: public status: public supervisor: - first_name: Jan full_name: Maas, Jan id: 4C5696CE-F248-11E8-B48F-1D18A9856A87 last_name: Maas orcid: 0000-0002-0845-1338 title: Functional inequalities and convergence of stochastic processes tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: dissertation user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd year: '2024' ... --- _id: '17344' abstract: - lang: eng text: 'This file contains the Mathematica notebook associated with the paper Effect of assortative mating and sexual selection on polygenic barriers to gene flow. It contains the numerical approximations, analyses, and simulations used in the study. ' acknowledged_ssus: - _id: ScienComp article_processing_charge: No author: - first_name: Parvathy full_name: Surendranadh, Parvathy id: 455235B8-F248-11E8-B48F-1D18A9856A87 last_name: Surendranadh orcid: 0000-0001-6395-386X - first_name: Himani full_name: Sachdeva, Himani last_name: Sachdeva citation: ama: Surendranadh P, Sachdeva H. Mathematica notebook for “Effect of assortative mating and sexual selection on polygenic barriers to gene flow.” 2024. doi:10.15479/AT:ISTA:17344 apa: Surendranadh, P., & Sachdeva, H. (2024). Mathematica notebook for “Effect of assortative mating and sexual selection on polygenic barriers to gene flow.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:17344 chicago: Surendranadh, Parvathy, and Himani Sachdeva. “Mathematica Notebook for ‘Effect of Assortative Mating and Sexual Selection on Polygenic Barriers to Gene Flow.’” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/AT:ISTA:17344. ieee: P. Surendranadh and H. Sachdeva, “Mathematica notebook for ‘Effect of assortative mating and sexual selection on polygenic barriers to gene flow.’” Institute of Science and Technology Austria, 2024. ista: Surendranadh P, Sachdeva H. 2024. Mathematica notebook for ‘Effect of assortative mating and sexual selection on polygenic barriers to gene flow’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:17344. mla: Surendranadh, Parvathy, and Himani Sachdeva. Mathematica Notebook for “Effect of Assortative Mating and Sexual Selection on Polygenic Barriers to Gene Flow.” Institute of Science and Technology Austria, 2024, doi:10.15479/AT:ISTA:17344. short: P. Surendranadh, H. Sachdeva, (2024). date_created: 2024-07-29T14:01:43Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-01-14T13:02:59Z ddc: - '576' department: - _id: GradSch - _id: NiBa doi: 10.15479/AT:ISTA:17344 file: - access_level: open_access checksum: 75bdbc7ad7cc6afe4459bc4a8824a302 content_type: application/octet-stream creator: psurendr date_created: 2024-07-29T13:51:11Z date_updated: 2024-07-29T13:51:11Z file_id: '17345' file_name: Submission.nb file_size: 726132 relation: main_file success: 1 file_date_updated: 2024-07-29T13:51:11Z has_accepted_license: '1' month: '07' oa: 1 oa_version: Published Version publisher: Institute of Science and Technology Austria status: public title: Mathematica notebook for 'Effect of assortative mating and sexual selection on polygenic barriers to gene flow' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository _id: '17361' abstract: - lang: eng text: 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. acknowledgement: The authors are grateful to Boris Khesin for valuable comments on the MW symplectic structure and S. Ishida thanks Albert Chern for insightful discussions on space curves and Chris Wojtan for his continuous support. M. Bauer was partially supported by NSF grant DMS-1953244 and by the Binational Science Foundation (BSF). S. Ishida was partially supported by ERC Consolidator Grant 101045083 “CoDiNA” funded by the European Research Council. Some figures were generated by the software Houdini and its education license was provided by SideFX. article_processing_charge: No arxiv: 1 author: - first_name: Martin full_name: Bauer, Martin last_name: Bauer - first_name: Sadashige full_name: Ishida, Sadashige id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425 last_name: Ishida orcid: 0000-0002-3121-3100 - first_name: Peter W. full_name: Michor, Peter W. last_name: Michor citation: ama: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves. arXiv. doi:10.48550/arXiv.2407.19908 apa: Bauer, M., Ishida, S., & Michor, P. W. (n.d.). Symplectic structures on the space of space curves. arXiv. https://doi.org/10.48550/arXiv.2407.19908 chicago: Bauer, Martin, Sadashige Ishida, and Peter W. Michor. “Symplectic Structures on the Space of Space Curves.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2407.19908. ieee: M. Bauer, S. Ishida, and P. W. Michor, “Symplectic structures on the space of space curves,” arXiv. . ista: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves. arXiv, 10.48550/arXiv.2407.19908. mla: Bauer, Martin, et al. “Symplectic Structures on the Space of Space Curves.” ArXiv, doi:10.48550/arXiv.2407.19908. short: M. Bauer, S. Ishida, P.W. Michor, ArXiv (n.d.). date_created: 2024-08-01T06:34:08Z date_published: 2024-07-29T00:00:00Z date_updated: 2026-04-28T09:59:01Z day: '29' department: - _id: GradSch - _id: ChWo doi: 10.48550/arXiv.2407.19908 external_id: arxiv: - '2407.19908' keyword: - space of space curves - symplectic stuctures language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2407.19908 month: '07' oa: 1 oa_version: Preprint project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: arXiv publication_status: draft related_material: record: - id: '20551' relation: dissertation_contains status: public - id: '21743' relation: later_version status: public status: public title: Symplectic structures on the space of space curves type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ...