--- 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 license: https://creativecommons.org/licenses/by/4.0/ 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 license: https://creativecommons.org/licenses/by-sa/4.0/ main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2403.06833 month: '03' oa: 1 oa_version: Preprint publication: arXiv publication_status: published related_material: link: - relation: software url: ' https://github.com/egozverev/Shold-It-Be-Executed-Or-Processed' status: public title: Can LLMs separate instructions from data? And what do we even mean by that? tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: preprint user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- OA_place: repository OA_type: green _id: '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_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_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' ... --- _id: '14769' abstract: - lang: eng text: 'For a set of points in Rd, the Euclidean k-means problems consists of finding k centers such that the sum of distances squared from each data point to its closest center is minimized. Coresets are one the main tools developed recently to solve this problem in a big data context. They allow to compress the initial dataset while preserving its structure: running any algorithm on the coreset provides a guarantee almost equivalent to running it on the full data. In this work, we study coresets in a fully-dynamic setting: points are added and deleted with the goal to efficiently maintain a coreset with which a k-means solution can be computed. Based on an algorithm from Henzinger and Kale [ESA''20], we present an efficient and practical implementation of a fully dynamic coreset algorithm, that improves the running time by up to a factor of 20 compared to our non-optimized implementation of the algorithm by Henzinger and Kale, without sacrificing more than 7% on the quality of the k-means solution.' acknowledgement: This project has received funding from the Euro-pean Research Council (ERC) under the EuropeanUnion’s Horizon 2020 research and innovation programme (Grant agreement No. 101019564 “The De-sign of Modern Fully Dynamic Data Structures (Mo-DynStruct)” and the Austrian Science Fund (FWF)project Z 422-N, project “Static and Dynamic Hierar-chical Graph Decompositions”, I 5982-N, and project“Fast Algorithms for a Reactive Network Layer (Re-actNet)”, P 33775-N, with additional funding from thenetidee SCIENCE Stiftung, 2020–2024.D. Sauplic has received funding from the Euro-pean Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreementNo 101034413. article_processing_charge: No arxiv: 1 author: - 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: David full_name: Saulpic, David id: f8e48cf0-b0ff-11ed-b0e9-b4c35598f964 last_name: Saulpic - first_name: Leonhard full_name: Sidl, Leonhard id: 8b563fd0-b441-11ee-9101-a3891c61efa6 last_name: Sidl citation: ama: 'Henzinger M, Saulpic D, Sidl L. Experimental evaluation of fully dynamic k-means via coresets. In: 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments. Society for Industrial and Applied Mathematics; 2024:220-233. doi:10.1137/1.9781611977929.17' apa: 'Henzinger, M., Saulpic, D., & Sidl, L. (2024). Experimental evaluation of fully dynamic k-means via coresets. In 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments (pp. 220–233). Alexandria, VA, United States: Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611977929.17' chicago: Henzinger, Monika, David Saulpic, and Leonhard Sidl. “Experimental Evaluation of Fully Dynamic K-Means via Coresets.” In 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, 220–33. Society for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/1.9781611977929.17. ieee: M. Henzinger, D. Saulpic, and L. Sidl, “Experimental evaluation of fully dynamic k-means via coresets,” in 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, Alexandria, VA, United States, 2024, pp. 220–233. ista: 'Henzinger M, Saulpic D, Sidl L. 2024. Experimental evaluation of fully dynamic k-means via coresets. 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments. ALENEX: Workshop on Algorithm Engineering and Experiments, 220–233.' mla: Henzinger, Monika, et al. “Experimental Evaluation of Fully Dynamic K-Means via Coresets.” 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, Society for Industrial and Applied Mathematics, 2024, pp. 220–33, doi:10.1137/1.9781611977929.17. short: M. Henzinger, D. Saulpic, L. Sidl, in:, 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments, Society for Industrial and Applied Mathematics, 2024, pp. 220–233. conference: end_date: 2024-01-08 location: Alexandria, VA, United States name: 'ALENEX: Workshop on Algorithm Engineering and Experiments' start_date: 2024-01-07 corr_author: '1' date_created: 2024-01-09T16:22:47Z date_published: 2024-01-04T00:00:00Z date_updated: 2025-04-14T13:50:50Z day: '04' department: - _id: MoHe doi: 10.1137/1.9781611977929.17 ec_funded: 1 external_id: arxiv: - '2310.18034' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.18034 month: '01' oa: 1 oa_version: Preprint page: 220-233 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 - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: 2024 Proceedings of the Symposium on Algorithm Engineering and Experiments publication_identifier: eisbn: - '9781611977929' publication_status: published publisher: Society for Industrial and Applied Mathematics quality_controlled: '1' scopus_import: '1' status: public title: Experimental evaluation of fully dynamic k-means via coresets type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2024' ... --- _id: '14794' abstract: - lang: eng text: "Mosaic analysis with double markers (MADM) technology enables the sparse labeling of genetically defined neurons. We present a protocol for time-lapse imaging of cortical projection neuron migration in mice using MADM. We describe steps for the isolation, culturing, and 4D imaging of neuronal dynamics in MADM-labeled brain tissue. While this protocol is compatible with other single-cell labeling methods, the MADM approach provides a genetic platform for the functional assessment of cell-autonomous candidate gene function and the relative contribution of non-cell-autonomous effects.\r\n\r\nFor complete details on the use and execution of this protocol, please refer to Hansen et al. (2022),1 Contreras et al. (2021),2 and Amberg and Hippenmeyer (2021).3" acknowledged_ssus: - _id: Bio - _id: PreCl acknowledgement: We thank Florian Pauler for discussion and his expert technical support. This research was supported by the Scientific Service Units (SSU) at IST Austria through resources provided by the Imaging and Optics Facility (IOF) and Preclinical Facility (PCF). A.H.H. was a recipient of a DOC Fellowship (24812) of the Austrian Academy of Sciences. article_number: '102795' article_processing_charge: Yes article_type: review author: - first_name: Andi H full_name: Hansen, Andi H id: 38853E16-F248-11E8-B48F-1D18A9856A87 last_name: Hansen - first_name: Simon full_name: Hippenmeyer, Simon id: 37B36620-F248-11E8-B48F-1D18A9856A87 last_name: Hippenmeyer orcid: 0000-0003-2279-1061 citation: ama: Hansen AH, Hippenmeyer S. Time-lapse imaging of cortical projection neuron migration in mice using mosaic analysis with double markers. STAR Protocols. 2024;5(1). doi:10.1016/j.xpro.2023.102795 apa: Hansen, A. H., & Hippenmeyer, S. (2024). Time-lapse imaging of cortical projection neuron migration in mice using mosaic analysis with double markers. STAR Protocols. Elsevier. https://doi.org/10.1016/j.xpro.2023.102795 chicago: Hansen, Andi H, and Simon Hippenmeyer. “Time-Lapse Imaging of Cortical Projection Neuron Migration in Mice Using Mosaic Analysis with Double Markers.” STAR Protocols. Elsevier, 2024. https://doi.org/10.1016/j.xpro.2023.102795. ieee: A. H. Hansen and S. Hippenmeyer, “Time-lapse imaging of cortical projection neuron migration in mice using mosaic analysis with double markers,” STAR Protocols, vol. 5, no. 1. Elsevier, 2024. ista: Hansen AH, Hippenmeyer S. 2024. Time-lapse imaging of cortical projection neuron migration in mice using mosaic analysis with double markers. STAR Protocols. 5(1), 102795. mla: Hansen, Andi H., and Simon Hippenmeyer. “Time-Lapse Imaging of Cortical Projection Neuron Migration in Mice Using Mosaic Analysis with Double Markers.” STAR Protocols, vol. 5, no. 1, 102795, Elsevier, 2024, doi:10.1016/j.xpro.2023.102795. short: A.H. Hansen, S. Hippenmeyer, STAR Protocols 5 (2024). corr_author: '1' date_created: 2024-01-14T23:00:56Z date_published: 2024-03-15T00:00:00Z date_updated: 2025-04-15T07:32:40Z day: '15' ddc: - '570' department: - _id: SiHi doi: 10.1016/j.xpro.2023.102795 external_id: pmid: - '38165800' file: - access_level: open_access checksum: 4644d537451c5c114a9d7c7829b65bba content_type: application/pdf creator: dernst date_created: 2024-07-16T12:04:46Z date_updated: 2024-07-16T12:04:46Z file_id: '17264' file_name: 2024_STARProtoc_Hansen.pdf file_size: 3758943 relation: main_file success: 1 file_date_updated: 2024-07-16T12:04:46Z has_accepted_license: '1' intvolume: ' 5' issue: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 2625A13E-B435-11E9-9278-68D0E5697425 grant_number: '24812' name: Molecular mechanisms of radial neuronal migration publication: STAR Protocols publication_identifier: eissn: - 2666-1667 publication_status: published publisher: Elsevier quality_controlled: '1' related_material: link: - relation: software url: http://github.com/hippenmeyerlab scopus_import: '1' status: public title: Time-lapse imaging of cortical projection neuron migration in mice using mosaic analysis with double markers 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: 5 year: '2024' ... --- _id: '14795' abstract: - lang: eng text: Metazoan development relies on the formation and remodeling of cell-cell contacts. Dynamic reorganization of adhesion receptors and the actomyosin cell cortex in space and time plays a central role in cell-cell contact formation and maturation. Nevertheless, how this process is mechanistically achieved when new contacts are formed remains unclear. Here, by building a biomimetic assay composed of progenitor cells adhering to supported lipid bilayers functionalized with E-cadherin ectodomains, we show that cortical F-actin flows, driven by the depletion of myosin-2 at the cell contact center, mediate the dynamic reorganization of adhesion receptors and cell cortex at the contact. E-cadherin-dependent downregulation of the small GTPase RhoA at the forming contact leads to both a depletion of myosin-2 and a decrease of F-actin at the contact center. At the contact rim, in contrast, myosin-2 becomes enriched by the retraction of bleb-like protrusions, resulting in a cortical tension gradient from the contact rim to its center. This tension gradient, in turn, triggers centrifugal F-actin flows, leading to further accumulation of F-actin at the contact rim and the progressive redistribution of E-cadherin from the contact center to the rim. Eventually, this combination of actomyosin downregulation and flows at the contact determines the characteristic molecular organization, with E-cadherin and F-actin accumulating at the contact rim, where they are needed to mechanically link the contractile cortices of the adhering cells. acknowledged_ssus: - _id: Bio - _id: PreCl acknowledgement: "We are grateful to Edwin Munro for their feedback and help with the single particle analysis. We thank members of the Heisenberg and Loose labs for their help and feedback on the manuscript, notably Xin Tong for making the PCS2-mCherry-AHPH plasmid. Finally, we thank the Aquatics and Imaging & Optics facilities of ISTA for their continuous support, especially Yann Cesbron for assistance with the laser cutter. This work was supported by an ERC\r\nAdvanced Grant (MECSPEC) to C.-P.H." article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Feyza N full_name: Arslan, Feyza N id: 49DA7910-F248-11E8-B48F-1D18A9856A87 last_name: Arslan orcid: 0000-0001-5809-9566 - first_name: Edouard B full_name: Hannezo, Edouard B id: 3A9DB764-F248-11E8-B48F-1D18A9856A87 last_name: Hannezo orcid: 0000-0001-6005-1561 - first_name: Jack full_name: Merrin, Jack id: 4515C308-F248-11E8-B48F-1D18A9856A87 last_name: Merrin orcid: 0000-0001-5145-4609 - first_name: Martin full_name: Loose, Martin id: 462D4284-F248-11E8-B48F-1D18A9856A87 last_name: Loose orcid: 0000-0001-7309-9724 - first_name: Carl-Philipp J full_name: Heisenberg, Carl-Philipp J id: 39427864-F248-11E8-B48F-1D18A9856A87 last_name: Heisenberg orcid: 0000-0002-0912-4566 citation: ama: Arslan FN, Hannezo EB, Merrin J, Loose M, Heisenberg C-PJ. Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts. Current Biology. 2024;34(1):171-182.e8. doi:10.1016/j.cub.2023.11.067 apa: Arslan, F. N., Hannezo, E. B., Merrin, J., Loose, M., & Heisenberg, C.-P. J. (2024). Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts. Current Biology. Elsevier. https://doi.org/10.1016/j.cub.2023.11.067 chicago: Arslan, Feyza N, Edouard B Hannezo, Jack Merrin, Martin Loose, and Carl-Philipp J Heisenberg. “Adhesion-Induced Cortical Flows Pattern E-Cadherin-Mediated Cell Contacts.” Current Biology. Elsevier, 2024. https://doi.org/10.1016/j.cub.2023.11.067. ieee: F. N. Arslan, E. B. Hannezo, J. Merrin, M. Loose, and C.-P. J. Heisenberg, “Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts,” Current Biology, vol. 34, no. 1. Elsevier, p. 171–182.e8, 2024. ista: Arslan FN, Hannezo EB, Merrin J, Loose M, Heisenberg C-PJ. 2024. Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts. Current Biology. 34(1), 171–182.e8. mla: Arslan, Feyza N., et al. “Adhesion-Induced Cortical Flows Pattern E-Cadherin-Mediated Cell Contacts.” Current Biology, vol. 34, no. 1, Elsevier, 2024, p. 171–182.e8, doi:10.1016/j.cub.2023.11.067. short: F.N. Arslan, E.B. Hannezo, J. Merrin, M. Loose, C.-P.J. Heisenberg, Current Biology 34 (2024) 171–182.e8. corr_author: '1' date_created: 2024-01-14T23:00:56Z date_published: 2024-01-08T00:00:00Z date_updated: 2025-09-04T11:39:10Z day: '08' ddc: - '570' department: - _id: CaHe - _id: EdHa - _id: MaLo - _id: NanoFab doi: 10.1016/j.cub.2023.11.067 ec_funded: 1 external_id: isi: - '001154500400001' pmid: - '38134934' file: - access_level: open_access checksum: 51220b76d72a614208f84bdbfbaf9b72 content_type: application/pdf creator: dernst date_created: 2024-01-16T10:53:31Z date_updated: 2024-01-16T10:53:31Z file_id: '14813' file_name: 2024_CurrentBiology_Arslan.pdf file_size: 5183861 relation: main_file success: 1 file_date_updated: 2024-01-16T10:53:31Z has_accepted_license: '1' intvolume: ' 34' isi: 1 issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: 171-182.e8 pmid: 1 project: - _id: 260F1432-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '742573' name: Interaction and feedback between cell mechanics and fate specification in vertebrate gastrulation publication: Current Biology publication_identifier: eissn: - 1879-0445 issn: - 0960-9822 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Adhesion-induced cortical flows pattern E-cadherin-mediated cell contacts 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: 34 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '14797' abstract: - lang: eng text: We study a random matching problem on closed compact 2 -dimensional Riemannian manifolds (with respect to the squared Riemannian distance), with samples of random points whose common law is absolutely continuous with respect to the volume measure with strictly positive and bounded density. We show that given two sequences of numbers n and m=m(n) of points, asymptotically equivalent as n goes to infinity, the optimal transport plan between the two empirical measures μn and νm is quantitatively well-approximated by (Id,exp(∇hn))#μn where hn solves a linear elliptic PDE obtained by a regularized first-order linearization of the Monge-Ampère equation. This is obtained in the case of samples of correlated random points for which a stretched exponential decay of the α -mixing coefficient holds and for a class of discrete-time Markov chains having a unique absolutely continuous invariant measure with respect to the volume measure. acknowledgement: "NC has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No 948819).\r\nFM is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the SPP 2265 Random Geometric Systems. FM has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2044 -390685587, Mathematics Münster: Dynamics–Geometry–Structure. FM has been funded by the Max Planck Institute for Mathematics in the Sciences." article_processing_charge: Yes (in subscription journal) article_type: original arxiv: 1 author: - first_name: Nicolas full_name: Clozeau, Nicolas id: fea1b376-906f-11eb-847d-b2c0cf46455b last_name: Clozeau - first_name: Francesco full_name: Mattesini, Francesco last_name: Mattesini citation: ama: Clozeau N, Mattesini F. Annealed quantitative estimates for the quadratic 2D-discrete random matching problem. Probability Theory and Related Fields. 2024;190:485-541. doi:10.1007/s00440-023-01254-0 apa: Clozeau, N., & Mattesini, F. (2024). Annealed quantitative estimates for the quadratic 2D-discrete random matching problem. Probability Theory and Related Fields. Springer Nature. https://doi.org/10.1007/s00440-023-01254-0 chicago: Clozeau, Nicolas, and Francesco Mattesini. “Annealed Quantitative Estimates for the Quadratic 2D-Discrete Random Matching Problem.” Probability Theory and Related Fields. Springer Nature, 2024. https://doi.org/10.1007/s00440-023-01254-0. ieee: N. Clozeau and F. Mattesini, “Annealed quantitative estimates for the quadratic 2D-discrete random matching problem,” Probability Theory and Related Fields, vol. 190. Springer Nature, pp. 485–541, 2024. ista: Clozeau N, Mattesini F. 2024. Annealed quantitative estimates for the quadratic 2D-discrete random matching problem. Probability Theory and Related Fields. 190, 485–541. mla: Clozeau, Nicolas, and Francesco Mattesini. “Annealed Quantitative Estimates for the Quadratic 2D-Discrete Random Matching Problem.” Probability Theory and Related Fields, vol. 190, Springer Nature, 2024, pp. 485–541, doi:10.1007/s00440-023-01254-0. short: N. Clozeau, F. Mattesini, Probability Theory and Related Fields 190 (2024) 485–541. corr_author: '1' date_created: 2024-01-14T23:00:57Z date_published: 2024-10-01T00:00:00Z date_updated: 2025-09-04T11:43:43Z day: '01' ddc: - '510' department: - _id: JuFi doi: 10.1007/s00440-023-01254-0 ec_funded: 1 external_id: arxiv: - '2303.00353' isi: - '001136206200002' file: - access_level: open_access checksum: 34f44cad6a210ff66791ee37e590af2c content_type: application/pdf creator: dernst date_created: 2025-01-09T08:10:54Z date_updated: 2025-01-09T08:10:54Z file_id: '18788' file_name: 2024_ProbTheoryRelatFields_Clozeau.pdf file_size: 880117 relation: main_file success: 1 file_date_updated: 2025-01-09T08:10:54Z has_accepted_license: '1' intvolume: ' 190' isi: 1 language: - iso: eng month: '10' oa: 1 oa_version: Published Version page: 485-541 project: - _id: 0aa76401-070f-11eb-9043-b5bb049fa26d call_identifier: H2020 grant_number: '948819' name: Bridging Scales in Random Materials publication: Probability Theory and Related Fields publication_identifier: eissn: - 1432-2064 issn: - 0178-8051 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Annealed quantitative estimates for the quadratic 2D-discrete random matching problem 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: 190 year: '2024' ... --- APC_amount: 3393,38 EUR DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '14802' abstract: - lang: eng text: Frequency-stable lasers form the back bone of precision measurements in science and technology. Such lasers typically attain their stability through frequency locking to reference cavities. State-of-the-art locking performances to date had been achieved using frequency modulation based methods, complemented with active drift cancellation systems. We demonstrate an all passive, modulation-free laser-cavity locking technique (squash locking) that utilizes changes in spatial beam ellipticity for error signal generation, and a coherent polarization post-selection for noise resilience. By comparing two identically built proof-of-principle systems, we show a frequency locking instability of 5×10−7 relative to the cavity linewidth at 10 s averaging. The results surpass the demonstrated performances of methods engineered over the last five decades, potentially enabling an advancement in the precision control of lasers, while creating avenues for bridging the performance gaps between industrial grade lasers with scientific ones due to the afforded simplicity and scalability. acknowledgement: We thank Rishabh Sahu and Sebastian Wald for technical contributions to the experiment. Funding by Institute of Science and Technology Austria. article_processing_charge: Yes article_type: original author: - first_name: Fritz R full_name: Diorico, Fritz R id: 2E054C4C-F248-11E8-B48F-1D18A9856A87 last_name: Diorico orcid: 0000-0002-4947-8924 - first_name: Artem full_name: Zhutov, Artem id: 0f02ed6a-b514-11ee-b891-8379c5f19cb7 last_name: Zhutov - first_name: Onur full_name: Hosten, Onur id: 4C02D85E-F248-11E8-B48F-1D18A9856A87 last_name: Hosten orcid: 0000-0002-2031-204X citation: ama: 'Diorico FR, Zhutov A, Hosten O. Laser-cavity locking utilizing beam ellipticity: accessing the 10−7 instability scale relative to cavity linewidth. Optica. 2024;11(1):26-31. doi:10.1364/optica.507451' apa: 'Diorico, F. R., Zhutov, A., & Hosten, O. (2024). Laser-cavity locking utilizing beam ellipticity: accessing the 10−7 instability scale relative to cavity linewidth. Optica. Optica Publishing Group. https://doi.org/10.1364/optica.507451' chicago: 'Diorico, Fritz R, Artem Zhutov, and Onur Hosten. “Laser-Cavity Locking Utilizing Beam Ellipticity: Accessing the 10−7 Instability Scale Relative to Cavity Linewidth.” Optica. Optica Publishing Group, 2024. https://doi.org/10.1364/optica.507451.' ieee: 'F. R. Diorico, A. Zhutov, and O. Hosten, “Laser-cavity locking utilizing beam ellipticity: accessing the 10−7 instability scale relative to cavity linewidth,” Optica, vol. 11, no. 1. Optica Publishing Group, pp. 26–31, 2024.' ista: 'Diorico FR, Zhutov A, Hosten O. 2024. Laser-cavity locking utilizing beam ellipticity: accessing the 10−7 instability scale relative to cavity linewidth. Optica. 11(1), 26–31.' mla: 'Diorico, Fritz R., et al. “Laser-Cavity Locking Utilizing Beam Ellipticity: Accessing the 10−7 Instability Scale Relative to Cavity Linewidth.” Optica, vol. 11, no. 1, Optica Publishing Group, 2024, pp. 26–31, doi:10.1364/optica.507451.' short: F.R. Diorico, A. Zhutov, O. Hosten, Optica 11 (2024) 26–31. corr_author: '1' date_created: 2024-01-15T10:25:38Z date_published: 2024-01-20T00:00:00Z date_updated: 2025-09-04T12:13:27Z day: '20' ddc: - '530' department: - _id: OnHo doi: 10.1364/optica.507451 external_id: isi: - '001202817000004' file: - access_level: open_access checksum: eb99ca7d0fe73e22f121875175546ed7 content_type: application/pdf creator: dernst date_created: 2024-01-17T08:53:16Z date_updated: 2024-01-17T08:53:16Z file_id: '14824' file_name: 2023_Optica_Diorico.pdf file_size: 4558986 relation: main_file success: 1 file_date_updated: 2024-01-17T08:53:16Z has_accepted_license: '1' intvolume: ' 11' isi: 1 issue: '1' keyword: - Atomic and Molecular Physics - and Optics - Electronic - Optical and Magnetic Materials language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: 26-31 publication: Optica publication_identifier: issn: - 2334-2536 publication_status: published publisher: Optica Publishing Group quality_controlled: '1' scopus_import: '1' status: public title: 'Laser-cavity locking utilizing beam ellipticity: accessing the 10−7 instability scale relative to cavity linewidth' 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: 11 year: '2024' ... --- _id: '14820' abstract: - lang: eng text: "We consider a natural problem dealing with weighted packet selection across a rechargeable link, which e.g., finds applications in cryptocurrency networks. The capacity of a link (u, v) is determined by how many nodes u and v allocate for this link. Specifically, the input is a finite ordered sequence of packets that arrive in both directions along a link. Given (u, v) and a packet of weight x going from u to v, node u can either accept or reject the packet. If u accepts the packet, the capacity on link (u, v) decreases by x. Correspondingly, v's capacity on \r\n increases by x. If a node rejects the packet, this will entail a cost affinely linear in the weight of the packet. A link is “rechargeable” in the sense that the total capacity of the link has to remain constant, but the allocation of capacity at the ends of the link can depend arbitrarily on the nodes' decisions. The goal is to minimise the sum of the capacity injected into the link and the cost of rejecting packets. We show that the problem is NP-hard, but can be approximated efficiently with a ratio of (1+E) . (1+3) for some arbitrary E>0." acknowledgement: We thank Mahsa Bastankhah and Mohammad Ali Maddah-Ali for fruitful discussions about different variants of the problem. This work is supported by the European Research Council (ERC) Consolidator Project 864228 (AdjustNet), 2020-2025, the ERC CoG 863818 (ForM-SMArt), and the German Research Foundation (DFG) grant 470029389 (FlexNets), 2021-2024. article_number: '114353' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Stefan full_name: Schmid, Stefan last_name: Schmid - first_name: Jakub full_name: Svoboda, Jakub id: 130759D2-D7DD-11E9-87D2-DE0DE6697425 last_name: Svoboda orcid: 0000-0002-1419-3267 - first_name: Michelle X full_name: Yeo, Michelle X id: 2D82B818-F248-11E8-B48F-1D18A9856A87 last_name: Yeo orcid: 0009-0001-3676-4809 citation: ama: 'Schmid S, Svoboda J, Yeo MX. Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation. Theoretical Computer Science. 2024;989. doi:10.1016/j.tcs.2023.114353' apa: 'Schmid, S., Svoboda, J., & Yeo, M. X. (2024). Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation. Theoretical Computer Science. Elsevier. https://doi.org/10.1016/j.tcs.2023.114353' chicago: 'Schmid, Stefan, Jakub Svoboda, and Michelle X Yeo. “Weighted Packet Selection for Rechargeable Links in Cryptocurrency Networks: Complexity and Approximation.” Theoretical Computer Science. Elsevier, 2024. https://doi.org/10.1016/j.tcs.2023.114353.' ieee: 'S. Schmid, J. Svoboda, and M. X. Yeo, “Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation,” Theoretical Computer Science, vol. 989. Elsevier, 2024.' ista: 'Schmid S, Svoboda J, Yeo MX. 2024. Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation. Theoretical Computer Science. 989, 114353.' mla: 'Schmid, Stefan, et al. “Weighted Packet Selection for Rechargeable Links in Cryptocurrency Networks: Complexity and Approximation.” Theoretical Computer Science, vol. 989, 114353, Elsevier, 2024, doi:10.1016/j.tcs.2023.114353.' short: S. Schmid, J. Svoboda, M.X. Yeo, Theoretical Computer Science 989 (2024). corr_author: '1' date_created: 2024-01-16T13:40:41Z date_published: 2024-03-21T00:00:00Z date_updated: 2025-12-02T14:02:37Z day: '21' ddc: - '000' department: - _id: KrCh - _id: KrPi doi: 10.1016/j.tcs.2023.114353 ec_funded: 1 external_id: isi: - '001168211400001' file: - access_level: open_access checksum: efd5b7e738bf845312ba53889a3e13e4 content_type: application/pdf creator: dernst date_created: 2024-07-16T12:02:25Z date_updated: 2024-07-16T12:02:25Z file_id: '17263' file_name: 2024_TheorComputerScience_Schmid.pdf file_size: 603570 relation: main_file success: 1 file_date_updated: 2024-07-16T12:02:25Z has_accepted_license: '1' intvolume: ' 989' isi: 1 keyword: - General Computer Science - Theoretical Computer Science language: - iso: eng month: '03' oa: 1 oa_version: Published Version project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: Theoretical Computer Science publication_identifier: issn: - 0304-3975 publication_status: published publisher: Elsevier quality_controlled: '1' related_material: record: - id: '19985' relation: earlier_version status: public scopus_import: '1' status: public title: 'Weighted packet selection for rechargeable links in cryptocurrency networks: Complexity and approximation' 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: 989 year: '2024' ... --- _id: '14828' abstract: - lang: eng text: Production of hydrogen at large scale requires development of non-noble, inexpensive, and high-performing catalysts for constructing water-splitting devices. Herein, we report the synthesis of Zn-doped NiO heterostructure (ZnNiO) catalysts at room temperature via a coprecipitation method followed by drying (at 80 °C, 6 h) and calcination at an elevated temperature of 400 °C for 5 h under three distinct conditions, namely, air, N2, and vacuum. The vacuum-synthesized catalyst demonstrates a low overpotential of 88 mV at −10 mA cm–2 and a small Tafel slope of 73 mV dec–1 suggesting relatively higher charge transfer kinetics for hydrogen evolution reactions (HER) compared with the specimens synthesized under N2 or O2 atmosphere. It also demonstrates an oxygen evolution (OER) overpotential of 260 mV at 10 mA cm–2 with a low Tafel slope of 63 mV dec–1. In a full-cell water-splitting device, the vacuum-synthesized ZnNiO heterostructure demonstrates a cell voltage of 1.94 V at 50 mA cm–2 and shows remarkable stability over 24 h at a high current density of 100 mA cm–2. It is also demonstrated in this study that Zn-doping, surface, and interface engineering in transition-metal oxides play a crucial role in efficient electrocatalytic water splitting. Also, the results obtained from density functional theory (DFT + U = 0–8 eV), where U is the on-site Coulomb repulsion parameter also known as Hubbard U, based electronic structure calculations confirm that Zn doping constructively modifies the electronic structure, in both the valence band and the conduction band, and found to be suitable in tailoring the carrier’s effective masses of electrons and holes. The decrease in electron’s effective masses together with large differences between the effective masses of electrons and holes is noticed, which is found to be mainly responsible for achieving the best water-splitting performance from a 9% Zn-doped NiO sample prepared under vacuum. acknowledgement: This work was supported by the Technology Innovation Program (20011622, Development of Battery System Applied High-Efficiency Heat Control Polymer and Part Component) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). Author acknowledge to Prof. Tsunehiro Takeuchi from Toyota Technological Institute, Nagoya, Japan for the support of computational resources. article_processing_charge: No article_type: original author: - first_name: Gundegowda Kalligowdanadoddi full_name: Kiran, Gundegowda Kalligowdanadoddi last_name: Kiran - first_name: Saurabh full_name: Singh, Saurabh id: 12d625da-9cb3-11ed-9667-af09d37d3f0a last_name: Singh orcid: 0000-0003-2209-5269 - first_name: Neelima full_name: Mahato, Neelima last_name: Mahato - first_name: Thupakula Venkata Madhukar full_name: Sreekanth, Thupakula Venkata Madhukar last_name: Sreekanth - first_name: Gowra Raghupathy full_name: Dillip, Gowra Raghupathy last_name: Dillip - first_name: Kisoo full_name: Yoo, Kisoo last_name: Yoo - first_name: Jonghoon full_name: Kim, Jonghoon last_name: Kim citation: ama: Kiran GK, Singh S, Mahato N, et al. Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity. ACS Applied Energy Materials. 2024;7(1):214-229. doi:10.1021/acsaem.3c02519 apa: Kiran, G. K., Singh, S., Mahato, N., Sreekanth, T. V. M., Dillip, G. R., Yoo, K., & Kim, J. (2024). Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity. ACS Applied Energy Materials. American Chemical Society. https://doi.org/10.1021/acsaem.3c02519 chicago: Kiran, Gundegowda Kalligowdanadoddi, Saurabh Singh, Neelima Mahato, Thupakula Venkata Madhukar Sreekanth, Gowra Raghupathy Dillip, Kisoo Yoo, and Jonghoon Kim. “Interface Engineering Modulation Combined with Electronic Structure Modification of Zn-Doped NiO Heterostructure for Efficient Water-Splitting Activity.” ACS Applied Energy Materials. American Chemical Society, 2024. https://doi.org/10.1021/acsaem.3c02519. ieee: G. K. Kiran et al., “Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity,” ACS Applied Energy Materials, vol. 7, no. 1. American Chemical Society, pp. 214–229, 2024. ista: Kiran GK, Singh S, Mahato N, Sreekanth TVM, Dillip GR, Yoo K, Kim J. 2024. Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity. ACS Applied Energy Materials. 7(1), 214–229. mla: Kiran, Gundegowda Kalligowdanadoddi, et al. “Interface Engineering Modulation Combined with Electronic Structure Modification of Zn-Doped NiO Heterostructure for Efficient Water-Splitting Activity.” ACS Applied Energy Materials, vol. 7, no. 1, American Chemical Society, 2024, pp. 214–29, doi:10.1021/acsaem.3c02519. short: G.K. Kiran, S. Singh, N. Mahato, T.V.M. Sreekanth, G.R. Dillip, K. Yoo, J. Kim, ACS Applied Energy Materials 7 (2024) 214–229. corr_author: '1' date_created: 2024-01-17T12:48:35Z date_published: 2024-01-08T00:00:00Z date_updated: 2024-10-09T21:07:53Z day: '08' department: - _id: MaIb doi: 10.1021/acsaem.3c02519 external_id: isi: - '001138342900001' intvolume: ' 7' isi: 1 issue: '1' keyword: - Electrical and Electronic Engineering - Materials Chemistry - Electrochemistry - Energy Engineering and Power Technology - Chemical Engineering (miscellaneous) language: - iso: eng month: '01' oa_version: None page: 214-229 publication: ACS Applied Energy Materials publication_identifier: issn: - 2574-0962 publication_status: published publisher: American Chemical Society quality_controlled: '1' scopus_import: '1' status: public title: Interface engineering modulation combined with electronic structure modification of Zn-doped NiO heterostructure for efficient water-splitting activity type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 7 year: '2024' ...