--- OA_place: repository _id: '19013' abstract: - lang: eng text: We study the singularities of the moduli space of degree e maps from smooth genus g curves to an arbitrary smooth hypersurface of low degree. For e large compared to g, we show that these moduli spaces have at worst terminal singularities. Our main approach is to study the jet schemes of these moduli spaces by developing a suitable form of the circle method. article_processing_charge: No arxiv: 1 author: - first_name: Jakob full_name: Glas, Jakob id: d6423cba-dc74-11ea-a0a7-ee61689ff5fb last_name: Glas - first_name: 'Matthew ' full_name: 'Hase-Liu, Matthew ' last_name: Hase-Liu citation: ama: Glas J, Hase-Liu M. Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method. arXiv. doi:10.48550/arXiv.2412.14923 apa: Glas, J., & Hase-Liu, M. (n.d.). Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method. arXiv. https://doi.org/10.48550/arXiv.2412.14923 chicago: Glas, Jakob, and Matthew Hase-Liu. “Terminal Singularities of the Moduli Space of Curves on Low Degree Hypersurfaces and the Circle Method.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2412.14923. ieee: J. Glas and M. Hase-Liu, “Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method,” arXiv. . ista: Glas J, Hase-Liu M. Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method. arXiv, 10.48550/arXiv.2412.14923. mla: Glas, Jakob, and Matthew Hase-Liu. “Terminal Singularities of the Moduli Space of Curves on Low Degree Hypersurfaces and the Circle Method.” ArXiv, doi:10.48550/arXiv.2412.14923. short: J. Glas, M. Hase-Liu, ArXiv (n.d.). corr_author: '1' date_created: 2025-02-07T12:04:11Z date_published: 2024-12-19T00:00:00Z date_updated: 2025-04-15T08:05:40Z day: '19' department: - _id: TiBr doi: 10.48550/arXiv.2412.14923 external_id: arxiv: - '2412.14923' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2412.14923 month: '12' oa: 1 oa_version: Preprint publication: arXiv publication_status: draft related_material: record: - id: '18295' relation: earlier_version status: public status: public title: Terminal singularities of the moduli space of curves on low degree hypersurfaces and the circle method 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: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '19028' abstract: - lang: eng text: The stochastic nature of modern Monte Carlo (MC) rendering methods inevitably produces noise in rendered images for a practical number of samples per pixel. The problem of denoising these images has been widely studied, with most recent methods relying on data-driven, pretrained neural networks. In contrast, in this paper we propose a statistical approach to the denoising problem, treating each pixel as a random variable and reasoning about its distribution. Considering a pixel of the noisy rendered image, we formulate fast pair-wise statistical tests—based on online estimators—to decide which of the nearby pixels to exclude from the denoising filter. We show that for symmetric pixel weights and normally distributed samples, the classical Welch t-test is optimal in terms of mean squared error. We then show how to extend this result to handle non-normal distributions, using more recent confidence-interval formulations in combination with the Box-Cox transformation. Our results show that our statistical denoising approach matches the performance of state-of-the-art neural image denoising without having to resort to any computation-intensive pretraining. Furthermore, our approach easily generalizes to other quantities besides pixel intensity, which we demonstrate by showing additional applications to Russian roulette path termination and multiple importance sampling. acknowledgement: 'We would like to thank Lukas Lipp for fruitful discussions, Károly Zsolnai-Fehér and Jaroslav Křivánek for valuable contributions to early versions of this work, and Bernhard Kerbl for help with our CUDA implementation. Moreover, we would like to thank the creators of the scenes we have used: Wig42 for “Wooden Staircase” (Fig. 1), “Grey and White Room” (Fig. S6), and “Modern Living Room” (Fig. S8); nacimus for “Bathroom” (Fig. 3, S5); NovaZeeke for “Japanese Classroom” (Fig. 4, 6); Beeple for “Zero-Day” (Fig. 8); Jay-Artist for “White Room” (Fig. S7); Mareck for “Contemporary Bathroom” (Fig. 2); Christian Freude for “Glass Caustics” (Fig. S10); and Benedikt Bitterli for “Veach Ajar” (Fig. 7, S2), “Veach MIS” (Fig. S4), and “Fur Ball” (Fig. S11). This work has received funding from the Vienna Science and Technology Fund (WWTF) project ICT22-028 (“Toward Optimal Path Guiding for Photorealistic Rendering”) and the Austrian Science Fund (FWF) project F 77 (SFB “Advanced Computational Design”).' article_number: '68' article_processing_charge: Yes (in subscription journal) author: - first_name: Hiroyuki full_name: Sakai, Hiroyuki last_name: Sakai - first_name: Christian full_name: Freude, Christian last_name: Freude - first_name: Thomas full_name: Auzinger, Thomas id: 4718F954-F248-11E8-B48F-1D18A9856A87 last_name: Auzinger orcid: 0000-0002-1546-3265 - first_name: David full_name: Hahn, David id: 357A6A66-F248-11E8-B48F-1D18A9856A87 last_name: Hahn - first_name: Michael full_name: Wimmer, Michael last_name: Wimmer citation: ama: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. A statistical approach to Monte Carlo denoising. In: Proceedings - SIGGRAPH Asia 2024 Conference Papers. Association for Computing Machinery; 2024. doi:10.1145/3680528.3687591' apa: 'Sakai, H., Freude, C., Auzinger, T., Hahn, D., & Wimmer, M. (2024). A statistical approach to Monte Carlo denoising. In Proceedings - SIGGRAPH Asia 2024 Conference Papers. Tokyo, Japan: Association for Computing Machinery. https://doi.org/10.1145/3680528.3687591' chicago: Sakai, Hiroyuki, Christian Freude, Thomas Auzinger, David Hahn, and Michael Wimmer. “A Statistical Approach to Monte Carlo Denoising.” In Proceedings - SIGGRAPH Asia 2024 Conference Papers. Association for Computing Machinery, 2024. https://doi.org/10.1145/3680528.3687591. ieee: H. Sakai, C. Freude, T. Auzinger, D. Hahn, and M. Wimmer, “A statistical approach to Monte Carlo denoising,” in Proceedings - SIGGRAPH Asia 2024 Conference Papers, Tokyo, Japan, 2024. ista: 'Sakai H, Freude C, Auzinger T, Hahn D, Wimmer M. 2024. A statistical approach to Monte Carlo denoising. Proceedings - SIGGRAPH Asia 2024 Conference Papers. SA: SIGGRAPH Asia, 68.' mla: Sakai, Hiroyuki, et al. “A Statistical Approach to Monte Carlo Denoising.” Proceedings - SIGGRAPH Asia 2024 Conference Papers, 68, Association for Computing Machinery, 2024, doi:10.1145/3680528.3687591. short: H. Sakai, C. Freude, T. Auzinger, D. Hahn, M. Wimmer, in:, Proceedings - SIGGRAPH Asia 2024 Conference Papers, Association for Computing Machinery, 2024. conference: end_date: 2024-12-06 location: Tokyo, Japan name: 'SA: SIGGRAPH Asia' start_date: 2024-12-03 date_created: 2025-02-16T23:02:34Z date_published: 2024-12-03T00:00:00Z date_updated: 2025-12-02T13:58:56Z day: '03' ddc: - '000' doi: 10.1145/3680528.3687591 external_id: isi: - '001441591200068' file: - access_level: open_access checksum: 89f63b9237224362ec33430af9152700 content_type: application/pdf creator: dernst date_created: 2025-04-15T12:53:24Z date_updated: 2025-04-15T12:53:24Z file_id: '19563' file_name: 2024_SIGGRAPH_Sakai.pdf file_size: 14791980 relation: main_file success: 1 file_date_updated: 2025-04-15T12:53:24Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: Proceedings - SIGGRAPH Asia 2024 Conference Papers publication_identifier: isbn: - '9798400711312' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' scopus_import: '1' status: public title: A statistical approach to Monte Carlo denoising 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: publisher OA_type: hybrid _id: '19051' abstract: - lang: eng text: This paper corrects an error in an earlier work of the author. article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Timothy D full_name: Browning, Timothy D id: 35827D50-F248-11E8-B48F-1D18A9856A87 last_name: Browning orcid: 0000-0002-8314-0177 citation: ama: Browning TD. The polynomial sieve and equal sums of like polynomials. International Mathematics Research Notices. 2024;2024(13):10165-10168. doi:10.1093/imrn/rnae066 apa: Browning, T. D. (2024). The polynomial sieve and equal sums of like polynomials. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnae066 chicago: Browning, Timothy D. “The Polynomial Sieve and Equal Sums of like Polynomials.” International Mathematics Research Notices. Oxford University Press, 2024. https://doi.org/10.1093/imrn/rnae066. ieee: T. D. Browning, “The polynomial sieve and equal sums of like polynomials,” International Mathematics Research Notices, vol. 2024, no. 13. Oxford University Press, pp. 10165–10168, 2024. ista: Browning TD. 2024. The polynomial sieve and equal sums of like polynomials. International Mathematics Research Notices. 2024(13), 10165–10168. mla: Browning, Timothy D. “The Polynomial Sieve and Equal Sums of like Polynomials.” International Mathematics Research Notices, vol. 2024, no. 13, Oxford University Press, 2024, pp. 10165–68, doi:10.1093/imrn/rnae066. short: T.D. Browning, International Mathematics Research Notices 2024 (2024) 10165–10168. corr_author: '1' date_created: 2025-02-18T07:15:50Z date_published: 2024-07-01T00:00:00Z date_updated: 2025-09-09T12:16:45Z day: '01' ddc: - '510' department: - _id: TiBr doi: 10.1093/imrn/rnae066 external_id: isi: - '001196957300001' file: - access_level: open_access checksum: b625b8adf018d2a97591813c1fc17b96 content_type: application/pdf creator: dernst date_created: 2025-02-18T07:56:36Z date_updated: 2025-02-18T07:56:36Z file_id: '19052' file_name: 2024_IMRN_Browning.pdf file_size: 205750 relation: main_file success: 1 file_date_updated: 2025-02-18T07:56:36Z has_accepted_license: '1' intvolume: ' 2024' isi: 1 issue: '13' language: - iso: eng month: '07' oa: 1 oa_version: Published Version page: 10165-10168 publication: International Mathematics Research Notices publication_identifier: eissn: - 1687-0247 issn: - 1073-7928 publication_status: published publisher: Oxford University Press quality_controlled: '1' related_material: record: - id: '254' relation: earlier_version status: public scopus_import: '1' status: public title: The polynomial sieve and equal sums of like polynomials 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: 2024 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 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: '19307' abstract: - lang: eng text: "This repository contains the data, scripts, SAM codes and files required to reproduce the results of the manuscript \"The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation\" submitted to the Geophysical Research Letters (GRL).\r\n\r\nBrief description of project: This project aims to examine the impact of rain evaporation removal or reduction in the planetary boundary layer (PBL) on convective self aggregation (CSA). Non-rotating radiative-convective equilibrium (RCE) simulations were conducted with the System for Atmospheric Modeling (SAM) cloud resolving model. Rain evaporation in the lowest 1 km was progressively reduced and the effect on CSA was investigated. The physical processes underlying this type of aggregation (referred to in the manuscript as no-evaporation CSA, or NE-CSA) were analyzed and described. \r\nThe default SAM code base (version 6.10.8) can be downloaded from here: http://rossby.msrc.sunysb.edu/~marat/SAM.html" article_processing_charge: No author: - first_name: Yi-Ling full_name: Hwong, Yi-Ling id: 1217aa61-4dd1-11ec-9ac3-f2ba3f17ee22 last_name: Hwong orcid: 0000-0001-9281-3479 - first_name: Caroline J full_name: Muller, Caroline J id: f978ccb0-3f7f-11eb-b193-b0e2bd13182b last_name: Muller orcid: 0000-0001-5836-5350 citation: ama: Hwong Y-L, Muller CJ. Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation. 2024. doi:10.5281/ZENODO.10687169 apa: Hwong, Y.-L., & Muller, C. J. (2024). Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation. Zenodo. https://doi.org/10.5281/ZENODO.10687169 chicago: Hwong, Yi-Ling, and Caroline J Muller. “Data - The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation.” Zenodo, 2024. https://doi.org/10.5281/ZENODO.10687169. ieee: Y.-L. Hwong and C. J. Muller, “Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation.” Zenodo, 2024. ista: Hwong Y-L, Muller CJ. 2024. Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation, Zenodo, 10.5281/ZENODO.10687169. mla: Hwong, Yi-Ling, and Caroline J. Muller. Data - The Unreasonable Efficiency of Total Rain Evaporation Removal in Triggering Convective Self-Aggregation. Zenodo, 2024, doi:10.5281/ZENODO.10687169. short: Y.-L. Hwong, C.J. Muller, (2024). corr_author: '1' date_created: 2025-03-07T08:39:40Z date_published: 2024-02-21T00:00:00Z date_updated: 2025-09-04T13:16:39Z day: '21' ddc: - '550' department: - _id: CaMu doi: 10.5281/ZENODO.10687169 has_accepted_license: '1' main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.8369509 month: '02' oa: 1 oa_version: Published Version publisher: Zenodo related_material: record: - id: '15186' relation: used_in_publication status: public status: public title: Data - The unreasonable efficiency of total rain evaporation removal in triggering convective self-aggregation 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_reference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: publisher OA_type: diamond _id: '19408' abstract: - lang: eng text: 'Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past. In this work, we take a step back, and ask: "Why should one care about continual learning in the first place?". We set the stage by examining recent continual learning papers published at four major machine learning conferences, and show that memory-constrained settings dominate the field. Then, we discuss five open problems in machine learning, and even though they might seem unrelated to continual learning at first sight, we show that continual learning will inevitably be part of their solution. These problems are model editing, personalization and specialization, on-device learning, faster (re-)training and reinforcement learning. Finally, by comparing the desiderata from these unsolved problems and the current assumptions in continual learning, we highlight and discuss four future directions for continual learning research. We hope that this work offers an interesting perspective on the future of continual learning, while displaying its potential value and the paths we have to pursue in order to make it successful. This work is the result of the many discussions the authors had at the Dagstuhl seminar on Deep Continual Learning, in March 2023.' alternative_title: - TMLR article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Eli full_name: Verwimp, Eli last_name: Verwimp - first_name: Rahaf full_name: Aljundi, Rahaf last_name: Aljundi - first_name: Shai full_name: Ben-David, Shai last_name: Ben-David - first_name: Matthias full_name: Bethge, Matthias last_name: Bethge - first_name: Andrea full_name: Cossu, Andrea last_name: Cossu - first_name: Alexander full_name: Gepperth, Alexander last_name: Gepperth - first_name: Tyler L. full_name: Hayes, Tyler L. last_name: Hayes - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: Christopher full_name: Kanan, Christopher last_name: Kanan - first_name: Dhireesha full_name: Kudithipudi, Dhireesha last_name: Kudithipudi - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 - first_name: Martin full_name: Mundt, Martin last_name: Mundt - first_name: Razvan full_name: Pascanu, Razvan last_name: Pascanu - first_name: Adrian full_name: Popescu, Adrian last_name: Popescu - first_name: Andreas S. full_name: Tolias, Andreas S. last_name: Tolias - first_name: Joost full_name: Van De Weijer, Joost last_name: Van De Weijer - first_name: Bing full_name: Liu, Bing last_name: Liu - first_name: Vincenzo full_name: Lomonaco, Vincenzo last_name: Lomonaco - first_name: Tinne full_name: Tuytelaars, Tinne last_name: Tuytelaars - first_name: Gido M. full_name: Van De Ven, Gido M. last_name: Van De Ven citation: ama: 'Verwimp E, Aljundi R, Ben-David S, et al. Continual learning: Applications and the road forward. Transactions on Machine Learning Research. 2024;2024.' apa: 'Verwimp, E., Aljundi, R., Ben-David, S., Bethge, M., Cossu, A., Gepperth, A., … Van De Ven, G. M. (2024). Continual learning: Applications and the road forward. Transactions on Machine Learning Research. Transactions on Machine Learning Research.' chicago: 'Verwimp, Eli, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, et al. “Continual Learning: Applications and the Road Forward.” Transactions on Machine Learning Research. Transactions on Machine Learning Research, 2024.' ieee: 'E. Verwimp et al., “Continual learning: Applications and the road forward,” Transactions on Machine Learning Research, vol. 2024. Transactions on Machine Learning Research, 2024.' ista: 'Verwimp E, Aljundi R, Ben-David S, Bethge M, Cossu A, Gepperth A, Hayes TL, Hüllermeier E, Kanan C, Kudithipudi D, Lampert C, Mundt M, Pascanu R, Popescu A, Tolias AS, Van De Weijer J, Liu B, Lomonaco V, Tuytelaars T, Van De Ven GM. 2024. Continual learning: Applications and the road forward. Transactions on Machine Learning Research. 2024.' mla: 'Verwimp, Eli, et al. “Continual Learning: Applications and the Road Forward.” Transactions on Machine Learning Research, vol. 2024, Transactions on Machine Learning Research, 2024.' short: E. Verwimp, R. Aljundi, S. Ben-David, M. Bethge, A. Cossu, A. Gepperth, T.L. Hayes, E. Hüllermeier, C. Kanan, D. Kudithipudi, C. Lampert, M. Mundt, R. Pascanu, A. Popescu, A.S. Tolias, J. Van De Weijer, B. Liu, V. Lomonaco, T. Tuytelaars, G.M. Van De Ven, Transactions on Machine Learning Research 2024 (2024). date_created: 2025-03-16T23:01:25Z date_published: 2024-04-12T00:00:00Z date_updated: 2025-03-20T09:21:02Z day: '12' ddc: - '000' department: - _id: ChLa external_id: arxiv: - '2311.11908' file: - access_level: open_access checksum: 0714e12f7423cd098976ed9974561155 content_type: application/pdf creator: dernst date_created: 2025-03-20T09:02:18Z date_updated: 2025-03-20T09:02:18Z file_id: '19426' file_name: 2024_TMLR_Verwimp.pdf file_size: 1367966 relation: main_file success: 1 file_date_updated: 2025-03-20T09:02:18Z has_accepted_license: '1' intvolume: ' 2024' language: - iso: eng month: '04' oa: 1 oa_version: Published Version publication: Transactions on Machine Learning Research publication_identifier: eissn: - 2835-8856 publication_status: published publisher: Transactions on Machine Learning Research quality_controlled: '1' scopus_import: '1' status: public title: 'Continual learning: Applications and the road forward' 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: repository OA_type: green _id: '19425' abstract: - lang: eng text: "We demonstrate that periodically driven quantum rotors provide a promising and broadly applicable platform to implement multi-gap topological phases, where groups of bands can acquire topological invariants due to non-Abelian braiding of band degeneracies. By adiabatically varying the periodic kicks to the rotor we find nodal-line braiding, which causes sign flips of topological charges of band nodes and can prevent them from annihilating, indicated by non-zero values of the %non-Abelian patch Euler class. In particular, we report\r\non the emergence of an anomalous Dirac string phase arising in the strongly driven regime, a truly out-of-equilibrium phase of the quantum rotor. This phase emanates from braiding processes involving all (quasienergy) gaps and manifests itself with edge states at zero angular momentum. Our results reveal direct applications in state-of-the-art experiments of quantum rotors, such as linear molecules driven by periodic far-off-resonant laser pulses or artificial\r\nquantum rotors in optical lattices, whose extensive versatility offers precise modification and observation of novel non-Abelian topological properties. " acknowledgement: "We thank G. M. Koutentakis, S. Wimberger, J. G. E. Harris, T. Enss and A. Ghazaryan for fruitful discussions. M.L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). R.-J. S. acknowledges funding from a EPSRC ERC underwrite grant EP/X025829/1, a EPSRC New Investigator Award grant EP/W00187X/1, as well as Trinity College, Cambridge. F.N.U. acknowledges support from the Marie ¨Sk lodowska-Curie programme of the European Commission [Grant No. 893915], Simons Investigator Award\r\n[Grant No. 511029] and Trinity College Cambridge." article_number: '2408.16848' article_processing_charge: No arxiv: 1 author: - first_name: Volker full_name: Karle, Volker id: D7C012AE-D7ED-11E9-95E8-1EC5E5697425 last_name: Karle orcid: 0000-0002-6963-0129 - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 - first_name: Adrien full_name: Bouhon, Adrien last_name: Bouhon - first_name: Robert-Jan full_name: Slager, Robert-Jan last_name: Slager - first_name: F. Nur full_name: Ünal, F. Nur last_name: Ünal citation: ama: Karle V, Lemeshko M, Bouhon A, Slager R-J, Ünal FN. Anomalous multi-gap topological phases in periodically driven quantum  rotors. arXiv. doi:10.48550/arXiv.2408.16848 apa: Karle, V., Lemeshko, M., Bouhon, A., Slager, R.-J., & Ünal, F. N. (n.d.). Anomalous multi-gap topological phases in periodically driven quantum  rotors. arXiv. https://doi.org/10.48550/arXiv.2408.16848 chicago: Karle, Volker, Mikhail Lemeshko, Adrien Bouhon, Robert-Jan Slager, and F. Nur Ünal. “Anomalous Multi-Gap Topological Phases in Periodically Driven Quantum  Rotors.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2408.16848. ieee: V. Karle, M. Lemeshko, A. Bouhon, R.-J. Slager, and F. N. Ünal, “Anomalous multi-gap topological phases in periodically driven quantum  rotors,” arXiv. . ista: Karle V, Lemeshko M, Bouhon A, Slager R-J, Ünal FN. Anomalous multi-gap topological phases in periodically driven quantum  rotors. arXiv, 2408.16848. mla: Karle, Volker, et al. “Anomalous Multi-Gap Topological Phases in Periodically Driven Quantum  Rotors.” ArXiv, 2408.16848, doi:10.48550/arXiv.2408.16848. short: V. Karle, M. Lemeshko, A. Bouhon, R.-J. Slager, F.N. Ünal, ArXiv (n.d.). date_created: 2025-03-20T07:48:23Z date_published: 2024-08-29T00:00:00Z date_updated: 2026-04-07T11:48:53Z day: '29' department: - _id: MiLe doi: 10.48550/arXiv.2408.16848 ec_funded: 1 external_id: arxiv: - '2408.16848' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2408.16848 month: '08' oa: 1 oa_version: Preprint project: - _id: 2688CF98-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '801770' name: 'Angulon: physics and applications of a new quasiparticle' publication: arXiv publication_status: draft related_material: record: - id: '19393' relation: dissertation_contains status: public status: public title: Anomalous multi-gap topological phases in periodically driven quantum rotors type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_type: closed access _id: '19446' abstract: - lang: eng text: This Comment explores new approaches to enrich large-scale population data, including incorporating macro-environmental and digital health measures. acknowledgement: Funded by the European Union. Complementary funding was received by the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (10041392 and 10038599). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union, the European Health and Digital Executive Agency (HADEA) or UKRI. The European Union, HADEA and UKRI cannot be held responsible for them. This work received also support from Chinese Ministry for Science and Technology (MOST), the Horizon 2020-funded European Research Council Advanced Grant ‘STRATIFY’ (695313), the German Research Foundation (COPE; 675346; NE 1383/15-1 (CoviDrug)) and the National Natural Science Foundation of China grant 82150710554. article_processing_charge: No article_type: letter_note author: - first_name: Frauke full_name: Nees, Frauke last_name: Nees - first_name: Paul full_name: Renner, Paul last_name: Renner - first_name: Nathalie E. full_name: Holz, Nathalie E. last_name: Holz - first_name: Elli full_name: Polemiti, Elli last_name: Polemiti - first_name: Sebastian full_name: Siehl, Sebastian last_name: Siehl - first_name: Sören full_name: Hese, Sören last_name: Hese - first_name: Kerstin full_name: Schepanski, Kerstin last_name: Schepanski - first_name: Gunter full_name: Schumann, Gunter last_name: Schumann - first_name: Henrik full_name: Walter, Henrik last_name: Walter - first_name: Andreas full_name: Heinz, Andreas last_name: Heinz - first_name: Markus full_name: Ralser, Markus last_name: Ralser - first_name: Sven full_name: Twardziok, Sven last_name: Twardziok - first_name: Nilakshi full_name: Vaidya, Nilakshi last_name: Vaidya - first_name: Antoine full_name: Bernas, Antoine last_name: Bernas - first_name: Emin full_name: Serin, Emin last_name: Serin - first_name: Marcel full_name: Jentsch, Marcel last_name: Jentsch - first_name: Esther full_name: Hitchen, Esther last_name: Hitchen - first_name: Hedi full_name: Kebir, Hedi last_name: Kebir - first_name: Tristram A. full_name: Lett, Tristram A. last_name: Lett - first_name: Jean Charles full_name: Roy, Jean Charles last_name: Roy - first_name: Roland full_name: Eils, Roland last_name: Eils - first_name: Ulrike Helene full_name: Taron, Ulrike Helene last_name: Taron - first_name: Tatjana full_name: Schütz, Tatjana last_name: Schütz - first_name: Jamie full_name: Banks, Jamie last_name: Banks - first_name: Tobias full_name: Banaschewski, Tobias last_name: Banaschewski - first_name: Karina full_name: Jansone, Karina last_name: Jansone - first_name: Nina full_name: Christmann, Nina last_name: Christmann - first_name: Andreas full_name: Meyer-Lindenberg, Andreas last_name: Meyer-Lindenberg - first_name: Heike full_name: Tost, Heike last_name: Tost - first_name: Nathalie full_name: Holz, Nathalie last_name: Holz - first_name: Emanuel full_name: Schwarz, Emanuel last_name: Schwarz - first_name: Argyris full_name: Stringaris, Argyris last_name: Stringaris - first_name: Maja full_name: Neidhart, Maja last_name: Neidhart - first_name: Beke full_name: Seefried, Beke last_name: Seefried - first_name: Rieke full_name: Aden, Rieke last_name: Aden - first_name: Ole A. full_name: Andreassen, Ole A. last_name: Andreassen - first_name: Lars T. full_name: Westlye, Lars T. last_name: Westlye - first_name: Dennis full_name: Van Der Meer, Dennis last_name: Van Der Meer - first_name: Sara full_name: Fernandez, Sara last_name: Fernandez - first_name: Rikka full_name: Kjelkenes, Rikka last_name: Kjelkenes - first_name: Helga full_name: Ask, Helga last_name: Ask - first_name: Michael full_name: Rapp, Michael last_name: Rapp - first_name: Mira full_name: Tschorn, Mira last_name: Tschorn - first_name: Sarah Jane full_name: Böttger, Sarah Jane last_name: Böttger - first_name: Andre full_name: Marquand, Andre last_name: Marquand - first_name: Gaia full_name: Novarino, Gaia id: 3E57A680-F248-11E8-B48F-1D18A9856A87 last_name: Novarino orcid: 0000-0002-7673-7178 - first_name: Lena full_name: Marr, Lena id: 4406F586-F248-11E8-B48F-1D18A9856A87 last_name: Marr - first_name: Mel full_name: Slater, Mel last_name: Slater - first_name: Guillem Feixas full_name: Viapiana, Guillem Feixas last_name: Viapiana - first_name: Francisco Eiroa full_name: Orosa, Francisco Eiroa last_name: Orosa - first_name: Jaime full_name: Gallego, Jaime last_name: Gallego - first_name: Alvaro full_name: Pastor, Alvaro last_name: Pastor - first_name: Andreas J. full_name: Forstner, Andreas J. last_name: Forstner - first_name: Per full_name: Hoffmann, Per last_name: Hoffmann - first_name: Markus M. full_name: Nöthen, Markus M. last_name: Nöthen - first_name: Isabelle full_name: Claus, Isabelle last_name: Claus - first_name: Abigail full_name: Miller, Abigail last_name: Miller - first_name: Carina M. full_name: Mathey, Carina M. last_name: Mathey - first_name: Stefanie full_name: Heilmann-Heimbach, Stefanie last_name: Heilmann-Heimbach - first_name: Peter full_name: Sommer, Peter last_name: Sommer - first_name: Myrto full_name: Patraskaki, Myrto last_name: Patraskaki - first_name: Johannes full_name: Wilbertz, Johannes last_name: Wilbertz - first_name: Karen full_name: Schmitt, Karen last_name: Schmitt - first_name: Viktor full_name: Jirsa, Viktor last_name: Jirsa - first_name: Spase full_name: Petkoski, Spase last_name: Petkoski - first_name: Séverine full_name: Pitel, Séverine last_name: Pitel - first_name: Lisa full_name: Otten, Lisa last_name: Otten - first_name: Anastasios Polykarpos full_name: Athanasiadis, Anastasios Polykarpos last_name: Athanasiadis - first_name: Charlie full_name: Pearmund, Charlie last_name: Pearmund - first_name: Bernhard full_name: Spanlang, Bernhard last_name: Spanlang - first_name: Elena full_name: Alvarez, Elena last_name: Alvarez - first_name: Mavi full_name: Sanchez, Mavi last_name: Sanchez - first_name: Arantxa full_name: Giner, Arantxa last_name: Giner - first_name: Tianye full_name: Jia, Tianye last_name: Jia - first_name: Yanting full_name: Gong, Yanting last_name: Gong - first_name: Yunman full_name: Xia, Yunman last_name: Xia - first_name: Xiao full_name: Chang, Xiao last_name: Chang - first_name: Vince full_name: Calhoun, Vince last_name: Calhoun - first_name: Jingyu full_name: Liu, Jingyu last_name: Liu - first_name: Ameli full_name: Schwalber, Ameli last_name: Schwalber - first_name: Paul full_name: Thompson, Paul last_name: Thompson - first_name: Nicholas full_name: Clinton, Nicholas last_name: Clinton - first_name: Sylvane full_name: Desrivières, Sylvane last_name: Desrivières - first_name: Allan H. full_name: Young, Allan H. last_name: Young - first_name: Bernd full_name: Stahl, Bernd last_name: Stahl - first_name: George full_name: Ogoh, George last_name: Ogoh citation: ama: Nees F, Renner P, Holz NE, et al. Large-scale population data enrichment in mental health research. Nature Mental Health. 2024;2(10):1124-1127. doi:10.1038/s44220-024-00316-z apa: Nees, F., Renner, P., Holz, N. E., Polemiti, E., Siehl, S., Hese, S., … Ogoh, G. (2024). Large-scale population data enrichment in mental health research. Nature Mental Health. Springer Nature. https://doi.org/10.1038/s44220-024-00316-z chicago: Nees, Frauke, Paul Renner, Nathalie E. Holz, Elli Polemiti, Sebastian Siehl, Sören Hese, Kerstin Schepanski, et al. “Large-Scale Population Data Enrichment in Mental Health Research.” Nature Mental Health. Springer Nature, 2024. https://doi.org/10.1038/s44220-024-00316-z. ieee: F. Nees et al., “Large-scale population data enrichment in mental health research,” Nature Mental Health, vol. 2, no. 10. Springer Nature, pp. 1124–1127, 2024. ista: Nees F, Renner P, Holz NE, Polemiti E, Siehl S, Hese S, Schepanski K, Schumann G, Walter H, Heinz A, Ralser M, Twardziok S, Vaidya N, Bernas A, Serin E, Jentsch M, Hitchen E, Kebir H, Lett TA, Roy JC, Eils R, Taron UH, Schütz T, Banks J, Banaschewski T, Jansone K, Christmann N, Meyer-Lindenberg A, Tost H, Holz N, Schwarz E, Stringaris A, Neidhart M, Seefried B, Aden R, Andreassen OA, Westlye LT, Van Der Meer D, Fernandez S, Kjelkenes R, Ask H, Rapp M, Tschorn M, Böttger SJ, Marquand A, Novarino G, Marr L, Slater M, Viapiana GF, Orosa FE, Gallego J, Pastor A, Forstner AJ, Hoffmann P, Nöthen MM, Claus I, Miller A, Mathey CM, Heilmann-Heimbach S, Sommer P, Patraskaki M, Wilbertz J, Schmitt K, Jirsa V, Petkoski S, Pitel S, Otten L, Athanasiadis AP, Pearmund C, Spanlang B, Alvarez E, Sanchez M, Giner A, Jia T, Gong Y, Xia Y, Chang X, Calhoun V, Liu J, Schwalber A, Thompson P, Clinton N, Desrivières S, Young AH, Stahl B, Ogoh G. 2024. Large-scale population data enrichment in mental health research. Nature Mental Health. 2(10), 1124–1127. mla: Nees, Frauke, et al. “Large-Scale Population Data Enrichment in Mental Health Research.” Nature Mental Health, vol. 2, no. 10, Springer Nature, 2024, pp. 1124–27, doi:10.1038/s44220-024-00316-z. short: F. Nees, P. Renner, N.E. Holz, E. Polemiti, S. Siehl, S. Hese, K. Schepanski, G. Schumann, H. Walter, A. Heinz, M. Ralser, S. Twardziok, N. Vaidya, A. Bernas, E. Serin, M. Jentsch, E. Hitchen, H. Kebir, T.A. Lett, J.C. Roy, R. Eils, U.H. Taron, T. Schütz, J. Banks, T. Banaschewski, K. Jansone, N. Christmann, A. Meyer-Lindenberg, H. Tost, N. Holz, E. Schwarz, A. Stringaris, M. Neidhart, B. Seefried, R. Aden, O.A. Andreassen, L.T. Westlye, D. Van Der Meer, S. Fernandez, R. Kjelkenes, H. Ask, M. Rapp, M. Tschorn, S.J. Böttger, A. Marquand, G. Novarino, L. Marr, M. Slater, G.F. Viapiana, F.E. Orosa, J. Gallego, A. Pastor, A.J. Forstner, P. Hoffmann, M.M. Nöthen, I. Claus, A. Miller, C.M. Mathey, S. Heilmann-Heimbach, P. Sommer, M. Patraskaki, J. Wilbertz, K. Schmitt, V. Jirsa, S. Petkoski, S. Pitel, L. Otten, A.P. Athanasiadis, C. Pearmund, B. Spanlang, E. Alvarez, M. Sanchez, A. Giner, T. Jia, Y. Gong, Y. Xia, X. Chang, V. Calhoun, J. Liu, A. Schwalber, P. Thompson, N. Clinton, S. Desrivières, A.H. Young, B. Stahl, G. Ogoh, Nature Mental Health 2 (2024) 1124–1127. date_created: 2025-03-23T23:01:28Z date_published: 2024-10-01T00:00:00Z date_updated: 2025-03-25T08:28:39Z day: '01' department: - _id: GaNo doi: 10.1038/s44220-024-00316-z intvolume: ' 2' issue: '10' language: - iso: eng month: '10' oa_version: None page: 1124-1127 publication: Nature Mental Health publication_identifier: eissn: - 2731-6076 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Large-scale population data enrichment in mental health research type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2 year: '2024' ... --- OA_type: closed access _id: '19470' abstract: - lang: eng text: When food is freely available, eating occurs without energy deficit. While agouti-related peptide (AgRP) neurons are likely involved, their activation is thought to require negative energy balance. To investigate this, we implemented long-term, continuous in vivo fiber-photometry recordings in mice. We discovered new forms of AgRP neuron regulation, including fast pre-ingestive decreases in activity and unexpectedly rapid activation by fasting. Furthermore, AgRP neuron activity has a circadian rhythm that peaks concurrent with the daily feeding onset. Importantly, this rhythm persists when nutrition is provided via constant-rate gastric infusions. Hence, it is not secondary to a circadian feeding rhythm. The AgRP neuron rhythm is driven by the circadian clock, the suprachiasmatic nucleus (SCN), as SCN ablation abolishes the circadian rhythm in AgRP neuron activity and feeding. The SCN activates AgRP neurons via excitatory afferents from thyrotrophin-releasing hormone-expressing neurons in the dorsomedial hypothalamus (DMHTrh neurons) to drive daily feeding rhythms. article_processing_charge: No article_type: original author: - first_name: Amelia May Barnett full_name: Douglass, Amelia May Barnett id: de5f6fda-80fb-11ef-996f-a8c4ecd8e289 last_name: Douglass orcid: 0000-0001-5398-6473 - first_name: Hakan full_name: Kucukdereli, Hakan last_name: Kucukdereli - first_name: Joseph C. full_name: Madara, Joseph C. last_name: Madara - first_name: Daqing full_name: Wang, Daqing last_name: Wang - first_name: Chen full_name: Wu, Chen last_name: Wu - first_name: Elijah D. full_name: Lowenstein, Elijah D. last_name: Lowenstein - first_name: Jenkang full_name: Tao, Jenkang last_name: Tao - first_name: Bradford B. full_name: Lowell, Bradford B. last_name: Lowell citation: ama: Douglass AM, Kucukdereli H, Madara JC, et al. Acute and circadian feedforward regulation of agouti-related peptide hunger neurons. Cell Metabolism. 2024;37(3):708-722.e5. doi:10.1016/j.cmet.2024.11.009 apa: Douglass, A. M., Kucukdereli, H., Madara, J. C., Wang, D., Wu, C., Lowenstein, E. D., … Lowell, B. B. (2024). Acute and circadian feedforward regulation of agouti-related peptide hunger neurons. Cell Metabolism. Elsevier. https://doi.org/10.1016/j.cmet.2024.11.009 chicago: Douglass, Amelia M., Hakan Kucukdereli, Joseph C. Madara, Daqing Wang, Chen Wu, Elijah D. Lowenstein, Jenkang Tao, and Bradford B. Lowell. “Acute and Circadian Feedforward Regulation of Agouti-Related Peptide Hunger Neurons.” Cell Metabolism. Elsevier, 2024. https://doi.org/10.1016/j.cmet.2024.11.009. ieee: A. M. Douglass et al., “Acute and circadian feedforward regulation of agouti-related peptide hunger neurons,” Cell Metabolism, vol. 37, no. 3. Elsevier, p. 708–722.e5, 2024. ista: Douglass AM, Kucukdereli H, Madara JC, Wang D, Wu C, Lowenstein ED, Tao J, Lowell BB. 2024. Acute and circadian feedforward regulation of agouti-related peptide hunger neurons. Cell Metabolism. 37(3), 708–722.e5. mla: Douglass, Amelia M., et al. “Acute and Circadian Feedforward Regulation of Agouti-Related Peptide Hunger Neurons.” Cell Metabolism, vol. 37, no. 3, Elsevier, 2024, p. 708–722.e5, doi:10.1016/j.cmet.2024.11.009. short: A.M. Douglass, H. Kucukdereli, J.C. Madara, D. Wang, C. Wu, E.D. Lowenstein, J. Tao, B.B. Lowell, Cell Metabolism 37 (2024) 708–722.e5. date_created: 2025-04-03T12:27:39Z date_published: 2024-03-04T00:00:00Z date_updated: 2025-07-10T11:51:40Z day: '04' doi: 10.1016/j.cmet.2024.11.009 extern: '1' external_id: pmid: - '39719709' intvolume: ' 37' issue: '3' language: - iso: eng month: '03' oa_version: None page: 708-722.e5 pmid: 1 publication: Cell Metabolism publication_identifier: issn: - 1550-4131 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Acute and circadian feedforward regulation of agouti-related peptide hunger neurons type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19486' abstract: - lang: eng text: Consider the family of elliptic curves En:y2=x3+n2, where n varies over positive cubefree integers. There is a rational 3-isogeny ϕ from En to E^n:y2=x3−27n2 and a dual isogeny ϕ^:E^n→En. We show that for almost all n, the rank of Selϕ(En) is 0, and the rank of Selϕ^(E^n) is determined by the number of prime factors of n that are congruent to 2mod3 and the congruence class of nmod9. acknowledgement: The author would like to thank Peter Koymans and Carlo Pagano for helpful discussions. article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Yik Tung full_name: Chan, Yik Tung id: c4c0afc8-9262-11ed-9231-d8b0bc743af1 last_name: Chan orcid: 0000-0001-8467-4106 citation: ama: Chan S. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. 2024;2024(9):7571-7593. doi:10.1093/imrn/rnad266 apa: Chan, S. (2024). The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnad266 chicago: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.” International Mathematics Research Notices. Oxford University Press, 2024. https://doi.org/10.1093/imrn/rnad266. ieee: S. Chan, “The 3-isogeny selmer groups of the elliptic curves y2=x3+n2,” International Mathematics Research Notices, vol. 2024, no. 9. Oxford University Press, pp. 7571–7593, 2024. ista: Chan S. 2024. The 3-isogeny selmer groups of the elliptic curves y2=x3+n2. International Mathematics Research Notices. 2024(9), 7571–7593. mla: Chan, Stephanie. “The 3-Isogeny Selmer Groups of the Elliptic Curves Y2=x3+n2.” International Mathematics Research Notices, vol. 2024, no. 9, Oxford University Press, 2024, pp. 7571–93, doi:10.1093/imrn/rnad266. short: S. Chan, International Mathematics Research Notices 2024 (2024) 7571–7593. date_created: 2025-04-05T10:50:33Z date_published: 2024-05-01T00:00:00Z date_updated: 2025-07-10T11:51:44Z day: '01' doi: 10.1093/imrn/rnad266 extern: '1' external_id: arxiv: - '2211.06062' intvolume: ' 2024' issue: '9' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2211.06062 month: '05' oa: 1 oa_version: Preprint page: 7571-7593 publication: International Mathematics Research Notices publication_identifier: eissn: - 1687-0247 issn: - 1073-7928 publication_status: published publisher: Oxford University Press quality_controlled: '1' scopus_import: '1' status: public title: The 3-isogeny selmer groups of the elliptic curves y2=x3+n2 type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 2024 year: '2024' ... --- OA_place: repository OA_type: green _id: '19510' abstract: - lang: eng text: "We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called\r\nMICROADAM that specifically minimizes memory overheads, while maintaining\r\ntheoretical convergence guarantees. We achieve this by compressing the gradient\r\ninformation before it is fed into the optimizer state, thereby reducing its memory\r\nfootprint significantly. We control the resulting compression error via a novel\r\ninstance of the classical error feedback mechanism from distributed optimization [Seide et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which\r\nthe error correction information is itself compressed to allow for practical memory\r\ngains. We prove that the resulting approach maintains theoretical convergence\r\nguarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MICROADAM can be implemented efficiently\r\non GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence competitive to that of the uncompressed\r\nAdam baseline, with lower memory usage and similar running time. Our code is\r\navailable at https://github.com/IST-DASLab/MicroAdam." acknowledged_ssus: - _id: CampIT acknowledgement: The authors thank Razvan Pascanu, Mahdi Nikdan and Soroush Tabesh for their valuable feedback, the IT department from Institute of Science and Technology Austria for the hardware support and Weights and Biases for the infrastructure to track all our experiments. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101034413. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Ionut-Vlad full_name: Modoranu, Ionut-Vlad id: 449f7a18-f128-11eb-9611-9b430c0c6333 last_name: Modoranu - first_name: Mher full_name: Safaryan, Mher id: dd546b39-0804-11ed-9c55-ef075c39778d last_name: Safaryan - first_name: Grigory full_name: Malinovsky, Grigory last_name: Malinovsky - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Thomas full_name: Robert, Thomas id: de632733-1457-11f0-ae22-b5914b8c1c41 last_name: Robert - first_name: Peter full_name: Richtárik, Peter last_name: Richtárik - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik, P., & Alistarh, D.-A. (2024). MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In 38th Conference on Neural Information Processing Systems (Vol. 37). Neural Information Processing Systems Foundation.' chicago: 'Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'I.-V. Modoranu et al., “MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence,” in 38th Conference on Neural Information Processing Systems, 2024, vol. 37.' ista: 'Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P, Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems. , Advances in Neural Information Processing Systems, vol. 37.' mla: 'Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. corr_author: '1' date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:32:52Z day: '20' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2405.15593' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2405.15593 month: '12' oa: 1 oa_version: Preprint project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' related_material: link: - relation: software url: https://github.com/IST-DASLab/MicroAdam scopus_import: '1' status: public title: 'MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19511' abstract: - lang: eng text: We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits. QuaRot rotates LLMs in a way that removes outliers from the hidden state without changing the output, making quantization easier. This computational invariance is applied to the hidden state (residual) of the LLM, as well as to the activations of the feed-forward components, aspects of the attention mechanism, and to the KV cache. The result is a quantized model where all matrix multiplications are performed in 4 bits, without any channels identified for retention in higher precision. Our 4-bit quantized LLAMA2-70B model has losses of at most 0.47 WikiText-2 perplexity and retains 99% of the zero-shot performance. We also show that QuaRot can provide lossless 6 and 8 bit LLAMA-2 models without any calibration data using round-to-nearest quantization. Code is available at github.com/spcl/QuaRot. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Saleh full_name: Ashkboos, Saleh last_name: Ashkboos - first_name: Amirkeivan full_name: Mohtashami, Amirkeivan last_name: Mohtashami - first_name: Maximilian L. full_name: Croci, Maximilian L. last_name: Croci - first_name: Bo full_name: Li, Bo last_name: Li - first_name: Pashmina full_name: Cameron, Pashmina last_name: Cameron - first_name: Martin full_name: Jaggi, Martin last_name: Jaggi - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Torsten full_name: Hoefler, Torsten last_name: Hoefler - first_name: James full_name: Hensman, James last_name: Hensman citation: ama: 'Ashkboos S, Mohtashami A, Croci ML, et al. QuaRot: Outlier-free 4-bit inference in rotated LLMs. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Ashkboos, S., Mohtashami, A., Croci, M. L., Li, B., Cameron, P., Jaggi, M., … Hensman, J. (2024). QuaRot: Outlier-free 4-bit inference in rotated LLMs. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Ashkboos, Saleh, Amirkeivan Mohtashami, Maximilian L. Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan-Adrian Alistarh, Torsten Hoefler, and James Hensman. “QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'S. Ashkboos et al., “QuaRot: Outlier-free 4-bit inference in rotated LLMs,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Ashkboos S, Mohtashami A, Croci ML, Li B, Cameron P, Jaggi M, Alistarh D-A, Hoefler T, Hensman J. 2024. QuaRot: Outlier-free 4-bit inference in rotated LLMs. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Ashkboos, Saleh, et al. “QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: S. Ashkboos, A. Mohtashami, M.L. Croci, B. Li, P. Cameron, M. Jaggi, D.-A. Alistarh, T. Hoefler, J. Hensman, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-09 date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:33:12Z day: '20' department: - _id: DaAl external_id: arxiv: - '2404.00456' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2404.00456 month: '12' oa: 1 oa_version: Preprint publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' related_material: link: - relation: software url: https://github.com/spcl/QuaRot scopus_import: '1' status: public title: 'QuaRot: Outlier-free 4-bit inference in rotated LLMs' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19512' abstract: - lang: eng text: "Differential privacy with gradual expiration models the setting where data items\r\narrive in a stream and at a given time t the privacy loss guaranteed for a data item\r\nseen at time (t − d) is εg(d), where g is a monotonically non-decreasing function.\r\nWe study the fundamental continual (binary) counting problem where each data\r\nitem consists of a bit, and the algorithm needs to output at each time step the sum of\r\nall the bits streamed so far. For a stream of length T and privacy without expiration\r\ncontinual counting is possible with maximum (over all time steps) additive error\r\nO(log2\r\n(T)/ε) and the best known lower bound is Ω(log(T)/ε); closing this gap\r\nis a challenging open problem.\r\nWe show that the situation is very different for privacy with gradual expiration by\r\ngiving upper and lower bounds for a large set of expiration functions g. Specifically,\r\nour algorithm achieves an additive error of O(log(T)/ε) for a large set of privacy\r\nexpiration functions. We also give a lower bound that shows that if C is the additive\r\nerror of any ε-DP algorithm for this problem, then the product of C and the privacy\r\nexpiration function after 2C steps must be Ω(log(T)/ε). Our algorithm matches\r\nthis lower bound as its additive error is O(log(T)/ε), even when g(2C) = O(1).\r\nOur empirical evaluation shows that we achieve a slowly growing privacy loss\r\nwith significantly smaller empirical privacy loss for large values of d than a natural\r\nbaseline algorithm." acknowledgement: 'Monika Henzinger: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101019564) and the Austrian Science Fund (FWF) grant DOI 10.55776/Z422, grant DOI 10.55776/I5982, and grant DOI 10.55776/P33775 with additional funding from the netidee SCIENCE Stiftung, 2020–2024. Joel Daniel Andersson and Rasmus Pagh are affiliated with Basic Algorithms Research Copenhagen (BARC), supported by the VILLUM Foundation grant 16582, and are also supported by Providentia, a Data Science Distinguished Investigator grant from Novo Nordisk Fonden. Teresa Anna Steiner is supported by a research grant (VIL51463) from VILLUM FONDEN. This work was done while Teresa Anna Steiner was a Postdoc at the Technical University of Denmark. Jalaj Upadhyay’s research was funded by the Rutgers Decanal Grant no. 302918 and an unrestricted gift from Google.' alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Joel Daniel full_name: Andersson, Joel Daniel last_name: Andersson - first_name: Monika H full_name: Henzinger, Monika H id: 540c9bbd-f2de-11ec-812d-d04a5be85630 last_name: Henzinger orcid: 0000-0002-5008-6530 - first_name: Rasmus full_name: Pagh, Rasmus last_name: Pagh - first_name: Teresa Anna full_name: Steiner, Teresa Anna last_name: Steiner - first_name: Jalaj full_name: Upadhyay, Jalaj last_name: Upadhyay citation: ama: 'Andersson JD, Henzinger M, Pagh R, Steiner TA, Upadhyay J. Continual counting with gradual privacy expiration. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Andersson, J. D., Henzinger, M., Pagh, R., Steiner, T. A., & Upadhyay, J. (2024). Continual counting with gradual privacy expiration. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: Andersson, Joel Daniel, Monika Henzinger, Rasmus Pagh, Teresa Anna Steiner, and Jalaj Upadhyay. “Continual Counting with Gradual Privacy Expiration.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024. ieee: J. D. Andersson, M. Henzinger, R. Pagh, T. A. Steiner, and J. Upadhyay, “Continual counting with gradual privacy expiration,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37. ista: 'Andersson JD, Henzinger M, Pagh R, Steiner TA, Upadhyay J. 2024. Continual counting with gradual privacy expiration. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: Andersson, Joel Daniel, et al. “Continual Counting with Gradual Privacy Expiration.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024. short: J.D. Andersson, M. Henzinger, R. Pagh, T.A. Steiner, J. Upadhyay, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-09 corr_author: '1' date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:33:22Z day: '20' department: - _id: MoHe ec_funded: 1 external_id: arxiv: - '2406.03802' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.03802 month: '12' oa: 1 oa_version: Preprint project: - _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62 call_identifier: H2020 grant_number: '101019564' name: The design and evaluation of modern fully dynamic data structures - _id: 34def286-11ca-11ed-8bc3-da5948e1613c grant_number: Z00422 name: Efficient algorithms - _id: bda196b2-d553-11ed-ba76-8e8ee6c21103 grant_number: I05982 name: Static and Dynamic Hierarchical Graph Decompositions - _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe grant_number: P33775 name: Fast Algorithms for a Reactive Network Layer publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: Continual counting with gradual privacy expiration type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19515' abstract: - lang: eng text: "Neural models learn data representations that lie on low-dimensional manifolds,\r\nyet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show\r\nthat this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks.\r\nTo this end, we introduce a multi-purpose framework to the representation learning\r\ncommunity, which allows to: (i) compare different spaces in an interpretable way\r\nand measure their intrinsic similarity; (ii) find correspondences between them, both\r\nin unsupervised and weakly supervised settings, and (iii) to effectively transfer\r\nrepresentations between distinct spaces. We validate our framework on various\r\napplications, ranging from stitching to retrieval tasks, and on multiple modalities,\r\ndemonstrating that Latent Functional Maps can serve as a swiss-army knife for\r\nrepresentation alignment" acknowledgement: MF is supported by the MSCA IST-Bridge fellowship which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034413. ER and VM are supported by the PNRR MUR project PE0000013-FAIR. MP is supported by the Sapienza grant "Predicting and Explaining Clinical Trial Outcomes", prot. RG12218166FA3F13. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Marco full_name: Fumero, Marco id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650 last_name: Fumero - first_name: Marco full_name: Pegoraro, Marco last_name: Pegoraro - first_name: Valentino full_name: Maiorca, Valentino last_name: Maiorca - first_name: Francesco full_name: Locatello, Francesco id: 26cfd52f-2483-11ee-8040-88983bcc06d4 last_name: Locatello orcid: 0000-0002-4850-0683 - first_name: Emanuele full_name: Rodolà, Emanuele last_name: Rodolà citation: ama: 'Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. Latent functional maps: A spectral framework for representation alignment. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Fumero, M., Pegoraro, M., Maiorca, V., Locatello, F., & Rodolà, E. (2024). Latent functional maps: A spectral framework for representation alignment. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Fumero, Marco, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, and Emanuele Rodolà. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, and E. Rodolà, “Latent functional maps: A spectral framework for representation alignment,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. 2024. Latent functional maps: A spectral framework for representation alignment. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Fumero, Marco, et al. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, E. Rodolà, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-09 corr_author: '1' date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:36:51Z day: '20' department: - _id: FrLo ec_funded: 1 external_id: arxiv: - '2406.14183' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2406.14183 month: '12' oa: 1 oa_version: Preprint project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: 'Latent functional maps: A spectral framework for representation alignment' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19517' abstract: - lang: eng text: "In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce cycle consistency of the permutations when merging n ≥ 3 models, allowing circular compositions of permutations to be computed without accumulating error along the path. We qualitatively and quantitatively motivate the need for such a constraint, showing its benefits when merging sets of models in scenarios spanning varying architectures and datasets. We finally show that, when coupled\r\nwith activation renormalization, our approach yields the best results in the task." acknowledgement: "This work is supported by the ERC grant no.802554 (SPECGEO), PRIN 2020 project\r\nno.2020TA3K9N (LEGO.AI), and PNRR MUR project PE0000013-FAIR. Marco Fumero is supported by the MSCA IST-Bridge fellowship which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034413. We thank Simone Scardapane for the helpful feedback on the paper." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Donato full_name: Crisostomi, Donato last_name: Crisostomi - first_name: Marco full_name: Fumero, Marco id: 1c1593eb-393f-11ef-bb8e-ab4f1e979650 last_name: Fumero - first_name: Daniele full_name: Baieri, Daniele last_name: Baieri - first_name: Florian full_name: Bernard, Florian last_name: Bernard - first_name: Emanuele full_name: Rodolà, Emanuele last_name: Rodolà citation: ama: 'Crisostomi D, Fumero M, Baieri D, Bernard F, Rodolà E. C2M3: Cycle-consistent multi-model merging. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Crisostomi, D., Fumero, M., Baieri, D., Bernard, F., & Rodolà, E. (2024). C2M3: Cycle-consistent multi-model merging. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Crisostomi, Donato, Marco Fumero, Daniele Baieri, Florian Bernard, and Emanuele Rodolà. “C2M3: Cycle-Consistent Multi-Model Merging.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, and E. Rodolà, “C2M3: Cycle-consistent multi-model merging,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Crisostomi D, Fumero M, Baieri D, Bernard F, Rodolà E. 2024. C2M3: Cycle-consistent multi-model merging. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Crisostomi, Donato, et al. “C2M3: Cycle-Consistent Multi-Model Merging.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, E. Rodolà, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-09 corr_author: '1' date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:36:59Z day: '20' department: - _id: FrLo ec_funded: 1 external_id: arxiv: - '2405.17897' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2405.17897 month: '12' oa: 1 oa_version: Preprint project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: 'C2M3: Cycle-consistent multi-model merging' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19518' abstract: - lang: eng text: "The rising footprint of machine learning has led to a focus on imposing model\r\nsparsity as a means of reducing computational and memory costs. For deep neural\r\nnetworks (DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics\r\ninspired by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al.,\r\n1989, Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature\r\ninformation to make better pruning decisions. Yet, these results still lack a solid\r\ntheoretical understanding, and it is unclear whether they can be improved by\r\nleveraging connections to the wealth of work on sparse recovery algorithms. In this\r\npaper, we draw new connections between these two areas and present new sparse\r\nrecovery algorithms inspired by the OBS framework that comes with theoretical\r\nguarantees under reasonable assumptions and have strong practical performance.\r\nSpecifically, our work starts from the observation that we can leverage curvature\r\ninformation in OBS-like fashion upon the projection step of classic iterative sparse\r\nrecovery algorithms such as IHT. We show for the first time that this leads both\r\nto improved convergence bounds under standard assumptions. Furthermore, we\r\npresent extensions of this approach to the practical task of obtaining accurate sparse\r\nDNNs, and validate it experimentally at scale for Transformer-based models on\r\nvision and language tasks." acknowledged_ssus: - _id: CampIT acknowledgement: The authors thank the anonymous NeurIPS reviewers for their useful comments and feedback, the IT department from the Institute of Science and Technology Austria for the hardware support, and Weights and Biases for the infrastructure to track all our experiments. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Maria Skłodowska-Curie grant agreement No 101034413. alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Diyuan full_name: Wu, Diyuan id: 1a5914c2-896a-11ed-bdf8-fb80621a0635 last_name: Wu - first_name: Ionut-Vlad full_name: Modoranu, Ionut-Vlad id: 449f7a18-f128-11eb-9611-9b430c0c6333 last_name: Modoranu - first_name: Mher full_name: Safaryan, Mher id: dd546b39-0804-11ed-9c55-ef075c39778d last_name: Safaryan - first_name: Denis full_name: Kuznedelev, Denis last_name: Kuznedelev - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Wu, D., Modoranu, I.-V., Safaryan, M., Kuznedelev, D., & Alistarh, D.-A. (2024). The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Wu, Diyuan, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, and Dan-Adrian Alistarh. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, and D.-A. Alistarh, “The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. 2024. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Wu, Diyuan, et al. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-09 corr_author: '1' date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T11:37:10Z day: '20' department: - _id: DaAl - _id: MaMo ec_funded: 1 external_id: arxiv: - '2408.17163' intvolume: ' 37' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2408.17163 month: '12' oa: 1 oa_version: Preprint project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 38th Conference on Neural Information Processing Systems publication_identifier: issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: 'The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: publisher OA_type: gold _id: '19519' abstract: - lang: eng text: There has been significant interest in "extreme" compression of large language models (LLMs), i.e. to 1-2 bits per parameter, which allows such models to be executed efficiently on resource-constrained devices. Existing work focused on improved one-shot quantization techniques and weight representations; yet, purely post-training approaches are reaching diminishing returns in terms of the accuracy-vs-bit-width trade-off. State-of-the-art quantization methods such as QuIP# and AQLM include fine-tuning (part of) the compressed parameters over a limited amount of calibration data; however, such fine-tuning techniques over compressed weights often make exclusive use of straight-through estimators (STE), whose performance is not well-understood in this setting. In this work, we question the use of STE for extreme LLM compression, showing that it can be sub-optimal, and perform a systematic study of quantization-aware fine-tuning strategies for LLMs.We propose PV-Tuning - a representation-agnostic framework that generalizes and improves upon existing fine-tuning strategies, and provides convergence guarantees in restricted cases.On the practical side, when used for 1-2 bit vector quantization, PV-Tuning outperforms prior techniques for highly-performant models such as Llama and Mistral. Using PV-Tuning, we achieve the first Pareto-optimal quantization for Llama-2 family models at 2 bits per parameter. acknowledgement: "Authors would like to thank Vage Egiazarian, Andrei Panferov and Ruslan Svirschevski for their\r\nhelp and advice on AQLM codebase and running large-scale experiments. We also thank Philip\r\nZmushko and Artem Fedorov for helpful discussions during the early stages of our research. The research of Kai Yi, Konstantin Burlachenko, and Peter Richtárik reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) – Center of Excellence for Generative AI, under award number 5940. We would also like to thank our NeurIPS reviewers for their helpful suggestions, we specifically highlight p3Lv’s suggestions to consider smaller codebook sizes and evaluate PV-Tuning with QuIP#, both of which produced interesting findings. Finally, we thank the open-source contributors from llama.cpp9 and the LocalLlama10 community for discussions and inspirations on practical use cases of quantized language models, and in particular, Yalda Shabanzadeh and Arthur Aardvark for their help with improving the codebase." alternative_title: - Advances in Neural Information Processing Systems article_processing_charge: No arxiv: 1 author: - first_name: Vladimir full_name: Malinovskii, Vladimir last_name: Malinovskii - first_name: Denis full_name: Mazur, Denis last_name: Mazur - first_name: Ivan full_name: Ilin, Ivan last_name: Ilin - first_name: Denis full_name: Kuznedelev, Denis last_name: Kuznedelev - first_name: Konstantin full_name: Burlachenko, Konstantin last_name: Burlachenko - first_name: Kai full_name: Yi, Kai last_name: Yi - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Peter full_name: Richtarik, Peter last_name: Richtarik citation: ama: 'Malinovskii V, Mazur D, Ilin I, et al. PV-tuning: Beyond straight-through estimation for extreme LLM compression. In: 38th Conference on Neural Information Processing Systems. Vol 37. Neural Information Processing Systems Foundation; 2024.' apa: 'Malinovskii, V., Mazur, D., Ilin, I., Kuznedelev, D., Burlachenko, K., Yi, K., … Richtarik, P. (2024). PV-tuning: Beyond straight-through estimation for extreme LLM compression. In 38th Conference on Neural Information Processing Systems (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.' chicago: 'Malinovskii, Vladimir, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan-Adrian Alistarh, and Peter Richtarik. “PV-Tuning: Beyond Straight-through Estimation for Extreme LLM Compression.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.' ieee: 'V. Malinovskii et al., “PV-tuning: Beyond straight-through estimation for extreme LLM compression,” in 38th Conference on Neural Information Processing Systems, Vancouver, Canada, 2024, vol. 37.' ista: 'Malinovskii V, Mazur D, Ilin I, Kuznedelev D, Burlachenko K, Yi K, Alistarh D-A, Richtarik P. 2024. PV-tuning: Beyond straight-through estimation for extreme LLM compression. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.' mla: 'Malinovskii, Vladimir, et al. “PV-Tuning: Beyond Straight-through Estimation for Extreme LLM Compression.” 38th Conference on Neural Information Processing Systems, vol. 37, Neural Information Processing Systems Foundation, 2024.' short: V. Malinovskii, D. Mazur, I. Ilin, D. Kuznedelev, K. Burlachenko, K. Yi, D.-A. Alistarh, P. Richtarik, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024. conference: end_date: 2024-12-15 location: Vancouver, Canada name: 'NeurIPS: Neural Information Processing Systems' start_date: 2024-12-10 date_created: 2025-04-06T22:01:32Z date_published: 2024-12-20T00:00:00Z date_updated: 2025-05-14T10:49:20Z day: '20' ddc: - '000' department: - _id: DaAl external_id: arxiv: - '2405.14852' file: - access_level: open_access checksum: 54d36f947887e26d0e568b512167001a content_type: application/pdf creator: dernst date_created: 2025-04-07T09:17:10Z date_updated: 2025-04-07T09:17:10Z file_id: '19521' file_name: 2024_NeurIPS_Malinovskii.pdf file_size: 939712 relation: main_file success: 1 file_date_updated: 2025-04-07T09:17:10Z has_accepted_license: '1' intvolume: ' 37' language: - iso: eng month: '12' oa: 1 oa_version: Published Version publication: 38th Conference on Neural Information Processing Systems publication_identifier: isbn: - '9798331314385' issn: - 1049-5258 publication_status: published publisher: Neural Information Processing Systems Foundation quality_controlled: '1' scopus_import: '1' status: public title: 'PV-tuning: Beyond straight-through estimation for extreme LLM compression' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 37 year: '2024' ... --- OA_place: repository OA_type: green _id: '19520' abstract: - lang: eng text: Vertebrates exhibit a wide range of motor behaviors, ranging from swimming to complex limb-based movements. Here we take advantage of frog metamorphosis, which captures a swim-to-limb-based movement transformation during the development of a single organism, to explore changes in the underlying spinal circuits. We find that the tadpole spinal cord contains small and largely homogeneous populations of motor neurons (MNs) and V1 interneurons (V1s) at early escape swimming stages. These neuronal populations only modestly increase in number and subtype heterogeneity with the emergence of free swimming. In contrast, during frog metamorphosis and the emergence of limb movement, there is a dramatic expansion of MN and V1 interneuron number and transcriptional heterogeneity, culminating in cohorts of neurons that exhibit striking molecular similarity to mammalian motor circuits. CRISPR/Cas9-mediated gene disruption of the limb MN and V1 determinants FoxP1 and Engrailed-1, respectively, results in severe but selective deficits in tail and limb function. Our work thus demonstrates that neural diversity scales exponentially with increasing behavioral complexity and illustrates striking evolutionary conservation in the molecular organization and function of motor circuits across species. acknowledged_ssus: - _id: Bio acknowledgement: "We would like to thank the members of the Sweeney Lab (especially Stavros Papadopoulos and\r\nSophie Gobeil) for their contributions to this project and, in addition to the lab, Graziana Gatto\r\nand Mario de Bono, for discussion, and support. We are also grateful to Tom Jessell and Chris\r\nKintner for their scientific insight and mentorship during the conception of this project. This\r\nproject would also not have been possible with the technical support of the Matthias Nowak,\r\nVerena Mayer and the Aquatics as well as the Imaging and Optics Facility support teams\r\n(ISTA). In addition, we thank our funding sources for providing the resources to do these\r\nexperiments: FTI Strategy Lower Austria Dissertation Grant Number FT121-D-046 (D.V.);\r\nHorizon Europe ERC Starting Grant Number 101041551 (L.B.S., F.A.T. and D.V); Special\r\nResearch Program (SFB) of the Austrian Science Fund (FWF) Project number F7814-B (L.B.S);\r\nNINDS 5R35NS116858 (J.S.D); CZI grant DAF2020-225401 (DOI): 10.37921/120055ratwvi\r\n(R.H.); NIH grant number R01NS123116 (J.B.B); American Lebanese Syrian Associated\r\nCharities (ALSAC) (J.B.B.); German Academic Exchange Service (DAAD) IFI Grant Number\r\n57515251-91853472 (Z.H.); and Project A.L.S. (S.B-M.). " article_processing_charge: No author: - first_name: David full_name: Vijatovic, David id: cf391e77-ec3c-11ea-a124-d69323410b58 last_name: Vijatovic - first_name: 'Florina Alexandra ' full_name: 'Toma, Florina Alexandra ' id: 2f73f876-f128-11eb-9611-b96b5a30cb0e last_name: Toma - first_name: Zoe P full_name: Harrington, Zoe P id: a8144562-32c9-11ee-b5ce-d9800628bda2 last_name: Harrington orcid: 0009-0008-0158-4032 - first_name: Christoph M full_name: Sommer, Christoph M id: 4DF26D8C-F248-11E8-B48F-1D18A9856A87 last_name: Sommer orcid: 0000-0003-1216-9105 - first_name: Robert full_name: Hauschild, Robert id: 4E01D6B4-F248-11E8-B48F-1D18A9856A87 last_name: Hauschild orcid: 0000-0001-9843-3522 - first_name: Alexandra J. full_name: Trevisan, Alexandra J. last_name: Trevisan - first_name: Phillip full_name: Chapman, Phillip last_name: Chapman - first_name: Mara full_name: Julseth, Mara id: 1cf464b2-dc7d-11ea-9b2f-f9b1aa9417d1 last_name: Julseth - first_name: Susan full_name: Brenner-Morton, Susan last_name: Brenner-Morton - first_name: Mariano I. full_name: Gabitto, Mariano I. last_name: Gabitto - first_name: Jeremy S. full_name: Dasen, Jeremy S. last_name: Dasen - first_name: Jay B. full_name: Bikoff, Jay B. last_name: Bikoff - first_name: Lora Beatrice Jaeger full_name: Sweeney, Lora Beatrice Jaeger id: 56BE8254-C4F0-11E9-8E45-0B23E6697425 last_name: Sweeney orcid: 0000-0001-9242-5601 citation: ama: Vijatovic D, Toma FA, Harrington ZP, et al. Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis. bioRxiv. doi:10.1101/2024.09.20.614050 apa: Vijatovic, D., Toma, F. A., Harrington, Z. P., Sommer, C. M., Hauschild, R., Trevisan, A. J., … Sweeney, L. B. (n.d.). Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis. bioRxiv. https://doi.org/10.1101/2024.09.20.614050 chicago: Vijatovic, David, Florina Alexandra Toma, Zoe P Harrington, Christoph M Sommer, Robert Hauschild, Alexandra J. Trevisan, Phillip Chapman, et al. “Spinal Neuron Diversity Scales Exponentially with Swim-to-Limb Transformation during Frog Metamorphosis.” BioRxiv, n.d. https://doi.org/10.1101/2024.09.20.614050. ieee: D. Vijatovic et al., “Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis,” bioRxiv. . ista: Vijatovic D, Toma FA, Harrington ZP, Sommer CM, Hauschild R, Trevisan AJ, Chapman P, Julseth M, Brenner-Morton S, Gabitto MI, Dasen JS, Bikoff JB, Sweeney LB. Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis. bioRxiv, 10.1101/2024.09.20.614050. mla: Vijatovic, David, et al. “Spinal Neuron Diversity Scales Exponentially with Swim-to-Limb Transformation during Frog Metamorphosis.” BioRxiv, doi:10.1101/2024.09.20.614050. short: D. Vijatovic, F.A. Toma, Z.P. Harrington, C.M. Sommer, R. Hauschild, A.J. Trevisan, P. Chapman, M. Julseth, S. Brenner-Morton, M.I. Gabitto, J.S. Dasen, J.B. Bikoff, L.B. Sweeney, BioRxiv (n.d.). corr_author: '1' date_created: 2025-04-07T08:48:28Z date_published: 2024-09-27T00:00:00Z date_updated: 2025-05-14T11:40:13Z day: '27' department: - _id: LoSw - _id: TiVo - _id: Bio - _id: NiBa doi: 10.1101/2024.09.20.614050 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1101/2024.09.20.614050 month: '09' oa: 1 oa_version: Preprint project: - _id: bd73af52-d553-11ed-ba76-912049f0ac7a grant_number: FTI21-D-046 name: Development of V1 interneuron diversity during swim-to-walk transition of Xenopus metamorphosis - _id: ebb66355-77a9-11ec-83b8-b8ac210a4dae grant_number: '101041551' name: Development and Evolution of Tetrapod Motor Circuits - _id: c08e9ad1-5a5b-11eb-8a69-9d1cf3b07473 grant_number: CZI01 name: Tools for automation and feedback microscopy publication: bioRxiv publication_status: submitted status: public title: Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository _id: '19545' abstract: - lang: eng text: "We prove the Eigenstate Thermalisation Hypothesis for Wigner matrices\r\nuniformly in the entire spectrum, in particular near the spectral edges, with a\r\nbound on the fluctuation that is optimal for any observable. This complements\r\nearlier works of Cipolloni et. al. (Comm. Math. Phys. 388, 2021; Forum Math.,\r\nSigma 10, 2022) and Benigni et. al. (Comm. Math. Phys. 391, 2022; arXiv:\r\n2303.11142) that were restricted either to the bulk of the spectrum or to\r\nspecial observables. As a main ingredient, we prove a new multi-resolvent local\r\nlaw that optimally accounts for the edge scaling." acknowledgement: Supported by ERC Advanced Grant “RMTBeyond” No. 101020331. article_processing_charge: No arxiv: 1 author: - first_name: Giorgio full_name: Cipolloni, Giorgio id: 42198EFA-F248-11E8-B48F-1D18A9856A87 last_name: Cipolloni orcid: 0000-0002-4901-7992 - first_name: László full_name: Erdös, László id: 4DBD5372-F248-11E8-B48F-1D18A9856A87 last_name: Erdös orcid: 0000-0001-5366-9603 - first_name: Sven Joscha full_name: Henheik, Sven Joscha id: 31d731d7-d235-11ea-ad11-b50331c8d7fb last_name: Henheik orcid: 0000-0003-1106-327X citation: ama: Cipolloni G, Erdös L, Henheik SJ. Eigenstate thermalisation at the edge for Wigner matrices. arXiv. doi:10.48550/arXiv.2309.05488 apa: Cipolloni, G., Erdös, L., & Henheik, S. J. (n.d.). Eigenstate thermalisation at the edge for Wigner matrices. arXiv. https://doi.org/10.48550/arXiv.2309.05488 chicago: Cipolloni, Giorgio, László Erdös, and Sven Joscha Henheik. “Eigenstate Thermalisation at the Edge for Wigner Matrices.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2309.05488. ieee: G. Cipolloni, L. Erdös, and S. J. Henheik, “Eigenstate thermalisation at the edge for Wigner matrices,” arXiv. . ista: Cipolloni G, Erdös L, Henheik SJ. Eigenstate thermalisation at the edge for Wigner matrices. arXiv, 10.48550/arXiv.2309.05488. mla: Cipolloni, Giorgio, et al. “Eigenstate Thermalisation at the Edge for Wigner Matrices.” ArXiv, doi:10.48550/arXiv.2309.05488. short: G. Cipolloni, L. Erdös, S.J. Henheik, ArXiv (n.d.). corr_author: '1' date_created: 2025-04-11T08:19:22Z date_published: 2024-12-17T00:00:00Z date_updated: 2026-04-07T12:37:11Z day: '17' department: - _id: LaEr doi: 10.48550/arXiv.2309.05488 ec_funded: 1 external_id: arxiv: - '2309.05488' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2309.05488 month: '12' oa: 1 oa_version: Preprint project: - _id: 62796744-2b32-11ec-9570-940b20777f1d call_identifier: H2020 grant_number: '101020331' name: Random matrices beyond Wigner-Dyson-Mehta publication: arXiv publication_status: draft related_material: record: - id: '19540' relation: dissertation_contains status: public status: public title: Eigenstate thermalisation at the edge for Wigner matrices 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: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ... --- OA_place: repository _id: '19547' abstract: - lang: eng text: "For correlated real symmetric or complex Hermitian random matrices, we prove\r\nthat the local eigenvalue statistics at any cusp singularity are universal.\r\nSince the density of states typically exhibits only square root edge or cubic\r\nroot cusp singularities, our result completes the proof of the\r\nWigner-Dyson-Mehta universality conjecture in all spectral regimes for a very\r\ngeneral class of random matrices. Previously only the bulk and the edge\r\nuniversality were established in this generality [arXiv:1804.07744], while cusp\r\nuniversality was proven only for Wigner-type matrices with independent entries\r\n[arXiv:1809.03971, arXiv:1811.04055]. As our main technical input, we prove an\r\noptimal local law at the cusp using the Zigzag strategy, a recursive tandem of\r\nthe characteristic flow method and a Green function comparison argument.\r\nMoreover, our proof of the optimal local law holds uniformly in the spectrum,\r\nthus also re-establishing universality of the local eigenvalue statistics in\r\nthe previously studied bulk [arXiv:1705.10661] and edge [arXiv:1804.07744]\r\nregimes." acknowledgement: "Supported by the ERC Advanced Grant \"RMTBeyond\"\r\nNo. 101020331." article_processing_charge: No arxiv: 1 author: - first_name: László full_name: Erdös, László id: 4DBD5372-F248-11E8-B48F-1D18A9856A87 last_name: Erdös orcid: 0000-0001-5366-9603 - first_name: Sven Joscha full_name: Henheik, Sven Joscha id: 31d731d7-d235-11ea-ad11-b50331c8d7fb last_name: Henheik orcid: 0000-0003-1106-327X - first_name: Volodymyr full_name: Riabov, Volodymyr id: 1949f904-edfb-11eb-afb5-e2dfddabb93b last_name: Riabov citation: ama: Erdös L, Henheik SJ, Riabov V. Cusp universality for correlated random matrices. arXiv. doi:10.48550/arXiv.2410.06813 apa: Erdös, L., Henheik, S. J., & Riabov, V. (n.d.). Cusp universality for correlated random matrices. arXiv. https://doi.org/10.48550/arXiv.2410.06813 chicago: Erdös, László, Sven Joscha Henheik, and Volodymyr Riabov. “Cusp Universality for Correlated Random Matrices.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2410.06813. ieee: L. Erdös, S. J. Henheik, and V. Riabov, “Cusp universality for correlated random matrices,” arXiv. . ista: Erdös L, Henheik SJ, Riabov V. Cusp universality for correlated random matrices. arXiv, 10.48550/arXiv.2410.06813. mla: Erdös, László, et al. “Cusp Universality for Correlated Random Matrices.” ArXiv, doi:10.48550/arXiv.2410.06813. short: L. Erdös, S.J. Henheik, V. Riabov, ArXiv (n.d.). corr_author: '1' date_created: 2025-04-11T08:48:21Z date_published: 2024-11-03T00:00:00Z date_updated: 2026-04-07T12:37:11Z day: '03' department: - _id: LaEr doi: 10.48550/arXiv.2410.06813 ec_funded: 1 external_id: arxiv: - '2410.06813' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2410.06813 month: '11' oa: 1 oa_version: Preprint project: - _id: 62796744-2b32-11ec-9570-940b20777f1d call_identifier: H2020 grant_number: '101020331' name: Random matrices beyond Wigner-Dyson-Mehta publication: arXiv publication_status: draft related_material: record: - id: '20322' relation: later_version status: public - id: '20575' relation: dissertation_contains status: public - id: '19540' relation: dissertation_contains status: public status: public title: Cusp universality for correlated random matrices type: preprint user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2024' ...